Discussion Paper - Applying Earth observation to support ...€¦ · Earth observation (EO):...

Applying Earth observation

to support the detection of

non-authorised water

abstractions

Discussion paper on the

development of Copernicus

tools and services

Report for the European Commission, DG Environment

Document information

CLIENT European Commission, DG Environment

CONTRACT

REFERENCE

070307/2013/SFRA/660810/ENV.C1 - under the Framework contract

ENV.D.I/FRA/2012/0014 “Framework contract to provide services to

support the development and implementation of EU freshwater

policies”

PROJECT NAME Applying Earth observation to detect non-authorised water

abstractions

PROJECT OFFICER Thomas Petitguyot

DATE 17 November 2014

AUTHORS Sarah Lockwood, Marion Sarteel, and Shailendra Mudgal

Bio by Deloitte

Dr. Anna Osann, Prof. Dr. Alfonso Calera

Universidad de Castilla-La Mancha (UCLM)

KEY CONTACTS Shailendra Mudgal

[email protected]

Or

Sarah Lockwood

[email protected]

Disclaimer:

The information and views set out in this report are those of the authors and do not necessarily reflect

the official opinion of the Commission. The Commission does not guarantee the accuracy of the data

included in this study. Neither the Commission nor any person acting on the Commission’s behalf may

be held responsible for the use which may be made of the information contained therein.

mailto:[email protected]

mailto:[email protected]

Use of Earth observation by water managers to detect and manage non-authorised water abstractions

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Contents

Document information ..............................................................................................................................3

Glossary ....................................................................................................................................................3

1. Introduction ........................................................................................................................................5

1.1. Context ........................................................................................................................ 5

1.2. Objectives .................................................................................................................... 6

1.3. Approach ..................................................................................................................... 6

2. Opportunities from the use of EO for the detection of non-authorised water abstraction in the EU .7

2.1. Identified water managers’ needs for the detection and monitoring of water abstractions7

2.2. Potential of EO products for detecting and monitoring abstractions ........................... 7

2.3. EO applications for the monitoring of non-authorised water abstractions ................. 13

2.4. Possible range of complementary applications ......................................................... 14

2.5. Discussion of the potential of EO applications .......................................................... 15

3. Requirements for the detection of non-authorised abstractions and complementary applications 19

3.1. Technical requirements for the monitoring of water abstractions .............................. 19

3.2. Legal and administrative requirements for the detection of non-authorised abstractions21

3.3. Operational requirements .......................................................................................... 22

4. Current challenges at MS level ...................................................................................................... 23

4.1. EO technical capacity ................................................................................................ 23

4.2. Governance and political commitment ...................................................................... 27

4.3. Definition of water rights and availability of corresponding information .................... 28

4.4. Local data availability ................................................................................................ 28

4.5. Economic aspects...................................................................................................... 29

4.6. Summary ................................................................................................................... 30

5. Options for tools and services to be developed at EU level........................................................... 31

5.1. The requirement and opportunity to act at the EU level ............................................ 31

5.2. Identification of relevant options and recommendations ........................................... 32

5.3. Suggested roadmap for scenario selection and implementation .............................. 42

6. Further research needs .................................................................................................................. 45

6.1. Maps of irrigated areas .............................................................................................. 45

6.2. Evapotranspiration and abstraction monitoring ......................................................... 45

6.3. Governance ............................................................................................................... 46

References ............................................................................................................................................ 47


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Tables

Table 1: EO products suitability for the identified water managers’ needs, including

complementary applications ............................................................................................................ 17

Table 2: Overall characteristics of EO products for detecting non-authorised abstractions of types

1 and 2 .............................................................................................................................................. 19

Table 3: Overview of local data requirements, source of data and availability .................................. 28

Table 4: Opportunities and barriers to the use of Earth Observation for the detection of non-

authorised abstraction ..................................................................................................................... 30

Table 5: Overview of the scenarios for the development of EU products and services for the

detection and/or monitoring of non-authorised abstractions ........................................................... 32

Table 6: Summary of main characteristics of each scenario .............................................................. 38

Table 7: Overview of responsibilities for each scenario and step in procedure to detect non-

authorised abstraction ..................................................................................................................... 39

Table 8: Requirements for the development of Copernicus water abstraction tools and services ..... 41

Table 9: Roadmap towards implementation: overview. .............................................................. 43

Figures

Figure 1: Different temporal phenology patterns of wheat and corn both irrigated as it is

described by NDVI .............................................................................................................................. 9

Figure 2: Different NDVI magnitude patterns of irrigated and non-irrigated wheat ............................ 9

Figure 3: Different NDVI’s magnitude patterns of wheat with different levels of irrigation ............... 10

Figure 4: Example of annual map of irrigated areas in La-Mancha Oriental aquifer (Spain). Parcels

in yellow (red) indicate irrigation before 1984 (after 1984) ............................................................... 10

Figure 5: Overview of steps in using EO for detecting non-authorised abstractions.......................... 12

Figure 6: Overview of processing steps from crop water requirements (CWR) to water abstraction . 12

Figure 7: Overview of steps for the detection of non-authorised abstractions .................................. 14

Figure 8: Characteristics of water rights and suitability for the EO-based detection of non-

authorised abstractions .................................................................................................................... 22

Figure 9: Overview of demonstrative and operational EO tools and services. Asterisks indicate

methodology to obtain CWR ............................................................................................................ 23


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Glossary

Crop coefficient (Kc): the ratio of evapotranspiration observed for a given crop over that

observed for the calibrated reference crop under the same conditions

Crop phenology: study of the plants’ biological cycle throughout the year and its

seasonal and inter-annual response to climate variations, using different parameters

that describe the seasonal behaviour of the vegetation.

Crop water requirements (CWR): "amount of water required for compensating the

evapotranspiration loss from the cropped field" (Allen et al. 1998).

Earth observation (EO): collection of data and information about the Earth system using

remote sensing systems (mostly satellite-based, but also sensors on-board aircraft or

drones can be used).

Evapotranspiration (ET): transport of water from land surfaces into the atmosphere by

soil evaporation and plant transpiration.

Flow meter: an instrument for monitoring, measuring, or recording the rate of flow,

pressure, or discharge of water.

Ground truth: in-situ observations for the purpose of calibration and validation of EO-

derived parameters.

In-situ: refers to observations obtained through airborne sensors and ground based

installations.

Irrigation water requirements (IWR): crop water requirements minus effective (i.e.

usable for plant) precipitation.

Local data: all types of data (field measurements, databases, knowledge, and expertise

of local/regional crops and their phenology) acquired at local scale, i.e. for a

geographically-limited area.

Non-authorised abstractions: there are two types of non-authorised abstraction for

irrigation purposes. Both types can be either permanent or occurring during periods of

special restrictions (e.g. drought):

abstraction for irrigation in areas that do not have the necessary water

rights (non-authorised abstractions of the first type); and/or

irrigation water consumption beyond the legally allowed or assigned water

volume (non-authorised abstractions of the second type).

Normalized Differential Vegetation Index (NDVI): index that gives a measure of the

vegetative cover on the land surface. Dense vegetation shows up very strongly in the

imagery, and areas with little or no vegetation are also clearly identified.

Reflectance: the ratio of the intensity of reflected radiation to that of the radiation

incident on a surface.

Satellite image: digital image (e.g. of a land surface) obtained from a satellite-borne

sensor. EO sensors provide multispectral images covering several bands in the solar


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(and in some cases also thermal) spectrum.

Spatial resolution (of a satellite image): size of pixel corresponding to the size of the

surface area measured on the ground (depends on the sensor used and the satellite's

orbit).

Supplemental irrigation: ad-hoc irrigation depending on weather conditions.

Surface water balance (SWB): equation describing the flow of water through the Earth’s

surface.

Surface energy balance (SEB): flow of energy into and out of the Earth at the land

surface.

Water rights: authorisation to abstract water, characterised by a combination of criteria

related to:

water resources used: quantity and quality of the water, the source and

location;

characteristics of use: use, location and duration; and

administration of the right: ownership and transfer, security and

enforcement.


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1. Introduction

1.1. Context

Over-abstraction, or the abstraction of more water than is sustainably available, regularly triggers or

increases water deficits in the EU. It is currently considered the second most common pressure on the

ecological status of water bodies in the Union. The water restrictions or natural shortages it engenders

are a potential cause of conflicts between competing uses and could have substantial socio-economic

consequences.

In 2011, a quantitative target was set within the Roadmap for a Resource Efficient Europe1,

recommending a water abstraction level below 20% of the available renewable water resources. In the

2012 Blueprint to Safeguard Europe’s Water Resources2 document, the European Commission

reinforced its commitment for better water management, in accordance to the 3rd

Implementation

Report on the WFD3 that assessed the 2009 River Basin Management Plans and the 2012 policy

review of the Strategy on Water Scarcity and Droughts4. The Blueprint highlights the issue of non-

authorised water abstraction and recalls the responsibility of Member States on law enforcement. Non-

authorised abstraction consists in the abstraction of water without permits or beyond authorised

amounts, either over a year, or during a restricted period of time where the use of water is rationed.

Earth Observation (EO), in particular European Union’s Copernicus programme (ex-GMES5), was

indicated in the Blueprint to Safeguard Europe's Water Resources as a promising approach to

address quantitative issues related to water, through the detection of possible cases of non-authorised

abstraction and as a complement to the often limited field data available. Copernicus is composed of

three elements: (i) a space component with the development of the fleet of EU Sentinel satellites and

with an access to data from other satellites, (ii) a service component including atmosphere, land,

marine, emergency management, security and climate change services and (iii) an in situ component.

Several EO projects recently developed in the EU were co-funded in the framework of the Seventh

Framework Programme and linked to space-based applications research serving European society

(2007-2013) and the European Programme for the establishment of a European capacity for Earth

Observation, now known as “Copernicus”6. In 2014, new regulations were adopted, establishing a new

phase for the Programme which will feature new operational services by year 2020. The land

monitoring service addresses a broad range of environmental policies (water, biodiversity, nature,

soils, forest, waste…); a step by step approach was decided, starting with the multi-purpose products

at a global and European scale (such as the Pan-European land cover products CORINE Land Cover

and new High Resolution Land Cover layer). A local component providing detailed information on

targeted areas of interest will be progressively implemented including Urban Atlas, riparian areas

mapping and Natura 2000 sites monitoring. It might be extended to additional areas of interest and

1 http://ec.europa.eu/environment/resource_efficiency/pdf/com2011_571.pdf

2 http://ec.europa.eu/environment/water/blueprint/index_en.htm

3 http://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52012DC0670

4 http://ec.europa.eu/environment/water/quantity/pdf/non-paper.pdf

5 Global monitoring for environment and security services

6 www.copernicus.eu/


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further thematic services, taking into account users’ requirements and the outcomes of precursor

activities and studies such as this one.

1.2. Objectives

The purpose of this discussion paper is to assess the opportunity to develop a Copernicus service

dedicated to the management of abstractions for irrigation and provide a basis for the discussion on its

key technical specifications. It is aimed at the European Commission, in particular the DG ENV and

the DG ENTR, as well as the Research Executive Agency and the members of the Copernicus User

Forum.

1.3. Approach

This document is one of the outcomes of a study started in October 2013 and conducted on behalf of

the European Commission by BIO by Deloitte with Universidad de Castilla-La Mancha (UCLM)

(“Applying EO tools and services to detect non-authorised water abstractions”). Another deliverable is

the stakeholders Guidance document. Following the terms of the contract, it focuses mostly on non-

authorised abstractions in irrigation, although also exploring complementary applications based on

Earth observation in the field of water management. The document is based on UCLM’s long-standing

expertise in the use of EO for irrigation management (developed in many projects, e.g. SIRIUS

www.sirius-gmes.es), on lessons learnt from literature review, stakeholders’ interviews, case studies,

a series of four workshops7 and from the consultation of international EO experts and representatives

of water authorities. The list of consulted stakeholders, as well as further technical details and case

studies are available in the annexes to the Guidance document.

7 held in Spain jointly with Portugal, in Italy, in France, and in Greece.


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2. Opportunities from the use of EO

for the detection of non-authorised

water abstraction in the EU

2.1. Identified water managers’ needs for the detection and monitoring of

water abstractions

During workshops and interviews, stakeholders expressed their need for a better knowledge of water

abstractions and specifically on irrigation uses, irrigation infrastructures (channels, tanks), irrigated

areas and abstraction volumes. The operational needs emerged included different aspects:

continuous monitoring of irrigated areas and volumes abstracted from well-established

areas;

ensuring the reliability of self-declarations on water abstractions;

optimising field inspections to ensure compliance with legal requirements on water

abstraction;

ensuring compliance in regards to seasonal water restrictions in the event of drought

management.

Complementary needs included:

regularising historical water rights by means of a better identification of water users;

mapping of hydraulic works;

ex-post assessment on the implementation and efficiency of water management

systems, providing different time scales (crisis or structural) as a base for to adopting

future actions;

adjustment of water prices and implementation of a volume-based fee system;

support to irrigation scheduling to increase efficiency.

2.2. Potential of EO products for detecting and monitoring abstractions

The detection, of non-authorised abstractions requires:

all irrigated areas to have the necessary water rights clearly established (non-

authorised abstractions of the first kind); and/or

irrigation water consumption to remain within the legally allowed or assigned water

volume (non-authorised abstractions of the second kind).

Both kinds can be either permanent or limited to exceptional restriction periods of (e.g. in the event of

drought).


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In the first case, all irrigated areas need to be identified and cross-checked with all available

information/databases on irrigable areas (i.e. areas with a legal right to irrigate). Depending on

national and/or regional legislation, the legal right to irrigate may be linked to the land, an abstraction

point or a water source, either permanently or for limited periods of time (e.g. seasonal restrictions). In

the second case, irrigation water consumption should be monitored and cross-checked with regulated

allocation and/or hydrological planning data.

In either case, geospatial legal reference data (e.g. cadastral water rights, allocation to current

hydrological plan) is always needed. This also applies to any in-situ, non-EO-based approach. The

availability of legal reference data is a key condition for the detection of non-authorised abstractions

and is closely linked to the implementation of INSPIRE Directive and SEIS (Shared environmental

Information system) principles.

Earth observation (EO) can supply two key products to monitor abstractions and thus to support the

detection of non-authorised abstractions:

maps of irrigated areas (for non-authorised abstractions); and

maps of irrigation water consumption and abstracted volumes (second case).

For a detailed description of how these maps can be obtained see the following sections 2.2.1 and

2.2.2.

The detection of hydraulic works (channels, tanks) as sources of abstraction cannot be obtained with

High Resolution EO images, but requires orthophotography (airborne) or the employment of very-high

resolution satellites (<1m, like WV-2) (available at commercial image cost), implying a high cost and a

relevant resource intensity. This specific circumstance is not within the scope of this document, but

could be further investigated.

2.2.1 Potential of EO products for monitoring abstractions

The detection of irrigated areas (providing a clear identification of the exact location and areal extent)

requires land-use/land-cover maps that allow distinguishing irrigated from non-irrigated crops. A

successful and widely used methodology (also adopted and operationalized in the FP7 project

SIRIUS) implies the use of a supervised “multi-temporal classification” based on a time series of EO

images.

These images provide the temporal evolution of crops and vegetation during growing season through

spectral reflectances, derived Vegetation Indices, (like NDVI, the Normalized Difference Vegetation

Index, EVI, SAVI and others), and even, when available, surface temperatures. The classification

process based on temporal pattern recognition is based on the resulting differences of the canopy on

the above mentioned parameters that allow relating each pixel to a vegetation class. The classes need

to be defined on the basis of field work and previous knowledge of crop phenology in a given area.

The crop classification is the prerequisite for the identification of irrigated areas and the exact period of

irrigation.

The above mentioned process allows distinguishing:

different categories of crops (e.g. wheat from corn), as illustrated in Figure 1;

irrigated crops from non-irrigated crops within a same category of crops, , as illustrated

for wheat in Figure 2. This process also allows the detection of intermediate irrigation

magnitudes (e.g. high irrigation volumes vs. low irrigation volumes), as illustrated in

Figure 3.

The identification of plots that receive supplemental irrigation (i.e. less amount of water, but in a well-

selected timeframe) usually implies more difficulties: under this practice plots show lower contrast

compared to rainfed or irrigated plots of the same crop. Supplemental irrigation is usually used when

water stress occurs, and is employed both in extensive herbaceous annual crops and woody crops. In


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this case, precipitations data is needed to distinguish irrigation (the vegetation index, reflecting the

plant water status, does not differentiate between water coming from rainfall or irrigation). Where the

use of EO is not sufficient (e.g. woody crops with sparse ground cover, like vine or olive), in-situ

information like cartography based on very high resolution orthophotos can be used.

The whole procedure is not automatic. It requires a precise knowledge of crops and crop phenology,

and needs further corroboration by experienced operators. Figure 4 shows an example of an annual

map of irrigated areas (a comparison of the situation before and after the introduction of a new Water

Law in Spain).

Figure 1: Different temporal phenology patterns of wheat and corn both irrigated as it is

described by NDVI

Note: NDVI represents relative photosynthetic size of the canopy. These NDVI values can be converted to crop coefficient

values to estimate crop evapotranspiration.

Figure 2: Different NDVI magnitude patterns of irrigated and non-irrigated wheat

Note: Two parcels of wheat with the same sowing date and similar development at the beginning of the cycle (due to sufficient

amount of rainfall) exhibit a different behaviour in the dry months as a consequence of no irrigation in one of them (green dots).

CORN WHEAT


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Figure 3: Different NDVI’s magnitude patterns of wheat with different levels of irrigation

Note: Two parcels of wheat under different water supply: fully irrigated (red) and not well irrigated due a failure of pumping

(green). Sowing date is later in fall than in previous figure (leading to not enough rainfall during early phenology stage).

Figure 4: Example of annual map of irrigated areas in La-Mancha Oriental aquifer (Spain).

Parcels in yellow (red) indicate irrigation before 1984 (after 1984)


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2.2.2 Monitoring of abstracted volumes

The detection of non-authorised abstractions of the second type (volumes) requires mapping crop

water consumption over time during the growing season. This can be accomplished by using the same

time series of images used for the detection of irrigated areas, nonetheless requires further processing

(Figure 5 and Figure 6).

The basis of irrigation water requirements calculations is:

time series of reflectances and vegetation index (VI) maps can be converted into maps

of basal crop coefficient, the basic input for the widely used FAO56 model on crop

evapotranspiration calculations. The crop coefficient is analogous to a transpiration

coefficient. The FAO56 approach is the most widely used and operationally mature

method since the seventies to retrieve evapotranspiration from agricultural crops. The

crop coefficient can be obtained directly from linear relationships with vegetation indices

(Glenn et al. 2011) and/or from reflectance data and a series of intermediate

relationships involving fAPAR or LAI. The degree of accuracy of both methods is similar

(D’Urso et al. 2011), but as pointed out by Glenn et al. (2007) « it makes sense to use

VIs directly rather than convert them into LAI or fc estimates ». The VI-based basal crop

coefficient approach is operationally mature and can use spectral data from all sensors

at higher spatial resolution in order to obtain dense time series of images. The

upcoming Sentinel-2 constellation (providing suitable spectral imagery at 10 m of spatial

resolution and a revisit time of less than a week) could be a definitive tool in this

approach. Interpolation of consecutive images is used to fill the gaps (e.g. due to cloud

cover), and the product of basal crop coefficient and daily reference evapotranspiration

from the agro-meteorological station subsequently provides daily crop water

requirements on a pixel by pixel basis.

remote sensing of evapotranspiration can also be obtained from surface temperature

images by using additional techniques based on surface energy balance (Bastiaanssen

et al. 1998). Given that of all operational high-resolution satellites only Landsat currently

provides surface temperature (and with a revisit time of 16 days), and considering its

thermal channel spatial resolution of 100 m pixel size, this procedure is complementary

with the one previously described, and can provide an independent quality control in

suitable areas. The upcoming Sentinel-3 will also provide a thermal band, albeit at the

coarse pixel resolution of 1 km.

EO-driven soil water balance, according to FAO56, enables to calculate irrigation

water requirements on a pixel by pixel basis. Precipitation and soil hydraulic

characteristics are required. Following FAO56 procedures it is possible to calculate

irrigation water requirements under water stress situations, as is used either in

controlled deficit irrigation or in the case of supplemental irrigation. Knowledge of the

desired water stress degree is required, which needs local calibration. EO-based soil

moisture data can be useful in some cases, although its spatial resolution is only 250m

minimum pixel size (PROBA) or 500m (Sentinel-1).

the calculation of abstracted volumes require the efficiency figures of both irrigation

systems and irrigation distribution/storage.

Figure 5 and Figure 6 show the flux diagram of the whole process (including indications on the

sources of uncertainty and accuracy).

More sophisticated methods have been developed (e.g., D’Urso, 2005), going through a series of

intermediate physical parameters, like leaf area index (LAI). Comparative studies (D’Urso and Calera,

2006) show that the accuracy of these methods is similar to the direct NDVI approach.


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Figure 5: Overview of steps in using EO for detecting non-authorised abstractions.

Note: Crop Water Requirements (CRW) can be obtained either directly from visible and near-infrared (NIR) bands (left strand*)

or through the surface energy balance from additional thermal bands (right hand strand**).

The variation of water storage determine the irrigation requirements

Maps ofET/CWR* or ET/CWR**

Derived from the water balance. A simple approach (one layer model) is described in the FAO-56 manual.

Maps of Capillarity rise (estimated)

Maps ofRun-off (estimated)

Maps ofDeep percolation

Net irrigation water requirements

Maps ofPrecipitation

Precipitation data from

agrostations

Spatially distributedSoil Water Balance

I=ET+DP-Pp-RO-?S

Maps of variation in water storage in the soil

Water consumed at irrigation scheme

Water abstraction/Water demand

Uncertainties:-Precision of the LU/LC map-Efficiencies of the irrigation systems

Uncertainties:Efficiencies of the distribution/storage systems

Uncertainties:-Precision of soil maps(soil depth and hydraulic properties)-Knowledge of irrigation strategies (deficit irrigation?)

Figure 6: Overview of processing steps from crop water requirements (CWR) to water

abstraction


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2.3. EO applications for the monitoring of non-authorised water

abstractions

Where water rights are clearly defined and corresponding information is available, EO products

can support the detection of non-authorised abstractions by allowing:

The continuous monitoring of irrigated areas and abstracted volumes over large areas;

A better targeting of field inspections (the technology is recognised as able to provide

more objective sets of data compared to field inspection methods, and appears to be a

powerful decision making tool to direct and guide the detailed field inspection, e.g. by

providing targeted mission roadmaps for field technicians;) or even

Legal action in Member States, provided there is a legal recognition of EO as evidence:

the use of Earth Observation-derived data, considered as objective, can indeed provide

legal evidence in the event of legal conflicts. In a recent sentence (2012), the Spanish

Supreme Court validated remote sensing as a method for the recognition of water rights

and detection of illegal abstraction and for the identification of irrigated surfaces. The

Supreme Court decision established for the first time the requirements that this

methodology must meet in order to be considered as acceptable evidence in legal

proceedings: the provided service on the identification of irrigated areas must include

the presence in court of the individualised report about a plot, introduced by a senior

expert, who is responsible for its completion and quality.

The EU project Aperture (ENV4-CT97-437) investigated if and how high spatial resolution Earth

Observation (EO) data can be accurately and effectively used in judicial and extra-judicial proceedings

related to the implementation of environmental law. The main outcome of the legal investigations was

that there were no major constraints in the use of satellite images either in the court or in

administrative proceedings. The need for an expert who would explain technical matters in the court

cases was however strongly supported (Aperture Final Report, unpublished document, 2000), similarly

to the Spanish Supreme Court sentence.

A recent ESA study report states: "Broadly, the conclusion reached is that internationally there are no

major insurmountable barriers to the use of EO information by courts and administrative tribunals."

(European Space Agency, 2012). Purdy and Leung (2012) provide a summary of legal consideration

in the EU.

Figure 7 presents an overview of the steps leading to the detection of non-authorised abstractions

using EO images.


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Acquisition

of EO

images

Local ancillary

data (e.g. climate,

land use, water

balance)

Processing and development of maps

of irrigated areas and maps of water

consumption, incl. field validation

+

Estimation and mapping of water abstractions

Local ancillary data (e.g.

irrigation efficiency)

+

Comparison of legal reference data with EO-assisted estimates of abstractions

Verification on the field through targeted inspections Recognition of EO products as legal

evidence (e.g. in Spain)

Collection of legal reference data on water

rights

Detection of non-authorised abstractions

Identification of anomalies / suspicions of non-authorised abstractions

Figure 7: Overview of steps for the detection of non-authorised abstractions

EO products provide key information on water abstraction through the identification of irrigation

activities and the monitoring of abstracted volumes, both for routine and drought management.

However, like for in-situ non EO-based approaches, this information needs to be compared to legal

reference data to actually detect possible cases of non-authorised abstractions. Existence of water

rights and availability of corresponding information are a prerequisite for the detection of non-

authorised abstractions, irrespective of the method used to control abstractions.

2.4. Possible range of complementary applications

Due to the potential absence of prerequisites such as water rights, it is not always possible to detect

non-authorised abstractions in Member States (see Chapter 3). However, the EO products described

above allow for the development of a range of complementary applications that can meet some of the

water managers’ needs identified in section 2.1 and, more generally, contribute to the sustainable

management of abstractions and water resources in general.

In particular, EO maps can be used for the two following complementary applications:

where water rights are under a transition from historic to moderns management (e.g.

entry in force of new water regulations e.g. in Spain), EO products can help to detect

irrigated areas and regularise water rights. Similarly, maps of irrigated areas can

support the procedure of extinction of water rights after protracted non-use. Maps of

irrigated areas can be retrospectively obtained from satellite data for many years and

allow new assignments and a general review of water rights;

better understanding of irrigated areas, irrigation requirements and abstractions, in

order to better inform the development and/or revision of land and water management

plans;

ex-post assessment of the efficiency of water saving actions taken on the catchment at

different time scales (multi-annual, annual, monthly);


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implementation of a volume-based system of water fees and control of the accuracy of

self-declarations.

The detailed crop maps obtained with EO images can also be used to support the updating of

Copernicus land service products or the control of the implementation of CAP measures such as the

development of catch crops in France8.

More generally, maps of irrigated areas from satellite data can be shared and verified independently

by many stakeholders and thus provide tools for participation and conflict resolution.

The same EO maps could also be used to contribute to the following EEA indicators (source: Digest of

EEA indicators 2014, EEA Technical report 8/2014):

climate change indicators (growing season for agricultural crops, agrophenology, water-

limited crop productivity, irrigation water requirement);

water indicators (use of freshwater resources).

2.5. Discussion of the potential of EO applications

Earth Observation features a wider range of technical assets when compared to field observation

alone. EO can provide detailed maps of irrigated areas and monitor the level of water consumption in

large geographical areas (e.g. watershed scale) where field measurements provide point values of

evapotranspiration (ET) for a specific location, but fail in providing the ET on a regional scale. Images

can be obtained on a daily or weekly basis, depending on the resolution required. EO time series

provide basic products that can support and optimise field inspections by guiding it to selected areas.

Moreover, crop temporal pattern as recorded by EO products can be used as legal evidence in court (

with a recognised EO expert as a supporting testimony). Several comparative analyses show that

Earth Observation systems present a good accuracy compared to field measurement techniques of

crop water (Castaño et al., 2010; Cuesta et al., 2005) and water abstractions (Garrido-Rubio et al.,

2014). As an example, the application of SAMIR showed that the overall difference with field

measurements was only of 5% over 160 days. The average difference may however reach 18% of the

soil water balance at a weekly scale (Er-Raki et al., 2011). Current technical challenges (essentially

due to cloudiness and to the difficulties of differentiation of specific crop classes) can be solved with

more sophisticated approaches (e.g. interpolation of images, additional ground truthing, water balance

model) (see Chapter 3 on requirements).

EO provides estimates of water abstractions that are subject to uncertainties, and is not intended to

replace individual metering of abstraction points by means of flowmeters (although these may also be

subject to uncertainties).

The use of EO products is particularly valid to ensure the respect of routine water management

(controlling the compliance with standard water rights), where inspections can be carried out all along

the growing season. When an anomaly is identified on the EO maps, operators can rapidly verify the

conformity of abstractions directly on the field.

Under specific conditions, the use of EO products is also valid for drought management involving

seasonal water restrictions. Controlling the respect of these restrictions appears particularly

challenging, as they are generally applied on limited short periods of time, requiring great reactivity of

water managers to identify cases of non-compliance. EO can be a helpful tool in this respect as it can

8 Projet TETIS. Méthode d’évaluation par imagerie satellitaire des anomalies de cultures intermédiaires pour pièges à nitrate

(CIPAN) GEOSUD - DDT79. Presentation available at: www.cete-sud-

ouest.equipement.gouv.fr/IMG/pdf/06_DDT79_SatCIPAN.pdf


| 16

cover large areas, help target inspections and provide an opportunity to foster equity, easing

participation and transparency. The possibility to identify NDVI variations based on short term and

sometimes only partial water restrictions (vs. bans) has been questioned, but it is not a matter of

technical limitations in terms of time of EO images acquisition and processing (weekly images can be

available and, currently, the processing time from image acquisition to a final, ready for use product is

around two days on average, according to current Landsat8 production chain). The upcoming pair of

Sentinel-2 satellites are expected to provide at least a similar performance, at a higher spatial

resolution. The main possible shortcoming could rather be related to the magnitude and temporal

response of the canopy to small and short-term water restrictions. However, the identification of

irrigated areas from the time series of EO imagery is simplified in the event of drought, as a

consequence of lower rainfall usually implies water shortage in the root zone of crops, resulting in an

increase of the contrast between canopy of irrigated and non-irrigated crops, both spectrally and

temporally, as shown in Figure 2 and Figure 3. Therefore, images reliably explain the impact of

watering ban on vegetation development and thus enable a timely reaction to drive field inspection.

In the following cases, the use of time series of EO imagery for drought management has namely

been shown to be reliable (e.g., Calera et al., 2013):

irrigation was not allowed in sensible areas where the impact of abstraction could have

put at risk the ecosystem survival (river running dry along the path in which the river

loses water by infiltration).

reduction of abstraction were possible by decreasing the allowable amount of water per

unit of area for each water management unit (farm or group of farms with the same

source of water). A first estimate was based on the determination of the irrigated area in

each management unit. Monitoring showed that shifting irrigated crops from summer to

spring was a very effective measure to decrease abstraction.

Extensive assessments of costs of field measurements approaches alone and compared to their

combination with Earth Observation-derived information were carried out within the FP7 SIRIUS

project for pilot areas in Spain, Italy, Turkey, and Brazil. They showed that the cost of Earth

Observation services could be significantly lower than the cost of field measurement and inspection

alone. Water users associations have already demonstrated their willingness to pay for such services.


| 17

In summary, the use of Earth Observation-derived data to monitor water abstraction is a mature

system, and several service providers with demonstrated operational capacity have shown to be

ready to implement it. The new coming Sentinel data generation will provide new opportunities to

improve this system, in particular with Sentinel 2 (10m resolution) and possibly by taking advantage of

Sentinel 3 (thermal band at 1 km pixel resolution) or Sentinel-1 (soil moisture) data

The key benefits of EO-based monitoring of abstractions include:

large geographical coverage at adequate spatial and temporal resolution;

good demonstrated accuracy;

vastly increased efficiency of surveillance and inspection;

objective assessment tool and trusted by water users as such;

vastly reduced monitoring cost and needs for human resources;

acceptance by users and demonstrated high interest of representatives of water authorities.

additional benefits coming from the development of additional land-use and climate-relevant

datasets;

However, there is still little knowledge about its practical use, assets and shortcomings for the

detection and management of non-authorised water abstractions in specific situations beyond the

experiences of SIRIUS and a few other initiatives. The key barrier to the detection of non-

authorised abstractions, irrespective of the method used (in situ or EO), remains the existence of

defined water rights and the availability of corresponding spatially explicit information. Where

water rights are not defined, however, EO products can be used for a range of intermediary

applications meeting identified needs of water managers.

Table 1 highlights EO products suitability for the identified water managers’ needs.

Table 1: EO products suitability for the identified water managers’ needs, including

complementary applications

Identified needs of

water managers

EO

suitability

EO potential and assets Shortcomings and barriers

Ensuring the reliability

of self-declaration of

water abstractions

High EO enables full area

coverage to detect

“suspicious” parcels

Difficulty to clearly differentiate perennial crops (need for additional in-situ data)

Technicians may need training

Optimisation of

inspections

(abstractions control)

Implementation of

volume-based fees

and reliability of self-

declarations

High

EO enables full area

coverage to detect

“suspicious” parcels and

direct field inspections

there

Difficulty to clearly differentiate

perennial crops (needs additional in-

situ data)


Dependent upon the existence and

availability of legal reference data


| 18

Identified needs of

water managers

EO

suitability

EO potential and assets Shortcomings and barriers

Respect of restrictions

for drought

management

Medium EO enables full area

coverage to detect

“suspicious” parcels

Difficulties to detect partial and short-

term restrictions in perennial crops

(needs additional in-situ data and/or

higher resolution EO time series)

Requires reactivity in the processing of

information and procedures in place to

handle on-demand requests by

organisations in charge of managing

water restrictions


Dependent upon the timescale and

nature of restriction measures

Monitoring of irrigated

areas and abstracted

volumes for a better

water management

High

Large area coverage at

adequate spatial and

temporal resolution

Users in many areas not yet prepared

(technically, expertise, administrative

structures)

Regularisation of

historical water rights

(e.g. Spain)

High EO provides the only

means to document

irrigated parcels in past

years (back to 1972)

Recognition as legal evidence achieved

e.g. in Spain, but not in most other MS

Mapping of hydraulic

works

Low

Ex-post assessment of

the efficiency of

actions taken, at

different time scales

High EO-based indices (water

abstraction vs planning;

irrigated vs irrigable)

enable full area

coverage at adequate

resolution

Users training and uptake needed

Adjusting water price

and implement

volume-based fees

High Based on estimation of

abstracted volumes


Irrigation scheduling High Near-real-time large

area coverage at

adequate spatial

resolution



| 19

3. Requirements for the detection of

non-authorised abstractions and

complementary applications

3.1. Technical requirements for the monitoring of water abstractions

Table 2 highlights the technical requirements for the implementation of an operational EO-based

system for the monitoring of abstractions (identification of irrigated areas and mapping of abstracted

volumes). It also presents associated advantages and shortcomings in terms of accuracy, costs and

applicability.

Table 2: Overall characteristics of EO products for detecting non-authorised abstractions of

types 1 and 2

Requirements Type 1

Mapping irrigated areas

Type 2

Mapping water consumption

Crop water requirements Irrigation water

requirements

Irrigation water

abstraction

Basic EO data

requirements

See box below

Time series of satellite images in visible and near-infrared (NIR) spectrum range (thermal

range optional)

EO

satellite/sensor

Sensor

resolution

Resolvable

parcel size

Image cost Comments

Landsat-8 30 m

1 ha free standard

processing

Landsat-8-

thermal

120m 15 ha free surface energy

balance (SEB)

Landsat-8 type

(Spot, IRS, DMC)

20-30 m 1 ha 1,000-5,000

€ per

100x100 km2

standard

processing

Sentinel-2 10 m 0.1 ha free planned 2015

Sentinel-3

(thermal)

1 km 10 km2 free SEB

commercial very-

high-resolution

around 1 m 0.03 ha 1,000-5,000

€ per 10x10

km2

increases

processing effort

(many small

images)


| 20

Requirements Type 1

Mapping irrigated areas

Type 2

Mapping water consumption

Crop water requirements Irrigation water

requirements

Irrigation water

abstraction

Required EO image

frequency and

period

every 2-4 weeks during

growing season

every 1-2 weeks during growing season

Additional data

requirements

ground truth visits to a

small sample of fields

agro-met station

Ground truth

precipitation;

soil water

storage

irrigation

system

efficiency

local information about main crops phenology and development (for initial methodological validation in a new area and later to improve accuracy of both types)

existing land use/land cover maps (useful basis to start with, but not necessary

orthophotos or EO very high spatial resolution (useful for woody crops)

Required models none (FAO-56)

(Allen et al., 1998)

soil water

balance,

FAO56

none

Uncertainties (depend on ground

truth sampling)

soil evaporation* soil

parameters

system

heterogeneity

Accuracy

92% identified correctly

(Calera et al., 2013)

+-5-10% uncertainty for

herbaceous crops

Comparable with eddy

covariance uncertainty &

FAO56 based on field

work

(Glenn et al., 2011;

Tasumi and Allen, 2007)

10-20%

(Campos et al.,

2010; Glenn et

al., 2011;

Melton et al.,

2012b)

10-20%

(Garrido-Rubio

et al., 2014)

Cost** 1-3 €/km2/year 2-4 €/km

2/year ***

(implementation and maintenance of ground truth not included)

Limitations/barriers very small parcels

low contrast between irrigated and non-irrigated crops

woody crops with management under water stress, supplemental Irrigation

very frequent full cloud cover that can limit the number of exploitable images (in particular in Northern-Western EU).

Overcoming barriers SENTINEL2 (10 m pixel size) as the basis of constellation to obtain dense time series. Use of very high spatial resolution, as RapidEye, Quickbird, Ikonos, Pleiades, WorldView, etc.

Multiannual monitoring; dry years increase contrast between irrigated and non-irrigated crops.

Management of

drought and crisis

The designed system enables enforcement of Annual Exploitation Plan

Weekly monitoring enables to detect non authorised irrigation

Legend:

CWR=crop water requirements; IWR=irrigation water requirements; IWA=Irrigation Water Applied

* in crops that do not cover the soil completely, e.g. vineyards, olive and orchard trees

** Source: large-scale production line cost estimation in context of FP7 project SIRIUS

*** Cost depending on available local information: cadastre, maps of hydraulic properties of soil, (agro)meteorological stations

network, ground truth.


| 21

Time and spatial resolution requirements for EO images

The detection of non-authorised abstractions of the first type (irrigated areas) requires land-use/land-

cover maps that allow for a distinction of irrigated crops. This is accomplished by multi-temporal

classification on the basis of a time series of EO images (for details of this processing, see the next

section). It is not possible to rely exclusively on existing land use maps (e.g. cadastral, CORINE Land

Cover) as they may not be updated and may be based on classes that do not take into account

irrigated vs. non-irrigated areas. The time resolution required by water managers and commissaries

for this purpose is to have a fairly cloud-free image (<10% cloud) every 2-4 weeks from about 2 weeks

before the start of the growing season until its end.

The detection of non-authorised abstractions of the second type (abstracted volumes) requires

mapping crop water consumption over time during the growing season. This is accomplished by using

the same time series of images as above in type 1, but with further processing (Figure 1 and Figure 2).

The time resolution requirement for this purpose is slightly different from type 1 mentioned above in

that one fairly cloud-free image is needed every 1-2 weeks, again from shortly before the beginning of

the growing season until its end.

For both types, obtaining images covering at least 90% of the area is normally sufficient.

The required time resolution for both purposes can best be achieved on the basis of a multi-sensor

constellation, integrating data from all available EO platforms. The corresponding inter-sensor cross-

calibration algorithms have been developed (Martínez-Beltrán et al., 2009) and a comprehensive

cross-calibration database has since been established.

The required spatial resolution for both types depends on the parcel size statistics in the area. Yet, the

current Landsat-8 type satellites can be used in most areas. This takes into account the need to

always aggregate 3x3 sensor pixels (compensating for geo-referencing uncertainties) in order to

obtain a stably geo-localised time series (i.e. making sure that the same pixel is in the same place in

all image-based maps) (Martínez-Beltrán et al., 2009). With the advent of Sentinel-2 (expected to

deliver operationally in 2015) parcels from 0.1 ha size can be resolved. In areas with particularly small

parcels, commercial satellites offer higher resolution, albeit at higher image and processing cost.

Sensors on-board low flying aircraft, ultralights, or drones accomplish the same, while offering more

local flexibility at high cost. The combination of various sensors and various data sources (i.e. the full

use of the multi-sensor constellation mentioned above) is essential for better efficiency of such tool.

3.2. Legal and administrative requirements for the detection of non-

authorised abstractions

As highlighted in Chapter 2, the definition and availability of spatially explicit information on water

rights (irrigable areas, abstraction allowances) against which to compare the EO-assisted products

(maps of irrigated areas and abstracted water volumes) is a prerequisite for the detection of non-

authorised abstractions. This is true irrespective of the method used (in-situ or EO-based). Depending

on their existence, status of implementation and nature, water rights are more or less suitable to the

detection of non-authorised abstractions, as illustrated in Figure 8. These water rights requirements

are sufficient to compare with EO products and better target field inspections.

Furthermore, optional requirements are the recognition of EO-based evidence in administrative and

legal procedures (e.g. sanctioning in case of abstraction beyond authorised volumes or outside

authorised periods, formal recognition of water rights from earlier legal situations) and the anchoring of

EO-based monitoring and control in national policies.


| 22

Figure 8: Characteristics of water rights and suitability for the EO-based detection of non-

authorised abstractions

3.3. Operational requirements

The required infrastructure and expertise for using the service requires very little infrastructure

and expertise: a standard PC or laptop and some training of the user is usually sufficient. Users are

not required to have any technical or informatics background.


| 23

4. Current challenges at MS level

In light of the requirements to develop EO-based tools and services for the detection of non-authorised

abstractions, MS are likely to encounter several challenges, as identified in previous projects and

through the interviews, case studies and series of workshops.

4.1. EO technical capacity

Following the discussion on requirements from chapter 3, MS would need dense time series of high

resolution imagery covering the entire crop growing season, and more specifically bi-weekly EO

images from a high-resolution (HR) Virtual Constellation (multi-sensor time series at 10-30m

resolution). These can be acquired with satellites like Landsat8 and DeIMOS, which are currently

available. A significant step forward in simplifying operations is expected from Sentinel-2, to be

launched later in 2014, which will be able to “see” spatial scales from 0.1ha. In areas where smaller

spatial scales prevail, RapidEye (5m by 5m) provides a high-quality reliable solution.

Beyond the availability of EO images, the challenge that could be encountered by MS rather lies in the

operation of EO-based monitoring. So far, EO-assisted abstractions monitoring has been introduced

only in two MS (Spain and Italy), at regional and/or local level. In contrast, EO has been used for

general water management in several MS, either at (pre)operational or test/demonstration level

(Figure 9). As the EO and supporting data needed for both purposes are the same, the step from

water management application to the focus on abstractions monitoring is quite straightforward

technically. It implies, however, more information, as well as appropriate legal and administrative

structures to be in place.

Figure 9: Overview of demonstrative and operational EO tools and services. Asterisks indicate

methodology to obtain CWR

There are tools and services for similar applications (agricultural sector, farm advisory, drought

monitoring, land-use) available in several MS, which partly require and use the same basic data.

These synergies can be explored.


| 24

4.1.1 Services or applications for water management

MINARET (MonitorINg irrigated AgriculturRe ET) has been developed by the Regional Government of

Andalucía and the Spanish Research Council Córdoba as a planning and operational tool for routinely

monitoring crop water consumption in the irrigated lands of the Guadalquivir basin (Southern Spain). It

has been made available to the corresponding river basin authority for use in their operations. Like

SIRIUS, it is based on the Kc-VI approach (González-Dugo et al. 2013).

The Dutch company WaterWatch has offered products and services to monitor crop water

consumption for over 15 years and has carried out relevant studies in many African countries as well

as in Saudi Arabia and Yemen. Their approach is based on the SEBAL algorithm (developed by

Bastiaanssen et al. 1998), which estimates the components of the surface energy balance (SEB) from

Earth Observation data. It requires Earth Observation image data both in the solar and thermal

spectral range. The latter limits the achievable spatial resolution (e.g. Landsat solar channels at 30m

resolution, thermal at 120m, which means that only parcels larger than 15 ha can be resolved, in

contrast to 1 ha for solar channels).

The University of Idaho Department of Water Resources has further developed SEBAL into

METRIC (using a simplified calibration approach). Their initial services to monitor crop water

consumption and irrigation water abstractions for nearby water users associations have been

extended to other areas across the U.S. (Allen et al. 2007).

4.1.2 Services or applications for irrigation advisory (or irrigation scheduling)

They are also based on the mapping of crop water requirements. Many of the Earth Observation-

based irrigation advisory services also offer crop inventories, which provide the necessary information

for detecting irrigated areas. Of the nine examples provided in Figure 9, five are fully operational

(TOPS-SIMS, IrrisatSMS, IRRISAT, SIRIUS-IFAS, FieldLook), while the others have been applied and

demonstrated in individual cases.

TOPS-SIMS is a fully operational prototype for mapping crop water requirements (centred on irrigation

advisory), but also allows for monitoring crop water consumption (Melton et el. 2012).

The SIRIUS Integrated Farm Advisory Service has been established in the Spanish La Mancha

area. It offers irrigation scheduling as well as advice on fertilization and yield forecast (Calera et al.

2013a).

FieldLook has been developed by WaterWatch (now under the roof of eLEAF) and is based on

SEBAL (requiring thermal imagery).

The Italian IRRISAT service has been providing irrigation scheduling to large areas in the Campania

region (Vuolo et al. 2013).

IrrisatSMS (currently discontinued) offered irrigation advisory by SMS to farmers in Australia and

California (Hornbuckle et al. 2009a).


| 25

IrriSATSMS

The IrriSATSMS system combines satellite images data, meteorological data and location

coordinates to determine how much water crops have used and how long farmers need to run their

irrigation system each day.

IrriSATSMS uses satellite images derived from Landsat 5. These satellite images are collected

across Australia every 14-20 days.

Daily, customised, irrigation water management information is then sent out to irrigators by SMS

messaging. The service sends actual pump run times based on your individual irrigation

application rates, there by simplifying irrigation decisions.

IrriSATSMS (currently discontinued) was used to9:

manage and benchmark water use efficiency across 26 000 ha of irrigated cotton (in

2010/2011)

help 175 peri-urban irrigators in the Hawkesbury -Nepean Catchment

assist winegrape growers in the Murrumbidgee and Murray Valleys manage 60 000

megalitres of high security irrigation water for vineyard irrigation

SAMIR is a tool for estimating agricultural water requirements based on the Kc-VI approach,

combined with a distributed soil water balance model. It has been integrated with a range of models in

a decision-support system for irrigation water monitoring and management for use by the Tensift river

basin authority (Morocco) (Er-Raki et al., 2011).

AGRASER is a very complete system of farm advisory and has also been extensively used by

Mexican insurance companies.

Isareg and GISAREG have been developed by the University of Lisboa and applied in Portugal as

well as in Latin America.

TELERIEG has investigated irrigation advisory with a special focus on woody crops (e.g. citrus trees),

also using very-high resolution imagery from airborne sensors.

9 CSIRO website : www.csiro.au/Organisation-Structure/Flagships/Sustainable-Agriculture-Flagship/Irrisat-Project/Using-

IrriSATSMS.aspx


| 26

TELERIEG PROJECT

The main objective of the TELERIEG project was to develop knowledge and tools on the

application of remote sensing and geographic information systems for the improvement of

irrigation water management and the response to natural risks affecting agriculture in the

Southwest Europe (Erena et Lόpez Francos, 2012).

The final results were an automated image processing system that generates daily maps with

useful parameter for irrigation management, and a geoportal gathering agro-climatic and

cartographic information, adapted to the INSPIRE Directive (Berthoumieu, 2012).

Concerning the application of remote sensing for the improvement of water management, three

different spatial resolutions for Earth Observation have been investigated:

Observation from a short distance or above canopy with a near infrared and visible

camera coupled with a thermal camera (10cm pixels) for irrigation scheduling in

vineyards (Bellvert and Girona, 2012), irrigation management of citrus trees

(Jiménez-Bello et al., 2012)

Observation with cameras installed on small aircrafts or satellite with high resolution

as SPOT, DMC (10cm to few meters pixels) for the estimation of actual

evapotranspiration (García Galiano and García Cárdenas, 2012)

Observation with satellites with a lower resolution as Landsat 5 (120 m) and 7 (60

m), HJ (China), NOAA, MODIS (240m for visible and 960 m for thermal) (50m to a

km pixel)

Using airborne platforms enables to get a high spatial resolution. However, this system has a cost

of about 150 000 to 200 000 € for the equipment plus about 150 €/h for the flight (or a bit less

depending on the aircraft cruise speed) (Berthoumieu, 2012).

4.1.3 Services or applications to support precision farming

The advent of Earth Observation via satellite systems has enabled the development of crop monitoring

systems and precision farming tools which are substantial assets for modern agriculture.

Satellite images enable providing information on current field and crop conditions that are essential for

agricultural management such as the NDVI index, the nitrogen deficit, soil moisture, weather forecast,

for a specific location. Several commercial companies offer crop monitoring tools that inform farmers

about their crops conditions and provide management advices at key stages of crop growth. These

tools are real decision support systems for farm management and help farmers to plan and carry out

agricultural operations. Remote management of crops makes it possible to target problematic areas

early in advance and react fast, thereby reducing the need for soil sample collection and analysis and

consequent late interventions10

.

These services or applications to support precision farming generally focus on input management, but

not necessarily on irrigation management. Consequently, they often do not provide maps of crop water

requirements (i.e. the information necessary for detecting non-authorized abstractions of the second

kind), neither do they provide maps of irrigated areas (i.e. the information required for the detection of

non-authorised abstractions of the first kind). However, they are based on Earth Observation images

which can be used to derive the information required to detect non-authorised abstractions. In

10

http://centres.insead.edu/social-innovation/what-we-do/documents/Farmstar.pdf


| 27

addition, they normally also either provide or have access to the local data needed to calculate crop

water consumption. Scale might be another issue here, as precision farming services often focus on

smaller areas, albeit at higher resolution.

Most relevant examples include Farmstar (used in France, Germany, Australia, Canada and South

Africa11

), Cropio12

(North America, Europe and CIS), FarmSat13

(France, USA and Australia),

FieldLook (Netherlands, Poland, Ukraine, Canada) and AgroSat (Spain).

FARMSTAR

Farmstar is a precision agriculture technology, developed by EADS-Astrium since 1996 and

operational in France since 2002.Since 2006, EADS-Astrium and Arvalis work together to develop

and commercialise Farmstar.

Astrium Services have access to a large satellite imagery and aerial data catalogue: SPOT satellite

family, Pleiades, TerraSAR-X, TanDEM-X, FORMOSAT-2 and DEIMOS14

. They operate satellite

and aircraft themselves and have thus full control over data acquisition.

Satellite data are combined with agronomic models that incorporate weather data and plot

characteristics informed by farmers (location, crop variety, date of sowing depth and type of soil,

type of irrigation system). Prescription maps are then generated and sent to farmers within 5

days15

.

Subscribing to the Farmstar service cost on average 10 € per hectare.

However, if Farmstar was implemented with a great success in France (250,000 ha in 2006)16

,

trials in the UK experienced problems. The main explanation is that unlike in France, farmers

cooperatives in the UK are fewer and work differently. British farmers seem to be more suspicious

concerning the productivity of ‘new-fangled’ technologies. Also, the weather and clouds presence

make clear satellite images more difficult.

In conclusion, “dual use” of irrigation advisory initiatives for detection of non-authorised abstractions

would involve additional steps of image processing and product generation (calculation of ET); the

derivation of maps of irrigated areas (from a time series of images); and possibly a mosaicking of

smaller images into a larger area.

4.2. Governance and political commitment

Water management happens in a complex web of institutions and other actors, at a range of

interacting spatial and temporal scales and over a range of equally interconnected governance and

policy levels (farm, local/regional, MS, EU). The acceptance of Earth Observation-derived information

for the monitoring of non-authorised abstractions seems to depend on local awareness of the issue

and political commitment to secure compliance with water rights, as well as on the support of

associations accompanying water users for such technologies and on the history of the use of Earth

Observation services for other purposes. The major difference in governance structure is introduced

by the water source. In areas irrigated from groundwater, we often find large numbers of individual

11

www.spectralcameras.com/files/Applications/precision-farming_farmstar.pdf 12

https://cropio.com/media/cropio_en.pdf 13

www.farmsatpro.geosys-na.com/Portal/Public/LoginGeosys.aspx?ReturnUrl=/portal/default.aspx 14

Astrium website : http://www.astrium-geo.com/en/209-our-activity 15

centres.insead.edu/social-innovation/what-we-do/documents/Farmstar.pdf 16

www.spectralcameras.com/files/Applications/precision-farming_farmstar.pdf


| 28

private farm holdings, sometimes (e.g. in Spain) grouped into an Irrigation Community. Areas mainly

irrigated from surface water are normally governed by Water User Associations (which are also in

charge of the land reclamation and channel networks). The monitoring and control of abstractions

(areas and volumes) are easier in surface water areas, where technical and social monitoring and

control mechanisms are normally in place already. Examples in Spain for instance show that

groundwater aquifers governed by an Irrigation Community can manage the abstractions of their

members quite well.

4.3. Definition of water rights and availability of corresponding

information

The common denominator to any approach for the detection of non-authorised water abstractions is

the availability of legal data regarding water rights. Where water rights are defined, data is mostly

available at the user level. Legal data on water rights are available in some MS, but not everywhere.

This means that there is not always the required legal reference data available against which to

contrast the EO-based monitoring data (see Annex 5 to the Guidance document about the availability

of water rights in the different countries).The compilation of water rights in a given area may therefore

require a combined top-down / bottom-up approach.

4.4. Local data availability

Table 3 provides an overview of the local data requirements and current knowledge about existing

sources and availability.

Table 3: Overview of local data requirements, source of data and availability

Datasets Details Sources Sources and data availability

Meteorological

data

Daily agro-

meteorological

data and rain

gauge data for

the calculation of

crop water

consumption

(agro)meteorological

station networks from

MS or WMO (World

Meteorological

Organisation) (in-situ

data)

Alternative options:

Meteorological satellites

(e.g., Meteosat Second

Generation) – data

products provided by

LSA SAF17

or numerical

weather prediction model

output data

Available in all MS

Potential synergies with the

Copernicus in situ coordination –

agreements foreseen with

Eumetnet

Soil maps of

water retention

Currently available pan-European

products or MS soil data

17

Land Surface Analysis Satellite Applications Facility; landsaf.meteo.pt


| 29

Datasets Details Sources Sources and data availability

capacity facilitated by the European Soil

Portal hosted by JRC18

Local information

about main crops

phenology and

development

FAO database can be

used as basic

reference19

National databases

Currently available in some MS

and some areas

Abstraction

monitoring data

Flow meter (in-

situ) data in

selected locations

for calibration and

continuous

ground truthing of

crop water

consumption

Monitoring networks Available in some MS, for

pressurised systems Not

available for flood irrigation

systems (like in the South of

France)

Existing land

use/land cover

maps (optional)

CORINE Land Cover20

;

Copernicus new High

Resolution layers (1 ha

resolution) and 'local'

component products

(Riparian areas

mapping)

LPIS (for parcel

delineation and land

cover/land use)21

Available in all MS for 2006.

Currently being produced for

2012 data.

Access and availability conditions

vary widely across MS.

4.5. Economic aspects

Based on currently active services, the cost of implementing and running such a service has been

estimated at the order of 1-3 €/km2/year. This cost estimate is based on long-term precursor services

implemented in Spain and Italy as well as on the outcome of the FP7 project SIRIUS, where a fully

operational production line was developed and implemented in 8 countries on 4 continents.

According to the views expressed by workshop participants (users), the cost for abstractions

monitoring and control (as mandated by EU and national policy) should be assumed by the EU and

each MS administration. In contrast, if the service is intended to provide water management and

irrigation scheduling support (which helps farmers improving their business profit) then users have

expressed readiness to pay for it.

In the example of Junta Central de Regantes La Mancha Oriental (where an EO-assisted water

management and abstractions control service has been running for over 15 years), the annual service

cost has been shared equally between users, River-basin authority, and Regional Government.

18

Eurosoils.jrc.es.europa.eu 19

http://www.fao.org/nr/climpag/aw_3_en.asp 20

http://land.copernicus.eu/pan-european/corine-land-cover 21

http://ies.jrc.ec.europa.eu/our-activities/support-for-member-states/lpis-iacs.html


| 30

4.6. Summary

Table 4 summarises the key factors in the implementation of an EO-assisted system or service for the

monitoring and control of non-authorised abstractions, keeping in mind that their importance may vary

depending on the local contexts.

Table 4: Opportunities and barriers to the use of Earth Observation for the detection of non-

authorised abstraction

Opportunities Barriers Variable factors

Technical Large geographical coverage

Good accuracy in identification of irrigated areas and mapping of abstracted volumes

Reliance on cloud conditions

Difficulty to identify small cultivated parcels

Calculation of abstracted volumes for some crop types require special ground truth calibration

Need for Earth Observation capabilities

Multi-sensor constellation.

Future Sentinel-2

Information on water rights and land uses

Economic Reduced costs and needs for human resources

Investments: costs of data / cost of human resources

Average cost of service: 1-3 €/km

2/year

Existing capabilities at water management authority (GIS, image processing)

Private providers to outsource the entire service or parts of it (image processing, webGIS)

Societal Acceptance and high interest demonstrated by representatives of water authorities

Participatory tool that facilitates collaboration between all stakeholders, transparency, conflict resolution

Little knowledge of the use of such technologies for the detection and management of non-authorised water abstractions.

Local challenges related to water

Support of key water users associations

Administrative

and legal

Efficiency of surveillance and inspection

Objective assessment

Consideration as legal evidence in at least one MS (Spain)

No water rights OR water rights based on flows OR based on volumes allocated to abstraction sources

Absence of information on water rights and detailed land uses (issue shared with other approaches for the detection of non-authorised abstractions)

Still little consideration as legal evidence

Political commitment to secure compliance with water rights

Governance in areas irrigated with groundwater (very large number of individual abstraction points) is often more difficult than with surface water (network of channels and abstraction points managed by users association)


| 31

5. Options for tools and services to

be developed at EU level

5.1. The requirement and opportunity to act at the EU level

The requirement to monitor and control water abstractions has been defined in the Water Framework

Directive (WFD). Over-abstraction of water is also highlighted in the Blueprint to Safeguard Europe’s

Water Resources as a significant pressure impeding the achievement of Water Framework Directive’s

(WFD) good status objectives. At present it is mostly the Southern MS that are affected by the issue at

stake. However, several South-Eastern and Eastern MS have expressed their concern that non-

authorised abstractions may be a dormant issue which will soon become important to deal with and to

be prepared for. Non-authorised abstractions, which remain out of record, may play a substantial role

in over-abstraction22

. Therefore, the detection of non-authorised abstractions provides a crucial

opportunity for improving water balances across the EU and helps achieve WFD objectives.

However, despite substantial progress in water management throughout the EU and some EO pilot

cases, most MS dealing with the issue of non-authorised abstractions consider they still lack tools and

resources that enable them to detect and manage these unsustainable practices.

The Blueprint clearly indicates Earth Observation (EO) - in particular the EU’s Global Monitoring for

Environment and Security (GMES, recently renamed Copernicus) - as a promising tool and instrument

for the detection of non-authorised abstractions. The Blueprint Impact assessment (SWD/2012 382

final) assessed this recommendation by analysing policy options for metering (as the necessary action

for the volumetric measurement of water use, and, therefore, as a necessary basis for an effective

pricing policy). Option 2a, which consists in “Mapping all EU large irrigated areas via the GMES

initiative and matching these areas with water abstraction permits to help Member States enforce

them and tackle illegal abstractions”, is one of the four options initially considered to promote

metering. The Blueprint Impact assessment states that implementing Option 2a on Copernicus would

enhance water governance at the river basin and local level and is expected to be more effective and

efficient than in situ-based inspections alone. The document deems appropriate for the Blueprint “to

refer to the possibility for the Commission to foster metering take up through the enforcement of article

9 of the WFD and through the enhanced use of Copernicus mapping to support Member States in

monitoring water abstraction at catchment level.”(p.48)

The Blueprint impact assessment also reminds that 60% of EU river basins are international, shared

by two or more countries, and that action taken by a single or a few MS is not sufficient to sustainably

tackle quantitative aspects of water management across national boundaries.

Furthermore, monitoring of abstractions is a pre-condition for the proper implementation of pricing

policies in water stressed areas (WFD Art. 9). In order to be effective in cross-border basins and to

22

Which can also be due to over-allocation of water to users in a river basin, e.g. due to an overestimation of the available amounts, or to economic or political pressure.


| 32

prevent negative effects on the internal market, economic incentives to use water at its true cost for

society should be applied in a consistent fashion in the EU. Therefore, also the metering principles,

tools and instruments should be consistent across all MS.

Yet, the need to act at EU level is triggered not only by the need to ensure consistency of water

allocation mechanisms in transboundary basins and a level playing field in the implementation of the

WFD, but also by the economies of scale and quality improvements that can be achieved by common

methodologies and datasets.

Most MS already confronted with water scarcity have shown high interest in the development of EO-

based water abstraction monitoring to complement more conventional approaches, such as the

reliance on flow meters. However, the implementation of EO-based tools and services has, so far, be

confined to isolated experiences as MS often lack expertise and the necessary enabling environment

to develop EO products for this purpose. A better knowledge of abstractions in general would support

the overall water management, and the same EO tools and services used for the detection of non-

authorised abstractions actually support a range of intermediary applications. One of them could be

the possibility to feed the EEA water-related set of indicators and thus help refine EU trends regarding

water uses and water balances.

The production of the basic map of irrigated areas and of the map of water irrigation requirements are

based on the same EO images for all EU countries, which highlights the opportunity to optimise efforts

to produce pan-European standard tools, and possibly services, to be used as such, or further

processed, within MS. Furthermore, for a system based on global data, as is the case with EO

images, cross-border interoperability is an important consideration, and adherence to and compliance

with the INSPIRE framework will also pave the way for a pan-European approach. This principle has

for instance been applied to a prototype (SIRIUS) from the first design of its services to their

implementation in 8 countries on 4 continents.

Copernicus has been conceived as a European monitoring system, in support of European policies as

well as of European platforms and sensors. Abstractions monitoring will provide a clear case of an

application that fills Copernicus with “life on the ground”, in true connection with and support of the

users that most need it (water managers at all levels, from farm to aquifer, irrigation scheme, river-

basin, and MS government), as well as in support of and supported by EU private sector capacities.

5.2. Identification of relevant options and recommendations

This section gives an overview of the different options (scenarios) for the development of Copernicus

water abstraction products, tools, and services identified in the study. These scenarios result in

different types of products, from a basic map of irrigated areas to detailed information on suspicions of

non-authorised abstractions. Member States are increasingly involved in data collection and

production from scenario 1 (not involved) to scenario 4 (full collaboration).

Table 5: Overview of the scenarios for the development of EU products and services for the

detection and/or monitoring of non-authorised abstractions

Scenario Narrative

1 EU Basic overview EU products based on space data and existing pan-EU

databases alone

2a EU-MS Low accuracy EU-MS product based on EU space data and local data from

readily available databases produced by the EU and MS – Low

accuracy


| 33

Scenario Narrative

2b EU-MS High accuracy EU-MS product based on EU space data and EU and local data

from databases produced by the EU and MS and field work –

High accuracy

3 EU-MS-local Complete

legal check

EU-MS product based on the combination of EU space data,

local technical data produced by the EU and MS and MS legal

data

These options differ by the type of governance, degree of sophistication of processing, requirements

of supporting data or models, and involvement of specialised (non-automatic) quality control and/or

local knowledge and expertise. All have in common the basic requirement of dense time series (one

image coverage every 4 weeks, unless specified otherwise below) of high-resolution images (10-30m

pixel) in the solar spectrum, which can be delivered by Sentinel-2 and which have been delivered by a

wide range of EO satellites since 1982 (e.g., Landsat4-8, Spot, IRS, Aster, Formosat, CBERS, Rapid

Eye, Ikonos, Pleiades), and of high-quality basic processing to level-2 products (reflectance values,

radiometrically calibrated, georeferenced with high-precision, and possibly atmospherically corrected).

The primary coverage of interest includes irrigable (as defined in some MS hydrological plans) and

irrigated areas (from the same source and from Eurostat, EEA, FAO, and MS statistics databases),

which would put the focus on Southern MS and would concern about 8% of agricultural areas (i.e.,

154 539 km2). Should the detection exercise be more limited spatially for financial and/or resource

issues, a refined zoom could be relevant on areas fed predominantly by groundwater , in EU roughly

50 000 km2 (about 35% of irrigated areas)

23 (because these have traditionally been more difficult to

monitor by non-EO means, so there is less information available in existing databases).

Each scenario is assessed below following the six steps for EO-based detection of non-authorised

abstractions:

1- Acquisition of EO data and basic processing (radiometric, geometric, and atmospheric

correction and generation of basic products: NDVI maps and colour composites);

2- Generation of overview products (rough estimates of crop phenological curves as well as of

crop coefficient (Kc), crop water requirements (CWR), and irrigation water requirements (IWR)

maps);

3- Generation of maps of irrigated areas and phenology;

4- Generation of maps of Kc, CWR and IWR;

5- Generation of maps of water consumption and water abstraction;

6- Detection of non-authorised abstractions by comparison with legal data of abstraction rights

and water allocation.

They are screened against criteria including:

responsibilities of stakeholders for the production of data, processing and use (EU, MS,

local) ;

accuracy;

data requirements ;

costs.

23

Siebert et al., HydrolEarth.Syst.Sci, 14, 1863-1880, 2010.


| 34

The specificities of each scenario are described in the text below and summarised in comparative

tables (Table 6, Table 7 and Table 8).

Scenario 1: EU product based on space data and existing pan-EU databases alone

The simplest scenario would provide two strands of very basic overview products.

The first strand would be space data and basic products (from EU and other sources) delivered

through a large-scale operational, largely automated production line under the responsibility of the EU

(i.e. Copernicus)). This entails the generation of maps of NDVI (and possibly other simple indices

and/or biophysical variables at large), which can be fully automated, plus maps of colour combination,

the generation of which needs manual supervision by experienced operators. The NDVI overview

maps can be used as a starting point for area selection for zooming in. The time series of NDVI maps

can also be used to extract “NDVI curves” (see below in scenario 2), which can give a very rough idea

of crop phonological curves. The colour combination maps give a first overview of crop vigour and

plant status. They can also be used at any water governance level as a first approximation for

directing field inspection.

The second strand would make use of existing pan-EU (or global) databases of non-EO data

(agrometeorological, precipitation, crop phenology) to calculate first estimates of phenology, crop

coefficient, CWR, and IWR. Both could be implemented fully on the EU level (Copernicus) and

integrated into the Copernicus governance mechanisms.

These products will be of practical use only to water managers (at national and regional level) with

some EO previous experience, so that they can visually assess crop status over wide areas (e.g. in

order to identify hot spots that need more in-depth monitoring). Their advantage lies in their low cost

and the availability of long time series (back to the first years of Landsat coverage in 1972) for

quantitative change assessment. Scenario 1 is a low cost scenario drawing from EO and existing

large-scale databases. The cost for setting up the operational production line will mostly depend on

more detailed specifications than those currently available (for instance, Sentinel-2 data will probably

have NDVI and colour composite included in level-2 products, which would substantially decrease the

costs). For illustrative purposes, however, the cost of implementing scenario 1 for Iberian peninsula

(Spain+Portugal, minus the islands) for one year, based on free images (currently Landsat),

processing & product generation, plus making products available to user via webGIS would be in the

order of magnitude of 0.20-0.50 €/km2/year (depending on the salary level of the personnel, here

calculated in ES).

Scenario 2 (version 2a and 2b): EU-MS product based on EU space data and

supporting local data produced by the EU and MS

Scenario 2 builds on scenario 1 and brings the added value of including the use of non-EU data

available at MS (all levels of water governance). These supporting local data are required for a more

accurate calculation of crop coefficient, crop water requirement, irrigation water requirement and

further products (irrigation water consumption and abstraction), as well as for the mapping of irrigated

areas. Three different kinds of products and/or services can be produced within scenario 2:

maps of irrigated areas ;

calculation of crop coefficient, evapotranspiration / crop water requirements, and

irrigation water requirements;

calculation of irrigation water consumption and abstraction.

For each of these products, two levels of sophistication (and thus, accuracy) can be achieved:

one without ground truthing, leading to scenario 2a (including the production of irrigated

area maps), but with additional information from readily available databases from the

EU and MS compared to scenario 1. There, the processing and product generation for


| 35

scenario 2a can be fully produced at EU level, based on available pan-EU, MS and/or

FAO databases; and

one with ground truthing (defined as calibration and validation of EO-data products

using in-situ data) in addition to readily available databases, leading to scenario 2b.

There, a collaborative mechanism for EU-MS synergy similar to the Copernicus Land

Monitoring, in particular CORINE is required.

i- A set of annual maps of irrigated areas are obtained from supervised crop classification based

mainly on the temporal evolution of NDVI (which in turn is used to generate crop phenological curves).

This requires a temporal resolution of one image coverage every 2-4 weeks during crop growth

periods. The spatial resolution obviously depends on the parcel size statistics of the area. Sentinel-2

will cover 80-90% of parcels in most areas and MS (estimate based on agricultural statistics).

The basic EO data products for this purpose are “NDVI profiles” per pixel (i.e., graphs of NDVI values

versus time, extracted from a time series of images over the same area, which by themselves are still

meaningless). These temporal profiles are available from scenario 1.

The next step is the analysis of these temporal profiles based on local data (including in-situ). It

requires information on the major crop classes in a given MS and their phenology. Knowledge of the

phenological curves is required for the main crops in each area (both irrigated and non-irrigated) as

the contrast between these curves allows for differentiating and thus identifying the crops. Regional

agronomic knowledge and ground truth are the main sources to obtain it. For crops that are more

difficult to identify from EO alone further supporting local information may be required (e.g.

orthophotos or very-high-resolution EO in the case of woody crops, see Table 2).

Colour composite images (RGB24

), generated at EU level (see scenario 1 above), provide visual

support for the classification process. A dynamic visualization webGIS with SOLAP (spatial online

analysis) capability, like SPIDER developed in SIRIUS, is an essential tool for this purpose, as it

allows efficient generation and visual comparison of NDVI temporal profiles and their correlation with

all available supporting data. Expertise and experience in performing this kind of analysis is required in

order to derive meaningful crop classification (based on and including the generation of the

phenological curves of all major crops).

From there, two levels of accuracy can be achieved, following versions 2a and 2b of this scenario:

an EU-wide approach (scenario version 2a, under EU responsibility) would entail the

use of all available and relevant pan-EU (Copernicus, EEA, JRC, Eurostat), MS and

FAO databases on regional soils, crops, and agronomy. Depending on the quantity and

quality of available information in each area the accuracy of resulting classification may

be lower than indicated in the previous chapters. The percentage error of mis-

classification would depend on the areal percentage of “difficult” crops (e.g., woody,

low-ground-cover vegetables, supplemental irrigation areas). However, this approach

would still provide extremely useful information at EU and MS level about irrigated

areas, possibly also their historical development, and current hotspots that would need

more detailed investigation ;

a more accurate approach (scenario version 2b, under shared EU-MS responsibility)

would entail the use of in-situ data involving local/regional collaboration. This would

24

RGB is the common denominator of all high-resolution sensors in the virtual multi-sensor constellation. NIR (Near-infrared

spectroscopy) and SWIR (Shortwave Infrared Imagery) may allow higher spatial resolution but are not included in all satellites in

the virtual constellation.


| 36

need to be integrated into MS governance mechanisms. A CORINE-like mechanism25

of EU-MS collaboration would probably be the most effective and efficient way of

integrating both levels (EO and local) in this case.

In both versions of scenario 2, harmonisation of data, methodology and end-products needs to be

achieved both at MS level (e.g. between different regions or river basins) and at EU level (e.g.,

definition of interoperability standards, agreement on overall relevant crop classes).

Crop classification, and subsequent mapping of irrigated areas, is a multi-annual process in which

knowledge is accumulative improving itself with a basic set-up of the first classification in year 1 and

subsequent annual updates over a period of 3-5 years, at the end of which all major crops in the area

will have been included. Further annual updates are required to follow the dynamic changes in land-

cover patterns, in particular in irrigated areas.

The annual costs for this scenario are estimated to range between 1 and 3 €/km2/year for versions 2a

and 2b, respectively. These costs include in both cases an overhead of 20% for infrastructure

elements, like computer facilities and field instrumentation maintenance, as well as costs for personnel

to perform the image processing and classification generation in the office. In addition, version 2b

includes costs related to the field work corresponding to ground truth calibration and validation,

corresponding to about the same costs as classification without ground truthing (i.e. about 1

€/km2/year).

ii- The calculation of crop coefficient, evapotranspiration / crop water requirements, and

irrigation water requirements26

on a pixel basis (for the subsequent mapping) requires the same

time series of EO images as product i) but at a slightly higher temporal resolution. A temporal

resolution of one image every 1-2 weeks is normally sufficient and the spatial resolution requirement is

the same as above.

Data layers required for this purpose include all previously mentioned products. NDVI and colour

composite maps can be fed from the EU level, while crop classification and maps of phenology could

come either from the (harmonised) EU level (scenario version 2a) or from MS level directly (scenario

version 2b).

The calculation of crop coefficient from NDVI has been demonstrated to be robust and reliably

applicable to a wide range of crops and conditions, although further research could be desirable (see

Chapter 6). Management under controlled deficit irrigation - as could be the case of grape vines and

olives – can be included by means of a combined surface water and energy balance approach. The

subsequent calculation steps - from crop coefficient to crop water requirements and from there to

irrigation requirements - require local/regional data of reference evapotranspiration, precipitation, and

soil characteristics respectively. These data can come either:

from local agro-meteorological stations or MS station networks (in which case this step

would be included in MS governance mechanism) ; or

from EU-wide or global sources (WMO, ECMWF, synergies with the Copernicus land

programme, weather satellites, soil moisture and precipitation satellites).

Version 2a promotes an EU-wide direct approach, under EU responsibility, based on classification and

crop coefficient from EU and MS databases and agromet data coming from any EU-wide network or

from weather satellites (Meteosat Second Generation, currently at experimental stage).

25

CORINE is based on a pan- EU-defined scheme of 44 land cover clases, managed by the EEA, with actual classification products developed in and provided by each MS. 26

With the sequence of calculation steps and required local data indicated in Table 8.


| 37

Version 2b promotes a more accurate approach with in-situ calibration, under shared EU-MS

responsibility.

Optionally, in order to double-check and/or improve the accuracy of evapotranspiration maps, it is

proposed to derive evapotranspiration from EO data on the surface energy balance. It would require

data from the thermal spectrum (currently only Landsat at high resolution, with 120m pixels in the

thermal range; in the future also Sentinel-3 with 1km pixels in the thermal), with local data

requirements similar to the NDVI-based approaches.

The products obtained here do not only serve the immediate focus of this study (i.e. abstractions

monitoring), but also open the way for a wide range of further complementary applications. These

include EO-assisted hydrological planning and monitoring (at national, regional, local level) and

irrigation scheduling (farm advisory at regional and local level), embedded in the corresponding level

governance mechanisms (MS, river basin authority, regional governments, water user associations).

They also provide continuous large area data that could feed into environmental indices (national,

EEA, JRC) and input for numerical weather prediction models (national weather services, ECMWF).

The annual cost of this scenario is estimated to be around 1 €/km2/year, on top of the cost for

classification, for scenarios 2a and 2b, respectively. This includes a 20% infrastructure overhead and

cost of personnel for analysis and product generation. Field calibration and validation cost about the

same as these infrastructure and personnel costs, which again means that the scenario 2a version

cost is roughly 50% of the scenario 2b version cost.

iii- The calculation of irrigation water consumption and abstraction builds on ii) above

(calculation of CWR and IWR). The calculation of irrigation water consumption and abstracted

volumes requires taking into account the efficiencies of the irrigation system and of the conveyor

systems (i.e. the transport of water from its source, e.g. well, pond, canal, to the irrigation system).

This requires local data on system efficiencies. The accuracy of these estimates depends on accuracy

of system efficiency data, which is generally owned by local and regional user organisations. This

opens the way to the monitoring of abstracted water volumes, which can be used e.g. in hydrological

planning and control (national, regional, local levels). While the basic data layers can still come from

EU level (see above, scenario version 2a or 2b), the necessary local data on system efficiencies are

more local and tied to local governance structures. Therefore, these final products/services

can only be generated with a reasonable accuracy at plot level in a scenario version 2b

context.

The annual cost needs to be estimated on a case-by-case basis. If efficiency data are easily available,

the cost would basically be for field calibration/validation (adding about 10-20% to the cost of ii). If

efficiency data are not yet available, cost is likely to be in the same order of magnitude as the

classification step of version 2b.

Summary for Scenario 2

Scenario 2 offers a complete portfolio of data products and information for agricultural water

management and abstractions monitoring, with cost increasing according to the level of completeness.

Its “fast-track” option (version 2a) could provide a suite of products generated at EU level based on

information provided by the EU and MS and managed under the responsibility of the EU, while version

2b could be the basis for more accurate and detailed products and services incorporated into MS

services, with MS in charge of final quality control and service provision.

Scenario 2a products could be generated fully at EU level using relevant EU coordinating mechanisms

(Copernicus, JRC, EEA), while scenario 2b products would need to be generated via an integrated

EU-MS approach, possibly similar to the CORINE process. The collaboration with local/regional

service providers in either version would always have the advantage of fully benefiting from locally

available expertise and proximity to the local area.

The final products could be delivered at both the national level (relevance to MS competencies) and at


| 38

the EU level (in order to enable their use in transboundary river basin management and EU-wide

databases). The most relevant option would be to provide an EU-wide service based on the collection

of data at the national and/or regional level and their harmonisation at the EU level.

Scenario 3: EU-MS product based on the combination of EU space data, local

technical data produced by the EU and MS and MS legal data

A third, complete scenario consists of all of the above, plus databases on legal water rights, which

enables the monitoring and control of irrigated areas and water abstractions. Databases of legal water

rights are in the competencies of MS and/or regional or local water authorities (by means of

delegation), so the corresponding governance would need to be coupled to these levels.

The details for this scenario are the same as above in scenario 2, the only difference being the

availability of geospatial legal reference data. Therefore, the cost is also essentially the same as for

scenario 2. The additional human resources required to actually analyse the comparison of EO-based

abstraction data with the legal reference (which by itself is assumed to be integrated as an additional

layer in the system) is on one hand small compared to the overall task and is on the other hand part of

the routine tasks of the user, independently of any EO-based service.

It should be noted though that this analysis step requires an easy-to-use webGIS that facilitated the

visualization and online analysis of the complete portfolio of data and information products. Therefore,

the users would need some minimum technical capabilities and understanding in order to upload their

legal reference data to the webGIS.

Comparative analysis of the three scenarios:

Table 6 synthesises the main characteristics of each scenario.

Table 6: Summary of main characteristics of each scenario

Scenario Accuracy Required non-EO data Cost

from large-scale

databases

from field work

1: Basic

overview

very low Y N low

2a: Reduced

accuracy

low Y N moderate

2b: High

accuracy

high Y Y high

3: Complete

legal check

high N Y very high

Legend:

Shortcomings Advantages

Table 7 gives an overview of the key elements of the different scenarios to implement this procedure

under the umbrella of Copernicus. Table 8 p.40 summarises the corresponding data requirements.

Details are described in the text below.


| 39

Table 7: Overview of responsibilities for each scenario and step in procedure to detect non-

authorised abstraction

a. Data sources:

Step Scenario 1 2a 2b 3 Comments

1 EO data & basic

products (NDVI

maps & curves,

colour composite)

EU EU EU EU Use of Sentinels data

and the Copernicus

space Data Access

mechanism for the

access to other

contributing missions

(high and very-high-

resolution data)To be

complemented with

Landsat if

appropriate

2 Overview products

(rough phenological

curves, rough

estimate of Kc,

CWR, IWR)

EU EU+MS EU+MS EU+MS To be complemented

with international

databases (e.g.,

FAO)

3 Maps of irrigated

areas & phenology

EU+MS EU+MS EU+MS MS data also from

RBA+WUA

4 Maps of Kc, CWR,

IWR

EU+MS EU+MS EU+MS MS data also from

RBA+WUA

5 Maps of

consumption,

abstraction

MS MS

MS data mostly local

6 Check against legal

data (abstraction

rights, water

allocation)

MS

From all levels of

water governance

within MS

b. Processing and product generation:


1 EO data & basic

products (NDVI

maps & curves,

colour composite)

EU EU EU EU multi-sensor

constellation

2 Overview products

(rough phenological

curves, rough

estimate of Kc,

CWR, IWR)

EU EU EU EU EO data

+ non-EO pan-EU

& MS databases

(no field

validation)

3 Maps of irrigated

areas & phenology

EU EU+MS EU+MS “EU+MS” in

CORINE-like

mechanism


| 40


4 Maps of Kc, CWR,

IWR

EU EU+MS EU+MS idem

5 Maps of

consumption,

abstraction

MS MS all levels in MS

6 Check against legal

data (abstraction

rights, water

allocation)

MS all levels in MS

c. Possible uses and targeted users:

Step Scenario Use purpose

1 EO data & basic products (NDVI maps &

curves, colour composite)

rough indication for directing field inspections

2 Overview products (rough phenological

curves, rough estimate of Kc, CWR, IWR)

identification of hotspots

3 Maps of irrigated areas & phenology monitoring of irrigated areas

4 Maps of Kc, CWR, IWR monitoring of irrigation requirements

5 Maps of consumption, abstraction monitoring of abstractions

6 Check against legal data (abstraction rights,

water allocation)

detection of non-authorised abstractions

Legend:

WUA=Water Users Association

RBA=River Basin Authority

Targeted users mostly include the relevant services of the European Commission, the River basin

authorities, the water users associations (including farmers), as well as other interested stakeholders

such as non-governmental organisations.


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Table 8 summarises the requirements for implementing each scenario.

Table 8: Requirements for the development of Copernicus water abstraction tools and services

Pan-EU level

(provided by

Copernicus)

Technical requirement Local data requirement /

operational level

Scenario 1

NDVI maps &

temporal

profiles

dense time series of high-

resolution (10 m max)

optical images (Sentinel-2

& coordinated missions);

radiometrically calibrated

and georeferenced with

high precision, possibly

atmospherically corrected;

conversion of digits into

NDVI;

extraction of NDVI temporal

profiles by SOLAP drilling

no local data requirements;

suitable for large-scale operational production line

Maps of colour

combination

same as above;

conversion of red-green-

blue bands into colour

combination

no local data requirement; large-scale operational

production line with manual supervision by

experienced operator

Maps of Kc,

CWR, IWR

(uncalibrated)

conversion NDVIKc

(generic equation);

conversion ET=Kc*Eto ;

IWR=CWR-precip

ETo from Meteosat or WMO or weather prediction

model;

precip from PROBA;

large-scale operational production line

Scenario 2 - i 2a 2b

Maps of

irrigated areas;

georeferenced

crop phenology

curves

same as above;

crop classification based on

temporal evolution of NDVI

ground truth data from

EU &/or MS databases

(of crop identification);

large-scale operational

production line

manual supervision of

classification;

+ ground truth data from

field (in-situ);

manually controlled

production line


| 42

Pan-EU level

(provided by

Copernicus)

Technical requirement Local data requirement /

operational level

Scenario 2 –ii 2a 2b

Crop coefficient

maps;

Crop water

requirement

maps;

Irrigation water

requirements;

same as above +

conversion NDVIKc;

conversion ET=Kc*Eto ;

IWR=CWR-precip


EU/MS databases:

ETo from agromet

network WMO or

Meteosat or weather

prediction models;

precipitation data from

EO (PROBA);

large-scale operational

production line

+ ground truth data from

field work & local

stations (agromet & rain

gauges) (in-situ);

manually controlled

production line

Scenario 2 –iii 2a 2b

Irrigation water

consumption &

abstraction

multiply by system &

conveyor efficiencies

-

(no EU level data)

efficiencies of irrigation

systems and conveyor

systems (local data);


flow meters (in-situ);

manually controlled

production line

Scenario 3

Integrated

abstractions

monitoring and

control service

same as all above, in

overlay with spatial water

rights database

-

(no EU level data)

legal data on water

rights (MS, RBA, WUA);

locally controlled

production line

5.3. Suggested roadmap for scenario selection and implementation

As requested in the Blueprint impact assessment, a Guidance document for MS has been developed

in the context of this study. So, the first step in the roadmap towards implementation is to inform MS

authorities in the context of the WFD about the document and the options. MS can then review their

situation and check their enabling environment against the requirements for implementation of EO-

based abstractions monitoring. Once MS have identified missing capabilities at MS level, they can

proceed to assess financing needs and potential funding sources.

In parallel, the identified scenarios should be assessed at EU level (in particularly in the context of

Copernicus) against their own capabilities (in terms of strategy, synergy with other sectors, and

financing) before implementing the selected scenario at EU level (contracting, specification and set-up

of the production line and distribution mechanisms, documentation). From that moment on the

continuous production of pan-EU maps of irrigated areas and abstracted volumes would be launched.


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Table 9 summarises the main steps in the process, where steps 1-4 run in parallel in MS and EU.

Table 9: Roadmap towards implementation: overview.

MS level Step 1 Information to Member States in the context of WFD about the

development of a pan-EU product, its possible applications, and

communication about the requirements at different scales to be able to

use it: water rights / GIS, etc.;

Step 2 Status of the enabling environment in Member States, at different

scales, against the identified requirements to be able to use the EU

products;

Step 3 Assessment of funding needs from MS based on existing capabilities;

Step 4 Identification of funding options from the EU and Member States;

EU level Step 1 Scenario selection at EU level (in particular in the context of

Copernicus);

Step 2 Implementation at EU level;

Step 3 Production of the EU-wide maps for irrigated areas and water use, and

publication of EU guidelines for their use at national level;

Step 4 Production of documentation (manuals, demonstration cases) for MS

that supports implementation (i.e. for use at regional/local level).


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This page is left intentionally blank.


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6. Further research needs

6.1. Maps of irrigated areas

Fully automated EO-based crop classification procedures have recently been published (e.g., Yan and

Roy, 2014), based on a very small number of validation cases. More research is needed in order to

understand the limitations of applicability of these object-based algorithms. They also rely on dense

time series of EO images, i.e. the same data as used in the multi-temporal phenology-based approach

proposed in this study. The potential synergies and application strategies need to be investigated.

In general, further research should be conducted to improve the capability to identify irrigated areas

based on captured reflectances and temperature by a dense time series of EO images at the

adequate spatial and temporal scales. Concrete research needs include:

to build a multisensor constellation to alleviate cloudiness;

the full exploitation of new Sentinels and Copernicus data and services

to integrate all classification procedures to improve the reliability of maps of irrigated

areas, by incorporating all EO and non-EO sources. Particular attention should be given

to crops that receive supplementary irrigation and woody crops with management under

stress.

6.2. Evapotranspiration and abstraction monitoring

Remote sensing estimation of evapotranspiration, i.e. crop water requirements, based on crop

coefficient estimates from multispectral imagery is mature and operational and has been validated in a

wide range of conditions. However, further calibration and validation of the relationship between basal

crop coefficient and vegetation indices (and biophysical parameters in general) should still be

performed for more conditions.

Also further studies on complementary aspects of water balance (NDVI)-based and energy balance

(thermal)-based approaches to estimate crop evapotranspiration could help increase the accuracy of

products in scenario 2 (both the fast-track 2a and the high-accuracy 2b versions). In particular, a

combined strategy to explore the advantages and avoid the pitfalls of each approach should be

developed and validated in a representative range of cases.

Maps of reference evapotranspiration and past rainfall, which are based on meteorological data, could

be improved by incorporating the most recent advances of meteorological modelling research.

Better monitoring and mapping of water stress would also require additional research involving EO

and non-EO aspects. EO could address the mapping of water stress by coupling actual ET estimates

using recorded temperature and surface energy balance modelling together with soil water balance

fed by crop coefficients derived from multispectral imagery. Difficulties arise, however, in 3D crops

with a large fraction of bare soil.

Soil moisture estimates based on radar (e.g., Sentinel-1, SMOS, SMAP) could help improve the

accuracy of CWR and IWR products, but need to be further developed for operational purposes and

carefully calibrated/validated for a representative range of cases.

Space-based precipitation data (e.g., Proba-V) can increase the operational potential and decrease


| 46

the cost of products, but further calibration/validation studies are needed.

6.3. Governance

Further research needs concern the adequate governance types for the implementation of EO-based

tools and services, for both the mapping of irrigated areas and the monitoring of abstraction and the

strategy to drive field inspections. In particular the legal conditions need to be analysed and adequate

ways need to be sought to establish the policy, legislative, and administrative framework that will

eventually allow for recognising the EO-based maps of irrigated areas and abstracted volumes as

legal evidence. The situation in Spain (where EO-based information has been accepted as legal

evidence by the Supreme Court) can serve as a guiding reference here, as well as the body of

knowledge available in the U.S. on forensic GIS and EO.


| 47

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