www.csiro.au

Modeling of Actual Crop Water Consumption Using Optical-Thermal Satellite and Airborne Data Over

Murrumbidgee Catchment

Mohsin Hafeez, Shahbaz Khan, Kaishan Song, Umair Rabbani, Jeff Walker

CSIRO Land and Water Division, Wagga WaggaInternational Centre of Water for Food Security (IC Water), Charles Sturt University

University of Melbourne, Melbourne

Outline

• Research Issues in ET modeling

• Satellite Data

• Initial Results

• Still to achieve science goals from NAFE Campaign

• Ongoing Research Projects

Water losses and gains are part of the water cycle

gain

loss

ET is important at all scales

Key Research Issues

• ET is coupled mass/energy process, linking the energy and water cycles

• Estimation of ET is critical for on-farm and regional models in irrigation systems

• ET is the largest water balance component after rainfall and irrigation input

• Water quantification (i.e. productive and non-productive use) is important for irrigated agriculture.

Current state-of-the-art Approaches for measuring Actual ET

• In-situ measurement (Bowen ratio tower, & soil water balance, etc.)

• Air-borne measurement (fluxes)

• Satellite measurement

High Spatial Resolution (ASTER and Landsat)

High Temporal Resolution (MODIS and NOAA-AVHRR)

• Modelling Approaches (plant to catchment)

The challenge in ET Modeling….We can’t cover everything all of the time…

• in-situ observations:(lysimeters, flux towers)

• aircraft observations:(fluxes, concentrations)

• modelling:(leaf …. region)

cover almost nothing but most of the time

cover almost everything but hardly ever

only pretends to cover everything all of the time

• satellite observations:(AVHRR, MODIS … )

cover everything all of the time but not at the time when we want!

Modified from Dr Schmid, Indiana University

Methods for Quantification of Actual ET

Empirical direct methods

Characterizing crop water status through the cumulative temperature difference (Ts-Ta)

Residual methods of the energy budget

Combination of empirical relationship and physical modules (SEBAL, SEBS)

Deterministic methods

Soil-Vegetation-Atmosphere Transfer models (SVAT)

Vegetation Index methods

National Airborne Field Experimentation (NAFE) 2006

Yanco area(2500 km2)

Kyeamba CreekCatchment (600 km2)

Satellite Data Over NAFE Campaign

Remote Sensing for Actual ET Estimation

ET is calculated as a “residual” of the energy balance

ET = R - G - Hn

Rn

G (heat to ground)

H (heat to air) ET

The energy balance includes all major sources (Rn) and consumers (ET, G, H) of energy

Basic Truth: Evaporation consumes Energy

(radiation from sun and sky)

Adapted from IDAHAO

Pre-Processing of SEBAL Modelling

•Quality Check Using TiSEG Software

•NDVI

•Surface Albedo

•Surface Emissivity

•Surface Temperature

•LAI

Solving Energy Balance at Satellite Overpass

ET = R - G - Hn

October 31 Using SEBAL-METRIC

Aqua MODIS (Spatial Resolution 1000 m)

November 11 Using SEBAL-METRIC

Terra MODIS (Spatial Resolution 1000 m)

November 14 Using SEBAL-METRIC

Terra MODIS (Spatial Resolution 1000 m)

November 22 Using SEBAL-METRIC

Terra MODIS (Spatial Resolution 1000 m)

Seasonal Actual Evapotranspiration Yanco Area

±Terra MODIS (Spatial Resolution 1000 m)

April –March

Reference ET Using Penman Monteith Method

0

2

4

6

8

10

12

14

16

290 300 310 320 330 340

Julian Day

Re

fre

nc

e E

T (

mm

/da

y)

CSIRO Weather Station at Y11- Morundah

Still to Achieve Scientific Goals from NAFE

•Actual crop water consumption and deficit using ASTER, Landsat 5 TM and NOAA-AVHRR satellite data over Yanco and Kyeamba Creek

•Uncertainty analysis of remote sensing based algorithms;

•Processing high resolution airborne thermal data;

•Modelling using airborne data to estimate actual evapotranspiration:

•Up-down scaling approaches in ET; and

•Validation of remote sensing derived ET by ground fluxes.

Surface Energy Balance System (SEBS)

(Su, 2002)

Thermal Data on November 16, 2006

Work in Progress From NAFE CampaignSpatial Actual ET

•TRI-Spectral NDVI Scanner

•Thermal Imager

•LIDAR

•Eddy Covariance Flux Tower

Processing of Thermal Imager Data

•Lens calibrations

•Correction of various distortions

•Aircraft Altitude & terrain

•Georeferencing of composite data

•Methods: Atmospheric corrections using ATCOR

•LST Retrieval: Qin et al., 2001, Sobrino et al., 2003

•Composite Images: Autopano, FLIR Image Builder, ENSOMOSAIC

Ongoing Research Projects

•Mapping, Monitoring and Modelling of Hydrological Parameters in the Lower Murrumbidgee Catchment of Australia; DLR-UNESCO HELP Project

•Development of SAM-ET for mapping water productivity in Australian Irrigated Ecosystems; NWC Project•Spatio-temporal water accounting framework through coupling of ground and remote sensing data at on-farm and regional level in a real time environment; NWC Project

•System Harmonisation through Regional Irrigation Business Partnerships (RIBP) across Australia; CRC IF Project

Remote Sensing Based Water Productivity Algorithm

•Develop a spatio-temporal water accounting and productivity framework through coupling of ground and remote sensing data at on-farm and regional level;

•Develop a remote sensing based algorithm for estimating an actual ET (SAM-ET) in irrigation systems within Australian agro-climate context and validate them with in-situ measurements e.g. by flux towers;

•Establish up-to-date land cover and land use classification of the irrigation areas using high spatial resolution TerraSAR-X, Rapid Eye and ALOS/PRISM data;

•Develop biophysical remote sensing models for monitoring of vegetation health through parameterization of NDVI, LAI, and NPP using high spatial and temporal resolution satellites;

•Uncertainty analysis of different ET based algorithms: and

•Develop a Spatial Algorithm for Mapping water productivity and vegetation health frameworks at different scales (farm, irrigation system and basin).

Water Accounting at Catchment Scale

Water Accounting Concept (IWMI, 1998)

Scaling Issues in ET

• Up-Down scaling Approaches

• Satellites: GEOS (4000m) - Terra/MODIS (1000m) - ASTER (90m) - Landsat 5 TM/ 7 ETM+ (30m)

• Airborne observations at a range of altitudes (500ft to 10,000ft) equipped with Thermal Imager and NDVI scanner (1m to 20m)

• Drone observations at 500 ft altitudes equipped with Thermal Imager and NDVI scanner (<1m)

•Ground observations from sensorscope stations located at strategic points

•Verifications and validations with

•Point observation networks using weather stations

•Eddy covariance system (100m)

•Large - Very large Aperture Scintillometers (5000m to 10,000m)

Drone – Monitoring of Hotspots in Irrigation Areas

Description:Flight weight: ca. 8-12 kg

Time of flight: > 90 min

Range: ca. 5 km

Max speed: 30 km

Max flight hight: ca. 4500 m

Carrying capacity: ca. 5-6 kg

Engine: 2-stroke, 5.5 h

Sensor:Digital camera, Multispectral camera, Thermal imager

Fail save functions

Real time transfer of GPS position data and camera field of view to operator

Terra SAR-X – Lower Murrumbidgee Catchment

Mission: Monitoring of earth surfaces with daily revisit

5 identical small satellites in a common orbital plane

5 bands (400-800nm), resolution: 6.5 m, swath: 78 km

Launch: November 2007

Commercial initiative of RapidEye AG (www.rapideye.de) with financial support from DLR

Scientific exploitation and data access through DLR

Coleambally Irrigation Area (CIA) – Demonstration Site

RapidEye High resolution imagery everyday everywhere

Way Forward

•Actual crop water consumption and deficit using ASTER, Landsat 5 TM and NOAA-AVHRR satellite data over Yanco and Kyeamba Creek;

•Uncertainty analysis of remote sensing based algorithms and validation of derived ET by ground fluxes;

•Modelling using airborne data to estimate actual evapotranspiration;

•Mapping, Monitoring and Modelling of Hydrological Parameters in the Lower Murrumbidgee Catchment of Australia;

•Development of SAM-ET for mapping water productivity in Australian irrigated Ecosystems; and

•Spatio-temporal water accounting framework through coupling of ground and remote sensing data at on-farm and regional level in a near real time environment.

www.csiro.au

Thank You