ESA’s Crop Monitoring And Early Warning Service

http://www.gmfs.info26/10/2011, Nairobi, Kenya

Global Monitoring for Food Security 3

ESA’s Crop MonitoringAnd

Early Warning Service

http://www.gmfs.infoNairobi 26/10/2011

Outline

This presentation

• Background• Partners• Services

• Early Warning• Agricultural Monitoring• CFSAM


ESA Stage 3 Background

• GMFS started in 2003• New contract GMFS3 -> 2013• Continuation of services to Stage 2 users• Focus is on Sustainability


4

Partners

Nr. Company Country General competences, tasks & responsibilities

01 VITO Belgium Project management, early warning, Medium/(high) resolution optical and training , information packaging, promotionSecondment to FAO through visiting scientists programme

02 Consorzio ITA

Italy Agriculture Mapping with optical remote sensing and ground statistics - Validation methodology definition – Training: MALAWI

03 EARS Netherlands FAST Meteosat based Early Warning Services for African regions. Generation of rainfall and evapotranspiration data fields. Production of crop yield forecasts. Provision of dedicated user software (Imageshow 2) and training.

04 EFTAS Germany Agriculture Mapping with optical remote sensing and ground statistics, with radar data - Validation data collection - Service network support: SUDAN

05 SARMAP Switzerland Agriculture mapping with radar – Software programming – Training package: RCMRD

06 ULg Belgium Early warning support and Service portfolio evolution for Early Warning Services: AGRHYMET

07 GeoVILLE

Austria Soil Moisture Indicator products


Early Warning

Crop Yield and Vegetation Monitoring Service

FAST Service

Soil Moisture Monitoring Service

Agricultural Monitoring

Support to the Optimization of the National Agricultural Survey Service

Agricultural Mapping Service

SAR Knowledge Transfer Service

CFSAM Support

Support to Crop and Food Supply Assessment Mission Service

ZimbabweMoA

Senegal CSE

Overall ManagementVITO

Southern AfricaRegional coordinator

ESATechnical officer

User Board Scientific Board

West AfricaRegional coordinator

AGRHYMET(CRA)

East AfricaRegional coordinator

RCMRD

West Africa region East Africa region Southern Africa region

MozambiqueINAM

EthiopiaMoARD

SudanFMoAF

MalawiMoAFS

MaliLaboSEP

Service Groups 7 GMFS3 Services



• Crop Yield and Vegetation Monitoring• Soil Moisture Analysis• FAST


MERISVegetation

ASCATSoil moisture

MSGRainfall

Radiation

SUPPORT EW ANALYSIS

TRAIN HOW TO USE THE DATA


3 independant sources of information Convergence of evidence analysis


Context: In Sahel Region the start of Season is a first indicator of crop development success or failure (Approach published in the CILLS bulletin)

Qualitative Analysis: Early or Late Start of the Growing Season

Profile Matching Approach: - Compare the fAPAR profile for the 3 first months of the 2011 season with the average 1999-2010

- Display the shift that have the best fit with the Long Term Average

- Give an overview of the anticipated or delayed area at pixel level

Crop Yield and Vegetation Monitoring

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

0 3 6 9 12 15 18 21 24 27 30 33 36

Decades

ND

VI

current year historic year

Phase/shift



Qualitative Analysis of ongoing Growing Season for Agricultural Monitoring based on low Resolution DataContext: Standard Anomaly Maps based on comparison with LTA give a good spatial overview of anomalies, this approach adds duration and intensity to the maps

Cluster analysis : - Iso data classification based on the relativedifference between 10 day VI and - Display classified map with the corresponding classes profiles



Yield Estimation based on Low Resolution Monitoring

Context: Non Parametric Yield ForecastSimilar Years to Yield estimate

Similarty Analysis : - CROP MAP !- for each pixel the most similar year is found- Display classified map - Per ADMIN percentage

TABLE1: Percentage of similar year to 2009 per administrative area

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008Addis Ababa ETH001 10% 9% 0% 8% 14% 8% 6% 10% 10% 23% 100%Amhara ETH003 7% 2% 1% 12% 23% 19% 21% 2% 6% 8% 100%Harari ETH007 36% 3% 9% 2% 25% 4% 0% 8% 12% 2% 100%Oromiya ETH008 9% 4% 8% 17% 11% 13% 9% 13% 7% 9% 100%Somali ETH009 6% 4% 8% 9% 9% 10% 4% 15% 4% 31% 100%Southern ETH010 10% 5% 7% 20% 7% 10% 8% 12% 6% 15% 100%Tigray ETH011 4% 2% 1% 11% 20% 17% 17% 6% 15% 6% 100%

TABLE2: WHEAT yield statistics from CFSA

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008Addis Ababa 11.97 11.69 11.815 11.94 12.45 18.71 17.32 13.87 17.32 13.87Amhara 7.38 9.4 10.36 8.87 12.93 14.5 15.24 15.94 15.24 15.94Harari 6.08 6.37 12.43 6.37 12.43 12.43 12.43 11.16 12.43 11.16Oromiya 13.23 13.27 14.12 12.04 18.82 17.02 18.17 17.64 18.17 17.64Somali 7.73 4.9 5.2 7.98 16.27 9.55 7.32 4.46 7.32 4.46South Gonder 5.59 6.28 7.31 8.91 8.78 7.54 8.48 10.41 8.48 10.41Tigray 10.3 8.95 9.78 6.56 13.15 9.79 13.32 14.79 13.32 14.79

TABLE3 = TABLE1 X TABLE2: Test of calculation of the estimated WHEAT yield for 2009

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008Calculated yield

for 2009Addis Ababa ETH001 1.25 1.07 0.00 0.98 1.73 1.48 1.10 1.45 1.81 3.20 14.08Amhara ETH003 0.49 0.15 0.08 1.10 2.91 2.71 3.25 0.32 0.98 1.22 13.21Harari ETH007 2.20 0.18 1.09 0.14 3.07 0.48 0.00 0.92 1.43 0.18 9.70Oromiya ETH008 1.13 0.57 1.12 2.07 2.12 2.14 1.57 2.27 1.33 1.65 15.97Somali ETH009 0.46 0.22 0.40 0.72 1.51 0.91 0.31 0.68 0.26 1.39 6.85Southern ETH010 0.53 0.34 0.52 1.80 0.65 0.77 0.65 1.21 0.49 1.56 8.53Tigray ETH011 0.45 0.18 0.09 0.71 2.65 1.71 2.24 0.90 2.04 0.91 11.87



USERS

Participatory Development

Yield Forecasting


Soil Moisture MonitoringFAST Service


Agricultural Monitoring

• Support to the Optimization of the National Agricultural Survey Service

• Agricultural Mapping• SAR Knowledge Transfer


“ASO” service: “support to the optimisation of national surveys”. In collaboration with JRC this service is focussed to consultancy on the introduction of area frame sampling approaches , making use of EO data which i stechnically sound and sustainable in the Malawian context

ASO in Malawi

1. EO data are used for the design of the sampling frame and its realisation: location of sample points, interpretation and classification, masking and stratification. Points vs. segments

2. EO data are used with GPS for survey optimisation and execution, e.g. maps for identifying the sampled points, planning of itineraries, control (synergies with field area measurements for APES, etc. see also proposals by MoAFS on the use of IT , FAO)

3. EO data are also used as auxiliary variables (land cover/land use classified images) to improve the accuracy in the estimation of the crop acreage by means of ad hoc statistical procedures.

Point frame: 500 m spacing , - approx. 4 points per km2

to be interpreted and classifiedBased on a simple LCLU legend

Up to 58.000 points are visited on the ground (sampling rate around 15 %)


2 Scales, Medium Resolution, High Resolution

LCCS support

• High Resolution Satellite Image coverage NKOR• Multi resolution segmentation of NKOR• Harmonized field work

• OutputsÞ Data & processing support for current LCCS mappingÞ recent HR Satellite image mosaic on state levelÞ Segmentation layerÞ Field work

ASO + AM North Sudan


GMFS fAPAR-EoG North Sudan 2010

ASO + AM in North-Sudan

GMFS fAPAR-EoG North Sudan 2005

Change maps:Context: there is a huge variability in the extent of growth (EoG) in North Sudan, based on MERIS-FR fAPAR images and analysis is made on this difference. Maps can be used in support of the Agricultural Survey

• Indication upon differences in growth activities

• Training on use of change maps is currently ongoing (September 2011)

http://www.gmfs.info

CFSAM support to FAO/WFP

FAO publication explaining the importance of Environmental Remote Sensing indicators for monitoring, using amongst others ESA-MERIS RR fAPAR (data processing + methodology = GMFS)


Thank you !

Questions?

ESA’s Crop Monitoring And Early Warning Service

Documents

Transcript of ESA’s Crop Monitoring And Early Warning Service