COST Action 718Meteorological Applications for Agriculture

4th Management Committee and Working Groups Meeting27 – 28 September 2001

Budapest, Hungary

Applications Software developed by FAOApplications Software developed by FAOfor Management offor Management of AgrometeorologicalAgrometeorological and and

Remotely Sensed DataRemotely Sensed Data

Michele BernardiEnvironment and Natural Resources Service

Food and Agriculture Organization of the United Nations

1. FAO - World Agricultural Information Center 2. FAO - SDRN3. FAO - G.I.S.4. FAO - ARTEMIS5. FAO - AGROMET6. Real-time world-wide meteorological data7. World-wide climatic database8. Global digital datasets9. Crop monitoring and yield forecasting10. Development of agrometeorology tools11. Agroclimatic database12. SEDI13. ADDAPIX14. RADAR15. Conclusions

OverviewOverview

1. 1. WAICENTWAICENT

Sustainable Development Department

SDRN at Work in the World

2. Environment and Natural Resources Service 2. Environment and Natural Resources Service (SDRN)(SDRN)

It’s the integrated response to FAO’s environmental and natural resources programme requirements.

SDRN deals with sustainable development and natural resources and environment management issues in order to:• develop spatial information infrastructures that enable information access, sharing and lower costs; • promote integrated data and information use for food security and sustainable development; • develop methodologies, guidelines and policies for resource management and environmental monitoring.

SDRN has a multidisciplinary team of experts in remote sensing, geographical information systems,agro-meteorology, environment, energy, ecology, and economics.

GIS is used to integrate, process, analyse and display digital spatial and non spatial data. Layers of thematic information can be integrated to provide new insights into sustainable development problems.

Layer 1: Hydrography rivers and lakes.

Layer 3: Infrastructure

Original data can be from a range of projections and scales.

Layer 4: Soils

Layer 2: Elevation contours

In this example, many thematic layers were integrated to obtain a GISinformation infrastructure for Burundi.

Layer 5: Major land-cover types

3. Geographic Information System (3. Geographic Information System (GISGIS))GIS refers to computerised information storage, processing and retrieval systems, specifically designed to cope with spatial data and its corresponding attribute information.

Several layers are combined in thisGIS output. The 3D effect is generated by the GIS from the elevation data.

4. Advanced Real4. Advanced Real--Time EnvironmentalTime EnvironmentalMonitoring Information System Monitoring Information System

((ARTEMISARTEMIS))Since August 1988, ARTEMIS has been operationally receiving, processing, archiving and disseminating low-resolution remote sensing imagery in support of FAO’s programmes on early warning for food security, migrant pest anddisease control.

The ARTEMIS system was implemented by FAO in close co-operation withNASA Goddard Space Flight Centre, USA, the National Aerospace Laboratory (NLR) of the Netherlands and the University of Reading, U.K. with funding support from the Government of the Netherlands.

The ARTEMIS archive contains products derived from four different series of satellites by a number of processing centres and are available at daily, 10-daily, bi-weekly and/or monthly intervals.They can be seen at http://METART.FAO.ORG

FAOFAO--ARTEMISARTEMIS

SSCKiruna, Sweden

NASA GSFCGreenbelt,

USA

NOAA/NESDISSuitland, USA

CNESToulouse, France

EC/JRC/SAIIspra, Italy

NASDA/JMAJAPAN

EUMETSATDarmstadt, Germany

USAIDFEWS

VITOMol, Belgium

University of MiamiMiami, USA

NOAA-CPCSuitland, USA

Japan Met. Agency

Tokyo, Japan

USAID FEWS

USERS

FAO Global Informationand Early Warning System

on Food and Agriculture(GIEWS)

FAO Emergency Centre forLocust Operations (ECLO)

FAO Animal Health Service(AGAH)

Southern Africa DevelopmentCommunity, RegionalRemote Sensing Unit

(SADC)

Various national and internationalresearch organisations

Normalized Difference Vegetation Index

Africa

Cold Cloud Duration Africa

Estimated Rainfall Africa

Estimated RainfallAfrica

Normalized Difference Vegetation Index Central America

Normalized Difference Vegetation IndexSouth America

Cold Cloud DurationSouth East Asia

Short Wave Infrared(VEGETATION)

Geostationary and Near Polar Orbiting Satellites

4. 4. ARTEMISARTEMIS

Yellow - Vegetation Index (NDVI)Blue - Cold Cloud Duration

Near real-time satellite data coverage

4.4. ARTEMISARTEMIS

4.4. ARTEMISARTEMIS

Datasets archive

Continent/Product: Source: Resolution Coverage Update Start Africa: • Cold Cloud Duration FAO-ARTEMIS 7.6km Africa Daily 1988• Estimated Rainfall FAO-ARTEMIS 7.6km W/E Africa Dekadal 1988• Interpolated Est. Rainfall NOAA/FEWS 7.6km Africa Dekadal 1995• NDVI - GAC NASA-GSFC/FEWS 7.6km Africa Dekadal 1982• NDVI - HRPT Centre AGRHYMET 1km W. Africa Dekadal 1997• NDVI - HRPT DMS-Zimbabwe 2km S. Africa Dekadal 1997• NDVI - VEGETATION SPOT/JRC 1km Africa Dekadal 1998 Asia/Pacific: • Cold Cloud Duration Univ. of Hawaii/JMA 5km Asia/Pacific Daily 1996 South/Central America: • NDVI – GAC

Global• NDVI - VEGETATION

NASA-GSFC SPOT/JRC

8km

1km

S/C America

Global

Dekadal

Dekadal

1982

1998

4.4. ARTEMISARTEMISMeteosat (Visible Channel 21 September 2001 12:30 GMT)

Meteosat (Thermal Infrared 21 September 2001 12:34 GMT)4.4. ARTEMISARTEMIS

Cold Cloud Duration hours (11-20 September 2001)4.4. ARTEMISARTEMIS

4.4. ARTEMISARTEMISRainFall Estimate (11-20 September 2001)

SPOT-4 VEGETATION1-10 March 2001

4.4. ARTEMISARTEMIS

Real-Time Satellite Data Coverage

SPOT-4 VEGETATION

4.4. ARTEMISARTEMIS

5. Agrometeorology5. Agrometeorology

�Agroclimatic databases: (i) collection of world-wide monthly time series andnormals for about 25000 stations; (ii) dissemination of a database of African sub-national crop statistics; (iii) reference unit on climate, and climate change and agriculture.

�Development and standardization of tools and methods of agroclimaticdatabases and application software: it includes spatial interpolation of climatic variables, impact assessments and agroclimatic risk.

�Crop monitoring ad yield forecasting: based on rainfall reports from FAOcountry representatives, GTS data, satellite imagery and crop specific water balance model. The outputs are provided to the Global Information and Early Warning System on Food and Agriculture (GIEWS).

Main activities 1/2

�Climate-related risk in agriculture: analysis of climate risk and vulnerability applied to agricultural production and attempts to quantify, at a national and global scale, the losses actually suffered by agriculture due to climate variability.

�Disaster Impact Assessment: evaluation of methodologies for rapid evaluation of geophysical disaster impact as related to the agricultural sector, and development of a detailed disaster impact database as a tool for impact simulation.

5. Agrometeorology5. Agrometeorology

Main activities 2/2

6. Real6. Real--time worldtime world--wide meteorological datawide meteorological data

20010430-999 42 -999 0 6/------- 600942 35 0 1 6/-///WW- 601043 37 1 1 6/////R/R 6011

-999 39 -999 0 6/------- 601256 43 23 23 6//MMRR/W 603053 42 -999 0 6//FF//// 604357 44 -999 0 6//////// 604957 45 11 11 6//////// 605261 48 0 0 6////W/// 6058

7. World7. World--wide climatic databasewide climatic database

7. World7. World--wide climatic databasewide climatic database

7. World7. World--wide climatic databasewide climatic database

8. Global digital 8. Global digital datasetsdatasets

8. Global digital 8. Global digital datasetsdatasets

8. Global digital 8. Global digital datasetsdatasets

9. Crop monitoring and yield forecasting9. Crop monitoring and yield forecasting

Rainfall monitoring

9. Crop monitoring and yield forecasting9. Crop monitoring and yield forecasting

WEST AFRICA - Cropping season 2001

Total rainfall (mm) from 1st to 31st August

Crop yield forecast for Sahelian Countries

9. Crop monitoring and yield forecasting9. Crop monitoring and yield forecasting

10. Development of10. Development of agrometeorologyagrometeorology toolstools

one title lineone line with V variable namesL lines with an optional "label" and V data items

"Mali 1990 Sorghum yield""","WSI","YLD","LAT","Long""Kayes",95.8,758,14.5,-11"Bafoulabe",92.6,843,14,-10"Diema",88.7,792,14.5,-9.5"Kenieba",98.8,760,13,-11.1

Mali 1990, SorghumWSI YLD LAT Lon

Kayes 95.8 758 14.5 -11.0Bafoulabe 92.6 843 14.0 -10.0Diema 88.7 792 14.5 -19.5Kenieba 98.8 760 13.0 -11.1

10. Development of10. Development of agrometeorologyagrometeorology toolstools

Standards on data file format

Cropping season: 2000FAO Water Satisfaction Index for Maize

as listed in file waWm00OA.DATStation:1 Crop:1 Station Name:BF02DSSN Elevation:274 m Crop type:MaizeCycle length:18 dekads(C) Total water requirements:686 mm(TWR)Normal water requirements:686 mm(TWRNor)Planting dekad:10(P)Pre-season Kcr : .15Maximum soil water storage:70 mm(H or WHC)Effective/Total rain:100%(E or EfR%)

DEK NOR ACT WRK PET KCR WR AvW SW S/D INDEX8 7 6 6 64 .15 10 -3 09 10 12 12 63 .15 9 3 3

10 11 28 28 58 .20 12 16 19 0 10011 15 18 18 56 .20 11 7 26 0 10012 20 45 45 54 .20 11 34 60 0 10013 29 4 4 52 .32 16 -12 48 0 10014 34 14 14 50 .61 30 -16 32 0 10015 38 77 77 49 .90 44 33 65 0 10016 38 53 53 47 1.19 56 -3 62 0 10017 42 28 28 46 1.25 58 -30 32 0 10018 47 31 31 44 1.25 55 -24 8 0 10019 55 31 31 43 1.25 54 -23 0 -15 9820 62 43 43 42 1.25 53 -10 0 -10 9621 68 34 34 41 1.25 51 -17 0 -17 9422 82 71 71 39 1.25 49 22 22 0 9423 84 132 132 39 1.25 49 83 70 35 9424 82 79 79 39 1.14 45 34 70 34 9425 72 41 41 39 .96 37 4 70 4 9426 63 67 67 40 .78 31 36 70 36 9427 50 47 47 41 .60 25 22 70 22 94

Surplus: 131mm (EXWT) Deficit: 42mm (DEFWT) ETA:645mm% data avail:100% (%AVAIL)Norm.index: 97% (IndxNor)

10. Development of10. Development of agrometeorologyagrometeorology toolstools

Faoindex (crop water balance)

Faomet (various tools)

10. Development of10. Development of agrometeorologyagrometeorology toolstools

AgroMetShell (integration of agromet software)

10. Development of10. Development of agrometeorologyagrometeorology toolstools

CCDSolRad at

ground level estimated

method params

method

SolRad atground levelmeasured

calib & valid

import

database SolRadmeasured

estimatestatistics

calibration

PotSolRad

distributedparameters

geo ref

datafile

spat int

10. Development of10. Development of agrometeorologyagrometeorology toolstools

RadEstSatellite (global solar radiation estimation from satellite)

10. Development of10. Development of agrometeorologyagrometeorology toolstools

LocClim (estimator for local climate) for PC

LocClim (estimator for local climate) for Internet

10. Development of10. Development of agrometeorologyagrometeorology toolstools

10. Development of10. Development of agrometeorologyagrometeorology toolstools

LocClim (estimator for local climate)

10. Development of10. Development of agrometeorologyagrometeorology toolstools

WinDisp (map and image display and analysis software)

10. Development of10. Development of agrometeorologyagrometeorology toolstools

WinDisp (map and image display and analysis software)

11. 11. AgroclimaticAgroclimatic databasedatabase

Crop Production System Zones for the Horn of

Africa

11. 11. AgroclimaticAgroclimatic databasedatabase

Sub-national agroclimaticstatistics for Africa

SEDI is a simple and straightforward method for “assisted” interpolation. The method can be applied to any parameter of which the values are available for a number of geographical locations, as long as a 'background' field is available that has a negative or positive relation to the parameter that needs to be interpolated.

Three requirements are a prerequisite for the successful application of the SEDImethod:� The availability of the parameter to interpolate as point data at different geographical locations (e.g. rainfall, potential evapotranspiration, crop yields)� The availability of a background parameter in the form of a regularly spaced grid (or field) for the same geographical area (e.g. CCD, NDVI, altitude).� A relation between the two parameters (negative of positive; rainfall/CCD is positive, PET/altitude is negative). A Spearman rank correlation test can reveal whether a relation exists, and how strong this relation is.

SEDI - Spatial Interpolation Method 1/6

12. 12. SEDISEDI

Rainfall (mm) for Zimbabwe second dekad of January 1991

Point data

12. 12. SEDISEDISEDI - Spatial Interpolation Method 2/6

Digital satellite data: Cold Cloud Duration image for Zimbabwe Second dekad of January 1991

12. 12. SEDISEDISEDI - Spatial Interpolation Method 3/6

12. 12. SEDISEDI

Extracting 5 or 9 pixels per point value and calculating the ratio

Ratio =StationValue

HighestPossiblePixelValue PixelValue−

SEDI - Spatial Interpolation Method 4/6

Creation of a grid from the irregularly spaced ratio's

12. 12. SEDISEDI

SEDI - Spatial Interpolation Method 5/6

Creating the SEDI image from ratio grid and background image

12. 12. SEDISEDISEDI - Spatial Interpolation Method 6/6

Spatial interpolation of agroclimatic variables

12. 12. SEDISEDI

Spatial interpolation of climatic variables

12. 12. SEDISEDI

Spatial interpolation of climatic variables

12. 12. SEDISEDI

PREPDATA Merges suitably a temporal sequence ofimages

ACOMPIX

NGPIX Non-hierarchical Clustering of Pixels

Principal Components Analysis of the tablepixels x time variables

DISPLAY Displays profiles of pixels and classesin graphic form

BOUND (optional) selects the region(s) to which torestrict the clustering procedure

EXTRACT into a classified image for DISPLAY

SELECT Selects only pixels belonging to someclasses for further processing

Converts any partition computed by NGPIX

13. 13. ADDAPIXADDAPIX

Program chaining for zoning mode

13. 13. ADDAPIXADDAPIX

Program chaining for monitoring mode

13. 13. ADDAPIXADDAPIX

Zoning mode: Agroecologicalzones of Morocco using NOAA-GAC NDVI data

13. 13. ADDAPIXADDAPIX

Monitoring mode: Assessment of 1999 Cereal Production in Western Africa using NOAA-GAC NDVI data

13. 13. ADDAPIXADDAPIX

Monitoring mode

14. RADAR14. RADAR

Transferfrom local magnitude

to intensity

Knowledge base,transfer functions

ΦΦΦΦM →→→→ I

evaluation oflocal magnitudes

Model base,geophysical modeling

Direct measurement of intensity

Integral overparcel components and area

to obtain damage

Calculation ofdamage for each component

Data base,Values of each component of

each parcel

Transferfrom intensity

to percentage loss

Knowledge base,transfer functions

ΨΨΨΨI →→→→ L

EVENT

Rapid Agricultural Disaster Assessment Routine

Parcel 4Town

Parcel 1Town

Parcel 2Field

Parcel 5Pasture

Parcel 3Wood

P

Data Base forParcel 6Type of Parcel LakeName Lake PleasentType of event TyphonIntensity 9

Basic systemLabor 25Fishing plots 9

Activities systemFishing 3000Gathering 100

Life supporting systemRepopulation plots4

Activities supporting systemDocks3Boats20Shelters2

Model Assess

14. RADAR14. RADAR

Rapid Agricultural Disaster Assessment Routine

Rapid Agricultural Disaster Assessment Routine

14. RADAR14. RADAR

14. RADAR14. RADAR

Rapid Agricultural Disaster Assessment Routine

Rapid Agricultural Disaster Assessment Routine

14. RADAR14. RADAR

15. Conclusions15. Conclusions

� Expertise in: GIS, remote sensing and agrometeorology

� Availability of global environmental digital datasets

� Meteorological and remote sensing data collection and analysis at near real-time

� Development of methodologies/models and tools for datastandardization, collection, spatialization, analysis and dissemination

� Networking and information sharing

� Development of Integrated Information Management Systems

FAO capabilities

THE EN

D

Thank you

FAO WEB sites:Environmental Information:

http://www.fao.org/sd/Endef_en.htm

Links of Agrometeorology:http://www.fao.org/sd/ENlin_en.htm

ARTEMIS and Agrometeorological data:http://metart.fao.org/default.htm

WinDisp:http://www.fao.org/WAICENT/faoinfo/economic/giews/

english/windisp/windisp.htm

FAO-WMO Agrometeorology Internet Conference:Agromet-L@mailserv.fao.org