OPAG 2Support Systems for

Agrometeorological Services

Federica Rossi

CAgM MG Meeting Obnisk 10-13 June 2008

NASA's Terra satellite April 15, 2008

May 5, 2008,

OPAG 2ICSAS- Support Systems forAgrometeorological Services

•Structure and organization•Activities and actions

•Links with previous ICSAS activites•Planning for the future

Chair Federica ROSSI RA VI (Italy)Co-Chair Enrique PALACIOS RA III (Ecuador)

Members:

Mohamed DAWOD RA I (Egypt)Huailiang CHEN RA II (China)Miguel EGANA RA III (Chile)Adriana CORTEZ RA IV (Venezuela)Flaviana HILARIO RA V (Philippines)Svetlana KORSAKOVA RA VI (Ukraine)

- Structure and organization

OPAG 2 Support Systems for Agrometeorological Services

2.2 ET Collection and Evaluation of Operational AgrometeorologicalTools and Methodologies

(R.Stern)

2.3 ET Communication of Agrometeorological Productsand Services(P.Hayman)

Organization based on terms of reference

(b) To review the operational applications of current agrometeorological data, analytical tools, and information delivery systems at the national and regional levels.

Leading group: Korsakova animateur, Dawod, Chen, Rossi.

(c) To make recommendations on procedures, methodologies and resources to improve the regional-based capability for

operational applications.

Leading group: Hilario, animateur, Egana, Cortez, Palacios.

OPAG 2 Support Systems for Agrometeorological Services

Activities and actions

a.To review and coordinate the activities of the OPAG, as well as additional related activities of priority to the Commission, in order to ensure their effective implementation and adoption within Membercountries.

Rossi and Palacios.

Term of reference:

1500 visits/month

22.000 visits/month !!!!

To have a basis of discussion and a actualovieview to develop terms b and c,

a questionnarie has been prepared by Rossi and Korsakova and distributed.

General ideas for the survey:1-what we have now

2- how we use it3- which is the demand

4- how to improve the offer

AVAILABILITY OF PRODUCTS AND SERVICES (AND ACTUAL RESOURCES)

Which kind of products and services provided by National Hydrological and Meteorological Services (NMHSs) are operationally used in your Country (RA)? Please list some

……To what extent new techniques such as remote sensing, GIS, modelling are used for data collection, analysis and operational recommendations ?

GIS

Plant growth models

Agroclimatic indices and simulation models

Remote sensing

NeverOccasionallyOftenAlways

WHAT IS AVAILABLE SO FAR?

3- Are equipment and devices for data collection and analyses made by national or foreign companies?

Workstations

Measurement stations

Foreign National Equipment

4- Is the budgetary allocations for meteorological-agrometeorological services sufficient?

Allocation for agrometeorology

Allocation for meteorology

Largely insufficient

InsufficientFairSufficient

There are state programs (if yes, specify details when possible ) on re-equipment of NMHSs by modern communications facilities, equipment and devices for data gathering?

New data-management equipments

New data-acquisition equipments

New communication facilities

Specify details if available

in preparationNoYes

What is your consideration about the degree of the public investments in National Meteorological and Agrometeorological Services?

Degree of public investment

Low FairGood Excellent

4- What is, in general, the degree of utilization of NMHSs products and services?

Degree of utilization

Low FairGood Excellent

Kind of use and frequency of use of different NMHSs products and services used in agrometeorology in your Country (RA)

Other (please, specify)

Protect ecosystems/biodiversity

Health improvement or safeguard

Reduce or mitigate hazards/ disasters

Better use of resources (water, soil, etc.)

Improve sustainability

Improve the farming techniques

neveroccasionallyoftenalwaysKind of use

How we useproductsand services?

How important is the demand of agrometeorological services in your Country/RA at the national and regional levels?

Demand of agrometeorological services

Fair at regional level

Important at regional level

Fair at national level

Important at national level

WHICH IS THE DEMAND ?

How the demand of the users is studied to understand the needs of other public services and users for weather, climate and agrometeorological information and in the perspective to create a demand for them?

Attempt to create a demand

Attempt to understand the needs of other services to use weather, climate, agrometinformation

Null Low FairGood Excellent

Which kinds of improvements are needed to increase the value of agrometeorological products and services for agricultural production?

Other (please, specify)

More public diffusion of the meaning of “agrometeorology”.

More technological information

More training of the personnel

More facilities to use agrometeorological products

More knowledge about the agrometeorological products

More knowledge about the potentialities offered by agrometeorology

Not importantNot so important

ImportantVery important

Kind of improvement

HOW CAN THE OFFER BE IMPROVED?

Which kinds of improvements are needed to increase the value of agrometeorological products and services for civil and economy uses?

Other (please, specify)

More exchange of information between stakeholders

More public diffusion of the meaning of “agrometeorology” and of its applications .

More technological information

More training of the personnel belonging to different administrations

More facilities to use agrometeorological products

More knowledge about type and usefulness of agrometeorological products

Improved dissemination of their potential utility

Not importantNot so important

ImportantVery important

Kind of improvement

Are advertising media used to demonstrate the value of agrometeorological services for decision-makers in agriculture, technician, farmers?

Web

e-mail

TV-radio

Newspaper

neveroccasionallyOften Always Kind of media

Are advertising media used for demonstration the value of agrometeorological services to decision-makers, politicians, the private sector and public at large?

Web

e-mail

TV-radio

Newspaper

neveroccasionallyOften Always Kind of media

Answers from 67 Countries:

…IN THE MEANTIME…

(b) To review the operational applications of current agrometeorological data, analytical tools, and information delivery systems at the national and regional levels.

(c) To make recommendations on procedures, methodologies and resources to improve the regional-based capability for operational applications

DATA COLLECTION AND USE AS INPUT DATA, AGROMET INDEXES

NUMERICAL MODELS

REMOTE SENSING

GIS -THEMATIC MAPS

AGROMET SUPPORT SYSTEMS

AGROMETEOROLOGICAL INDEXES

ADVANTAGESPowerful tools, frequently ( how, where?) used at operational level, providing the end-users with useful information (which ones?) to manage and plan agricultural activities (which one? where?).

Accumulation of materials concerning interrelated plant/weather observation, increasing efficiency of computer technology: multifactorial relationships are being developed which fully express the complex impacts of climate on plant productivity.

Gain understanding and visualizing of the real present and possible future use of agrometindexes.

THE NEED:

THE AVAILABILITY:

Drought

Frost

Direct crop response

Simulated crop response

Excess rain

Snow coverHeat stress

Distribution of the number of agrometeorological indices in researchaccording to their purpose.

02468

1012141618

DRO

UG

HT

HEA

T ST

RESS

EXC

ESS

RAIN

FRO

ST

SNO

W C

OVE

R

CRO

P RE

SP.

P&D

OTH

ER

Nb. o

f cou

ntrie

s

Event

Water balance

Operational use of agrometeorological indices in European countries

The top parameter for plantgrowth

NEEDS AND LIMITATIONS:

Data availability: meteo data have to be collected by specific stations or by existing network of stations. Hourly or daily data are required.Data transmission: depending on the indices, data need to be manually collected or automatically transmitted with a regular time step;Data control: indices are the result of a combination of

different data, it is particularly important that single data are controlled and eventually corrected before indices calculation to avoid the propagation of errors;Calculation of the indices: automatic procedures can be set up for the indices calculation;Elaboration of results: single values, but also annual trends and interannual variability can be considered to obtain information concerning the management of the systems and their planning.Use and diffusion: Big part of the agromet indices is a part and form agrometeorological forecast. Agrometeorological forecast may be applied at both national and regional levels influencing market planning, agricultural policy, insurance policy as well as farming practices.

INPUT DATA

INDEX

USE

A question: is the use of indices be possibly improved? Which ones? How?

Data sources (data owners, location, property of stations, short-term records, compatibility…)

The problem of scaleSpatial scale (in situ, local conditions, integration for spatiatization….)Time scale (usually daily info, too short in some cases….)

ComplexityMore complex approaches (process oriented models) are still very limited in operational applications (especially crop yield models), except the more simple ones focused on irrigation scheduling or the already widely applied models for pest and disease management.

The need of standardization

An example:

Drought indices: to quantify the lack of water during plant growth. The general problem of these indices is to include the physical and biological properties of the particular crop in order to reflect its sensitivity and limitations towards the lack of water supply during the vegetation period. Some indices in use define

the meteorological drought which does not in all cases describe the real shortage of water for the crops. Further problem of defining the drought is the

time step used to calculate the particular indices.

The major part of the indices in use are rather complex and deal with water balance components and precipitation measures. Water balance components

are used in various forms in almost all the Countries.

Main users: farmers, extension services.

From the standard indices SPI, PDSI, percent of normal precipitation and rainfall percentiles are in operational use.

Reconnaissance Drought Index (RDI) which uses potential

evapotranspiration in conjunction withprecipitation as the variables

Gennady Menzhulin. Personal communication

1) Koloskov . P.I. Report of Temir Experimental Field on 1907-1908. St.Petersburg. 1910. Koloskov P.I. On the Problem of Agroclimatic Regionalization of USSR Territory. Transactions of the Research Institute of Agrometeorology. 1958, vol.6.

ARIDITY INDEX E0=a(e0-e)E0 – daily potential evapotranspiratione0 – saturate water vapour pressure e – real water vapor pressure a – empirical constant (usually 0.804)Used in researches2) Oldekopff E.M. On Evaporation over River Basins. Yuryev, 1911. ARIDITY INDEX=(PRECIP)/ad d - deficit of water vapor saturation a - empirical constant (usually 232)Used in researches 3) Selyaniniv G.T., Leonteyevsky N.P. Climatic Conditions of Crops in Kamenno-Steppe

Agrometeteorological Station. Leningrad. 1930.Selyaninov G.T. The Nature and Dynamics of The Droughts. In Book: Droughts in the USSR, their Nature, Recurrences and Impact ao Crops Yields. Leningrad, Gidrometeoizdat, 1958.

HYDROTHERMAL COEFF = Sum (Precip)/Sum (Temp) In three summer months

Also = Sum (Precip)/0.1Sum (Temp>10 0C)Very popular in operation practice and researches even in present 3) David R.E. Agricultural Meteorology. State Publ.Company SELKHOZGIZ. 1936.

ARIDITY INDEX = E0 – PrecipE0 – potential evapotranspirationTotally for warm period of the year

Used in Researchers4) Shostakovitch V.B. On the Comprising of Soil Formations with Climatic Conditions. In book:

Proceedings of 2nd Congress on Soil Science. Vol.5. Moscow. 1932.ARIDITY INDEX = 10Sum (Precip)/Sum(Temp in vegetation period in 0C)Used in researches5) Bova N.V. On Climatic Study of Droughts in USSR South-East Areas. Bulletin of USSR

Academy of Sciences. Geography. and Geophysics Ser. 1946. N3. ARIDITY INDEX K = 10(H+Q)/Sum(temp)H – water amount in 100 cm soil layer available for plantsQ – total precipitation (from beginning of spring till beginning of drought) Sum (temp) – Sum of positive temperatures (0C) from spring

Beginning of drought when K=1.5 Used in researches6) Ivanov N.N. Landscape and Climatic Zones of Continents. Bulletin of All-Union Geographical

Society. New Ser. 1948. vol.1.ARIDITY INDEX E0 = 0.0018(25+t 0C)2(100-a) E0 – potential evaporation fro open water surface t – mean monthly temperature a – mean monthly relative humidity (%)

Used in researches

7) Skvortsov A.A. On the Question of Heat and Water Exchange in the Surface Air. Transactions of Middle Asian State University. 1950, vol.22, N6.

ARIDITY INDEX -= Ea/EstE0 - real evapotranspirationEst - Evaporation from the standard water surface

Used in researches 8) Budyko M.I. et al. Climate Change and National Plan of Environment Modification of Arid

USSR Areas. Leningrad. Gidrometeoizdat. 1952. RADIATION INDEX OF ARIDITY = (Annual Radiation Balance) / LPL- latent heat P – Total annual precipitation

Used in argroclimatological division into regions (in researches)9) Kostin S.I. Climatic Zones in the Central and Eastern Regions of Forest-Steppe and Steppe

Territories of Russian Plain. Transactions of Voronezh State University. 1954. vol.30 ARIDITY INDEX K = (Precip)/dn(1+0.004t 0C)2Precip = rainfall in some period d – mean humidity deficit for some periodt – meat air temperature for the same period

Not sufficient moistening - when K=0.7-0.9 Arid conditions - when K=0.7-0.5Very arid - K<0.5Used in researches10) Budagovsky A.I. Meteorological Factors and Soil Moisture Influence on Evaporation. In book:

Irrigation Problems in Lower Amu-Darya River. Moscow, USSR RAS Publ. 1956.ARIDITY INDEX = Delta E = E0-EE0 – potential evapotranspiration (in vegetationn period) E – actual evapotranspiration (in vegetation period)

Used in researches 10) Nesterov V.G., Maksimov S.A. Droughts and their Prevention. Moscow. 1958.

ARIDITY INDEX = Sum(td)t- air surface temperature at 13 o’clockd- deficit of air humidity at 13 o’clock 9 in mbar)

Used in researches 11) Buchinsky I.E. Climatic Assessments of Heat Waves in Ukraine Agricultural Areas. In book:

Paper Presented to the Scientific Council of Ukrainian Agriculture Ministry.. Leningrad. Gidrometeoizdat. 1958.

Alpatyev – Buchinsky Pluviothermal Coefficient = Sum (Precip)/0.3Sum (Temp)Total for three summer mobthsUsed in researches 12) Shashko D.I. Agroclimatic Division into Districts of USSR Terrirory. Moscow. GUGR Publ.. 1969.ARIDITY INDEX = Sum(Precip)/(Sum (Deficit of air humidity) (in vegetation period) Used in operation practice and in researches. Used in division into regions 13) Koshelenko I.V., Volevakha V.A. Partial Impacts of Several Meteorological Factors on Droughts Formation. Transactions of Ukrainian Research Hydrometeorological Institute. 1971. N101.

DROUGHT INDEX – When Precipitation less 40% of Climate NormalUsed in researches 14) Ped’ L.A. On the New Drought and Over-moistening Index. Transactions of the USSR Hydrometeorological Center. 1975. Vol.156.c.19-39

ARIDITY INDEX = Delta(temp)/Sigma(temp) –Delta(Precip)/Sigma(Precip) –- Delta(Soil Moisture)/Sigma (Soil Moisture)

Delta(temp) – Surface air temperature deviation (in some month or vegetation period)Sigma(temp) – Standard temperature deviation (30 years)Delta(Precip) – Monthly precip deviation (in some month or vegetation period)Sigma(Precip) - Standard precip deviation Delta(Soil Moisture) – Soil moisture content deviation (in 100 cm) (month or period)

Dissemination of information in agrometeorology: the personalized irrigation guided scheduling for farmers

CONSORZIO DI BONIFICA DI SECONDO GRADO PER IL CANALE EMILIANO ROMAGNOLO BOLOGNA

WATER BALANCE AS A SERVICE IN WATER BALANCE AS A SERVICE IN AGRICULTUREAGRICULTURE

IRRINET

WATER

BALANCE

The service is active from 1985, and has allowed to the farmers using this facility to save an average 20 % water.

Now, telematic applications (videotel, internet) are offering very good results at farm and local level.

WATER BALANCE IRRINETWATER BALANCE IRRINET

www.consorziocer.itwww.consorziocer.it

•input: data on crop type and irrigation plants, meteo data fromSMR and radar

•output: time and amount of the irrigation, soilhumidity

The irrigation service- IRRINET

Meteo data Soil data base

Cropparameters

CER

Irrinet-sms

The sms service uses IRRINET data to send personalized messages. This opportunity allowed to greatly increase the number of users

This consortium through a Call Center operates to book and todistribute water (also directly opening the bulkheads of channels) , indicating to each farmer how much water isneeded. The service is consulted 5000 times a year

Tradition-high value → optimal irrigation strategies

Overview on the main limitations of:

•Excess rain indices

•Heat stress (complex funcions of t, duration, rate of increase, thresholds dependent on crops type and stages. Prediction isusually in general weather forecast, specific heat stress to bestudied more?)

•Frost

•Specific events (fires)

•Pest and diseases monitoring

1Lack of quality of weather data

5Daily weather data not available in time

11Low quality of soil data like parameter value describing texture, organic content, soil physical indicators (too high spatial resolution for field applications etc.)

12Lack of availability of biological data for validation of models (long term yield of specific varieties etc.)

NumberType of limitation

Named limitations connected to the use of crop growth models

•The answers reflect practical problems connected to socio-economic conditions and local administration of data in different regions (lack of relevant biological data), and the general low quality and availability of the soil physical data for modelling purposes. • Outputs (qualitative and quantitative) have to be analyzed, understood, interpreted eventually correct before operational application use by trained personel.

nononononononoCROPSYSTnoSerbia

nononononononoCERES MAIZE

CERES WHEATRomania

nononoIUNG-PIB IUNG-PIB IUNG-PIB nonoIUNG-PIB Poland

nonononononoENGNORnoKONORNorway

nononono

WOFл/SUCRWOF/SUCRnonoWOFOST/SUCROSNetherland

nononononononoWOFOSTWOFOSTItaly

nononoAMBETIAMBETIAMBETIAMBETIAMBETIAMBETI/SIMONTOGermany

STICSnonononoSTICSSTICSSTICSSTICSFrance

Irrigation modelnonononono

grassland modelnonoDenmark

WOFOSTnonononononoWOFOSTWOFOSTBulgaria

nononoAMBETIAMBETIAMBETIAMBETI/FAO-GRAMAMBETIAMBETIAustria

OtherGrapeAppleSugar beatPotatoOilseed (Rape)GrasslandMaizeWheat

Application still very limited,.Crop simulation models, depending on their complexity, need more or less various input

data. The model DSSAT (Decision Support System for Agrotechnology Transfer) for example,

needs information about weather, soil, management and genetics.

More More complexcomplex approachesapproaches: : processprocess orientedoriented modelsmodels in in CropCrop indexesindexes

Output is on local scale. As crop simulation models produce a point output, spatial data analysis techniques and geographical information system (GIS) can help to increase information of these outputs to larger areas.

Crop simulation modelsin combination withRemote Sensing and GIS

Forecasting cropyield

Precisionfarming

Agro-ecologicalzoning

One example of application is the CGMS (Crop Growth Monitoring System) of MARS (Monitoring Agriculture with Remote Sensing) (http://mars.jrc.it/). The project uses two crop models (WOFOST and LINGRA) and Arc/Info for operational yield forecasting of main crops in the European Union

Agro-Ecological Zones system, developed by FAO and Int Inst Applied Systems Analysis IIASA. The approach enables rational land-use planning on the basis of an inventory of land resources and an evaluation of their biophysical limitations and potentials for crop production

System or computer based methodology for capturing, storing, editing, analyzing,managing, querying, displaying geo-referred datadata and associated attributes.attributes.

Geographical Information Systems

G I S

Digital maps

Hardware

Software

Skills and rules

Data

Data creation (map digitalization, …)Data capture (remote sensing, …)

Data Projections

Spatial analysis, data modelling, geo-statistic, data and map overlay, ...

Thematic maps, cartography, decision supporting tool

Data elaborationData input Data Output

Field of application Field of application (any field wich the attributes are related with the territor)(any field wich the attributes are related with the territor)

1. DERIVATION

INTERPOLATION (SPATIAL ANALYSIS,

GEOSTATISTICS)

DATA INVECTOR GEOGRAFICAL DATA

(E.G. BOUNDARIES, URBAN, RIVERS,ISOLINES, PUNCTUAL OBSERVATION )

POINT CLIMATOLOGICAL DATA(E.G. WEATHER STATION OBSERVATIONS)

DATA ENHANCEMENTINTERPOLATION

(SPATIAL ANALYSIS, GEOSTATISTICS)

RASTER GEOGRAPH. DATA(E.G. DIGITAL TERRAIN MODELS,

REMOTE SENSING IMAGES)

DATA EXTRACTIONEXTRAPOLATION

MODELLINGE.g. meteorological forecast

modelsGIS-Ready Weather Data

(POES Images, GOES Images, NEXRAD, GRIB (models), METAR)

DATA OUT / VISUALIZATIONTHEMATIC MAPS

DATA OUT TERTIARY DATA

2. CUSTOMIZATION

topological overlay

SPATIAL CLIMATIC DATASET theme / layer 1

OTHER GEOGRAFICAL DATASET theme / layer 2

OTHER GEOGRAFICAL DATASET theme / layer 3

OTHER GEOGRAFICAL DATASET theme / layer N

GISGIS

GISGIS

Soil parameters, Morphology,Aspect, Slope, Hillshade, etc.

Rainfalls, temperatures,sunshine hours, wind, humidity, etc.

GISGIS

GISGIS

Large amounts of spatial data

Extensive estimation of agro-climatic parameters

Precision mappingVisualizing the land useVegetation indexes Drought effects on plants , modelization of the potential evapotranspiration by overlaying temperature and DEM-derived slope, aspect and elevation data.

Decision supporting tool for preventing drought damages

Maps of combined soils, nutrients, climate, water stress, fertility and predicted yield. Estimation of yield via crop simulation models.Zoning

Monitoring biogenic emissions and agricultural pollution.Wind maps

Produce climate zones to select site suitability for biomass resources (forests, crops).

Topoclimatic studies > Maps of Frost prediction, modelling the stagnation of cold air using a DEM.

Free Open Sources GIS software

WellWell--known open source GIS software:known open source GIS software:

• GRASS GIS (Geographic Resources Analysis Support System) • Quantum GIS• MapServer• uDig• OpenJUMP• gvSIG• OSGeo• MapTools

Libraries (GDAL/OGR, GeoTools, PostGIS) of non-proprietary and open data formats, as shape file (vector data) and Geotiff (raster data).Open Geospatial Consortium (OGC) standards for networked servers Web Map Service (WMS) and Web Feature Service (WFS).

Web and web service oriented applications.

Money saving.

Models –applications using Remote Sensing

Enrique Palacios

Models – Remote Sensing

1. Roles of remote sensing in modeling NPP

2. Production efficiency models3. Continuity of the global satellite data4. Errors in satellite derived variables5. Achievements with the satellite data

record6. Future for production efficiency models

1. Roles of Remote Sensing in modelling

• Land Cover mapping– Classification– Ordination (continuous fields)

• Phenology (seasonality)• Surface meteorology

– Air temperature– Humidity– Rainfall– Soil moisture

• Leaf Area Index• Absorbed fraction of incident photosynthetically active

radiation (Fapar)

2. Production Efficiency Models

• Principles– Remote measurement of absorbed PAR

• Fapar• Incident shortwave solar radiation

– Light Use (Efficiency) ε (gMJ-1)• A conservative parameter?• More conservative for Gross Primary

Calibrated εS(Solar Surface

Irradiance)

FPAR

*

APAR=

M

*

=

W, T Scalars

*

CASA

ε*

Field-NPP(for select surface sites)

APAR

/

= Field-ε(for select surface sites)

calibration

1/(W * T1 * T2)*

Calibrated ε

CASA ε Calibration

BiologicalvariablesNDVI

LAI

Plant functionaltype

Climate variables

Temperature

VPD

Rainfall

FPAR

GPP

Ra

Satellite remote sensing

Soil moisture

LUE

NPP

Efficiency Model

PAR

Atmospheric CO2

Temperature

Water vapor pressure deficit

Soil moisture

Radiation

εgross,max

εgross

σ

Photo-synthetic enzyme

efficiency

Stomatal CO2

conductance

Lightuse

efficiency

Determination of Light Use Efficiency

“stress”

User’s Guide GPP and NPP(MOD17A2/A3) Products NASA MODIS Land Algorithm

December 2, 2003

3. Achievements with the satellite data record

(non-meteorological applications)• Vegetation dynamics – data at 8-15d intervals• 23 year record

– El Niño events– Large areas of tropical rainforest cleared– Several major fire events at global scale

• Land cover at 1km2 resolution with global coverage – includes natural and human disturbance

4. Continuity of global near-daily VIS-NIR-TIR observations

• Time line of sensors AVHRR – NPOESS– AVHRR 1981-2007– MODIS 2000-2008– VIIRS 2007-

• NASA CAN project– Create an improved AVHRR data set

• Apply Pathfinder II preprocessing• Adapt atmospheric and BRDF corrections from MODIS

processing– Create an equivalent MODIS data set for best continuity– Apply a similar transitional analysis to the MODIS-VIIRS

transition• User group to be established

AVHRR (1981-present) data issues

• Calibration• Orbital drift• Geolocation• Sampling of global area coverage• Limited bands for atmospheric correction• Lack of application of best processing to

public archive

The AVHRR instrumental record Calibration

The AVHRR instrumental record

From:

An Extended AVHRR 8-km NDVI Data Set Compatible with MODIS and SPOT Vegetation NDVI Data

Compton J. Tucker, Jorge E. Pinzon, Molly E. Brown, Daniel A. Slayback, Edwin W. Pak, Robert Mahoney, Eric F. Vermote, and Nazmi El Saleous

Submitted to International Journal of Remote Sensing

The AVHRR instrumental record Available vegetation index data

setsN

DV

I

ND

VI

Rat

io

From:Quantitative comparison of four AVHRR global data sets for land applicationsMolly E. Brown, Jorge E. Pinzon, Compton J. Tucker

Channel 2NIR

Channel 1VIS

Channel 3MIR

Channel 4TIR

Channel 5TIR

Digital numbers 0 -1023

CalibrationAtmospheric correction

BRDF normalization

Spectral Vegetation Indices(e.g., NDVI)

Fraction of incidentPhotosynthetically Active Radiation

absorbed by leaves (FAPAR)

Leaf Area Index (LAI)Incident

PhotosyntheticallyActive Radiation (PAR)

Burned area

Growingseason length

CalibrationAtmospheric correction

Surface temperatureTs

Air temperatureTa

Active fire

Net Primary Production (NPP)Crop yield

Carbon & Nitrogen cycles

Landcover

mapping

5. Errors in satellite derivations of surface properties

Revision of the potential applications of Remote sensing in:

CROP MONITORING

(NDVI, LAI).

(Ex: applications as USDA Foreign Agric Serv toimprove foreign market access: use of satellite-ground based information, MODIS info analysisdesigned and implemented.MODIS Rapid ResponseTeam, high quality data, spatial res 250m -1 km, avalable 2-4 after acquisition.

RADAR IMAGERY for

•crop identification and area estimation

(Ex: RADARSAT in China forbackscatter of various land covers, AirSAR (C, L, P bands to identifydifferent roughness backscattering)

SATELLITE for cropidentification BroadBand optical and microwave satellites)

… AND OTHER APPLICATIONS (soil moisture, surface temperature, vegetation status, fishing zone identification…)

RS ia a valuable source of data especially at regional scale.

LIMITATIONS:

Data availability

Lenght of recording periods

Limited mapping capabilities

Requirement of expertize and pcfacilities

Costs

Reports prepared and available for ICSAS

F. Hilario- Assessment of the use of modern technologies (remote sensing information) in agrometeorological applications.

•Satellite platforms

•Potential applications in agrometeorology in region V

•(crop monitoring, drought monitoring, potentialities and limitations)

F.RossiF.Rossi, P. , P. CalancaCalanca, A. , A. SusnikSusnik Use of numerical weather products in Use of numerical weather products in and applications of seasonal forecasts to and applications of seasonal forecasts to agrometeorologyagrometeorology in Europein Europe

COST 734 team: Survey of agrometeorological practices and application in Europe (regarding climate change impacts).

•The Commission agreed that one of the major issues to be addressed is the current constraints in the delivery of agrometeorological products and advisories.

• The Commission noted that ICSAS had developed a matrix that showed different channels of dissemination of these products andadvisories (a set of 12 channels ranging from radio and television to organization of field days) and the level of accessibility to these channels for various groups (9 groups ranging from isolated farmers to large cooperatives and companies).

• This matrix clarifies the issue of delivery of agrometeorological products and advisories and what can be done to improve the current situation.

Links with previous ICSAS activites

AVAILABLE CHANNELS FOR THE DISSEMINATION OF AGROMETEOROLOGICALPRODUCTS AND ADVISORIES TO DIFFERENT GROUPS OF USERS

xxxxxxxxxxx9 - Companiesxxxxxxxxxxx8 - Cooperativesxxxxx7 - Farmer Unionsxxxxxxxxxxx6 - Large Farmersxxxxxxxx5 - Medium Farmers xxxx4 - Small Farmers

xxx3 - Villagexx2 - Small Communityxx1 - Isolated farmers

1211109876543210Groups

*Channels for dissemination of products and advisories

0 = no technology 5 = phone (mobile) 10 = weather forecast1 = radio 6 = public extension service 11 = NGO2 = television 7 = private extension service 12 = field day organization3 = leaflet (hard copy) 8 = news papers4 = Internet 9 = specific bulletins

Communication of the products should take into account local capabilities (channels) and local need (contents)

Info: timely and relevant to the action

Users are different, effort to translate the info

10-1-6 The Commission concurred with ICSAS on the need to develop and implement the following two projects based on the prioritization exercise carried out for the recommendations of

ETAC:

(a) “MODIS (Moderate Resolution Imaging Spectral Radiometer) Applications to Agriculture”. Currently AVHRR data are being usedin operational systems in agriculture. It is important to investigate

the new operational applications of MODIS in the RussianFederation, Ukraine, Brazil, Argentina and USA;

LINKs with previous ICSAS ACTIVITIES

b- Transferring Research Results and Tools to OperationalApplications in Agriculture - Developing a transparent protocol”.

There are a number of research results and tools that are beingdeveloped continuously in the area of agroclimatic analysis and mapping, but their applications at the field level tend to lag behind.

This time lag is often quite long and it is important to develop a transparent protocol, e.g. Grapevine, Peach and Apple in Europe, Coffee in Africa.

OPAG 2 Support Systems for Agrometeorological Services

2.2 ET Collection and Evaluation of Operational AgrometeorologicalTools and Methodologies(R. Stern)

2.3 ET Communication of Agrometeorological Productsand Services(P. Hayman)

KenyaOctober 2008 Australia about March 2009

Thank you