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A Bottom-up Approach to A Bottom-up Approach to Characterize Crop Functioning Characterize Crop Functioning From VEGETATION Time seriesFrom VEGETATION Time series

Toulouse, FranceBucharest, Fundulea, Romania

F. Oro.(1), F. Baret (1), C. Lauvernet (1), R. Vintila (2), N. Rochdi (1) H. de Boissezon (3)

National Institute for Agronomy researchDepartment of Agronomy and Environment

(1) INRA,CSE,Avignon,France(2) ICPA, Bucarest, Romania(3) CNES, Toulouse, Franceforo@avignon.inra.fr

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Introduction

Context Yield estimation/forecasting at regional/national/continental/global scales is required for improved security and market management. Users are governments, FAO, NGOs, traders… This question is part of GMES issues

The monitoring of crops at these scales is currently only accessible operationally from large swath sensors such as VEGETATION that provides enough revisit frequency

Problem: Difficulty to monitor each individual crop because of mixed pixels

HRV VGT

1km

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To develop and evaluate a method to estimate crop production with SPOT/VEGETATION data

Objective

Approach: forcing a crop growth model with LAI dynamics derived from remote sensing: allow to integrate soil & climate available information within the growth model

Stics

Production

Soil characteristics

Meteorological dataCultural practices

LAIdynamics

How to derive LAI dynamics of specific crops from VEGETATION time series???

SPOTVEGETATION

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ri(t)

RT model

LAIi(t)

MODLAI

A bottom-up approach to retrieve LAI dynamics

Simulated VEGETATION

time series

)()(1

trctRn

i

ii

AGREGATION

Ci

ClassificationSPOT/HRV20x20 m²

Measured Temperatures

[LAImax,Ti,Ts,a,b]MODLAI parameters

Comparison

ActualVEGETATION

time series

Ajustingparameters

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Detailed objectives of the study

evaluate the approach in two steps:

1- develop the approach based on simulations using a series of SPOT/HRV images

-Define the LAI dynamics models for different covers-Get prior information on the distribution of the parameters-Evaluate RT models for reflectance simulation-and to investigate the sources of uncertainties

2- Evaluate the approach over actual VEGETATION data

The study is based on the ADAM experiment

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10 Km x 10 Km

www.medias.obs-mip.fr/adam

The ADAM experiment

Romania

•

Fundulea

Wheat 32%

Maize 36%

Forest 4%

Water 2%

Pea 8%

Alfalfa 6%

Other 12%

Focus on wheat crops

The data collected in 2000-2001Satellite data Meteo/atmosphere Vegetation variables Soil variables39 SPOT/HRV images16 ERS/RadarsatVEGETATION data

TemperatureRadiationRainfall …Aerosol Opt. Thick.

LAIBiomass & distributionChlorophyllMoistureYield

TextureOrganic matterMoisture profilesBulk densityChemistry …

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Results: Temporal profiles of reflectances of each cover

WheatMaizeForestWaterPea Alfalfa

Extraction of 100 pixels for each cover class in the red and near infrared bands

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Results: Deriving LAI temporal profiles

Consistent Canopy variable retrievals

Inversion of RT model (SAIL+PROSPECT) over the 100 pixels

LAILeaf angle

Hot-spot Leaf structure Chlorophyll

Leaf dry matter Soil brightness

LAIInverting RT model (SAIL+PROSPECT) to get canopy variablesExample of the wheat crop

Good consistency of

Retrieved variables

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temperature

LAI

Ti Ts

a

b

LAI m

ax

a, b: rate of growth and senescence

Ti, Ts: Significant dates of the life cycle of cultures

Results: Adjusting LAI dynamics modelRetrieving MODLAI parameters [LAImax,Ti,Ts,a,b] for the 100 pixels and each class

Example of the wheat crop

Good description of theDynamics of LAI values

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Results: prior distribution of MODLAI parameters

Good consistency of the distribution of parameters

Computation of the distribution of the MODLAI parameters:It will constitute the prior distribution used in the bottom-up approach

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CONCLUSIONInterest of the proposed bottom-up approach:•Innovative approach to combine

- few high spatial resolution images (land cover classification) - with high temporal frequency medium resolution temporal series

•Less dependant on scaling

Potential problems•Variability within one cover class? But using mixed models would allow to account for•Impact of the performances of the models used ?: MODLAI and RT models?•Effect of the VEGETATION registration? apply the approach to resolution larger than 1km?

Status of the study•The study is still under development… next steps:

•Adjusting the MODLAI parameters to complete the bottom-up loop•Evaluate the sources of uncertainties: registration, variability within cover class, …•Apply the approach to actual VEGETATION data and evaluate the performances•Compare the performances of this bottom up approach with top-down approaches (desagregation)•Force the STICS growth model to evaluate the performances of yield estimation