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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, [email protected]
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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