Antwerp march A Bottom-up Approach to Characterize Crop Functioning From VEGETATION Time series Toulouse, France Bucharest, 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 research Department of Agronomy and Environment (1) INRA,CSE,Avignon,France (2) ICPA, Bucarest, Romania (3) CNES, Toulouse, France

Antwerp march 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

Antwerp march 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 data Cultural practices LAI dynamics How to derive LAI dynamics of specific crops from VEGETATION time series??? SPOT VEGETATION

Antwerp march r i (t) RT model LAI i (t) MODLAI A bottom-up approach to retrieve LAI dynamics Simulated VEGETATION time series AGREGATION CiCi Classification SPOT/HRV 20x20 m² Measured Temperatures [LAI max,T i,  T s,a,b] MODLAI parameters Comparison Actual VEGETATION time series Ajusting parameters

Antwerp march 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

Antwerp march Km x 10 Km 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 Satellite dataMeteo/atmosphereVegetation variablesSoil variables 39 SPOT/HRV images 16 ERS/Radarsat VEGETATION data Temperature Radiation Rainfall … Aerosol Opt. Thick. LAI Biomass & distribution Chlorophyll Moisture Yield Texture Organic matter Moisture profiles Bulk density Chemistry …

Antwerp march Results: Temporal profiles of reflectances of each cover Wheat Maize ForestWaterPeaAlfalfa Extraction of 100 pixels for each cover class in the red and near infrared bands

Antwerp march Results: Deriving LAI temporal profiles Consistent Canopy variable retrievals Inversion of RT model (SAIL+PROSPECT) over the 100 pixels LAI Leaf angle Hot-spot Leaf structure Chlorophyll Leaf dry matter Soil brightness LAI Inverting RT model ( SAIL+PROSPECT ) to get canopy variables Example of the wheat crop Good consistency of Retrieved variables

Antwerp march TiTi TsTs a b LAI max a, b : rate of growth and senescence T i,  T s : Significant dates of the life cycle of cultures Results: Adjusting LAI dynamics model Retrieving MODLAI parameters [LAI max,T i,  T s,a,b] for the 100 pixels and each class Example of the wheat crop Good description of the Dynamics of LAI values

Antwerp march 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 LAI max TsTs b TiTi

Antwerp march CONCLUSION Interest 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