Issue
Agronomie
Volume 24, Number 6-7, September-November 2004
Crop model STICS (Simulateur mulTIdisciplinaire pour les Cultures Standard)
Page(s) 437 - 444
DOI http://dx.doi.org/10.1051/agro:2004034
Agronomie 24 (2004) 437-444
DOI: 10.1051/agro:2004034

Use of SPOT4-VEGETATION satellite data to improve pasture production simulated by STICS included in the ISOP French system

C. Di Bellaa, R. Faivreb, F. Rugetc, B. Seguinc, M. Guérifc, B. Combalc, M. Weissc and C. Rebellaa

a  Instituto de Clima y Agua - CIRN - INTA, Los Reseros y Las Cabañas S/N, Castelar (1712), Buenos Aires, Argentina
b  Biométrie et IA, INRA, Toulouse, BP 27, 31326 Castanet-Tolosan Cedex, France
c  CSE, Bâtiment Climat - INRA, Site Agroparc, 84914 Avignon Cedex 9, France

(Received 12 January 2003; accepted 11 June 2004)

Abstract - In France, pastures represent a significant land-cover type, which mainly sustains husbandry production. For this reason, it is of great benefit to develop real-time monitoring of pasture biomass production, taking into account its spatial and temporal variability. The absence of low-cost methods applicable to large regions has oriented French stakeholders to the use of growth simulation models adequately informed through spatialised databases (such as the ISOP system). Remote-sensing data may be considered a potential tool to improve simulations by objective observations in a real-time framework and the aim of this work was to evaluate this potential role of remote sensing. Thirteen forage regions (administrative partitioning of the French territory for pastures and grasslands) were selected in France, differing by their soil, climatic and land-cover characteristics. SPOT4-VEGETATION satellite images (1 km2 resolution) were used to provide the spectral signature corresponding to pure pasture, using subpixel estimation methods. This information was then related to growth variables calculated by the STICS-pasture model (included in the ISOP system). We found that the best relations were obtained between a middle infrared-based vegetation index (SWVI) calculated from the elementary reflectance bands of the satellite, and the leaf area index (LAI) calculated from STICS. The use of these relations first showed the ability of satellite data to provide real-time estimations of growth status variables. Second, the comparison between both types of data showed that spatial and temporal differences existed between satellite and model information, mainly during the harvesting periods. This result could contribute to improving the model evaluations on a regional scale.


Key words: pastures / remote sensing / vegetation index / biomass production / STICS / regional scale

Corresponding author: C. Di Bella cdibella@cnia.inta.gov.ar

© INRA, EDP Sciences 2004