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A comparison of two coupling methods for improving a sugarcane model yield estimation with a NDVI-derived variable.

Morel Julien, Martiné Jean-François, Bégué Agnès, Todoroff Pierre, Petit Michel. 2012. A comparison of two coupling methods for improving a sugarcane model yield estimation with a NDVI-derived variable.. In : Conference SPIE Remote Sensing for Agriculture, Ecosystems, and Hydrology, Edinburgh, United Kindom, 24 - 27 September 2012. s.l. : s.n., 10 p. Conference SPIE Remote Sensing for Agriculture, Ecosystems, and Hydrology, Edinburgh, Royaume-Uni, 14 September 2012/27 September 2012.

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Abstract : Coupling remote sensing data with crop model has been shown to improve accuracy of the model yield estimation. MOSICAS model simulates sugarcane yield in controlled conditions plot, based on different variables, including the interception efficiency index (?i). In this paper, we assessed the use of remote sensing data to sugarcane growth modeling by 1) comparing the sugarcane yield simulated with and without satellite data integration in the model, and 2) comparing two approaches of satellite data forcing. The forcing variable is the interception efficiency index (?i). The yield simulations are evaluated on a data set of cane biomass measured on four on-farm fields, over three years, in Reunion Island. Satellite data are derived from a SPOT 10 m resolution time series acquired during the same period. Three types of simulations have been made: a raw simulation (where the only input data are daily precipitations, daily temperatures and daily global radiations), a partial forcing coupling method (where MOSICAS computed values of ?i have been replaced by NDVI computed ?i for each available satellite image), and complete forcing method (where all MOSICAS simulated ?i have been replaced by NDVI computed ?i). Results showed significant improvements of the yield's estimation with complete forcing approach (with an estimation of the yield 8.3 % superior to the observed yield), but minimal differences between the yields computed with raw simulations and those computed with partial forcing approach (with a mean overestimation of respectively 34.7 and 35.4 %). Several enhancements can be made, especially by optimizing MOSICAS parameters, or by using other remote sensing index, like NDWI. (Résumé d'auteur)

Classification Agris : F01 - Crops
U10 - Computer science, mathematics and statistics
U30 - Research methods

Auteurs et affiliations

  • Morel Julien, CIRAD-PERSYST-UPR SCA (REU)
  • Martiné Jean-François, CIRAD-PERSYST-UPR SCA (REU)
  • Bégué Agnès, CIRAD-ES-UMR TETIS (FRA)
  • Todoroff Pierre, CIRAD-PERSYST-UPR SCA (REU)
  • Petit Michel

Source : Cirad - Agritrop (https://agritrop.cirad.fr/566947/)

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