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

Julien Morel; Jean-François Martiné; Agnès Bégué; Pierre Todoroff; Michel Petit

doi:10.1117/12.975688

19 October 2012 A comparison of two coupling methods for improving a sugarcane model yield estimation with a NDVI-derived variable

Julien Morel, Jean-François Martiné, Agnès Bégué, Pierre Todoroff, Michel Petit

Author Affiliations +

Proceedings Volume 8531, Remote Sensing for Agriculture, Ecosystems, and Hydrology XIV; 85310E (2012) https://doi.org/10.1117/12.975688
Event: SPIE Remote Sensing, 2012, Edinburgh, United Kingdom

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 (Ε_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temperatures and daily global radiations), a partial forcing coupling method (where MOSICAS computed values of Ε_i have been replaced by NDVI computed Ε_i for each available satellite image), and complete forcing method (where all MOSICAS simulated Ε_i have been replaced by NDVI computed Ε_i).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.

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Julien Morel, Jean-François Martiné, Agnès Bégué, Pierre Todoroff, and Michel Petit "A comparison of two coupling methods for improving a sugarcane model yield estimation with a NDVI-derived variable", Proc. SPIE 8531, Remote Sensing for Agriculture, Ecosystems, and Hydrology XIV, 85310E (19 October 2012); https://doi.org/10.1117/12.975688

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