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and daily global radiations), a partial forcing coupling method (where MOSICAS computed values of epsilon(i) have been replaced by NDVI computed epsilon(i) for each available satellite image), and complete forcing method (where all MOSICAS simulated epsilon(i) have been replaced by NDVI computed epsilon(i)).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

Keywords

sugarcane; yield; growth model; NDVI; interception efficiency; forcing

Published in

Proceedings of SPIE
2012, number: 8531
Title: Remote sensing for agriculture, ecosystems, and hydrology XIV : 24-26 September 2012, Edinburgh, United Kingdom
ISBN: 9780819492715
Publisher: SPIE

Conference

Remote Sensing for Agriculture, Ecosystems, and Hydrology XIV

UKÄ Subject classification

Agricultural Science


Publication identifier

DOI: https://doi.org/10.1117/12.975688

Permanent link to this page (URI)

https://res.slu.se/id/publ/89028