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Remote Sens. 2014, 6(7), 6620-6635;

Toward a Satellite-Based System of Sugarcane Yield Estimation and Forecasting in Smallholder Farming Conditions: A Case Study on Reunion Island

CIRAD-UPR AIDA, Station de Ligne-Paradis, 7 chemin de l'IRAT, FR-97410 Saint-Pierre, La Réunion, France
CIRAD-UMR TETIS, 500 rue Jean-François Breton, FR-34093 Montpellier, France
Syndicat du Sucre de la Réunion, 40, route Gabriel Macé, FR-97492 Sainte-Clotilde Cedex, La Réunion, France
CIRAD-UPR AIDA, Station de la Bretagne, 40 chemin de Grand Canal, FR-97743 Saint-Denis, La Réunion, France
Institut de Recherche pour le Développement (IRD), 911 avenue Agropolis, FR-34394 Montpellier Cedex 05, France
Author to whom correspondence should be addressed.
Received: 5 May 2014 / Revised: 4 July 2014 / Accepted: 9 July 2014 / Published: 18 July 2014
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Estimating sugarcane biomass is difficult to achieve when working with highly variable spatial distributions of growing conditions, like on Reunion Island. We used a dataset of in-farm fields with contrasted climatic conditions and farming practices to compare three methods of yield estimation based on remote sensing: (1) an empirical relationship method with a growing season-integrated Normalized Difference Vegetation Index NDVI, (2) the Kumar-Monteith efficiency model, and (3) a forced-coupling method with a sugarcane crop model (MOSICAS) and satellite-derived fraction of absorbed photosynthetically active radiation. These models were compared with the crop model alone and discussed to provide recommendations for a satellite-based system for the estimation of yield at the field scale. Results showed that the linear empirical model produced the best results (RMSE = 10.4 t∙ha−1). Because this method is also the simplest to set up and requires less input data, it appears that it is the most suitable for performing operational estimations and forecasts of sugarcane yield at the field scale. The main limitation is the acquisition of a minimum of five satellite images. The upcoming open-access Sentinel-2 Earth observation system should overcome this limitation because it will provide 10-m resolution satellite images with a 5-day frequency. View Full-Text
Keywords: sugarcane; yield estimation; model; remote sensing sugarcane; yield estimation; model; remote sensing
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Morel, J.; Todoroff, P.; Bégué, A.; Bury, A.; Martiné, J.-F.; Petit, M. Toward a Satellite-Based System of Sugarcane Yield Estimation and Forecasting in Smallholder Farming Conditions: A Case Study on Reunion Island. Remote Sens. 2014, 6, 6620-6635.

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