Spectral Model for Soybean Yield Estimate Using MODIS/EVI Data

Anibal Gusso, Jorge Ricardo Ducati, Mauricio Roberto Veronez, Damien Arvor, Luiz Gonzaga da Silveira

Environmental Engineering, UNISINOS, S?o Leopoldo, Brazil.
Graduate Program in Geology, UNISINOS, S?o Leopoldo, Brazil.
Institut de Recherche Pour le Développement (IRD), Montpellier, France.
Remote Sensing Center and Astronomy Department, UFRGS, Porto Alegre, Brazil.
Graduate Program in Applied Computing, UNISINOS, Sao Leopoldo, Brazil.

DOI: 10.4236/ijg.2013.49117                

Abstract

Attaining reliable and timely agricultural estimates is very important everywhere, and in Brazil, due to its characteristics, this is especially true. In this study, estimations of crop production were made based on the temporal profiles of the Enhanced Vegetation Index (EVI) obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) images. The objective was to evaluate the coupled model (CM) performance of crop area and crop yield estimates based solely on MODIS/EVI as input data in Rio Grande do Sul State, which is characterized by high variability in seasonal soybean yields, due to different crop development conditions. The resulting production estimates from CM were compared to official agricultural statistics of Brazilian Institute of Geography and Statistics (IBGE) and the National Company of Food Supply (CONAB) at different levels from 2000/2001 to 2010/2011 crop years. Results obtained with CM indicate that its application is able to generate timely production estimates for soybean both at municipality and local levels. Validation estimates with CM at State level obtained R² = 0.95. Combining all cropping years at municipality level, estimates were highly correlated to official statistics from IBGE, with R² = 0.91 and RMSD = 10,840 tons. Spatially interpolated comparisons of yield maps obtained from the CM estimates and IBGE data also showed visual similarity in their spatial distribution. Local level comparisons were performed and presented R² = 0.95. Implications of this work point out that time-series analysis of production estimates are able to provide anticipated spatial information prior to the soybean harvest.

Keywords

Remote Sensing; Coupled Model; Soy Yield; Forecast; Satellite Images

Conflicts of Interest

The authors declare no conflicts of interest.

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