A geostatistical approach for modeling soybean crop area and yield based on census and remote sensing data

Abstract

Advances in satellite imagery and remote sensing have enabled the acquisition of spatial data at several different resolutions. Geographic information systems (GIS) and geostatistics can be used to link geographic data from different sources. This article discusses the need to improve soybean crop detection and yield prediction by linking census data, GIS, remote sensing, and geostatistics. The proposed approach combines Brazilian Institute of Geography and Statistics (IBGE) census data with an eight-day enhanced vegetation index (EVI) time series derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data to monitor soybean areas and yields in Mato Grosso State, Brazil. In situ data from farms were used to validate the obtained results. Binomial areal kriging was used to generate maps of soybean occurrence over the years, and Gaussian areal kriging was used to predict soybean crop yield census data inside detected soybean areas, which had a downscaling effect on the results. The global accuracy and the Kappa index for the soybean crop detection were 92.1% and 0.84%, respectively. The yield prediction presented 95.09% accuracy considering the standard deviation and probable error. Soybean crop detection and yield monitoring can be improved by this approach.