da Silva, CA; Leonel, AHS; Rossi, FS; Correia, WLF; Santiago, DD; de Oliveira, JF; Teodoro, PE; Lima, M; Capristo-Silva, GF (2020). Mapping soybean planting area in midwest Brazil with remotely sensed images and phenology-based algorithm using the Google Earth Engine platform. COMPUTERS AND ELECTRONICS IN AGRICULTURE, 169, 105194.

Soybean is the main crop of the Brazilian agribusiness. The near-real-time monitoring of this crop is important in the production estimate, identification of the progress, and location of the crops. It is also crucial for governmental surveillance institutions regarding sanitary break. Thus, this study aimed to estimate and map soybean areas in almost real time using temporal series multispectral images and vegetation indices (near-infrared and red) in the Google Earth Engine system in the state of Mato Grosso, Brazil. A multitemporal algorithm of the Perpendicular Vegetation Index (PVI) of MODIS, OLI, and MSI images of the 2016/2017 crop yr(-1) was created from the identification of soybean areas using the Perpendicular Crop Enhancement Index (PCEI). The use of the MODIS images for the monitoring of soybean areas using the Google Earth Engine platform was a viable and promising automated alternative for large-scale soybean area estimates.