Mapping Cropland Extent By Asynchronous Fusion Of Optical And Active Microwave Imagery

In this study, a machine learning based algorithm is developed for dynamically classifying and determining area of agricultural land-use using optical and active microwave remote sensing. It includes a gradient boosted machine trained in the US using labels derived from the United States Department of Agriculture National Agricultural Statistics Service crop land data layers (CDL). This is then applied to the state of Mato Grosso in Brazil with a hidden Markov model applied to temporally stabilize the land-cover predictions. High resolution remote sensing products such as land surface temperature, normalized difference vegetation index, nadir adjusted bidirectional reflectance, land-cover and radar backscatter were used to develop the classification model. The results in the US were validated using a hold-out set with CDL labels. The results in Brazil were validated using the harvested area statistics reported by Companhia Nacional de Abastecimento.