Rural communities rely on smallholder maize farms for subsistence agriculture, the main driver of local economic activity and food security. However, their planted area estimates are unknown in most developing countries. This study explores use Sentinel-1 Sentinel-2 data to map farms. The random forest (RF), support vector (SVM) machine learning algorithms model stacking (ST) were applied. Resu...

Remote sensing offers a way to map crop types across large spatio-temporal scales at low costs. However, mapping is challenging in heterogeneous, smallholder farming systems, such as those India, where field sizes are often smaller than the resolution of historically available imagery. In this study, we examined potential relatively new, high-resolution imagery (Sentinel-1, Sentinel-2, and Plan...

The use of remote sensing images to detect forest changes is great significance for resource management. With the development and implementation deep learning algorithms in change detection, a large number models have been designed multi-phase images. Although synthetic aperture radar (SAR) data strong potential application detection tasks, most existing learning-based optical imagery. Therefor...