Temporal Series Crop Classification Study in Rural China Based on Sentinel-1 SAR Data

Crop classification is one of the focused topics in remote sensing study nowadays. Optical imageries, although providing many information, are often contaminated with cloud or other weather effects while SAR imageries are more resilience to those. Temporal series data are often used to improve classification accuracy, especially in crop classification. This paper investigates the usage of temporal series SAR imagery on crop classification in vast rural area of China. The selected area of interest has a complicated, heavily mixed agriculture as well as lots of non-agriculture landcovers. Total 9 classes are considered, 6 of them being crop types. A pixel-based classifier using subspace k-th Nearest Neighbor (KNN) algorithm is applied to open source Sentinel-1 polarized SAR data. The study results in an overall accuracy of 97.23% for all 9 classes in 5-fold cross validation, indicating a promising application for agricultural monitoring using SAR data.