Classification of croplands through fusion of optical and sar time series data

Many satellite sensors including Landsat series have been extensively used for land cover classification. Studies have been conducted to mitigate classification problems associated with the use of single data (e.g., such as cloud contamination) through multi-sensor data fusion and the use of time series data. This study investigated two areas with different environment and climate conditions: one in South Korea and the other in US. Cropland classification was conducted by using multi-temporal Landsat 5, Radarsat-1 and digital elevation models (DEM) based on two machine learning approaches (i.e., random forest and support vector machines). Seven classification scenarios were examined and evaluated through accuracy assessment. Results show that SVM produced the best performance (overall accuracy of 93.87%) when using all temporal and spectral data as input variables. Normalized Difference Water Index (NDWI), SAR backscattering, and Normalized Difference Vegetation Index (NDVI) were identified as more contributing variables than the others for cropland classification.