High-Resolution Snow Cover Mapping with Gaofen-1 Optical Satellite Images

The detailed satellite mapping of seasonal snow cover, with many spectral bands from High-resolution remote sensors, is largely investigated recently. Snow cover maps can be extracted from optical data using relatively simple approaches given the difference in spectral reflectance. However, some high-resolution images of illuminated snow-covered surfaces suffered from limited detector saturation due to overexposure. Also, these detailed images involve large data volumes that prohibit complex analysis. This study developed and automated four algorithms, including Random Forest Model (RF), Maximum Likelihood Classifier (MLC), the Threshold method based on Water-resistant Snow Index (WSI), and the Blue Snow Threshold method (BST) respectively, for discriminating snow from other surface types with Gaofen-1 optical satellite images. To reduce the impact of overexposure on the extraction of snow-cover areas, a simple cross-calibration methodology has been utilized before estimation. Image pairs from the Operational Land Imager (OLI) on Landsat-8 and Wide Field of View (WFV) on Gaofen-1 were used to verify the radiometric calibration of WFV with respect to the well-calibrated OLI sensor. Different algorithms' performance was tested by utilizing Gaofen-2 Multi-Spectral (PMS) sensor data for validation. RF-derived estimates of Snow Cover Areas (SCA) did better in terms of overall accuracy. Due to simplicity and efficiency, these algorithms have the potential to be used to develop high spatiotemporal resolution maps of SCA.