Snow Cover Detection Using Multi-Temporal Remotely Sensed Images of Fengyun-4A in Qinghai-Tibetan Plateau

Differentiating between snow and clouds presents a formidable challenge in the context of mapping snow cover over the Qinghai-Tibetan Plateau (QTP). The frequent presence of cloudy conditions severely complicates the discrimination of snow cover from satellite imagery. To accurately monitor the spatiotemporal evolution of snow cover, it is imperative to address these challenges and enhance the segmentation schemes employed for snow cover assessment. In this study, we devised a pixel-wise classification algorithm based on Support Vector Machine (SVM) called the 3-D Orientation Gradient algorithm (3-D OG), which captures the variations of the gradient direction of snow and clouds in spatiotemporal dimensions based on geostationary satellite Fengyun-4A (FY-4A) multi-spectral and multi-temporal optical imagery. This algorithm assumes that the speed and direction of clouds and snow are different in the process of movement leading to their discrepancy of gradient characteristics in time and space. Therefore, in this algorithm, the gradient of the images in the spatiotemporal dimensions is calculated first, and then the movement angle and trend are obtained based on that. Finally, the feature space is composed of the multi-spectral image, gradient image, and movement feature maps, which are used as the input of the SVM. Our results demonstrate that the proposed algorithm can identify snow and clouds more accurately during snowfall by utilizing the FY-4A's high temporal resolution image. Weather station data, which was collected during snowstorms in the QTP, were used for evaluating the accuracy of our algorithm. It is demonstrated that the overall accuracy of snow cover segmentation by using the 3-D OG algorithm is improved by at least 12% and 10% as compared to snow products of Fengyun-2 and MODIS, respectively. Overall, the proposed algorithm has overcome the axial swing errors existing in Geostationary satellites and is successfully applied to cloud and snow segmentation in QTP. Furthermore, our study underscores that the visible and near-infrared bands of Fengyun-4A can be used for near real-time snow cover monitoring with high performance using the 3-D OG algorithm.

Automated summary

This study presents a pixel-wise classification algorithm based on Support Vector Machine (SVM) for differentiating between snow and clouds over the Qinghai-Tibetan Plateau. The algorithm, called the 3-D Orientation Gradient algorithm (3-D OG), captures the variations of the gradient direction of snow and clouds in spatiotemporal dimensions. The results demonstrate that the proposed algorithm can accurately identify snow and clouds during snowfall, and outperforms existing algorithms in terms of accuracy. The algorithm utilizes the high temporal resolution image from the geostationary satellite Fengyun-4A (FY-4A) for near real-time snow cover monitoring.