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This study evaluates the accuracy of the U.S. interactive multisensor snow and ice mapping system (IMS) 1 km product with recall, precision, false alarm rate, and overall accuracy by utilizing ground-observed snow depth data from meteorological stations in China over six snow seasons spanning from 2015 to 2020. Furthermore, it examines the correlation between recall and snow depth, as well as the correlation between overall accuracy and snow depth. The results reveal the following: The precision typically peaks during the middle of the snow season, predominantly exceeding 80% but rarely reaching 100%, with minimal spatial variation. The precision generally ranges from 75% to 80%, exhibiting slight spatial variations during this period. The overestimation rate remains below 5%, seldom exceeding 10%. The overall accuracy mostly exceeds 95%, showing no significant spatial variations. Across both China and its five regions, the recall demonstrates a significant logarithmic correlation with snow depth, while the overall accuracy exhibits an almost horizontal linear correlation with snow depth once it exceeds 3 cm. [ABSTRACT FROM AUTHOR]

The precipitation estimation with high spatial and temporal resolution over Tibet is of great significance for monitoring the stability of the plateau ecosystem and early warning of natural disasters. In response to the problem of the unsatisfactory accuracy in traditional real-time precipitation inversion methods and quantitative accuracy estimation products in this region. This paper adopts deep-learning methods to explore the corresponding relationship between precipitation and multi-band observation data from geostationary satellite imagery. The U-High Resolution Network (U-HRNet) as backbone is fully exploited to generate multi-scale features and fused using attention gates to extract high-resolution spatial representations representing precipitation clouds, thereby achieving precipitation estimation of target clouds at different scales. In addition, in response to the characteristics of high precipitation intensity and drastic cloud cluster changes in summer convective weather within a short period of time, a multi-head attention mechanism is introduced to extract temporal information from time series inputs and fuse it with spatial features to improve the accuracy of real-time precipitation estimation. Compared with the operational precipitation products and baseline deep-learning models, the classification metrics such as Probability of detection (POD), False Alarm Ratio (FAR) and Critical success index (CSI) show that the proposed model (U-HRNet+) has better performance in test set and has high generalization ability for estimating short-term heavy rainfall over Tibet and its surrounding areas in summer. Therefore, the proposed model has the potential to serve as a more accurate and reliable satellite-based precipitation estimation product. [ABSTRACT FROM AUTHOR]

Fine spatiotemporal resolution snow monitoring at the watershed scale is crucial for the management of snow water resources. This research proposes a cloud removal algorithm via snow grain size (SGS) gap-filling based on a space–time extra tree, which aims to address the issue of cloud occlusion that limits the coverage and time resolution of long-time series snow products. To fully characterize the geomorphic characteristics and snow duration time of the Kaidu River Basin (KRB), we designed dimensional data that incorporate spatiotemporal information. Combining other geographic and snow phenological information as input for estimating SGS. A spatiotemporal extreme tree model was constructed and trained to simulate the nonlinear mapping relationship between multidimensional inputs and SGS. The estimation results of SGS can characterize the snow cover under clouds. This study found that when the cloud cover is less than 70%, the model's estimation of SGS meets expectations, and snow cover reconstruction achieves good results. In specific cloud removal cases, compared to traditional spatiotemporal filtering and multi-sensor fusion, the proposed method has better detail characterization ability and exhibits better performance in snow cover reconstruction and cloud removal in complex mountainous environments. Overall, from 2000 to 2020, 66.75% of snow products successfully removed cloud coverage. This resulted in a decrease in the annual average cloud coverage rate from 52.46% to 34.41% when compared with the MOD10A1 snow product. Additionally, there was an increase in snow coverage rate from 21.52% to 33.84%. This improvement in cloud removal greatly enhanced the time resolution of snow cover data without compromising the accuracy of snow identification. [ABSTRACT FROM AUTHOR]

The Qinghai-Tibetan Plateau (QTP) snow cover information acquisition of the high precision spatial and temporal characteristics is of great significance for the research on its land surface atmosphere coupled system and global climate change effects. The Moderate Resolution Imaging Spectro-radiometer (MODIS) daily snow cover products (MOD10A1 and MYD10A1) have been widely used in long time series of spatial and temporal variation analysis, but they are limited to be used because of high cloud cover ratio. In this paper, a 7-day rolling combination algorithm was presented to eliminate cloud obscuration, and the whole cloud amount falls below 7 %. The ground station in situ measurements verify that the overall precision is more than 90 %. The presented algorithm guaranteed the same spatial resolution and temporal resolution, and has higher precision than products MOD10A1 and MYD10A1. The MODIS 7-day rolling combination snow cover datasets products were obtained between 2003 and 2014 in the QTP, and the snow cover area of spatial and temporal variation was analyzed. The change characteristics of snow cover duration was also studied combining with the Digital Elevation Model data. Results show that the snow cover area of the whole QTP has a slowly decreased trend, but increases in autumn. Thus, the snow cover proportion of annual periodic and unstable in different elevations has the highest correlation with area of the elevation. [ABSTRACT FROM AUTHOR]

The normalized difference snow index (NDSI), a key part of the snow-mapping algorithm for extracting snow information from remotely sensed imageries, has been frequently employed in acquiring snow cover extent in the past decades. A linear regression analysis has been frequently used in estimating snow cover fraction (SCF) on a subpixel basis by developing a statistical relationship between NDSI and SCF. In this letter, a comparison of NDSI, ratio snow index (RSI), and difference snow index (DSI) in estimating SCF is carried out in mountainous area of northwestern China. Three kinds of fitting methods, i.e., linear fitting, logarithmic curve fitting, and exponential curve fitting, are employed in this comparison for the purpose of finding out a best statistical fitting relationship between SCF and snow index. The results show that RSI and DSI are both good substitutes for NDSI in SCF estimation due to high R-squares, up to 0.83, of fitted lines between snow indexes and SCF, respectively. Furthermore, exponential fitting is considered to be of the highest robustness in the three fitting methods of interest for all the snow indexes studied. [ABSTRACT FROM PUBLISHER]