With the high resolution of optical data and the lack of weather effects of passive microwave data, we developed an algorithm to map daily cloud-free fractional snow cover (FSC) based on the Moderate Resolution Imaging Spectroradiometer (MODIS) standard daily FSC product, the Advanced Microwave Scanning Radiometer (AMSR2) snow water equivalent (SWE) product and digital elevation data. We then used the algorithm to produce a daily cloud-free FSC product with a resolution of 500 m for regions in China. In addition, we produced a high-resolution FSC map using a Landsat 8 Operational Land Imager (OLI) image as a true value to test the accuracy of the cloud-free FSC product developed in this study. The analysis results show that the daily cloud-free FSC product developed in this study can completely remove clouds and effectively improve the accuracy of snow area monitoring. Compared to the true value, the mean absolute error of our product is 0.20, and its root mean square error is 0.29. Thus, the synthesized product in this study can improve the accuracy of snow area monitoring, and the obtained snow area data can be used as reliable input parameters for hydrological and climate models. The land cover type and terrain factors are the main factors that limit the accuracy of the daily cloud-free FSC product developed in this study. These limitations can be further improved by improving the accuracy of the MODIS standard snow product for complicated underlying surfaces.

It is important to predict snow disasters to prevent and reduce hazards in pastoral areas. In this study, we build a potential risk assessment model based on a logistic regression of 33 snow disaster events that occurred in Qinghai Province. A simulation model of the snow disaster early warning is established using a back propagation artificial neural network (BP-ANN) method and is then validated. The results show: (1) the potential risk of a snow disaster in the Qinghai Province is mainly determined by five factors. Three factors are positively associated, the maximum snow depth, snow-covered days (SCDs), and slope, and two are negative factors, annual mean temperature and per capita gross domestic product (GDP); (2) the key factors that contribute to the prediction of a snow disaster are (from the largest to smallest contribution): the mean temperature, probability of a spring snow disaster, potential risk of a snow disaster, continual days of a mean daily temperature below −5 °C, and fractional snow-covered area; and (3) the BP-ANN model for an early warning of snow disaster is a practicable predictive method with an overall accuracy of 80%. This model has quite a few advantages over previously published models, such as it is raster-based, has a high resolution, and has an ideal capacity of generalization and prediction. The model output not only tells which county has a disaster (published models can) but also tells where and the degree of damage at a 500 m pixel scale resolution (published models cannot).

The Qinghai Lake Basin acts as a natural barrier, preventing the western desert from spreading eastward. This is an important link in preserving the ecological stability of the northeastern region of the Qinghai–Tibet Plateau (QTP). Therefore, quantitative research into the net primary productivity (NPP) of vegetation and its driving force in the Qinghai Lake Basin is required. The effects of land use/cover change (LUCC) and climate change on NPP in the Qinghai Lake Basin were studied using R-contribution ratio and partial correlation analysis methods using MOD17A3H products, Land Use/Land Cover (LULC) data, and meteorological data. (1) The LULC of the Qinghai Lake Basin showed a trend that “the area of grassland, cultivated land, and unused land continued to decrease, while the area of other LULC types increased” from 2000 to 2020, according to the study’s findings. Grassland, water bodies, construction land, and unused land dominated the mutual transformation of LULC types. (2) The NPP of the basin showed a growing trend, with a growth rate of 3.93 gC·m^–2·a^–1 before 2010 and 0.88 gC·m^–2·a^–1 after 2010. Significant regional heterogeneity was found in NPP, with gradients decreasing from southeast to northwest. (3) The impact of LUCC on overall NPP changes had gradually increased. Climate change has been the primary driver of NPP changes in the Qinghai Lake Basin over the last 20 years.

The timely and accurate acquisition of information on the distribution of the crop planting structure in the Loess Plateau of eastern Gansu Province, one of the most important agricultural areas in Western China, is crucial for promoting fine management of agriculture and ensuring food security. This study uses multi-temporal high-resolution remote sensing images to determine optimal segmentation scales for various crops, employing the estimation of scale parameter 2 (ESP2) tool and the Ratio of Mean Absolute Deviation to Standard Deviation (RMAS) model. The Canny edge detection algorithm is then applied for multi-scale image segmentation. By incorporating crop phenological factors and using the L1-regularized logistic regression model, we optimized 39 spatial feature factors—including spectral, textural, geometric, and index features. Within a multi-level classification framework, the Random Forest (RF) classifier and Convolutional Neural Network (CNN) model are used to classify the cropping patterns in four test areas based on the multi-scale segmented images. The results indicate that integrating the Canny edge detection algorithm with the optimal segmentation scales calculated using the ESP2 tool and RMAS model produces crop parcels with more complete boundaries and better separability. Additionally, optimizing spatial features using the L1-regularized logistic regression model, combined with phenological information, enhances classification accuracy. Within the OBIC framework, the RF classifier achieves higher accuracy in classifying cropping patterns. The overall classification accuracies for the four test areas are 91.93%, 94.92%, 89.37%, and 90.68%, respectively. This paper introduced crop phenological factors, effectively improving the extraction precision of the shattered agricultural planting structure in the Loess Plateau of eastern Gansu Province. Its findings have important application value in crop monitoring, management, food security and other related fields.