Merging satellite and rain gauge data by combining accurate quantitative rainfall from stations with spatial continuous information from remote sensing observations provides a practical method of estimating rainfall. However, generating high spatiotemporal rainfall fields for catchment-distributed hydrological modeling is a problem when only a sparse rain gauge network and coarse spatial resolution of satellite data are available. The objective of the study is to present a satellite and rain gauge data-merging framework adapting for coarse resolution and data-sparse designs. In the framework, a statistical spatial downscaling method based on the relationships among precipitation, topographical features, and weather conditions was used to downscale the 0.25° daily rainfall field derived from the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) precipitation product version 7. The nonparametric merging technique of double kernel smoothing, adapting for data-sparse design, was combined with the global optimization method of shuffled complex evolution, to merge the downscaled TRMM and gauged rainfall with minimum cross-validation error. An indicator field representing the presence and absence of rainfall was generated using the indicator kriging technique and applied to the previously merged result to consider the spatial intermittency of daily rainfall. The framework was applied to estimate daily precipitation at a 1 km resolution in the Qinghai Lake Basin, a data-scarce area in the northeast of the Qinghai-Tibet Plateau. The final estimates not only captured the spatial pattern of daily and annual precipitation with a relatively small estimation error, but also performed very well in stream flow simulation when applied to force the geomorphology-based hydrological model (GBHM). The proposed framework thus appears feasible for rainfall estimation at high spatiotemporal resolution in data-scarce areas.

Historical patterns of snow cover and snowmelt are shifting due to climate warming and perhaps some human activities, threatening natural water resources and the ecological environment. Passive microwave remote sensing provides quantitative data for snow mass evaluation. Here, we evaluated the long-term impact of climate warming on snowmelt rates, using snow water equivalent (SWE) datasets derived from passive microwave remotely sensed data over China’s three main stable snow cover regions during the past 40 years (1981–2020). The results showed that higher ablation rates in spring were found in locations with a deeper SWE because of high snowmelt rates that occurred in late spring and early summer in areas with a deeper snowpack. Annual maximum SWE (snow water equivalent) has declined across two out of the three main mountains of China’s snow cover regions over the past 40 years under climate warming. The maximum and mean snowmelt rate was ca. 30 and 3 mm/day, respectively, over the three regions. Further, due to SWE being reduced in these deep snowpack areas, moderate and high rates of snowmelt showed trends of decline after 2000. Accordingly, an earlier snow onset day (average 0.6~0.7 day/a) and slower snowmelt rates characterized the mountainous areas across the three main snow cover regions. The slower snowmelt rate is also closely related to vegetation improvement over the three main stable snow cover regions. Therefore, not only vegetation in spring but also streamflow and other ecological processes could be affected by the pronounced changes in SWE and snowmelt rates. These findings strengthen our understanding of how to better assess ecological and environmental changes towards the sustainable use of freshwater resources in spring and earlier summer months in snow-rich alpine regions.

With snow cover changing worldwide in several worrisome ways, it is imperative to determine both the variability in snow cover in greater detail and its relationship with ongoing climate change. Here, we used the satellite-based snow cover extent (SCE) dataset of National Oceanic and Atmospheric Administration (NOAA) to detect SCE variability and its linkages to climate over the 1967–2018 periods across the Northern Hemisphere (NH). Interannually, the time series of SCE across the NH reveal a substantial decline in both spring and summer (−0.54 and −0.71 million km²/decade, respectively), and this decreasing trend corresponded with rising spring and summer temperatures over high-latitude NH regions. Among the four seasons, the temperature rise over the NH was the highest in winter (0.39 °C/decade, p < 0.01). More precipitation in winter was closely related to an increase of winter SCE in mid-latitude areas of NH. Summer precipitation over the NH increased at a significant rate (1.1 mm/decade, p < 0.01), which likely contribute to the accelerated reduction of summer’s SCE across the NH. However, seasonal sensitivity of SCE to temperature changes differed between the Eurasian and North American continents. Thus, this study provides a better understanding of seasonal SCE variability and climatic changes that occurred at regional and hemispheric spatial scales in the past 52 years.

Mapping is a fundamental application of remote sensing images, and the accurate evaluation of remote sensing image information extraction using artificial intelligence is critical. However, the existing evaluation method, based on Intersection over Union (IoU), is limited in evaluating the extracted information’s boundary accuracy. It is insufficient for determining mapping accuracy. Furthermore, traditional remote sensing mapping methods struggle to match the inflection points encountered in artificial intelligence contour extraction. In order to address these issues, we propose the mean inflection point distance (MPD) as a new segmentation evaluation method. MPD can accurately calculate error values and solve the problem of multiple inflection points, which traditional remote sensing mapping cannot match. We tested three algorithms on the Vaihingen dataset: Mask R-CNN, Swin Transformer, and PointRend. The results show that MPD is highly sensitive to mapping accuracy, can calculate error values accurately, and is applicable for different scales of mapping accuracy while maintaining high visual consistency. This study helps to assess the accuracy of automatic mapping using remote sensing artificial intelligence.