Habitat suitability assessment is critical for wildlife population conservation and management planning. The MaxEnt model is widely used in species habitat suitability modeling. In order to investigate the habitat status of the black-necked crane in the Zoige grassland wetland ecological function area, this study evaluated the habitat suitability of the black-necked crane using the MaxEnt model with 152 occurrence records and 13 environmental variables. Based on the Akaike information criterion corrected for the small sample size, the best optimal parameter combination (feature class LQPHT, regularization multiplier 3.0) was selected. The results show that the Maxent model had good accuracy with an area under the curve (AUC) value of 0.895. Distance to roads, average summer precipitation, distance to lakes, and altitude are the dominant environmental variables affecting the potential distribution of black-necked cranes, with the contribution rates of 31.6%, 29.8%, 14.3%, and 8.2%, respectively. The suitable habitat area of black-necked cranes is mainly distributed in and around the Zoige Wetland National Nature Reserve, accounting for 46.49% of the Zoige Grassland Wetland National Key Ecological Function Zone. The potential distribution area has a tendency to spread to Hongyuan County in the south of the functional zone, and the unsuitable habitat is mainly distributed in the high-altitude area in the southwest of the functional zone. This study recommends focusing on the distribution area of black-necked cranes around Zoige Wetland National Nature Reserve and Hongyuan County to improve conservation strategies and strengthen protection efforts.

Accurate high-resolution soil moisture mapping is critical for surface studies as well as climate change research. Currently, regional soil moisture retrieval primarily focuses on a spatial resolution of 1 km, which is not able to provide effective information for environmental science research and agricultural water resource management. In this study, we developed a quantitative retrieval framework for high-resolution (250 m) regional soil moisture inversion based on machine learning, multisource data fusion, and in situ measurement data. Specifically, we used various data sources, including the normalized vegetation index, surface temperature, surface albedo, soil properties data, precipitation data, topographic data, and soil moisture products from passive microwave data assimilation as input parameters. The soil moisture products simulated based on ground model simulation were used as supplementary data of the in situ measurements, together with the measured data from the Maqu Observation Network as the training target value. The study was conducted in the Zoige region of the Tibetan Plateau during the nonfreezing period (May–October) from 2009 to 2018, using random forests for training. The random forest model had good accuracy, with a correlation coefficient of 0.885, a root mean square error of 0.024 m³/m³, and a bias of −0.004. The ground-measured soil moisture exhibited significant fluctuations, while the random forest prediction was more accurate and closely aligned with the field soil moisture compared to the soil moisture products based on ground model simulation. Our method generated results that were smoother, more stable, and with less noise, providing a more detailed spatial pattern of soil moisture. Based on the permutation importance method, we found that topographic factors such as slope and aspect, and soil properties such as silt and sand have significant impacts on soil moisture in the southeastern Tibetan Plateau. This highlights the importance of fine-scale topographic and soil property information for generating high-precision soil moisture data. From the perspective of inter-annual variation, the soil moisture in this area is generally high, showing a slow upward trend, with small spatial differences, and the annual average value fluctuates between 0.3741 m³/m³ and 0.3943 m³/m³. The intra-annual evolution indicates that the monthly mean average soil moisture has a large geographical variation and a small multi-year linear change rate. These findings can provide valuable insights and references for regional soil moisture research.

This study assesses the impact of climate change and human activities on vegetation dynamics (kNDVI) on the Qinghai-Tibet Plateau (QTP) between 2000 and 2022, considering both lag and cumulative effects. Given the QTP’s high sensitivity to climate change and human activities, it is imperative to understand their effects on vegetation for the sustainable development of regional and national terrestrial ecosystems. Using MOD13Q1 NDVI and climate and human activity data, we applied methods such as Sen-MK, lag and cumulative effect analysis, improved residual analysis, and geographical detector analysis. The outcomes were as follows. (1) The vegetation kNDVI on the QTP showed an overall fluctuating growth trend between 2000 and 2022; improved regions were more significant than degraded regions, with improved regions primarily distributed in humid and semi-humid areas with favorable climate conditions, and degraded regions primarily in arid and semi-arid areas; this implies that climate conditions have a significant impact on vegetation changes on the QTP. (2) The analysis of lag and cumulative effects revealed that temperature and precipitation have a substantial cumulative effect on vegetation kNDVI on the QTP. The vegetation kNDVI showed a lag effect of 0 months and a cumulative effect of 1 month for temperature, and a lag effect of 0 months and a cumulative effect of 2 months for precipitation, respectively. (3) Improved residual analysis based on lag and cumulative effects revealed that human activities positively contributed 66% to the changes in vegetation kNDVI on the QTP, suggesting a notable positive impact of human activities. Geographical detector analysis indicated that, among different human activity factors affecting vegetation kNDVI changes, the explanatory power in 2005 and 2015 ranked as X3 (livestock density) > X1 (population density) > X2 (per capita GDP) > X4 (artificial afforestation density) > X5 (land use type), and in 2020, as X3 > X4 > X1 > X5 > X2. The explanatory power of afforestation density and land use type has relatively increased, indicating that recent efforts in ecological protection and restoration on the QTP, including developing artificial forest areas and afforestation programs, have considerably contributed to vegetation greening.