Maximal light-use efficiency (LUE), ɛ_max, is a measure of the conversion efficiency of the photosynthetically active radiation absorbed by plants to net primary productivity (NPP), based on the principle that an optimal plant productive capability exists when the LUE is at its maximum. ɛ_max is an important parameter for modelling regional NPP and is conventionally applied at the biome level using a constant value. In this study, we estimated type-specific ɛ_max values for three dominant land cover types on the Mongolian Plateau: 0.621 g C MJ^–1 (MJ is the mega Joules and 1 MJ = 10⁶ J) for meadow steppe, 0.534 g C MJ^–1 for typical steppe, and 0.520 g C MJ^–1 for desert steppe. With these steppe-specific modified ɛ_max values, we were able to examine changes in NPP for 2001–2015 on the plateau, as well as their likely responses to regional climate change. The use of different ɛ_max values for each steppe type improved the accuracy of the Carnegie Ames Stanford Approach (CASA) Biosphere model predictions of grassland NPP by 18.8% (R² = 0.48 to R² = 0.57; R² is the coefficient of determination) over the observation period. Previous studies based on a constant ɛ_max (0.541 g C MJ^–1) appear to underestimate ɛ_max and NPP in meadow steppe, highlighting the importance of setting type-specific ɛ_max values for different land cover types in the remote-sensing modelling of grassland NPP. However, more detailed maps of biome sub-classes, with species composition, would be valuable for future attempts to determine appropriate ɛ_max values. The growing season (April–October) NPP on the plateau increased significantly from 2001 to 2015, with an annual increment of 4.44 g C m^–2 y^–1. This trend was strongly governed by the change in summer NPP across the plateau. In comparison, NPP in the spring and autumn did not influence the change in total NPP, which was likely due to their relatively small values. Summer precipitation and the related drought stress were the chief factors responsible for plateau-scale NPP changes, due to the high proportion of summer precipitation and NPP in the annual totals. This may induce important environmental features, such as dzud (a Mongolian term for a severe winter) and desertification on the plateau.

The escalation in the population of livestock coupled with inadequate precipitation has caused a reduction in pasture biomass, thereby resulting in diminished grassland carrying capacity (GCC) and pasture degradation. In this research, net primary productivity (NPP) data, sourced from the Global Land Surface Satellite (GLASS) and Moderate Resolution Imaging Spectroradiometer (MODIS) datasets from 1982 to 2020, were initially transformed into aboveground biomass (AGB) estimates. These estimates were subsequently utilized to evaluate and assess the long-term trends of GCC across Mongolia. The MODIS data indicated an upward trend in AGB from 2000 to 2020, whereas the GLASS data reflected a downward trend from 1982 to 2018. Between 1982 and 2020, climatic analysis uncovered robust positive correlations between AGB and precipitation (R > 0.80) and negative correlations with temperature (R < −0.60). These climatic alterations have led to a reduction in AGB, further impairing the regenerative capacity of grasslands. Concurrently, livestock numbers have generally increased since 1982, with a decrease in certain years due to dzud and summer drought, leading to the increase in the GCC. GCC assessment found that 37.5% of grasslands experienced severe overgrazing and 31.9–40.7% was within sustainable limits. Spatially, the eastern region of Mongolia could sustainably support current livestock numbers; the western and southern regions, as well as parts of northern Mongolia, have exhibited moderate to critical levels of grassland utilization. A detailed analysis of GCC dynamics and its climatic impacts would offer scientific support for policymakers in managing grasslands in the Mongolian Plateau.

Changes in grassland vegetation coverage (GVC) and their causes in the China–Mongolia–Russia Economic Corridor (CMREC) region have been a hot button issue regarding the ecological environment and sustainable development. In this paper, multi-source remote sensing (RS) data were used to obtain GVC from 2000 to 2023 based on random forest (RF) regression inversion. The nonlinear characteristics such as the number of mutations, magnitude of mutations, and time of mutations were detected and analyzed using the BFAST model. Driving factors such as climatic factors were introduced to quantitatively explain the driving mechanism of GVC changes. The results showed that: (1) RF model is the optimal model for the inversion of GVC in this region. The R² of the RF training set reached 0.94, the RMSE of the test set was 12.86%, the correlation coefficient between the predicted and actual values was 0.76, and the CVRMSE was 18.07%. (2) During the period of 2000–2023, the number of mutations in GVC ranged from 0 to 5, and there were at least 1 mutation in 58.83% of the study area. The years with the largest proportion of mutations was 2010, followed by 2016, accounting for 14.57% and 11.60% of all mutations, respectively. The month with the highest percentage of mutations was October, and followed by June, accounting for 31.73% and 22.19% of all mutations, respectively. (3) The sustained and stable positive effect was shown by precipitation on GVC before and after the maximum mutation. Wind speed was a negative effect on GVC in areas with more severe desertification, such as Inner Mongolia, China and parts of Mongolia. On the other hand, GVC was reduced by the wind speed before and after the maximum mutations. Therefore, to guarantee the ecological security of the CMREC, governments should formulate new countermeasures to prevent desertification in the region according to the laws of nature and strengthen international cooperation.

This study analyzed the spatiotemporal changes in grassland NDVI from 2000 to 2020 in the eastern route of the China–Mongolia–Russia Economic Corridor, a region with frequent ecological–economic interactions, and explained the main driving factors, influencing patterns, and degrees of grassland NDVI changes in different regions. Based on MODIS NDVI data, the study employs emerging spatiotemporal hotspot analysis, Maximum Relevance Minimum Redundancy (mRMR) feature selection, and Gaussian Process Regression (GPR) to reveal the spatiotemporal variation characteristics of grassland NDVI, while identifying long-term stable trends, and to select the most relevant and non-redundant factors to analyze the main driving factors of grassland NDVI change. Partial dependence plots were used to visualize the response and sensitivity of grassland NDVI to various factors. The results show the following: (1) From 2000 to 2020, the NDVI of grassland in the study area showed an overall upward trend, from 0.61 to 0.65, with significant improvement observed in northeastern China and northeastern Russia. (2) Spatiotemporal hotspot analysis indicates that 51% of the area is classified as persistent hotspots for grassland NDVI, mainly distributed in Russia, whereas 12% of the area is identified as persistent cold spots, predominantly located in Mongolia. (3) The analysis of key drivers reveals that precipitation and land surface temperature are the dominant climatic factors shaping grassland NDVI trends, while the effects of soil conditions and human activity vary regionally. In China, NDVI is primarily driven by land surface temperature (LST), GDP, and population density; in Mongolia, precipitation, LST, and GDP exert the strongest influence; whereas in Russia, livestock density and soil organic carbon play the most significant roles. (4) For the whole study area, in persistent cold spot areas of grassland NDVI, the negative effects of rising land surface temperature were most pronounced, reducing NDVI by 36% in the 25–40 °C range. The positive effects of precipitation on NDVI were most evident under low to moderate precipitation conditions, with the effects diminishing as precipitation increased. Soil moisture and soil pH have stronger effects in persistent hotspot areas. Regarding human activity factors, the livestock factor in Mongolia shows an inverted U-shaped relationship with NDVI, and increasing population density contributed to grassland degradation in persistent cold spots. Proper grazing intensity regulation strategy is crucial in these areas with inappropriate grazing intensity, while social and economic activities promoted vegetation cover improvement in persistent hotspots in China and Russia. These findings provide practical insights to guide grassland ecosystem restoration and ensure sustainable development along the eastern route of the China–Mongolia–Russia Economic Corridor. China should prioritize ecological compensation policies. Mongolia needs to integrate traditional nomadic grazing with modern practices. Russia should focus on strengthening regulatory frameworks to prevent the over-exploitation of grasslands. Especially for persistent cold spot areas of grassland NDVI in Mongolia and Russia that are prone to grassland degradation, attention should be paid to the significant negative impact of livestock on grassland.