Leaf area index (LAI) is one of the key biophysical indicators for characterizing the growth and status of vegetation and is also used in modeling earth system processes. Machine learning algorithms (MLAs) such as random forest regression (RFR), artificial neural network regression (ANNR) and support vector regression (SVR) based on satellite data have been widely used for the estimation of LAI. However, the selection of input variables has a great impact on the estimation performance of MLAs. In this study, we aimed to improve the LAI inversion model of Inner Mongolia grassland based on MLAs incorporating empirical knowledge. Firstly, we used the ANNR, SVR and RFR approaches, respectively, to rank the input variables including vegetation indices, climate factors, soil factors and topography factors and found that Normalized Difference Phenology Index (NDPI) contributed the most to LAI estimation. Secondly, we selected four sets of input variables, namely, all variables—A, model selected variables—B, overlapping variables—C and self-defined variables—D, respectively. Subsequently, we built twelve LAI estimation models (RFR-A, RFR-B, RFR-C, etc.) based on three MLAs and four sets of input variables. The evaluation of them showed the RFR produced higher prediction accuracy, followed by ANNR and SVR. Furthermore, the RFR-D presented the highest accuracy in predicting LAI (R² = 0.55, RMSE = 0.37 m²/m², MAE = 0.29 m²/m²). Finally, we compared our results with MODIS LAI and GEOV2 LAI products and found that all of them showed a similar spatial distribution of grassland LAI in the four sub-regions covering all grassland types, but our model exhibited larger LAI values in the desert steppe and smaller LAI values in the others. These findings demonstrated that MLAs incorporating empirical knowledge could improve the accuracy of modelling LAI and further study is necessary to reduce the uncertainty in LAI mapping in grassland.

The services provided by grassland ecosystems are important and irreplaceable in maintaining the balance and stability of ecosystems. The spatiotemporal variations of grassland ecosystem service value (ESV) and its influencing factors in Inner Mongolia from 2000 to 2019 were studied in this paper. Based on the socio-economic data, remote sensing data, geographic data, and meteorological data, a dynamic ESV assessment method based on the equivalent factors was used to calculate the grassland ESV for each year. The spatiotemporal dynamic variation and future trend of grassland ESV were studied by coefficient of variation index (CV), Theil–Sen median trend analysis, Mann–Kendall test, and Hurst index, and the Geodetector was used to determine the main factors affecting the distribution of ESV. The results indicated that (1) the annual average grassland ESV of Inner Mongolia was higher in the northeast than in southwest, the average ESV was 2.0794 million CNY/km², and the pixels were concentrated from 1 to 3 million CNY/km², accounting for 75.46% of the study area; (2) during the study period, the average grassland ESV increased slowly with time at an annual growth rate of 0.2, and the total ESV decreased first and then increased with the change in grassland area; (3) the average volatility was 0.16, and pixels with CV values between 0.1 and 0.2 accounted for 69.2% of the study area, indicating the fluctuation of ESV was relatively stable during the study period; (4) 37.16% of the grassland ESV in Inner Mongolia decreased slightly and 41.77% increased slightly during these years, and the two parts showed opposite trends in the future; and (5) the single factor influencing the spatial distribution of grassland ESV was mainly normalized vegetation index (NDVI) and precipitation, and the multi-factor interactions were NDVI∩slope and NDVI∩precipitation. All influencing factors exhibited a stronger impact through the two-factor interaction. This study can provide reference values for the policymaking of natural resource conservation or restoration.

(1) Estimation of grazing livestock intake is the basis for studying animal–plant relationships and the nutritional status of grazing livestock and has important implications for grassland composition and productivity. (2) We used the saturated alkanes method to determine the feed intake and vegetation nutrient digestibility of livestock at different grazing intensities and in different months. (3) We found that C₃₁ had the highest concentration in both pasture and fecal output, and the average recovery of C₃₁ was 77.99%. The different grazing intensities significantly affected livestock intake. As the grazing intensity increased, there was a decreasing trend of livestock intake and the highest livestock feed intake was 6.11 kg DM/day in light grazing. With the increase in grazing season months, the highest livestock intake was 6.67 kg DM/day in the cold period in September. The month also had a significant effect on the digestibility of livestock for all nutrient variables when compared to the grazing intensity. Livestock weight and medium palatability species are more important for livestock intake. (4) Our study provides a more accurate measurement of grazing livestock intake, which can be used as a reference for the scientific management of grazing livestock and the rational use of grazing pastures.

The interrelation between grassland vegetation greenness and hydrothermal conditions on the Tibetan Plateau demonstrates a significant correlation. However, understanding the spatial patterns and the degree of this correlation, especially in relation to minimum and maximum air temperatures across various vertical gradient zones of the Plateau, necessitates further examination. Utilizing the normalized difference phenology index (NDPI) and considering four distinct hydrothermal conditions (minimum, maximum, mean temperature, and precipitation) during the growing season, an analysis was conducted on the correlation of NDPI with hydrothermal conditions across plateau elevations from 2000 to 2021. Results indicate that the correlation between vegetation greenness and hydrothermal conditions on the Tibetan Plateau grasslands is spatially varied. There is a pronounced negative correlation of greenness to maximum temperature and precipitation in the northeastern plateau, while areas exhibit stronger positive correlations to mean temperature. Additionally, as elevation increases, the positive correlation and sensitivity of alpine grassland vegetation greenness to minimum temperature significantly intensify, contrary to the effects observed with maximum temperature. The correlations between greenness and mean temperature in relation to elevational changes primarily exhibit a unimodal pattern across the Tibetan Plateau. These findings emphasize that the correlation and sensitivity of grassland vegetation greenness to hydrothermal conditions are both elevation-dependent and spatially distinct.

Leaf area index (LAI) is a key indicator of vegetation structure and function, and its products have a wide range of applications in vegetation condition assessment and usually act as important input parameters for ecosystem modeling. Grassland plays an important role in regional climate change and the global carbon cycle and numerous studies have focused on the product-based analysis of grassland vegetation changes. However, the performance of various LAI products and their discrepancies across different grassland types in drylands remain unclear. Therefore, it is critical to assess these products prior to application. We evaluated the accuracy of four commonly used LAI products (GEOV2, GLASS, GLOBMAP, and MODIS) using LAI reference maps based on both bridging and cross-validation approaches. Under different grassland types, the GLASS LAI performed better in meadow steppe (R² = 0.26, RMSE = 0.41 m²/m²) and typical steppe (R² = 0.32, RMSE = 0.38 m²/m²); the GEOV2 LAI performed better in desert steppe (R² = 0.39, RMSE = 0.30 m²/m²). When we assessed their spatial and temporal discrepancies during the period from 2010 to 2019, the four LAI products overall showed a high spatial and temporal consistency across the region. Compared with GLASS LAI, the most consistent to least consistent correlations can be ordered by GEOV2 LAI (R² = 0.94), MODIS LAI (R² = 0.92), and GLOBMAP LAI (R² = 0.87). The largest differences in LAI throughout the year occurred in July for all grassland types. Limited by the location and number of sample plots, we mainly focused on spatial and temporal variations. The spatial heterogeneity of land surface is pervasive, especially in vast grassland areas with rich grassland types, and the results of this study can provide a basis for the application of the product in different grassland types. Furthermore, it is essential to develop highly accurate and reliable satellite-based LAI products focused on grassland from the regional to the global scale according to these popular approaches, which is the next step in our work plan.