Net Primary Productivity (NPP) is not only the main parameter for estimating carbon sequestration and oxygen release in ecosystems and measuring terrestrial carbon cycle, but also the main index for evaluating ecosystem health. In view of the limited application of domestic satellites in remote sensing monitoring of grassland NPP, a set of NPP inversion model of grassland NPP in Inner Mongolia was constructed based on FY-3D/MERSI2 data. The NPP of grassland under clear sky condition in Inner Mongolia was obtained by using a strict cloud detection algorithm, which was driven by remote sensing data products and CLDAS meteorological assimilation data, combined with light energy utilization model and ecological process model. In this study, lattice meteorological data with high resolution are introduced, which greatly improves the precision of inversion results. At the same time, based on the observation data and MODIS products, multiple relationship models of above-ground biomass, the Fraction Photosynthetic Active Radiation Absorption Ratio (FPAR) and Normalized Difference Vegetation Index (NDVI) in different months (from May to August) of grassland growth period in Inner Mongolia were constructed, and process parameters such as Leaf Area Index (LAI) and FPAR could be directly estimated based on FY-3D data. By comparing several key ecological process parameters with MODIS products, it is found that they have good correlation and spatial consistency. Finally, the grass observation data of 18 ecological meteorological observation stations in June 2021 were compared with the estimated results, showing that there was a good consistency between them, with a correlation coefficient of 0.86. The NPP inversion using FY-3D/MERSI2 can fully present the general state of vegetation productivity in Inner Mongolia.

Based on soil moisture and meteorological elements data at four pasture observation stations in typical steppe of Inner Mongolia from 1981 to 2015, the correlations between multi-time-scales meteorological drought indexes and soil relative moisture at different depths in spring, summer and autumn were comparatively analyzed, and the applicability of these drought indexes to drought monitoring was explored in typical steppe of Inner Mongolia. And on this basis, the prediction models of soil relative moisture at different depths were built by using multiple linear regression method in different seasons. The results show that the soil relative moisture at 0-20 cm depth in three seasons was affected by atmospheric water balance in the past two months, while the timescales influencing soil relative moisture at 0-50 cm and 0-100 cm depths were different in typical steppe of Inner Mongolia. In spring, the soil relative moistures at 0-50 cm and 0-100 cm depths were significantly regulated by previous annual precipitation. In summer, the soil relative moisture at 0-50 cm depth was closely related to atmospheric moisture balance in the past two months, while the soil drought at 0-100 cm depth was mainly controlled by precipitation deficit in the past 2-6 months. In autumn, the soil relative moisture at 0-50 cm depth was significantly influenced by previous precipitation for 3-6 months, while that at 0-100 cm depth was closely correlated with the balance between precipitation and evapotranspiration in the past three months, and the influence of meteorological droughts in the past six months and above was obvious. CI, MCI and PDSI had higher correlation with soil relative moisture at different depths than other meteorological drought indices in different seasons due to taking into account the comprehensive effects of long-term and short-term atmospheric water deficit. The established prediction models of soil relative moisture based on previous meteorological drought indexes could better capture the change of soil moisture in typical steppe, and they could support the drought monitoring and predication of pasture to some extent.