Assessment of the grassland carrying capacity for winter-spring period in Mongolia

The grassland ecosystems of Mongolia are among the most sensitive to global climate change because of the arid and semiarid climate. As a key source of primary productivity for livestock, the quantification of the carrying capacity in grassland for the winter and early spring period is crucial for sustainable livestock management and livelihoods of herders in Mongolia. In this study, we used remote sensing data and ancillary data to propose a framework to estimate the aboveground biomass(AGB) and the carrying capacity of grassland (GCC) using the Google Earth Engine (GEE) environment. We analysed the spatial and temporal changes in the GCC for the winter-spring period in Mongolia during 2000–2020, and the grassland carrying status index for winter-spring period (GCSIW) was proposed to reflect grassland utilization and livestock carrying status over the past 21 years. Our study demonstrated the effectiveness of AGB and GCC estimation using the Carnegie-Ames-Stanford Approach (CASA) model with the root-to-crown ratio method within the GEE environment. The AGB model validation showed good performance with an R2 of 0.67–0.71 and RMSE of 22.91–28.94 g/m2. Significant increases in AGB and GCC over the 21 years were found in Mongolian grasslands and most provinces. The average GCSIW increased significantly during 2000–2020 in the whole country and all provinces, indicating the increasing stocking density and the overexploited status of grassland in recent years. The multiregression analysis further showed that the dramatic increase in livestock populations contributed 87.5% and 55%-99% to the variations in the GSCIW for the grassland and seventeen provinces, respectively. These results will be useful and helpful in supporting sustainable grassland management and the sustainable livelihoods of herders in Mongolia

Assessment of Drought Impact on Net Primary Productivity in the Terrestrial Ecosystems of Mongolia from 2003 to 2018

Drought has devastating impacts on agriculture and other ecosystems, and its occurrence is expected to increase in the future. However, its spatiotemporal impacts on net primary productivity (NPP) in Mongolia have remained uncertain. Hence, this paper focuses on the impact of drought on NPP in Mongolia. The drought events in Mongolia during 2003–2018 were identified using the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI). The Boreal Ecosystem Productivity Simulator (BEPS)-derived NPP was computed to assess changes in NPP during the 16 years, and the impacts of drought on the NPP of Mongolian terrestrial ecosystems was quantitatively analyzed. The results showed a slightly increasing trend of the growing season NPP during 2003–2018. However, a decreasing trend of NPP was observed during the six major drought events. A total of 60.55–87.75% of land in the entire country experienced drought, leading to a 75% drop in NPP. More specifically, NPP decline was prominent in severe drought areas than in mild and moderate drought areas. Moreover, this study revealed that drought had mostly affected the sparse vegetation NPP. In contrast, forest and shrubland were the least affected vegetation types

Performance and the Optimal Integration of Sentinel-1/2 Time-Series Features for Crop Classification in Northern Mongolia

Accurate and early crop-type maps are essential for agricultural policy development and food production assessment at regional and national levels. This study aims to produce a crop-type map with acceptable accuracy and spatial resolution in northern Mongolia by optimizing the combination of Sentinel-1 (S1) and Sentinel-2 (S2) images with the Google Earth Engine (GEE) environment. A total of three satellite data combination scenarios are set, including S1 alone, S2 alone, and the combination of S1 and S2. In order to avoid the impact of data gaps caused by clouds on crop classification, this study reconstructed the time series of S1 and S2 with a 10-day interval using the median composite method, linear moving interpolation, and Savitzky–Golay (SG) filter. Our results indicated that crop-type classification accuracy increased with the increase in data length to all three data combination scenarios. S2 alone has higher accuracy than S1 alone and the combination of S1 and S2. The crop-type map with the highest accuracy was generated using S2 data from 150 days of the year (DOY) (11 May) to 260 DOY (18 September). The OA and kappa were 0.93 and 0.78, respectively, and the F1-score for spring wheat and rapeseed were 0.96 and 0.80, respectively. The classification accuracy of the crop increased rapidly from 210 DOY (end of July) to 260 DOY (August to mid-September), and then it remained stable after 260 DOY. Based on our analysis, we filled the gap of the crop-type map with 10 m spatial resolution in northern Mongolia, revealing the best satellite combination and the best period for crop-type classification, which can benefit the achievement of sustainable development goals 2 (SDGs2)