The aim of this study was to develop a robust methodology to estimate pasture biomass across the huge land surface of Mongolia (1.56 × 10⁶ km²) using high-resolution Landsat 8 satellite data calibrated against field-measured biomass samples. Two widely used regression models were compared and adopted for this study: Partial Least Squares (PLS) and Random Forest (RF). Both methods were trained to predict pasture biomass using a total of 17 spectral indices derived from Landsat 8 multi-temporal satellite imagery as predictor variables. For training, reference biomass data from a field survey of 553 sites were available. PLS results showed a satisfactory correlation between field measured and estimated biomass with coefficient of determination (R²) = 0.750 and Root Mean Square Error (RMSE) = 101.10 kg ha⁻¹. The RF regression gave similar results with R² = 0.764, RMSE = 98.00 kg ha⁻¹. An examination of feature importance found the following vegetation indices to be the most relevant: Green Chlorophyll Index (CL_green), Simple Ratio (SR), Wide Dynamic Range Vegetation Index (WDRVI), Enhanced Vegetation Index EVI₁ and Normalized Difference Vegetation Index (NDVI) indices. With respect to the spectral reflectances, Red and Short Wavelength Infra-Red2 (SWIR₂) bands showed the strongest correlation with biomass. Using the developed PLS models, a spatial map of pasture biomass covering Mongolia at a spatial resolution of 30 m was generated. Our study confirms the high potential of RF and PLS regression (PLSR) models to predict pasture biomass. The computationally simpler PLSR model is preferred for applications involving large regions. This method can be implemented easily, provided that sufficient reference data and cloud-free observations are available.

The objective of this research was to develop a robust statistical model to estimate climatologies (2002–2017) of monthly average near-surface air temperature (Ta) over Mongolia using Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) time series products and terrain parameters. Two regression models were analyzed in this study linking automatic weather station data (Ta) with Earth observation (EO) images: Partial least squares (PLS) and random forest (RF). Both models were trained to predict Ta climatologies for each of the twelve months, using up to 17 variables as predictors. The models were applied to the entire land surface of Mongolia, the eighteenth largest but most sparsely populated country in the world. Twelve of the predictor variables were derived from the LST time series products of the Terra MODIS satellite. The LST MOD11A2 (collection 6) products provided thermal information at a spatial resolution of 1 km and with 8-day temporal resolution from 2002 to 2017. Three terrain variables, namely, elevation, slope, and aspect, were extracted using a Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM), and two variables describing the geographical location of weather stations were extracted from vector data. For training, a total of 8544 meteorological data points from 63 automatic weather stations were used covering the same period as MODIS LST products. The PLS regression resulted in a coefficient of determination (R²) between 0.74 and 0.87 and a root-mean-square error (RMSE) from 1.20 °C to 2.19 °C between measured and estimated monthly Ta. The non-linear RF regression yielded even more accurate results with R² in the range from 0.82 to 0.95 and RMSE from 0.84 °C to 1.93 °C. Using RF, the two best modeled months were July and August and the two worst months were January and February. The four most predictive variables were day/nighttime LST, elevation, and latitude. Using the developed RF models, spatial maps of the monthly average Ta at a spatial resolution of 1 km were generated for Mongolia (~1566 × 10⁶ km²). This spatial dataset might be useful for various environmental applications. The method is transparent and relatively easy to implement.

Accurately estimating shrub biomass in arid and semi-arid regions is critical for understanding ecosystem productivity and carbon stocks at both local and global scales. Due to the short and sparse features of shrubs, capturing the shrub biomass accurately by satellite observations is challenging. Previous studies mostly used ground samples and satellite observations to estimate shrub biomass by establishing a direct connection, which was often hindered by the limited number of ground samples and spatial scale mismatch between samples and observations. Unmanned aerial vehicles (UAVs) provide opportunities to obtain more samples that are in line with the aspects of satellite observations (i.e., scale) for regional-scale shrub biomass estimations accurately with low costs. However, few studies have been conducted based on the air-space-ground-scale connection assisted by UAVs. Here we developed a framework for estimating 10 m shrub biomass at a regional scale by integrating ground measurements, UAV, Landsat, and Sentinel-1/2 observations. First, the spatial distribution map of shrublands and non-shrublands was generated in 2023 in the Helan Mountains of Ningxia province, China. This map had an F1 score of 0.92. Subsequently, the UAV-based shrub biomass map was estimated using an empirical model between the biomass and the crown area of shrubs, which was aggregated at a 10 m × 10 m grid to match the spatial resolution of Sentinel-1/2 images. Then, a regional-scale estimation model of shrub biomass was developed with a random forest regression (RFR) approach driven by ground biomass measurements, UAV-based biomass, and the optimal satellite metrics. Finally, the developed model was used to produce the biomass map of shrublands over the study area in 2023. The uncertainty of the resultant biomass map was characterized by the pixel-level standard deviation (SD) using the leave-one-out cross-validation (LOOCV) method. The results suggested that the integration of multi-scale observations from the ground, UAVs, and satellites provided a promising approach to obtaining the regional shrub biomass accurately. Our developed model, which integrates satellite spectral bands and vegetation indices (R² = 0.62), outperformed models driven solely by spectral bands (R² = 0.33) or vegetation indices (R² = 0.55). In addition, our estimated biomass has an average uncertainty of less than 4%, with the lowest values (30%) and biomass production (>300 g/m²). This study provides a methodology to accurately monitor the shrub biomass from satellite images assisted by near-ground UAV observations as well as ground measurements.

Using Hovsgol National Park as an example, we have made an assessment of the changes in landscapes occurring from 1992 to 2008, determined the chief causes of them and outlined the principal management tasks regarding specially protected natural territories focusing on minimizing the level of negative effects. We examine the ecological problems faced by the National Park and the associated hazards and suggest recommendations on an optimization of management of the Park with a view to decrease the possible occurrence of challenging ecological hazards.