A new soil moisture index for monitoring drought occurrence and intensity is presented. The index is based on the integration of different remote sensing products and in situ observations. Due to a shortage of precipitation, droughts reduce vegetation productivity, and thus, aggravate the impact of moisture stress on pastureland. The spatial distribution of soil moisture index with high-resolution images in Mongolia is still being one of the essential goals in the remote sensing and rangeland community. Specifically, we examined a new composite Gobi soil moisture index (GI) based on the combination of Ocean Salinity (SMOS) Soil Moisture, several products from the MODIS satellite, and in situ Soil Moisture (SM) observations. A multiple linear regression method was used for the estimation of GI soil moisture index. The former includes the surface soil moisture from the Soil Moisture and Ocean Salinity (SMOS) mission, the Moderate Resolution Imaging Spectroradiometer (MODIS) derived land surface temperature (LST), normalized difference vegetation index (NDVI), potential evapotranspiration (PET). The latter includes a standardized precipitation index (SPI) from in-situ data. The validation of the approach is based on the relationship between SPI and in-situ soil moisture (SM) observations, and their comparison to remote sensing (RS) – derived indices. The results show that the correlation was statistically significant between GI and in-situ SM observations from the meteorological stations at 10 – 15 cm depths (p < 0.0001). The correlation between GI and SPI, as represented by the correlation coefficient (r) was 0.64. The GI empirical equations that utilize at least three key atmospheric variables are (a) NDVI, (b) land surface temperature, and (c) potential evapotranspiration. The established new GI soil moisture index was retrieved at the 1 km spatial resolution for Southwest Mongolia from 2000 to 2018, and their two summer months (July, August) were used for monitoring drought and vegetation response to the varying soil/climatic conditions. Now, based on the assessment of drought severity, the new soil moisture index allowed us to assess a large-scale spatial coherence of droughts across the Southwestern part of Mongolia.

Manually collected snow data are often considered as ground truth for many applications such as climatological or hydrological studies. However, there are many sources of uncertainty that are not quantified in detail. For the determination of water equivalent of snow cover (SWE), different snow core samplers and scales are used, but they are all based on the same measurement principle. We conducted two field campaigns with 9 samplers commonly used in observational measurements and research in Europe and northern America to better quantify uncertainties when measuring depth, density and SWE with core samplers. During the first campaign, as a first approach to distinguish snow variability measured at the plot and at the point scale, repeated measurements were taken along two 20 m long snow pits. The results revealed a much higher variability of SWE at the plot scale (resulting from both natural variability and instrumental bias) compared to repeated measurements at the same spot (resulting mostly from error induced by observers or very small scale variability of snow depth). The exceptionally homogeneous snowpack found in the second campaign permitted to almost neglect the natural variability of the snowpack properties and focus on the separation between instrumental bias and error induced by observers. Under such measurement conditions, the uncertainty in bulk snow density estimation is about 5% for an individual instrument and is close to 10% among different instruments. Results confirmed that instrumental bias exceeded both the natural variability and the error induced by observers, even in the case when observers were not familiar with a given snow core sampler.