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Development of a New Soil Moisture Index Using SMOS Satellite Soil Moisture Products: Case study in Southwestern Mongolia
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  • Oyudari Vova,
  • Pavel Groisman,
  • Martin Kappas,
  • Tsolmon Renchin,
  • Steven Fassnacht
Oyudari Vova
University of Göttingen

Corresponding Author:oyudari.vova@geo.uni-goettingen.de

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Pavel Groisman
Hydrology Science and Services Corporation
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Martin Kappas
University of Goettingen
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Tsolmon Renchin
National University of Mongolia
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Steven Fassnacht
Ecosystem Science and Sustainability - Watershed Science Department, Colorado State University; Cooperative Institute for Research in the Atmosphere, Fort Collins, USA
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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.