ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume IV-2/W5
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W5, 485–491, 2019
https://doi.org/10.5194/isprs-annals-IV-2-W5-485-2019
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W5, 485–491, 2019
https://doi.org/10.5194/isprs-annals-IV-2-W5-485-2019

  29 May 2019

29 May 2019

SOIL MOISTURE ANALYSIS USING MULTISPECTRAL DATA IN NORTH CENTRAL PART OF MONGOLIA

E. Natsagdorj1,2, T. Renchin2, P. De Maeyer1, B. Tseveen3, C. Dari4, and E. Dashdondog5 E. Natsagdorj et al.
  • 1Dept. of Geography, Faculty of Science, Ghent University, 9000 Ghent, Belgium
  • 2NUM-ITC-UNESCO Laboratory for Space Science and Remote Sensing, National University of Mongolia, Ulaanbaatar, Mongolia
  • 3Dept. of Environment and Forest Engineering, National University of Mongolia, Ulaanbaatar, Mongolia
  • 4Dept. of Management, School of Business, National University of Mongolia, Ulaanbaatar, Mongolia
  • 5Dept. of Physics, School of Arts and Sciences, National University of Mongolia, Ulaanbaatar, Mongolia

Keywords: Soil moisture, satellite, modelling, ground truth measurement, moisture index

Abstract. Soil moisture (SM) content is one of the most important environmental variables in relation to land surface climatology, hydrology, and ecology. Long-term SM data-sets on a regional scale provide reasonable information about climate change and global warming specific regions. The aim of this research work is to develop an integrated methodology for SM of kastanozems soils using multispectral satellite data. The study area is Tuv (48°40′30″N and 106°15′55″E) province in the forest steppe zones in Mongolia. In addition to this, land surface temperature (LST) and normalized difference vegetation index (NDVI) from Landsat satellite images were integrated for the assessment. Furthermore, we used a digital elevation model (DEM) from ASTER satellite image with 30-m resolution. Aspect and slope maps were derived from this DEM. The soil moisture index (SMI) was obtained using spectral information from Landsat satellite data. We used regression analysis to develop the model. The model shows how SMI from satellite depends on LST, NDVI, DEM, Slope, and Aspect in the agricultural area. The results of the model were correlated with the ground SM data in Tuv province. The results indicate that there is a good agreement between output SM and SM of ground truth for agricultural area. Further research is focused on moisture mapping for different natural zones in Mongolia. The innovative part of this research is to estimate SM using drivers which are vegetation, land surface temperature, elevation, aspect, and slope in the forested steppe area. This integrative methodology can be applied for different regions with forest and desert steppe zones.