Abstract
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.
Highlights
Soil moisture (SM) presents an important environmental indicator controlling and regulating the interaction between the atmosphere and the land surface
Where: REF is the spectral reflectance of the surface; Lsat is the satellite spectral radiance for given spectral bands; Lhaze is the upwelling spectral radiance, value derived from image using darkobject criteria; calculated by using the dark object criteria; TAUv is the atmospheric transmittance along the path from ground to sensor, assumed to be 1 because of nadir look angle; Eo is the solar spectral irradiance; TZ is the solar zenith angle; TAUz is the atmospheric transmittance along the path from the sun to the ground surface; Edown is the down welling spectral irradiance at the atmosphere (Chavez, 1996)
We assume SM derived from satellite and it depends on variables such as land surface temperature (LST), normalized difference vegetation index (NDVI), elevation, aspect and slope
Summary
Soil moisture (SM) presents an important environmental indicator controlling and regulating the interaction between the atmosphere and the land surface. Understanding the spatial and temporal variability of moisture patterns is critically important for food security in Mongolia, and other regions of central Asia. For this reason, it is essential to make research on SM and other suitable drivers for development of agricultural land in Mongolia. Mohamed used the normalized day-night surface temperature difference index (NTDI) with moisture availability (ma) over Mongolian Steppe during the growing season, and showed a significant inverse exponential correlation with ma This result indicates that the NTDI is useful as a surrogate of moisture availability in the steppe terrain of central Asia (Mohamed and Kimura, 2014). This paper proposes that it is important to consider elevation, slope, and aspect for SM in mountainous areas
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