Surface Soil Moisture Inversion and Distribution Based on Spatio-Temporal Fusion of MODIS and Landsat

Sinan Wang, Wenjun Wang (), Yingjie Wu and Shuixia Zhao
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Sinan Wang: Yinshanbeilu Grassland Eco-Hydrological National Observation and Research Station, China Institute of Water Resources and Hydropower Research, Hohhot 010018, China
Wenjun Wang: Yinshanbeilu Grassland Eco-Hydrological National Observation and Research Station, China Institute of Water Resources and Hydropower Research, Hohhot 010018, China
Yingjie Wu: Yinshanbeilu Grassland Eco-Hydrological National Observation and Research Station, China Institute of Water Resources and Hydropower Research, Hohhot 010018, China
Shuixia Zhao: Yinshanbeilu Grassland Eco-Hydrological National Observation and Research Station, China Institute of Water Resources and Hydropower Research, Hohhot 010018, China

Sustainability, 2022, vol. 14, issue 16, 1-15

Abstract: Soil moisture plays an important role in hydrology, climate, agriculture, and ecology, and remote sensing is one of the most important tools for estimating the soil moisture over large areas. Soil moisture, which is calculated by remote sensing inversion, is affected by the uneven distribution of vegetation and therefore the results cannot accurately reflect the spatial distribution of the soil moisture in the study area. This study analyzes the soil moisture of different vegetation covers in the Wushen Banner of Inner Mongolia, recorded in 2016, and using Landsat and MODIS images fused with multispectral bands. Firstly, we compared and analyzed the ability of the visible optical and short-wave infrared drought index (VSDI), the normalized differential infrared index (NDII), and the short-wave infrared water stress index (SIWSI) in monitoring the soil moisture in different vegetation cover soils. Secondly, we used the stepwise multiple regression analysis method in order to correlate the multispectral fusion bands with the field-measured soil water content and established a soil moisture inversion model based on the multispectral fusion bands. As the results show, there was a strong correlation between the established model and the measured soil water content of the different vegetation cover soils: in the bare soil, R2 was 0.86; in the partially vegetated cover soil, R2 was 0.84; and in the highly vegetated cover soil, R2 was 0.87. This shows that the established model could better reflect the actual condition of the surface soil moisture in the different vegetation covers.

Keywords: soil moisture; drought index; drought; remote sensing; desert steppe (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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