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

Author

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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
    Institute of Water Resources for Pastoral Area, 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
    Institute of Water Resources for Pastoral Area, 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
    Institute of Water Resources for Pastoral Area, 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
    Institute of Water Resources for Pastoral Area, China Institute of Water Resources and Hydropower Research, Hohhot 010018, China)

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.

Suggested Citation

  • Sinan Wang & Wenjun Wang & Yingjie Wu & Shuixia Zhao, 2022. "Surface Soil Moisture Inversion and Distribution Based on Spatio-Temporal Fusion of MODIS and Landsat," Sustainability, MDPI, vol. 14(16), pages 1-15, August.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:16:p:9905-:d:885183
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    References listed on IDEAS

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    1. Zhihui Yang & Jun Zhao & Jialiang Liu & Yuanyuan Wen & Yanqiang Wang, 2021. "Soil Moisture Retrieval Using Microwave Remote Sensing Data and a Deep Belief Network in the Naqu Region of the Tibetan Plateau," Sustainability, MDPI, vol. 13(22), pages 1-19, November.
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    Cited by:

    1. Jiaqi Lu & Xifeng Zhang & Shuiming Liang & Xiaowei Cui, 2023. "Spatiotemporal Dynamics of Vegetation Index in an Oasis-Desert Transition Zone and Relationship with Environmental Factors," Sustainability, MDPI, vol. 15(4), pages 1-18, February.

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