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Abstract
Soil moisture, especially surface soil moisture (SSM), plays an important role in the development of various natural hazards that result from extreme weather events such as drought, flooding, and landslides. There have been many remote sensing methods for soil moisture retrieval based on microwave or optical thermal infrared (TIR) measurements. TIR remote sensing has been popular for SSM retrieval due to its fine spatial and temporal resolutions. However, because of limitations in the penetration of optical TIR radiation and cloud cover, TIR methods can only be used under clear sky conditions. Microwave SSM retrieval is based on solid physical principles, and has advantages in cases of cloud cover, but it has low spatial resolution. For applications at the local scale, SSM data at high spatial and temporal resolutions are important, especially for agricultural management and decision support systems. Current remote sensing measurements usually have either a high spatial resolution or a high temporal resolution, but not both. This study aims to retrieve SSM at both high spatial and temporal resolutions through the fusion of Moderate Resolution Imaging Spectroradiometer (MODIS) and Land Remote Sensing Satellite (Landsat) data. Based on the universal triangle trapezoid, this study investigated the relationship between land surface temperature (LST) and the normalized difference vegetation index (NDVI) under different soil moisture conditions to construct an improved nonlinear model for SSM retrieval with LST and NDVI. A case study was conducted in Iowa, in the United States (USA) (Lat: 42.2°~42.7°, Lon: −93.6°~−93.2°), from 1 May 2016 to 31 August 2016. Daily SSM in an agricultural area during the crop-growing season was downscaled to 120-m spatial resolution by fusing Landsat 8 with MODIS, with an R2 of 0.5766, and RMSE from 0.0302 to 0.1124 m3/m3.
Highlights
Soil moisture is an important element of the global environment and land surface system, and plays an essential role in the crop growing season
The results show that this method can be applied to regional Surface soil moisture (SSM) monitoring with success
The 16-day temporal resolution of Land Remote Sensing Satellite (Landsat) data may lead to missing valuable crop growth and development information during the growing season
Summary
Soil moisture is an important element of the global environment and land surface system, and plays an essential role in the crop growing season. It is one of the most important parameters for evaluating potential agricultural drought conditions [1,2,3]. There is growing recognition of the importance of soil moisture in the environmental cycle, with the surface layer acting as an interface between the land and the atmosphere. Surface soil moisture (SSM) has a crucial impact on the exchange of water and heat energy between the atmosphere and the land surface through transpiration or evaporation [4,5,6,7]. SSM is an important parameter in the land surface model, closely linking the water vapor in the atmosphere, surface water, and groundwater in the study of the entire ecological system [4,11,12]
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