Abstract
Abstract. Soil moisture is an essential variable of environment and climate change, which affects the energy and water exchange between soil and atmosphere. The estimation of soil moisture is thus very important in geoscience, while at same time also challenging. Satellite remote sensing provides an efficient way for large-scale soil moisture distribution mapping, and microwave remote sensing satellites/sensors, such as Soil Moisture and Ocean Salinity (SMOS), Advanced Microwave Scanning Radiometer (AMSR), and Soil Moisture Active Passive (SMAP) satellite, are widely used to retrieve soil moisture in a global scale. However, most microwave products have relatively coarse resolution (tens of kilometres), which limits their application in regional hydrological simulation and disaster prevention. In this study, the SMAP soil moisture product with spatial resolution of 9km is downscaled to 750m by fusing with VIIRS optical products. The LST-EVI triangular space pattern provides the physical foundation for the microwave-optical data fusion, so that the downscaled soil moisture product not only matches well with the original SMAP product, but also presents more detailed distribution patterns compared with the original dataset. The results show a promising prospect to use the triangular method to produce finer soil moisture datasets (within 1 km) from the coarse soil moisture product.
Highlights
Soil moisture is a key variable of surface and atmospheric system, which plays an important role in the process of precipitation distribution, infiltration, runoff and latent heat flux, etc. (Molero et al, 2016; Mccoll et al, 2017)
Global soil moisture products have been developed from observations by several passive microwave satellite remote sensing sensors worldwide, including Soil Moisture and Ocean Salinity (SMOS), Advanced Microwave Scanning Radiometer (AMSR-1/2), and Soil Moisture Active Passive (SMAP) satellite
Tangnaihai River basin is located at the cross region of two scans of Visible Infrared Imaging Radiometer Suite (VIIRS) mission, where the “Bow-tie effect” exists (Seaman et al, 2015), resulting in the “pixel-trims” within the observations of this area
Summary
Soil moisture is a key variable of surface and atmospheric system, which plays an important role in the process of precipitation distribution, infiltration, runoff and latent heat flux, etc. (Molero et al, 2016; Mccoll et al, 2017). Estimations of large-scale surface soil moisture distribution can be applied to flood and drought monitoring, numerical weather forecasting, climate risk assessment, and crop growth modelling (Robinson et al, 2008; Martinez et al, 2016; Anna et al, 2018). Active microwave (radar) remote sensing has higher spatial resolution and stronger penetration, but is always with long revisit period and high cost, and sensitive to surface roughness and vegetation biomass, resulting in complex data processing and modelling. Despite of the significant contribution to global environment and climate change studies, these products is still with relatively coarse footprint (tens of kilometres) due to limited SNR of microwave radiometers, which hinders their application in regional hydrological simulation and hazards (drought/flood) monitoring
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