Abstract
Soil moisture (SM) applications in terrestrial hydrology require higher spatial resolution soil moisture products than those provided by passive microwave remote sensing instruments (grid resolution of 9 km or larger). In this investigation, an innovative algorithm that uses visible/infrared remote sensing observations to downscale Advanced Microwave Scanning Radiometer 2 (AMSR2) coarse spatial resolution SM products was developed and implemented for use with data provided by the Advanced Microwave Scanning Radiometer 2 (AMSR2). The method is based on using the Normalized Difference Vegetation Index (NDVI) modulated relationships between day/night SM and temperature change at corresponding times. Land surface model output variables from the North America Land Data Assimilation System (NLDAS), remote sensing data from the Moderate-Resolution Imaging Spectroradiometer (MODIS), and Advanced Very High Resolution Radiometer (AVHRR) were used in this methodology. The functional relationships developed using NLDAS data at a grid resolution of 12.5 km were applied to downscale AMSR2 JAXA (Japan Aerospace Exploration Agency) SM product (25 km) using MODIS land surface temperature (LST) and NDVI observations (1 km) to produce the 1 km SM estimates. The downscaled SM estimates were validated by comparing them with ISMN (International Soil Moisture Network) in situ SM in the Black Bear–Red Rock watershed, central Oklahoma between 2015–2017. The overall statistical variables of the downscaled AMSR2 SM validation R2, slope, RMSE and bias, demonstrate good accuracy. The downscaled SM better characterized the spatial and temporal variability of SM at watershed scales than the original SM product.
Highlights
Satellite technology is a practical approach to monitoring Earth surface hydrological characteristics especially in regions with limited ground measurements [1,2,3,4,5,6,7,8]
For examining performance of the downscaling algorithm at different regions and seasons, the R2 and RMSE between θ and corresponding ∆Ts of descending overpass times using the North America Land Data Assimilation System (NLDAS) data between 1981 and 2016 by each month between April and September are mapped in Figures 2 and 3
The other months from October to March were excluded for building the downscaling model, as the NLDAS Soil moisture (SM) had poor correlations with in situ observations in the northern Continental United States (CONUS) region during cold months and was suggested not to be used, due to the biases caused by frozen soil water content [72]
Summary
Satellite technology is a practical approach to monitoring Earth surface hydrological characteristics especially in regions with limited ground measurements [1,2,3,4,5,6,7,8]. Besides the above two types of methods, a number of SM downscaling approaches using high spatial resolution visible/infrared band remotely sensed products have been explored in previous studies. We implemented the SM downscaling algorithm from [46] to AMSR2 radiometer-based SM products retrieved from JAXA Version 3 SM [49,50] over the Continental United States (CONUS) region and examined the performance of this algorithm over regions with different SM conditions This SM downscaling algorithm is based on the relationships between SM, temperature change derived from MODIS Aqua/Terra data, and vegetation index. It is necessary to develop downscaling algorithms to enhance the spatial resolution of passive microwave SM products instead of using NLDAS SM outputs for various applications
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