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Open AccessFeature PaperArticle

AMSR2 Soil Moisture Downscaling Using Temperature and Vegetation Data

1
School of Earth Ocean and Environment, University of South Carolina, Columbia, SC 29208, USA
2
Hydrological Sciences Branch, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
3
Hydrology and Remote Sensing Laboratory, Beltsville Agricultural Research Center, United States Department of Agriculture, Beltsville, MD 20705, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(10), 1575; https://doi.org/10.3390/rs10101575
Received: 16 July 2018 / Revised: 11 September 2018 / Accepted: 20 September 2018 / Published: 1 October 2018
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. View Full-Text
Keywords: AMSR2; passive microwave soil moisture; soil moisture downscaling AMSR2; passive microwave soil moisture; soil moisture downscaling
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MDPI and ACS Style

Fang, B.; Lakshmi, V.; Bindlish, R.; Jackson, T.J. AMSR2 Soil Moisture Downscaling Using Temperature and Vegetation Data. Remote Sens. 2018, 10, 1575. https://doi.org/10.3390/rs10101575

AMA Style

Fang B, Lakshmi V, Bindlish R, Jackson TJ. AMSR2 Soil Moisture Downscaling Using Temperature and Vegetation Data. Remote Sensing. 2018; 10(10):1575. https://doi.org/10.3390/rs10101575

Chicago/Turabian Style

Fang, Bin; Lakshmi, Venkat; Bindlish, Rajat; Jackson, Thomas J. 2018. "AMSR2 Soil Moisture Downscaling Using Temperature and Vegetation Data" Remote Sens. 10, no. 10: 1575. https://doi.org/10.3390/rs10101575

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