AMSR2 Soil Moisture Downscaling Using Temperature and Vegetation Data

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...

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Main Authors: Bin Fang, Venkat Lakshmi, Rajat Bindlish, Thomas J. Jackson
Format: Article
Language:English
Published: MDPI AG 2018-10-01
Series:Remote Sensing
Subjects:
Online Access:http://www.mdpi.com/2072-4292/10/10/1575
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spelling doaj-9f107446767a48e79211bb19853679952020-11-25T01:05:26ZengMDPI AGRemote Sensing2072-42922018-10-011010157510.3390/rs10101575rs10101575AMSR2 Soil Moisture Downscaling Using Temperature and Vegetation DataBin Fang0Venkat Lakshmi1Rajat Bindlish2Thomas J. Jackson3School of Earth Ocean and Environment, University of South Carolina, Columbia, SC 29208, USASchool of Earth Ocean and Environment, University of South Carolina, Columbia, SC 29208, USAHydrological Sciences Branch, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USAHydrology and Remote Sensing Laboratory, Beltsville Agricultural Research Center, United States Department of Agriculture, Beltsville, MD 20705, USASoil 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.http://www.mdpi.com/2072-4292/10/10/1575AMSR2passive microwave soil moisturesoil moisture downscaling
collection DOAJ
language English
format Article
sources DOAJ
author Bin Fang
Venkat Lakshmi
Rajat Bindlish
Thomas J. Jackson
spellingShingle Bin Fang
Venkat Lakshmi
Rajat Bindlish
Thomas J. Jackson
AMSR2 Soil Moisture Downscaling Using Temperature and Vegetation Data
Remote Sensing
AMSR2
passive microwave soil moisture
soil moisture downscaling
author_facet Bin Fang
Venkat Lakshmi
Rajat Bindlish
Thomas J. Jackson
author_sort Bin Fang
title AMSR2 Soil Moisture Downscaling Using Temperature and Vegetation Data
title_short AMSR2 Soil Moisture Downscaling Using Temperature and Vegetation Data
title_full AMSR2 Soil Moisture Downscaling Using Temperature and Vegetation Data
title_fullStr AMSR2 Soil Moisture Downscaling Using Temperature and Vegetation Data
title_full_unstemmed AMSR2 Soil Moisture Downscaling Using Temperature and Vegetation Data
title_sort amsr2 soil moisture downscaling using temperature and vegetation data
publisher MDPI AG
series Remote Sensing
issn 2072-4292
publishDate 2018-10-01
description 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.
topic AMSR2
passive microwave soil moisture
soil moisture downscaling
url http://www.mdpi.com/2072-4292/10/10/1575
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