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A New Optical Remote Sensing Technique for High-Resolution Mapping of Soil Moisture
The recently developed OPtical TRApezoid Model (OPTRAM) has been successfully applied for watershed scale soil moisture (SM) estimation based on remotely sensed shortwave infrared (SWIR) transformed reflectance (TR(SWIR)) and the normalized difference vegetation index (NDVI). This study is aimed at...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7931970/ https://www.ncbi.nlm.nih.gov/pubmed/33693360 http://dx.doi.org/10.3389/fdata.2019.00037 |
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author | Babaeian, Ebrahim Sidike, Paheding Newcomb, Maria S. Maimaitijiang, Maitiniyazi White, Scott A. Demieville, Jeffrey Ward, Richard W. Sadeghi, Morteza LeBauer, David S. Jones, Scott B. Sagan, Vasit Tuller, Markus |
author_facet | Babaeian, Ebrahim Sidike, Paheding Newcomb, Maria S. Maimaitijiang, Maitiniyazi White, Scott A. Demieville, Jeffrey Ward, Richard W. Sadeghi, Morteza LeBauer, David S. Jones, Scott B. Sagan, Vasit Tuller, Markus |
author_sort | Babaeian, Ebrahim |
collection | PubMed |
description | The recently developed OPtical TRApezoid Model (OPTRAM) has been successfully applied for watershed scale soil moisture (SM) estimation based on remotely sensed shortwave infrared (SWIR) transformed reflectance (TR(SWIR)) and the normalized difference vegetation index (NDVI). This study is aimed at the evaluation of OPTRAM for field scale precision agriculture applications using ultrahigh spatial resolution optical observations obtained with one of the world's largest field robotic phenotyping scanners located in Maricopa, Arizona. We replaced NDVI with the soil adjusted vegetation index (SAVI), which has been shown to be more accurate for cropped agricultural fields that transition from bare soil to dense vegetation cover. The OPTRAM was parameterized based on the trapezoidal geometry of the pixel distribution within the TR(SWIR)-SAVI space, from which wet- and dry-edge parameters were determined. The accuracy of the resultant SM estimates is evaluated based on a comparison with ground reference measurements obtained with Time Domain Reflectometry (TDR) sensors deployed to monitor surface, near-surface and root zone SM. The obtained results indicate an SM estimation error between 0.045 and 0.057 cm(3) cm(−3) for the near-surface and root zone, respectively. The high resolution SM maps clearly capture the spatial SM variability at the sensor locations. These findings and the presented framework can be applied in conjunction with Unmanned Aerial System (UAS) observations to assist with farm scale precision irrigation management to improve water use efficiency of cropping systems and conserve water in water-limited regions of the world. |
format | Online Article Text |
id | pubmed-7931970 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-79319702021-03-09 A New Optical Remote Sensing Technique for High-Resolution Mapping of Soil Moisture Babaeian, Ebrahim Sidike, Paheding Newcomb, Maria S. Maimaitijiang, Maitiniyazi White, Scott A. Demieville, Jeffrey Ward, Richard W. Sadeghi, Morteza LeBauer, David S. Jones, Scott B. Sagan, Vasit Tuller, Markus Front Big Data Big Data The recently developed OPtical TRApezoid Model (OPTRAM) has been successfully applied for watershed scale soil moisture (SM) estimation based on remotely sensed shortwave infrared (SWIR) transformed reflectance (TR(SWIR)) and the normalized difference vegetation index (NDVI). This study is aimed at the evaluation of OPTRAM for field scale precision agriculture applications using ultrahigh spatial resolution optical observations obtained with one of the world's largest field robotic phenotyping scanners located in Maricopa, Arizona. We replaced NDVI with the soil adjusted vegetation index (SAVI), which has been shown to be more accurate for cropped agricultural fields that transition from bare soil to dense vegetation cover. The OPTRAM was parameterized based on the trapezoidal geometry of the pixel distribution within the TR(SWIR)-SAVI space, from which wet- and dry-edge parameters were determined. The accuracy of the resultant SM estimates is evaluated based on a comparison with ground reference measurements obtained with Time Domain Reflectometry (TDR) sensors deployed to monitor surface, near-surface and root zone SM. The obtained results indicate an SM estimation error between 0.045 and 0.057 cm(3) cm(−3) for the near-surface and root zone, respectively. The high resolution SM maps clearly capture the spatial SM variability at the sensor locations. These findings and the presented framework can be applied in conjunction with Unmanned Aerial System (UAS) observations to assist with farm scale precision irrigation management to improve water use efficiency of cropping systems and conserve water in water-limited regions of the world. Frontiers Media S.A. 2019-11-05 /pmc/articles/PMC7931970/ /pubmed/33693360 http://dx.doi.org/10.3389/fdata.2019.00037 Text en Copyright © 2019 Babaeian, Sidike, Newcomb, Maimaitijiang, White, Demieville, Ward, Sadeghi, LeBauer, Jones, Sagan and Tuller. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Big Data Babaeian, Ebrahim Sidike, Paheding Newcomb, Maria S. Maimaitijiang, Maitiniyazi White, Scott A. Demieville, Jeffrey Ward, Richard W. Sadeghi, Morteza LeBauer, David S. Jones, Scott B. Sagan, Vasit Tuller, Markus A New Optical Remote Sensing Technique for High-Resolution Mapping of Soil Moisture |
title | A New Optical Remote Sensing Technique for High-Resolution Mapping of Soil Moisture |
title_full | A New Optical Remote Sensing Technique for High-Resolution Mapping of Soil Moisture |
title_fullStr | A New Optical Remote Sensing Technique for High-Resolution Mapping of Soil Moisture |
title_full_unstemmed | A New Optical Remote Sensing Technique for High-Resolution Mapping of Soil Moisture |
title_short | A New Optical Remote Sensing Technique for High-Resolution Mapping of Soil Moisture |
title_sort | new optical remote sensing technique for high-resolution mapping of soil moisture |
topic | Big Data |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7931970/ https://www.ncbi.nlm.nih.gov/pubmed/33693360 http://dx.doi.org/10.3389/fdata.2019.00037 |
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