Rice Inundation Assessment Using Polarimetric UAVSAR Data
Abstract Irrigated rice requires intense water management under typical agronomic practices. Cost effective tools to improve the efficiency and assessment of water use is a key need for industry and resource managers to scale ecosystem services. In this research we advance model‐based decomposition...
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2021-03-01
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Online Access: | https://doi.org/10.1029/2020EA001554 |
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doaj-0a521ae2d0fc4407a89913065f623d8e2021-03-26T21:46:39ZengAmerican Geophysical Union (AGU)Earth and Space Science2333-50842021-03-0183n/an/a10.1029/2020EA001554Rice Inundation Assessment Using Polarimetric UAVSAR DataXiaodong Huang0Benjamin R. K. Runkle1Mark Isbell2Beatriz Moreno‐García3Heather McNairn4Michele L. Reba5Nathan Torbick6Applied Geosolutions LLC Durham NH USADepartment of Biological & Agricultural Engineering University of Arkansas Fayetteville AR USAIsbell Farms England AR USADepartment of Biological & Agricultural Engineering University of Arkansas Fayetteville AR USAAgriculture and Agri‐Food Canada Ottawa CanadaUSDA Agricultural Research Service Jonesboro AR USAApplied Geosolutions LLC Durham NH USAAbstract Irrigated rice requires intense water management under typical agronomic practices. Cost effective tools to improve the efficiency and assessment of water use is a key need for industry and resource managers to scale ecosystem services. In this research we advance model‐based decomposition and machine learning to map inundated rice using time‐series polarimetric, L‐band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) observations. Simultaneous ground truth observations recorded water depth inundation during the 2019 crop season using instrumented fields across the study site in Arkansas, USA. A three‐component model‐based decomposition generated metrics representing surface‐, double bounce‐, and volume‐scattering along with a shape factor, randomness factor, and the Radar Vegetation Index (RVI). These physically meaningful metrics characterized crop inundation status independent of growth stage including under dense canopy cover. Machine learning (ML) comparisons employed Random Forest (RF) using the UAVSAR derived parameters to identify cropland inundation status across the region. Outcomes show that RVI, proportion of the double‐bounce within total scattering, and the relative comparison between the double‐bounce and the volume scattering have moderate to strong mechanistic ability to identify rice inundation status with Overall Accuracy (OA) achieving 75%. The use of relative ratios further helped mitigate the impacts of far range incidence angles. The RF approach, which requires training data, achieved a higher OA and Kappa of 88% and 71%, respectively, when leveraging multiple SAR parameters. Thus, the combination of physical characterization and ML provides a powerful approach to retrieving cropland inundation under the canopy. The growth of polarimetric L‐band availability should enhance cropland inundation metrics beyond open water that are required for tracking water quantity at field scale over large areas.https://doi.org/10.1029/2020EA001554inundation mappingmachine learningpolarimetricriceUAVSAR |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xiaodong Huang Benjamin R. K. Runkle Mark Isbell Beatriz Moreno‐García Heather McNairn Michele L. Reba Nathan Torbick |
spellingShingle |
Xiaodong Huang Benjamin R. K. Runkle Mark Isbell Beatriz Moreno‐García Heather McNairn Michele L. Reba Nathan Torbick Rice Inundation Assessment Using Polarimetric UAVSAR Data Earth and Space Science inundation mapping machine learning polarimetric rice UAVSAR |
author_facet |
Xiaodong Huang Benjamin R. K. Runkle Mark Isbell Beatriz Moreno‐García Heather McNairn Michele L. Reba Nathan Torbick |
author_sort |
Xiaodong Huang |
title |
Rice Inundation Assessment Using Polarimetric UAVSAR Data |
title_short |
Rice Inundation Assessment Using Polarimetric UAVSAR Data |
title_full |
Rice Inundation Assessment Using Polarimetric UAVSAR Data |
title_fullStr |
Rice Inundation Assessment Using Polarimetric UAVSAR Data |
title_full_unstemmed |
Rice Inundation Assessment Using Polarimetric UAVSAR Data |
title_sort |
rice inundation assessment using polarimetric uavsar data |
publisher |
American Geophysical Union (AGU) |
series |
Earth and Space Science |
issn |
2333-5084 |
publishDate |
2021-03-01 |
description |
Abstract Irrigated rice requires intense water management under typical agronomic practices. Cost effective tools to improve the efficiency and assessment of water use is a key need for industry and resource managers to scale ecosystem services. In this research we advance model‐based decomposition and machine learning to map inundated rice using time‐series polarimetric, L‐band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) observations. Simultaneous ground truth observations recorded water depth inundation during the 2019 crop season using instrumented fields across the study site in Arkansas, USA. A three‐component model‐based decomposition generated metrics representing surface‐, double bounce‐, and volume‐scattering along with a shape factor, randomness factor, and the Radar Vegetation Index (RVI). These physically meaningful metrics characterized crop inundation status independent of growth stage including under dense canopy cover. Machine learning (ML) comparisons employed Random Forest (RF) using the UAVSAR derived parameters to identify cropland inundation status across the region. Outcomes show that RVI, proportion of the double‐bounce within total scattering, and the relative comparison between the double‐bounce and the volume scattering have moderate to strong mechanistic ability to identify rice inundation status with Overall Accuracy (OA) achieving 75%. The use of relative ratios further helped mitigate the impacts of far range incidence angles. The RF approach, which requires training data, achieved a higher OA and Kappa of 88% and 71%, respectively, when leveraging multiple SAR parameters. Thus, the combination of physical characterization and ML provides a powerful approach to retrieving cropland inundation under the canopy. The growth of polarimetric L‐band availability should enhance cropland inundation metrics beyond open water that are required for tracking water quantity at field scale over large areas. |
topic |
inundation mapping machine learning polarimetric rice UAVSAR |
url |
https://doi.org/10.1029/2020EA001554 |
work_keys_str_mv |
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1724201776409739264 |