Enhanced Polarimetric Radar Vegetation Index and Integration with Optical Index for Biomass Estimation in Grazing Lands Across the Contiguous United States
Grazing lands are crucial for agricultural productivity, ecological stability, and carbon sequestration, underscoring the importance of monitoring vegetation biomass for the effective management of these ecosystems. Remote sensing data, including optical vegetation indices (VIs) like the Normalized...
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MDPI AG
2024-11-01
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| author | Jisung Geba Chang Simon Kraatz Martha Anderson Feng Gao |
| author_facet | Jisung Geba Chang Simon Kraatz Martha Anderson Feng Gao |
| author_sort | Jisung Geba Chang |
| collection | DOAJ |
| description | Grazing lands are crucial for agricultural productivity, ecological stability, and carbon sequestration, underscoring the importance of monitoring vegetation biomass for the effective management of these ecosystems. Remote sensing data, including optical vegetation indices (VIs) like the Normalized Difference Vegetation Index (NDVI), are widely used to monitor vegetation dynamics due to their simplicity and high sensitivity. In contrast, radar-based VIs, such as the Polarimetric Radar Vegetation Index (PRVI), offer additional advantages, including all-weather imaging capabilities, a wider saturation range, and sensitivity to the vegetation structure information. This study introduces an enhanced form of the PRVI, termed the Normalized PRVI (NPRVI), which is calibrated to a 0 to 1 range, constraining the minimum value to reduce the background effects. The calibration and range factor were derived from statistical analysis of PRVI components across vegetated regions in the Contiguous United States (CONUS), using dual-polarization C-band Sentinel-1 and L-band ALOS-PALSAR data on the Google Earth Engine (GEE) platform. Machine learning models using NPRVI and NDVI demonstrated their complementarity with annual herbaceous biomass data from the Rangeland Analysis Platform. The results showed that the Random Forest Model outperformed the other machine learning models tested, achieving R<sup>2</sup> ≈ 0.51 and MAE ≈ 498 kg/ha (relative MAE ≈ 32.1%). Integrating NPRVI with NDVI improved biomass estimation accuracy by approximately 10% compared to using NDVI alone, highlighting the added value of incorporating radar-based vegetation indices. NPRVI may enhance the monitoring of grazing lands with relatively low biomass compared to other vegetation types, while also demonstrating applicability across a broad range of biomass levels and in diverse vegetation covers. |
| format | Article |
| id | doaj-art-a836f75d58e54a5c968d6f5df954d0f5 |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj-art-a836f75d58e54a5c968d6f5df954d0f52025-08-20T02:50:40ZengMDPI AGRemote Sensing2072-42922024-11-011623447610.3390/rs16234476Enhanced Polarimetric Radar Vegetation Index and Integration with Optical Index for Biomass Estimation in Grazing Lands Across the Contiguous United StatesJisung Geba Chang0Simon Kraatz1Martha Anderson2Feng Gao3Hydrology and Remote Sensing Laboratory, USDA Agricultural Research Service, Beltsville, MD 20705, USAHydrology and Remote Sensing Laboratory, USDA Agricultural Research Service, Beltsville, MD 20705, USAHydrology and Remote Sensing Laboratory, USDA Agricultural Research Service, Beltsville, MD 20705, USAHydrology and Remote Sensing Laboratory, USDA Agricultural Research Service, Beltsville, MD 20705, USAGrazing lands are crucial for agricultural productivity, ecological stability, and carbon sequestration, underscoring the importance of monitoring vegetation biomass for the effective management of these ecosystems. Remote sensing data, including optical vegetation indices (VIs) like the Normalized Difference Vegetation Index (NDVI), are widely used to monitor vegetation dynamics due to their simplicity and high sensitivity. In contrast, radar-based VIs, such as the Polarimetric Radar Vegetation Index (PRVI), offer additional advantages, including all-weather imaging capabilities, a wider saturation range, and sensitivity to the vegetation structure information. This study introduces an enhanced form of the PRVI, termed the Normalized PRVI (NPRVI), which is calibrated to a 0 to 1 range, constraining the minimum value to reduce the background effects. The calibration and range factor were derived from statistical analysis of PRVI components across vegetated regions in the Contiguous United States (CONUS), using dual-polarization C-band Sentinel-1 and L-band ALOS-PALSAR data on the Google Earth Engine (GEE) platform. Machine learning models using NPRVI and NDVI demonstrated their complementarity with annual herbaceous biomass data from the Rangeland Analysis Platform. The results showed that the Random Forest Model outperformed the other machine learning models tested, achieving R<sup>2</sup> ≈ 0.51 and MAE ≈ 498 kg/ha (relative MAE ≈ 32.1%). Integrating NPRVI with NDVI improved biomass estimation accuracy by approximately 10% compared to using NDVI alone, highlighting the added value of incorporating radar-based vegetation indices. NPRVI may enhance the monitoring of grazing lands with relatively low biomass compared to other vegetation types, while also demonstrating applicability across a broad range of biomass levels and in diverse vegetation covers.https://www.mdpi.com/2072-4292/16/23/4476biomassCONUSgrazing landspolarimetric radar vegetation indexPALSARSentinel-1 |
| spellingShingle | Jisung Geba Chang Simon Kraatz Martha Anderson Feng Gao Enhanced Polarimetric Radar Vegetation Index and Integration with Optical Index for Biomass Estimation in Grazing Lands Across the Contiguous United States Remote Sensing biomass CONUS grazing lands polarimetric radar vegetation index PALSAR Sentinel-1 |
| title | Enhanced Polarimetric Radar Vegetation Index and Integration with Optical Index for Biomass Estimation in Grazing Lands Across the Contiguous United States |
| title_full | Enhanced Polarimetric Radar Vegetation Index and Integration with Optical Index for Biomass Estimation in Grazing Lands Across the Contiguous United States |
| title_fullStr | Enhanced Polarimetric Radar Vegetation Index and Integration with Optical Index for Biomass Estimation in Grazing Lands Across the Contiguous United States |
| title_full_unstemmed | Enhanced Polarimetric Radar Vegetation Index and Integration with Optical Index for Biomass Estimation in Grazing Lands Across the Contiguous United States |
| title_short | Enhanced Polarimetric Radar Vegetation Index and Integration with Optical Index for Biomass Estimation in Grazing Lands Across the Contiguous United States |
| title_sort | enhanced polarimetric radar vegetation index and integration with optical index for biomass estimation in grazing lands across the contiguous united states |
| topic | biomass CONUS grazing lands polarimetric radar vegetation index PALSAR Sentinel-1 |
| url | https://www.mdpi.com/2072-4292/16/23/4476 |
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