Low-variance estimation of live fuel moisture content using VIIRS data through radiative transfer model
The Canopy Live Fuel Moisture Content (LFMC) is a pivotal factor in wildfire risk assessment within the fire triangle model, representing the ratio of canopy moisture content to its dry weight. Against the backdrop of degraded Moderate Resolution Imaging Spectroradiometer (MODIS) performance and the...
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Elsevier
2025-02-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843224006691 |
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| author | Shuai Yang Rui Chen Binbin He Yiru Zhang |
| author_facet | Shuai Yang Rui Chen Binbin He Yiru Zhang |
| author_sort | Shuai Yang |
| collection | DOAJ |
| description | The Canopy Live Fuel Moisture Content (LFMC) is a pivotal factor in wildfire risk assessment within the fire triangle model, representing the ratio of canopy moisture content to its dry weight. Against the backdrop of degraded Moderate Resolution Imaging Spectroradiometer (MODIS) performance and the underutilization of Visible Infrared Imaging Radiometer Suite (VIIRS) in LFMC inversion, this study harnessed the coupled radiative transfer models (RTMs) to probe the spectral sensitivity of the VIIRS to LFMC and pinpoint the optimal band combination for LFMC inversion. To tackle the challenge of ill-posed inversion, we leveraged the correlation coefficient matrix to mitigate erroneous combinations of free parameters in the construction of the lookup table. Results showcase that VIIRS-based LFMC inversion yields marginally superior accuracy (R2= 0.57, R2= 0.32) for both grassland and forest types, with VIIRS-based inversion demonstrating a lower relative root mean square error (rRMSE = 5.84%), compared to results from the MODIS. By scrutinizing LFMC trends alongside precipitation (PP) data in four forest fires spanning from 2019 to 2022 in southwest China, varied degrees of LFMC decrease preceding fire outbreaks. Those results substantiated the validity of the proposed method for wildfire warning. Consequently, our study asserts the reliability of VIIRS in LFMC inversion, positioning it as a viable substitute and extension of MODIS. VIIRS offers continuous and effective product support for wildfire warning assessment, enhancing our ability to monitor and mitigate wildfire risks. |
| format | Article |
| id | doaj-art-03ebaeb726a14214a8a13f71e637c0b9 |
| institution | OA Journals |
| issn | 1569-8432 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
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| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj-art-03ebaeb726a14214a8a13f71e637c0b92025-08-20T02:15:32ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322025-02-0113610431110.1016/j.jag.2024.104311Low-variance estimation of live fuel moisture content using VIIRS data through radiative transfer modelShuai Yang0Rui Chen1Binbin He2Yiru Zhang3School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, Sichuan, 611731, ChinaSchool of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, Sichuan, 611731, ChinaCorresponding author.; School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, Sichuan, 611731, ChinaSchool of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, Sichuan, 611731, ChinaThe Canopy Live Fuel Moisture Content (LFMC) is a pivotal factor in wildfire risk assessment within the fire triangle model, representing the ratio of canopy moisture content to its dry weight. Against the backdrop of degraded Moderate Resolution Imaging Spectroradiometer (MODIS) performance and the underutilization of Visible Infrared Imaging Radiometer Suite (VIIRS) in LFMC inversion, this study harnessed the coupled radiative transfer models (RTMs) to probe the spectral sensitivity of the VIIRS to LFMC and pinpoint the optimal band combination for LFMC inversion. To tackle the challenge of ill-posed inversion, we leveraged the correlation coefficient matrix to mitigate erroneous combinations of free parameters in the construction of the lookup table. Results showcase that VIIRS-based LFMC inversion yields marginally superior accuracy (R2= 0.57, R2= 0.32) for both grassland and forest types, with VIIRS-based inversion demonstrating a lower relative root mean square error (rRMSE = 5.84%), compared to results from the MODIS. By scrutinizing LFMC trends alongside precipitation (PP) data in four forest fires spanning from 2019 to 2022 in southwest China, varied degrees of LFMC decrease preceding fire outbreaks. Those results substantiated the validity of the proposed method for wildfire warning. Consequently, our study asserts the reliability of VIIRS in LFMC inversion, positioning it as a viable substitute and extension of MODIS. VIIRS offers continuous and effective product support for wildfire warning assessment, enhancing our ability to monitor and mitigate wildfire risks.http://www.sciencedirect.com/science/article/pii/S1569843224006691Fuel moisture contentRadiative transfer modelVIIRSMODISSensitivity analysisWildfire |
| spellingShingle | Shuai Yang Rui Chen Binbin He Yiru Zhang Low-variance estimation of live fuel moisture content using VIIRS data through radiative transfer model International Journal of Applied Earth Observations and Geoinformation Fuel moisture content Radiative transfer model VIIRS MODIS Sensitivity analysis Wildfire |
| title | Low-variance estimation of live fuel moisture content using VIIRS data through radiative transfer model |
| title_full | Low-variance estimation of live fuel moisture content using VIIRS data through radiative transfer model |
| title_fullStr | Low-variance estimation of live fuel moisture content using VIIRS data through radiative transfer model |
| title_full_unstemmed | Low-variance estimation of live fuel moisture content using VIIRS data through radiative transfer model |
| title_short | Low-variance estimation of live fuel moisture content using VIIRS data through radiative transfer model |
| title_sort | low variance estimation of live fuel moisture content using viirs data through radiative transfer model |
| topic | Fuel moisture content Radiative transfer model VIIRS MODIS Sensitivity analysis Wildfire |
| url | http://www.sciencedirect.com/science/article/pii/S1569843224006691 |
| work_keys_str_mv | AT shuaiyang lowvarianceestimationoflivefuelmoisturecontentusingviirsdatathroughradiativetransfermodel AT ruichen lowvarianceestimationoflivefuelmoisturecontentusingviirsdatathroughradiativetransfermodel AT binbinhe lowvarianceestimationoflivefuelmoisturecontentusingviirsdatathroughradiativetransfermodel AT yiruzhang lowvarianceestimationoflivefuelmoisturecontentusingviirsdatathroughradiativetransfermodel |