TY - JOUR
T1 - Sub-daily live fuel moisture content estimation from Himawari-8 data
AU - Quan, Xingwen
AU - Chen, Rui
AU - Yebra, Marta
AU - Riaño, David
AU - Resco de Dios, Víctor
AU - Li, Xing
AU - He, Binbin
AU - Nolan, Rachael H.
AU - Griebel, Anne
AU - Boer, Matthias M.
AU - Sun, Yuanqi
N1 - Publisher Copyright:
© 2024 Elsevier Inc.
PY - 2024/7/1
Y1 - 2024/7/1
N2 - Live fuel moisture content (LFMC) is a crucial variable affecting fire ignition and spread. Satellite remote sensing has been effective in estimating LFMC over large spatial scales, but continuous sub-daily (e.g., every 10 mins to hourly during daylight) LFMC monitoring from space is yet to be accomplished. Using the geostationary satellite Himawari-8 temporally dense observations every 10 mins, this study designed a generalized reduced gradient (GRG) numerical optimization method coupled with PROSAILH_5B radiative transfer model (RTM) to track the sub-daily LFMC dynamics. This method simultaneously accounted for the changing sun-target-sensor geometry bi-directional reflectance distribution function (BRDF) effect on Himawari-8 AHI reflectance. LFMC field measurements from Australia and China validated the LFMC estimation from Himawari-8 AHI. In addition, they were also compared to estimates from two broadly used polar-orbiting satellites, the Landsat-8 OLI and Terra+Aqua MODIS. At the sub-daily scale, the LFMC estimated using the GRG method from Himawari-8 AHI yielded reasonable accuracy (R2 = 0.61, rRMSE = 20.78%). When averaged to a daily scale, the accuracy of LFMC estimation based on the Himawari-8 AHI was lower (R2: 0.60-0.61, rRMSE = 25.38%-26.58%) than that based on the Landsat-8 OLI (R2: 0.68-0.79, rRMSE = 18.11%-25.89%) and Terra+Aqua MODIS (R2: 0.63-0.76, rRMSE = 19.73%-25.84%). However, after removing some heterogeneous measurements, the difference in the accuracy of LFMC estimates among these three data sources got smaller and improved (R2: 0.72-0.82, rRMSE = 17.96%-23.84%). Furthermore, the method proved its feasibility and applicability to identify fire danger conditions through two wildfire case studies: one in Queensland (Australia, 2019) and another in Xichang (China, 2020). These studies showed that the wildfires started when the Himawari-8 AHI-based sub-daily LFMC reached its daily minimum. Therefore, this study serves as a foundational step toward estimating sub-daily LFMC dynamics, an important yet overlooked factor in assessing sub-daily fire danger and behavior.
AB - Live fuel moisture content (LFMC) is a crucial variable affecting fire ignition and spread. Satellite remote sensing has been effective in estimating LFMC over large spatial scales, but continuous sub-daily (e.g., every 10 mins to hourly during daylight) LFMC monitoring from space is yet to be accomplished. Using the geostationary satellite Himawari-8 temporally dense observations every 10 mins, this study designed a generalized reduced gradient (GRG) numerical optimization method coupled with PROSAILH_5B radiative transfer model (RTM) to track the sub-daily LFMC dynamics. This method simultaneously accounted for the changing sun-target-sensor geometry bi-directional reflectance distribution function (BRDF) effect on Himawari-8 AHI reflectance. LFMC field measurements from Australia and China validated the LFMC estimation from Himawari-8 AHI. In addition, they were also compared to estimates from two broadly used polar-orbiting satellites, the Landsat-8 OLI and Terra+Aqua MODIS. At the sub-daily scale, the LFMC estimated using the GRG method from Himawari-8 AHI yielded reasonable accuracy (R2 = 0.61, rRMSE = 20.78%). When averaged to a daily scale, the accuracy of LFMC estimation based on the Himawari-8 AHI was lower (R2: 0.60-0.61, rRMSE = 25.38%-26.58%) than that based on the Landsat-8 OLI (R2: 0.68-0.79, rRMSE = 18.11%-25.89%) and Terra+Aqua MODIS (R2: 0.63-0.76, rRMSE = 19.73%-25.84%). However, after removing some heterogeneous measurements, the difference in the accuracy of LFMC estimates among these three data sources got smaller and improved (R2: 0.72-0.82, rRMSE = 17.96%-23.84%). Furthermore, the method proved its feasibility and applicability to identify fire danger conditions through two wildfire case studies: one in Queensland (Australia, 2019) and another in Xichang (China, 2020). These studies showed that the wildfires started when the Himawari-8 AHI-based sub-daily LFMC reached its daily minimum. Therefore, this study serves as a foundational step toward estimating sub-daily LFMC dynamics, an important yet overlooked factor in assessing sub-daily fire danger and behavior.
KW - Geostationary satellite
KW - Himawari-8
KW - Live fuel moisture content
KW - Numerical optimization
KW - Radiative transfer model
KW - Sub-daily scale
KW - Wildfires
UR - http://www.scopus.com/inward/record.url?scp=85192182531&partnerID=8YFLogxK
U2 - 10.1016/j.rse.2024.114170
DO - 10.1016/j.rse.2024.114170
M3 - Article
AN - SCOPUS:85192182531
SN - 0034-4257
VL - 308
JO - Remote Sensing of Environment
JF - Remote Sensing of Environment
M1 - 114170
ER -