Estimating Live Fuel Moisture Using SMAP L-Band Radiometer Soil Moisture for Southern California, USA
1
Center of Excellence in Earth Systems Modeling and Observations (CEESMO), Chapman University, Orange, CA 92866, USA
2
NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(13), 1575; https://doi.org/10.3390/rs11131575
Received: 8 June 2019 / Revised: 1 July 2019 / Accepted: 1 July 2019 / Published: 3 July 2019
(This article belongs to the Special Issue Modeling and Monitoring Climate Extremes and Impacts on Natural-Human Systems)
Live fuel moisture (LFM) is a field-measured indicator of vegetation water content and a crucial observation of vegetation flammability. This study presents a new multi-variant regression model to estimate LFM in the Mediterranean ecosystem of Southern California, USA, using the Soil Moisture Active Passive (SMAP) L-band radiometer soil moisture (SMAP SM) from April 2015 to December 2018 over 12 chamise (Adenostoma fasciculatum) LFM sites. The two-month lag between SMAP SM and LFM was utilized either as steps to synchronize the SMAP SM to the LFM series or as the leading time window to calculate the accumulative SMAP SM. Cumulative growing degree days (CGDDs) were also employed to address the impact from heat. Models were constructed separately for the green-up and brown-down periods. An inverse exponential weight function was applied in the calculation of accumulative SMAP SM to address the different contribution to the LFM between the earlier and present SMAP SM. The model using the weighted accumulative SMAP SM and CGDDs yielded the best results and outperformed the reference model using the Moderate Resolution Imaging Spectroradiometer (MODIS) Visible Atmospherically Resistance Index. Our study provides a new way to empirically estimate the LFM in chaparral areas and extends the application of SMAP SM in the study of wildfire risk.
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Keywords:
Live fuel moisture; SMAP soil moisture; MODIS; wildfire; multi-variate regression model; chaparral
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MDPI and ACS Style
Jia, S.; Kim, S.H.; Nghiem, S.V.; Kafatos, M. Estimating Live Fuel Moisture Using SMAP L-Band Radiometer Soil Moisture for Southern California, USA. Remote Sens. 2019, 11, 1575.
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