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Foliage fuel load (FFL) is a critical factor affecting crown fire intensity and rate of spread. Satellite observations provide the potential for monitoring FFL dynamics across large areas. Previous studies commonly used empirical methods to estimate FFL, which potentially lacks reproducibility. This study applied Landsat 7 ETM+ and 8 OLI data for FFL retrieval using radiative transfer model (RTM) and machine learning method. To this end, the GeoSail, SAIL and PROSPECT RTMs were firstly coupled together to model the near realistic scenario of a twolayered forest structure. Secondly, available ecological information was applied to constrain the coupled RTM modeling phases, in order to decrease the probability of generating unrealistic simulations. Thirdly, the coupled RTMs were linked to three machine learning models -random forest, support vector machine and multi-layer perceptron -as well as a traditional lookup table. Finally, the performance of each method was validated by FFL measurements from Southwest China and Sweden. The resulting multi-layer perceptron (R 2 = 0.77, RMSE = 0.13 and rRMSE = 0.43) outperformed the other three methods. Evaluation of applicability of the FFL estimates was conducted in a southwest China forest where two forest fires occurred in 2014 and 2020. The FFL dynamics from 2013 through 2020 showed that fire was likely to occur when the FFL accumulated to a critical point (around 27×10 6 kg), highlighting the relevance of remote sensing-derived FFL estimates for understanding potential fire occurrence.

Burn severity mapping is critical to quantifying fire impact on key ecological processes and post-fire forest management. Satellite remote sensing has the advantages of high spatial-temporal resolution and large-scale monitoring and provides a more efficient way to evaluate forest fire burn severity than traditional field or aerial surveys. However, the proportion of tree canopy cover (TCC) affects the spectral signal received by remote sensing sensors from the background charcoal and ash. Consequently, not considering this factor normally leads a spectral confusion in burn severity retrieval. In this study, the burn severity of two Qinyuan forest fires was estimated using a coupled Radiative Transfer Model (RTM) and Sentinel-2A Multi-Spectral Instrument (MSI) reflectance data. A two-layer Canopy Reflectance Model (ACRM) RTM was coupled with the GeoSail RTM by replacing the spectra of the background input of GeoSail RTM to simulate the spectra of the three-layered forests for burn severity retrieval measured as the Composite Burn Index (CBI). The TCC data was then served to RTM parameterization and constrain the backward inversion procedure of the coupled RTM to alleviate spectral confusion. Finally, the inversion retrievals were evaluated using 163 field measured CBI. The coupled RTM can simulate the radiative transfer characteristics of three-layer vegetation and has greater potential to accurately estimate burn severity worldwide. To evaluate the merit of our proposed method, the CBI was estimated through coupled RTM inversion with TCC constraint (CP_RTM+TCC), coupled RTM inversion with global optimal search (CP-RTM+GOS), Forest Reflectance and Transmittance (FRT) RTM inversion with TCC constraint (FRT+TCC), and random forest (RF) algorithm. The results showed that the method proposed in this study (CP_RTM+TCC) yielded the highest estimation accuracy (R2 = 0.92, RMSE = 0.2) among the four methods used as benchmark, indicating its reasonable ability to assist forest managers to better understand post-fire vegetation regeneration and forest management.

In 2018, the megafire episodes on record occurred in California, causing a large number of civilian deaths and damages. As an important part of the "fire environment triangle," the fuel moisture content (FMC) of both live (LFMC) and dead (DFMC) vegetation were broadly accepted as the important drivers of wildfire ignition and spread, but their effects on the 2018 megafires in California were less explored. Here, we explored and compared the effects of LFMC and DFMC on the 2018 megafire in California, allowing for highlighting the role of different types of FMC in megafire risk assessment. The LFMC was collected from the global LFMC product. We used three indices obtained from the Canadian Forest Service Fire Weather Index Rating System as a proxy of DFMC products, including the fine fuel moisture code, duff moisture code (DMC), and drought code. We analyzed the long-term series (2001-2018) of these four indices in California to test whether these indices were indicative of the occurrence of the megafire, and which of the index was the most powerful driving the 2018 megafires. The results showed that all these indices were correlated with the fires in California. The LFMC showed the highest correlation with the fire occurrence between 2001 and 2018, whereas the DMC performed a major role in driving the 2018 megafire in California. This study presented the insights that the LFMC and DMC should be carefully considered in future operational fire risk assessments for megafire prescription, suppression, and response. Index Terms-Drought code (DC), duff moisture code (DMC), fine fuel moisture code (FFMC), fire risk, live fuel moisture content (LFMC), megafire, remote sensing. I. INTRODUCTION W ILDFIRE is a common natural disaster worldwide. However, as climate change progresses, it has led to Manuscript

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