As climate change intensifies, the increasing frequency of wildfires is attracting attention worldwide. Changes in live fuel moisture content (LFMC), a key factor influencing fire occurrence and spread, are closely related to wildfires. Here, we presented a study to explore the global LFMC dynamic trend between 2000 and 2018 based on the satellite-derived LFMC dataset. We adopted the Mann-Kendall trend test, a non-parametric trend test commonly used in hydrological and climatic analysis, to analyze global LFMC changes. The experiment is divided into two parts: a global analysis of MK trends at the pixel level and regional LFMC annual variation curves. The results show an upward trend in global LFMC from June to August, but a larger area of no clear trend exists in the other months.

Live fuel moisture content (LFMC) is a key variable affecting fire occurrence and is an important precondition for building a fire risk forecasting system. Meteorological indices and soil moisture are commonly used variables for estimating LFMC, however, few studies have focused on their long-term cumulative and lagged effects on LFMC. In this study, we first (1) assessed the lagged effect of meteorology and soil moisture on LFMC, then (2) extracted the time characteristics from the long-term time series of them for estimating LFMC, (3) and ultimately establish an empirical model to achieve the LFMC estimation in the western U.S. The accuracy of the model developed in this study reached 0.57 for overall R2and 27.20% for RMSE. Three different types of vegetation cover were classified and R2and RMSE were 0.61 and 25.94% for shrublands, 0.45 and 25.86% for savanna and 0.57 and 27.62% for grassland.

Severe wildfires increased and threaten in southwest Sichuan Province China, causing serious death and damage to this region. The local government launched a series of strict policies on wildfire management in 2014, including control of anthropogenic ignition sources and quick response on wildfire suppression when a fire is detected. This study aimed to examine the effectiveness of these policies in containing wildfire in this region based on multiple remotely sensed observation data. We examined the fire event count (FEC) change between 2001 and 2021 based on multiple satellite-based fire products and found the FEC markedly decreased since 2015. Since wildfire occurrence is a non-linear process resulting from interactions between weather, topography, fuel, and anthropogenic factors, to explore which factor led to such FEC decline, we examined three wildfire weather-related variables - the vapor pressure deficit (VPD), Canada forest fire weather index (FWI), synthetical wildfire danger index (WDI), and three fuel load-related variables - foliage fuel load (FFL), aboveground biomass (AGB), and solar-induced chlorophyll fluorescence (SIF). We found no significant (p>0.05) increase or decline in wildfire weather trend was observed, whereas a significant fuel load accumulation trend (p<0.05) was identified across this region between 2001 and 2021. As the topography factor is stable, this study, with the lengths of remote sensing observation, demonstrated the effectiveness of the management paradigm containing wildfire over southwest China.

With the deepening understanding of fire research and the rapid development of remote sensing technology, remote sensing data has become the main basis for fire monitoring. This study aimed to test the accuracy of four selected fire products based on fire events in Sichuan Province, including VNPI4IMGTDL_NRT (hereinafter referred to as VNPI4DL), MCD64Al, Fire_CCI, MCDI4ML. The reference data is provided by Sichuan Forestry and Grassland Bureau, and the fire products are verified by the spatio-temporal cluster analysis method and classification accuracy evaluation method. The results show VNP14DL has the highest fire detection accuracy, with Fl-score reaching 0.490 in 2014, followed by MCD14ML (Fl-score=0.457), MCD64Al (Fl-score=0.390), and Fire_CCI (Fl-score=0.330).

Fuel moisture content (FMC) of live vegetation is a crucial wildfire risk and spread rate driver. Current wildfire warnings generally use historical FMC data, which leads to inaccurate predictions. The accuracy of FMC forecasting can provide data support for assessing wildfire danger. Long short-term memory (LSTM) is a special recurrent neural network that learns long-term dependencies. It is suitable for predicting time series with both long-term and short-term dependencies. This study intends to use the LSTM model to forecast FMC. The experiment is divided into three parts: the conventional model; the LSTM model with FMC as the only feature variable; the LSTM model with seven feature variables, included FMC, maximum temperature (Tmax), minimum temperature (Tmin), mean temperature (Tmean), mean dew point temperature (Tdmean), maximum vapor pressure deficit (VPDmax), minimum vapor pressure deficit (VPDmin). The results of the LSTM model outperform that of the conventional model, and the effect of selecting multiple parameters as feature variables reached the best accuracy.

In the absence of historical fire records, end-users intend to adopt free satellite-derived fire products, including global burn area (BA) and active fire (AF) products, to understand the historical fire dynamics for better forest management. Previous literature evaluated the accuracy of these fire products in regions with different environments, but no study evaluated the performance of these fire products in fire-prone, cloudy and mountainous areas. This study contributed to filling this gap, through the first evaluation of four broadly used fire products: MODIS-based MCD64A1, MCD14ML, VIIRS-based VNP14DLIMGTDL_NRT (hereafter simplified as VNP14DL), and ESA Fire_CCI51 over subtropical China. Two methods were applied to this end, the spatio-temporal clustering algorithm based on official historical fire records and the density-based random sampling and estimation method from the Landsat 8 fire scenes. The results show that both the AF and BA products show poor fire detection ability in this area (with all the F1-Score < 0.5). Among them, the VNP14DL performed best, followed by MCD14ML, Fire_CCI51, and MCD64A1. The MCD14ML had the best detection capability for small fires (< 50 hectares). AF products have an overall higher fire detection ability than BA products. These findings provide insights for the improvement of fire detection algorithms of these fire products in the fire-prone, cloudy and mountainous area over subtropical China.