Forest canopy fuel moisture content (FMC) is a critical factor in assessing the vulnerability of a specific area to forest fires. The conventional FMC estimation method, which relies on look-up tables and loss functions, cannot to elucidate the relationship between FMC and simulated data from look-up tables. This study proposes a novel approach for estimating FMC by combining enhanced vegetation index (EVI) and normalized difference moisture index (NDMI). The method employs the PROSAIL + PROGeoSAIL two-layer coupled radiation transfer model to simulate the vegetation index, the water index, and the FMC value, targeting the prevalent double-layer structure in the study area’s vegetation distribution. Additionally, a look-up table is constructed through numerical analysis to investigate the relationships among vegetation indices, water indices, and FMC. The results reveal that the polynomial equations incorporating vegetation and water indices as independent variables exhibit a strong correlation with FMC. Utilizing the EVI–NDMI joint FMC estimation method enables the direct estimation of FMC. The collected samples from Dali were compared with the estimated values, revealing that the proposed method exhibits superior accuracy (R2 = 0.79) in comparison with conventional FMC estimation methods. In addition, we applied this method to estimate the FMC in the Chongqing region one week before the 2022 forest fire event, revealing a significant decreasing trend in regional FMC leading up to the fire outbreak, highlighting its effectiveness in facilitating pre-disaster warnings.

Evapotranspiration (ET) is a critical hydro-thermal parameter for effective water resource management, particularly in land use. However, accurately estimating ET in mountainous regions is challenging due to terrain effects. In this study, we coupled a surface net radiation model accounting for topographic effect, with the Surface Energy Balance Algorithm for Land (SEBAL) to estimate daily ET and analyze the impact of terrain on ET. We compared ET estimated from SEBAL, using Landsat 8/9 datasets, with those from MOD16A2, PML-V2, and ETMonitor at Potatso National Park, China, from 2017 to 2022. The result showed that ET estimated by SEBAL exhibited more detailed spatial distributions influenced by terrain. ET estimated by SEBAL agreed well with the PML-V2, with an R2 RMSE and bias of 0.81, 1.02 mm/day, and 0.94 mm/day, respectively. Terrain effects on ET can be generalized as follows: spatial distributions of daily ET exhibit a bimodal pattern with increasing altitude, with ET peaks occurring at 2900-3200 and 3500-3800 m, respectively. Daily ET notably increases from north-to-northeast-to-east aspects, remains stable from east-to-south-to-southwest, and gradually decreases from southwest-to-west-to-northwest aspects. ET peaks were observed on slopes of 15-25°, with significant increases followed by decreases as the slope increased, and the maximum difference was 0.69 mm/day on west slopes. Additionally, ET peaks shifted to higher slopes on east-to-south slope compared to west-to-north slope, with a difference of less than 0.51 mm/day. This study presents a robust approach for accurately estimating ET and analyzing the effects of terrain on ET in mountainous areas.

Accurately estimating sensible heat (H) and latent heat (LE) in mountainous areas is a significant challenge due to the influence of complex topography factors. Currently, most models have been developed to estimate surface heat flux for flat surfaces without considering the effect of complex geometric terrain structures. In this study, the Surface Energy Balance Algorithm for Land coupled with a mountainous net surface radiation (R_n) calculation method (MSEBAL) was proposed to accurately estimate H and LE in the upstream catchment regions of the Heihe River Basin (HRB). The R_n was estimated by correcting the solar incoming radiation components using topographic factors, including slope, aspect, sky view factor (SVF), and terrain configuration factor (TCF). The SEBAL and MSEBAL models were applied to satellite remote sensing data from Landsat 8 images and ground-observed datasets. In situ measurements from the eddy covariance (EC) system of the A’rou superstation were used to validate the estimation accuracy of R_n, LE, and H by MSEBAL. The results show that R_n, LE, and H estimated by the MSEBAL exhibit good consistency with the validation of in situ measurements. The R_n estimated by MSEBAL showed a decrease in RMSE from 164.32 to 51.31 W/m² and a reduction in absolute bias from 154.50 to 10.50 W/m² compared to SEBAL. The H and LE estimated by MSEBAL exhibit low RMSE and bias, with values of 31.96 and -17.93 W/m² for H, and 35.67 and -6.18 W/m2 for LE, respectively, compared to SEBAL. The spatial pattern of surface heat fluxes exhibited variations with complex terrain changes. H and LE were found to be higher at mountain peaks, while lower values were observed in valleys. Additionally, H and LE were greater on east and south-facing slopes that receive more solar radiation compared to west and north-facing slopes. This study provides an effective tool for estimating surface heat fluxes over mountainous regions.

Evapotranspiration (ET) plays a pivotal role in the water and energy cycles, yet the accuracy of ET products and their driving mechanisms varies significantly across complex topographies. This study systematically evaluates five ET products and investigates their driving mechanisms across the Tibetan Plateau (TP), the Yunnan–Guizhou Plateau (YGP), and the lowlands of China. The results show that, while ET products generally achieve higher accuracy in lowlands, the PML_V2 and ETMonitor demonstrate superior performance and sensitivity in the challenging plateau environments. Distinct regional heterogeneity was observed: the TP is dominated by thermal radiation factors, the YGP is constrained by both thermal and moisture conditions, and the lowlands are driven by the synergistic interaction of thermal, moisture, and vegetation factors. Unlike previous studies, which often treat these regions homogeneously, this work highlights the critical necessity of region-specific model selection to mitigate uncertainties arising from terrain changes. These findings advance understanding of ET discrepancies, facilitate the selection of appropriate ET products, promote model optimization in ET retrieval research, and enhance water management and hydrological modeling under varying topographical conditions.