Mapping global drought-induced forest mortality based on multiple satellite vegetation optical depth data

The frequency and intensity of global drought events are continuously increasing, posing an elevated risk of forest mortality worldwide. Accurately understanding the impact of drought on forests, particularly the distribution of mortality due to drought, is crucial for scientifically understanding global ecological drought. Atmospheric indicators and soil moisture are typically correlated with tree growth and influence tree water status and drought severity; however, they do not directly represent forest drought conditions. Optical vegetation indices reflect forest mortality but are affected by response delays, low temporal resolution, and cloud contamination. Therefore, the accuracy of current assessment methods for global drought-induced forest mortality, which are based on meteorological and vegetation variables, still needs improvement. To address this challenge, we utilized vegetation optical depth (VOD) data to characterize the changes in forest canopy moisture due to drought. VOD is a parameter that describes the transmissivity of vegetation in the microwave band and is closely related to forest water content and biomass, with longer wavelengths and greater penetration capabilities than visible and near-infrared remote sensing signals. We calculated the annual variation of VOD (ΔVOD) as a supplementary indicator to enhance the accuracy of monitoring and modeling of global drought-induced forest mortality. We integrated VOD with vegetation indices, meteorological data, terrain, and other variables to construct a predictive model of forest mortality due to drought and used this model to generate a series of global maps depicting drought-induced forest mortality. The results indicated that variables related to VOD contributed significantly to the mortality model compared with those based on vegetation or meteorological variables. Furthermore, ΔVOD exhibited a higher correlation with reference mortality rates compared to relative water content, the enhanced vegetation index, and climate water deficit. Notably, by validating the model fit with reference mortality rates, we found that incorporating ΔVOD into the model improved the accuracy of the global forest mortality map from R² = 0.45 to R² = 0.63. By optimizing the training points using a two-stage correlation threshold between ΔVOD and the reference mortality, map accuracy was further improved to R² = 0.72. This study highlights the effectiveness of VOD, particularly ΔVOD, as a direct indicator of vegetation water content variation, for predicting drought-induced forest mortality. The global forest mortality map obtained from 2014 to 2018 is of significant value for the further analysis of forest carbon variations induced by extreme global drought events.