2024 is the hottest year on record, accompanied by extreme precipitation, droughts and fires. The global atmospheric CO2 growth rate in 2024 reached a historic high of 3.73 ppm yr-1, significantly surpassing the previous record set during the 2015/16 El Niño event. Here, we investigate the causes and underlying mechanisms of this record-high growth rate by combining satellite-based atmospheric inversions and estimates of gross primary production and fire emissions. We find that the record-high CO2 growth rate is due to large reductions in the land CO2 sink. This is dominated by a dramatic increase in total ecosystem respiration, which occurred primarily in grass and shrub lands, owing to compound hot-wet climatic conditions in 2024. Given the projected increase in the frequency and intensity of compound pluvial-hot extremes under warming, changes in ecosystem respiration will become more drastic and cause positive feedback to climate warming.

Over the past few decades, China has been engaged in improving maize (Zea mays L.) production to ensure food security. However, it remains unclear whether the spring and summer maize planting regions had different responses to a changing climate. Therefore, this study aims to use smart agricultural techniques to analyze the sensitivities of maize growth to recent climate change in China during 2000-2020. The main objectives are to reveal differences in the relationship between climate and agriculture across the two regions, and to provide insights into optimizing agronomic practices in space and time. Satellite-based observations indicated that summer maize faced more challenging growth conditions (i.e., significant warming and drying) than spring maize during the last two decades. However, as the spring maize regions shifted toward favourable cooler and wetter conditions, its growth and yield increase rates were mostly higher than those of summer maize. To estimate the sensitivities of maize growth to climate factors and atmospheric carbon dioxide concentration (CO2), we employed random forest-based simulation experiments. The results demonstrated that spring maize growth was more sensitive to climate and CO2 than summer maize during the period. We found that the sensitivities were significantly different between the two regions. It seems that spring maize growth was more sensitive, likely due to its predominantly rain-fed nature, making it more vulnerable to climate fluctuations. Conversely, summer maize showed greater resistance, likely buffered by more well-developed irrigation facilities, allowing it to withstand adverse environments. Specifically, spring maize's high sensitivity to water supply illustrates the necessity of expanding irrigation to mitigate extreme events, while summer maize's exposure to heat stress calls for the deployment of heat-tolerant hybrids. These findings provide an evidence-based framework for region specific adaptation strategies, such as breeding improved plant architecture, optimizing planting density, and promoting the northward expansion of cropping systems, thereby ensuring crop yield stability under diverse climate challenges. Overall, this study provides decision-makers with essential support for developing adaptation strategies, highlighting the differential impacts of climate change on crops across various regions.

Climate change has significantly impacted tropical water use efficiency (WUE), defined as the ratio of gross primary productivity (GPP) to evapotranspiration (ET). However, the spatiotemporal dynamics and controlling factors of WUE in these regions-particularly the effects of extreme El Nino events-remain unclear. Using multiple satellite-derived GPP and ET datasets with large-scale observations, here we quantified WUE trends from 2001 to 2020 and assessed the impact of the 2015/16 El Nino drought on WUE in the tropics. Our analysis revealed a significant upward trend in tropical WUE, increasing at a rate of 0.007 f 0.001 g C kg-1 H2O yr-1 (mean f standard deviation), with the largest increase observed in tropical Asia (0.01 f 0.001 g C kg-1 H2O yr-1). Spatially, three independent remote sensing-driven datasets consistently showed a significant WUE increase in 32%-54% of tropical regions, while only 1%-3% experienced a significant decline. Furthermore, tropical ecosystems exhibited a substantial increase in GPP (5.47 f 0.60 g C m-2 yr-1), with the highest growth rate in tropical Asia (11.45 f 0.37 g C m-2 yr-1), whereas ET showed minor changes. This suggests that WUE changes in tropical ecosystems are primarily driven by increases of GPP rather than ET. Further analysis identified leaf area as the dominant factor influencing WUE, GPP, and ET trends across the tropics. We also found that the extreme drought during the 2015/16 El Nino event resulted in a net decrease in WUE (-0.03 f 0.01 g C kg-1 H2O), which transitioned to a net increase (0.04 f 0.01 g C kg-1 H2O) by 2016/17. Compared to satellite-driven results, most land surface models captured the direction of tropical WUE trends but simulated a slower rate of change, with substantial variation in predicted trend intensities among models. This study advances our understanding of tropical ecosystem WUE dynamics and provides critical insights for predicting future WUE changes under ongoing climate change, informing strategies for carbon sequestration and water resource management in vulnerable tropical regions.

Vegetation resilience may undergo critical regime shifts under persistent climate change. What remains unknown is whether vegetation resilience responds to climate change gradually or abruptly (a sudden and persistent level shift in the intercept rather than the slope) across the globe. Integrating multiple satellite-derived and modeled datasets, we delineated distinct global vegetation resilience patterns from 2000 to 2023 and subsequently employed interpretable machine learning to identify the drivers of resilience shifts in regions experiencing abrupt change. We found that abrupt changes were prevalent in both positive and negative vegetation resilience changes. Regions dominated by humid forests exhibited a higher proportion of negative abrupt changes compared to shrubs and grasses in arid areas. Negative abrupt shifts in resilience peaked during periods of intensified drought and fire activity, but these nonlinear abrupt changes subsided in recent years. We further revealed that species richness mainly influences resilience dynamics in regions undergoing abrupt change. In areas with negative abrupt changes, increased soil moisture (SM) emerges as the primary driver of enhanced resilience. In contrast, increased SM weakened resilience trends in positive abrupt-change regions. Our study reveals more complex patterns in global vegetation resilience, providing scientific guidance for the development of targeted ecosystem conservation and restoration measures.

Mountain ecosystems are highly sensitive to climate change, as they regulate carbon–water dynamics that underpin critical ecosystem services. Satellite remote sensing serves as a powerful tool for large-scale monitoring in mountainous regions where ground-based measurements are scarce. However, it remains unclear how satellite-derived gross primary productivity (GPP) and evapotranspiration (ET) vary with elevation and the magnitude of discrepancies across different datasets. This case study focuses on Nepal to systematically investigate the spatiotemporal consistency of six GPP products (EC-LUE, GOSIF, MODIS, MuSyQ, PML_v2, and VPM) and three ET products (ETMonitor, MODIS, and PML_v2) during 2001–2016, with validation against eddy covariance flux measurements. Our results indicate that no single dataset outperforms others across all elevational gradients. Based on the relatively superior datasets (VPM for GPP and PML_v2 for ET), we reveal a strong elevation dependence of GPP, ET, and water use efficiency (WUE = GPP/ET): The highest multi-year mean values are observed in lowland regions (< 200 m), and the greatest interannual variability occurs in midland zones (1,000–3,000 m). Across most datasets, GPP and ET exhibit consistent upward trends, accompanied by a concurrent decline in WUE. Notably, at the pixel scale, only 11.2%, 33.3%, and 0.5% of terrestrial areas show consistent long-term trends in GPP, ET, and WUE, respectively. Such inconsistencies significantly hinder efforts to elucidate carbon–water coupling processes in mountainous ecosystems. Our findings indicate that sustained increases in vegetation productivity may exacerbate hydrological water loss in Nepal, while also underscoring the urgent need for targeted improvements to satellite-derived products.

Satellite solar-induced chlorophyll fluorescence (SIF) provides a new opportunity to quantify vegetation photosynthesis at large scales, but individual satellite observations are usually constrained by their respective limitations regarding spatial continuity or temporal coverage. Although various reconstructed SIF products have already been generated, their spatiotemporal variability remains highly dependent on selected explanatory variables, and complementary information from multiple sensors has not been fully exploited. Here, we developed a multisource SIF fusion framework to integrate the respective strengths of the Global Ozone Monitoring Experiment-2 (GOME-2), Orbiting Carbon Observatory-2 (OCO-2), and TROPOspheric Monitoring Instrument (TROPOMI) products, to generate a fusion-based global continuous SIF time series with high quality (FSIF). Specifically, a statistical spatiotemporal fusion (STF) approach is first employed to preliminarily merge the spatial resolution and timespan advantages across GOME-2 and TROPOMI SIF observations, and followed by a light gradient boosting machine (LightGBM) model to extract the information from auxiliary data and OCO-2 SIF. FSIF covers the full period observed by the three sensors from 2008 to 2022, with a 0.05 degrees and 8-day resolution. The spatial distribution of FSIF is consistent with TROPOMI SIF ( R-2=0.91 , p <0.001) primarily attributable to the STF process, which effectively constrains spatial patterns and value ranges. Fusion with OCO-2 observations further enhances the relationship between FSIF and the eddy covariance (EC)-based gross primary productivity (GPP) with an R(2)difference of 0.09, which benefits from the superiority of OCO-2 SIF in GPP representation. Extensive factorial experiments also declare the importance of fusing each sensor and the significance of coupling STF-machine learning (ML) models.

Abstract. Accurate estimation of regional-scale terrestrial carbon budgets is of great importance but remains challenging. With particular advantages, the Long Short-Term Memory (LSTM) networks method shows potential in improving regional carbon budget upscaling estimations. Here, based on LSTM, we upscale regional net ecosystem carbon exchange (NEE) with available flux tower measurements and satellite land surface observations in North America. With well-established ecosystem-specific LSTMs, we produced monthly NEE at a spatial resolution of 0.1° × 0.1° over 2001–2021 (labelled as MemoryFlux). Unlike existing upscaling estimates, our dataset properly identified the Midwest Corn Belt as a region of large seasonal carbon uptake during peak growing seasons, a feature revealed by previous top-down studies and recognized as a model benchmark. Moreover, the estimated seasonal variations of NEE by MemoryFlux coincided well with those by atmospheric inversions, i.e., the ensemble mean of Orbiting Carbon Observatory-2 Model Intercomparison Project (OCO-2 v10 MIP; r = 0.96, p < 0.001) and CarbonTracker2022 (CT2022) (r = 0.97, p < 0.001). The mean annual NEE was estimated at -1.27 ± 0.12 Pg C yr-1, aligning more closely with the inversions (-0.83 to -0.70 Pg C yr-1) than existing upscaling estimates (-3.30 to -1.68 Pg C yr-1) do. In addition, our estimate plausibly captured the NEE spatial anomalies caused by all the recent extreme drought and flood events. We further confirmed that considering memory effects was critical for better indicating interannual variability and spatial anomalies of NEE induced by climate extremes. MemoryFlux provides an improved bottom-up estimation of North American NEE, largely narrowing the gap with top-down inversions. This dataset can be downloaded at https://doi.org/10.5281/zenodo.20482274 (Huang and He, 2026).

Leaf longevity is a fundamental plant trait that largely explains ecosystem functional dynamics in global pantropical moist forests. However, the signs, magnitudes, and mechanisms of the spatiotemporal variations in leaf longevity with ongoing climate change are still lacking. Using both ground measurements and gridded leaf age-dependent leaf area index data, we map the continental-scale variability of annual mean leaf longevity across pantropical moist forests over 2001–2023. We find a biome-dependent and converging trend in leaf longevity under climate change. In Amazon and tropical Asia with long leaf longevity (> ~1.8 years), leaf longevity decreases due to rising temperature and intensified atmospheric dryness. In contrast, an increasing trend is observed in Congo and subtropical Asia where forests have short leaf longevity (<~1.8 years). These responses cause a convergence of pantropical short and long leaf longevity into a middle longevity range, with maximization of plant functional traits, photosynthesis, and species evenness, which are expected to better resist climate variability. Our study provides emerging evidence for large-scale structural and functional adaptions across pantropical moist forests and is helpful for predicting climate-driven risks to ecosystem stability. The longevity of leaves determines the overall duration of photosynthesis for plants. This study suggests that climate change drives leaf longevity convergence toward intermediate ranges, which, by altering leaf traits and enhancing photosynthetic capacity, strengthens ecosystem stability and is closely linked to vegetation diversity.

Recent satellite observations have revealed the patterns and drivers of large-scale afternoon photosynthetic depression (APD). However, existing APD monitoring is limited because APD does not directly reflect physiological stress since it can occur under normal conditions, and monthly metrics used in previous studies obscure rapid, sub-daily fluctuations. To address this gap, we developed an Afternoon Photosynthetic Depression Index (APDI) using the morning–afternoon contrast in photosynthetic efficiency from eddy-covariance flux tower data. Then using two satellite-based hourly gross primary production (GPP) products (BEPS, FLUXCOM), we assessed the ability of APDI to detect extreme APD events (EAPD), and quantify their coupling with extreme vapor pressure deficit (EVPD) globally. Results showed that APDI effectively captured afternoon depression in photosynthesis, which intensified under increasing environmental stress. Site and global analyses showed the strongest temporal correlations of APDI–VPD, and significantly higher APDI in drylands compared to non-drylands (p < 0.001). Further validation showed that BEPS GPP agreed better with site observations (R2 = 0.45–0.46) than FLUXCOM (R2 = 0.34–0.36). Independent geostationary GPP and solar-induced chlorophyll fluorescence (SIF) datasets further confirmed the strong association between APDI and environmental drivers. Using BEPS-derived APDI, EAPD and EVPD occurred globally and showed spatial co-occurrence with mean frequency of 1.55 and 1.76 events yr−1, lasting 6.34 and 8.55 days with severity of 2.56 and 17.05 kPa, respectively. The EAPD duration and severity exhibited significant increasing trends from 2001 to 2019 (0.031 days yr−1 and 0.020 yr−1), and exhibited temporally coherent variations with EVPD (0.057 days yr−1 and 0.144 kPa yr−1), underscoring their link with atmospheric dryness. Quantifying GPP losses reveals that while EAPD accounts for only 0.6% of annual production, it drives 9.6% of interannual productivity fluctuations on average, particularly during extreme climatic episodes. In conclusion, this study provides a diagnostic tool to quantify the impacts of atmospheric dryness on ecosystem functioning, offering a new perspective for assessing terrestrial ecosystem vulnerability to extreme atmospheric dryness.

Climatic and anthropogenic disturbances have led to intense small-scale tree cover loss in global forests. However, it remains unclear when forest attributes at a large scale (e.g., 0.05° resolution) will decline in response to such sub-grid (e.g., 30-m) tree cover losses within forest ecosystems. Utilizing global maps of forest attribute proxies, we discover that vegetation greenness, canopy structure, composition, and photosynthesis function can all increase under limited tree cover loss, indicating a widely existing safety margin in global forests that is primarily buffered by a positive edge effect of landscape fragmentation within forest ecosystems. The safety margin varies across biomes (tropical: 7.7%; temperate: 3.7%; boreal: 1.0%) and is often positively correlated with ecosystem resistance. In addition, about 35.7% of the remaining global forests have exceeded the safety margin. Our finding contrasts with the conventional perception that sub-grid tree cover losses are inevitably associated with declines in forest attributes and functions. It provides quantitative information for mitigating forest degradation and has strong implications for sustainable forest management practices.

Intrinsic water use efficiency (iWUE) at the leaf level measures water expenditures by terestrial plants during photosynthesis, yet its global spatiotemporal dynamics and responses to water stress remain poorly understood. Using machine-learning models and carbon isotope observations in C3 foliage, here we elucidate global patterns, trends, and water-stress responses of leaf iWUE. We find high iWUE in cold, arid regions and lower values in warm, humid areas. From 2001 to 2020, global iWUE increases at 0.2 ± 0.02 μmol mol-1 year-1, with strong biome specific differences. Grasslands exhibit the highest mean iWUE but the slowest increase, whereas evergreen broadleaf forests show the lowest iWUE yet the fastest increase. iWUE rises with increasing water stress, but the rate of growth diminishes as water stress intensifies. Vapor pressure deficit influence iWUE more broadly than soil moisture. The ecological optimality model reproduces the spatial patterns of leaf iWUE and identifies vapor pressure deficit as the dominant driver, but overestimates mean iWUE and its trend. Our findings suggest that increasing water stress may slow the rate of global iWUE increase as the climate continues to warm. Climate change is altering how plants balance carbon gain and water loss. This study maps global leaf-level water-use efficiency over the past two decades, showing it is highest in regions that are cold or dry, increasing worldwide, and strongly influenced by atmospheric dryness.

Climate change is projected to intensify water stress in many ecosystems and poses threats to their stability, which can be quantified through ecosystem resistance and resilience. Relevant studies mostly focused on multi-year or annual droughts, and in spatially homogeneous or species-specific ecosystems. However, resilience and resistance within complex ecosystems, where different plants exhibit different adaptations and recovery behaviours, are less understood. Using productivity data from satellite-derived GOSIF (Global Orbiting Carbon Observatory-2 Solar-Induced Fluorescence) and flux towers, we examined vegetation responses to short-term (<1 year) water stress events from 2000 to 2018 along the North Australia Tropical Transect, which spans a 1600 mm rainfall gradient and transitions from seasonal mesic to non-seasonal arid ecosystems. We define resistance as productivity maintained during stress relative to a multi-year average baseline, and resilience as the extent to which productivity recovered one year after stress relative to the same baseline. Our results show that ecosystem resistance to water stress was lowest in semi-arid regions but higher in both arid and mesic regions, while ecosystem resilience showed the opposite pattern. These spatial patterns occurred regardless of seasonality and were mainly associated with dominant vegetation type. Woody savanna-dominated mesic regions exhibited highest resistance (0.82 +/- 0.13, p < 0.001) and lowest resilience (0.26 +/- 0.19, p < 0.001), shrublands in arid areas had intermediate values of both resistance (0.81 +/- 0.14, p < 0.001) and resilience (0.27 +/- 0.22, p < 0.001), while the grasslands in semi-arid regions had low resistance (0.78 +/- 0.15, p < 0.001) and high resilience (0.38 +/- 0.24, p < 0.001). The highest likelihood (>75.0 %) of full recovery (i.e., exceeding baseline after one year) occurred during the wet season in mesic regions, likely due to energy limitation, while arid regions showed a lower likelihood (57.0 %). This study provides a remote sensing framework for quantifying ecosystem resistance and resilience under water stress.

Abstract Peatlands store about one‐third of total global soil carbon. Vegetation composition strongly regulates peatland carbon dynamics. Global warming and climate‐driven ecohydrological changes are expected to alter peatland vegetation composition, necessitating accurate simulation of vegetation dynamics to predict future fate of peatland carbon. We incorporated six plant functional types (PFTs) into the ORCHIDEE‐PEAT model to represent bryophytes (mosses), C3 graminoids (sedges and grasses), boreal broadleaf deciduous shrubs, boreal needleleaf evergreen trees, tropical evergreen and raingreen (water‐driven deciduous) trees growing in peatlands. The introduction and elimination of each PFT in response to bioclimatic conditions, as well as sapling establishment, growth, mortality, and competition among PFTs, are explicitly modeled. Simulated vegetation distributions align well with site‐level observations from West Siberian wetlands, where extensive vegetation composition measurements are available for model evaluation. The model slightly overestimated gross primary productivity (GPP) across 60 sites. Evaluation using global satellite‐derived land cover, leaf area index and GPP data was encouraging, though challenges lie in the lack of observational data specific to peatlands. From 1901 to 2020, simulated tropical peatland vegetation composition remains relatively stable. In northern peatlands, as a result of warming and declining water table, bryophyte and C3 graminoid cover decrease by 0.2 (13%) and 0.1 (13%) million km2, respectively, while shrub and tree cover increase by 0.3 (75%) and 0.03 (2%) million km2, respectively. The impacts of these vegetation shift on peatland carbon balance can be explored in future studies using the model, which integrates peatland vegetation dynamics with peatland‐specific hydrology and carbon cycling.

Global greening and browning, as evidenced by changes in leaf area index (LAI) derived from satellite observations, indicate how ecosystems respond to rising atmospheric CO 2 , climate change, and human interventions. However, uncertainties in satellite LAI records have led to conflicting conclusions about global trends and their drivers. Here, by developing a refined Advanced Very High-Resolution Radiometer LAI dataset with reduced radiometric and geometric uncertainties, we find a sustained global greening during 1982–2021 (0.035 m 2 m –2 decade –1 ). Global greening is largely driven by the continuous CO 2 fertilization (1982–2001: 72%; 2002–2021: 77%), while land-use management determines regional patterns. An ensemble of 15 Earth system models also simulates persistent greening (0.050 ± 0.044 m 2 m –2 decade –1 ), but fails to reproduce the observed spatial patterns of greening and drivers. These findings provide observational evidence for sustained global greening, with no clear signs of slowing CO 2 fertilization effect under current CO 2 and climatic conditions.

Atmospheric vapor pressure deficit (VPD) is a key climatic factor that influences vegetation productivity and the global carbon cycle. With ongoing climate warming, VPD has been rising globally. However, the effects of this increase on gross primary production (GPP), especially under different driving mechanisms, remain unclear. This uncertainty limits our ability to predict terrestrial ecosystem responses. In this study, we examined the spatial heterogeneity and climatic drivers of VPD impacts on GPP using three global datasets—FLUXCOM GPP, GOSIF GPP, and VPM GPP—from 2000 to 2018. We classified VPD increases into three types: temperature-driven, combined temperature and relative humidity-driven, and relative humidity-driven. Using trend analysis, partial correlation, ridge regression, and random forest models, we identified a distinct latitudinal gradient in VPD-GPP relationships, presenting an “N-shaped” pattern. VPD positively influenced GPP near the equator and at high latitudes, but showed predominantly negative effects in mid-latitudes. This spatial variation was shaped by the background climate conditions and the interaction of water and energy-related factors. In regions where temperature and humidity changed synchronously, VPD effects on GPP were often neutral or positive. In contrast, asynchronous changes—particularly those dominated by humidity declines—tended to intensify negative impacts. Our findings highlight the diverse vegetation responses to different drivers of VPD increase. They also emphasize the importance of correctly representing regional VPD heterogeneity in ecosystem modeling and future carbon cycle projections.

Leaf age structure strongly regulates canopy photosynthesis in Amazon rainforests yet its large-scale patterns and dynamics remain poorly understood. Here we map the fraction of leaf area of photosynthetically efficient young leaves (fyoung) using remote sensing data and assess its spatiotemporal variability from 2001 to 2023. We find that fyoung varies markedly with elevation and canopy height: tall or mountain forests (canopy ≥32 m or elevation ≥300 m) exhibit higher fyoung than short or lowland forests, reflecting higher leaf turnover driven by stronger radiation, greater atmospheric dryness and longer dry seasons. Across the basin, fyoung increased significantly in 85.2% of forests during 2001-2023, linked to decreasing precipitation, rising sunlight, intensifying atmospheric dryness and lengthening dry seasons. This widespread trend towards more juvenile leaves is projected to persist under future climate change. Our findings reveal a fundamental shift in Amazon leaf age structure and highlight its importance for predicting future photosynthetic responses in a warmer, drier climate.

In 2024, the global annual growth rate of atmospheric CO2 (CGR) surged to a record of 3.73 ppm year-1-the highest since 1959-exceeding the 1.5°C climate threshold for the first time. However, the drivers behind this unprecedented rise remain poorly understood. Here, we employed a machine-learning approach integrating satellite-derived gross primary productivity (GPP) and climatic data to estimate the 2024 land sink (SLAND) at approximately 2.21 ± 0.25 GtC year-1, which had a striking decline of 1.01 GtC year-1 compared to the 2014-2023 average. The reduction in SLAND, most pronounced in tropical regions, contributed ~50% to the 2024 CGR increase-a larger impact than that of fossil fuel emissions or ocean sinks. Moreover, using partial least squares structural equation modelling, we further explained the underlying mechanism for the reduction of the SLAND in 2024, which was caused by hotter and drier conditions leading to a larger increase in respiration than photosynthesis. Our findings underscore the urgent need to investigate the mechanisms behind the declining carbon sink amid persistent global greening. These results challenge previous assumptions about the long-term stability of the terrestrial carbon sink and highlight society's growing dependence on adaptive strategies to mitigate climate warming.

Live Fuel Moisture Content (LFMC) is a critical determinant of wildfire ignition and spread. Accurate forecasting of LFMC dynamics, particularly at a two-week timescale, is essential for early wildfire danger assessment. While satellite remote sensing provides valuable current and historical observations, it lacks the ability to predict future LFMC dynamics. Meanwhile, although weather forecasts are relatively reliable over short timescales (up to two weeks), LFMC models based solely on meteorological inputs often fall short, particularly when predicting conditions at the species level. To address these limitations, this study introduces a species-specific approach that integrates MODIS-derived LFMC data into the biophysical process-based MEDFATE model to optimize LFMC simulations and enable short-term forecasting based on daily weather projections. A global sensitivity analysis was conducted to identify key input parameters for different tree species within MEDFATE. These parameters guided the development of a cost function that quantifies discrepancies between model-simulated and fieldmeasured LFMC, enabling species-specific model calibration. To enhance model optimization, the global optimal DEoptim algorithm was combined with four-dimensional variational data assimilation (4D-Var) to integrate MODIS-derived LFMC estimates into MEDFATE. Using weather projections, the optimized MEDFATE model produced LFMC forecasts at about a two-week timescale. Time-series measurements of LFMC dynamics for Quercus faginea, Quercus ilex, and Pinus halepensis in Spain, Pinus ponderosa in the USA, and Eucalyptus species in Australia demonstrated that model calibration improved daily LFMC estimates (R2 increased from 0.22 to 0.31; RMSE reduced from 18.71% to 16.04%). Further incorporation of MODIS-derived LFMC data significantly enhanced accuracy (R2 = 0.56; RMSE = 9.75%). Validation across seven wildfire events in Spain, Australia, and the USA confirmed the effectiveness and operational relevance of the approach for early fire warning. These findings underscore the potential of integrating satellite remote sensing and meteorological data into biophysical process-based models to improve tree species-specific LFMC prediction and support proactive fire management.