During extensive periods without rain, known as dry-downs, decreasing soil moisture (SM) induces plant water stress at the point when it limits evapotranspiration, defining a critical SM threshold (θ_crit). Better quantification of θ_crit is needed for improving future projections of climate and water resources, food production, and ecosystem vulnerability. Here, we combine systematic satellite observations of the diurnal amplitude of land surface temperature (dLST) and SM during dry-downs, corroborated by in-situ data from flux towers, to generate the observation-based global map of θ_crit. We find an average global θ_crit of 0.19 m³/m³, varying from 0.12 m³/m³ in arid ecosystems to 0.26 m³/m³ in humid ecosystems. θ_crit simulated by Earth System Models is overestimated in dry areas and underestimated in wet areas. The global observed pattern of θ_crit reflects plant adaptation to soil available water and atmospheric demand. Using explainable machine learning, we show that aridity index, leaf area and soil texture are the most influential drivers. Moreover, we show that the annual fraction of days with water stress, when SM stays below θ_crit, has increased in the past four decades. Our results have important implications for understanding the inception of water stress in models and identifying SM tipping points.

Emerging new-generation geostationary satellites have broadened the scope for studying the diurnal cycle of ecosystem functions. We exploit observations from the Geostationary Operational Environmental Satellite-R series to examine the effect of a severe U.S. heatwave in 2020 on the diurnal variations of ecosystem photosynthesis. We find divergent responses of photosynthesis to the heatwave across vegetation types and aridity gradients, with drylands exhibiting widespread midday and afternoon depression in photosynthesis. The diurnal centroid and peak time of dryland gross primary production (GPP) substantially shift toward earlier morning times, reflecting notable water and heat stress. Our geostationary satellite-based method outperforms traditional radiation-based upscaling methods from polar-orbiting satellite snapshots in estimating daily GPP and GPP loss during heatwaves. These findings underscore the potential of geostationary satellites for diurnal photosynthesis monitoring and highlight the necessity to consider the increased diurnal asymmetry in GPP under stress when evaluating carbon-climate interactions.

Forests in Europe experienced record-breaking dry conditions during the summer of 2022. The direction in which various forest types respond to climate extremes during their growing season is contingent upon an array of internal and external factors. These factors include the extent and severity of the extreme conditions and the tree ecophysiological characteristics adapted to environmental cues, which exhibit significant regional variations. In this study, we aimed to (1) quantify the extent and severity of the extreme soil and atmospheric dryness in 2022 in comparison to the two most extreme years in the past (2003 and 2018), (2) quantify the response of different forest types to atmospheric and soil dryness in terms of canopy browning and photosynthesis, and (3) relate the functional characteristics of the forests to the emerging responses observed remotely at the canopy level. For this purpose, we used spatial meteorological datasets between 2000 and 2022 to identify conditions with extreme soil and atmospheric dryness. We used the near-infrared reflectance of vegetation (NIR_v), derived from the Moderate Resolution Imaging Spectroradiometer (MODIS), and the global OCO-2 solar-induced fluorescence (GOSIF) as an observational proxy for ecosystem gross productivity to quantify the response of forests at the canopy level. In summer 2022, southern regions of Europe experienced exceptionally pronounced atmospheric and soil dryness. These extreme conditions resulted in a 30 % more widespread decline in GOSIF across forests compared to the drought of 2018 and 60 % more widespread decline compared to the drought of 2003. Although the atmospheric and soil drought scores were more extensive and severe (indicated by a larger observed maximum z score) in 2018 compared to 2022, the negative impact on forests, as indicated by declined GOSIF, was significantly larger in 2022. Different forest types were affected to varying degrees by the extreme conditions in 2022. Deciduous broadleaf forests were the most negatively impacted due to the extent and severity of the drought within their distribution range. In contrast, areas dominated by evergreen needleleaf forest (ENF) in northern Europe experienced a positive soil moisture (SM) anomaly and minimal negative vapour pressure deficit (VPD) in 2022. These conditions led to enhanced canopy greening and stronger solar-induced fluorescence (SIF) signals, benefiting from the warming. The higher degree of canopy damage in 2022, despite less extreme conditions, highlights the evident vulnerability of European forests to future droughts.

Accurate estimation and monitoring of forest aboveground biomass (AGB) are essential for understanding carbon dynamics, managing forest resources, and guiding environmental policies. However, the spatial and temporal patterns, dynamics, and driving factors of forest AGB in China over recent decades remain insufficiently understood, hindering ecosystem analysis and forest management strategies. This study combines multi-source remote sensing data with residual neural networks (ResNets) to develop the first 30 m resolution annual China Forest AGB dataset (1985–2023) with uncertainty quantification. Validation results confirm the robustness of the ResNets model, achieving an R2 of 0.91, RMSE of 16.49 Mg ha−1, and Bias of 0.50 Mg ha−1 against GEDI footprint AGBD, and an R2 of 0.63, RMSE of 68.26 Mg ha−1, and Bias of −19.87 Mg ha−1 against independent multi-year ground survey data. The dataset reveals a notable increase in China's average forest aboveground biomass density (AGBD) from 95.74 ± 11.30 Mg ha−1 in 1985 to 122.69 ± 13.94 Mg ha−1 in 2023. During this period, total forest aboveground carbon (AGC) stock rose from 5.50 ± 0.23 PgC to 13.97 ± 0.87 PgC, establishing China's forests as a significant carbon sink over the past four decades, with a net carbon sink of 0.22 ± 0.01 PgC yr−1, offsetting 11.5 %–14.9 % of China's annual fossil fuel and industrial carbon dioxide emissions. Forest growth contributed 65.1 % (5.75 PgC) of the total AGC increase, while forest expansion accounted for 34.9 % (3.09 PgC). This dataset provides critical information for forest carbon accounting in China and offers valuable insights for climate change mitigation, ecosystem conservation, and sustainable land management. The dataset is available at https://doi.org/10.5281/zenodo.12620984 (Part I: 1985–1993, Cai et al., 2025a), https://doi.org/10.5281/zenodo.12637101 (Part II: 1994–2001, Cai et al., 2025b), https://doi.org/10.5281/zenodo.12655492 (Part III: 2002–2008, Cai et al., 2025c), https://doi.org/10.5281/zenodo.12658255 (Part IV: 2009–2015, Cai et al., 2025d), https://doi.org/10.5281/zenodo.12742210 (Part V: 2016–2021, Cai et al., 2025e), https://doi.org/10.5281/zenodo.12747329 (Part VI: 2022-2023, Cai et al., 2025f).