Zhongbo Su

Remote‐sensing spectral observations are widely used to monitor crop growth and productivity, yet their potential for agrohydrological forecasting remains limited by a lack of mechanistic links to crop and soil processes. Existing models often prescribe canopy states, simplify physiological responses, or omit explicit spectral simulations, limiting their ability to use spectral signals to constrain key crop and soil variables. To address this, we developed the STEMMUS‐SCOPE‐PG model, integrating a process‐based plant growth (PG) module into the STEMMUS‐SCOPE framework. In this system, simulated carbon assimilation and soil moisture jointly drive dynamic changes in leaf area index (LAI) and biomass, which in turn feed back into canopy radiative transfer, enabling bidirectional coupling between spectral signals (e.g., reflectance and solar‐induced chlorophyll fluorescence (SIF)) and hydrological fluxes. The model was evaluated using three years of field data from maize (C4) and soybean (C3) AmeriFlux sites (US‐Bo1 and US‐Ne3), together with MODIS/TROPOMI satellite observations. Results show that STEMMUS‐SCOPE‐PG reproduced LAI and biomass across both sites, and captured seasonal SIF dynamics consistent with TROPOMI observations. Relative to the original SCOPE and STEMMUS‐SCOPE configurations, the model reduced mean RMSE by 28.8% and 28.3% for gross primary productivity, and by 15.4% and 14.3% for evapotranspiration, respectively. Moreover, we revealed a linear relationship between daily LAI change and SIF during vegetative stages, and a nonlinear relationship during reproductive stages, highlighting phenological effects on crop‐SIF dynamics. These findings demonstrate the value of linking crop physiology with satellite observations, supporting applications in precision agriculture and land‐surface modeling.

Accurate and reliable latent heat (LH) and sensible heat (SH) flux data are crucial for determining energy exchange on the Tibetan Plateau (TP) and for studying land–atmosphere interactions and their impacts on the climate. Improving the accuracy of heat flux datasets over the TP and comprehensively assessing their reliability and uncertainty, especially in data-sparse regions, remain insufficiently explored. This study utilized the Optuna–categorical boosting (CatBoost) model to fuse daily TP heat flux datasets from 2000 to 2022, incorporating five reanalysis datasets (CRA, ERA5, MERRA2, NCEP, and JRA55) and environmental variables. Shapley Additive exPlanation (SHAP) analysis and the generalized three-cornered-hat (TCH) method were applied for result interpretation and spatiotemporal uncertainty quantification, respectively. The results showed that the fusion method can significantly increase the accuracy of the data, i.e., the Pearson CC can be increased from 0.74 to 0.85 for the LH and from 0.52 to 0.70 for the SH. Moreover, it can significantly decrease the uncertainty of the fused LH data and decrease the uncertainty of the fused SH data in general (the average relative uncertainty (RU) was 14.84% for LH and 27.88% for SH). Dewpoint and LST are found to be the most important environmental factors influencing the LH and SH simulations (with average contribution rates of 12.9% and 15.6%, respectively). When the dewpoint exceeds 266.56 K and the LST exceeds 13.79 °C, their influence on LH and SH shifts from negative to positive. Wetlands and glaciers are the land cover types with the highest uncertainty in LH and SH data, respectively. The Qaidam Basin shows the highest mean uncertainties among all the climatic zones. The machine learning-based fusion method developed by this paper not only effectively enhances the quality of fused data but also provides valuable insights for future site expansion and further data quality improvement through its uncertainty analysis findings.

Water potential gradients drive water flow within and between soils and plants, and the internal plant water potential controls a wide range of physiological processes including photosynthesis, growth, and mortality. Notwithstanding this clear relevance for many critical aspects of ecosystem function, water potential data have historically been relatively inaccessible and unnetworked. The absence of a centralized repository for plant water potential time series limits our ability to integrate a wealth of ecophysiological information from other networks and from remote sensing. Closing this gap is necessary to address unresolved questions about plant responses to drought and heat stress, and to make confident predictions about plant and ecosystem function in a warming world. Here, we introduce the PSInet database -- a global collection of plant water potential time series from 285 datasets representing 523 species. We present the workflow that guided database development and evaluate its key features. Through a series of preliminary analyses, we then highlight the potential of the PSInet database for applications including: a) advancing plant water use strategy frameworks; b) disentangling the impacts of soil versus atmospheric drought stress; c) assessing the long-held assumption of pre-dawn equilibration of ecosystem water potential; d) understanding the risk of drought-driven mortality; and e) benchmarking remote-sensing data products and land-surface models.

Accurate quantification of ecohydrological processes is essential for effective water and carbon management in terrestrial ecosystems. Traditional simulations mainly rely on mechanistic models, yet their accuracy is often limited by inconsistencies in representing physical processes and uncertainties in parameterization. Integrating remote sensing signals offers a promising way to reduce these uncertainties and enhance model applicability. In this study, in-situ observations from a wheat cropland in the Guanzhong Plain were used to simulate gross primary productivity (GPP) and latent heat flux (LE) by comparing a forward model (STEMMUS-SCOPE) with a remote sensing-driven inverse model (STEMMUS-MLR). We further examined the role of solar-induced chlorophyll fluorescence (SIF), an emerging proxy for photosynthesis, as an input to improve mechanistic modeling of GPP and LE. Results show that STEMMUS-MLR outperformed STEMMUS-SCOPE in estimating water and carbon fluxes, demonstrating that incorporating SIF effectively reduces bias associated with uncertainties in parameters and forcing data. The contribution of SIF was quantified using Random Forest regression and Shapley additive explanations (SHAP), revealing that SIF markedly reduced the dependence of GPP and LE simulations on shortwave radiation (SW), air temperature (Ta), and leaf area index (LAI). These findings highlight the critical role of SIF in ecohydrological modeling of semi-arid cropland ecosystems and provide a scientific basis for advancing process understanding and improving the precision management of water and carbon budgets in terrestrial ecosystems.

Hight-quality and Long-term measurements of land-atmosphere fluxes are vital for climate monitoring and Land Surface models (LSMs) benchmarking. Eddy covariance provides key in-situ data for theory and LSMs evaluation, but most flux towers lack continuous soil-plant-atmosphere measurements. Here, we present a long-term global dataset of water, energy and carbon fluxes, along with the corresponding above and below-ground hydrological, photosynthetic, and radiative data derived from the STEMMUS-SCOPE model simulations at 170 sites. In-situ observed fluxes data from PLUMBER2 and soil moisture (SM) data from FLUXNET2015 are employed to validate the effectiveness of the STEMMUS-SCOPE dataset. Results demonstrate that, without site-specific model tuning or calibration, and driven solely by global parameters and forcing datasets, simulated net radiation, latent heat flux, sensible heat flux, gross primary production, net ecosystem exchange, and SM datasets consistently agree with available in-situ measurements (median KGE: −0.03 to 0.80; median R²: 0.46 to 0.97; median rRMSE: 4.09% to 29.11%). This dataset supplements the existing ecosystem flux and SM network, enhancing our understanding of ecosystem functioning.

The Tibetan Plateau, a critical regulator of the global water cycle and climate system, represents a highly sensitive region to environmental changes, with significant implications for sustainable development. This study focuses on Nam Co Lake, the third largest lake on the Tibetan Plateau, and investigates the hydrochemical evolution of the lake and the driving mechanisms in regard to the lake–river–groundwater system within the Nam Co Basin over the last 20 years. Our findings provide critical insights for sustainable water resource management in regard to fragile alpine lake ecosystems. The hydrochemical analyses revealed distinct temporal patterns in the total dissolved solids, showing an increasing trend during the 2000s, followed by a decrease in the 2010s. Piper diagrams demonstrated a gradual change in the anion composition from the Cl type to the HCO₃ type over the study period. The ion ratio analyses identified rock weathering (particularly silicate, halite, sulfate, and carbonate weathering), ion exchange, and evaporation processes as primary controlling processes, with notable differences between water bodies: while all four weathering processes contributed to the lake’s water chemistry, only halite and carbonate weathering influenced river and groundwater compositions. The comparative analysis revealed more pronounced ion exchange processes in lake water than in river and groundwater systems. Climate change impacts were manifested through two primary mechanisms: (1) enhanced evaporation, leading to elevated ion concentrations and isotopic enrichment; and (2) temperature-related effects on the water chemistry through increased dilution from precipitation and glacial meltwater. Understanding these mechanisms is essential for developing adaptive strategies to maintain water security and ecosystem sustainability. The relationships established between climate drivers and hydrochemical responses provide a scientific basis for predicting future changes and informing sustainable management practices for inland lake systems across the Tibetan Plateau.

The soil health assessment has evolved from focusing primarily on agricultural productivity to an integrated evaluation of soil biota and biotic processes that impact soil properties. Consequently, soil health assessment has shifted from a predominantly physicochemical approach to incorporating ecological, biological and molecular microbiology indicators. This shift enables a comprehensive exploration of soil microbial community properties and their responses to environmental changes arising from climate change and anthropogenic disturbances. Despite the increasing availability of soil health indicators (physical, chemical, and biological) and data, a holistic mechanistic linkage has not yet been fully established between indicators and soil functions across multiple spatiotemporal scales. This article reviews the state‐of‐the‐art of soil health monitoring, focusing on understanding how soil‐microbiome‐plant processes contribute to feedback mechanisms and causes of changes in soil properties, as well as the impact these changes have on soil functions. Furthermore, we survey the opportunities afforded by the soil‐plant digital twin approach, an integrative framework that amalgamates process‐based models, Earth Observation data, data assimilation, and physics‐informed machine learning, to achieve a nuanced comprehension of soil health. This review delineates the prospective trajectory for monitoring soil health by embracing a digital twin approach to systematically observe and model the soil‐plant system. We further identify gaps and opportunities, and provide perspectives for future research for an enhanced understanding of the intricate interplay between soil properties, soil hydrological processes, soil‐plant hydraulics, soil microbiome, and landscape genomics.

Drought-induced plant hydraulic failure is one of the main factors for large-scale plant mortality. Understanding the response of the plants to water stress is of paramount importance to elucidate the dynamics of water, energy and carbon fluxes under drought conditions. In this study, we implemented the plant hydraulics pathway in STEMMUS-SCOPE (hereafter as STEMMUS-SCOPE-PHS) by considering xylem vulnerability, and validated the model at a karst site in Chongqing, China. Plant water potentials of root, stem and leaf are calculated in STEMMUS-SCOPE-PHS. A leaf water potential-based plant water stress factor (PHWSF) replaces the original soil moisture-based water stress factor to represent the effect of water stress on plant growth. Results show that the PHWSF captures the diurnal dynamics of water stress. The STEMMUS-SCOPE-PHS improves the simulation of diurnal dynamics of latent heat flux, net ecosystem exchange and gross primary production compared to STEMMUS-SCOPE with the value of Kling-Gupta efficiency (KGE) increasing from 0.74 to 0.83, 0.57 to 0.76, and 0.57 to 0.80, respectively. This research delineates the plant hydraulic responses to water stress and highlights the importance of leaf water potential in reflecting the plant water stress.

Drought-induced plant hydraulic failure is one of the main factors for large-scale plant mortality. Understanding the response of the plants to water stress is of paramount importance to elucidate the dynamics of water, energy and carbon fluxes under drought conditions. In this study, we implemented the plant hydraulics pathway in STEMMUS-SCOPE (hereafter as STEMMUS-SCOPE-PHS) by considering xylem vulnerability, and validated the model at a karst site in Chongqing, China. Plant water potentials of root, stem and leaf are calculated in STEMMUS-SCOPE-PHS. A leaf water potential-based plant water stress factor (PHWSF) replaces the original soil moisture-based water stress factor to represent the effect of water stress on plant growth. Results show that the PHWSF captures the diurnal dynamics of water stress. The STEMMUS-SCOPE-PHS improves the simulation of diurnal dynamics of latent heat flux, net ecosystem exchange and gross primary production compared to STEMMUS-SCOPE with the value of Kling-Gupta efficiency (KGE) increasing from 0.74 to 0.83, 0.57 to 0.76, and 0.57 to 0.80, respectively. This research delineates the plant hydraulic responses to water stress and highlights the importance of leaf water potential in reflecting the plant water stress.

Drought-induced plant hydraulic failure is one of the main factors for large-scale plant mortality. Understanding the response of the plants to water stress is of paramount importance to elucidate the dynamics of water, energy and carbon fluxes under drought conditions. In this study, we implemented the plant hydraulics pathway in STEMMUS-SCOPE (hereafter as STEMMUS-SCOPE-PHS) by considering xylem vulnerability, and validated the model at a karst site in Chongqing, China. Plant water potentials of root, stem and leaf are calculated in STEMMUS-SCOPE-PHS. A leaf water potential-based plant water stress factor (PHWSF) replaces the original soil moisture-based water stress factor to represent the effect of water stress on plant growth. Results show that the PHWSF captures the diurnal dynamics of water stress. The STEMMUS-SCOPE-PHS improves the simulation of diurnal dynamics of latent heat flux, net ecosystem exchange and gross primary production compared to STEMMUS-SCOPE with the value of Kling-Gupta efficiency (KGE) increasing from 0.74 to 0.83, 0.57 to 0.76, and 0.57 to 0.80, respectively. This research delineates the plant hydraulic responses to water stress and highlights the importance of leaf water potential in reflecting the plant water stress.