Hydrological remote sensing

The Surface Water and Ocean Topography (SWOT) satellite mission introduces unprecedented 2-D radar interferometry to inland hydrology, aiming to evaluate water bodies down to 0.0625 km 2 . Standard operational products, such as LakeSP, are designed for global applicability but may exhibit suboptimal design choices for particular hydrological regimes. To address this, a customized processing workflow utilizing low-level SWOT L2 HR PIXC vD data was evaluated using 37 small Danish lakes (0.25 to 40 km 2 ) against daily in-situ gauge data from July 2023 to May 2026. The workflow achieves a median root-mean-square error (RMSE) of 5.57 cm and a Pearson correlation coefficient (PCC) of 0.91, similar to LakeSP. Crucially, this approach retains a median of 125 valid timestamps per lake compared to 60 timestamps preserved by the filtered LakeSP product, effectively doubling the temporal resolution and enabling the capture of higher-frequency hydrological variations. Additionally, we find indications that SWOT's PIXC data contains time-varying residual roll-errors over Danish lakes that affect ca 18 % of all passes with more than 10 observed lakes covering Denmark.

Knowing water amounts destined for agricultural practices, essentially consisting of irrigation water use (IWU), is essential for water resources management. Although explicit and consistent information on IWU is generally lacking worldwide, remote sensing and modeling platforms offer valuable solutions for estimating it. The objective of this work is to perform an intercomparison between satellite‐based IWU products and modeling reconstructions over four main irrigation hotspots worldwide: the Contiguous United States (CONUS), the Ebro basin (Spain), India, and the Murray‐Darling basin (Australia). Remote‐sensing‐based IWU estimated through Soil‐Moisture‐Inversion (SM‐Inversion) and SM‐Delta approaches are considered. As modeled data, five Global Hydrological Models are evaluated: H08, PCR‐GLOBWB, WaterGAP, LPJmL, and QUAlloc. Reference irrigation volumes are exploited to assess which source is more reliable. Over CONUS and Murray‐Darling basin, lower discrepancies between data sources (satellite or modeling) are detected. Referring to spatially‐averaged IWU magnitudes, the comparison over CONUS provides ranging from 9.52 to 12.21 mm/month, between 3.24 and 7.06 mm/month, and Pearson correlation () from 0.77 to 0.90. The same intervals for the Murray‐Darling basin are 8.04–17.70 mm/month for , 4.81–12.63 mm/month for , and 0.42–0.85 for . Consequently, over such areas, similar performances in reproducing actual irrigation amounts by satellite estimates and modeling reconstructions, when available, are found. Over the Ebro basin, where the strongest discrepancies are found, satellite‐based data sets outperform model outputs in reproducing reference data. The lack of in situ observations for India prevents a clear assessment of the results, but insights on differences introduced by parametrizations are established.

Flood peaks can develop and recede within a day, making them difficult to capture with satellites with multi‐day revisit intervals. Here, we exploit daily observations during the Calibration and Validation phase of the SWOT mission to examine a short‐lived flood in May 2023 along the Du Gouffre River in Canada. Despite channel widths below 100 m, SWOT tracked day‐to‐day changes in water surface elevation, capturing a rapid rise of approximately 5 m consistent in timing and magnitude with in situ measurements. We derived flood extent based on classification and confidence information. The results show partial spatial overlap with an independent post‐event flood hazard map, with differences primarily associated with reduced classification confidence in vegetated floodplains and narrow reaches. Some areas not identified as water may reflect erosion, sediment deposition, or post‐peak conditions. These results highlight the value of high‐frequency wide‐swath radar altimetry for monitoring transient floods.

The TV-SAT signals received by the ground antennas of Satellite Microwave Links (SMLs) can be opportunistically used for detecting and quantifying precipitation. Consequently, SMLs have the potential to serve as low-cost rainfall sensors complementing conventional instruments. However, a major challenge for opportunistic sensors, including both SMLs and their terrestrial counterpart – Commercial Microwave Links (CMLs) – is related to data ownership and accessibility constraints, possibly hindering progress in the development of processing tools and validation studies. This limitation highlights the importance of openly accessible datasets. While CML open datasets are already available, there are no large-scale SML datasets in public repositories. To address this gap, we introduce the OpenSat4Weather dataset, a comprehensive and openly accessible collection of measurements from 215 SML sensors deployed in Southern France, over a five-month period from August to December 2022. The dataset is accessible at https://doi.org/10.5281/zenodo.16530166 (Nebuloni et al., 2025b). OpenSat4Weather also includes concurrent conventional measurements, namely, 6 min rainfall depths from 113 operational rain gauges, and radar-based estimates of rainfall intensity along each SML path. The radar data were derived from the gauge-adjusted weather radar product PANTHERE provided by Météo-France. Additionally, the dataset stores ERA5 reanalysis data of the 0 °C isotherm height for rain height estimation, which are essential for accurately converting the received signal level into rainfall intensity. In this paper, we detail the OpenSat4Weather data preparation process and draw statistics of data availability. We then provide a descriptive analysis of the dataset, including rainfall characteristics, derived from rain gauges, statistical properties of the SML received signal levels, and a comparison between SML and radar data. In addition, we illustrate examples of disturbances and anomalous patterns observed in raw SML signals. Finally, we provide some practical guidelines for processing and analyzing SML data. Our ultimate goal is to promote open research initiatives aimed at accelerating the development of SML-based applications. Enhancing rainfall monitoring by opportunistic sensors has the potential to provide substantial benefits, particularly in regions where conventional…

We present a robust, global method to reconcile absolute water surface elevations (WSE) from the Surface Water and Ocean Topography (SWOT) mission with a large‐scale hydrodynamic model by iteratively correcting river bathymetry. After removing sensor and representativeness bias, we update riverbed elevations under DEM and min‐depth feasibility constraints and interpolate corrections along reaches. Applied worldwide at 0.05° resolution over 2023–2025, the approach reduces the global median absolute WSE bias (3.10 m → 0.18 m), preserving the same global median correlation (0.38). A modest ∼4% increase in the standard deviation ratio (1.07 → 1.11) indicates slight variance inflation. Independent evaluation over 2015–2025 at 16,746 Hydroweb sites and 2123 GRDC gauges, restricted to reaches with updated beds, confirms substantially reduced bias, negligible change in both correlation and variance, and no significant change in global median discharge skill. When combined with models, this bias‐aware bathymetry will enable a refined numerical representation of floods globally.

SWOT river discharge products rely on the SWOT River Database (SWORD), designed mainly for single‐thread channels. We assess how well SWORD represents ∼65,000 river bifurcations from the Global River Topology (GRIT) network. SWORD geographically intersects both downstream branches at 88.2% of bifurcations, but only 2.0% of these have distinct reach IDs per branch. At structurally usable bifurcations, summed branch discharge exceeds the upstream value by a median factor of 2.1 in every algorithm tested, and the closure error is independent of branch width asymmetry. A width‐based redistribution ( Q ∝ W 1.52 ) enforces closure by construction; at the Athabasca–Embarras bifurcation, when anchored to the upstream gauge discharge and using Landsat‐derived branch widths, the redistribution matches the gauged Embarras flow fraction ( Q EM / Q upstream ) within 0.004 whereas SoS V3 underestimates it by 0.089. Improved multi‐channel topology in SWORD‐like frameworks remains the longer‐term path to branch‐resolved discharge.

Estimation of surface soil moisture (SSM) is fundamental for hydrological monitoring and agricultural management, particularly in drought-prone regions such as central–northeastern Argentina. Data assimilation provides a robust approach for estimating SSM by integrating land surface models with remote sensing data. Satellite missions such as SMAP and SMOS deliver global SSM products; however, these observations often exhibit systematic biases arising from instrument noise, indirect measurements, and temporal and spatial heterogeneities. Since standard data assimilation techniques assume unbiased observations, bias correction is required to ensure optimal assimilation. This work evaluates off-line observation bias-correction methods within a land data assimilation framework based on the Noah-MP land surface model and an ensemble Kalman filter, focusing on the 2022 dry season in the endorheic Pampas region. During dry conditions, SSM probability density functions differ significantly among soil textures within the bin. To address sampling errors in cumulative distribution function (CDF) matching, we propose an approach in which climatological statistics are computed using homogeneous soil texture pixels, and a 45-day moving temporal window is applied to smooth CDF evolution. The proposed method is compared with standard CDF matching and normal deviate scaling. Results indicate that improved statistical sampling in CDF matching produces a measurable impact on SSM estimates, particularly during dry periods. It shows better agreement with independent in-situ soil measurements than standard CDF matching, provided that the soil texture classifications at the in-situ site and in the model grid cell are consistent.

Large-scale hydrological models like CTRIP and MGB are essential for simulating river dynamics and supporting large-scale climate studies. Their accuracy can be significantly improved through satellite data assimilation. This study leverages the stand-alone value of 20 years of ESA Climate Change Initiative (CCI) high-resolution discharge and water surface elevation (WSE) products (2000–2020) for improving large-scale hydrological simulations through data assimilation. To evaluate the added-value of these produtcs across contrasting modelling and hydrological contexts, we assimilate altimetry-derived discharge, multispectral-imagery-derived discharge, and WSE anomalies into two existing ensemble Kalman Filter frameworks: HyDAS in CTRIP, a global-scale physically based and uncalibrated river-routing model, and HYFAA in MGB, a calibrated semi-distributed regional hydrological model. The experiments are conducted over the Niger and Congo basins, which differ in hydrological variability, river-network structure, wetland influence, and CCI product availability. Across the experiments, discharge assimilation generally outperformed WSE anomaly assimilation because discharge is directly represented in the routing models, providing a more direct and physically consistent correction, while WSE requires consistency between observed and simulated rating curves to be converted into effective discharge corrections. In the Niger basin, where the seasonal signal and station coverage better constrain the main river dynamics, assimilating altimetry-derived discharge led to the strongest improvement in MGB, increasing the median Nash-Sutcliffe Efficiency (NSE) to 0.83 and the correlation coefficient to 0.94. WSE anomaly assimilation was beneficial in specific cases, particularly when the baseline simulation was poor and when observed and simulated rating curves were well aligned. Temporal data density in discharge assimilation emerged as a key driver of performance gains. Assimilating high-frequency discharge data from multispectral imagery significantly reduced bias, from 1.2 to near 1 in MGB, and from 2.3 to 1.78 in CTRIP (median values), supporting hydrological assessments related to long-term variability. Furthermore, the higher temporal resolution allowed for better capture of flow variability, with Kling-Gupta Efficiency γ approaching 1.0 in MGB, which is relevant for…

Historical peatland drainage resulted in surface subsidence and reduced water storage capacity (WSC), a process that can be exacerbated by climate change. We assessed the effects of rewetting on WSC using mesocosm experiments in climate chambers, field measurements, and Interferometric Synthetic Aperture Radar (InSAR) analysis. Results showed that rewetting significantly increased WSC under both present and future RCP 4.5 climates, with stronger effects where Sphagnum moss recovery may restore surface porosity. Rewetting enhances WSC under shallow antecedent water levels (WLs) and high precipitation, despite reduced vertical storage, due to living Sphagnum moss and porous near‐surface peat. Field validation confirmed increased WSC adjacent to the original ditches. In addition, InSAR analysis detected accelerated peat uplift post‐rewetting compared with pristine peatland conditions. These results demonstrate that rewetting effectively restores the flood control function of drained boreal peatlands, strengthens their resilience under climate change, and supports ecosystem recovery and climate adaptation strategies.

Watershed streamflow is often the focus of hydrological model calibration and evaluation, despite other potential objectives, including water quality management, flood protection, or agricultural management. When hydrological models are calibrated on streamflow, intermediate processes such as those affecting soil-moisture are not necessarily well represented. This research evaluated whether calibration using downscaled and bias-corrected satellite soil-moisture improves prediction of field-scale soil-moisture relative to conventional streamflow-based calibration. In this work, downscaled satellite soil-moisture and streamflow data are used to calibrate a soil and water assessment tool – variable source area model initialized using a terrain informed process to create hydrologic response units. In-situ soil-moisture measurements at 25 locations across a 4.2-ha mixed-grass pasture located in southwestern Virginia were used to estimate field-scale average soil-moisture variability for model evaluation. Leveraging downscaled satellite soil-moisture data substantially improved estimation of temporal soil-moisture variability without affecting the model streamflow performance. The multi-objective calibration using streamflow and satellite soil-moisture improved soil-moisture performance while maintaining streamflow performance comparable to streamflow-only calibration. These results demonstrate the potential for satellite soil-moisture–informed calibration to improve internal hydrologic state estimation in small, saturation excess watersheds. Furthermore, these results highlight the importance of coupling statistical performance gains with evaluation of hydrologic realism when extending such approaches to broader modeling applications.

Floods are among the most damaging disasters worldwide, necessitating high-resolution modeling of where terrain and environmental conditions predispose societies to flooding. While geospatial data are invaluable for flood susceptibility mapping, comprehensive and globally consistent datasets remain scarce. We present the Global Flood Susceptibility Map (GFSM v1), a globally harmonized 30 m flood susceptibility dataset produced by incorporating multi-source flood conditioning factors accounting for topographic, hydrological, meteorological, and anthropogenic characteristics within a machine learning framework. A gradient-boosted tree model was trained on ~17,000 curated grid tiles with ~30.45 million training samples, using a global flood inundation dataset as training labels and nine conditioning factors (elevation, slope, aspect, wetness index, vegetation index, height above nearest drainage, water/roads proximity, and rainfall frequency). The model was deployed based on geopolitical and climate strata (units n = 192) to generate global pixel-level susceptibility. Rigorous cross-validation indicates strong model predictive accuracy (median area-under-curve ~0.95), and the output captures localized flood-prone regions worldwide. GFSM v1 offers a global open-source, high-resolution resource to support informed flood risk management, particularly in data-sparse regions.

Long-term monitoring of global river discharge has been hindered by the uneven distribution of gauging stations and limited data accessibility, a challenge that is particularly acute in China. Although China contains one of the world's densest river networks, high-frequency in situ discharge observations remain largely unavailable in the international public domain. To address this gap, we compiled daily discharge records from 1196 gauges across China, comprising approximately 2.33 ×10 6 observations – 39 times as many gauges as are currently available for the region in the Global Runoff Data Centre (GRDC). Leveraging this unprecedented collection of in situ discharge records, along with river width time series derived from Landsat and Sentinel-2 imagery and gauge-specific hydraulic geometry relationships, we reconstructed and extended daily river discharge observations for 310 gauges from 1990 to 2024, resulting in the China Daily River Discharge Records (CDR 2 ) dataset. Compared with existing global satellite-derived discharge products, CDR 2 expands the number of available gauges in China by more than fivefold while delivering substantially improved performance, achieving a median Kling–Gupta efficiency of 0.66 during validation. Moreover, uncertainties propagated from the fitted hydraulic geometry parameters remain low, with a median relative uncertainty of only 11.89 % in the reconstructed discharge estimates. Sensitivity analyses further indicate that discharge estimation accuracy increases markedly with greater river width variability and stronger hydraulic sensitivity. Trend analysis reveals that nearly 65 % of gauges exhibit declining discharge over 1990–2024, with a median relative trend of −0.21 % yr −1 , most pronounced in the Haihe, Liaohe, Yellow River, and middle Yangtze River basins. As the most extensive satellite-derived, gauge-constrained river discharge dataset currently available for China at daily resolution, CDR 2 bridges a critical geographic gap in global river monitoring and provides a valuable benchmark for future discharge estimation, hydrological research, water resources management, and the calibration and evaluation of satellite missions. The CDR 2 dataset is publicly available on Zenodo at https://doi.org/10.5281/zenodo.22231453 (Wang and Li, 2026).

Ground‐based Global Navigation Satellite System (GNSS) vertical displacements are increasingly used to infer terrestrial water storage (TWS) variations, but their applicability remains uncertain where weak hydrological signals coexist with non‐hydrological deformation processes. This study evaluates the potential of GNSS‐derived vertical displacements to capture TWS variability in Northeast China (NEC), a representative cold region characterized by weak hydrologic variability, a sparse GNSS network, and widespread freeze‐thaw processes. Vertical displacements from GNSS, GRACE, and the Noah land surface model during 2012–2023 were compared. At the regional scale, network‐mean GNSS displacements calculated using all examined stations were moderately correlated with GRACE‐ and Noah‐based estimates ( R = 0.61 and 0.68, respectively). At the station scale, GNSS captured long‐term and interannual TWS variations at most stations, but its seasonal cycle differed markedly from GRACE and Noah. Agreement between GNSS and GRACE degraded substantially in winter ( R = 0.30), possibly due in part to non‐elastic deformation associated with freeze‐thaw processes. Regional‐average snow water equivalent was weakly correlated with station‐mean vertical displacements from GNSS ( R = 0.10), GRACE ( R = 0.28), and Noah ( R = −0.21), while correlations between TWS anomalies and vertical displacements were −0.60, −0.88, and −0.91, respectively. These results suggest that snow mass alone does not dominate seasonal TWS variability in NEC. Overall, GNSS vertical displacements capture regional and interannual TWS variations in cold regions, highlighting their potential for large‐scale hydrological monitoring. However, possible non‐elastic deformation associated with freeze‐thaw processes requires further investigation to improve GNSS‐based hydrological applications in complex cold‐region environments.

Accurate estimation of terrestrial water storage (TWS) variations is important for understanding climate change and managing water resources. Satellite gravimetry has been a unique tool for this purpose, but has inherently low spatial resolution. Dense networks of the Global Navigation Satellite System (GNSS) receivers may provide a higher resolution alternative by observing crustal deformation due to changing water mass loads. However, GNSS observations contain noise and other residual signals from various sources that are unrelated to elastic loading, which is troublesome in TWS applications. In this study, we estimated monthly TWS changes on a 0.25° grid in California from January 2019 to December 2023 using GNSS data, after developing a method to separate elastic loading signals through a Combined Empirical Orthogonal Function (CEOF) analysis with Gravity Recovery and Climate Experiment Follow‐On (GRACE‐FO). The corrected GNSS data were used to estimate TWS change via a regularized linear inversion, revealing clear seasonal variations and long‐term decline from January 2019 to October 2022, followed by an abrupt increase associated with atmospheric river events from October 2022 to March 2023. GNSS‐based estimates are validated with in situ lake water elevation data, and provide additional spatial detail compared to GRACE‐FO mass concentration solutions and hydrological model estimates. These results improve the potential of GNSS‐based TWS estimates in regional hydrological analyses, water resource management, and climate change assessment.

Surface Water and Ocean Topography (SWOT) provides water surface elevation (WSE) that can constrain river hydraulics through data assimilation (DA), but basin‐scale impacts of assimilating real observations remain uncertain under heterogeneous availability and regulation. We assimilate node‐level SWOT WSE into a large‐scale hydrodynamic model over the Ohio River Basin using an ensemble Kalman filter and test river‐network‐aware localization. Assimilation improves discharge skill at most of the 66 USGS gages, including sites without direct SWOT overpasses, with ∼60% showing higher KGE and ~3% degrading. These improvements suggest that SWOT WSE assimilation extends beyond direct observations, with localization adding benefit. Impacts are governed by transferable constraints, including quality control (QC), residual observation uncertainty, and river‐network context. Comparison with SWOT‐derived discharge at 27 overlap sites provides complementary context and supports using WSE as a direct hydrodynamic constraint. These results show that SWOT WSE assimilation improves discharge estimates under real‐world constraints, supporting water management in data‐sparse and transboundary basins globally.

In the WRR 50th Anniversary Special Collection, Lettenmaier et al. (2015, https://doi.org/10.1002/2015WR017616 ) provided a timely account of how satellite remote sensing evolved from a niche capability in hydrology into a mainstream driver of hydrologic discovery, as the community shifted from imagery toward validated Earth Observation (EO) products and open‐access data systems. In this Commentary for the WRR 60th Anniversary Special Collection, we revisit and extend that synthesis and argue that the next step is not just more EO data, but a more accountable remote sensing hydrology. The challenge is to integrate remote sensing more directly into hydrologic science questions in ways that ground networks alone cannot provide consistently across scales. We center this Commentary on a core question: How can EO be used, credibly and consistently, to quantify where water is stored, how it moves, and how the hydrologic cycle is changing, in ways that improve hydrologic understanding and decision value beyond what ground networks alone can provide? We summarize key advances since 2015 that sharpen water‐cycle observability, expand analysis‐ready data systems and computation, and accelerate hybrid inference, while highlighting persistent challenges that continue to limit credible hydrologic inference near thresholds and extremes. We conclude with a science‐driven agenda focused on closing observability gaps, improving record coherence, advancing uncertainty‐aware inference, strengthening end‐to‐end traceability from measurements to societal impacts, and sustaining climate‐quality EO records for hydrologic science in a changing climate, while recognizing that these advances will depend on continuity of observing capability and credible follow‐on mission strategies.

Drought is increasingly reshaping terrestrial ecosystems under climate change, yet how aridity modulates the climatic sensitivities of satellite‐derived green‐up rate (GR) and senescence rate (SR) in natural ecosystems remains poorly understood at the global scale. Here, we combine two decades of satellite observations and climate data sets to identify the primary climatic drivers (temperature, precipitation, solar radiation, soil moisture, and vapor pressure deficit) of maximum GR and SR across 764 global ecoregions and to quantify their sensitivities to interannual variability in climate‐forcing accumulation rates of these drivers. We reveal contrasting temperature‐response patterns along the aridity gradient in arid versus humid ecoregions. In arid ecoregions, decreasing aridity increases the GR sensitivity to temperature accumulation rates during the green‐up phase, whereas in humid ecoregions this sensitivity weakens and can even become negative as conditions become wetter. Similar, though asymmetric, those sensitivity reversal patterns are observed for SR. These findings demonstrate that aridity fundamentally reshapes vegetation‐climate interactions, with important implications for the stability of ecosystem carbon uptake under increasing hydroclimatic variability. By identifying critical transitions in climate sensitivities across aridity gradients, this study provides new insights for improving predictions of ecosystem responses to intensifying drought and for assessing risks to carbon sequestration under future climate change.

The construction and operation of reservoirs have fundamentally modified the natural hydrograph globally. However, complex seasonal operating patterns remain opaque due to data scarcity, limiting the accuracy of hydrological modeling and water management. Here we show that the Surface Water and Ocean Topography satellite overcomes these challenges by providing full‐coverage water surface elevation measurements for a basin‐wide network of small reservoirs. Applying a two‐step framework to 305 reservoirs (>0.5 km 2 ) in the Ganjiang River Basin (Yangtze), we found that single‐cycle regulation (33.4%) and dual‐cycle regulation (36.1%) patterns are dominant. Coupled with TimesNet deep learning, we predicted reservoir primary functions with an 86.0% accuracy and successfully disentangled multi‐purpose facilities. Our methodology characterizes reservoir regimes in data‐scarce basins, offering high‐resolution insights critical for understanding anthropogenic impacts on the global water cycle and supporting effective management.

Latent heat flux ( L E ) provides a direct representation of terrestrial evapotranspiration (ET) and plays a critical role in hydrological cycle studies, land surface model development, and the evaluation of remotely sensed evapotranspiration products. Although flux observations based on the eddy covariance technique are widely regarded as essential benchmark data for evapotranspiration estimation, existing ChinaFlux observations are generally limited by short observation periods and extensive data gaps, which substantially constrain their applicability in long-term change analyses and multi-scale studies. To address these limitations, we developed a gap-filling and temporal prolongation framework specifically designed for half-hourly L E and established a continuous ground-based benchmark dataset covering China for the period 2000–2024 based on observations from 50 ChinaFlux sites. The framework is built upon an automated machine learning approach (AutoML) and integrates ERA5-Land reanalysis data with MODIS vegetation indices, enabling accurate gap-filling within observation periods and reliable prolongation beyond observation intervals. Comprehensive evaluations demonstrate that the AutoML framework achieves high accuracy at the half-hourly scale across different gap-length scenarios, with an overall correlation coefficient (CC) of 0.862 and a root mean square error (RMSE) of 33.75 W m −2 , and it substantially outperforms conventional methods under long-gap conditions (i.e., 7 and 30 d) introduced in the artificial gap experiments. The forward and backward prolongation results show high consistency (CC values of 0.902 and 0.896, respectively) and exhibit robust temporal stability under varying training data lengths. Multi-timescale validations further indicate that the prolonged L E data reasonably reproduce diurnal variations, seasonal cycles, and interannual variability from half-hourly to daily and monthly scales. Comparisons with ChinaFlux observations under strict quality control (half-hourly L E observations with QA/QC = 0) reveal good consistency across different temporal scales, underlying surface types, and climate zones. Interpretability analysis based on Shapley Additive Explanations (SHAP) indicates that energy supply consistently dominates L E variability, while vegetation state and water availability modulate their relative importance…

The absence of a definitive follow‐on strategy for the Soil Moisture Active Passive (SMAP) mission, which has already surpassed its planned mission lifetime, presents a significant challenge for the hydrological science community. GNSS‐Reflectometry (GNSS‐R), which exploits reflected satellite navigation signals, has emerged as a rapidly growing alternative for soil moisture retrieval, increasingly positioned as a low‐cost path to global observation. The two approaches, however, differ fundamentally in how they sense the surface, and the hydrological science community should weigh these differences carefully before decisions are made that could compromise the continuity of this essential climate variable's data record. In this study, we compared soil moisture products from these two systems across seven perspectives: (a) observation characteristics, (b) spatial resolution, (c) coverage and frequency of observations, (d) vulnerability to radio frequency interference, (e) complementarity potential, (f) performance, and (g) cost considerations. Our assessment suggests that GNSS‐R is better understood as a complement to SMAP than as a replacement. A GNSS‐R constellation can deliver sub‐daily observations, and lower upfront costs, capabilities that genuinely extend what single‐platform missions can offer. At the same time, GNSS‐R retrievals exhibit variable spatial sampling and canopy attenuation, and the highest‐performing products are typically trained, calibrated, or benchmarked against SMAP (an implicit cost that the low‐cost framing of GNSS‐R does not fully capture). Taken together, these findings underscore the need for sustained investment in both SMAP‐class L‐band missions and the expanding GNSS‐R constellation, each contributing complementarity strengths to a resilient global soil moisture observing system.

We present the globally available dataset of Line-of-sight Gravity Differences (LGD) as a new data product to fill the long-standing gap of investigating sub-monthly surface mass change from the satellite gravimetry along-track perspective. The dataset is generated directly from Gravity Recovery and Climate Experiment Follow-on (GRACE-FO) Level-1B intersatellite ranging observations, positioned conceptually between Level-1B measurements and Level-2 monthly gravity field solutions. Provided as along-track time series, LGD delivers instantaneous gravity change at satellite altitude, thus ensuring the fidelity of measurements by avoiding the complex preprocessing, regularization, and filtering applied to monthly products. The instantaneous LGD measurements enable monitoring of sub-monthly terrestrial water storage variability and provide accurate information on timing and magnitude of water mass changes. GRACE-FO's ground track allows LGD retrievals every 5–6 d in most low and mid latitude regions, while the revisit interval becomes shorter toward higher latitudes. Our LGD data products also contain climatology in the form of along-track gravity variations. We demonstrate its potential through case studies, including along-track diagnosis of flash drought evolution in the southeastern United States and the characterization of sub-monthly hydrological extremes in global hotspots. Results show that the LGD dataset has the potential to resolve the critical timing (e.g., onset and peak) of hydrological extremes, which is unattainable from monthly products. The LGD dataset is available at https://doi.org/10.5281/zenodo.17766177 (Tang et al., 2025a), which is expected to pave the way for advances in sub-monthly hydrological monitoring and can serve as a unique constraint for hydrological and geophysical models, opening new opportunities for Earth system sciences.

Copernicus Land Monitoring Service (CLMS) produces biogeophysical maps of the global land surface. The CLMS portfolio so far did not include actual evapotranspiration (ETa), despite it being a direct link between the energy, water and carbon cycles and its importance for global food security, efficient water resources management and weather forecasting. However, a global CLMS ETa product was recently developed and entered operational production at the end of 2025. It consists of water flux (ETa, evaporation and transpiration) and energy flux (latent and sensible heat) sub-products, has a spatial resolution of 300 m, dekadal (10 d mean) temporal resolution for water fluxes and daily temporal resolution for instantaneous energy fluxes, is produced in near-real-time, and (like all other CLMS products) is distributed under free and open data policy. It is based mainly on Copernicus input data with primary satellite imagery coming from the observations of OLCI and SLSTR sensors on board of Sentinel-3 satellites. Such product fills a gap in previously existing global and operational ETa products, thus satisfying a wide range of potential users' needs. In this paper, we describe, evaluate and justify the various design choices taken during the development of the ETa product, ranging from cloud masking and gap-filling, through derivation of biophysical traits, radiation components and weather forcings to spatial sharpening of the land surface temperature observations. Those data were then used to drive two ETa models: TSEB-PT and ETLook. A prototype implementation of the ETa processing chain was used to produce ETa data (using both models individually and their ensemble) across a globally representative range of climatic zones and plant functional types, which was validated against measurements from 206 Eddy Covariance flux tower sites. The ensemble resulted in the all-site root mean squared error (RMSE) of 0.87 mm d −1 (relative RMSE of 47 %, site range: 0.31–1.96 mm d −1 ), bias of 0.01 mm d −1 (relative bias of 0.6 %, site range: −1.39–1.50 mm d −1 ) and correlation coefficient of 0.81 (site mean of 0.77), which compares well with WaPOR global ETa dataset and therefore supports the operational production of global, near-real time, Copernicus-based 300 m ETa maps. Finally, we propose a number of potential evolutions of the CLMS ETa product, including enlarging…

The Surface Water and Ocean Topography (SWOT) satellite mission offers unprecedented estimates of global river discharge. This study provides a first continental‐scale assessment of SWOT Consensus discharge performance across South America and compares it with a large‐scale hydrodynamic model and in situ observations. Consensus generally exhibits higher skill with a median Kling‐Gupta Efficiency (0.43) across the 569 benchmarked reaches. Performance varies systematically with physiographic settings. Random Forest and Spearman correlation analyses identify slope and distance to outlet as the top‐ranked controls among the variables tested, though no single variable explains performance, reflecting its multi‐dimensional nature. Consensus discharge inherits systematic bias from its machine learning prior, and substantial unexplained variability highlights that further work is needed to fully diagnose low‐skill reaches.

Irrigation alters the terrestrial water cycle, yet its spatial distribution and temporal variability remain poorly constrained because existing data sets often rely on indirect proxies or inventories rather than observations tied to land‐surface water‐balance dynamics. Here, we introduce a wavelet‐based method to detect irrigation from spectral differences between modeled and satellite‐observed soil‐moisture time series, implemented using Noah–MP simulations and Soil Moisture and Ocean Salinity (SMOS) observations. The method, applied across the contiguous United States, exploits differences in seasonal to sub‐seasonal soil‐moisture variability between irrigation‐off model simulations and satellite observations, particularly in the 6–18‐month band, enabling irrigation detection. The resulting soil‐moisture‐based irrigation classification complements high‐resolution optical irrigation data sets, while reflecting the coarse‐scale information contained in SMOS soil moisture. When incorporated into Noah–MP simulations, it improves the distribution of simulated irrigation water use. This framework supports scalable irrigation detection from satellite soil moisture and improved irrigation representation in Earth system models.

Interferometric Synthetic Aperture Radar (InSAR) offers a potential pathway for measuring seasonal snow water equivalent (SWE) and snow depth at spatial and temporal scales needed for basin‐scale hydrology. First proposed for seasonal snow more than 25 years ago, InSAR uses repeat‐pass radar phase differences to estimate changes in SWE or depth caused by microwave propagation through snow. This review synthesizes InSAR‐based snow monitoring from early theoretical and satellite demonstrations through recent tower, airborne, and spaceborne studies. We summarize the retrieval physics, evaluate published validation results, compare performance across wavelengths and snow environments, and identify barriers to operational implementation. The literature shows that InSAR can provide high‐resolution, spatially distributed information on snow accumulation and ablation, especially under dry‐snow conditions when temporal coherence is maintained and snow phase can be separated from atmospheric, vegetation, soil‐moisture, and other non‐snow contributions. Longer wavelengths, particularly L‐band, help maintain coherence, reduce phase‐wrapping ambiguity, and support retrievals in some vegetated environments, making the NASA–ISRO Synthetic Aperture Radar (NISAR) mission a timely opportunity for basin‐scale testing. Remaining challenges include correcting ionospheric and tropospheric delays; defining stable reference phases; identifying, correcting, or flagging coherence loss and phase‐unwrapping errors; accounting for forest‐canopy interactions; collecting spatially distributed validation data; and scaling site demonstrations to regional or global products. Operational use will require algorithmic maturation, robust validation, and integration with other remote sensing and modeling approaches. If these challenges are addressed, InSAR‐capable satellites could become an important component of an integrated global snow observation suite.

Reservoir hypsometric relationships are crucial in determining water storage changes and modeling hydrological processes. Previous studies have demonstrated that uncertainties in reservoir surface area observations are a primary source of error in satellite‐derived storage estimates. However, little research has examined how variability in hyposometric data sets influences storage estimation accuracy. In addition to evaluating the impacts of hypsometric data set variability on storage estimation, this study introduced a new, simple hypsometric satellite and geometry‐based (SAGE) approach. SAGE combines remote sensing and geometry‐based methods to generate a global hypsometric data set for 7,250 reservoirs worldwide using the Global Reservoir and Dam (GRanD) database, and its performance is compared with existing data sets. The SAGE‐based results were validated against in situ Elevation‐Volume (E‐V), Area‐Volume (A‐V) relationships and storage dynamics from 132 reservoirs in the United States and Vietnam. Validation results showed strong performance, with median Normalized Root Mean Square Error (NRMSE) of 9.2% for A‐V curves and 12.4% for E‐V curves, compared to higher median NRMSE values from other global data sets, exceeding 20% for E‐V curves and ranging from 12.5% to 32.3% for A‐V curves. Reservoir storage dynamics were then estimated by integrating satellite‐derived surface areas from the Global Reservoir Surface Area Data set (GRSAD) with the SAGE‐derived AEV curves. The SAGE‐based approach significantly improved storage estimates, reducing errors by over 20% compared to other global AEV products, based on in situ storage data from 109 reservoirs in the United States and Vietnam. Overall, this study improved the accuracy of hypsometric relationships and storage dynamics, producing a more robust product for global hydrological applications.

Poyang Lake, China's largest floodplain lake, plays a crucial role in regional hydrology by seasonally regulating water to mitigate floods and sustain extensive wetland ecosystems. However, complex hydrodynamics and significant spatial hydraulic gradients often invalidate the traditional “flat‐water” assumption, making reliable estimation of the lake's true seasonal buffering capacity a persistent challenge. To address this, we developed a SWOT‐based Floodplain Lake wAter volume and Storage cHange (SWOT‐FLASH) framework. By integrating two‐dimensional water surface elevations from the SWOT mission with reconstructed full‐lake bathymetry, this method explicitly resolves spatial surface gradients to estimate a massive seasonal buffering capacity of 22.71 ± 1.96 km 3 for Poyang Lake, accounting for 95.10% of the maximum lake water storage recorded from July 2023 to July 2025. Beyond revealing the lake's critical regional flood‐mitigation role, this framework introduces a physically realistic tool for quantifying lake water storage dynamics in complex river‐floodplain lake systems worldwide.

Land surface temperature (LST) is an essential climate variable in geophysical, ecological, and environmental research. Remote sensing provides a unique observation approach for obtaining large-scale LST products. However, current official LST datasets (such as FY-4A) are limited by the unaddressed thermal radiation directionality effect and suffer from the spatial discontinuities due to the pervasive presence of clouds. Moreover, geostationary LST products have relatively coarser resolution than those of polar-orbiting satellites due to trade-off between spatial and temporal resolutions. Based on the official hourly FY-4A LST dataset, this study proposes a novel framework for generating angular-normalized, cloud-filled, and 0.01°-downscaled LST (ANCFDS-LST) product, encompassing directional ( T dir ), nadir ( T nadir ), and hemispherical ( T hemi ) LST layers. First, the angular-normalized T nadir and T hemi were generated using a time-evolving kernel driven model (TEKDM) with the inputs of multi-temporal FY-4A T dir . Subsequently, hypothetical clear-sky LST was predicted using a CatBoost model optimized via Bayesian methods. The cloudy-sky LST values were then derived through a cloud radiation force (CRF) correction. Finally, the 0.05° all-weather T dir , T nadir , and T hemi values were downscaled to 0.01° resolution using an improved hybrid downscaling algorithm (IHDA) combining kernel- and fusion-based methods. Taking the daytime clear-sky near-nadir VNP21A1 LST as reference, the 0.05° T dir before angular-normalization has a root mean squared difference (RMSD) of 4.53 K and a mean bias difference (MBD) of −1.85 K, whereas the angularly normalized T nadir has a much smaller RMSD of 2.56 K and a better MBD of 0.06 K. For the all-weather T hemi , temperature-based validation over 8 sites in the Heihe River Basin and Australia shows a root mean squared error (RMSE) and mean bias error (MBE) of 2.48 and −0.83 K under clear-sky conditions, 3.65 and −1.43 K under cloudy-sky conditions. After the spatial downscaling, the 0.01° all-weather T hemi exhibits abundant texture details. The generated LST products over the FY-4A disk from 2018 to 2023 exhibit enhanced angular consistency, spatial continuity, and finer resolution, offering valuable support for subsequent LST-related applications. The ANCFDS-LST data is freely available at…

Given the insurmountable challenge of measuring all rivers in situ, global river models serve as the foundation of freshwater knowledge past, present, and future. We adopt an inductive empirical framework based on Surface Water and Ocean Topography (SWOT) satellite measurements to assess these models. After controlling for SWOT data quality, we examine 68,347 individual river reaches representing ∼38% of global discharge. We find river models currently struggle in areas of heavy economic development, multi‐channel rivers, arid areas, and many Arctic rivers. After controlling for these expected errors, we find better skill as rivers get wider and that parts of Siberia and China are particularly difficult to model. We also find large variability and spatial heterogeneity to model performance, resisting oversimplification. Our results suggest that leveraging SWOT observations within river models will improve them, but river models must adapt their structure to represent realistic hydraulics to do so.

Snowmelt runoff onset timing represents a critical hydrological parameter, particularly in mountainous regions where seasonal snow serves as a natural reservoir for downstream water resources. Despite this importance, high-resolution observations of snowmelt runoff onset across complex terrain are limited, due to challenges from sparse in situ monitoring networks, intermittent optical remote sensing data, and coarse passive microwave remote sensing data. To address this gap, we prepared a global snowmelt runoff onset timing dataset (https://doi.org/10.5281/zenodo.16953614, Gagliano et al., 2026) for the 10-year period spanning 2015–2024, with 80 m spatial resolution and 9.5 d average temporal resolution. We created this dataset by identifying backscatter minima empirically associated with runoff onset in a time series of Sentinel-1 C-band SAR images, with detection constrained by a custom MODIS-derived snow phenology dataset. We evaluated our dataset using in situ snow pillow estimates of runoff onset from 1116 automated weather stations across the Western United States, British Columbia, Norway, and Nepal, finding a median timing difference of −2.0 d and a median absolute deviation of 10.0 d. The local agreement between our runoff onset estimates and snow pillow runoff onset estimates varies with site-specific variables like forest cover fraction, SWE, and dataset temporal resolution. We characterized these dependencies to provide empirically-derived thresholds for quality filtering as well as guidance for interpretation and use of our products. The dataset includes global annual runoff onset products for each water year, annual local temporal resolution products for each water year, and 10-year composites of median runoff onset, median absolute deviation, and local temporal resolution. This unique combination of high spatial resolution, global coverage, and decade-long temporal coverage provides unprecedented detail for the study of snowmelt runoff onset across snow-covered regions. Our snowmelt runoff onset dataset enables an improved understanding of mountain hydrological processes and informs water resource management in snow-dominated watersheds.

Wetland restoration under the EU Nature Restoration Law prioritises the recovery of surface water connectivity, yet the ways in which water moves and connects across European wetlands remains poorly understood, hindering effective restoration and monitoring. To address this, we analysed ~50 European Ramsar wetlands between 2015 and 2024 using Sentinel-1 satellite imagery and a set of connectivity metrics that capture the spatiotemporal reorganisation of surface water bodies. We show that wetlands with similar inundated areas exhibited very different connectivity patterns, demonstrating that surface water area alone cannot adequately represent wetland hydrological behaviour. Connectivity stability, measured from the repeatability of annual inundation patterns and the timing of connectivity changes, exhibited a strong latitudinal gradient, with northern peatland-dominated wetlands showing the most predictable behaviour. Higher connectivity stability was associated with later snowmelt ( r = 0.57-0.70, p < 0.05) and fewer annual growing degree days ( r = –0.46 to −0.55, p < 0.05). These findings establish a pan-European baseline of surface water connectivity and support hydrologically informed wetland monitoring, restoration, and management.

Agricultural weather data are fundamental for the accurate estimate of evapotranspiration (ET), irrigation scheduling, and water-use accounting. In particular, reference ET provides a standardized atmospheric demand for water loss from a hypothetical well-watered grass (ETo) or alfalfa (ETr); however, weather stations may not adequately represent such climatic conditions. Weather data commonly contain errors from poor siting, sensor drift, and network management deficiencies. No standardized dataset exists over the contiguous United States (CONUS). Systematic errors affect ETo/ETr calculations and derived products. Notably, satellite-based platforms like OpenET require agricultural weather data to bias correct gridded reference ET to interpolate between satellite overpasses. CONUS-AgWeather is a benchmark dataset of daily agricultural weather data (precipitation, solar radiation, air temperature, humidity, wind speed, ETo, ETr) from 793 stations. This dataset contains 4,191,808 days (11,484 station-years, 1981–2020) and was produced through standardized and systematic quality control procedures and open-source software packages for time series inspection, outlier detection, corrections, and ETo/ETr calculations. CONUS-AgWeather is intended primarily to support OpenET in the Western U.S. but has broader applications.

In the development of satellite precipitation climate data records, enormous effort has been expended to ensure that satellite precipitation products (SPPs) match the most accurate in situ gauge measurements. In this work, we evaluate the accuracy of satellite precipitation estimates covering a range of rainfall accumulation from the subdaily to the monthly scales. Using the U.S. Climate Reference Network (USCRN) as a reference, results show that Climate Prediction Center morphing technique (CMORPH) and Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) satellite products performed better at accumulation scales greater than 12 h than at smaller time scales. Both products exhibited strong contrast between the western and eastern halves of the contiguous United States (CONUS), with the best results obtained over the eastern United States. Normalized SPP estimations of rainfall event maxima for heavy precipitation events—those at or above the 90th percentile at each station—are consistent across the CONUS, with SPP estimates generally below the station observations. Residual analysis at the 90th percentile indicates that SPPs tend to underestimate extreme precipitation when compared to in situ observations, with very few exceptions. Generally, the SPPs underestimate the top-10 percentile observations by a factor of around 0.25–0.5 with respect to the in situ 90th percentile threshold. For maximum precipitation, SPP estimates diverge from gauges significantly at the 1–3-h accumulation time scales. Between 6 and 12 h, the SPP estimates tend to stabilize, and the differences between the SPPs and USCRN are reduced thereafter regardless of the location or for the entire CONUS. The use of satellite precipitation estimates for extreme event quantification at time scales shorter than 12 h should be done with caution.

Satellite-based precipitation products (SPPs) face persistent challenges over mountains due to complex orographic processes. This study uses high-resolution radar-gauge precipitation data and environmental reanalysis parameters to evaluate IMERG V07B Final Run (IM-F) performance across six mountain ranges (U.S. West Coast, Rockies, East Coast; French Alps, Central Massif, Pyrenees). We condition the analysis on environmental clusters defined by orographic upward motion, moisture flux convergence, and stability, to address two questions: (1) To what extent do environmental parameters explain the distribution and extremes of radar-observed precipitation in each mountain range? (2) How much of the variation in IM-F retrieval error across these mountain ranges is explained by environmental parameters? Results show that environmental conditions strongly control precipitation distributions – for instance, convective windward regimes yield the highest median and 99th-percentile rain rates, whereas stable, stratiform conditions produce much lower rainfall totals. Conditional validation reveals that a substantial portion of IM-F error is attributable to these environmental factors: precipitation detection skill and bias vary systematically with cluster conditions. Cluster 1 (unstable upslope flow) produces frequent heavy precipitation that IM-F underestimates when cloud ice is minimal, whereas Cluster 4 (stable, downslope conditions) is associated with light stratiform drizzle that IM-F often misses. These findings expose how orographic lift, moisture convergence, and stability interact to shape both precipitation extremes and satellite retrieval biases. This is the first study to systematically dissect IM-F error by environmental regimes across multiple global mountain ranges, providing novel insight into physical drivers of SPP errors. Building on these insights, we propose adaptive algorithm enhancements – for example, incorporating orographic uplift and moisture convergence indicators – to improve mountainous precipitation estimation. Our results underscore a new process-informed pathway for refining satellite QPE in complex terrain.

Rainfall variability in West Africa, driven by the West African Monsoon, poses significant challenges to agricultural productivity and livelihoods. In this context, understanding root-zone soil moisture (RZSM) dynamics is crucial since it serves as the primary water source for crops. While surface soil moisture (SSM) has been widely studied, research on RZSM remains limited. This study investigates RZSM dynamics across West Africa from 2003 to 2019 using multiple satellite-derived and model-based datasets, including ESA CCI v0.81, GLWS2.0, WaterGAP, CLM5.0, and in-situ observations. Results indicate that ESA CCI exhibits the strongest temporal and spatial alignment with ground measurements, whereas CLM5.0 and GLWS2.0 effectively capture latitudinal soil moisture (SM) gradients associated with climatic zones. A novel application of an analytical solution to Richards' equation was employed to translate surface moisture signals to deeper soil layers, demonstrating GLWS2.0's superior ability to reproduce seasonal patterns at various depths, notably in Benin and Niger. Despite challenges posed by sparse in-situ data and vegetation-induced signal attenuation, the study highlights the significant benefits of GRACE/-FO data assimilation in enhancing model accuracy. The proposed depth-projection methodology improves the vertical representation of SM, offering new insights into the dynamics of surface and subsurface water storage. These findings have important implications for agricultural forecasting, sustainable water resource management, and climate adaptation strategies in regions where accurate SM data are essential for resilience planning.

The Gravity Recovery and Climate Experiment (GRACE) and its Follow-On (GRACE-FO) missions provide observations of terrestrial water storage (TWS) dynamics from regional to global scales. However, their coarse spatio-temporal resolution limits the applicability in many hydrological studies. A joint collaboration between the National Aeronautics and Space Administration (NASA) and the European Space Agency (ESA), known as the Mass Change And Geosciences International Constellation (MAGIC), aims to launch a new paired mission, i.e., GRACE-C and NGGM (Next Generation Gravity Mission), to improve the monitoring of extreme events such as floods and droughts. The primary objective of this study is to examine the impact of the expected improvement in the spatial-temporal resolution and accuracy of NGGM and MAGIC on precipitation estimation by developing multiple synthetic experiments over Europe. The study employed the well-known SM2RAIN algorithm to estimate the precipitation accumulated between two consecutive TWS measurements. The total amount of water in the soil from the fifth generation ECMWF reanalysis for the land (ERA5L) is used as a proxy of TWS over the period 2003–2012, while ERA5L precipitation serves as a reference dataset. The results showed that SM2RAIN exhibited satisfactory performance at a daily temporal resolution, with a mean correlation coefficient of 0.86. Good agreement was obtained across most of Europe except in some areas of northern Italy, northeastern Europe (Estonia, Latvia) and some coastal regions. Synthetic experiments were developed by degrading the temporal resolution (5–10, 15, and 30 d) and by adding Gaussian errors to TWS data ranging from 1 to 40 mm. The results showed robust performance under moderate temporal degradation (5–10 d) and low measurement errors (< 5 mm), with correlations generally exceeding 0.75. However, performance deteriorates significantly when errors exceed 10–20 mm, with correlations dropping below 0.40 for an error of 42 mm. These findings identify critical design thresholds for NGGM/MAGIC missions, demonstrating that achieving a measurement accuracy better than 5–10 mm is crucial for reliable precipitation estimation across diverse European hydroclimatic conditions.

Whether surface and deeper water storage trends move in the same direction underpins assessments of freshwater availability. Theory predicts vertical coherence, yet it is untested globally and questioned under climate extremes and human management. Using depth‐sensitive satellite observations spanning surface soil moisture (SSM) (Soil Moisture Active Passive) and total terrestrial water storage (GRACE/FO) for 2015–2025, we assess the vertical organization of multi‐year land water change across ∼78 million km 2 of land. We find that vertical coherence dominates, with same‐sign surface and total storage trends across >70% of the analyzed area. Satellite Normalized Difference Vegetation Index reinforces this pattern, aligning with coherent trends and tracking deeper storage where surface and total trends diverge. Divergence is spatially clustered and linked to repeated hydroclimatic extremes or intensive groundwater abstraction. These results provide the first global observational test of vertical coherence in land water storage change, showing it remains dominant but can be weakened by depth‐selective climatic and anthropogenic pressures.

The Penman–Monteith–Leuning (PML) model is a widely recognized diagnostic framework for estimating coupled terrestrial evapotranspiration (ET) and gross primary production (GPP). To address the critical need for high-fidelity, long-term, and near-present eco-hydrological records, we developed the PML-V2.2 dataset, spanning from 1982 to 2025. Driven by observation-constrained Multi-Source Weighted-Ensemble Precipitation (MSWEP) and Multi-Source Weather (MSWX) meteorological variables, the dataset comprises three complementary products: (1) PML-V2.2a, an 8 d 500 m MODIS/VIIRS satellite-based product (2000–2024 and 2012–2025) optimized for near-present monitoring (updated annually); (2) PML-V2.2b, a half-month 0.1° AVHRR-based product (1982–2020) anchoring long-term climate attribution; and (3) PML-V2.2c, a consolidated half-month 0.1° record integrating the above products for seamless 44-year continuity (1982–2025). Our methodological framework features an expanded bottom-up calibration using 208 flux sites (∼ 1400 site-years) across various plant functional types (PFTs) and a refined parameterization that explicitly distinguishes between irrigated and rainfed croplands. This distinction effectively mitigated systematic biases in agricultural regions, reducing ET and GPP estimation errors by 8.7 % and 16.2 %, respectively. Performance evaluation reveals high accuracy across PFTs (cross-validation Nash-Sutcliffe Efficiency, NSE > 0.60, absolute bias < 5 %), while top-down water-balance validation across 56 large river basins during 1982–2016 and 152 basins during 2003–2020 confirms high reliability (NSE: 0.89–0.91) as compared with other products. The MODIS- and VIIRS-based PML-V2.2a datasets are internally consistent, and exhibit high agreement with PML-V2.2b during their overlapping period (NSE = 0.90 and 0.79 for annual ET and GPP anomalies), ensuring a seamless transition across satellite epochs. Based on the consolidated PML-V2.2c dataset, global terrestrial ET and GPP during 1982–2025 are estimated at 65.8 × 10 3 km 3 yr −1 (with 58.2 % from transpiration) and 143.4 PgC yr −1 , respectively. Long-term analysis reveals significant ( p < 0.05) increasing trends in GPP (0.343 PgC yr −2 ) and ET (0.019 × 10 3 km 3 yr −2 ) during 1982–2025, where vegetation greening impact on ET is partially offset by physiological water saving under rising atmospheric CO 2…

South and Southeast Asia is a major global hub for paddy rice cultivation, with the highest rice cropping intensity worldwide owing to its favorable hydrothermal conditions. The region has also experienced considerable spatiotemporal changes driven by climate change and anthropogenic activities. However, the absence of spatially explicit long-term datasets on paddy rice distribution and cropping intensity hinders effective agricultural and environmental management. This gap is particularly critical in the 21st century, as changes in climate, water resources, and food trade patterns increasingly reshape regional rice production systems. Using all available Landsat and Sentinel-2 archives, we refined a phenology-based algorithm to generate 30 m multi-year composite rice distribution and cropping-intensity products across South and Southeast Asia for four nominal reference years: 1995, 2005, 2015, and 2024. The algorithm addresses the challenge of detecting rice cropping intensity from long-term satellite time series and comprises three core steps: (1) identifying pixel-level rice phenological peaks using an enhanced peak detection method, thereby defining potential transplanting windows and minimizing monsoon-induced cloud and precipitation interference; (2) detecting paddy flooding signals and delineating rice cultivation areas based on phenological rules derived from the relationship between the Land Surface Water Index (LSWI) and Enhanced Vegetation Index (EVI); and (3) determining rice cropping intensity according to the number of valid crop peaks and associated flooding signals detected within the corresponding composite-period time series. The resulting maps were validated using 23 396 samples derived from a field photo library, visual interpretation of Sentinel-1/2 satellite imagery, and a sample migration algorithm. Across the four periods, the maps achieved overall accuracies ranging from 83.74 % to 87.60 %. In addition, the resulting products were compared with existing regional and period-specific rice datasets (e.g., NESEA-Rice10 and Open-SEA-Rice-10) for further evaluation. The comparisons demonstrated that the refined approach achieved higher accuracy and robustness in mapping both rice distribution and cropping intensity. When compared with official FAO statistics for South and mainland Southeast Asian countries, the derived maps yielded R 2…