Urban surface energy partitioning drives the urban thermal environment and the urban heat island (UHI) effect. Accurately estimating the Bowen ratio (β), a key indicator of sensible and latent heat flux balance, remains challenging in complex urban environments due to heterogeneous land cover and intricate 3D morphology. This study proposes an improved Simplified Surface Energy Balance Index (S-SEBI) method that integrates the Sky View Factor (SVF) into the dry-wet edge fitting process to enhance the accuracy of Bowen ratio estimation using remote sensing data. Taking Hong Kong's Kowloon Peninsula as the study area, the estimated β values were validated against ground-based meteorological observations and ENVI-met simulations. Results demonstrate that the improved S-SEBI model achieved an RMSE of 0.120 and MAE of 0.080. Spatial and temporal analysis reveals a clear relationship between β and urban morphological parameters, including building height (BH), density (BD), and fractional vegetation cover (FVC). Notably, a U-shaped relationship between β and BD is observed, with FVC consistently reducing β. Moreover, β increases with BH but levels off beyond a threshold, primarily due to building shadow effects: as shadow coverage expands and then stabilizes, sensible and latent heat fluxes also stabilize, leading to a steady surface Bowen ratio. This study highlights the effectiveness of incorporating urban geometric indicators into remote sensing models and provides a physically sound framework for assessing urban energy balance and climate mitigation strategies.

This study retrieves high-resolution atmospheric water vapor fields by processing Global Navigation Satellite System (GNSS) data from Hong Kong Continuously Operating Reference Stations (CORS) and six International GNSS Service (IGS) stations. Using the GAMIT software, we derived Precipitable Water Vapor (PWV) and Zenith Tropospheric Delay (ZTD) during four distinct heavy rainfall events. Statistical analysis reveals a strong temporal correlation between PWV/ZTD variations and observed rainfall. Based on these results, we propose two quantitative thresholds for short-term rainfall prediction: a PWV change rate exceeding ±10 mm/h and a ZTD change rate surpassing ±40 mm/h. These thresholds provide reliable indicators for estimating precipitation probability and intensity, demonstrating the practical value of GNSS-derived atmospheric parameters in nowcasting applications. The consistency of our findings with previous studies further supports the applicability of the proposed thresholds in operational meteorology.

A synergistic integration of physics-based and data-driven approaches has emerged as promising research field for terrestrial evapotranspiration (ET) estimation, enabling robust modeling of land-atmosphere interactions. This study proposes a hybrid model by integrating machine learning (ML)-based canopy surface resistance (r_s,c) estimation into the Shuttleworth-Wallace (S-W) dual-source scheme under the ETMonitor framework, replacing traditional physics-based r_s,c parameterization. Three ML algorithms, Random Forest (RF), Gradient Boosting Regression Tree (GBRT) and Deep Neural Network (DNN) were tested in the hybrid model. A reference dataset of r_s,c was derived by inverting S-W dual-source model with in-situ flux measurements. The model was trained on 179 global flux tower sites and independently validated on 45 sites. Three full ML-based models based on DNN, GBRT and RF, were also developed to estimate ET directly for comparison. The DNN-integrated hybrid model outperformed the original physics-based model, with Kling-Gupta Efficiency (KGE) increasing from 0.7 to 0.84 and coefficient of determination (R²) increasing from 0.66 to 0.72. The three full ML models showed comparable performance to the hybrid models. Notably, the physics-ML hybrid framework balances physical interpretability with data-driven efficiency, minimizing reliance on prior knowledge and avoiding over-parameterization.

The urban complex material and geometry characteristics result in a 3-D thermal heterogeneity and that limits the urban surface temperature (UST) retrieval. In this study, we improved the temperature and emissivity separation (TES) algorithm by incorporating thermal heterogeneity within mixed pixel (MP). The improvement was based on the discrete anisotropic radiative transfer (DART) model and applied to retrieve land surface temperature (LST) from Sustainable Development Goals Science Satellite 1 (SDGSAT-1). The TES algorithm retrieval approach for MP (TES-MP) algorithm was validated with ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) and the data simulated by the DART model, and the results show that it can reach good accuracy under complex urban conditions. Based on the simulated scenes from the Sheung Wan building in Hong Kong, the root mean squared error (RMSE) of the TES-MP algorithm is 0.85 K under thermal homogeneous conditions and 1.13 K under thermal heterogeneous conditions. Additionally, new high-reflectivity construction materials are common in urban areas, i.e., metal materials. It shows that the relationship between maximum-minimum difference (MMD) and minimum emissivity (ε_min) is not applicable to these materials. Thus, the impacts of such materials on the UST retrieval were evaluated. The results show that the higher the reflectivity and the fractional abundance of such materials, the larger the LST underestimation. Under nadir observation conditions, the proportion of high-reflectivity walls does not cause significant LST retrieval errors. The geometry and adjacency effects on retrieved LST were evaluated, and the results show that the TES-MP algorithm has some resistance to geometry and adjacency effects, thereby reducing errors in LST retrieval. This study provides a new view on retrieving LST of urban MPs and also suggests that three or more bands should be considered when setting up thermal infrared (TIR) sensors.

This study evaluates the effectiveness of hyperspectral data to retrieve chlorophyll a (Chl-a) concentrations using various Machine Learning (ML) methods, specifically to determine whether spectral reflectance can provide accurate estimations of Chl-a. The study aims to address the gap in understanding how hyperspectral measurements correlate with Chl-a concentrations and to explore the potential for improving water quality assessment by accurately estimating Chl-a concentrations, which is essential for environmental monitoring, especially in aquatic ecosystems. The method proposed is evaluated using different Chl-a concentrations defined by the experiment design using Rhodamine B. The main reason for preparing pre-defined solutions of Chl-a is to verify the sensitivity of spectral measurements to Chl-a concentrations. In this paper, we aim to measure the pure signature of the Chl-a in which spectral reflectance of each Chl-a concentration is measured with 10 replicates by the spectrometer HS-1000WFL3. Six ML methods were investigated; (i) the multilayer perceptron artificial neural network (MLPNN), (ii) the support vector regression (SVR), (iii) the random forest regression (RFR), (iv) the Gaussian process regression (GPR), (v) Relevance Vector Machine (RVM) and (vi) Extreme Gradient Boosting (XGboost). 70 % of the data is used in training the models and 30 % of the data was used for their validation. We applied two bands 446 nm and 595 nm that are highly correlated with Chl-a. The models are evaluated using coefficient of determination (R2), Nash-Sutcliffe efficiency (NSE), root-mean-square error (RMSE), and mean absolute error (MAE). The results for the input variable, band 595 nm achieved the best predictive accuracy using the MLPNN method with R2, NSE, RMSE and MAE of approximately ≈0.859, ≈0.853, ≈26.722 and ≈19.05, respectively. The research also aims to lay the groundwork for future studies in water quality monitoring and management, using hyperspectral data and ML to improve our understanding of aquatic environments.

The estimation of water requirements constitutes a critical prerequisite for delineating water scarcity hotspots and mitigating intersectoral competition, particularly in endorheic basins in arid or semi-arid regions where hydrological closure exacerbates resource allocation conflicts. Under conditions of water scarcity, water supplied locally by precipitation and shallow groundwater bodies should be taken into account to estimate the net water requirements to be met with water conveyed from off-site sources. This concept is embodied in the distinction of blue ET (BET) and green ET (GET). In this study, the Budyko hypothesis (BH) method was optimized to partition the total ET into GET and BET during 2001–2018 in the Heihe River Basin. In this region, a better knowledge of net water requirements is even more important due to water allocation policies which reduced water supply to irrigated lands in the last 15 years. This study proposes a modified BH method based on a new vegetation-specific parameter ((Formula presented.)) which was optimized for different vegetation types using precipitation and actual ET data obtained from remote sensing observations. The results show that the BH method partitioned GET and BET reasonably well, with a percent bias of 23.8% and 37.4% and a root mean square error of 84.8 mm/a and 113.6 mm/a, respectively, when compared with reported data, which are superior to that of the precipitation deficit and soil water balance methods. A sensitivity experiment showed that the BH method exhibits a low sensitivity to uncertainties of input data. The results documented differences in the contribution of GET and BET to total ET across different land cover types in the Heihe River Basin. As expected, rainfed forest and grassland ecosystems are predominantly governed by GET, with 81.3% and 87.2% of total ET, respectively. In contrast, croplands and shrublands are primarily regulated by BET, with contributions of 61.5% and 84.3% to total ET. The improved BH method developed in this study paves the way for further analyses of the net water requirements in arid and semi-arid regions.

Water volume, a fundamental characteristic of lakes, serves as a crucial indicator for understanding regional climate, ecological systems, and hydrological processes. However, limitations in existing estimation methods and datasets for water depth, such as the insufficient observation of small and medium-sized lakes and unclear temporal information, have hindered a comprehensive understanding of global lake water volumes. To address these challenges, this study develops a machine learning (ML)-based approach to estimate the dynamic water depths of global lakes. By incorporating various lake features and employing multiple innovative water depth extraction methods, we generated an extensive water depth dataset to train the model. Validation results demonstrate the model’s high accuracy, with the bias of −0.08 m, a MAE of 1.09 m, an RMSE of 4.78 m, and an R² of 0.95. The proposed method provides dynamic monthly estimates of global lake water depths and volumes in 2000~2020. This study offers a cost-effective and efficient solution for estimating global lake water dynamics, providing reliable data to support the monitoring, analysis, and management of regional and global lake systems.

The Advanced Microwave Scanning Radiometer-Earth Observing System/National Aeronautics and Space Administration (AMSR-E/NASA) daily global soil moisture (SM) product (2002–2011, 25-km resolution) has been widely used but exhibits limited sensitivity to intra-annual and interannual variability in many regions. This limitation is mainly attributed to inaccurate parameter values (A₀ and A₁), which account for vegetation transmittance and emission in the AMSR-E/NASA SM retrieval algorithm. To address this issue, we recalibrated A₀ and A₁ using in situ SM measurements from 13 observation networks (192 sites) and established their empirical relationships with fractional vegetation cover (FVC). Four dominant land cover types (i.e., bare soil, grassland, cropland, and forest) were considered due to their global representativeness and extensive coverage with in situ SM measurements. Based on these relationships, we generated global maps of A₀ and A₁ and produced an improved Global Daily AMSR-E SM dataset (GD_AMSR-E_SM; 2002–2011, 25-km resolution) using the Global Land Surface Satellite (GLASS) FVC dataset and AMSR-E observations. Validation against in situ SM measurements within six independent networks shows that the GD_AMSR-E_SM dataset achieves greater consistency with in situ SM measurements, with mean absolute error (MAE) and root-mean-square error (RMSE) values of 0.026 and 0.032 cm³/cm³, respectively. This represents average reductions of 20% and 26% compared with the AMSR-E/NASA, AMSR-E/Japan Aerospace Exploration Agency (JAXA), and AMSR-E/Land Parameter Retrieval Model (LPRM) SM products. The enhanced algorithm improves the accuracy and reliability of AMSR-E observations for long-term global SM monitoring.

Operational forest fire danger rating systems uses meteorological variables to estimate vegetation conditions and predict fire occurrence and spread. This study introduces a novel approach to relate live fuel conditions retrieved from MODIS optical and thermal bands with fire behaviour and the probability of extreme events. The analysis focusses on land surface temperature (LST) anomaly and on the perpendicular moisture index (PMI) to evaluate fire characteristics like burned area, duration, and rate of spread. Results show that PMI is a strong covariate of burned area and rate of spread but not fire duration, while LST anomaly is a strong covariate of burned area and fire duration, and a weak covariate of rate of spread. Comparing these findings with the Canadian forest fire weather index (FWI) system components reveals that LST anomaly and PMI are effective predictors of fire characteristics, potentially enhancing fire danger models and preparedness strategies.

Time series of spatially continuous satellite data are increasingly used for environmental studies. Among these, land surface temperature (LST), retrieved from data such as the MODerate resolution Imaging Spectroradiometer (MODIS), plays a vital role in numerous applications. However, cloud cover significantly reduces the number of usable pixelwise LST observations. Despite various documented methods for reconstructing missing LST pixels, challenges remain regarding their flexibility to handle varying gap percentages and reliance on multiple ancillary datasets. This study presents a flexible and automated technique to reconstruct missing LST pixels without relying on ancillary data. The approach combines three innovative techniques: global regression analysis, local regression analysis, and geospatial analysis. The missing pixels percentage of each day determines the appropriate technique to fill the gaps. The method was applied to daily Terra MODIS LST datasets (MOD11A1) at 1 km spatial resolution from 2002 to 2022. Two evaluation methods were conducted: comparing with in-situ measurements and introducing artificial gaps. The validation was demonstrated over the Heihe River basin in China and in four experimental areas worldwide with available ground measurements from FLUXNET. Validation with artificial gaps produced average root-mean-square error (RMSE) and mean absolute error (MAE) of 2.33 K and 1.76 K, respectively. In-situ measurements indicated superior performance with R ², RMSE, and MAE of 0.85, 4 K, and 3.4 K, outperforming two existing methods. The study demonstrates that the model accurately reconstructs missing pixels on heterogeneous surfaces under diverse conditions, effectively handling large datasets and complex gaps.

Climate change, population growth, and economic development exacerbate water scarcity. This study investigates the impact of drought on water availability in the Belt and Road region using high-resolution remote sensing data from 2001 to 2020. The results revealed an average water availability (precipitation minus evapotranspiration) of 249 mm/year and a declining trend in the Belt and Road region. Approximately 13% of the Belt and Road region faces water deficits (evapotranspiration exceeds precipitation), primarily in arid and semi-arid regions with high drought frequency. The area in the water deficit is expanding, and the intensity of the water deficit is increasing. The annual trend of water availability is strongly related to the frequency of droughts, i.e. water availability decreases with increased drought frequency. Drought exacerbates seasonal water stress in approximately one-third of the Belt and Road region, mainly in Europe and northern Asia, where drought frequently occurs during seasons with low water availability. The more severe the drought, the larger the negative anomaly in water availability. The critical role of evapotranspiration in seasonal water availability variability is also highlighted. This research underscores the importance of understanding drought-induced changes in water availability, which is crucial for sustainable water resource management.

Accurate estimation of urban land surface temperature (ULST) is critical for studying urban heat islands, but complex three-dimensional (3D) structures and materials in urban areas introduce significant adjacency effects into remote sensing retrievals. To investigate the influence of different factors on the adjacency effects, this study employed the DART model to quantify brightness temperature differences (ΔTb) of urban pixels by comparing their simulated radiance in two scenarios: (1) an isolated state (no adjacent buildings) and (2) an adjacent state (with surrounding buildings). ΔTb, representing the adjacency effect, was systematically analyzed across spatial resolutions (1–120 m), building geometry (building height BH, roof area index (Formula presented.), adjacent obstruction degree SVF_Obs.), and material reflectance (reflectance R = 0.05, 0.1, 0.15) to determine key influencing factors. The results demonstrate that (1) adjacency effects intensify significantly with higher spatial resolution (mean ΔTb ≈ 5 K at 1 m vs. ≈2 K at 30 m), with 60–90 m identified as the critical resolution range where the adjacency-induced error is attenuated to a level (ΔTb < 1 K) that is commensurate with the intrinsic uncertainty of current mainstream ULST algorithms; (2) increased building height, reduced density ((Formula presented.)), and greater adjacent obstruction (SVF_Obs.) exacerbate adjacency effects; (3) material emissivity (ε = 1 − R) is the dominant factor, where low-ε materials (high R) exhibit markedly stronger adjacency effects than geometric influences (e.g., ΔTb at R = 0.15 is approximately three times higher than at R = 0.05); and (4) temperature differences among surface components exert minimal influence on adjacency effects (ΔTb < 0.5 K). This study clarifies key factors driving adjacency effects in high-resolution ULST retrieval and defines the critical spatial resolution for simplifying inversions, providing essential insights for accurate urban temperature estimation.

Evaluating the performance of irrigation water use is essential for efficient and sustainable water resource management. However, existing approaches often lack systematic quantification of irrigation water consumption and fail to differentiate between the use of precipitation and anthropogenic appropriation of water flows. Building on the green–blue water concept, consumptive water use, assumed equal to actual evapotranspiration (ET_a), was partitioned into green ET (GET) and blue ET (BET) using remote sensing data and the Budyko hypothesis. A novel BET metric of consumptive irrigation water use was developed and applied to the irrigated lands in northwest China to evaluate the performance of irrigation from 2001 to 2021. The results showed that in terms of total available water resources (precipitation + gross irrigation water (GIW)) compared to irrigation water demand, estimated as reference evapotranspiration (ET₀), Ningxia has sufficient water supply to meet irrigation demand, while the Hexi Corridor faces increasing risks of unsustainable water use. The Hetao irrigation scheme has shifted from a fragile supply–demand balance to a situation where water demand far exceeds availability. In Xinjiang, the balance between water supply and demand is tight. Furthermore, when considering the available water (GIW) relative to the net irrigation water demand (ET₀-GET), the Hexi Corridor faces significant water deficits, and Ningxia and Xinjiang are close to meeting local irrigation water demands by relying on current water availability and efficient irrigation practices. It is noteworthy that the BET remains lower than the GIW in northwest China (excluding the Hexi Corridor in recent years). The ratio of the BET to GIW is an estimate of the scheme irrigation efficiency, which was equal to 0.54 for all irrigation schemes taken together. In addition, the irrigation water use efficiency, estimated as the ratio of BET to net irrigation water, was evaluated in detail, and it was found that in the last 10 years the irrigation water use efficiency improved in Ningxia, the Hetao irrigation scheme, and Xinjiang. However, the Hexi Corridor continues to face severe net irrigation water deficits, suggesting the likelihood of groundwater use to sustain irrigated agriculture. BET innovatively separates consumptive use of precipitation (green water) and consumptive use of irrigation (blue water), a critical advancement beyond conventional approaches’ estimates that merge these distinct hydrological components to help quantifying water use efficiency.

Optical fine and coarse spatial resolution multispectral images are essential for monitoring land surface processes but are often affected by gaps due to cloud contamination and other factors. Gap-filling methods are vital for overcoming these issues, yet existing approaches struggle to accurately reconstruct pixels impacted by undetected thin clouds and shadows, particularly in fine spatial resolution images. This study introduces a comprehensive gap-filling method that identifies and reconstructs invalid pixels in both fine and coarse spatial resolution images. The method combines different spatial and temporal gap-filling methods. The specific combination of methods is orchestrated to adapt to each image, mainly on the basis of the fractional abundance and spatial pattern of cloud cover. To evaluate the performance, experiments were conducted using MODIS (coarse-resolution) and Landsat/OLI (fine-resolution) images with artificial gaps (10% -90% ) introduced at varying positions in cloud-free reference images. For coarse-resolution images, the blue band showed the lowest root mean square error (RMSE) of 0.004 to 0.03, while the near-infrared (NIR) band had higher RMSE (0.01-0.05). The structural similarity index measure (SSIM) ranged from 0.96 to 0.73 as gap percentages increased. For fine-resolution images, random gaps were reconstructed most effectively, with RMSE values for the blue band between 0.005 and 0.01, and NIR errors ranging from 0.01 to 0.05. SSIM values ranged from 0.90 to 0.83 (blue) and 0.86 to 0.71 (NIR), confirming the method reliability for time-series analysis and data fusion applications.

Glaciers are crucial water resources in the Third Pole (the Tibetan Plateau and its surroundings) and are shrinking in response to climate change. Glacier albedo is an expression of glacier interactions with climate and dust/black carbon, and albedo reduction enhances glacier mass loss, but its changes and potential drivers remain poorly quantified. We leverage satellite observations to explore the variability of glacier albedo and understand its sensitivity to potential drivers and its future evolution. We find that glacier albedo has declined during 2001–2020, but high interannual variability is also an important signal. These variations are highly sensitive to air temperature and snow conditions and to nearby dust/black carbon emission sources. Future changes to these drivers will lead to further decreases of 2.9%–12.5% in glacier albedo by 2100 under different warming scenarios. These findings highlight the importance of albedo in glacier future evolution and the urgency of action to mitigate climate warming.

Improving irrigation water management is a key concern for the agricultural sector, and it requires extensive and comprehensive tools that provide a complete knowledge of crop water use and requirements. This study presents a novel methodology to explicitly estimate daily gross and net crop water requirements, actual crop water use, and irrigation efficiency of center pivot irrigation systems, by mainly utilizing the Sentinel-2 MultiSpectral Instrument (MSI) imagery at the farm scale. ETMonitor model is adapted to estimate actual water use (as the sum of canopy transpiration and evaporation of water intercepted by canopy and evaporation from soil) at daily/10-m resolution, benefiting from the high-resolution Sentinel-2 data and thus to assess the irrigation efficiency at the farm scale. The gross irrigation water requirement is estimated from the net crop water requirement and the water loss, including the water droplet evaporation directly into the air during application before droplets fall on the canopy and canopy interception loss. The method was applied to a pilot farmland with two major crops (wheat and potato) in the Inner Mongolia Autonomous Region of China, where modern equipment and appropriate irrigation methods are deployed for efficient water use. The estimated actual crop water use showed good agreement with the ground observations, e.g. the determination coefficients range from 0.67 to 0.81 and root mean square errors range from 0.56 mm/day to 1.24 mm/day for wheat and potato when comparing the estimated evapotranspiration with the measurement by the eddy covariance system. It also showed that the losses of total irrigated volume were 25.4% for wheat and 23.7% for potato, respectively, and found that the water allocation was insufficient to meet the water requirement in this irrigated area. This suggests that the amount of water applied was insufficient to meet the crop water requirement and the inherent water losses in the center pivot irrigation system, which imply the necessity to improve the irrigation practice to use the water more efficiently.

In the Tibetan Plateau (TP) region, the foreseeable increase in air temperature may have profound and complex effects on the local hydrological cycle, and is likely to increase water loss from the land surface to the atmosphere through evapotranspiration (ET). Quantifying ET and its regulatory mechanisms are major challenges for understanding the water cycle and land-atmosphere interactions in the TP region. We evaluated the performance of several Earth observation-based ET datasets in the TP region, and explored the spatiotemporal variation of ET in the same region. The accuracy of different global ET datasets was evaluated, and ETMonitor and PML-V2 provide the best accuracy with overall high correlation, low bias, and low root mean square error. ETMonitor ET is also the only product with both high spatial (~1 km) and temporal (daily) resolution. ETMonitor ET may reflect the effect of mountain topography on ET better than other global products, i.e., ET values are higher in the humid valleys with denser vegetation cover and higher soil moisture, and ET values are lower on the mountain slopes at higher elevations with less vegetation cover and colder climate. Other ET products failed to capture the spatial patterns of ET in the mountainous regions, and this suggests that the spatial resolution is not the only dominant factor leading to the poorer performance of these ET products in the mountain regions of the TP. The results show that multi-year average ET is 339 mm/yr in the TP region during 2000-2021, which accounts for about 51% of the total precipitation in the TP region. From 2000 to 2021, ET over the Tibetan Plateau shows an overall increasing trend with large spatial variability.