The recent capabilities of the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) photon counting lidar in monitoring water levels have been demonstrated through its precise elevation measurement and small footprint. The accuracy in water level measurements is, however, significantly impacted by the first photon bias, especially when photon-counting detectors are fully saturated due to particular reflections from calm water surfaces. In this study, we propose an analytical model to correct the first photon bias in scenarios where the ICESat-2/Advanced Topographic Altimeter System (ATLAS) is fully saturated. Notably, the model innovatively recovers and estimates the required signal level using after-pulses, which are typically considered as noise. These after-pulses can be used to effectively estimate the signal level when the detector is fully saturated. The experiment analysis, conducted on eight ICESat-2 ground tracks over calm water surfaces near the Great Lakes and the Tibetan Plateau, indicates that the actual received signal photons can surpass 200 and in some cases, reach up to 600 counts for strong beams, introducing a first photon bias exceeding 15 cm. The findings prove that 1) after-pulses can be used to retrieve water surface elevation and reflectance when the primary surface return is distorted by detector saturation and 2) calm waters reflect 5–40 times more than ice and snow surfaces, where first photon bias is a predominant error in water level measurements. The method holds great significance for the accurate monitoring of water levels in small inland water bodies using ICESat-2 and may also inform the design of lidar systems for inland water observations.

As a vital resource for the Earth’s ecological environment, effective extraction of surface water has a profound impact on human livelihoods and socio-economic development. Currently, water indices based on satellite imagery are widely used for surface water extraction. However, their accuracy remains significantly limited in complex scenarios—such as urban areas with high- and low-reflectivity buildings and shadow interference, sediment-laden or eutrophic water bodies, and intricate water–land transition zones. These limitations typically manifest in the failure to detect smaller water bodies, unclear water edges, and the presence of surrounding noise. To address these challenges, this study introduces a novel and practical water-body index—the Harmonized Landsat-Sentinel Water Index (HLSWI)—based on the Harmonized Landsat and Sentinel-2 dataset. HLSWI integrates the complementary spectral characteristics of the Landsat OLI and Sentinel-2 MSI sensors and optimizes classification thresholds via the ISO-Data clustering algorithm. HLSWI’s effectiveness is assessed through comparisons with seven commonly adopted water indices across 14 study areas worldwide and three representative complex scenarios, covering various surface types, including wetlands, arid zones, and urban aquatic environments. Experimental findings indicate that HLSWI outperforms the other seven water indices, reducing total water extraction errors by an average of 1.18% to 5.55% and improving overall accuracy and the Kappa coefficient by 1.42%–3.63% and 0.02–0.05, respectively. Therefore, HLSWI serves as a simple yet effective tool for surface water detection using optical remote sensing imagery and provides essential technical support for water extraction in complex environments.

The Gravity Recovery and Climate Experiment (GRACE) satellite provides an unprecedented tool for monitoring large-scale terrestrial water storage (TWS) changes. Yet, its coarse resolution restricts its effectiveness in areas with complex hydrogeological environments, such as southwestern China. To address this limitation, we propose a novel method to improve the spatial resolution of GRACE observations. Our approach leverages a deep learning downscaling model that integrates generative adversarial networks (GANs) and transformer attention mechanisms to derive the spatial patterns of TWS variations. The model incorporates the estimated total water storage changes from GRACE and some hydrological variables—including the digital elevation model (DEM), soil moisture, evapotranspiration, temperature, and precipitation—to enhance the resolution and accuracy of GRACE data. By implementing this method, we successfully increased the spatial resolution of GRACE observations from 0.25° to 0.05°. The advanced neural network downscaling model can accurately characterize local water storage variations, with Nash–Sutcliffe efficiency (NSE) values ranging from 0.58 to 0.92. Moreover, this model not only significantly increases the spatial resolution but also maintains the spatial distribution, offering valuable insights for regional water resources management and fostering small-scale hydrological research. The results have profound implications for sustainable water resources management and climate change assessment.

Surface soil moisture plays a pivotal role in various hydrological processes. Precisely assessing soil moisture with high resolution is crucial for effective water resource management, informed agricultural decision-making, and in-depth climate change research. While passive microwave remote sensing is a primary technology for regional soil moisture monitoring, its practical application is hindered by data discontinuity and low resolution. To address these challenges, we propose an integrated learning framework to enhance the continuity and resolution of soil moisture data across China. Leveraging low-resolution passive microwave soil moisture data, moderate-resolution assimilated soil moisture data, and multiple high-resolution ancillary inputs, the network effectively captures the spatiotemporal dynamics of soil moisture through integrating gap-filling, multisource fusion, and spatial downscaling processes. Validation against in situ data demonstrates the significant enhancements achieved by the proposed method, with an average R value of 0.706 and an average unbiased root mean square error of 0.055 m3/m3. Comparative analysis further confirms its superior accuracy and robustness across diverse regions. These findings highlight the potential of this integrated learning framework to advance hydrological applications, enhance agricultural production, and support climate research.

Automatic building extraction from high-resolution aerial and satellite images has many practical applications, such as urban planning and disaster management. However, the complex appearance and various scales of buildings in remote-sensing images bring a challenge for building extraction. In this study, we developed a novel multiscale building extraction method based on refined attention pyramid networks (RAPNets). We built an encoder–decoder structure, and combine atrous convolution, deformable convolution, attention mechanism, and pyramid pooling module to improve the performance of feature extraction in the encoding path. Moreover, the salient multiscale features were extracted by embedding the convolutional block attention module into the lateral connections. Finally, the refined feature pyramid structure was adopted in the decoding path to fuse the multiscale features to obtain the final extraction results. Experiments on two standard data sets (Inria aerial image labeling data set and xBD data set) show that our method achieves reliable results and outperforms the comparing methods.

Knowledge of reservoir bathymetry is essential for many studies on terrestrial hydrological and biogeochemical processes. However, there are currently no cost-effective approaches to derive reservoir bathymetry at the global scale. This study explores the potential of generating high-resolution global bathymetry using elevation data collected by the 532-nm Advanced Topographic Laser Altimeter System (ATLAS) onboard the Ice, Cloud, and Land Elevation Satellite (ICESat-2). The novel algorithm was developed and tested using the ICESat-2 airborne prototype, the Multiple Altimeter Beam Experimental Lidar (MABEL), with Landsat-based water classifications (from 1982 to 2017). MABEL photon elevations were paired with Landsat water occurrence percentiles to establish the elevation- area (E-A) relationship, which in turn was applied to the percentile image to obtain partial bathymetry over the historic dynamic range of reservoir area. The bathymetry for the central area was projected to achieve the full bathymetry. The bathymetry image was then embedded onto the digital elevation model (DEM). Results were validated over Lake Mead against survey data. Results over four transects show coefficient of determination (R2) values from 0.82 to 0.99 and root-mean-square error (RMSE) values from 1.18 to 2.36 m. In addition, the E-A and elevation-storage (E-S) curves have RMSEs of 1.56 m and 0.08 km3, respectively. Over the entire dynamic reservoir area, the derived bathymetry agrees very well with independent survey data, except for within the highest and lowest percentile bands. With abundant overpassing tracks and high spatial resolution, the newly launched ICESat-2 should enable the derivation of bathymetry over an unprecedented number of reservoirs.

Advances in remote sensing paved a way to understand the landuse and landcover features from the eye of space borne sensors. Spectral indices are used to analyze these features. This study focuses on the two very important orthogonal indices for water mapping - Universal Pattern Decomposition Method (UPDM) and Tasseled Cap Transformation (TCT) by considering Landsat 8 data. Results are compared with another very famous water index Modified Normalized Difference Water Index (MNDWI). It was found that wetness index of TCT didn't give good visual interpretation of water body in highly dense vegetative areas. So, greenness index of TCT was used for water delineation. And then results were compared with MNDWI. For good visual interpretation, UPDM seems better than TCT.

This study introduced the vegetation index based on the universal pattern decomposition method (VIUPD) and then applied on a new sensor - Landsat 8 Operational Land Imager (OLI). VIUPD is a valuable sensor-independent spectral analysis method. Each pixel is described as the linear mixture of standard spectral patterns for water, vegetation, soil and supplementary patterns included when necessary. In the present paper, processing procedure about the data acquisition, radiometric calibration and atmospheric correction have been elaborated. The normalized reflectance (P) of four standard samples resampled to OLI has been listed. For validation of the results, Normalized Difference Vegetation Index (NDVI) and VIUPD have been calculated for comparison. The results showed that VIUPD is more sensitive to the vegetation amount change even in the high vegetation coverage, while the NDVI is more rapidly saturated in high vegetation cover area. In addition, VIUPD is more sensitive to the soil background than NDVI.