Remote Sensing of Environment最新文献

{"title":"Sediment accumulation at the Amazon coast observed by satellite gravimetry","authors":"Earthu H. Oh ,&nbsp;Ki-Weon Seo ,&nbsp;Taehwan Jeon ,&nbsp;Jooyoung Eom ,&nbsp;Jianli Chen ,&nbsp;Clark R. Wilson","doi":"10.1016/j.rse.2025.114688","DOIUrl":"10.1016/j.rse.2025.114688","url":null,"abstract":"<div><div>Terrestrial sediment transport through large rivers exerts a significant impact on coastal morphology, marine ecosystems, and human livelihoods. Accurately measuring these sediment discharges has long been a challenge. Traditional in-situ methods fall short of providing comprehensive and continuous assessments of sediment dynamics due to spatiotemporal and economic constraints. While remote sensing techniques using satellite imagery have offered valuable insights into sediment transportation and deposition, their scope is primarily restricted to observing suspended sediment loads rather than total loads. Sediment accumulation at river margins will cause gravity increases observable by the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GFO) missions. Previous efforts to observe sediment signals lacked proper corrections for various GRACE/GFO issues, including leakage of signals from surrounding land, variations in nearby ocean mass, and noise levels that typically exceed sediment signal magnitudes. In this study, we develop a new approach to obtain a satellite gravity estimate of sediment accumulation along the Amazon coast where the largest amount of sediment deposition is expected. We address limitations in previous studies using three steps: Forward modeling to suppress leakage of signal from land to oceans; adjusting ocean mass change via the sea level equation; and filtering using empirical orthogonal functions. The estimated accumulation rate of sediment on the Amazon continental shelf is approximately 1301 Mtons per year for the period June 2002 to May 2023. This estimate is slightly higher than the results from field-based studies, which fall in the range 550 to 1030 Mtons per year.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"321 ","pages":"Article 114688"},"PeriodicalIF":11.1,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143547012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RealFusion: A reliable deep learning-based spatiotemporal fusion framework for generating seamless fine-resolution imagery","authors":"Dizhou Guo ,&nbsp;Zhenhong Li ,&nbsp;Xu Gao ,&nbsp;Meiling Gao ,&nbsp;Chen Yu ,&nbsp;Chenglong Zhang ,&nbsp;Wenzhong Shi","doi":"10.1016/j.rse.2025.114689","DOIUrl":"10.1016/j.rse.2025.114689","url":null,"abstract":"<div><div>Spatiotemporal fusion of multisource remote sensing data offers a viable way for precise and dynamic Earth monitoring. However, existing methods struggle with reliable spatiotemporal fusion in two commonly occurring yet complex scenarios: drastic surface changes, such as those caused by natural disasters and human activities, and poor image quality, which caused by thick cloud cover, cloud shadows, haze and noise. To address these challenges, this study proposes a Reliable deep learning-based spatiotemporal Fusion framework (RealFusion), designed to blend Landsat and MODIS imagery to generate daily seamless Landsat-like imagery. ReadFusion enhances fusion reliability through several advancements: (1) integrating diverse input data with complementary information, (2) implementing task decoupled architectures, (3) developing advanced restoration and fusion networks, (4) adopting adaptive training strategy, (5) and establishing a comprehensive accuracy assessment framework. Extensive experiments, comprising 25 trials in three distinct areas, demonstrate that RealFusion outperforms four methods proposed in recent years (Object-Level Hybrid SpatioTemporal Fusion Method, OL-HSTFM; Enhanced Deep Convolutional Spatiotemporal Fusion Network, EDCSTFN; Generative Adversarial Network-based SpatioTemporal Fusion Model, GAN-STFM; and Multilevel Feature Fusion with Generative Adversarial Network, MLFF-GAN). Notably, RealFusion is the only model capable of robustly and accurately reconstructing information of areas with drastic surface changes and poor image quality in experiments. RealFusion, thus, facilitates the reliable reconstruction of high-quality images in complex scenarios, marking a meaningful advancement in spatiotemporal fusion technique.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"321 ","pages":"Article 114689"},"PeriodicalIF":11.1,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143547014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating the potential of handheld mobile laser scanning for an operational inclusion in a national forest inventory – A Swiss case study","authors":"Daniel Kükenbrink,&nbsp;Mauro Marty,&nbsp;Nataliia Rehush,&nbsp;Meinrad Abegg,&nbsp;Christian Ginzler","doi":"10.1016/j.rse.2025.114685","DOIUrl":"10.1016/j.rse.2025.114685","url":null,"abstract":"<div><div>Close-range remote sensing technologies show great potential to support national forest inventories (NFIs). Mobile laser scanning (MLS) has performed especially well in recent studies, as it is capable of fast data acquisition while still delivering accurate information on NFI-relevant variables (e.g. tree position, diameter at breast height [DBH]). However, the performance of MLS acquisition under challenging conditions (e.g. steep terrain, dense understorey vegetation) has rarely been addressed. Here, on 29 NFI plots distributed over the entire country of Switzerland and representing various terrain and forest conditions, we evaluated the use and performance of handheld MLS acquisition, using a GeoSLAM ZebHorizon laser scanner, to deliver analysis-ready (with minimal occlusion) point clouds and to estimate primary tree variables such as tree position and DBH. Data acquisition was successful for all tested Swiss NFI plots. The average time needed for point cloud acquisition of a 50 × 50 m sample plot was 13.13 min. Over all tested NFI plots, we achieved a tree detection rate of 82.6%, with a relative root mean square error (RMSE) of 7.25%, a mean absolute error of 2.0 cm and a systematic prediction error (SPE) of 1.0 cm for DBH estimation. The terrain gradient had an especially strong influence on the accuracy of DBH estimation. Occlusion, as a metric for point cloud capacity to hold forest structural information for the entire canopy volume, was minimal when the suggested acquisition pattern was followed, at 9% on average for the entire canopy volume. However, the level of occlusion increased in the upper part of the canopy (<span><math><mo>&gt;</mo></math></span> 10 m above ground), to 15% on average.</div><div>Overall, handheld MLS effectively produced analysis-ready point clouds within the 15-minute acquisition time limitation for a 50 x 50 m area, even under challenging conditions, showing promise for future use in the Swiss NFI. However, the current accuracy of MLS-estimated forest variables like DBH and tree detection is insufficient to replace conventional measurements within an established NFI. Despite this, estimating NFI-relevant variables, even with lower accuracy, and expanding the area of measured tree variables could improve information on forest functions and services (e.g., protection and biodiversity), currently not possible to derive from NFI data due to the limited plot size. In the future, MLS-acquired point clouds could (1) shift from expert-assessed to more quantitative and reproducible variable estimation, and (2) enhance NFI with variables difficult to measure conventionally. This could open new pathways for assessing and monitoring forest structural diversity nationally and provide valuable calibration and validation data for large-scale forest assessments from airborne or space-borne acquisitions.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"321 ","pages":"Article 114685"},"PeriodicalIF":11.1,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143538480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning approach for reconstructing three-dimensional distribution of NO2 on an urban scale","authors":"Zhiguo Zhang ,&nbsp;Qihua Li ,&nbsp;Qihou Hu ,&nbsp;Jingkai Xue ,&nbsp;Ting Liu ,&nbsp;Zhijian Tang ,&nbsp;Fan Wang ,&nbsp;Chengxin Zhang ,&nbsp;Chuan Lu ,&nbsp;Zhiman Wang ,&nbsp;Meng Gao ,&nbsp;Cheng Liu","doi":"10.1016/j.rse.2025.114678","DOIUrl":"10.1016/j.rse.2025.114678","url":null,"abstract":"<div><div>The emission, transmission, and secondary generation of atmospheric pollutants occur not only in proximity to the ground but also at elevated altitudes. Vertical distribution plays a pivotal role in understanding the intricate mechanisms that govern atmospheric pollutants. Although ground-based remote sensing offers valuable insights into vertical pollutant profiles, it is limited to obtaining vertical profiles at specific locations, which cannot capture the spatially continuous distribution of vertical profiles at the city scale. Thus, these methods cannot fully support the understanding of high-altitude transport and vertical exchange of atmospheric pollutants. This study proposes a multimodal intermediate fusion (MIF) architecture based on a residual network. Using inputs such as satellite data and meteorological parameters tied to profiles retrieved by multi-axis differential optical absorption spectroscope (MAX-DOAS), we reconstruct the daytime high spatial resolution (1 × 1 × 0.1 km) and temporal resolution (15 min) NO₂ spatiotemporal distribution. The mean correlation between MIF reconstructions and ground-based in situ observations from the China National Environmental Monitoring Center reaches 0.813, and the correlation with TROPOMI tropospheric NO₂ data reaches 0.750–0.796 in the different distance groups. The MIF performance significantly exceeds that of the WRF-Chem model in terms of resolution and accuracy. The full-coverage high-resolution 3D NO₂ distribution allows us to analyze the distribution of NO₂ and estimate its transport, revealing an annual net output of 0.780 Gg of NO₂ from Hefei's urban areas. The vertical transport at high altitudes in urban areas is 28.2 % higher than that in the suburban regions. Notably, the annual high-altitude NO₂ transport from 8:00 to 16:00 local time constitutes approximately 23.7 % of the annual NO_X emissions in the Hefei urban area. During pollution events, the MIF reconstructions can identify two typical processes: 1) transport from elevated altitudes to ground level, and 2) external transport from the urban surface to high altitudes and then transport to suburban areas from elevated altitudes, ultimately reaching the suburban surface. The 3D NO₂ distribution reconstructed by MIF may help understand the impact of high-altitude transport and the vertical exchange of pollutants on atmospheric pollution and can serve as a scientific basis for air pollution control policies.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"321 ","pages":"Article 114678"},"PeriodicalIF":11.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143518881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Linking Kubelka-Munk and recollision probability theories for radiative transfer simulations in turbid canopy","authors":"Peiqi Yang ,&nbsp;Wout Verhoef ,&nbsp;Hongliang Fang ,&nbsp;Wenjie Fan ,&nbsp;Christiaan van der Tol","doi":"10.1016/j.rse.2025.114680","DOIUrl":"10.1016/j.rse.2025.114680","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Radiative transfer (RT) theories formulate vegetation radiative transfer models (RTMs) that link the biophysical properties of vegetation with remote sensing signals. Compared to classical RT theories, the recollision probability theory (also known as p-theory) is distinctive as it predicts some optical properties of vegetation canopies using fewer spectral invariants and simpler mathematical functions. This theory commonly employs an effective recollision probability (&lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;p&lt;/mi&gt;&lt;mi&gt;E&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt;) that is assumed to spectrally independent for vegetation-photon interactions, to describe the absorptive and reflective properties of a vegetation canopy at any wavelength. Most p-theory studies approximate &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;p&lt;/mi&gt;&lt;mi&gt;E&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; using the canopy-average recollision probability at different locations within the canopy, enabling its estimation based on canopy structural properties. However, the canopy-average recollision probability and &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;p&lt;/mi&gt;&lt;mi&gt;E&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; different meanings. As an effective parameter, &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;p&lt;/mi&gt;&lt;mi&gt;E&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; should be obtained by fitting the p-theory formulations, as done in previous studies using simulated and measured canopy optical properties for specific canopies. These studies have empirically shown that &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;p&lt;/mi&gt;&lt;mi&gt;E&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; generally increases with canopy leaf area and exhibits some spectral variability. Building on this empirical evidence, we explore an analytical expression for &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;p&lt;/mi&gt;&lt;mi&gt;E&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; and its dependence on leaf optical and canopy structural properties.&lt;/div&gt;&lt;div&gt;In this study, we link the recollision probability theory with the classical Kubelka-Munk (KM) theory, a two-stream RT theory which predicts canopy optical properties by solving the corresponding differential equations. By using the KM theory as applied in the SAIL model, we derive the analytical expression for the absorptance of vegetation canopies illuminated by diffuse radiation. This absorptance is then used to derive &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;p&lt;/mi&gt;&lt;mi&gt;E&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; based on its relationship with absorptance in the p-theory. In this way, we express &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;p&lt;/mi&gt;&lt;mi&gt;E&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; as a function of leaf albedo (&lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;ω&lt;/mi&gt;&lt;mi&gt;l&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt;) and canopy leaf area index (LAI, &lt;span&gt;&lt;math&gt;&lt;mi&gt;L&lt;/mi&gt;&lt;/math&gt;&lt;/span&gt;). Our findings demonstrate that, for a given canopy, &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;p&lt;/mi&gt;&lt;mi&gt;E&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; could be approximated as a function of LAI (&lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;p&lt;/mi&gt;&lt;mi&gt;L&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt;) by using Taylor series expansion. This approximation aligns with Stenberg's, 2007 canopy-average recollision probability, although the two have different meanings and are derived using different approaches. More importantly, we demonstrate","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"321 ","pages":"Article 114680"},"PeriodicalIF":11.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143526340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A gradient-based 3D nonlinear spectral model for providing components optical properties of mixed pixels in shortwave urban images","authors":"Zhijun Zhen ,&nbsp;Shengbo Chen ,&nbsp;Nicolas Lauret ,&nbsp;Abdelaziz Kallel ,&nbsp;Eric Chavanon ,&nbsp;Tiangang Yin ,&nbsp;Jonathan León-Tavares ,&nbsp;Biao Cao ,&nbsp;Jordan Guilleux ,&nbsp;Jean-Philippe Gastellu-Etchegorry","doi":"10.1016/j.rse.2025.114657","DOIUrl":"10.1016/j.rse.2025.114657","url":null,"abstract":"<div><div>Unmixing optical properties (OP) of land covers from coarse spatial resolution images is crucial for microclimate and energy balance studies. We propose the Unmixing Spectral method using Discrete Anisotropic Radiative Transfer (DART) model (US-DART), a novel approach for unmixing endmember OP in the shortwave domain from mono- or multispectral remotely sensed images. US-DART comprises four modules: pure pixel selection, linear spectral mixture analysis, gradient iterations, and spectral correlation. US-DART requires a surface reflectance image, a 3D mock-up with facets’ group information, and standard DART parameters (e.g., spatial resolution and skylight ratio) as inputs, producing an OP map for each scene element. The accuracy of US-DART is evaluated using two types of scenes (vegetation and urban) and images (Sentinel-2 surface reflectance and DART-simulated pseudo-satellite images). Results demonstrate a median relative error of approximately 0.1 % for pixel reflectance, with higher accuracy for opaque surfaces compared to translucent materials. Excluding co-registration errors and sensor noise, the median relative error of OP is typically around 1 % for opaque elements and 1–5 % for translucent elements with an accurate a priori “reflectance-transmittance” ratio. US-DART enhances our ability to derive detailed OP from coarse-resolution imagery, potentially enabling more accurate modeling of spatial resolution conversions, and energy dynamics, including albedo and shortwave radiation balance, across diverse environments.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"321 ","pages":"Article 114657"},"PeriodicalIF":11.1,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143511702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sea surface wind speed retrieval based on ICESat-2 ocean signal vertical distribution","authors":"Jinghong Xu ,&nbsp;Qun Liu ,&nbsp;Chong Liu ,&nbsp;Yatong Chen ,&nbsp;Peituo Xu ,&nbsp;Yue Ma ,&nbsp;Yifu Chen ,&nbsp;Yudi Zhou ,&nbsp;Han Zhang ,&nbsp;Wenbo Sun ,&nbsp;Suhui Yang ,&nbsp;Weige Lv ,&nbsp;Lan Wu ,&nbsp;Dong Liu","doi":"10.1016/j.rse.2025.114686","DOIUrl":"10.1016/j.rse.2025.114686","url":null,"abstract":"<div><div>Accurate retrieval of sea surface wind speed is crucial for ecological research and marine resource development. The advent of satellite technology provides a feasible approach for global wind speed retrieval. As a photon-counting lidar, ICESat-2 provides unparalleled details of the sea surface and has the potential for sea surface wind speed retrieval. To facilitate the retrieval of sea surface wind speed from ICESat-2, a vertical ocean signal distribution model of ICESat-2 was established, and then training samples were collected by changing the parameters and inputted into the back propagation neural network to fit the relationship between the ICESat-2 vertical distribution signal and the sea surface wind speed. The model considered both environmental factors (solar noise, atmospheric absorption, sea surface reflection, water backscattering, etc.) and hardware characteristics (the spatial and temporal distribution of laser energy, dead time, and dark noise of the detectors, etc.). The validation against MERRA-2 data revealed that the RMSE is 1.57 m/s for nighttime and 1.89 m/s for daytime, while buoy comparisons showed RMSE values of 1.53 m/s for nighttime and 1.82 m/s for daytime. Additionally, comparisons of global monthly mean results also agree well, underscoring the capability of ICESat-2 in sea surface wind speed retrieval.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"321 ","pages":"Article 114686"},"PeriodicalIF":11.1,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143511703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Climate change and shallow aquifers - Unravelling local hydrogeological impacts and groundwater decline-induced subsidence","authors":"Artur Guzy ,&nbsp;Adam Piasecki ,&nbsp;Wojciech T. Witkowski","doi":"10.1016/j.rse.2025.114682","DOIUrl":"10.1016/j.rse.2025.114682","url":null,"abstract":"<div><div>Climate change significantly compromises global water resources, particularly shallow aquifer systems, which are vulnerable to variations in precipitation and evapotranspiration. This study investigated the impacts of climate change on a shallow aquifer system in the Gniezno Lakeland, Poland, by analysing the relationship among ground motion, hydraulic head changes, surface water variations, and meteorological trends. We used EGMS-based InSAR ground motion products to detect subtle land surface displacements across the study area, combined with hydrogeological data, meteorological records, and surface water measurements. Our results revealed a slight but ongoing subsidence of −0.9 mm/year across the area, with variations observed across different land cover types and wetlands being the most affected. Seasonal oscillations in hydraulic head (8–50 cm) and ground motion (2–7 mm) highlighted the aquifer's elastic response to climate variability. Long-term meteorological data indicated a trend towards a drier climate, with annual increases in temperature (+0.5 °C), insolation (+6.8 h), and evaporation (+3.8 mm), coupled with decreasing humidity (−0.13 %/year). The annual negative vertical water exchange, exceeding the volume of groundwater extraction, suggests that climate-driven factors are the primary drivers of aquifer storage decline. The aquifer system exhibited greater resilience to climate change than surface water, as evidenced by its annual storage change (∼2 % of lake volume fluctuations). Localized subsidence patterns near the lake shoreline further underscored the interplay between surface water and groundwater in this region. Our findings provide insights into the impacts of climate change on shallow aquifer systems and highlight the importance of integrating multisource data for comprehensive hydrogeological assessments.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"321 ","pages":"Article 114682"},"PeriodicalIF":11.1,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143509753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Two-stage downscaling and correction cascade learning framework for generating long-time series seamless soil moisture","authors":"Jie Li ,&nbsp;Yingtao Wei ,&nbsp;Liupeng Lin ,&nbsp;Qiangqiang Yuan ,&nbsp;Huanfeng Shen","doi":"10.1016/j.rse.2025.114684","DOIUrl":"10.1016/j.rse.2025.114684","url":null,"abstract":"<div><div>Soil moisture (SM) is a key state variable in agricultural, hydrological, and ecological studies. Microwave remote sensing can retrieve soil moisture at regional or global scales, but is limited by coarse spatial resolution. In order to generate large-scale, spatiotemporally seamless soil moisture of high precision, we propose a two-stage downscaling and correction cascade learning framework by fusing multi-sourced remote sensing and in-situ data. Under the framework, the Hybrid Attention based residual dense Network for soil moisture Downscaling (HAND) is developed to downscale the Soil Moisture Active Passive (SMAP) SM products from 36 km to 1 km effectively. The Random Forest method is subsequently employed to correct the downscaled SM products by in-situ measurements and the 1 km seamless daily SM products of high precision are then generated. The soil moisture downscaling network adopts the Residual Dense connection Network (RDN) as the backbone and embeds a multi-factor interactive attention module, a cross-attention module, and the hybrid attention block with increased/ decreased receptive field, to comprehensively extract the complex relationships between geoscience parameters and soil moisture. The western continental of the United States is served as the study area of this paper, covering 2016–2020. The Pearson correlation (R, unitless) and the Unbiased Root-Mean-Square Error (UbRMSE, <span><math><msup><mi>m</mi><mn>3</mn></msup><mo>/</mo><msup><mi>m</mi><mn>3</mn></msup></math></span>) values of the HAND downscaled products with SMAP are 0.65 and 0.066 <span><math><msup><mi>m</mi><mn>3</mn></msup><mo>/</mo><msup><mi>m</mi><mn>3</mn></msup></math></span>, showing the ability of HAND model to achieve satisfactory accuracy while maintaining consistency with original SMAP products, as well as restoring fine spatial details. After the in-situ correction, the R and UbRMSE values of ten-folder cross validation against the in-situ SM reach 0.92 and 0.033<span><math><msup><mi>m</mi><mn>3</mn></msup><mo>/</mo><msup><mi>m</mi><mn>3</mn></msup></math></span>, while the metrics of SMAP SM against the in-situ SM are 0.46 and 0.083 <span><math><msup><mi>m</mi><mn>3</mn></msup><mo>/</mo><msup><mi>m</mi><mn>3</mn></msup></math></span>, which demonstrates great potential of the proposed method in water resources management at regional scale.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"321 ","pages":"Article 114684"},"PeriodicalIF":11.1,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143510103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing on-orbit radiometric performance of SDGSAT-1 MII for turbid water remote sensing","authors":"Wenkai Li ,&nbsp;Shilin Tang ,&nbsp;Liqiao Tian ,&nbsp;Hongmei Zhao ,&nbsp;Haibin Ye ,&nbsp;Wendi Zheng ,&nbsp;Yupeng Liu ,&nbsp;Ling Sun","doi":"10.1016/j.rse.2025.114683","DOIUrl":"10.1016/j.rse.2025.114683","url":null,"abstract":"&lt;div&gt;&lt;div&gt;The Sustainable Development Science Satellite 1 (SDGSAT-1) is the first satellite developed specifically for implementing the UN 2030 Agenda for Sustainable Development. The multispectral imager (MII) onboard SDGSAT-1 provides advanced capabilities for coastal and inland water environment analysis but requires comprehensive radiometric performance evaluation for effective water monitoring. In this study, the radiometric performance of SDGSAT-1 MII was evaluated for water quality monitoring by assessing its signal-to-noise ratio (SNR) and radiometric sensitivity to variations in suspended particulate matter (SPM). The SNR was statistically estimated from homogeneous waters over the South China Sea (SCS), and radiometric sensitivity was simulated using the Hydrolight software under varied SPM conditions. Results indicated that SDGSAT-1 MII exhibits significantly improved SNR and radiometric sensitivity compared to commonly used satellite sensors (Landsat-8 OLI and Sentinel-2B MSI), extensively applied in turbid water monitoring. Preliminary validation of SDGSAT-1 MII remote sensing reflectance (&lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;R&lt;/mi&gt;&lt;mi&gt;rs&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt;) derived from Acolite DSF against Aeronet-OC measurements demonstrated strong agreement at 438 nm (&lt;span&gt;&lt;math&gt;&lt;msup&gt;&lt;mi&gt;r&lt;/mi&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/msup&gt;&lt;/math&gt;&lt;/span&gt;=0.98 and &lt;em&gt;NRMSE&lt;/em&gt; = 9.04 %), 495 nm (&lt;span&gt;&lt;math&gt;&lt;msup&gt;&lt;mi&gt;r&lt;/mi&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/msup&gt;&lt;/math&gt;&lt;/span&gt;=0.94 and &lt;em&gt;NRMSE&lt;/em&gt; = 16.39 %), 553 nm (&lt;span&gt;&lt;math&gt;&lt;msup&gt;&lt;mi&gt;r&lt;/mi&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/msup&gt;&lt;/math&gt;&lt;/span&gt;=0.99 and &lt;em&gt;NRMSE&lt;/em&gt; = 7.97 %) and 657 nm (&lt;span&gt;&lt;math&gt;&lt;msup&gt;&lt;mi&gt;r&lt;/mi&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/msup&gt;&lt;/math&gt;&lt;/span&gt;=0.77 and &lt;em&gt;NRMSE&lt;/em&gt; = 35.85 %). In addition, retrieved &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;R&lt;/mi&gt;&lt;mi&gt;rs&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; from SDGSAT-1 MII showed good consistency with Sentinel-3B OLCI at 438 nm (&lt;span&gt;&lt;math&gt;&lt;msup&gt;&lt;mi&gt;r&lt;/mi&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/msup&gt;&lt;/math&gt;&lt;/span&gt;=0.7 and &lt;em&gt;NRMSE&lt;/em&gt; = 23.32 %), 495 nm (&lt;span&gt;&lt;math&gt;&lt;msup&gt;&lt;mi&gt;r&lt;/mi&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/msup&gt;&lt;/math&gt;&lt;/span&gt;=0.8 and &lt;em&gt;NRMSE&lt;/em&gt; = 19.55 %), 553 nm (&lt;span&gt;&lt;math&gt;&lt;msup&gt;&lt;mi&gt;r&lt;/mi&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/msup&gt;&lt;/math&gt;&lt;/span&gt;=0.96 and &lt;em&gt;NRMSE&lt;/em&gt; = 11.07 %) and 657 nm (&lt;span&gt;&lt;math&gt;&lt;msup&gt;&lt;mi&gt;r&lt;/mi&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/msup&gt;&lt;/math&gt;&lt;/span&gt;=0.93 and &lt;em&gt;NRMSE&lt;/em&gt; = 31.30 %). However, the consistency of &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mi&gt;R&lt;/mi&gt;&lt;mi&gt;rs&lt;/mi&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; at the 401 nm and near-infrared bands (776 nm and 854 nm) was substantially lower relative to other bands. Regarding SPM mapping in the PRE, spatial consistency between the SDGSAT-1 MII and reference sensors remained high (Landsat-8 OLI: &lt;span&gt;&lt;math&gt;&lt;msup&gt;&lt;mi&gt;r&lt;/mi&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/msup&gt;&lt;/math&gt;&lt;/span&gt;=0.78 and &lt;em&gt;NRMSE&lt;/em&gt; = 19.96 %, Sentinel-3B OLCI: &lt;span&gt;&lt;math&gt;&lt;msup&gt;&lt;mi&gt;r&lt;/mi&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/msup&gt;&lt;/math&gt;&lt;/span&gt;=0.78 and &lt;em&gt;NRMSE&lt;/em&gt; = 19.96 %). Overall, these findings highlight the strong potential of SDGSAT-1 MII for monitoring coastal turbid waters, while also noting certain spectral limitations, especially for the shorter (401 nm) and near-infra","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"321 ","pages":"Article 114683"},"PeriodicalIF":11.1,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143509754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}