Journal Description
Remote Sensing
Remote Sensing
is an international, peer-reviewed, open access journal about the science and application of remote sensing technology, and is published semimonthly online by MDPI. The Remote Sensing Society of Japan (RSSJ) and Japan Society of Photogrammetry and Remote Sensing (JSPRS) are affiliated with Remote Sensing and their members receive discounts on the article processing charge.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), Ei Compendex, PubAg, GeoRef, Astrophysics Data System, Inspec, dblp, and other databases.
- Journal Rank: JCR - Q1 (Geosciences, Multidisciplinary) / CiteScore - Q1 (General Earth and Planetary Sciences)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 24.9 days after submission; acceptance to publication is undertaken in 2.5 days (median values for papers published in this journal in the first half of 2025).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Companion journal: Geomatics.
- Journal Cluster of Geospatial and Earth Sciences: Remote Sensing, Geosciences, Quaternary, Earth, Geographies, Geomatics and Fossil Studies.
Impact Factor:
4.1 (2024);
5-Year Impact Factor:
4.8 (2024)
Latest Articles
A Spatiotemporal Subgrid Least Squares Approach to DEM Generation of the Greenland Ice Sheet from ICESat-2 Laser Altimetry
Remote Sens. 2025, 17(24), 4027; https://doi.org/10.3390/rs17244027 (registering DOI) - 13 Dec 2025
Abstract
Greenland, home to the largest ice sheet in the Northern Hemisphere, provides a crucial digital elevation model (DEM) for understanding polar climate evolution and valuable data for global climate change research. Based on ICESat-2 laser altimetry data collected from satellite observations over Greenland
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Greenland, home to the largest ice sheet in the Northern Hemisphere, provides a crucial digital elevation model (DEM) for understanding polar climate evolution and valuable data for global climate change research. Based on ICESat-2 laser altimetry data collected from satellite observations over Greenland between November 2020 and November 2021, the Shandong University of Science and Technology 2021 DEM (SDUST2021DEM) with 500 m grid resolution at the epoch of May 2021 was constructed using a spatiotemporally fitted subgrid least squares method. The precision of the DEM was evaluated by comparison with National Aeronautics and Space Administration IceBridge data and supplemented by GNSS station measurements. The median difference between the DEM and IceBridge data was −0.33 m, the mean deviation −0.58 m, and the median absolute deviation 2.31 m. The accuracy of SDUST2021DEM exhibits a clear spatial pattern: it is higher in the central ice sheet than at the margins, decreases in regions with complex terrain, and remains more reliable in areas characterized by gentle slopes and flat terrain. Overall, the SDUST2021DEM demonstrates stable accuracy and can reliably produce high-precision DEMs for a specific temporal epoch.
Full article
(This article belongs to the Special Issue Remote Sensing in Space Geodesy and Cartography Methods (Fourth Edition))
Open AccessArticle
Quantitative Assessment of Satellite-Observed Atmospheric CO2 Concentrations over Oceanic Regions
by
Xinyu He, Shuangling Chen, Jingyuan Xi and Yuntao Wang
Remote Sens. 2025, 17(24), 4026; https://doi.org/10.3390/rs17244026 (registering DOI) - 13 Dec 2025
Abstract
Atmospheric carbon dioxide in mole fraction (XCO2) is one of the key parameters in estimating CO2 fluxes at the air–sea interface. Satellite-derived column-averaged XCO2 has been widely used in the estimates of air–sea CO2 fluxes, yet the uncertainties
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Atmospheric carbon dioxide in mole fraction (XCO2) is one of the key parameters in estimating CO2 fluxes at the air–sea interface. Satellite-derived column-averaged XCO2 has been widely used in the estimates of air–sea CO2 fluxes, yet the uncertainties induced by using column-averaged XCO2 instead of atmospheric XCO2 in the ocean boundary layer have been generally unknown. In this study, based on an extensive dataset of atmospheric XCO2 measured in the ocean boundary layer from global ocean mooring arrays (N = 945,243) and historical cruises (N = 170,000) between 2002 and 2024, for the first time, we quantitatively evaluated the performance of four satellites, including the Greenhouse gases Observing SATellite (GOSAT and GOSAT-2), the Orbiting Carbon Observatory-2 (OCO-2), and the Atmospheric InfraRed Sounder (AIRS), in monitoring the atmospheric XCO2 over oceanic regions. The atmospheric XCO2 has been increasing from 375 ppm in 2002 to 417 ppm in 2024 based on the longest data record from AIRS. We found that the column-averaged atmospheric XCO2 can serve as a good proxy for atmospheric XCO2 in the ocean boundary layer, with associated uncertainties of 2.48 ppm (0.46%) for GOSAT, 1.01 ppm (0.24%) for GOSAT-2, 2.45 ppm (0.45%) for OCO-2, and 4.22 ppm (0.83%) for AIRS. We also investigated the consistency of these satellites in monitoring the growth rates of atmospheric XCO2 in the global ocean basins. Based on the longest data record from AIRS, the atmospheric XCO2 has been increasing at a rate of 1.87–1.97 ppm year−1 over oceanic regions in the past two decades. These findings contribute to improving the reliability of satellite-derived column-averaged XCO2 observations in the estimates of air–sea CO2 fluxes and support future efforts in monitoring ocean carbon dynamics through satellite remote sensing.
Full article
(This article belongs to the Section Ocean Remote Sensing)
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Open AccessArticle
Enhancing Plant Ecological Unit Mapping Accuracy with Auxiliary Data from Landsat-8 in a Heterogeneous Rangeland
by
Masoumeh Aghababaei, Ataollah Ebrahimi, Ali Asghar Naghipour, Esmaeil Asadi and Jochem Verrelst
Remote Sens. 2025, 17(24), 4025; https://doi.org/10.3390/rs17244025 (registering DOI) - 13 Dec 2025
Abstract
Mapping Plant Ecological Units (PEUs) support sustainable rangeland management. Yet, distinguishing them from multispectral imagery remains challenging due to high intra-class variability and spectral overlap. This study evaluates the contribution of auxiliary data layers to improve PEU classification from Landsat-8 OLI imagery in
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Mapping Plant Ecological Units (PEUs) support sustainable rangeland management. Yet, distinguishing them from multispectral imagery remains challenging due to high intra-class variability and spectral overlap. This study evaluates the contribution of auxiliary data layers to improve PEU classification from Landsat-8 OLI imagery in semi-arid rangelands of northeastern Iran. A random forest (RF) classifier was trained using field samples and multiple feature combinations, including spectral indices, topographic variables (DEM, slope, aspect), and principal component analysis (PCA) components. Classification performance was assessed using overall accuracy (OA), kappa coefficient, and non-parametric Friedman and post hoc tests to determine significant differences among scenarios. The results show that auxiliary features consistently enhanced classification performance as opposed to spectral bands alone. Integrating DEM and PCA layers yielded the highest accuracy (OA = 79.3%, κ = 0.71), with statistically significant improvement (p < 0.05). The findings demonstrate that incorporating topographic and transformed spectral information can effectively reduce class confusion and improve the separability of PEUs in complex rangeland environments. The proposed workflow provides a transferable approach for ecological unit mapping in other semi-arid regions facing similar environmental and management challenges.
Full article
(This article belongs to the Special Issue Vegetation Mapping through Multiscale Remote Sensing)
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Open AccessArticle
Study on the Spatiotemporal Variation of Vegetation Characteristics in the Three River Source Region Based on the CatBoost Model
by
Jun Wang, Siqiong Luo, Hongrui Ren, Xufeng Wang, Jingyuan Wang and Zisheng Zhao
Remote Sens. 2025, 17(24), 4024; https://doi.org/10.3390/rs17244024 (registering DOI) - 13 Dec 2025
Abstract
Under the ongoing trend of climate warming and increasing humidity on the Qinghai–Tibet Plateau, the Three River Source Region (TRSR) has shown strong sensitivity to global climate change. Its vegetation change is particularly worthy of attention and research. The Normalized Difference Vegetation Index
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Under the ongoing trend of climate warming and increasing humidity on the Qinghai–Tibet Plateau, the Three River Source Region (TRSR) has shown strong sensitivity to global climate change. Its vegetation change is particularly worthy of attention and research. The Normalized Difference Vegetation Index (NDVI) is a key indicator for assessing the growth status of vegetation. However, the insufficiency of existing NDVI datasets in terms of spatiotemporal continuity has limited the accuracy of long-term vegetation change studies. This study proposed a machine learning-based downscaling framework that integrates the Moderate-resolution Imaging Spectroradiometer (MODIS) NDVI and the Global Inventory Monitoring and Modeling System (GIMMS) NDVI data to reconstruct a long-term, high-resolution NDVI dataset. Unlike conventional statistical fusion approaches, the proposed framework employs machine learning-based nonlinear relationships to generate long-term, high-resolution NDVI data. Three machine learning algorithms—Random Forest (RF), LightGBM, and CatBoost—were evaluated. Their performance was validated using the MODIS NDVI as reference, with the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and Pearson’s correlation coefficient (R) as evaluation metrics. Based on model comparison, the CatBoost model was identified as the optimal algorithm for spatiotemporal data fusion (R2 = 0.9014, RMSE = 0.0674, MAE = 0.0445), significantly outperforming RF and LightGBM models and demonstrating stronger capability for NDVI spatiotemporal reconstruction. Using this model, a long-term, 1 km monthly GIMMS-MODIS NDVI dataset from 1982 to 2014 was successfully reconstructed. On the basis of this dataset, the spatiotemporal variation characteristics of vegetation in the TRSR from 1982 to 2014 were systematically analyzed. The research results show that: (1) The constructed long-series high-resolution NDVI dataset has a high consistency with MODIS NDVI data; (2) From 1982 to 2014, the NDVI in the TRSR showed an increasing trend, with an average growth rate of 0.0020/10a (p < 0.05). NDVI showed obvious spatial heterogeneity, characterized by a decreasing gradient from southeast to northwest. (3) The Yellow River source exhibited the most evident vegetation recovery, the Yangtze River Source area showed a moderate improvement, whereas the Lancang River Source area displayed little noticeable change. (4) Broad-leaved forests experienced the most significant growth, while cultivated vegetation displayed a marked tendency toward degradation. This study provides both a high-accuracy long-term NDVI product for the TRSR and a methodological foundation for advancing vegetation dynamics research in other high-altitude regions.
Full article
(This article belongs to the Special Issue Multisource Remote Sensing Data Fusion and Applications in Vegetation Monitoring (Second Edition))
Open AccessArticle
Post-Fire Vegetation Recovery Response: A Case Study of the 2020 Bobcat Fire in Los Angeles, California
by
Andrew Alamillo, Jingjing Li, Alireza Farahmand, Madeleine Pascolini-Campbell and Christine Lee
Remote Sens. 2025, 17(24), 4023; https://doi.org/10.3390/rs17244023 (registering DOI) - 13 Dec 2025
Abstract
Wildfires can drastically alter ecological landscapes in just a few days, while it takes years of post-fire recovery for vegetation to return to its former pre-fire state. Assessing changes in vegetation can help with understanding how the hydrological components in the wildfire-affected areas
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Wildfires can drastically alter ecological landscapes in just a few days, while it takes years of post-fire recovery for vegetation to return to its former pre-fire state. Assessing changes in vegetation can help with understanding how the hydrological components in the wildfire-affected areas contribute to potential vegetation shifts. This case study of the Los Angeles Bobcat Fire in 2020 uses Google Earth Engine (GEE) and Python 3.10.18 to access and visualize variations in Difference Normalized Burn Ratio (dNBR) area, Normalized Difference Vegetation Index (NDVI), and OpenET’s evapotranspiration (ET) across three dominant National Land Cover Database (NLCD) vegetation classes and dNBR classes via monthly time series and seasonal analysis from 2016 to 2024. Burn severity was determined based on Landsat-derived dNBR thresholds defined by the United Nations Office for Outer Space Affairs UN-Spider Knowledge Portal. Our study showed a general reduction in dNBR class area percentages, with High Severity (HS) dropping from 15% to 0% and Moderate Severity (MS) dropping from 45% to 10%. Low-Severity (LS) areas returned to 25% after increasing to 49% in May of 2022, led by vegetation growth. The remaining area was classified as Unburned and Enhanced Regrowth. Within our time series analysis, HS areas showed rapid growth compared to MS and LS areas for both ET and NDVI. Seasonal analysis showed most burn severity levels and vegetation classes increasing in median ET and NDVI values while 2024’s wet season median NDVI decreased compared to 2023’s wet season. Despite ET and NDVI continuing to increase post-fire, recent 2024 NLCD data shows most Forests and Shrubs remain as Grasslands, with small patches recovering to pre-fire vegetation. Using GEE, Python, and available satellite imagery demonstrates how accessible analytical tools and data layers enable wide-ranging wildfire vegetation studies, advancing our understanding of the impact wildfires have on ecosystems.
Full article
(This article belongs to the Special Issue Remote Sensing for Risk Assessment, Monitoring and Recovery of Fires)
Open AccessArticle
A Semi-Automatic Framework for Dry Beach Extraction in Tailings Ponds Using Photogrammetry and Deep Learning
by
Bei Cao, Yinsheng Wang, Yani Li, Xudong Zhu, Zicheng Yang, Xinlong Liu and Guangyin Lu
Remote Sens. 2025, 17(24), 4022; https://doi.org/10.3390/rs17244022 (registering DOI) - 13 Dec 2025
Abstract
The spatial characteristics of the dry beach in tailings ponds are critical indicators for the safety assessment of tailings dams. This study presents a method for dry beach extraction that combines deep learning-based semantic segmentation with 3D reconstruction, overcoming the limitations of 2D
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The spatial characteristics of the dry beach in tailings ponds are critical indicators for the safety assessment of tailings dams. This study presents a method for dry beach extraction that combines deep learning-based semantic segmentation with 3D reconstruction, overcoming the limitations of 2D methods in spatial analysis. The workflow includes four steps: (1) High-resolution 3D point clouds are reconstructed from UAV images, and the projection matrix of each image is derived to link 2D pixels with 3D points. (2) AlexNet and GoogLeNet are employed to extract image features and automatically select images containing the dry beach boundary. (3) A DeepLabv3+ network is trained on manually labeled samples to perform semantic segmentation of the dry beach, with a lightweight incremental training strategy for enhanced adaptability. (4) Boundary pixels are detected and back-projected into 3D space to generate consistent point cloud boundaries. The method was validated on two-phase UAV datasets from a tailings pond in Yunnan Province, China. In phase I, the model achieved high segmentation performance, with a mean Accuracy and IoU of approximately 0.95 and a BF of 0.8267. When applied to phase II without retraining, the model maintained stable performance on dam boundaries, while slight performance degradation was observed on hillside and water boundaries. The 3D back-projection converted 2D boundary pixels into 3D coordinates, enabling the extraction of dry beach point clouds and supporting reliable dry beach length monitoring and deposition morphology analysis.
Full article
(This article belongs to the Section Engineering Remote Sensing)
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Open AccessArticle
Dynamic Changes and Sediment Reduction Effect of Terraces on the Loess Plateau
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Chenfeng Wang, Xiaoping Wang, Xudong Fu, Xiaoming Zhang and Yunqi Wang
Remote Sens. 2025, 17(24), 4021; https://doi.org/10.3390/rs17244021 (registering DOI) - 13 Dec 2025
Abstract
Terraces are the main engineering of soil erosion control on the Loess Plateau, offering measures for sediment reduction and water conservation, as well as the potential for increasing agricultural productivity. Over the years, large-scale terrace construction has been undertaken; however, the management has
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Terraces are the main engineering of soil erosion control on the Loess Plateau, offering measures for sediment reduction and water conservation, as well as the potential for increasing agricultural productivity. Over the years, large-scale terrace construction has been undertaken; however, the management has been inadequate, especially in terms of long-term monitoring and mapping. Moreover, the sediment reduction effect of terrace construction is not yet fully understood. Therefore, this study utilizes Landsat series data, integrating remote sensing imaging principles with machine learning techniques to achieve long–term temporal sequence mapping of terraces at a 30 m spatial resolution on the Loess Plateau. The sediment reduction effect brought about by terrace construction on the Loess Plateau is quantified using a sediment reduction formula. The results show that Elevation (Ele.), red band (R), Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Near-infrared Reflectance of Vegetation (NIRv) are key parameters for remote sensing identification of terraces. These five remote sensing variables explain 88% of the terrace recognition variance. Coupling the Random Forest classification model with the LandTrendr algorithm allows for rapid time-series mapping of terrace spatial distribution characteristics on the Loess Plateau. The producer’s accuracy of terrace identification is 93.49%, the user’s accuracy is 93.81%, the overall accuracy is 88.61%, and the Kappa coefficient is 0.87. The LandTrendr algorithm effectively removes terraces affected by human activities. Terraces are mainly distributed in the southeastern Loess areas, including provinces such as Gansu, Shaanxi, and Ningxia. Over the past 30 years, the terrace area on the Loess Plateau has increased from 0.9790 million hectares in 1990 to 9.8981 million hectares in 2020. The sediment reduction effect is particularly notable, with an average reduction of 49.75% in soil erosion across the region. This indicates that terraces are a key measure for soil erosion control in the region and a critical strategy for improving farmland productivity. The data from this study provides scientific evidence for soil erosion control on the Loess Plateau and enhances the precision of terrace management.
Full article
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
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Open AccessArticle
Impact of Industrialization on the Evolution of Suspended Particulate Matter from MODIS Data (2002–2022): Case Study of Açu Port, Brazil
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Ikram Salah Salah, Vincent Vantrepotte, João Felipe Cardoso dos Santos, Manh Duy Tran, Daniel Schaffer Ferreira Jorge, Milton Kampel and Hubert Loisel
Remote Sens. 2025, 17(24), 4020; https://doi.org/10.3390/rs17244020 (registering DOI) - 12 Dec 2025
Abstract
The present study evaluates the influence of industrialization on suspended particulate matter (SPM) dynamics along the northern coast of Rio de Janeiro, focusing specifically on the Açu Port Industrial Complex (APIC). A 20-year MODIS-Aqua (1 km) dataset (2002–2022) was processed using the OC-SMART
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The present study evaluates the influence of industrialization on suspended particulate matter (SPM) dynamics along the northern coast of Rio de Janeiro, focusing specifically on the Açu Port Industrial Complex (APIC). A 20-year MODIS-Aqua (1 km) dataset (2002–2022) was processed using the OC-SMART atmospheric correction. For SPM estimation, a retrieval approach for coastal turbid waters that integrates two optimized bio-optical algorithms based on Optical Water Types (OWTs) was developed. The validity of this approach was substantiated through the utilization of the GLORIA in situ dataset and satellite matchups, which demonstrated its robust performance across a range of turbidity conditions. Its main innovation lies in the OWT-based fusion of two optimized SPM models, enabling robust retrievals across diverse coastal optical conditions. Statistical analyses based on Census X11 decomposition and the Seasonal Mann–Kendall test revealed strong spatial and temporal variability, with SPM concentrations increasing by up to 60% near the APIC during the study period, coinciding with dredging, port expansion, and sediment disposal. These findings indicate a pronounced anthropogenic signal, while spatial and temporal correlation analyses demonstrated that sediment dispersion is consistently directed northward, primarily controlled by currents and wind forcing. The results indicate that industrial activities augment the supply of sediments, while natural hydrodynamic processes govern their dispersion and transport, emphasizing the impact of human pressures and physical drivers on coastal sediments.
Full article
Open AccessArticle
A Spatial Framework for Assessing Irrigation Water Use in Overexploited Mediterranean Aquifers
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Esther López-Pérez, Juan Manzano-Juarez, Miguel Angel Jiménez-Bello, Alberto García-Prats, Carles Sanchis-Ibor, Adrià Rubio-Martín, Fatima Zahrae Boubekri, Abdellah Kajji, Paolo Tufoni, Luís Miguel Nunes and Manuel Pulido-Velazquez
Remote Sens. 2025, 17(24), 4019; https://doi.org/10.3390/rs17244019 - 12 Dec 2025
Abstract
Irrigated agriculture in Mediterranean semi-arid regions is increasingly constrained by aquifer depletion and climate change. Enhancing water use efficiency in the irrigation of perennial crops is essential for long-term agricultural sustainability. This study introduces a Spatial Irrigation Adequacy Index (SIAI), a normalized index
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Irrigated agriculture in Mediterranean semi-arid regions is increasingly constrained by aquifer depletion and climate change. Enhancing water use efficiency in the irrigation of perennial crops is essential for long-term agricultural sustainability. This study introduces a Spatial Irrigation Adequacy Index (SIAI), a normalized index expressing the deviation between actual evapotranspiration (ETa) and Crop Water Requirements (CWR). The framework was applied to assess irrigation performance in grapevine (Vitis vinifera), apple orchards (Malus domestica) and citrus tress (Citrus sinensis) across three groundwater-dependent systems: Requena-Utiel (Spain), Ain Timguenai (Morocco), and Campina de Faro (Portugal). ETa was estimated using Landsat 8 and 9 imageries processed with the SSEBop model, while crop water demand was calculated with the FAO-56 dual crop coefficient method incorporating site-specific agroclimatic data. Results revealed distinct crop-specific irrigation patterns: grapevines achieved near-optimal water use, apple orchards were generally over-irrigated, and citrus groves experienced persistent deficits. The framework enables scalable, transferable assessments of irrigation performance, supporting sustainable water management and adaptive irrigation under climate variability, with potential applications in digital farm management systems, water authority decision-making, and corporate ESG reporting frameworks.
Full article
(This article belongs to the Special Issue Remote Sensing Applications in Hydrology and Water Resources Management)
Open AccessArticle
Effects of Spatial Resolution on Assessing Cotton Water Stress Using Unmanned Aerial System Imagery
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Oluwatola Adedeji, Yazhou Sun, Sanai Li and Wenxuan Guo
Remote Sens. 2025, 17(24), 4018; https://doi.org/10.3390/rs17244018 - 12 Dec 2025
Abstract
Accurate detection of cotton water stress is essential for improving irrigation efficiency and yield prediction. Unmanned aerial system (UAS) imagery offers an effective means for high-throughput crop monitoring, yet its performance across spatial resolutions remains insufficiently characterized. This study aimed to (1) evaluate
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Accurate detection of cotton water stress is essential for improving irrigation efficiency and yield prediction. Unmanned aerial system (UAS) imagery offers an effective means for high-throughput crop monitoring, yet its performance across spatial resolutions remains insufficiently characterized. This study aimed to (1) evaluate the performance of UAS-derived Water Deficit Index (WDI) and Crop Water Stress Index (CWSI) across cotton growth stages and (2) examine how spatial resolution influences stress detection and yield prediction. Field experiments were conducted in Lubbock County, Texas, during the 2021–2022 growing seasons under three irrigation treatments (30%, 60%, and 90% ET replacement). Multispectral and thermal UAS imagery were processed to generate WDI and CWSI maps at spatial resolutions ranging from 0.1 to 4.0 m. Results showed that WDI outperformed CWSI at distinguishing water-stress levels, particularly during early growth stages. A 0.5 m resolution provided the best balance between detection accuracy and computational efficiency, whereas finer resolutions improved detection at the expense of processing time. Coarser resolutions (≥1 m) reduced accuracy due to spatial averaging and plot-mixing effects. These findings highlight the need to optimize UAS flight altitude and sensor configuration to achieve efficient, scalable, and precise cotton water-stress assessment and yield prediction.
Full article
(This article belongs to the Special Issue Applications of Unmanned Aerial Remote Sensing in Precision Agriculture)
Open AccessArticle
SAOF: A Semantic-Aware Optical Flow Framework for Fine-Grained Disparity Estimation in High-Resolution Satellite Stereo Images
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Dingkai Wang, Feng Wang, Jingyi Cao, Niangang Jiao, Yuming Xiang, Enze Zhu, Jingxing Zhu and Hongjian You
Remote Sens. 2025, 17(24), 4017; https://doi.org/10.3390/rs17244017 - 12 Dec 2025
Abstract
Disparity estimation from high-resolution satellite stereo images is critical for 3D reconstruction but remains challenging due to large disparities, complex structures, and textureless regions. To address this, we propose a Semantic-Aware Optical Flow (SAOF) framework for fine-grained disparity estimation, which enhances optical flow-based
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Disparity estimation from high-resolution satellite stereo images is critical for 3D reconstruction but remains challenging due to large disparities, complex structures, and textureless regions. To address this, we propose a Semantic-Aware Optical Flow (SAOF) framework for fine-grained disparity estimation, which enhances optical flow-based via a multi-level optimization incorporating sub-top pyramid re-PatchMatch, scale-adaptive matching windows, and multi-feature cost refinement. For improving the spatial consistency of the resulting disparity map, SAMgeo-Reg is utilized to produce semantic prototypes, which are used to build guidance embeddings for integration into the optical flow estimation process. Experiments on the US3D dataset demonstrate that SAOF outperforms state-of-the-art methods across challenging scenarios. It achieves an average endpoint error (EPE) of 1.317 and a D1 error of 9.09%.
Full article
Open AccessArticle
Multi-Domain Intelligent State Estimation Network for Highly Maneuvering Target Tracking with Non-Gaussian Noise
by
Zhenzhen Ma, Xueying Wang, Yuan Huang, Qingyu Xu, Wei An and Weidong Sheng
Remote Sens. 2025, 17(24), 4016; https://doi.org/10.3390/rs17244016 - 12 Dec 2025
Abstract
In the field of remote sensing, tracking highly maneuvering targets is challenging due to its rapidly changing patterns and uncertainties, particularly under non-Gaussian noise conditions. In this paper, we consider the problem of tracking highly maneuvering targets without using preset parameters in non-Gaussian
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In the field of remote sensing, tracking highly maneuvering targets is challenging due to its rapidly changing patterns and uncertainties, particularly under non-Gaussian noise conditions. In this paper, we consider the problem of tracking highly maneuvering targets without using preset parameters in non-Gaussian noise. We propose a multi-domain intelligent state estimation network (MIENet). It consists of two main models to estimate the key parameter for the Unscented Kalman Filter, enabling robust tracking of highly maneuvering targets under various intensities and distributions of observation noise. The first model, called a fusion denoising model (FDM), is designed to eliminate observation noise by enhancing multi-domain feature fusion. The second model, called a parameter estimation model (PEM), is designed to estimate key parameters of target motion by learning both global and local motion information. Additionally, we design a physically constrained loss function (PCLoss) that incorporates physics-informed constraints and prior knowledge. We evaluate our method on radar trajectory simulation and real remote sensing video datasets. Simulation results on the LAST dataset demonstrate that the proposed FDM can reduce the root mean square error (RMSE) of observation noise by more than 60%. Moreover, the proposed MIENet consistently outperforms the state-of-the-art state estimation algorithms across various highly maneuvering scenes, achieving this performance without requiring adjustment of noise parameters under non-Gaussian noise. Furthermore, experiments conducted on the real-world SV248S dataset confirm that MIENet effectively generalizes to satellite video object tracking tasks.
Full article
(This article belongs to the Section AI Remote Sensing)
Open AccessArticle
Assessing the Use of the Standardized GRACE Satellite Groundwater Storage Change Index for Quantifying Groundwater Drought in the Mu Us Sandy Land
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Yonghua Zhu, Longfei Zhou, Qi Zhang, Zhiming Han, Jiamin Li, Yan Chao, Xiaohan Wang, Hui Yuan, Jie Zhang and Bisheng Xia
Remote Sens. 2025, 17(24), 4015; https://doi.org/10.3390/rs17244015 - 12 Dec 2025
Abstract
The increasingly severe phenomenon of groundwater drought poses a dual threat to the development and construction of a region, as well as its ecological environment. Traditional groundwater drought monitoring methods rely on observation wells, which makes it difficult to obtain dynamic drought information
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The increasingly severe phenomenon of groundwater drought poses a dual threat to the development and construction of a region, as well as its ecological environment. Traditional groundwater drought monitoring methods rely on observation wells, which makes it difficult to obtain dynamic drought information in areas with limited measurement data. Based on Gravity Recovery and Climate Experiment (GRACE) satellite technology and data, the suitability of the standardized groundwater index (GRACE_SGI) was explored for drought characterization in the Mu Us Sandy Land. Multiscale and seasonal trend changes in groundwater drought in the study area from 2002 to 2021 were comprehensively identified. Subsequently, the characteristics of hysteresis time between the GRACE_SGI and the standardized precipitation index (SPI) were clarified. The results show that (1) different fitting functions impact the parameterized GRACE_SGI fitting results. The Anderson–Darling method was used to find the best-fitting function for groundwater data in the study area: the Pearson III distribution. (2) The gain and loss characteristics of the GRACE_SGI are similar, showing downward trends at different time scales, including seasonal scales. (3) The absolute values based on the maximum correlation coefficients between the SPI and the GRACE_SGI at different time scales were 0.1296, 0.2483, 0.2427, and 0.5224, with time lags of 0, 0, 12, and 11 months, respectively. The vulnerability of semiarid ecosystems to hydroclimatic changes is highlighted by these findings, and a satellite-based framework for monitoring groundwater drought in data-scarce regions is provided.
Full article
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
Open AccessArticle
Optimising Deep Learning-Based Segmentation of Crop and Soil Marks with Spectral Enhancements on Sentinel-2 Data
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Andaleeb Yaseen, Giulio Poggi, Sebastiano Vascon and Arianna Traviglia
Remote Sens. 2025, 17(24), 4014; https://doi.org/10.3390/rs17244014 - 12 Dec 2025
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This study presents the first systematic investigation into the influence of spectral enhancement techniques on the segmentation accuracy of specific soil and vegetation marks associated with palaeochannels. These marks are often subtle and can be seasonally obscured by vegetation dynamics and soil variability.
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This study presents the first systematic investigation into the influence of spectral enhancement techniques on the segmentation accuracy of specific soil and vegetation marks associated with palaeochannels. These marks are often subtle and can be seasonally obscured by vegetation dynamics and soil variability. Spectral enhancement methods, such as spectral indices and statistical aggregations, are routinely applied to improve their visual discriminability and interpretability. Despite recent progress in automated detection workflows, no prior research has rigorously quantified the effects of these enhancement techniques on the performance of deep learning–based segmentation models. This gap at the intersection of remote sensing and AI-driven analysis is critical, as addressing it is essential for improving the accuracy, efficiency, and scalability of subsurface feature detection across large and heterogeneous landscapes. In this study, two state-of-the-art deep learning architectures, U-Net and YOLOv8, were trained and tested to assess the influence of these spectral transformations on model performance, using Sentinel-2 imagery acquired across three seasonal windows. Across all experiments, spectral enhancement techniques led to clear improvements in segmentation accuracy compared with raw multispectral inputs. The multi-temporal Median Visualisation (MV) composite provided the most stable performance overall, achieving mean IoU values of 0.22 ± 0.02 in April, 0.07 ± 0.03 in August, and 0.19 ± 0.03 in November for U-Net, outperforming the full 12-band Sentinel-2 stack, which reached only 0.04, 0.02, and 0.03 in the same periods. FCC and VBB also performed competitively, e.g., FCC reached 0.21 ± 0.02 (April) and VBB 0.18 ± 0.03 (April), showing that compact three-band enhancements consistently exceed the segmentation quality obtained from using all spectral bands. Performance varied with environmental conditions, with April yielding the highest accuracy, while August remained challenging across all methods. These results highlight the importance of seasonally informed spectral preprocessing and establish an empirical benchmark for integrating enhancement techniques into AI-based archaeological and geomorphological prospection workflows.
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Open AccessArticle
Research on GPS Satellite Clock Bias Prediction Algorithm Based on the Inaction Method
by
Cong Shen, Huiwen Hu, Guocheng Wang, Lintao Liu, Dong Ren and Zhiwu Cai
Remote Sens. 2025, 17(24), 4013; https://doi.org/10.3390/rs17244013 - 12 Dec 2025
Abstract
Satellite clock bias exhibits complex, time-varying periodic characteristics due to environmental disturbances. Accurate modeling and prediction of periodic terms play a crucial role in improving the precision and stability of short-term predictions. Traditional models such as spectral analysis model (SAM) estimate the frequency,
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Satellite clock bias exhibits complex, time-varying periodic characteristics due to environmental disturbances. Accurate modeling and prediction of periodic terms play a crucial role in improving the precision and stability of short-term predictions. Traditional models such as spectral analysis model (SAM) estimate the frequency, amplitude, and phase of periodic terms through global fitting, which limits their ability to adapt to abrupt changes at the prediction boundary. To address this limitation, this paper proposes an improved spectral analysis model (IM-SAM) based on the inaction method (IM). The model employs IM to extract the instantaneous frequency, amplitude, and phase parameters of periodic terms precisely at the data endpoint, and utilizes the parameters of periodic terms at the data endpoint for prediction, effectively suppressing periodic fluctuations in prediction errors. Experimental results based on real GPS clock bias data demonstrate that the root mean square (RMS) of IM-SAM prediction errors is reduced by 19.14%, 14.39%, and 10.48% for 3 h, 6 h, and 12 h prediction tasks, respectively, compared with SAM. Furthermore, a kinematic precise point positioning experiment was performed using IM-SAM-predicted clock products and compared with the predicted half of IGS ultra-rapid clock products. The RMS of position error was reduced by 14.3%, 12.6%, and 7.9% in the east, north, and up directions, respectively. These results demonstrate the practical effectiveness and accuracy of IM-SAM in real-time clock prediction and GPS positioning applications.
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(This article belongs to the Special Issue Beidou/GNSS Positioning, Navigation and Timing: Methods and Technology (Third Edition))
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Open AccessArticle
Validation of the CERES Clear-Sky Surface Longwave Downward Radiation Products Under Air Temperature Inversion
by
Hao Sun, Qi Zeng, Wanchun Zhang and Jie Cheng
Remote Sens. 2025, 17(24), 4012; https://doi.org/10.3390/rs17244012 - 12 Dec 2025
Abstract
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This study assessed the performance of the Clouds and the Earth’s Radiant Energy System (CERES) surface longwave downward radiation (SLDR) products under the atmospheric temperature inversion (ATI) conditions for the first time. Three years of ground-measured SLDRs from 409 globally distributed stations across
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This study assessed the performance of the Clouds and the Earth’s Radiant Energy System (CERES) surface longwave downward radiation (SLDR) products under the atmospheric temperature inversion (ATI) conditions for the first time. Three years of ground-measured SLDRs from 409 globally distributed stations across four flux networks were employed, and the collocated MODIS atmospheric profile product was used to identify the ATI profiles at each flux station. All three SLDR estimate algorithms (Models A, B, and C) show a pronounced accuracy decline under ATI conditions, regardless of region (polar or non-polar) or time of day (daytime or nighttime). Under ATI conditions, the Bias/RMSE increases by approximately 10.0/5.0 W/m2 for Models A and B, 5.0/1.0 W/m2 for Model C. Sensitivity analysis reveals that the concurrent atmospheric moisture inversion (AMI) compounds this degradation; both the Bias and RMSE increase with the AMI intensity. These results underscore the need to refine CERES SLDR algorithms in the future.
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Open AccessArticle
A Region-Adaptive Phenology-Aware Network for Perennial Cash Crop Mapping Using Multi-Source Time-Series Remote Sensing
by
Yujuan Yang, Shi Cao, Xia Lu, Lina Ping, Xiang Fan, Meiling Liu, Qin Yang and Xiangnan Liu
Remote Sens. 2025, 17(24), 4011; https://doi.org/10.3390/rs17244011 - 12 Dec 2025
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Monitoring and identifying perennial cash crops is essential for optimizing agricultural resource allocation and supporting sustainable rural development. However, cross-regional recognition remains challenging due to cloud contamination, irregular mountainous topography, and climatic-driven phenological shifts. To address these issues, we propose a Region-Adaptive Multi-Head
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Monitoring and identifying perennial cash crops is essential for optimizing agricultural resource allocation and supporting sustainable rural development. However, cross-regional recognition remains challenging due to cloud contamination, irregular mountainous topography, and climatic-driven phenological shifts. To address these issues, we propose a Region-Adaptive Multi-Head Phenology-Aware Network (RAM-PAMNet) that incorporates three key innovations. First, a Multi-source Temporal Attention Fusion (MTAF) module dynamically fuses Sentinel-1 SAR and Sentinel-2 optical time series to enhance temporal consistency and cloud robustness. Second, a Region-Aware Module (RAM) encodes topographic and climatic factors to adaptively adjust phenological windows across regions. Third, a Multi-Head Phenology-Aware Module (MHA-PAM) captures short-, mid-, and long-term phenological rhythms while integrating region-modulated attention for adaptive feature learning. The model was trained and validated in Changde, Hunan (694 patches; augmented to 2776; 70%/15%/15% split) and independently tested in Yaan, Sichuan (574 patches), two regions with contrasting elevation, terrain complexity, and hydrothermal regimes. RAM-PAMNet achieved an OA of 83.3%, mean F1 of 78.8%, and mIoU of 65.4% in Changde, and maintained strong generalization in Yaan with an mIoU of 59.2% and a DecayRate of 9.5, outperforming all baseline models. These results demonstrate that RAM-PAMNet effectively mitigates regional phenological mismatches and improves perennial crop mapping across heterogeneous environments. The proposed framework provides an interpretable and region-adaptive solution for large-scale monitoring of tea, citrus, and grape.
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Open AccessArticle
Detecting Harmful Algae Blooms (HABs) on the Ohio River Using Landsat and Google Earth Engine
by
Douglas Kaiser and John J. Qu
Remote Sens. 2025, 17(24), 4010; https://doi.org/10.3390/rs17244010 - 12 Dec 2025
Abstract
Harmful Algal Blooms (HABs) in large river systems present significant challenges for water quality monitoring, with traditional in-situ sampling methods limited by spatial and temporal coverage. This study evaluates the effectiveness of machine learning techniques applied to Landsat spectral data for detecting and
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Harmful Algal Blooms (HABs) in large river systems present significant challenges for water quality monitoring, with traditional in-situ sampling methods limited by spatial and temporal coverage. This study evaluates the effectiveness of machine learning techniques applied to Landsat spectral data for detecting and quantifying HABs in the Ohio River system, with particular focus on the unprecedented 2015 bloom event. Our methodology combines Google Earth Engine (GEE) for satellite data processing with an ensemble machine learning approach incorporating Support Vector Regression (SVR), Neural Networks (NN), and Extreme Gradient Boosting (XGB). Analysis of Landsat 7 and 8 data revealed that the 2015 HAB event had both broader spatial extent (636.5 river miles) and earlier onset (5–7 days) than detected through conventional monitoring. The ensemble model achieved a correlation coefficient of 0.85 with ground-truth measurements and demonstrated robust performance in detecting varying bloom intensities (R2 = 0.82). Field validation using ORSANCO monitoring stations confirmed the model’s reliability (Nash-Sutcliffe Efficiency = 0.82). The integration of multispectral indices, particularly the Floating Algae Index (FAI) and Normalized Difference Chlorophyll Index (NDCI), enhanced detection accuracy by 23% compared to single-index approaches. The GEE-based framework enables near real-time processing and automated alert generation, making it suitable for operational deployment in water management systems. These findings demonstrate the potential for satellite-based HAB monitoring to complement existing ground-based systems and establish a foundation for improved early warning capabilities in large river systems through the integration of remote sensing and machine learning techniques.
Full article
(This article belongs to the Special Issue Remote Sensing for Monitoring Harmful Algal Blooms (Second Edition))
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Open AccessArticle
A Distributed Data Management and Service Framework for Heterogeneous Remote Sensing Observations
by
Hongquan Cheng, Huayi Wu, Jie Zheng, Zhenqiang Li, Kunlun Qi, Jianya Gong, Longgang Xiang and Yipeng Cao
Remote Sens. 2025, 17(24), 4009; https://doi.org/10.3390/rs17244009 - 12 Dec 2025
Abstract
Remote sensing imagery is a fundamental data source in spatial information science and is widely used in earth observation and geospatial applications. The explosive growth of such data poses significant challenges for online management and service, particularly in terms of storage scalability, processing
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Remote sensing imagery is a fundamental data source in spatial information science and is widely used in earth observation and geospatial applications. The explosive growth of such data poses significant challenges for online management and service, particularly in terms of storage scalability, processing efficiency, and real-time accessibility. To overcome these limitations, we propose DDMS, a distributed data management and service framework for heterogeneous remote sensing data that structures its functionality around three core components: storage, computing, and service. In this framework, a distributed integrated storage model is constructed by integrating file systems with database technologies to support heterogeneous data management, and a parallel computing model is designed to optimize large-scale image processing. To verify the effectiveness of the proposed framework, a prototype system was implemented and evaluated with experiments on representative datasets, covering both optical and InSAR images. Results show that DDMS can flexibly adapt to heterogeneous remote sensing data and storage backends while maintaining efficient data management and stable service performance. Stress tests further confirm its scalability and consistent responsiveness under varying workloads. DDMS provides a practical and extensible solution for large-scale online management and real-time service of remote sensing images. By enhancing modularity, scalability, and service responsiveness, the framework supports both research and practical applications that depend on massive earth observation data.
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(This article belongs to the Special Issue Spatial Modeling and Analysis with Geographical and Remote Sensing Data)
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Open AccessReview
Advancements in Satellite Observations of Inland and Coastal Waters: Building Towards a Global Validation Network
by
Dulcinea M. Avouris, Fernanda Maciel, Samantha L. Sharp, Susanne E. Craig, Arnold G. Dekker, Courtney A. Di Vittorio, John R. Gardner, Emma Goldsmith, Juan I. Gossn, Steven R. Greb, Brice K. Grunert, Daniela Gurlin, Mahesh Jampani, Rabia Munsaf Khan, Ben Lowin, Lachlan McKinna, Colleen B. Mouw, Igor Ogashawara, Sara Rivero Calle, Wilson Salls, Joan-Albert Sánchez-Cabeza, Blake Schaeffer, Bridget N. Seegers, Jari Silander, Emily A. Smail, Menghua Wang and Jeremy Werdelladd
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Remote Sens. 2025, 17(24), 4008; https://doi.org/10.3390/rs17244008 - 12 Dec 2025
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The use of satellite-based remote sensing imagery for water quality monitoring of inland and coastal waters has become widespread over the last few decades, with the expansion of, and investment in, operational Earth-observing missions. Satellite-based sensors are uniquely suited to provide synoptic, system-wide
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The use of satellite-based remote sensing imagery for water quality monitoring of inland and coastal waters has become widespread over the last few decades, with the expansion of, and investment in, operational Earth-observing missions. Satellite-based sensors are uniquely suited to provide synoptic, system-wide water quality parameter estimates that supplement traditional field-based sampling methods. The remote sensing of water quality parameter estimates is particularly valuable in systems with high temporal and spatial variability, as well as in areas that are difficult to access, or where agencies lack funding for routine monitoring. However, optically complex inland and coastal waters pose additional challenges for developing robust remote sensing retrieval models for optical properties and water quality parameters. One of the biggest challenges is collecting high quality field measurements that are used to calibrate and validate the retrieval algorithms. Here, we present the current status of satellite missions, field methods that include instruments used and commonly measured parameters, and repositories of historical field data that are relevant to inland and coastal water studies. We then present data requirements for model validation and highlight gaps in validation coverage. Finally, we provide considerations for future field campaigns to improve coordination with remote sensing data collection and ensure that field data is well suited for use in model or algorithm development.
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