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 the Japan Society of Photogrammetry and Remote Sensing (JSPRS) are affiliated with Remote Sensing, and their members receive a discount 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
Estimating Grazing Pressure from Satellite Time Series Without Reliance on Total Production
Remote Sens. 2025, 17(22), 3781; https://doi.org/10.3390/rs17223781 (registering DOI) - 20 Nov 2025
Abstract
Accurately assessing grazing impacts is essential for sustaining alpine grasslands. Conventional approaches often rely on total forage productivity, an indirect and uncertain proxy for forage availability. In this study, we propose a novel framework for estimating grazing pressure that integrates residual biomass with
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Accurately assessing grazing impacts is essential for sustaining alpine grasslands. Conventional approaches often rely on total forage productivity, an indirect and uncertain proxy for forage availability. In this study, we propose a novel framework for estimating grazing pressure that integrates residual biomass with grazing intensity, thereby overcoming the limitations and uncertainties inherent in total forage-based assessments. Our results reveal pronounced spatiotemporal variation in grazing intensity: lowland areas experienced the highest intensity early in the growing season, whereas upland areas became more heavily grazed later in the season. However, grazing intensity alone proved insufficient to explain grazing pressure or predict pasture degradation risk. Overlay analyses demonstrated that only 38.8% of high intensity areas identified as under high grazing pressure, and more than 40% of high intensity area exhibiting substantial aboveground biomass. These findings highlight the limited explanatory power of grazing intensity when considered in isolation. By explicitly incorporating standing biomass rather than relying merely on total production, the proposed framework reduces estimation uncertainty, enhances ecological realism, and provides a scalable, more accurate and practical tool for monitoring grassland utilization and degradation.
Full article
(This article belongs to the Section Ecological Remote Sensing)
Open AccessArticle
A Novel Framework Integrating Spectrum Analysis and AI for Near-Ground-Surface PM2.5 Concentration Estimation
by
Hanwen Qin, Qihua Li, Shun Xia, Zhiguo Zhang, Qihou Hu, Wei Tan and Taoming Guo
Remote Sens. 2025, 17(22), 3780; https://doi.org/10.3390/rs17223780 (registering DOI) - 20 Nov 2025
Abstract
Monitoring the horizontal distribution of PM2.5 within urban areas is of great significance, not only for environmental management but also for providing essential data to understand the distribution, formation, transport, and transformation of PM2.5 within cities. This study proposes a novel
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Monitoring the horizontal distribution of PM2.5 within urban areas is of great significance, not only for environmental management but also for providing essential data to understand the distribution, formation, transport, and transformation of PM2.5 within cities. This study proposes a novel approach—the Spectral Analysis-based PM2.5 Estimation Machine Learning (SAPML) model. This method uses a machine learning model trained with features derived from multi-azimuth and multi-elevation MAX-DOAS observations, specifically the oxygen dimer (O4) differential slant column densities (O4 dSCDs), and labels provided by near-surface ground measurements corresponding to each azimuthal direction, to estimate near-surface PM2.5 concentrations. This approach does not rely on meteorological data and enables multi-directional near-surface PM2.5 monitoring using only a single independent instrument. SAPML bypasses the intermediate retrieval of aerosol extinction coefficients and directly estimates PM2.5 concentrations from spectral analysis results, thereby avoiding the accumulation of errors. Using O4 dSCD data from multiple MAX-DOAS stations for model training eliminates inter-station conversion differences, allowing a single model to be applied across multiple sites. Station-based k-fold cross-validation yielded an average Pearson correlation coefficient (R) of 0.782, demonstrating the robustness and transferability of the method across major regions in China. Among the machine learning algorithms evaluated, Extreme Gradient Boosting (XGBoost) exhibited the best performance. Feature optimization based on importance ranking reduced data collection time by approximately 30%, while the correlation coefficient (R) of the estimation results decreased by only about 1.3%. The trained SAPML model was further applied to two MAX-DOAS stations in Hefei, HF-HD, and HFC, successfully resolving the near-surface PM2.5 spatial distribution at both sites. The results revealed clear intra-urban heterogeneity, with higher PM2.5 concentrations observed in the western industrial park area. During the same observation period, an east-to-west PM2.5 pollution transport event was captured: PM2.5 increases were first detected in the upwind direction at HF-HD, followed by the downwind direction at the same station, and finally at the downwind station HFC. These results indicate that the SAPML model is an effective approach for monitoring intra-urban PM2.5 distributions.
Full article
(This article belongs to the Section Atmospheric Remote Sensing)
Open AccessArticle
Monitoring Sand Dune Height Change in Kubuqi Desert Based on a Bistatic InSAR-Measured DEM Differential Method
by
Chenchen Li, Huiqiang Wang, Ruiping Li, Yanan Yu, Cunli Miao and Ning Wang
Remote Sens. 2025, 17(22), 3779; https://doi.org/10.3390/rs17223779 (registering DOI) - 20 Nov 2025
Abstract
Sand dune movements represent a critical global environment challenge. While previous studies have mainly focused on horizontal deformation, this study applies the bistatic InSAR technique to reconstruct high-precision digital elevation models (DEMs) of the desert terrain, enabling quantitative assessment of the height change
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Sand dune movements represent a critical global environment challenge. While previous studies have mainly focused on horizontal deformation, this study applies the bistatic InSAR technique to reconstruct high-precision digital elevation models (DEMs) of the desert terrain, enabling quantitative assessment of the height change in sand dunes by the DEM differential method. Although InSAR has been widely applied to monitor the surface deformation over the urban, mining, and landslide areas, its application in the desert area is still rare. In this study, the northwestern Kubuqi desert, where sand dunes are clearly distributed, was selected as the study area. Using the TanDEM-X bistatic InSAR data acquired on 26 December 2012 and 25 January 2018, we generated high-resolution DEMs with an estimated accuracy of RMSE ≈ 0.9 m in non-dune areas, as validated against ICESat-2 reference data. The high-precision DEM is attributed to the application of a parameterized modeling method, which also facilitates the effective implementation of the DEM differential method. Then, the t-test (i.e., a statistical hypothesis method) was used to estimate a minimum detectable height change (i.e., LoD) of approximately ±0.50 m and confirm the significance of observed elevation changes. Based on this, this reveals a net mean dune height decrease of 1.04 m during the study period. In addition, quantitative investigations on the vegetation coverage and the wind conditions provided further evidence supporting the observed reduction in dune height, suggesting that vegetation stabilization has likely inhibited sediment transport. This study demonstrates the potential of bistatic InSAR for monitoring desert geomorphological processes and provides scientific support for designing effective desertification control strategies.
Full article
(This article belongs to the Special Issue Advances in Surface Deformation Monitoring Using SAR Interferometry)
Open AccessArticle
A Multi-Sensor Machine Learning Framework for Field-Scale Soil Salinity Mapping Under Data-Scarce Conditions
by
Joyce Mongai Chindong, Jamal-Eddine Ouzemou, Ahmed Laamrani, Ali El Battay, Soufiane Hajaj, Hassan Rhinane and Abdelghani Chehbouni
Remote Sens. 2025, 17(22), 3778; https://doi.org/10.3390/rs17223778 - 20 Nov 2025
Abstract
Soil salinity severely constrains agricultural productivity and soil health, particularly in arid and semi-arid regions. Conventional salinity assessment methods are labor-intensive, time-consuming, and spatially limited. This study developed a data-scarce workflow integrating proximal sensing (EM38-MK2), very high-resolution multispectral imagery, and machine learning to
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Soil salinity severely constrains agricultural productivity and soil health, particularly in arid and semi-arid regions. Conventional salinity assessment methods are labor-intensive, time-consuming, and spatially limited. This study developed a data-scarce workflow integrating proximal sensing (EM38-MK2), very high-resolution multispectral imagery, and machine learning to map soil salinity at field scale in the semi-arid Sehb El Masjoune area, central Morocco. A total of 26 soil samples were analyzed for Electrical Conductivity (EC), and 500 Apparent Electrical Conductivity (ECa) measurements were collected and calibrated using the field samples. Spectral and topographic covariates derived from Unmanned Aerial Vehicle (UAV) and PlanetScope imagery supported model training using Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), Random Forest (RF), and a Stacked Ensemble Learning Model (ELM). Regression Kriging (RK) was applied to model residuals to improve spatial prediction. ELM achieved the highest accuracy (R2 = 0.87, RMSE ≈ 4.15), followed by RF, which effectively captured nonlinear spatial patterns. RK improved PLSR accuracy (by 11.1% for PlanetScope, 13.8% for UAV) but offered limited gains for RF, SVR, and ELM. SHAP analysis identified topographic covariates as the most influential predictors. Both UAV and PlanetScope delineated similar saline–sodic zones. The study demonstrates the following: (1) a scalable, data-efficient workflow for salinity mapping; (2) model and RK performance depend more on algorithmic design than sensor type; (3) interpretable ML and spatial modeling enhance understanding of salinity processes in semi-arid systems.
Full article
(This article belongs to the Special Issue Advancements in Remote, Areal, and Proximal Soil Sensing: Innovations in Measurement and Spatial Modelling)
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Open AccessArticle
Inaccurate DInSAR Time Series Underlie the Purported Evidence of a Recent, Rapid Ascent of a Magmatic Body in the Campi Flegrei Caldera (Italy)
by
Antonella Amoruso, Warner Marzocchi and Luca Crescentini
Remote Sens. 2025, 17(22), 3777; https://doi.org/10.3390/rs17223777 - 20 Nov 2025
Abstract
Ground deformation data are crucial for understanding the processes driving volcanic unrest. The current unrest at the Campi Flegrei caldera, Italy, presents a significant challenge, primarily due to the presence of inconsistencies between seismic data and recent ground deformation model outcomes. While there
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Ground deformation data are crucial for understanding the processes driving volcanic unrest. The current unrest at the Campi Flegrei caldera, Italy, presents a significant challenge, primarily due to the presence of inconsistencies between seismic data and recent ground deformation model outcomes. While there are no seismic indicators of magma movement during the period of unrest, the analysis of ground deformation yielded mutually contradictory results. Despite the indications from prior analyses that the shape of the ground deformation field remains almost constant over time, recent findings based on different DInSAR (Differential Synthetic Aperture Radar Interferometry) time series suggest the upward migration of an ellipsoidal magmatic body at shallow depths, as well as significant changes to its shape. These findings carry strong implications for the related volcanic risk. By comparing DInSAR and GPS (Global Positioning System) displacement time series in detail, we identified a bias in the DInSAR time series used to support the upward migration and shape changes in magmatic bodies. The results of this paper emphasize that the source of ground deformation during the current unrest at Campi Flegrei is quasi-stationary, with no clear evidence of magma migration.
Full article
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
Open AccessArticle
The Intertwined Factors Affecting Altimeter Sigma0
by
Graham D. Quartly
Remote Sens. 2025, 17(22), 3776; https://doi.org/10.3390/rs17223776 - 20 Nov 2025
Abstract
Radar altimeters receive radio-wave reflections from nadir and determine surface parameters from the strength and shape of the return signal. Over the oceans, the strength of the return, termed sigma0 ( ), is strongly related to the small-scale roughness of the
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Radar altimeters receive radio-wave reflections from nadir and determine surface parameters from the strength and shape of the return signal. Over the oceans, the strength of the return, termed sigma0 ( ), is strongly related to the small-scale roughness of the ocean surface and is used to estimate near-surface wind speed. However, a number of other factors affect , and these need to be estimated and compensated for when developing long-term consistent records spanning multiple missions. Aside from unresolved issues of absolute calibration, there are various geophysical factors (sea surface temperature, wave height and rain) that have an effect. The choice of the waveform retracking algorithm also affects the values, with the four-parameter Maximum Likelihood Estimator introducing a strong dependence on waveform-derived mispointing and the use of delay-Doppler processing leading to apparent variation with spacecraft radial velocity. As all of these terms have strong geographical correlations, care is required to disentangle these various effects in order to establish a long-term consistent record. This goal will enable a better investigation of the long-term changes in wind speed at sea.
Full article
Open AccessArticle
High-Precision Geolocation of SAR Images via Multi-View Fusion Without Ground Control Points
by
Anxi Yu, Huatao Yu, Yifei Ji, Wenhao Tong and Zhen Dong
Remote Sens. 2025, 17(22), 3775; https://doi.org/10.3390/rs17223775 - 20 Nov 2025
Abstract
Synthetic Aperture Radar (SAR) images generated via range-Doppler (RD) model-based geometric correction often suffer from non-negligible systematic geolocation errors due to cumulative impacts of platform positioning inaccuracies, payload time synchronization offsets, and atmospheric propagation delays. These errors limit the applicability of SAR data
[...] Read more.
Synthetic Aperture Radar (SAR) images generated via range-Doppler (RD) model-based geometric correction often suffer from non-negligible systematic geolocation errors due to cumulative impacts of platform positioning inaccuracies, payload time synchronization offsets, and atmospheric propagation delays. These errors limit the applicability of SAR data in high-precision geometric applications, especially in scenarios where ground control points (GCPs)—traditionally used for calibration—are inaccessible or costly to acquire. To address this challenge, this study proposes a novel GCP-free high-precision geolocation method based on multi-view SAR image fusion, integrating outlier detection, weighted fusion, and refined estimation strategies. The method first establishes a positioning error correlation model for homologous point pairs in multi-view SAR images. Under the assumption of approximately equal positioning errors, initial systematic error estimates are obtained for all arbitrary dual-view combinations. It then identifies and removes outlier images with inconsistent systematic errors via coefficient of variation analysis, retaining a subset of multi-view images with stable calibration parameters. A weighted fusion strategy, tailored to the geometric error propagation model, is applied to the optimized subset to balance the influence of angular relationships on error estimation. Finally, the minimum norm least-squares method refines the fusion results to enhance consistency and accuracy. Validation experiments on both simulated and actual airborne SAR images demonstrate the method’s effectiveness. For actual measured data, the proposed method achieves an average positioning accuracy improvement of 84.78% compared with dual-view fusion methods, with meter-level precision. Ablation studies confirm that outlier removal and refined estimation contribute 82.42% and 22.75% to accuracy gains, respectively. These results indicate that the method fully leverages multi-view information to robustly estimate and compensate for 2D systematic errors (range and azimuth), enabling high-precision planar geolocation of airborne SAR images without GCPs.
Full article
(This article belongs to the Special Issue Advances in Synthetic Aperture Radar: Calibration, Analysis and Application II)
Open AccessArticle
KFGOD: A Fine-Grained Object Detection Dataset in KOMPSAT Satellite Imagery
by
Dong Ho Lee, Ji Hun Hong, Hyun Woo Seo and Han Oh
Remote Sens. 2025, 17(22), 3774; https://doi.org/10.3390/rs17223774 - 20 Nov 2025
Abstract
Object detection in high-resolution satellite imagery is a critical technology for various applications, yet it faces persistent challenges due to extreme variations in object scale, orientation, and density. The development of numerous public datasets has been pivotal for advancing the field. To continue
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Object detection in high-resolution satellite imagery is a critical technology for various applications, yet it faces persistent challenges due to extreme variations in object scale, orientation, and density. The development of numerous public datasets has been pivotal for advancing the field. To continue this progress and expand the diversity of sensor data available for research, we introduce the KOMPSAT Fine-Grained Object Detection (KFGOD) dataset, a new large-scale benchmark for fine-grained object detection. KFGOD is uniquely constructed using 70 cm and 55 cm resolution optical imagery from the KOMPSAT-3 and 3A satellites, sources not covered by existing major datasets. It provides approximately 880,000 object instances across 33 fine-grained classes, encompassing a wide range of ships, aircraft, vehicles, and infrastructure. The dataset ensures high quality and sensor consistency, covering diverse geographical regions worldwide to promote model generalization. For precise localization, all objects are annotated with both oriented (OBB) and horizontal (HBB) bounding boxes. Comprehensive experiments with state-of-the-art detection models provide benchmark results and highlight the challenging nature of the dataset, particularly in distinguishing between visually similar fine-grained classes. The KFGOD dataset is publicly available and aims to foster further research in fine-grained object detection and analysis of high-resolution satellite imagery.
Full article
(This article belongs to the Special Issue Artificial Intelligence-Driven Methods for Remote Sensing Target and Object Detection (Third Edition))
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Open AccessReview
Evolution of Spaceborne SAR Missions in Earth Orbit
by
Marco D’Errico
Remote Sens. 2025, 17(22), 3773; https://doi.org/10.3390/rs17223773 - 20 Nov 2025
Abstract
A truly operational SAR was first flown in space in 1978 on board the Seasat satellite. Since then, its utilization first expanded to Canada, Europe and Japan, and then to countries with emerging space industries later on. Many technological developments have been crucial
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A truly operational SAR was first flown in space in 1978 on board the Seasat satellite. Since then, its utilization first expanded to Canada, Europe and Japan, and then to countries with emerging space industries later on. Many technological developments have been crucial in the evolution of SAR missions. The introduction of active phased array antennas enabled a variety of operation modes and expanded SAR data applications. Miniaturization of satellite technology allowed for a paradigm change in SAR mission development with the use of small and micro satellites in lieu of large spacecraft. The paper reviews 47 years of SAR missions in low Earth orbit and highlights evolution trends by analyzing 200 satellites.
Full article
(This article belongs to the Special Issue Advances in Spaceborne SAR—Technology and Applications (Second Edition))
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Open AccessArticle
Assessment of Multiple Satellite Precipitation Products over Italy
by
Gaetano Pellicone, Tommaso Caloiero, Roberto Coscarelli and Francesco Chiaravalloti
Remote Sens. 2025, 17(22), 3772; https://doi.org/10.3390/rs17223772 - 20 Nov 2025
Abstract
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Accurate rainfall estimation remains a critical challenge in hydrology, particularly in Italy, where complex topography and uneven rain-gauge distribution introduce major uncertainties. To address this gap, this study assessed five widely used satellite precipitation products, CHIRPS, GPM, HSAF, PDIRNOW, and SM2RAIN, against the
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Accurate rainfall estimation remains a critical challenge in hydrology, particularly in Italy, where complex topography and uneven rain-gauge distribution introduce major uncertainties. To address this gap, this study assessed five widely used satellite precipitation products, CHIRPS, GPM, HSAF, PDIRNOW, and SM2RAIN, against the high-resolution SCIA-ISPRA ground dataset. These products were selected because they represent distinct retrieval approaches (infrared–station hybrid, microwave integration, geostationary blending, neural-network infrared, and soil–moisture inversion) and offer diverse temporal and spatial resolutions suitable for both research and operational monitoring. The evaluation, conducted at daily, seasonal, and annual scales using categorical, continuous, and extreme-event indices, revealed that no single product performs optimally across all metrics. GPM achieved the most balanced and reliable performance overall, whereas PDIRNOW and SM2RAIN provided strong detection but frequent overestimation. CHIRPS yielded conservative estimates with few false alarms, while HSAF was less consistent, especially during winter. The results underscore that product suitability depends on the intended application: detection-oriented systems like PDIRNOW are preferable for flood forecasting, whereas conservative datasets like CHIRPS better support drought monitoring. Overall, integrating multiple products or adopting hybrid approaches is recommended to enhance precipitation assessment accuracy over complex Mediterranean terrains.
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Open AccessArticle
GRADE: A Generalization Robustness Assessment via Distributional Evaluation for Remote Sensing Object Detection
by
Decheng Wang, Yi Zhang, Baocun Bai, Xiao Yu, Xiangbo Shu and Yimian Dai
Remote Sens. 2025, 17(22), 3771; https://doi.org/10.3390/rs17223771 - 20 Nov 2025
Abstract
The performance of remote sensing object detectors often degrades severely when deployed in new operational environments due to covariate shift in the data distribution. Existing evaluation paradigms, which primarily rely on aggregate performance metrics such as mAP, generally lack the analytical depth to
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The performance of remote sensing object detectors often degrades severely when deployed in new operational environments due to covariate shift in the data distribution. Existing evaluation paradigms, which primarily rely on aggregate performance metrics such as mAP, generally lack the analytical depth to provide insights into the mechanisms behind such generalization failures. To fill this critical gap, we propose the GRADE (Generalization Robustness Assessment via Distributional Evaluation) framework, a multi-dimensional, systematic methodology for assessing model robustness. The framework quantifies shifts in background context and object-centric features through a hierarchical analysis of distributional divergence, utilizing Scene-level Fréchet Inception Distance (FID) and Instance-level FID, respectively. These divergence measures are systematically integrated with a standardized performance decay metric to form a unified, adaptively weighted Generalization Score (GS). This composite score serves not only as an evaluation tool but also as a powerful analytical tool, enabling the fine-grained attribution of performance loss to specific sources of domain shift—whether originating from scene variations or anomalies in object appearance. Compared to conventional single-dimensional evaluation methods, the GRADE framework offers enhanced interpretability, a standardized evaluation protocol, and reliable cross-model comparability, establishing a principled theoretical foundation for cross-domain generalization assessment. Extensive empirical validation on six mainstream remote sensing benchmark datasets and multiple state-of-the-art detection models demonstrates that the model rankings produced by the GRADE framework exhibit high fidelity to real-world performance, thereby effectively quantifying and explaining the cross-domain generalization penalty.
Full article
(This article belongs to the Special Issue Advanced Artificial Intelligence and Deep Learning for Remote Sensing (3rd Edition))
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Open AccessArticle
Validation of the MODIS Clumping Index: A Case Study in Saihanba National Forest Park
by
Siyang Yin, Ziti Jiao, Yadong Dong, Lei Cui, Anxin Ding, Feng Qiu, Qian Zhang, Yongguang Zhang, Xiaoning Zhang, Jing Guo, Rui Xie, Yidong Tong, Zidong Zhu, Sijie Li, Chenxia Wang and Jiyou Jiao
Remote Sens. 2025, 17(22), 3770; https://doi.org/10.3390/rs17223770 - 20 Nov 2025
Abstract
The clumping index (CI) describes the level of foliage grouping relative to the random distribution within the canopy. It plays a vital role in the derivation of other important parameters (e.g., the leaf area index, (LAI)) that are usually employed in hydrological, ecological
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The clumping index (CI) describes the level of foliage grouping relative to the random distribution within the canopy. It plays a vital role in the derivation of other important parameters (e.g., the leaf area index, (LAI)) that are usually employed in hydrological, ecological and climatological modeling. In recent years, several satellite-based CI products have been developed using multi-angle reflectance data. However, these products have been validated through the use of a “point-to-point” comparison, which rarely involves a quantitative analysis of spatial representativeness for field-measured CIs in most cases. In this study, we developed a methodological framework to validate the MODIS CI at three different data scales on the basis of intense field measurements, high-resolution unmanned aerial vehicle (UAV) observations and Landsat 8 data. This framework was used to understand the impacts of the scale issue and subpixel variance of the CI in the validation of the MODIS CI for a case study of 12 gridded 500 m pixels in Saihanba National Forest Park, Hebei, China. The results revealed that the MODIS CIs in the study area were in good agreement with the upscaled field CIs (R = 0.75, RMSE = 0.05, bias = 0.02) and UAV CIs. Through a comparison of the observed CIs along the 30 m transects with the 500 m MODIS CIs, we gained insight into the uncertainty caused by the direct “point-to-pixel” evaluation method, which ranged from −0.21~+0.27 for the 10th and 90th percentiles of the observed-MODIS CI error distribution for the twelve pixels. Moreover, semivariogram analysis revealed that the representativeness assessments based on high-resolution albedo and CI maps could reflect the spatial heterogeneity within pixels, whereas the CI map provided more information on the variation in vegetation structures. The average observational footprint needed for a spatially representative sample is approximately 209 m according to an analysis of the high-resolution CI map. The uncertainty of mismatched MODIS land cover types can lead to a deviation of 0.33 in CI estimates, and compared with the CLX method, the scaled-up CI method based on simple arithmetic averages tends to overestimate CIs. In summary, various validation efforts in this case study reveal that the accuracy of the MODIS CIs is generally reliable and in good agreement with that of the upscaled field CIs and UAV CIs; however, with the development of surface process modeling and remote sensing technology, substantial measurements of field CIs in conjunction with high-resolution remotely sensed CI maps derived from single-angle advanced methods are urgently needed for further validation and potential applications. Certainly, such a validation effort will help to improve the understanding of MODIS CI products, which, in turn, will further support the methods and applications of global geospatial information.
Full article
(This article belongs to the Special Issue Supporting Earth Observation and Human–Environment Interaction with Global Geospatial Information)
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Open AccessArticle
Fine-Grained Multispectral Fusion for Oriented Object Detection in Remote Sensing
by
Xin Lan, Shaolin Zhang, Yuhao Bai and Xiaolin Qin
Remote Sens. 2025, 17(22), 3769; https://doi.org/10.3390/rs17223769 - 20 Nov 2025
Abstract
Infrared–visible-oriented object detection aims to combine the strengths of both infrared and visible images, overcoming the limitations of a single imaging modality to achieve more robust detection with oriented bounding boxes under diverse environmental conditions. However, current methods often suffer from two issues:
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Infrared–visible-oriented object detection aims to combine the strengths of both infrared and visible images, overcoming the limitations of a single imaging modality to achieve more robust detection with oriented bounding boxes under diverse environmental conditions. However, current methods often suffer from two issues: (1) modality misalignment caused by hardware and annotation errors, leading to inaccurate feature fusion that degrades downstream task performance; and (2) insufficient directional priors in square convolutional kernels, impeding robust object detection with diverse directions, especially in densely packed scenes. To tackle these challenges, in this paper, we propose a novel method, Fine-Grained Multispectral Fusion (FGMF), for oriented object detection in the paired aerial images. Specifically, we design a dual-enhancement and fusion module (DEFM) to obtain the calibrated and complementary features through weighted addition and subtraction-based attention mechanisms. Furthermore, we propose an orientation aggregation module (OAM) that employs large rotated strip convolutions to capture directional context and long-range dependencies. Extensive experiments on the DroneVehicle and VEDAI datasets demonstrate the effectiveness of our proposed method, yielding impressive results with accuracies of 80.2% and 66.3%, respectively. These results highlight the effectiveness of FGMF in oriented object detection within complex remote sensing scenarios.
Full article
(This article belongs to the Special Issue Image Fusion and Object Detection Using Multi-Modal Remote Sensing Data)
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Open AccessEditorial
Multiplatform Remote Sensing Techniques for Active Tectonics, Seismotectonics, and Volcanic Hazard Assessment
by
Daniele Cirillo, Pietro Tizzani and Francesco Brozzetti
Remote Sens. 2025, 17(22), 3768; https://doi.org/10.3390/rs17223768 - 20 Nov 2025
Abstract
In recent years, the continuous improvement of remote sensing technologies has
profoundly strengthened our capacity to investigate the active deformation of the Earth’s
crust [1] [...] Full article
profoundly strengthened our capacity to investigate the active deformation of the Earth’s
crust [1] [...] Full article
(This article belongs to the Special Issue Multiplatform Remote Sensing Techniques for Active Tectonics, Seismotectonics, and Volcanic Hazard Assessment)
Open AccessArticle
Analysis of LightGlue Matching for Robust TIN-Based UAV Image Mosaicking
by
Sunghyeon Kim, Seunghwan Ban, Hongjin Kim and Taejung Kim
Remote Sens. 2025, 17(22), 3767; https://doi.org/10.3390/rs17223767 - 19 Nov 2025
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Recent advances in UAV (Unmanned Aerial Vehicle)-based remote sensing have significantly enhanced the efficiency of monitoring and managing agricultural and forested areas. However, the low-altitude and narrow-field-of-view characteristics of UAVs make robust image mosaicking essential for generating large-area composites. A TIN (triangulated irregular
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Recent advances in UAV (Unmanned Aerial Vehicle)-based remote sensing have significantly enhanced the efficiency of monitoring and managing agricultural and forested areas. However, the low-altitude and narrow-field-of-view characteristics of UAVs make robust image mosaicking essential for generating large-area composites. A TIN (triangulated irregular network)-based mosaicking framework is herein proposed to address this challenge. A TIN-based mosaicking method constructs a TIN from extracted tiepoints and the sparse point clouds generated by bundle adjustment, enabling rapid mosaic generation. Its performance strongly depends on the quality of tiepoint extraction. Traditional matching combinations, such as SIFT with Brute-Force and SIFT with FLANN, have been widely used due to their robustness in texture-rich areas, yet they often struggle in homogeneous or repetitive-pattern regions, leading to insufficient tiepoints and reduced mosaic quality. More recently, deep learning-based methods such as LightGlue have emerged, offering strong matching capabilities, but their robustness under UAV conditions involving large rotational variations remains insufficiently validated. In this study, we applied the publicly available LightGlue matcher to a TIN-based UAV mosaicking pipeline and compared its performance with traditional approaches to determine the most effective tiepoint extraction strategy. The evaluation encompassed three major stages—tiepoint extraction, bundle adjustment, and mosaic generation—using UAV datasets acquired over diverse terrains, including agricultural fields and forested areas. Both qualitative and quantitative assessments were conducted to analyze tiepoint distribution, geometric adjustment accuracy, and mosaic completeness. The experimental results demonstrated that the hybrid combination of SIFT and LightGlue consistently achieved stable and reliable performance across all datasets. Compared with traditional matching methods, this combination detected a greater number of tiepoints with a more uniform spatial distribution while maintaining competitive reprojection accuracy. It also improved the continuity of the TIN structure in low-texture regions and reduced mosaic voids, effectively mitigating the limitations of conventional approaches. These results demonstrate that the integration of LightGlue enhances the robustness of TIN-based UAV mosaicking without compromising geometric accuracy. Furthermore, this study provides a practical improvement to the photogrammetric TIN-based UAV mosaicking pipeline by incorporating a LightGlue matching technique, enabling more stable and continuous mosaicking even in challenging low-texture environments.
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Open AccessArticle
Super Resolution of Satellite-Based Land Surface Temperature Through Airborne Thermal Imaging
by
Raniero Raniero, Salim Malek and Fabio Remondino
Remote Sens. 2025, 17(22), 3766; https://doi.org/10.3390/rs17223766 - 19 Nov 2025
Abstract
Urban heat island pose a significant threat to public health and urban livability. UHI maps are created using satellite thermal data, a crucial source for earth monitoring and for delivering mitigation strategies. Nowadays there is still a resolution gap between high-resolution optical data
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Urban heat island pose a significant threat to public health and urban livability. UHI maps are created using satellite thermal data, a crucial source for earth monitoring and for delivering mitigation strategies. Nowadays there is still a resolution gap between high-resolution optical data and low-resolution satellite thermal imagery. This study introduces a novel deep learning approach—named Dilated Spatio-Temporal U-Net (DST-UNet)—to bridge this gap. DST-UNET is a modified U-Net architecture which incorporates dilated convolutions to address the multiscale nature of urban thermal patterns. The model is trained to generate high-resolution, airborne-like thermal maps from available, low-resolution satellite imagery and ancillary data. Our results demonstrate that the DST-UNet can effectively generalise across different urban environments, enabling municipalities to generate detailed thermal maps with a frequency far exceeding that of traditional airborne campaigns. This framework leverages open-source data from missions like Landsat to provide a cost-effective and scalable solution for continuous, high-resolution urban thermal monitoring, empowering more effective climate resilience and public health initiatives.
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(This article belongs to the Special Issue Remote Sensing for Land Surface Temperature and Related Applications)
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Open AccessArticle
High-Resolution Mapping and Spatiotemporal Dynamics of Cropland Soil Temperature in the Huang-Huai-Hai Plain, China (2003–2020)
by
Guofei Shang, Yiran Tian, Xiangyang Liu, Xia Zhang, Zhe Li and Shixin An
Remote Sens. 2025, 17(22), 3765; https://doi.org/10.3390/rs17223765 - 19 Nov 2025
Abstract
Soil temperature (ST) is a key regulator of crop growth, microbial activity, and soil biogeochemical processes, making its accurate estimation critical for agricultural monitoring. Focusing on the Huang-Huai-Hai (HHH) Plain, a major grain-producing region of China, we developed a monthly ST prediction framework
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Soil temperature (ST) is a key regulator of crop growth, microbial activity, and soil biogeochemical processes, making its accurate estimation critical for agricultural monitoring. Focusing on the Huang-Huai-Hai (HHH) Plain, a major grain-producing region of China, we developed a monthly ST prediction framework for two depths (0–5 cm and 5–15 cm) using Random Forest and recursive feature elimination with cross-validation. Based on ~3000 in situ records (2003–2020) and 19 geo-environmental covariates, we generated 1 km monthly cropland ST maps and examined their spatiotemporal dynamics. The models achieved consistently high accuracy (R2 ≥ 0.80; RMSE ≤ 1.9 °C; MAE ≤ 1.1 °C; NSE ≥ 0.8, Bias ≤ ±0.3 °C). Feature selection revealed clear month-to-month shifts in predictor importance: environmental variables dominated overall but followed a U-shaped pattern (decreasing then increasing importance), soil properties became more influential in spring–summer, and topography gained importance in autumn–winter. Interannually, cropland ST declined during 2003–2012 (−0.60 °C/decade at 0–5 cm; −0.52 °C/decade at 5–15 cm) but increased more rapidly during 2012–2020 (1.04 and 0.84 °C/decade, respectively). Seasonally, the largest amplitudes occurred in spring–summer (±0.5 °C at 0–5 cm; ±0.4 °C at 5–15 cm), with there being moderate fluctuations in autumn (±0.1 °C) and negligible changes in winter. These temporal dynamics exhibited pronounced spatial heterogeneity shaped by latitude, elevation, and soil type. Collectively, this study produces high-resolution monthly maps and a transparent variable-selection framework for cropland ST, providing new insights into soil thermal regimes and supporting precision agriculture and sustainable land management in the HHH Plain.
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(This article belongs to the Special Issue Spatio-Temporal Land Surface Temperature Retrieval Based on Ground-Based, Satellite Observations and Artificial Intelligence)
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Open AccessArticle
Observed Mesoscale Wind Response to Sea Surface Temperature Patterns: Modulation by Large-Scale Physical Conditions
by
Lorenzo F. Davoli, Agostino N. Meroni and Claudia Pasquero
Remote Sens. 2025, 17(22), 3764; https://doi.org/10.3390/rs17223764 - 19 Nov 2025
Abstract
Sea surface temperature (SST) gradients modulate surface wind variability at the mesoscale O(100 km), with relevant impacts on surface fluxes, rainfall, cloudiness and storms. The dependence of the SST-wind coupling mechanisms on physical environmental conditions has been proven using global ERA5 reanalysis
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Sea surface temperature (SST) gradients modulate surface wind variability at the mesoscale O(100 km), with relevant impacts on surface fluxes, rainfall, cloudiness and storms. The dependence of the SST-wind coupling mechanisms on physical environmental conditions has been proven using global ERA5 reanalysis data, regional observations and models. However, recent literature calls for the need of an observational confirmation to overcome the limitations of numerical simulations in representing such turbulent processes. Here, we employ O(10 km) MetOp A observations of surface wind and SST to verify the dependence of the downward momentum mixing (DMM) mechanism on large-scale wind U and atmospheric stability. We propose a simple empirical model describing how the coupling intensity varies as a function of U, where we account for the role of the characteristic SST length scale LSST and the boundary layer height h in determining the balance between the advective and response timescales, and therefore the decoupling of the atmospheric response from the SST forcing due to advection. Fitting such a model to the observations, we retrieve a scaling with U that depends on the atmospheric stability, in agreement with the literature. The physical interpretation from ERA5 is confirmed, albeit relevant discrepancies emerge in stable regimes and specific regional contexts. This suggests that global numerical models are not able to properly reproduce the coupling in certain conditions, which might have important implications for air–sea fluxes.
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(This article belongs to the Special Issue Observations of Atmospheric and Oceanic Processes by Remote Sensing)
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Open AccessArticle
An Azimuth-Continuously Controllable SAR Image Generation Algorithm Based on GAN
by
Yongjie Cui, Zhiqu Liu, Linian Ruan, Bowen Sheng, Ning Wang, Xiulai Xiao and Xiaolin Bian
Remote Sens. 2025, 17(22), 3763; https://doi.org/10.3390/rs17223763 - 19 Nov 2025
Abstract
The performance of deep learning models largely depends on the scale and quality of training data. However, acquiring sufficient, high-quality samples for specific observation scenarios is often challenging due to high acquisition costs. Unlike optical imagery, synthetic aperture radar (SAR) target images exhibit
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The performance of deep learning models largely depends on the scale and quality of training data. However, acquiring sufficient, high-quality samples for specific observation scenarios is often challenging due to high acquisition costs. Unlike optical imagery, synthetic aperture radar (SAR) target images exhibit strong nonlinear scattering variations with changing azimuth angles, making conventional data augmentation methods such as cropping or rotation ineffective. To tackle these challenges, this paper introduces an Azimuth-Continuously Controllable Generative Adversarial Network (ACC-GAN), which incorporates a continuous azimuth conditional variable to achieve precise azimuth-controllable target generation from dual-input SAR images. Our key contributions are threefold: (1) a continuous azimuth control mechanism that enables precise interpolation between arbitrary azimuth angles; (2) a dual-discriminator framework combining similarity and azimuth supervision to ensure both visual realism and angular accuracy; and (3) conditional batch normalization integrated with adaptive feature fusion to maintain scattering consistency. Experiments on the MSTAR dataset demonstrate that ACC-GAN effectively captures nonlinear azimuth-dependent transformations, generating high-quality images that improve downstream classification accuracy and validate its practical value for SAR data augmentation.
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(This article belongs to the Special Issue Big Data Era: AI Technology for SAR and PolSAR Image)
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Open AccessArticle
A Class-Aware Unsupervised Domain Adaptation Framework for Cross-Continental Crop Classification with Sentinel-2 Time Series
by
Shuang Li, Li Liu, Jinjie Huo, Shengyang Li, Yue Yin and Yonggang Ma
Remote Sens. 2025, 17(22), 3762; https://doi.org/10.3390/rs17223762 - 19 Nov 2025
Abstract
Accurate and large-scale crop mapping is crucial for global food security, yet its performance is often hindered by domain shift when models trained in one region are applied to another. This is particularly challenging in cross-continental scenarios where variations in climate, soil, and
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Accurate and large-scale crop mapping is crucial for global food security, yet its performance is often hindered by domain shift when models trained in one region are applied to another. This is particularly challenging in cross-continental scenarios where variations in climate, soil, and farming systems are significant. To address this, we propose PLCM (PSAE-LTAE + Class-aware MMD), an unsupervised domain adaptation (UDA) framework for crop classification using Sentinel-2 satellite image time series. The framework features two key innovations: (1) a Pixel-Set Attention Encoder (PSAE), which intelligently aggregates spatial features within parcels by assigning weights to individual pixels, enhancing robustness against noise and intra-parcel heterogeneity; and (2) a class-aware Maximum Mean Discrepancy (MMD) loss function that performs fine-grained feature alignment within each crop category, effectively mitigating negative transfer caused by domain shift while preserving class-discriminative information. We validated our framework on a challenging cross-continental, cross-year task, transferring a model trained on data from the source domain in the United States (2022) to an unlabeled target domain in Wensu County, Xinjiang, China (2024). The results demonstrate the robust performance of PLCM. While achieving a competitive overall Macro F1-score of 96.56%, comparable to other state-of-the-art UDA methods, its primary advantage is revealed in a granular per-class analysis. This analysis shows that PLCM provides a more balanced performance by particularly excelling at identifying difficult-to-adapt categories (e.g., Cotton), demonstrating practical robustness. Ablation studies further confirmed that both the PSAE module and the class-aware MMD strategy were critical to this performance gain. Our study shows that the PLCM framework can effectively learn domain-invariant and class-discriminative features, offering an effective and robust solution for high-accuracy, large-scale crop mapping across diverse geographical regions.
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(This article belongs to the Special Issue Advances in Remote Sensing for Crop Monitoring and Food Security)
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