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Combining Passive Infrared and Microwave Satellite Observations to Investigate Cloud Microphysical Properties: A Review
Characterising the Thematic Content of Image Pixels with Topologically Structured Clustering

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 23.9 days after submission; acceptance to publication is undertaken in 2.5 days (median values for papers published in this journal in the second half of 2024).
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

Impact Factor: 4.2 (2023); 5-Year Impact Factor: 4.9 (2023)

Imprint Information Journal Flyer Open Access ISSN: 2072-4292

Latest Articles

14 pages, 2072 KiB

Open AccessArticle

Investigation of the Application of Measured Meteorological Observations in Real-Time Precise Point Positioning

by Qinglan Zhang, Shirong Ye, Jingchao Xia, Peng Zhang, Dezhong Chen and Peng Jiang

Remote Sens. 2025, 17(10), 1773; https://doi.org/10.3390/rs17101773 - 19 May 2025

Tropospheric delay is the main error source that affects the further improvement of the accuracy of space geodesy. High-precision zenith tropospheric delay (ZTD) can be used as a prior value for precise point positioning (PPP) in global navigation satellite systems (GNSSs) to enhance [...] Read more.

Tropospheric delay is the main error source that affects the further improvement of the accuracy of space geodesy. High-precision zenith tropospheric delay (ZTD) can be used as a prior value for precise point positioning (PPP) in global navigation satellite systems (GNSSs) to enhance the speed and accuracy of real-time PPP solutions. Using the Saastamoinen ZTD model, we computed ZTDs using different meteorological elements. One ZTD was termed MZTD and was obtained from 80 reference sites in the China Mainland Crustal Movement Observation Network (CMONOC), the other was termed HZTD and was obtained from elements acquired from the improved version of the hourly global pressure and temperature atmospheric model (HGPT2). The results indicate that the accuracy of the MZTD was 12.94% higher than that of the HZTD, with the ZTDs estimated by post-processing GNSS values as the reference values. Additionally, the MZTD and HZTD were both applied as constraints to the PPP solution. The application of the MZTD constraints to the PPP floating-point solution resulted in a 28.9% improvement in accuracy and a 36.4% decrease in convergence time in the U-direction as a maximum, compared with the application of the HZTD constraints. Full article

23 pages, 25515 KiB

Open AccessArticle

UGC-Net: Uncertainty-Guided Cost Volume Optimization with Contextual Features for Satellite Stereo Matching

by Wonje Jeong and Soon-Yong Park

Remote Sens. 2025, 17(10), 1772; https://doi.org/10.3390/rs17101772 - 19 May 2025

Disparity estimation in satellite stereo images is a highly challenging task due to complex terrain, occlusions caused by tall buildings and structures, and texture-less regions such as roads, rivers, and building roofs. Recent deep learning-based satellite stereo disparity estimation methods have adopted cascade [...] Read more.

Disparity estimation in satellite stereo images is a highly challenging task due to complex terrain, occlusions caused by tall buildings and structures, and texture-less regions such as roads, rivers, and building roofs. Recent deep learning-based satellite stereo disparity estimation methods have adopted cascade multi-scale feature extraction techniques to address these challenges. However, the recent learning-based methods still struggle to effectively estimate disparity in the high ambiguity regions. This paper proposes a disparity estimation and refinement method that leverages variance uncertainty in the cost volume to overcome these limitations. The proposed method calculates variance uncertainty from the cost volume and generates uncertainty weights to adjust the cost volume based on this information. These weights are designed to emphasize geometric features in regions with low uncertainty while enhancing contextual features in regions with high uncertainty, such as occluded or texture-less areas. Furthermore, the proposed method introduces a pseudo volume, referred to as the 4D context volume, which extends the reference image’s features during the stereo-matching aggregation step. By integrating the 4D context volume into the aggregation layer of the geometric cost volume, our method effectively addresses challenges in disparity estimation, particularly in occluded and texture-less areas. For the evaluation of the proposed method, we use the Urban Semantic 3D dataset and the WHU-Stereo dataset. The evaluation results show that the proposed method achieves state-of-the-art performance, improving disparity accuracy in challenging regions. Full article

28 pages, 2805 KiB

Open AccessArticle

Enhancing Urban Understanding Through Fine-Grained Segmentation of Very-High-Resolution Aerial Imagery

by Umamaheswaran Raman Kumar, Toon Goedemé and Patrick Vandewalle

Remote Sens. 2025, 17(10), 1771; https://doi.org/10.3390/rs17101771 - 19 May 2025

Despite the growing availability of very-high-resolution (VHR) remote sensing imagery, extracting fine-grained urban features and materials remains a complex task. Land use/land cover (LULC) maps generated from satellite imagery often fall short in providing the resolution needed for detailed urban studies. While hyperspectral [...] Read more.

Despite the growing availability of very-high-resolution (VHR) remote sensing imagery, extracting fine-grained urban features and materials remains a complex task. Land use/land cover (LULC) maps generated from satellite imagery often fall short in providing the resolution needed for detailed urban studies. While hyperspectral imagery offers rich spectral information ideal for material classification, its complex acquisition process limits its use on aerial platforms such as manned aircraft and unmanned aerial vehicles (UAVs), reducing its feasibility for large-scale urban mapping. This study explores the potential of using only RGB and LiDAR data from VHR aerial imagery as an alternative for urban material classification. We introduce an end-to-end workflow that leverages a multi-head segmentation network to jointly classify roof and ground materials while also segmenting individual roof components. The workflow includes a multi-offset self-ensemble inference strategy optimized for aerial data and a post-processing step based on digital elevation models (DEMs). In addition, we present a systematic method for extracting roof parts as polygons enriched with material attributes. The study is conducted on six cities in Flanders, Belgium, covering 18 material classes—including rare categories such as green roofs, wood, and glass. The results show a 9.88% improvement in mean intersection over union (mIOU) for building and ground segmentation, and a 3.66% increase in mIOU for material segmentation compared to a baseline pyramid attention network (PAN). These findings demonstrate the potential of RGB and LiDAR data for high-resolution material segmentation in urban analysis. Full article

(This article belongs to the Special Issue Applications of AI and Remote Sensing in Urban Systems II)

21 pages, 6259 KiB

Open AccessArticle

Cross-Level Adaptive Feature Aggregation Network for Arbitrary-Oriented SAR Ship Detection

by Lu Qian, Junyi Hu, Haohao Ren, Jie Lin, Xu Luo, Lin Zou and Yun Zhou

Remote Sens. 2025, 17(10), 1770; https://doi.org/10.3390/rs17101770 - 19 May 2025

The rapid progress of deep learning has significantly enhanced the development of ship detection using synthetic aperture radar (SAR). However, the diversity of ship sizes, arbitrary orientations, densely arranged ships, etc., have been hindering the improvement of SAR ship detection accuracy. In response [...] Read more.

The rapid progress of deep learning has significantly enhanced the development of ship detection using synthetic aperture radar (SAR). However, the diversity of ship sizes, arbitrary orientations, densely arranged ships, etc., have been hindering the improvement of SAR ship detection accuracy. In response to these challenges, this study introduces a new detection approach called a cross-level adaptive feature aggregation network (CLAFANet) to achieve arbitrary-oriented multi-scale SAR ship detection. Specifically, we first construct a hierarchical backbone network based on a residual architecture to extract multi-scale features of ship objects from large-scale SAR imagery. Considering the multi-scale nature of ship objects, we then resort to the idea of self-attention to develop a cross-level adaptive feature aggregation (CLAFA) mechanism, which can not only alleviate the semantic gap between cross-level features but also improve the feature representation capabilities of multi-scale ships. To better adapt to the arbitrary orientation of ship objects in real application scenarios, we put forward a frequency-selective phase-shifting coder (FSPSC) module for arbitrary-oriented SAR ship detection tasks, which is dedicated to mapping the rotation angle of the object bounding box to different phases and exploits frequency-selective phase-shifting to solve the periodic ambiguity problem of the rotated bounding box. Qualitative and quantitative experiments conducted on two public datasets demonstrate that the proposed CLAFANet achieves competitive performance compared to some state-of-the-art methods in arbitrary-oriented SAR ship detection. Full article

(This article belongs to the Special Issue SAR Image Object Detection and Information Extraction: Methods and Applications)

18 pages, 2804 KiB

Open AccessArticle

Open and Free Sentinel-2 Mowing Event Data for Austria

by Petra Miletich, Marco Kirchmair, Janik Gregory Deutscher, Alexander Schippl and Manuela Hirschmugl

Remote Sens. 2025, 17(10), 1769; https://doi.org/10.3390/rs17101769 - 19 May 2025

The accurate detection of mowing events is important in many applications, including in agricultural contexts such as yield and fodder production, as well as biodiversity assessments, habitat modeling, and protected area monitoring. This work presents the first free and open dataset of mowing [...] Read more.

The accurate detection of mowing events is important in many applications, including in agricultural contexts such as yield and fodder production, as well as biodiversity assessments, habitat modeling, and protected area monitoring. This work presents the first free and open dataset of mowing events covering the entire Austrian territory for the year 2023 at a spatial resolution of 10 × 10 m. We use the Sentinel-2 time series of the Normalized Difference Vegetation Index (NDVI) to detect mowing events, and additionally, we use the mean of the two ShortWave InfraRed (SWIR) bands to exclude misclassification caused by remaining cloud artifacts and shadows. The validation procedure builds on a visual interpretation of the Panomax webcam archive complemented by a selection of field observations. The final validation dataset consists of 211 mowing events recorded in 85 different locations across Austria. In total, 77.73% of these mowing events were detected with a mean time delay of 4 days. The detection delay in summer was smaller than the values recorded in spring and fall. The pixel-based approach exhibited superior efficacy, especially for meadows with three or more mowing events, compared to the polygon-based approach. The results of our study are consistent with those of previous works demonstrating the capacity to produce high-quality mowing event data for various grassland areas in a fully automated manner, independent from training datasets. The results could be used in research on biodiversity or in practical applications such as agricultural policy support and control, fodder supply evaluation, or impact assessment in nature restoration efforts. Full article

(This article belongs to the Special Issue Advanced in Remote Sensing Approaches for Agricultural Monitoring at Field and Regional Scale)

22 pages, 14069 KiB

Open AccessArticle

DAU-YOLO: A Lightweight and Effective Method for Small Object Detection in UAV Images

by Zeyu Wan, Yizhou Lan, Zhuodong Xu, Ke Shang and Feizhou Zhang

Remote Sens. 2025, 17(10), 1768; https://doi.org/10.3390/rs17101768 - 19 May 2025

Drone object detection serves as a fundamental task for more advanced applications. However, drone images typically exhibit challenges such as small object sizes, dense distributions, and high levels of overlap. Traditional object detection networks struggle to achieve the required accuracy and efficiency under [...] Read more.

Drone object detection serves as a fundamental task for more advanced applications. However, drone images typically exhibit challenges such as small object sizes, dense distributions, and high levels of overlap. Traditional object detection networks struggle to achieve the required accuracy and efficiency under these conditions. In this paper, we propose DAU-YOLO, a novel object detection method tailored for drone imagery, built upon the YOLOv11 framework. To enhance feature extraction, a Receptive-Field Attention (RFA) module is introduced in the backbone, allowing adaptive convolution kernel adjustments across different local regions, thereby addressing the challenge of dense object distributions. In the neck, we propose a Dynamic Attention and Upsampling (DAU) module, which incorporates additional low-level features rich in small-object information. Furthermore, Scale-Diffusion Attention and Task-Aware Attention are employed to refine these features, significantly improving the network’s ability to detect small objects. To maintain an extremely lightweight architecture, the bottom-most Bottom–Up layer is removed, reducing model complexity without compromising detection accuracy. In the experiments, the proposed method achieves state-of-the-art (SOTA) performance on the VisDrone2019 dataset. On the validation set, DAU-YOLO(l) attains an

m A P_{50}

of 56.1%, surpassing the baseline YOLOv11(l) by 9.1% and the latest similar-structure method Drone-YOLO(l) by 4.8%, while maintaining only 28.9M parameters, almost half those of Drone-YOLO(l). In the discussion, we provide a detailed analysis of the improvements in small object detection as well as the trade-off between detection accuracy and inference speed. These results demonstrate the effectiveness of DAU-YOLO in addressing the challenges of drone object detection, offering a highly accurate and lightweight solution for real-time applications in complex aerial scenes. Full article

(This article belongs to the Section Remote Sensing Image Processing)

16 pages, 4452 KiB

Open AccessArticle

Augmenting Satellite Remote Sensing with AERONET-OC for Plume Monitoring in the Chesapeake Bay

by Samantha Lynn Smith, Stephanie Schollaert Uz, J. Blake Clark and Dirk Aurin

Remote Sens. 2025, 17(10), 1767; https://doi.org/10.3390/rs17101767 - 19 May 2025

Satellite observations provide broad spatial coverage of complex coastal environments but may lack temporal resolution to capture rapid changes in these dynamic systems. This study explores the potential of the recently installed NASA Aerosol Robotic Network Ocean Color (AERONET-OC) in the Chesapeake Bay, [...] Read more.

Satellite observations provide broad spatial coverage of complex coastal environments but may lack temporal resolution to capture rapid changes in these dynamic systems. This study explores the potential of the recently installed NASA Aerosol Robotic Network Ocean Color (AERONET-OC) in the Chesapeake Bay, USA, both for comparison with satellite remote sensing and to complement the satellite observations by filling temporal gaps at a fixed site. Using AERONET-OC’s effectiveness as a validation tool through comparisons with multi- and hyperspectral satellites, we find agreement between AERONET-OC and satellite remote sensing reflectance measurements in the Chesapeake Bay. We use AERONET-OC to estimate total suspended matter transport through the upper bay, revealing a 3-day lag of sediment plume transport from riverine discharge to the AERONET-OC site. During the 2023 Canadian wildfire smoke episode, AERONET-OC aerosol optical depth measurements in the Chesapeake Bay agree with satellite products while capturing diurnal variations that are not observable through daily satellite passes. This study demonstrates the potential of continuous in situ monitoring by AERONET-OC to complement satellite observations with higher frequency, important for capturing extreme events that may be missed by daily satellite overpass or are less frequent when cloudy. Full article

(This article belongs to the Special Issue Monitoring Coastal and Marine Environments Based on Remote Sensing Data)

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18 pages, 7465 KiB

Open AccessArticle

New Method for Single-Site Cloud-to-Ground Lightning Location Based on Tri-Pre Processing

by Bingzhe Dai, Qilin Zhang, Jie Li, Yi Liu and Minhong Zhao

Remote Sens. 2025, 17(10), 1766; https://doi.org/10.3390/rs17101766 - 19 May 2025

The single-site lightning detection system can provide timely and effective information on lightning activity in areas where a multi-site lightning network cannot be built. Using deep learning, the single-site lightning detection achieves better performance than traditional methods, but it is highly dependent on [...] Read more.

The single-site lightning detection system can provide timely and effective information on lightning activity in areas where a multi-site lightning network cannot be built. Using deep learning, the single-site lightning detection achieves better performance than traditional methods, but it is highly dependent on the quality of the training dataset. To address this, this paper proposes a method called Tri-Pre to improve dataset quality and thereby enhance the performance of single-site cloud-to-ground lightning detection based on deep learning. After using the Tri-Pre method, the location model’s distance estimation error decreases by 36.08%. For lightning with propagation distances greater than 1000 km, the average relative error of the results from the built model based on the Tri-Pre method is 3.78%. When verified using additional measured data, the model also shows satisfactory accuracy, particularly for lightning with propagation distances beyond 1000 km. Specifically, for lightning with propagation distances between 1500 and 1600 km, the average relative location error is approximately 5.46%. Full article

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28 pages, 4479 KiB

Open AccessReview

Advances in Research and Application of Techniques for Measuring Photosynthetically Active Radiation

by Jiahui Liu, Yefan Cai, Xiangcan Pei and Xiangyang Yu

Remote Sens. 2025, 17(10), 1765; https://doi.org/10.3390/rs17101765 - 19 May 2025

Photosynthetically Active Radiation (PAR) is a critical indicator of plant photosynthesis and ecosystem energy flux. Accurate measurement of PAR is essential in agriculture, ecological research, and environmental management. This paper reviews recent developments and applications of PAR measurement technologies, focusing on recent advances [...] Read more.

Photosynthetically Active Radiation (PAR) is a critical indicator of plant photosynthesis and ecosystem energy flux. Accurate measurement of PAR is essential in agriculture, ecological research, and environmental management. This paper reviews recent developments and applications of PAR measurement technologies, focusing on recent advances in commercial PAR sensors, low-cost sensors, and remote sensing techniques. First, we outline the definition of PAR and further discuss in situ measurements and indirect estimation approaches. The technical principles, sources of error, and practical applications of commercial PAR sensors are presented at the next section. On the contrary, we also introduce the potential of low-cost sensors, particularly emphasizing the role of citizen science initiatives and open-source technologies in promoting widespread PAR measurement. Furthermore, the technical principles underlying remote sensing-based PAR estimation on a global scale are summarized, along with the application prospects of machine learning and satellite-derived product analysis techniques. Finally, this paper comprehensively reviews the challenges confronting PAR measurement and explores potential sensor innovations that may arise from future technological advancements. Full article

(This article belongs to the Special Issue Remote Sensing for Soil Properties and Plant Ecosystems)

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29 pages, 5911 KiB

Open AccessArticle

Machine Learning-Based Estimation of foF2 and MUF(3000)F2 Using GNSS Ionospheric TEC Observations

by Yuhang Zhang, Ming Ou, Liang Chen, Yi Hao, Qinglin Zhu, Xiang Dong and Weimin Zhen

Remote Sens. 2025, 17(10), 1764; https://doi.org/10.3390/rs17101764 - 19 May 2025

This study developed machine learning models using different algorithms, including support vector machine (SVM), random forest (RF), and backpropagation neural network (BPNN), to estimate the critical frequency of the F2 layer (foF2) and the maximum usable frequency of the F2 layer for a [...] Read more.

This study developed machine learning models using different algorithms, including support vector machine (SVM), random forest (RF), and backpropagation neural network (BPNN), to estimate the critical frequency of the F2 layer (foF2) and the maximum usable frequency of the F2 layer for a 3000 km circuit (MUF(3000)F2) based on the total electron content (TEC) observed by global navigation satellite system (GNSS) receivers. The ionospheric dataset used comprised TEC, foF2, and MUF(3000)F2 measurements from 11 stations in China during a solar activity period (2008–2020). The results indicate that all three machine learning models performed better than the IRI-2020 model, with varying levels of accuracy. For foF2 (MUF(3000)F2) estimation, the root mean square error (RMSE) values at Kunming and Xi’an stations were reduced by approximately 38% (26%) and 18% (11%), respectively, compared to IRI-2020. During geomagnetic disturbances, all three models were able to reproduce the variations in both foF2 and MUF(3000)F2 parameters. Nevertheless, the RF model showed significantly better performance in foF2 estimation compared to the SVM and BPNN models. Full article

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32 pages, 8105 KiB

Open AccessArticle

Spatial Downscaling of Soil Moisture Product to Generate High-Resolution Data: A Multi-Source Approach over Heterogeneous Landscapes in Kenya

by Asnake Kassahun Abebe, Xiang Zhou, Tingting Lv, Zui Tao, Abdelrazek Elnashar, Asfaw Kebede, Chunmei Wang and Hongming Zhang

Remote Sens. 2025, 17(10), 1763; https://doi.org/10.3390/rs17101763 - 19 May 2025

Soil moisture (SM) estimates are essential for drought monitoring, hydrological modeling, and climate resilience planning applications. While satellite and model-derived SM products effectively capture SM dynamics, their coarse spatial resolutions (~10–36 km) hinder their ability to represent SM variability in heterogeneous landscapes influenced [...] Read more.

Soil moisture (SM) estimates are essential for drought monitoring, hydrological modeling, and climate resilience planning applications. While satellite and model-derived SM products effectively capture SM dynamics, their coarse spatial resolutions (~10–36 km) hinder their ability to represent SM variability in heterogeneous landscapes influenced by local factors. This study proposes a novel downscaling framework that employs an Artificial Neural Network (ANN) on a cloud-computing platform to improve the spatial resolution and representation of multi-source SM datasets. A data analysis was conducted by integrating Google Earth Engine (GEE) with the computing capabilities of the python language through Google Colab. The framework downscaled Soil Moisture Active Passive (SMAP), European Centre for Medium-Range Weather Forecasts Reanalysis 5th Generation (ERA5-Land), and Famine Early Warning Systems Network Land Data Assimilation System (FLDAS) at 500 m for Kenya, East Africa. This was achieved by leveraging ten input variables comprising elevation, slope, surface albedo, vegetation, soil texture, land surface temperatures (day and night), evapotranspiration, and geolocations. The coarse SM datasets exhibited spatiotemporal consistency, with a standard deviation below 0.15 m³/m³, capturing over 95% of the variability in the original data. Validation against in situ SM data at the station confirmed the framework’s reliability, achieving an average UbRMSE of less than 0.04 m³/m³ and a correlation coefficient (r) over 0.52 for each downscaled dataset. Overall, the framework improved significantly in r values from 0.48 to 0.64 for SMAP, 0.47 to 0.63 for ERA5-Land, and 0.60 to 0.69 for FLDAS. Moreover, the performance of FLDAS and its downscaled version across all climate zone is consistent. Despite the uncertainties among the datasets, the framework effectively improved the representation of SM variability spatiotemporally. These results demonstrate the framework’s potential as a reliable tool for enhancing SM applications, particularly in regions with complex environmental conditions. Full article

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24 pages, 97118 KiB

Open AccessArticle

TMBO-AOD: Transparent Mask Background Optimization for Accurate Object Detection in Large-Scale Remote-Sensing Images

by Tianyi Fu, Hongbin Dong, Benyi Yang and Baosong Deng

Remote Sens. 2025, 17(10), 1762; https://doi.org/10.3390/rs17101762 - 18 May 2025

Recent advancements in deep-learning and computer vision technologies, coupled with the availability of large-scale remote-sensing image datasets, have accelerated the progress of remote-sensing object detection. However, large-scale remote-sensing images typically feature extensive and complex backgrounds with small and sparsely distributed objects, which pose [...] Read more.

Recent advancements in deep-learning and computer vision technologies, coupled with the availability of large-scale remote-sensing image datasets, have accelerated the progress of remote-sensing object detection. However, large-scale remote-sensing images typically feature extensive and complex backgrounds with small and sparsely distributed objects, which pose significant challenges to detection performance. To address this, we propose a novel framework for accurate object detection, termed transparent mask background optimization for accurate object detection (TMBO-AOD), which incorporates a clear focus module and an adaptive filtering framework. The clear focus module constructs an empirical background pool using a Gaussian distribution and introduces transparent masks to prepare for subsequent optimization stages. The adaptive filtering framework can be applied to anchor-based or anchor-free models. It dynamically adjusts the number of candidates generated based on background flags, thereby optimizing the label assignment process. This approach not only alleviates the imbalance between positive and negative samples but also enhances the efficiency of candidate generation. Furthermore, we introduce a novel separated loss function that strengthens both foreground and background consistencies. Specifically, it focuses the model’s attention on foreground objects while enabling it to learn the consistency of background features, thus improving its ability to distinguish objects from the background. We employ YOLOv8 combined with our proposed optimizations to evaluate our model in many datasets, demonstrating improvements in both accuracy and efficiency. Additionally, we validate the effectiveness of our adaptive filtering framework in both anchor-based and anchor-free methods. When implemented with YOLOv5 (anchor based), the framework reduces the candidate generation time by 48.36%, while the YOLOv8 (anchor-free) implementation achieves a 46.81% reduction, both with maintained detection accuracy. Full article

(This article belongs to the Section Remote Sensing Image Processing)

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24 pages, 3707 KiB

Open AccessArticle

Comparison of a Continuous Forest Inventory to an ALS-Derived Digital Inventory in Washington State

by Thomas Montzka, Steve Scharosch, Michael Huebschmann, Mark V. Corrao, Douglas D. Hardman, Scott W. Rainsford, Alistair M. S. Smith and The Confederated Tribes and Bands of the Yakama Nation

Remote Sens. 2025, 17(10), 1761; https://doi.org/10.3390/rs17101761 - 18 May 2025

The monitoring and assessment of forest conditions has traditionally relied on continuous forest inventory (CFI) plots, where all plot trees are regularly measured at discrete locations, then plots are grouped as representative samples of forested areas via stand-based inventory expectations. Remote sensing data [...] Read more.

The monitoring and assessment of forest conditions has traditionally relied on continuous forest inventory (CFI) plots, where all plot trees are regularly measured at discrete locations, then plots are grouped as representative samples of forested areas via stand-based inventory expectations. Remote sensing data acquisitions, such as airborne laser scanning (ALS), are becoming more widely applied to operational forestry to derive similar stand-based inventories. Although ALS systems are widely applied to assess forest metrics associated with crowns and canopies, limited studies have compared ALS-derived digital inventories to CFI datasets. In this study, we conducted an analysis of over 1000 CFI plot locations on ~611,000 acres and compared it to a single-tree derived inventory. Inventory metrics from CFI data were forward modeled from 2016 to 2019 using the USDA Forest Service Forest Vegetation Simulator (FVS) to produce estimates of trees per acre (TPA), basal area (BA) per tree or per plot, basal area per acre (BAA), and volume per acre (VPA) and compared to the ALS-derived Digital Inventory^® (DI) of 2019. The CFI data provided greater on-plot tree counts, BA, and volume compared to the DI when limited to trees ≥5 inches DBH. On-plot differences were less significant for taller trees and increasingly diverged for shorter trees (<20 feet tall) known to be less detectable by ALS. The CFI volume was found to be 44% higher than the ALS-derived DI suggesting mean volume per acre as derived from plot sampling methods may not provide accurate results when expanded across the landscape given variable forest conditions not captured during sampling. These results provide support that when used together, CFI and DI datasets represent a powerful set of tools within the forest management toolkit. Full article

(This article belongs to the Special Issue Remote Sensing and Lidar Data for Forest Monitoring)

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21 pages, 1689 KiB

Open AccessArticle

Inherent Trade-Offs Between the Conflicting Aspects of Designing the Compact Global Navigation Satellite System (GNSS) Anti-Interference Array

by Xiangjun Li, Xiaoyu Zhao, Xiaozhou Ye, Zukun Lu, Feixue Wang and Peiguo Liu

Remote Sens. 2025, 17(10), 1760; https://doi.org/10.3390/rs17101760 - 18 May 2025

The Global Navigation Satellite System (GNSS) has emerged as a critical spatiotemporal infrastructure for ensuring the integrity of remote sensing data links. However, traditional GNSS antenna arrays, typically configured with the antenna spacing of half a wavelength, are constrained by the spatial limitations [...] Read more.

The Global Navigation Satellite System (GNSS) has emerged as a critical spatiotemporal infrastructure for ensuring the integrity of remote sensing data links. However, traditional GNSS antenna arrays, typically configured with the antenna spacing of half a wavelength, are constrained by the spatial limitations of remote sensing platforms. This limitation results in a restricted number of interference-resistant antennas, posing a risk of failure in scenarios involving distributed multi-source interference. To address this challenge, this paper focuses on the multidimensional trade-off problem in the design of compact GNSS anti-interference arrays under finite spatial constraints. For the first time, we systematically reveal the intrinsic relationships and game-theoretic mechanisms among key parameters, including the number of antennas, antenna spacing, antenna size, null width, coupling effects, and receiver availability. First, we propose a novel null width analysis method based on the steering vector correlation coefficient (SVCC), elucidating the inverse regulatory mechanism between increasing the number of antennas and reducing antenna spacing on null width. Furthermore, we demonstrate that increasing antenna size enhances the signal-to-noise ratio (SNR) while also introducing trade-offs with mutual coupling losses, which degrade SNR after compensation. Building on these insights, we innovatively propose a multi-objective optimization framework based on the non-dominated sorting genetic algorithm-II (NSGA-II) model, integrating antenna electromagnetic characteristics and signal processing constraints. Through iterative generation of the Pareto front, this framework achieves a globally optimal solution that balances spatial efficiency and anti-interference performance. Experimental results show that, under a platform constraint of 1 wavelength × 1 wavelength, the optimal number of antennas ranges from 15 to 17, corresponding to receiver availability rates of 89%, 72%, and 55%, respectively. Full article

25 pages, 7899 KiB

Open AccessArticle

Machine Learning-Based Alfalfa Height Estimation Using Sentinel-2 Multispectral Imagery

by Hazhir Bahrami, Karem Chokmani, Saeid Homayouni, Viacheslav I. Adamchuk, Rami Albasha, Md Saifuzzaman and Maxime Leduc

Remote Sens. 2025, 17(10), 1759; https://doi.org/10.3390/rs17101759 - 18 May 2025

Climate change is threatening the sustainability of crop yields due to an increasing frequency of extreme weather conditions, requiring timely agricultural monitoring. Remote sensing facilitates consistent and continuous monitoring of field crops. This study aimed to estimate alfalfa crop height through satellite images [...] Read more.

Climate change is threatening the sustainability of crop yields due to an increasing frequency of extreme weather conditions, requiring timely agricultural monitoring. Remote sensing facilitates consistent and continuous monitoring of field crops. This study aimed to estimate alfalfa crop height through satellite images and machine learning methods within the Google Earth Engine (GEE) Python API. Ground measurements for this study were collected over three years in four Canadian provinces. We utilized Sentinel-2 data to obtain satellite imagery corresponding to the same timeframe and location as the ground measurements. Three machine learning algorithms were employed to estimate plant height from satellite images: random forest (RF), support vector regression (SVR), and extreme gradient boosting (XGB). The efficacy of these algorithms has been assessed and compared. Several widely used vegetation indices, for instance normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and normalized difference red-edge (NDRE), were selected and assessed in this study. RF feature importance was utilized to determine the ranking of features from most to least significant. Several feature selection strategies were utilized and compared with the situation where all features are used. We demonstrated that RF and XGB surpassed SVR when assessing test data performance. Our findings showed that XGB and RF could predict alfalfa crop height with an R² of 0.79 and a mean absolute error (MAE) of around 4 cm Our findings indicated that SVR exhibited the lowest accuracy among the three algorithms tested, with R² of 0.69 and an MAE of 4.63 cm. The analysis of important features showed that normalized difference red edge (NDRE) and normalized difference water index (NDWI) were the most important variables in determining alfalfa crop height. The results of this study also demonstrated that using RF and feature selection strategies, alfalfa crop height can be estimated with comparably high accuracy. Given that the models were fully trained and developed in Python (v. 3.10), they can be readily implemented in a decision support system and deliver near real-time estimations of alfalfa crop height for farmers throughout Canada. Full article

(This article belongs to the Special Issue Machine Learning for Applications in Agriculture and Vegetation Using Remote Sensing)

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21 pages, 5535 KiB

Open AccessArticle

Using Multi-Angular Spectral Reflection of Dorsiventral Leaves to Improve the Transferability of PLSR Models for Estimating Leaf Biochemical Traits

by Dongjie Ran, Zhongqiu Sun and Shan Lu

Remote Sens. 2025, 17(10), 1758; https://doi.org/10.3390/rs17101758 - 17 May 2025

Leaf biochemical traits are crucial for understanding plant physiological status and ecological dynamics. Partial least squares regression (PLSR) models have been widely used to estimate leaf biochemical traits from spectral reflectance information. However, variations in sun–sensor geometry, the sensor field of view, and [...] Read more.

Leaf biochemical traits are crucial for understanding plant physiological status and ecological dynamics. Partial least squares regression (PLSR) models have been widely used to estimate leaf biochemical traits from spectral reflectance information. However, variations in sun–sensor geometry, the sensor field of view, and the random orientation of leaves can introduce multi-angular reflection properties that differ between leaf sides. In this context, the transferability of PLSR models across different leaf sides and viewing zenith angles (VZAs) remains unclear. This study investigated the potential of multi-angular spectral reflection from dorsiventral leaves to improve the transferability of PLSR models for estimating the leaf chlorophyll content (LCC) and equivalent water thickness (EWT). We compared models trained using multi-angular data from both leaf sides with models trained using nadir data (from the adaxial side, abaxial side, or their combination). The results show that the PLSR models trained with multi-angular data from both leaf sides outperformed the models trained with nadir data, achieving the highest accuracy in estimating biochemical traits (LCC: R² = 0.87, RMSE = 7.17 μg/cm², NRMSE = 10.71%; EWT: R² = 0.86, RMSE = 0.0015 g/cm², NRMSE = 10.00%). In contrast, the PLSR models trained using single-angle reflection from either the adaxial or abaxial side showed a lower estimation accuracy and greater variability across leaf sides and VZAs. The superior performance across datasets obtained under different measurement conditions (e.g., integrating spheres and leaf clips) further confirmed the improved generalizability of the PLSR model trained with multi-angular data from dorsiventral leaves. These findings highlight the potential of the multi-angular spectral reflection of dorsiventral leaves to enhance the estimation of biochemical traits across various leaf sides, viewing angles, and measurement conditions. They also underscore the importance of incorporating spectral diversity into model training for improved transferability. Full article

(This article belongs to the Special Issue Vegetation Biophysical Variables and Remote Sensing Applications (Second Edition))

19 pages, 40057 KiB

Open AccessArticle

Time-Series InSAR Monitoring of Permafrost-Related Surface Deformation at Tiksi Airport: Impacts of Climate Warming and Coastal Erosion on the Northernmost Siberian Mainland

by Qingkai Yan, Ze Zhang, Xianglong Li, Aoxiang Yan, Lisha Qiu, Andrei Zhang, Andrey Melnikov and Leonid Gagarin

Remote Sens. 2025, 17(10), 1757; https://doi.org/10.3390/rs17101757 - 17 May 2025

The Arctic is the fastest-warming region on Earth, exhibiting a pronounced “amplifying effect”, which has triggered widespread permafrost thaw and increased the risk of surface deformation. In the Arctic coastal lowlands, permafrost is also affected by shoreline retreat. The impact of these dual [...] Read more.

The Arctic is the fastest-warming region on Earth, exhibiting a pronounced “amplifying effect”, which has triggered widespread permafrost thaw and increased the risk of surface deformation. In the Arctic coastal lowlands, permafrost is also affected by shoreline retreat. The impact of these dual stressors on surface deformation processes in the Arctic coastal lowlands remains poorly understood, particularly in terms of how permafrost thaw and shoreline retreat interact to influence surface stability. To address this gap, we employed PS-InSAR technology to monitor surface deformation from 2017 to 2021 at Tiksi Airport, the northernmost airport on the Siberian mainland, situated adjacent to the Laptev Sea. The results show that Tiksi Airport experiences localized significant surface subsidence, with deformation velocity ranging from −42 to 39 mm/yr. The near-coastal area of Tiksi Airport is strongly influenced by the ocean. Specifically, for extreme subsidence deformation (around –40 mm/yr), the surface subsidence velocity increases by 0.2 mm/yr for every 100 m closer to the coastline. Analysis of these deformation characteristics suggests that the primary causes of subsidence are land surface temperature (LST) warming and erosion by the Laptev Sea, which together lead to increased permafrost thaw. By revealing the combined effects of climate warming and coastal erosion on permafrost stability, this study contributes to enhancing the understanding of infrastructure safety and quality of life for residents in Arctic coastal subsidence areas. Full article

(This article belongs to the Special Issue Advances in Cryosphere Monitoring: Integrating Multi-Source Remote Sensing and AI Technologies)

19 pages, 5852 KiB

Open AccessArticle

Remote Sensing of Particle Absorption Coefficient of Pigments Using a Two-Stage Framework Integrating Optical Classification and Machine Learning

by Xietian Xia, Shaohua Lei, Hui Lu, Zenghui Xu, Xiang Li, Xing Chen, Niancheng Hong, Jie Xu, Kun Shi and Jiacong Huang

Remote Sens. 2025, 17(10), 1756; https://doi.org/10.3390/rs17101756 - 17 May 2025

The particle absorption coefficient of pigments (a_ph(λ)), a critical indicator of phytoplankton spectral absorption properties, is essential for bio-optical models and water quality monitoring. To enhance the accuracy of a_ph(λ) retrieval in complex aquatic environments, this study proposes [...] Read more.

The particle absorption coefficient of pigments (a_ph(λ)), a critical indicator of phytoplankton spectral absorption properties, is essential for bio-optical models and water quality monitoring. To enhance the accuracy of a_ph(λ) retrieval in complex aquatic environments, this study proposes a novel two-stage framework integrating optical classification and machine learning regression. Focusing on inland waters—key areas for eutrophication monitoring—we first developed an intelligent clustering method combining Kernel Principal Angle-based Component (KPAC) dimensionality reduction and Chameleon Swarm Algorithm (CSA)-optimized k-medoids to classify water bodies into four optical types based on hyperspectral reflectance features. Subsequently, an XGBoost regression model with L1-norm feature selection was applied to inversely derive a_ph(440), a_ph(555), a_ph(675), and a_ph(709) for each class. Experimental results demonstrated that optical classification significantly improved inversion accuracy: the determination coefficients R² all exceeded 0.9 in classified datasets, with RMSE reduced by up to 93.1% compared to unclassified scenarios. This indicates that the strategy based on optical classification and regression inversion can effectively enhance the accuracy of pigment particle absorption coefficient inversions. In summary, this study, with the central objective of accurately measuring the pigment particle absorption coefficient, successfully developed a comprehensive set of optical classification and regression inversion methods applicable to various aquatic environments. This new scientific approach and powerful tool provide a means for monitoring and interpreting the pigment particle absorption characteristics in water bodies using remote sensing technology. Full article

(This article belongs to the Section Environmental Remote Sensing)

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18 pages, 5704 KiB

Open AccessArticle

Prediction of Sea Surface Chlorophyll-a Concentrations by Remote Sensing and Deep Learning

by Qingfeng Ruan, Delu Pan, Difeng Wang, Xianqiang He, Fang Gong and Qingjiu Tian

Remote Sens. 2025, 17(10), 1755; https://doi.org/10.3390/rs17101755 - 17 May 2025

Accurate prediction of the spatiotemporal distribution of chlorophyll-a (Chl-a) is essential for evaluating marine ecosystem health and predicting ecological disasters. Current methods struggle to capture short-term variability and periodic trends in Chl-a, especially in noise-prone coastal regions. This study aims to enhance the [...] Read more.

Accurate prediction of the spatiotemporal distribution of chlorophyll-a (Chl-a) is essential for evaluating marine ecosystem health and predicting ecological disasters. Current methods struggle to capture short-term variability and periodic trends in Chl-a, especially in noise-prone coastal regions. This study aims to enhance the prediction of marine Chl-a concentrations by introducing the chlorophyll-a concentration prediction model (ChlaPM), which was developed on the basis of a convolutional long short-term memory (ConvLSTM) network. The model integrates recent spatiotemporal feature extraction (RSTFE), periodic feature extraction (PFE), and denoising fusion (DNF) modules to effectively capture short-term spatiotemporal changes and periodic variations in Chl-a concentrations. In this study, the performance of ChlaPM in single-step and multistep predictions was evaluated using monthly average Chl-a remote sensing data spanning 1998–2023. The results indicate that compared with the RSTFE model, the ChlaPM model achieves substantial reductions in the root mean square error (RMSE) of 53.84%, 53.58%, and 49.70% for predicting Chl-a concentrations 1 month, 3 months, and 6 months into the future, respectively. These findings highlight the effectiveness of ChlaPM in addressing short-term variability and periodic trends and significantly enhances the accuracy of Chl-a prediction. Future work will focus on integrating additional relevant marine variables into the prediction model to further improve its prediction capabilities. Full article

(This article belongs to the Special Issue Artificial Intelligence for Ocean Remote Sensing (Second Edition))

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23 pages, 3335 KiB

Open AccessArticle

An Explainable Machine Learning Model for Predicting Macroseismic Intensity for Emergency Management

by Federico Mori and Giuseppe Naso

Remote Sens. 2025, 17(10), 1754; https://doi.org/10.3390/rs17101754 - 17 May 2025

Predicting macroseismic intensity from instrumental ground motion parameters remains a complex task due to the nonlinear relationship with observed damage patterns. An explainable machine learning model based on the XGBoost algorithm was developed to address the challenge. The model is trained on data [...] Read more.

Predicting macroseismic intensity from instrumental ground motion parameters remains a complex task due to the nonlinear relationship with observed damage patterns. An explainable machine learning model based on the XGBoost algorithm was developed to address the challenge. The model is trained on data from Italian earthquakes recorded between 1972 and 2016, linking ground motion recordings to MCS observations located within 3 km. The dataset has been enhanced with site-specific correction factors to better capture local amplification effects. Key input features include Arias Intensity, spectral accelerations at four representative periods (0.15 s, 0.4 s, 0.6 s, and 2 s), and site condition proxies, such as slope and Vs30. The model achieves strong predictive performance (RMSE = 0.73, R² = 0.76), corresponding to a 33% reduction in residual standard deviation compared to traditional GMICE-based regression methods. To ensure transparency, Shapley Additive Explanations (SHAPs) are used to quantify the contribution of each feature. Arias Intensity emerges as the dominant predictor, followed by spectral ordinates in line with structural response mechanics. As damage severity increases, feature importance shifts from PGA to PGV, while site-specific variables (slope, Vs30) act as refiners rather than amplifiers of shaking. The proposed approach enables near real-time prediction of local damage scenarios and supports data-driven decision-making in seismic emergency management. Full article

(This article belongs to the Special Issue Intelligent Perception of Disaster Information in Remote Sensing Imagery)

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