Previous Issue
Volume 17, September-2
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.6
4.1
Journals
Remote Sensing
Volume 17
Issue 19
remotesensing-logo

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Remote Sens., Volume 17, Issue 19 (October-1 2025) – 50 articles

  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.
Order results
Result details
Section
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
18 pages, 6145 KB  
Article
An Evaluation of Radiation Parameterizations in a Meso-Scale Weather Prediction Model Using Satellite Flux Observations
by Jihee Choi, Soonyoung Roh, Hwan-Jin Song, Sunghye Baek, Minjin Choi and Won-Jun Choi
Remote Sens. 2025, 17(19), 3312; https://doi.org/10.3390/rs17193312 (registering DOI) - 26 Sep 2025
Abstract
This study evaluates the forecast performance of four radiation parameterization schemes—the Rapid Radiative Transfer Model for General Circulation Models (RRTMG), its improved version RRTMG-K, the infrequently applied variant, RRTMG-K60x, and the neural network emulator, RRTMG-KNN, within a high-resolution numerical weather [...] Read more.
This study evaluates the forecast performance of four radiation parameterization schemes—the Rapid Radiative Transfer Model for General Circulation Models (RRTMG), its improved version RRTMG-K, the infrequently applied variant, RRTMG-K60x, and the neural network emulator, RRTMG-KNN, within a high-resolution numerical weather prediction (NWP) model. The evaluation uses satellite-derived observations of Outgoing Longwave Radiation (OLR) and Outgoing Shortwave Radiation (OSR) from the Clouds and the Earth’s Radiant Energy System (CERES) over the Korean Peninsula during 2020, including an extreme case study of Typhoon Haishen. Results show that RRTMG-K reduces RMSEs by 4.8% for OLR and 17.5% for OSR relative to RRTMG, primarily due to substantial bias reduction (42.3% for OLR, 60.4% for OSR). The RRTMG-KNN scheme achieves approximately 60-fold computational speedup while maintaining similar or slightly better accuracy than RRTMG-K; specifically, it reduces OLR errors by 1.2% and OSR errors by 1.6% compared to the infrequently applied RRTMG-K60x. In contrast, the infrequent application of RRTMG-K (RRTMG-K60x) slightly increases errors, underscoring the trade-off between computational efficiency and accuracy. These findings demonstrate the value of integrating advanced satellite flux observations and machine learning techniques into the evaluation and optimization of radiation schemes, providing a robust framework for improving cloud–radiation interaction representation in NWP models. Full article
(This article belongs to the Special Issue The Applications of Remote Sensing, Machine Learning, and Deep Learning in Atmospheric Radiative Transfer)
22 pages, 9049 KB  
Article
SAM–Attention Synergistic Enhancement: SAR Image Object Detection Method Based on Visual Large Model
by Yirong Yuan, Jie Yang, Lei Shi and Lingli Zhao
Remote Sens. 2025, 17(19), 3311; https://doi.org/10.3390/rs17193311 - 26 Sep 2025
Abstract
The object detection model for synthetic aperture radar (SAR) images needs to have strong generalization ability and more stable detection performance due to the complex scattering mechanism, high sensitivity of the orientation angle, and susceptibility to speckle noise. Visual large models possess strong [...] Read more.
The object detection model for synthetic aperture radar (SAR) images needs to have strong generalization ability and more stable detection performance due to the complex scattering mechanism, high sensitivity of the orientation angle, and susceptibility to speckle noise. Visual large models possess strong generalization capabilities for natural image processing, but their application to SAR imagery remains relatively rare. This paper attempts to introduce a visual large model into the SAR object detection task, aiming to alleviate the problems of weak cross-domain generalization and poor adaptability to few-shot samples caused by the characteristics of SAR images in existing models. The proposed model comprises an image encoder, an attention module, and a detection decoder. The image encoder leverages the pre-trained Segment Anything Model (SAM) for effective feature extraction from SAR images. An Adaptive Channel Interactive Attention (ACIA) module is introduced to suppress SAR speckle noise. Further, a Dynamic Tandem Attention (DTA) mechanism is proposed in the decoder to integrate scale perception, spatial focusing, and task adaptation, while decoupling classification from detection for improved accuracy. Leveraging the strong representational and few-shot adaptation capabilities of large pre-trained models, this study evaluates their cross-domain and few-shot detection performance on SAR imagery. For cross-domain detection, the model was trained on AIR-SARShip-1.0 and tested on SSDD, achieving an mAP50 of 0.54. For few-shot detection on SAR-AIRcraft-1.0, using only 10% of the training samples, the model reached an mAP50 of 0.503. Full article
(This article belongs to the Special Issue Big Data Era: AI Technology for SAR and PolSAR Image)
31 pages, 6023 KB  
Article
A Multimodal Ensemble Deep Learning Model for Wildfire Prediction in Greece Using Satellite Imagery and Multi-Source Remote Sensing Data
by Ioannis Papakis, Vasileios Linardos and Maria Drakaki
Remote Sens. 2025, 17(19), 3310; https://doi.org/10.3390/rs17193310 - 26 Sep 2025
Abstract
Wildfire events pose significant threats to global ecosystems, with Greece experiencing substantial economic losses exceeding EUR 1.7 billion in 2023 alone, generating immediate financial burdens while contributing to atmospheric carbon dioxide emissions and accelerating climate change effects. This study presents a group of [...] Read more.
Wildfire events pose significant threats to global ecosystems, with Greece experiencing substantial economic losses exceeding EUR 1.7 billion in 2023 alone, generating immediate financial burdens while contributing to atmospheric carbon dioxide emissions and accelerating climate change effects. This study presents a group of classification models for Greece wildfires utilizing historical datasets spanning 2017 to 2021, incorporating satellite-derived remote sensing data, topographical characteristics, and meteorological observations through a multimodal methodology that integrates satellite imagery processing with traditional numerical data analysis techniques. The framework encompasses multiple deep learning architectures, specifically implementing four standalone models comprising two convolutional neural networks optimized for spatial image processing and long short-term memory networks designed for temporal pattern recognition, extending classification approaches by incorporating visual satellite data alongside established numerical datasets to enable the system to leverage both spatial visual patterns and temporal numerical trends. The implementation employs an ensemble methodology that combines individual model classifications through systematic voting mechanisms, harnessing the complementary strengths of each architectural approach to deliver enhanced predictive capabilities and demonstrate the substantial benefits achieved through multimodal data integration for comprehensive wildfire risk assessment applications. Full article
(This article belongs to the Special Issue Environment Observation Analysis Based on Remote Sensing and Geospatial Artificial Intelligence)
►▼ Show Figures

Figure 1

27 pages, 3391 KB  
Article
Spatiotemporal Trends and Driving Factors of Global Impervious Surface Area Changes from 2001 to 2020
by Yihan Xia, Yanning Guan, Tao Yang, Jiaqi Qian, Zhishou Wei, Wutao Yao, Rui Deng, Chunyan Zhang and Shan Guo
Remote Sens. 2025, 17(19), 3309; https://doi.org/10.3390/rs17193309 - 26 Sep 2025
Abstract
The change in impervious surface area (ISA) is an important factor reflecting urban expansion. This study used the global ISA dataset to analyze the spatiotemporal changes in ISA from 2001 to 2020 worldwide, explored the hotspots and patterns of ISA expansion, and analyzed [...] Read more.
The change in impervious surface area (ISA) is an important factor reflecting urban expansion. This study used the global ISA dataset to analyze the spatiotemporal changes in ISA from 2001 to 2020 worldwide, explored the hotspots and patterns of ISA expansion, and analyzed the natural and socio-economic factors affecting ISA changes at three different levels, namely the continent, country, and city levels, by using the RF-SHAP method. The results are as follows: (1) The ISA has grown by 0.94 million km2. (2) ISA in regions such as Asia and Africa has expanded faster than the global average. Developed countries had lower expansion rates. The hotspots of the ISA change rate were relatively concentrated in eastern Asia. Hotspot areas were mainly distributed in Asia and eastern South America in the early stage of the study period and appeared in eastern Europe in the later stage. (3) Edge expansion is the main pattern. Upper-middle-income countries have the largest area of ISA expansion, followed by high-income countries. Cities in developed countries have more infilling expansion; cities in developing countries have more edge expansion. (4) At the continent and country level, social factors, especially GDP, have the greatest impact on ISA change. At the city level, natural factors play a more influential role. Full article
(This article belongs to the Special Issue Remote Sensing and Geospatial Analysis in Urban Environments in the Big Data Era)
18 pages, 7645 KB  
Article
Reliability of Satellite Data in Capturing Spatiotemporal Changes of Precipitation Extremes in the Middle Reaches of the Yellow River Basin
by Qianxi Yang, Qiuyu Xie and Ximeng Xu
Remote Sens. 2025, 17(19), 3308; https://doi.org/10.3390/rs17193308 - 26 Sep 2025
Abstract
Extreme precipitation in the Middle Reaches of the Yellow River Basin (MRYRB) has increased significantly and unevenly, heightening the urgency for rapid and accurate monitoring of such extremes. Satellite precipitation data have proved effective in capturing precipitation extremes but have not been validated [...] Read more.
Extreme precipitation in the Middle Reaches of the Yellow River Basin (MRYRB) has increased significantly and unevenly, heightening the urgency for rapid and accurate monitoring of such extremes. Satellite precipitation data have proved effective in capturing precipitation extremes but have not been validated in the MRYRB. Thus, station-interpolated data were used to validate the reliability of satellite data (GPM IMERG) in characterizing spatiotemporal changes in nine extreme precipitation indices across the entire MRYRB and its ten sub-basins from 2001 to 2022. The results show that all frequency, intensity, and cumulative amount indices exhibit significantly increasing trends. Spatially, extreme precipitation exhibits a clear southeast–northwest gradient. The higher values occur in the southeastern sub-basins. Characterized by high-intensity, short-duration precipitation, the central sub-basins exhibit the lower values of extreme precipitation indices, yet have experienced the most rapid upward trends in those indices. The comparative analysis demonstrates that GPM reliably reproduces indices such as the number of days and amounts with precipitation above a threshold (R10, R20, R95p), maximum precipitation over five days (RX5day), and total precipitation (PRCPTOT) (with regression slopes close to 1, coefficient of determination R2 and Nash-Sutcliffe efficiency (NSE) greater than 0.7, and residual sum of squares ratio (RSR) less than 0.6, with negligible relative bias), particularly in the southern sub-basins. However, it tends to underestimate continuous wet days (CWD) and total precipitation when precipitation is over the 99th percentile (R99p). These findings advance current understanding of GPM applicability at watershed scales and offer actionable insight for water-sediment prediction under the world’s changing climate. Full article
23 pages, 4793 KB  
Article
Contrasting Drydown Time Scales: SMAP L-Band vs. AMSR2 C-Band Brightness Temperatures Against Ground Observations and SMAP Products
by Hongxun Jiang, Shaoning Lv, Yin Hu and Jun Wen
Remote Sens. 2025, 17(19), 3307; https://doi.org/10.3390/rs17193307 - 26 Sep 2025
Abstract
Surface water loss, regulated by natural factors such as surface properties and atmospheric conditions, is a complex process across multiple spatiotemporal scales. This study compared the statistical characteristics of drydown time scale (τ) derived from multi-frequency microwave brightness temperatures (TB, including L-band and [...] Read more.
Surface water loss, regulated by natural factors such as surface properties and atmospheric conditions, is a complex process across multiple spatiotemporal scales. This study compared the statistical characteristics of drydown time scale (τ) derived from multi-frequency microwave brightness temperatures (TB, including L-band and C-band), SMAP (Soil Moisture Active Passive) soil moisture (SM) products, and in situ observation data. It mainly conducted a sensitivity analysis of τ to depth, climate type, vegetation coverage, and soil texture, and compared the sensitivity differences between signals of different frequencies. The statistical results of τ showed a pattern varying with sensing depth: C-band TB (0~3 cm) < L-band TB (0~5 cm) < in situ observation (4~8 cm), i.e., the shallower the depth, the faster the drying. τ was sensitive to Normalized Difference Vegetation Index (NDVI) when NDVI < 0.7 and climate types, but relatively insensitive to soil texture. The global median τ retrieved from TB aligned with the spatial pattern of climate classifications; drier climates and sparser vegetation coverage led to faster drying, and L-band TB was more sensitive to these factors than C-band TB. The attenuation magnitude of L-band TB was smaller than that of C-band TB, but the degree of change in its attenuation effect was greater than that of C-band TB, particularly regarding variations in NDVI and climate types. Furthermore, given the similar sensing depths of SMAP SM and L-band TB, their τ statistical characteristics were compared and found to differ, indicating that depth is not the sole reason SMAP SM dries faster than in situ observations. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Soil Property Mapping)
19 pages, 3619 KB  
Article
Surface Urban Heat Island Risk Index Computation Using Remote-Sensed Data and Meta Population Dataset on Naples Urban Area (Italy)
by Massimo Musacchio, Alessia Scalabrini, Malvina Silvestri, Federico Rabuffi and Antonio Costanzo
Remote Sens. 2025, 17(19), 3306; https://doi.org/10.3390/rs17193306 - 26 Sep 2025
Abstract
Extreme climate events such as heatwaves are becoming more frequent and pose serious challenges in cities. Urban areas are particularly vulnerable because built surfaces absorb and release heat, while human activities generate additional greenhouse gases. This increases health risks, making it crucial to [...] Read more.
Extreme climate events such as heatwaves are becoming more frequent and pose serious challenges in cities. Urban areas are particularly vulnerable because built surfaces absorb and release heat, while human activities generate additional greenhouse gases. This increases health risks, making it crucial to study population exposure to heat stress. This research focuses on Naples, Italy’s most densely populated city, where intense human activity and unique geomorphological conditions influence local temperatures. The presence of a Surface Urban Heat Island (SUHI) is assessed by deriving high-resolution Land Surface Temperature (LST) in a time series ranging from 2013 to 2023, processed with the Statistical Mono Window (SMW) algorithm in the Google Earth Engine (GEE) environment. SMW needs brightness temperature (Tb) extracted from a Landsat 8 (L8) Thermal InfraRed Sensor (TIRS), emissivity from Advanced Spaceborne and Thermal Emission Radiometer Global Emissivity Database (ASTERGED), and atmospheric correction coefficients from the National Center for Environmental Prediction and Atmospheric Research (NCEP/NCAR). A total of 64 nighttime images were processed and analyzed to assess long-term trends and identify the main heat islands in Naples. The hottest image was compared with population data, including demographic categories such as children, elderly people, and pregnant women. A risk index was calculated by combining temperature values, exposure levels, and the vulnerability of each group. Results identified three major heat islands, showing that risk is strongly linked to both population density and heat island distribution. Incorporating Local Climate Zone (LCZ) classification further highlighted the urban areas most prone to extreme heat based on morphology. Full article
(This article belongs to the Special Issue Monitoring Urban Environment and Temperature Change Using Remote Sensing)
►▼ Show Figures

Figure 1

21 pages, 7001 KB  
Article
CGNet: Remote Sensing Instance Segmentation Method Using Contrastive Language–Image Pretraining and Gated Recurrent Units
by Hui Zhang, Zhao Tian, Zhong Chen, Tianhang Liu, Xueru Xu, Junsong Leng and Xinyuan Qi
Remote Sens. 2025, 17(19), 3305; https://doi.org/10.3390/rs17193305 - 26 Sep 2025
Abstract
Instance segmentation in remote sensing imagery is a significant application area within computer vision, holding considerable value in fields such as land planning and aerospace. The target scales of remote sensing images are often small, the contours of different categories of targets can [...] Read more.
Instance segmentation in remote sensing imagery is a significant application area within computer vision, holding considerable value in fields such as land planning and aerospace. The target scales of remote sensing images are often small, the contours of different categories of targets can be remarkably similar, and the background information is complex, containing more noise interference. Therefore, it is essential for the network model to utilize the background and internal instance information more effectively. Considering all the above, to fully adapt to the characteristics of remote sensing images, a network named CGNet, which combines an enhanced backbone with a contour–mask branch, is proposed. This network employs gated recurrent units for the iteration of contour and mask branches and adopts the attention head for branch fusion. Additionally, to address the common issues of missed and misdetections in target detection, a supervised backbone network using contrastive pretraining for feature supplementation is introduced. The proposed method has been experimentally validated in the NWPU VHR-10 and SSDD datasets, achieving average precision metrics of 68.1% and 67.4%, respectively, which are 0.9% and 3.2% higher than those of the suboptimal methods. Full article
(This article belongs to the Special Issue Object Detection in Remote Sensing Images Based on Artificial Intelligence)
►▼ Show Figures

Figure 1

21 pages, 2807 KB  
Article
Discrimination of Multiple Foliar Diseases in Wheat Using Novel Feature Selection and Machine Learning
by Sen Zhuang, Yujuan Huang, Jie Zhu, Qingluo Yang, Wei Li, Yangyang Gu, Tongjie Li, Hengbiao Zheng, Chongya Jiang, Tao Cheng, Yongchao Tian, Yan Zhu, Weixing Cao and Xia Yao
Remote Sens. 2025, 17(19), 3304; https://doi.org/10.3390/rs17193304 - 26 Sep 2025
Abstract
Wheat, a globally vital food crop, faces severe threats from numerous foliar diseases, which often infect agricultural fields, significantly compromising yield and quality. Rapid and accurate identification of the specific disease is crucial for ensuring food security. Although progress has been made in [...] Read more.
Wheat, a globally vital food crop, faces severe threats from numerous foliar diseases, which often infect agricultural fields, significantly compromising yield and quality. Rapid and accurate identification of the specific disease is crucial for ensuring food security. Although progress has been made in wheat foliar disease detection using RGB imaging and spectroscopy, most prior studies have focused on identifying the presence of a single disease, without considering the need to operationalize such methods, and it will be necessary to differentiate between multiple diseases. In this study, we systematically investigate the differentiation of three wheat foliar diseases (e.g., powdery mildew, stripe rust, and leaf rust) and evaluate feature selection strategies and machine learning models for disease identification. Based on field experiments conducted from 2017 to 2024 employing artificial inoculation, we established a standardized hyperspectral database of wheat foliar diseases classified by disease severity. Four feature selection methods were employed to extract spectral features prior to classification: continuous wavelet projection algorithm (CWPA), continuous wavelet analysis (CWA), successive projections algorithm (SPA), and Relief-F. The selected features (which are derived by CWPA, CWA, SPA, and Relief-F algorithm) were then used as predictors for three disease-identification machine learning models: random forest (RF), k-nearest neighbors (KNN), and naïve Bayes (BAYES). Results showed that CWPA outperformed other feature selection methods. The combination of CWPA and KNN for discriminating disease-infected (powdery mildew, stripe rust, leaf rust) and healthy leaves by using only two key features (i.e., 668 nm at wavelet scale 5 and 894 nm at wavelet scale 7), achieved an overall accuracy (OA) of 77% and a map-level image classification efficacy (MICE) of 0.63. This combination of feature selection and machine learning model provides an efficient and precise procedure for discriminating between multiple foliar diseases in agricultural fields, thus offering technical support for precision agriculture. Full article
►▼ Show Figures

Figure 1

19 pages, 2814 KB  
Article
High-Frequency Monitoring and Short-Term Forecasting of Surface Water Temperature Using a Novel Hyperspectral Proximal Sensing System
by Xiayang Luo, Na Li, Yunlin Zhang, Yibo Zhang, Kun Shi, Boqiang Qin and Guangwei Zhu
Remote Sens. 2025, 17(19), 3303; https://doi.org/10.3390/rs17193303 - 26 Sep 2025
Abstract
The lake surface water temperature (LSWT) is one of the key indicators for monitoring and predicting changes in lake ecosystems, as it regulates numerous physical and biogeochemical processes. However, current LSWT measurements mainly rely on infrared thermometry and traditional in situ sensors, and [...] Read more.
The lake surface water temperature (LSWT) is one of the key indicators for monitoring and predicting changes in lake ecosystems, as it regulates numerous physical and biogeochemical processes. However, current LSWT measurements mainly rely on infrared thermometry and traditional in situ sensors, and lack effective short-term LSWT forecasting and early warning capabilities. To overcome these limitations, we established a high-frequency, real-time, and accurate monitoring and forecasting method for the LSWT based on a novel hyperspectral proximal sensing system (HPSs). An LSWT inversion method was constructed based on a deep neural network (DNN) algorithm with a satisfactory accuracy of R2 = 0.99, RMSE = 0.92 °C, MAE = 0.64 °C. An analysis of data collected from October 2021 to December 2023 revealed distinct seasonal fluctuations in the LSWT in the northern region of Lake Taihu, with the LSWT ranging from 2.61 °C to 38.52 °C. The hourly LSWT for the next three days was forecasted based on a long short-term memory (LSTM) model, with the accuracy having an R2 = 0.99, an RMSE = 1.01 °C, and an MAE = 0.87 °C. This study complements lake water quality monitoring and early warning systems and supports a deeper understanding of dynamic processes within lake physical systems. Full article
(This article belongs to the Special Issue Remote Sensing for Soil and Water Conservation and Sustainable Development in the Context of Climate Change)
►▼ Show Figures

Figure 1

24 pages, 7680 KB  
Article
Warm-Season Precipitation in the Eastern Pamir Plateau: Evaluation from Multi-Source Datasets and Elevation Dependence
by Mengying Yao, Junqiang Yao, Weiyi Mao and Jing Chen
Remote Sens. 2025, 17(19), 3302; https://doi.org/10.3390/rs17193302 - 26 Sep 2025
Abstract
As the Pamir Plateau is known as the “Water Tower of Central Asia”, accurate precipitation dataset is essential for the study of climate and hydrology in this region. Based on the monthly precipitation observations from 268 meteorological stations in the Eastern Pamir Plateau [...] Read more.
As the Pamir Plateau is known as the “Water Tower of Central Asia”, accurate precipitation dataset is essential for the study of climate and hydrology in this region. Based on the monthly precipitation observations from 268 meteorological stations in the Eastern Pamir Plateau (EPP) during the April-to-September warm season of 2010–2024, this paper comprehensively evaluates the applicability of eight multi-source precipitation datasets in complex terrains by using statistical indicators, constructs a skill-weighted ensemble mean dataset (Skill-Ens), and analyzes the elevation-dependent characteristics of precipitation in the EPP. The research findings are as follows: (1) The warm-season precipitation in the EPP shows a significant elevation-dependent feature, with the maximum precipitation altitude (MPA) in the range of 2400–2800 m. Precipitation is reduced above this elevation range, but a second MPA may appear in the glacier area above 4000 m. (2) Among the studied eight datasets, the first-generation Chinese Global Land-surface Reanalysis (CRA40/Land) performs the best overall. A long-term (1979–2020) high-resolution (1/30°) precipitation dataset for the Third Pole region (TPHiPr) can most accurately capture the elevation-dependent characteristics of precipitation, while the satellite datasets are relatively poor in this respect. (3) The skill-weighted ensemble mean dataset (Skill-Ens) constructed in this study can significantly improve precipitation estimation (DISO = 0.35), especially in the MPA region, and can accurately depict the elevation-dependent characteristics of precipitation as well (CC = 0.92). In a word, this paper provides the applicable options for precipitation data in complex terrain areas. With the Skill-Ens, the limitation of the individual dataset has been compensated for, which is of significant application value in improving the accuracy of hydrological simulations in high-elevation mountainous areas. Full article
(This article belongs to the Section Atmospheric Remote Sensing)
►▼ Show Figures

Figure 1

25 pages, 20535 KB  
Article
DWTF-DETR: A DETR-Based Model for Inshore Ship Detection in SAR Imagery via Dynamically Weighted Joint Time–Frequency Feature Fusion
by Tiancheng Dong, Taoyang Wang, Yuqi Han, Deren Li, Guo Zhang and Yuan Peng
Remote Sens. 2025, 17(19), 3301; https://doi.org/10.3390/rs17193301 - 25 Sep 2025
Abstract
Inshore ship detection in synthetic aperture radar (SAR) imagery poses significant challenges due to the high density and diversity of ships. However, low inter-object backscatter contrast and blurred boundaries of docked ships often result in performance degradation for traditional object detection methods, especially [...] Read more.
Inshore ship detection in synthetic aperture radar (SAR) imagery poses significant challenges due to the high density and diversity of ships. However, low inter-object backscatter contrast and blurred boundaries of docked ships often result in performance degradation for traditional object detection methods, especially under complex backgrounds and low signal-to-noise ratio (SNR) conditions. To address these issues, this paper proposes a novel detection framework, the Dynamic Weighted Joint Time–Frequency Feature Fusion DEtection TRansformer (DETR) Model (DWTF-DETR), specifically designed for SAR-based ship detection in inshore areas. The proposed model integrates a Dual-Domain Feature Fusion Module (DDFM) to extract and fuse features from both SAR images and their frequency-domain representations, enhancing sensitivity to both high- and low-frequency target features. Subsequently, a Dual-Path Attention Fusion Module (DPAFM) is introduced to dynamically weight and fuse shallow detail features with deep semantic representations. By leveraging an attention mechanism, the module adaptively adjusts the importance of different feature paths, thereby enhancing the model’s ability to perceive targets with ambiguous structural characteristics. Experiments conducted on a self-constructed inshore SAR ship detection dataset and the public HRSID dataset demonstrate that DWTF-DETR achieves superior performance compared to the baseline RT-DETR. Specifically, the proposed method improves mAP@50 by 1.60% and 0.72%, and F1-score by 0.58% and 1.40%, respectively. Moreover, comparative experiments show that the proposed approach outperforms several state-of-the-art SAR ship detection methods. The results confirm that DWTF-DETR is capable of achieving accurate and robust detection in diverse and complex maritime environments. Full article
(This article belongs to the Special Issue Deep Learning for Multi-Source Remote Sensing Image Interpretation: Exploring, Rethinking, and Limiting Breakthroughs)
►▼ Show Figures

Figure 1

29 pages, 23948 KB  
Article
CAGMC-Defence: A Cross-Attention-Guided Multimodal Collaborative Defence Method for Multimodal Remote Sensing Image Target Recognition
by Jiahao Cui, Hang Cao, Lingquan Meng, Wang Guo, Keyi Zhang, Qi Wang, Cheng Chang and Haifeng Li
Remote Sens. 2025, 17(19), 3300; https://doi.org/10.3390/rs17193300 - 25 Sep 2025
Abstract
With the increasing diversity of remote sensing modalities, multimodal image fusion improves target recognition accuracy but also introduces new security risks. Adversaries can inject small, imperceptible perturbations into a single modality to mislead model predictions, which undermines system reliability. Most existing defences are [...] Read more.
With the increasing diversity of remote sensing modalities, multimodal image fusion improves target recognition accuracy but also introduces new security risks. Adversaries can inject small, imperceptible perturbations into a single modality to mislead model predictions, which undermines system reliability. Most existing defences are designed for single-modal inputs and face two key challenges in multimodal settings: 1. vulnerability to perturbation propagation due to static fusion strategies, and 2. the lack of collaborative mechanisms that limit overall robustness according to the weakest modality. To address these issues, we propose CAGMC-Defence, a cross-attention-guided multimodal collaborative defence framework for multimodal remote sensing. It contains two main modules. The Multimodal Feature Enhancement and Fusion (MFEF) module adopts a pseudo-Siamese network and cross-attention to decouple features, capture intermodal dependencies, and suppress perturbation propagation through weighted regulation and consistency alignment. The Multimodal Adversarial Training (MAT) module jointly generates optical and SAR adversarial examples and optimizes network parameters under consistency loss, enhancing robustness and generalization. Experiments on the WHU-OPT-SAR dataset show that CAGMC-Defence maintains stable performance under various typical adversarial attacks, such as FGSM, PGD, and MIM, retaining 85.74% overall accuracy even under the strongest white-box MIM attack (ϵ=0.05), significantly outperforming existing multimodal defence baselines. Full article
(This article belongs to the Special Issue Advances in Multimodal Remote Sensing Data: Processing, Fusion and Applications)
►▼ Show Figures

Figure 1

34 pages, 27487 KB  
Article
Detection of Aguadas (Ponds) Through Remote Sensing in the Bajo El Laberinto Region, Calakmul, Campeche, Mexico
by Alberto G. Flores Colin, Nicholas P. Dunning, Armando Anaya Hernández, Christopher Carr, Felix Kupprat, Kathryn Reese-Taylor and Demián Hinojosa-Garro
Remote Sens. 2025, 17(19), 3299; https://doi.org/10.3390/rs17193299 - 25 Sep 2025
Abstract
This study explores the detection and classification of aguadas (ponds) in the Bajo El Laberinto region, in the Calakmul Biosphere Reserve, Campeche, Mexico, using remote sensing techniques. Lidar-derived digital elevation models (DEMs), orthophotos and satellite imagery from multiple sources were employed to identify [...] Read more.
This study explores the detection and classification of aguadas (ponds) in the Bajo El Laberinto region, in the Calakmul Biosphere Reserve, Campeche, Mexico, using remote sensing techniques. Lidar-derived digital elevation models (DEMs), orthophotos and satellite imagery from multiple sources were employed to identify and characterize these water reservoirs, which played a crucial role in ancient Maya water management and continued to be vital for contemporary wildlife. By comparing different visualization techniques and imagery sources, the study demonstrates that while lidar data provides superior topographic detail, satellite imagery—particularly with nominal 3 m, or finer, spatial resolution with a near-infrared band—offers valuable complementary data including present-day hydrological and vegetative characteristics. In this study, 350 aguadas were identified in the broader region. The shapes, canopy cover, and topographic positions of these aguadas were documented, and the anthropogenic origin of most features was emphasized. The paper’s conclusion states that combining various remote sensing datasets enhances the identification and understanding of aguadas, providing insights into ancient Mayan adaptive strategies and contributing to ongoing archaeological and ecological research. Full article
►▼ Show Figures

Figure 1

23 pages, 16110 KB  
Article
Integrating Sentinel-1/2 Imagery and Climate Reanalysis for Monthly Bare Soil Mapping and Wind Erosion Modeling in Shandong Province, China
by Aobo Liu and Yating Chen
Remote Sens. 2025, 17(19), 3298; https://doi.org/10.3390/rs17193298 - 25 Sep 2025
Abstract
Accurate identification of bare soil exposure and quantification of associated dust emissions are essential for understanding land degradation and air quality risks in intensively farmed regions. This study develops a monthly monitoring and modeling framework to quantify bare soil dynamics and wind erosion-induced [...] Read more.
Accurate identification of bare soil exposure and quantification of associated dust emissions are essential for understanding land degradation and air quality risks in intensively farmed regions. This study develops a monthly monitoring and modeling framework to quantify bare soil dynamics and wind erosion-induced particulate matter (PM) emissions across Shandong Province from 2017 to 2024. By integrating Sentinel-1/2 imagery, climate reanalysis, terrain and soil data, and employing a stacking ensemble classification model, we mapped bare soil areas at 10 m resolution with an overall accuracy of 93.1%. The results show distinct seasonal variation, with bare soil area peaking in winter and early spring, exceeding 25,000 km2 or 15% of the total area, which is far above the 6.4% estimated by land cover products. Simulations using the CLM5.0 dust module indicate that annual PM10 emissions from bare soil averaged (2.72 ± 1.09) × 105 tons across 2017–2024. Emissions were highest in March and lowest in summer months, with over 80% of the total emitted during winter and spring. A notable increase in emissions was observed after 2022, likely due to more frequent extreme wind events. Spatially, emissions were concentrated in coastal lowlands such as the Yellow River Delta and surrounding saline–alkali lands. Our approach explicitly advances traditional methods by generating monthly 10 m bare soil maps and linking satellite-derived dynamics with process-based dust emission modeling, providing a robust basis for targeted dust control and land management strategies. Full article
(This article belongs to the Section Environmental Remote Sensing)
►▼ Show Figures

Figure 1

24 pages, 8686 KB  
Article
Comparative Analysis of Multi-Source Evapotranspiration Products in Xinjiang, China
by Jing Chen, Chenzhi Ma, Junqiang Yao, Weiyi Mao, Gangyong Li and Jian Peng
Remote Sens. 2025, 17(19), 3297; https://doi.org/10.3390/rs17193297 - 25 Sep 2025
Abstract
Evapotranspiration (ET) is essential to the terrestrial water and energy cycle. Accurate evapotranspiration estimates are crucial for understanding global and regional climate change and effective water management. This research uses meteorological observations to provide insights into the spatial and temporal trend patterns of [...] Read more.
Evapotranspiration (ET) is essential to the terrestrial water and energy cycle. Accurate evapotranspiration estimates are crucial for understanding global and regional climate change and effective water management. This research uses meteorological observations to provide insights into the spatial and temporal trend patterns of potential evapotranspiration (PET) and evapotranspiration in Xinjiang. A comparative analysis was conducted on six remote sensing-based, land surface model-based, and reanalysis-based products across multiple temporal scales (yearly, seasonally, and monthly) and point-to-point spatial dimensions and impacts of different land cover types was explored. The results show that: (1) The annual PET in Xinjiang showed a significant increasing trend, but showed a significant decreasing trend in summer and autumn. The actual evapotranspiration increased significantly in autumn. (2) The simulation of ET products in Xinjiang exhibits pronounced spatial heterogeneity and seasonal dependency. The datasets demonstrated a superior ability to simulate evapotranspiration in the northern part of Xinjiang compared to the southern part. Product performance varied extremely widely in desert areas but was stable in oasis areas. (3) Significant discrepancies exist across the multiple datasets, with the reanalysis-based products demonstrating superior comprehensive performance. This study offers critical insights for the suitable selection of evapotranspiration products and model optimization in the hydro-meteorological research of Xinjiang. Full article
(This article belongs to the Special Issue Precipitation and Evapotranspiration Mechanisms in Drylands and Their Remote Sensing Retrieval & Simulation (Second Edition))
►▼ Show Figures

Figure 1

40 pages, 12558 KB  
Article
Integrating Multi-Source Remote Sensing and Spatial Metrics to Quantify Urban Park Design Effects on Surface Cool Islands in Mexicali, Mexico
by Alan García-Haro, Blanca Arellano and Josep Roca
Remote Sens. 2025, 17(19), 3296; https://doi.org/10.3390/rs17193296 - 25 Sep 2025
Abstract
The Surface Cool Island (SCI) refers to localized reductions in land surface temperature (LST) produced by features that enhance evapotranspiration, shading, and energy flux regulation. In arid urban areas, vegetated parks play a key role in mitigating heat through these mechanisms. This study [...] Read more.
The Surface Cool Island (SCI) refers to localized reductions in land surface temperature (LST) produced by features that enhance evapotranspiration, shading, and energy flux regulation. In arid urban areas, vegetated parks play a key role in mitigating heat through these mechanisms. This study evaluates how park vegetation structure and spatial configuration influence SCI intensity (ΔTmax) and extent (Lmax) using multi-seasonal, day–night satellite observations in Mexicali, Mexico. A total of 435 parks were analyzed using Landsat 8/9 TIRS (30 m) for LST and Sentinel-2 MSI (10 m) for vegetation mapping via NDVI thresholding and supervised random forest (RF) classification. On average, parks lowered daytime LST by 0.81 °C (max: 6.41 °C), with a mean Lmax of 120 m; nighttime cooling was weaker (avg. ΔTmax: 0.37 °C; Lmax: 48 m). RF-derived metrics explained SCI variability more effectively (R2 up to 0.64 for ΔTmax; 0.48 for Lmax) than NDVI-based metrics (R2 < 0.35), highlighting the value of object-based land cover classification in capturing vegetation structure. This remote sensing framework offers a scalable method for assessing urban cooling performance and supports climate-adaptive green space design in hot-arid cities. Full article
(This article belongs to the Special Issue Estimating the Ecological Services of Urban Green Infrastructures Using Remote Sensing)
►▼ Show Figures

Figure 1

28 pages, 2643 KB  
Article
Extraction and Prediction of Spatiotemporal Pattern Characteristics of Farmland Non-Grain Conversion in Yunnan Province Based on Multi-Source Data
by Xianguang Ma, Bohui Tang, Feng He, Liang Huang, Zhen Zhang and Dongguang Cui
Remote Sens. 2025, 17(19), 3295; https://doi.org/10.3390/rs17193295 - 25 Sep 2025
Abstract
Non-grain conversion threatens food security in karst mountainous regions where fragmented terrain and shallow soils create unique agricultural challenges. This study examines Yunnan Province (28% karst coverage) in the Yunnan-Guizhou Plateau, where cultivated land faces distinct pressures from limited soil depth (average < [...] Read more.
Non-grain conversion threatens food security in karst mountainous regions where fragmented terrain and shallow soils create unique agricultural challenges. This study examines Yunnan Province (28% karst coverage) in the Yunnan-Guizhou Plateau, where cultivated land faces distinct pressures from limited soil depth (average < 30 cm in karst areas) and poor water retention capacity. Using multi-source data (2001–2021) and an integrated Dynamic Spatial-Temporal Clustering Model (DSTCM), we quantify non-grain conversion through a clearly defined Non-Grain Conversion Index (NGCI = 0.35 × CPI + 0.25 × LUI + 0.20 × RSI + 0.20 × PSI). Results reveal the NGCI declined from 45.91 to 21.05, indicating a 54% intensification in conversion (lower values = higher conversion intensity). Spatial analysis shows significant clustering (Moran’s I = 0.57, p < 0.001), with karst areas experiencing 23% higher conversion rates than non-karst regions. Key drivers include soil fertility limitations (t = 2.35, p = 0.027), crop type transitions (t = 3.12, p = 0.047), and economic pressures (t = 2.88, p = 0.012). Model predictions (accuracy: 92.51% ± 2.3%) forecast continued intensification with NGCI reaching 9.31 by 2035 under current policies. Spatial distribution mapping reveals concentrated conversion hotspots in southeastern karst regions, with 73% of high-intensity conversion occurring in areas with >30% karst coverage. This research provides critical insights for managing cultivated land in karst landscapes facing unique geological constraints. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Land Cover Changes and Associated Environmental Effects: Progress, Challenges, and Opportunities)
►▼ Show Figures

Figure 1

23 pages, 6045 KB  
Article
Early Warning of Anthracnose on Illicium verum Through the Synergistic Integration of Environmental and Remote Sensing Time Series Data
by Junji Li, Yuxin Zhao, Tianteng Zhang, Jiahui Du, Yucai Li, Ling Wu and Xiangnan Liu
Remote Sens. 2025, 17(19), 3294; https://doi.org/10.3390/rs17193294 - 25 Sep 2025
Abstract
Anthracnose on Illicium verum Hook.f (I. verum) significantly affects the yield and quality of I. verum, and timely detection methods are urgently needed for early control. However, early warning is difficult due to two major challenges, including the sparse availability [...] Read more.
Anthracnose on Illicium verum Hook.f (I. verum) significantly affects the yield and quality of I. verum, and timely detection methods are urgently needed for early control. However, early warning is difficult due to two major challenges, including the sparse availability of optical remote sensing observations due to frequent cloud and rain interference, and the weak spectral responses caused by infestation during early stages. In this article, a framework for early warning of anthracnose on I. verum that combines high-frequency environmental (meteorological and topographical) data and Sentinel-2 remote sensing time-series data, along with a Time-Aware Long Short-Term Memory (T-LSTM) network incorporating an attentional mechanism (At-T-LSTM) was proposed. First, all available environmental and remote sensing data during the study period were analyzed to characterize the early anthracnose outbreaks, and sensitive features were selected as the algorithm input. On this basis, to address the issue of unequal temporal lengths between environmental and remote sensing time series, the At-T-LSTM model incorporates a time-aware mechanism to capture intra-feature temporal dependencies, while a Self-Attention layer is used to quantify inter-feature interaction weights, enabling effective multi-source features time-series fusion. The results show that the proposed framework achieves a spatial accuracy (F1-score) of 0.86 and a temporal accuracy of 83% in early-stage detection, demonstrating high reliability. By integrating remote sensing features with environmental drivers, this approach enables multi-feature collaborative modeling for the risk assessment and monitoring of I. verum anthracnose. It effectively mitigates the impact of sparse observations and significantly improves the accuracy of early warnings. Full article
(This article belongs to the Special Issue Application of Remote Sensing in Agroforestry (Third Edition))
►▼ Show Figures

Figure 1

23 pages, 3115 KB  
Article
Deep Learning-Based Prediction of Multi-Species Leaf Pigment Content Using Hyperspectral Reflectance
by Ziyu Wang and Duanyang Xu
Remote Sens. 2025, 17(19), 3293; https://doi.org/10.3390/rs17193293 - 25 Sep 2025
Abstract
Leaf pigment composition and concentration are crucial indicators of plant physiological status, photosynthetic capacity, and overall ecosystem health. While spectroscopy techniques show promise for monitoring vegetation growth, phenology, and stress, accurately estimating leaf pigments remains challenging due to the complex reflectance properties across [...] Read more.
Leaf pigment composition and concentration are crucial indicators of plant physiological status, photosynthetic capacity, and overall ecosystem health. While spectroscopy techniques show promise for monitoring vegetation growth, phenology, and stress, accurately estimating leaf pigments remains challenging due to the complex reflectance properties across diverse tree species. This study introduces a novel approach using a two-dimensional convolutional neural network (2D-CNN) coupled with a genetic algorithm (GA) to predict leaf pigment content, including chlorophyll a and b content (Cab), carotenoid content (Car), and anthocyanin content (Canth). Leaf reflectance and biochemical content measurements taken from 28 tree species were used in this study. The reflectance spectra ranging from 400 nm to 800 nm were encoded as 2D matrices with different sizes to train the 2D-CNN and compared with the one-dimensional convolutional neural network (1D-CNN). The results show that the 2D-CNN model (nRMSE = 11.71–31.58%) achieved higher accuracy than the 1D-CNN model (nRMSE = 12.79–55.34%) in predicting leaf pigment contents. For the 2D-CNN models, Cab achieved the best estimation accuracy with an nRMSE value of 11.71% (R2 = 0.92, RMSE = 6.10 µg/cm2), followed by Car (R2 = 0.84, RMSE = 1.03 µg/cm2, nRMSE = 12.29%) and Canth (R2 = 0.89, RMSE = 0.35 µg/cm2, nRMSE = 31.58%). Both 1D-CNN and 2D-CNN models coupled with GA using a subset of the spectrum produced higher prediction accuracy in all pigments than those using the full spectrum. Additionally, the generalization of 2D-CNN is higher than that of 1D-CNN. This study highlights the potential of 2D-CNN approaches for accurate prediction of leaf pigment content from spectral reflectance data, offering a promising tool for advanced vegetation monitoring. Full article
(This article belongs to the Section Forest Remote Sensing)
►▼ Show Figures

Figure 1

25 pages, 11479 KB  
Article
Improved Pixel Offset Tracking Method Based on Corner Point Variation in Large-Gradient Landslide Deformation Monitoring
by Dingyi Zhou, Zhifang Zhao and Fei Zhao
Remote Sens. 2025, 17(19), 3292; https://doi.org/10.3390/rs17193292 - 25 Sep 2025
Abstract
Aiming at the problems of feature matching difficulty and limited extension application in the existing pixel offset tracking method for large-gradient landslides, this paper proposes an improved pixel offset tracking method based on corner point variation. Taking the Jinshajiang Baige landslide as the [...] Read more.
Aiming at the problems of feature matching difficulty and limited extension application in the existing pixel offset tracking method for large-gradient landslides, this paper proposes an improved pixel offset tracking method based on corner point variation. Taking the Jinshajiang Baige landslide as the research object, the method’s effectiveness is verified using sentinel data. Through a series of experiments, the results show that (1) the use of VV (Vertical-Vertical) and VH (Vertical-Horizontal) polarisation information combined with the mean value calculation method can improve the accuracy and credibility of the circling of the landslide monitoring range, make up for the limitations of the single polarisation information, and capture the landslide range more comprehensively, which provides essential information for landslide monitoring. (2) The choice of scale factor has an essential influence on the results of corner detection, in which the best corner effect is obtained when the scale factor R is 2, which provides an essential reference basis for practical application. (3) By comparing traditional normalized and adaptive window cross-correlation methods with the proposed approach in calculating landslide offset distances, the proposed method shows superior matching accuracy and sliding direction estimation. (4) Analysis of pixels P1, P2, and P3 confirms the method’s high accuracy and reliability in landslide displacement assessment, demonstrating its advantage in tracking pixel offsets in large-gradient scenarios. Therefore, the proposed method offers an effective solution for large-gradient landslide monitoring, overcoming limitations of feature matching and limited applicability. It is expected to provide more reliable technical support for geological disaster management. Full article
(This article belongs to the Special Issue Multi-Source Remote Sensing Integrations in Geological Hazards Research)
►▼ Show Figures

Figure 1

17 pages, 5663 KB  
Article
Evaluating the Performance of Satellite-Derived Vegetation Indices in Gross Primary Productivity (GPP) Estimation at 30 m and 500 m Spatial Resolution
by Deli Cao, Xiaojuan Huang, Gang Liu, Lingwen Tian, Qi Xin and Yuli Yang
Remote Sens. 2025, 17(19), 3291; https://doi.org/10.3390/rs17193291 - 25 Sep 2025
Abstract
Vegetation indices (VIs) have been extensively employed as proxies for gross primary productivity (GPP). However, it is unclear how the spatial resolution effects the performance of VIs in GPP estimation in different biomes when matching the flux tower footprint. Here, we examined the [...] Read more.
Vegetation indices (VIs) have been extensively employed as proxies for gross primary productivity (GPP). However, it is unclear how the spatial resolution effects the performance of VIs in GPP estimation in different biomes when matching the flux tower footprint. Here, we examined the relationship with tower GPP between Landsat-footprint VIs and MODIS-footprint VIs. We first calculated Landsat-footprint VIs (e.g., Normalized Difference Vegetation Index (NDVI), enhanced vegetation index (EVI), two-band EVI (EVI2), near-infrared reflectance of vegetation (NIRv) and kernel Normalized Difference Vegetation Index (kNDVI)) averaged over all the pixels within the footprint and MODIS-footprint VIs with 3 × 3 km or 1 × 1 km around the tower, respectively. We then examined the relationship between Landsat- and MODIS-footprint VIs and tower GPP at monthly scale over 76 FLUXNET sites across ten vegetation types worldwide. The results showed that Landsat-footprint VIs had stronger performance than MODIS-footprint VIs for GPP estimation in all ecosystems, with high improvement on croplands, wetlands, and grasslands and moderate improvements on mixed forest, evergreen needleleaf forest, and deciduous broadleaf forest. Moreover, NIRv showed a stronger correlation with tower-based GPP than NDVI, EVI, EVI2, and kNDVI in ten ecosystems both at 30 m and 500 spatial resolutions. Our findings highlighted the critical role of VIs with high spatial resolution and footprint-aware matching in GPP estimation. Full article
(This article belongs to the Special Issue Remote Sensing for Environmental Monitoring in Cold and Arid Regions: Advances and Perspectives)
►▼ Show Figures

Figure 1

26 pages, 4007 KB  
Article
Carbon Benefits and Water Costs of Cover Crops by Assimilating Sentinel-2 and Landsat-8 Images in a Crop Model
by Taeken Wijmer, Rémy Fieuzal, Jean François Dejoux, Ahmad Al Bitar, Tiphaine Tallec and Eric Ceschia
Remote Sens. 2025, 17(19), 3290; https://doi.org/10.3390/rs17193290 - 25 Sep 2025
Abstract
The use of cover crops is one of the most effective practices for maintaining, or even improving, the carbon balance of agricultural soils, while offering various ecosystem benefits. However, replacing bare soil with cover crops can increase transpiration and potentially reduce the water [...] Read more.
The use of cover crops is one of the most effective practices for maintaining, or even improving, the carbon balance of agricultural soils, while offering various ecosystem benefits. However, replacing bare soil with cover crops can increase transpiration and potentially reduce the water available for subsequent cash crops. The study takes place in southwestern France where it is essential to strike a balance between carbon storage and water availability, and where agroecological practices are encouraged and water resources are limited and expected to diminish with climate change. In this study, estimates of cover crop biomass production, as well as of the components of the water and carbon cycles, are carried out using a hybrid approach, AgriCarbon-EO, combining modeling, remote sensing, and assimilation, with quantification of target variables and their uncertainties at decametric resolution. The SAFYE-CO2 agrometeorological model used in AgriCarbon-EO is calibrated to represent cover crops development, and simulated variables are compared with CO2 fluxes and evapotranspiration measured by eddy covariance (for NEE, R2 = 0.57, RMSE = 0.97 gC·m−2; for ETR, R2 = 0.42, RMSE = 0.87 mm), as well as to an extensive above-ground biomass dataset (R2 = 0.71, RMSE = 93.3 g·m−2). Knowing the local performance of the approach, a large-scale, decametric-resolution modeling exercise was carried out to simulate winter cover crops in southwestern France, over five contrasting fallow periods. The significant variability in cover crop phenology and above-ground biomass was characterized, and estimates of the amount of humified carbon added to the soil by cover crops were quantified at the pixel level. With amounts ranging from 40 to 130 gC·m−2 for most of the considered pixels, these new SOC values show clear trends as a function of cumulative evapotranspiration. However, the impact of cover crops on soil water content appears to be minimal due to spring precipitation. Full article
(This article belongs to the Special Issue Remote Sensing Application in the Carbon Flux Modelling)
►▼ Show Figures

Figure 1

22 pages, 23570 KB  
Article
Bundled-Images Based Geo-Positioning Method for Satellite Images Without Using Ground Control Points
by Zhenling Ma, Yuan Chen, Xu Zhong, Hong Xie, Yanlin Liu, Zhengjie Wang and Peng Shi
Remote Sens. 2025, 17(19), 3289; https://doi.org/10.3390/rs17193289 - 25 Sep 2025
Abstract
Bundle adjustment without Ground Control Points (GCPs) using stereo remote sensing images represents a reliable and efficient approach for realizing the demand for regional and global mapping. This paper proposes a bundled-images based geo-positioning method that leverages a Kalman filter to effectively integrate [...] Read more.
Bundle adjustment without Ground Control Points (GCPs) using stereo remote sensing images represents a reliable and efficient approach for realizing the demand for regional and global mapping. This paper proposes a bundled-images based geo-positioning method that leverages a Kalman filter to effectively integrate new image observations with their corresponding historical bundled images. Under the assumption that the noise follows a Gaussian distribution, a linear mean square estimator is employed to orient the new images. The historical bundled images can be updated with posterior covariance information to maintain consistent accuracy with the newly oriented images. This method employs recursive computation to dynamically orient the new images, ensuring consistent accuracy across all the historical and new images. To validate the proposed method, extensive experiments were carried out using two satellite datasets comprising both homologous (IKONOS) and heterogeneous (TH-1 and ZY-3) sources. The experiment results reveal that without using GCPs, the proposed method can meet 1:50,000 mapping standards with heterogeneous TH-1 and ZY-3 datasets and 1:10,000 mapping accuracy requirements with homologous IKONOS datasets. These experiments indicate that as the bundled images expand further, the image quantity growth no longer results in substantial improvements in precision, suggesting the presence of an accuracy ceiling. The final positioning accuracy is predominantly influenced by the initial bundled image quality. Experimental evidence suggests that when using the proposed method, the bundled image sets should exhibit superior precision compared to subsequently new images. In future research, we will expand the coverage to regional or global scales. Full article
►▼ Show Figures

Figure 1

31 pages, 15645 KB  
Article
RCF-YOLOv8: A Multi-Scale Attention and Adaptive Feature Fusion Method for Object Detection in Forward-Looking Sonar Images
by Xiaoxue Li, Yuhan Chen, Xueqin Liu, Zhiliang Qin, Jiaxin Wan and Qingyun Yan
Remote Sens. 2025, 17(19), 3288; https://doi.org/10.3390/rs17193288 - 25 Sep 2025
Abstract
Acoustic imaging systems are essential for underwater target recognition and localization, but forward-looking sonar (FLS) imagery faces challenges due to seabed variability, resulting in low resolution, blurred images, and sparse targets. To address these issues, we introduce RCF-YOLOv8, an enhanced detection framework based [...] Read more.
Acoustic imaging systems are essential for underwater target recognition and localization, but forward-looking sonar (FLS) imagery faces challenges due to seabed variability, resulting in low resolution, blurred images, and sparse targets. To address these issues, we introduce RCF-YOLOv8, an enhanced detection framework based on YOLOv8, designed to improve FLS image analysis. Key innovations include the use of CoordConv modules to better encode spatial information, improving feature extraction and reducing misdetection rates. Additionally, an efficient multi-scale attention (EMA) mechanism addresses sparse target distributions, optimizing feature fusion and improving the network’s ability to identify key areas. Lastly, the C2f module with high-quality feature fusion (C2f-Fusion) optimizes feature extraction from noisy backgrounds. RCF-YOLOv8 achieved a 98.8% mAP@50 and a 67.6% mAP@50-95 on the URPC2021 dataset, outperforming baseline models with a 2.4% increase in single-threshold accuracy and a 10.4% increase in multi-threshold precision, demonstrating its robustness for underwater detection. Full article
(This article belongs to the Special Issue Efficient Object Detection Based on Remote Sensing Images)
►▼ Show Figures

Figure 1

25 pages, 104808 KB  
Article
From the Moon to Mercury: Release of Global Crater Catalogs Using Multimodal Deep Learning for Crater Detection and Morphometric Analysis
by Riccardo La Grassa, Cristina Re, Elena Martellato, Adriano Tullo, Silvia Bertoli, Gabriele Cremonese, Natalia Amanda Vergara Sassarini, Maddalena Faletti, Valentina Galluzzi and Lorenza Giacomini
Remote Sens. 2025, 17(19), 3287; https://doi.org/10.3390/rs17193287 - 25 Sep 2025
Abstract
This study has compiled the first impact-crater dataset for Mercury with diameters greater than 400 m by a multimodal deep-learning pipeline. We present an enhanced deep learning framework for large-scale planetary crater detection, extending the YOLOLens architecture through the integration of multimodal inputs: [...] Read more.
This study has compiled the first impact-crater dataset for Mercury with diameters greater than 400 m by a multimodal deep-learning pipeline. We present an enhanced deep learning framework for large-scale planetary crater detection, extending the YOLOLens architecture through the integration of multimodal inputs: optical imagery, digital terrain models (DTMs), and hillshade derivatives. By incorporating morphometric data, the model achieves robust detection of impact craters that are often imperceptible in optical imagery alone, especially in regions affected by low contrast, degraded rims, or shadow-dominated illumination. The resulting catalogs LU6M371TGT for the Moon and ME6M300TGT for Mercury constitute the most comprehensive automated crater inventories to date, demonstrating the effectiveness of multimodal learning and cross-planet transfer. This work highlights the critical role of terrain information in planetary object detection and establishes a scalable, high-throughput pipeline for planetary surface analysis using modern deep learning tools. To validate the pipeline, we compare its predictions against the manually annotated catalogs for the Moon, Mercury, and several regional inventories, observing close agreement across the full diameter spectrum, revealing a high level of confidence in our approach. This work presents a spatial density analysis, comparing the spatial density maps of small and large craters highlighting the uneven distribution of crater sizes across Mercury. We explore the prevalence of kilometer-scale (1–5 km range) impact craters, demonstrating that these dominate the crater population in certain regions of Mercury’s surface. Full article
(This article belongs to the Special Issue Advancements in Deep Learning for Remote Sensing: Exploring Planetary and Earth Observation Applications)
►▼ Show Figures

Figure 1

21 pages, 7702 KB  
Article
Mechanisms and Predictability of Beaufort Sea Ice Retreat Revealed by Coupled Modeling and Remote Sensing Data
by Hongtao Nie, Zijia Zheng, Shuo Wei, Wei Zhao and Xiaofan Luo
Remote Sens. 2025, 17(19), 3286; https://doi.org/10.3390/rs17193286 - 25 Sep 2025
Abstract
The Beaufort Sea has experienced significant sea ice retreat in recent decades, driven by both thermodynamic and dynamic processes. This study investigates the drivers and predictability of summer sea ice retreat in the Beaufort Sea by integrating an ocean–sea ice model with satellite-derived [...] Read more.
The Beaufort Sea has experienced significant sea ice retreat in recent decades, driven by both thermodynamic and dynamic processes. This study investigates the drivers and predictability of summer sea ice retreat in the Beaufort Sea by integrating an ocean–sea ice model with satellite-derived sea ice concentration data and atmospheric reanalysis products. Model diagnostics from 1994 to 2019 reveal that thermodynamic processes dominate annual sea ice loss (approximately 90%), with vertical heat flux accounting for roughly 85% of total oceanic heat input. The summer sea ice minimum area and the day of opening, derived from either model results and satellite observations, have a strong correlation with R2 = 0.60 and R2 = 0.77, respectively, enabling regression equations based solely on remote sensing data. Further multiple linear regression incorporating preceding winter (January to April) accumulated temperature and easterly wind yields moderately robust forecasts of minimum sea ice area (R2 = 0.49) during 1998–2020. Additionally, analysis of reanalysis wind data shows that the timing of minimum sea ice area is significantly influenced by the frequency and intensity of sub-seasonal easterly wind events during melt season. These results demonstrate the critical importance of remote sensing in monitoring Arctic sea ice variability and enhancing seasonal prediction capability under a rapidly changing climate. Full article
(This article belongs to the Section Ocean Remote Sensing)
►▼ Show Figures

Figure 1

22 pages, 4173 KB  
Article
A Novel Nighttime Sea Fog Detection Method Based on Generative Adversarial Networks
by Wuyi Qiu, Xiaoqun Cao and Shuo Ma
Remote Sens. 2025, 17(19), 3285; https://doi.org/10.3390/rs17193285 - 24 Sep 2025
Abstract
Nighttime sea fog exhibits high frequency and prolonged duration, posing significant risks to maritime navigation safety. Current detection methods primarily rely on the dual-infrared channel brightness temperature difference technique, which faces challenges such as threshold selection difficulties and a tendency toward overestimation. In [...] Read more.
Nighttime sea fog exhibits high frequency and prolonged duration, posing significant risks to maritime navigation safety. Current detection methods primarily rely on the dual-infrared channel brightness temperature difference technique, which faces challenges such as threshold selection difficulties and a tendency toward overestimation. In contrast, the VIIRS Day/Night Band (DNB) offers exceptional nighttime visible-like cloud imaging capabilities, offering a new solution to alleviate the overestimation issues inherent in infrared detection algorithms. Recent advances in artificial intelligence have further addressed the threshold selection problem in traditional detection methods. Leveraging these developments, this study proposes a novel generative adversarial network model incorporating attention mechanisms (SEGAN) to achieve accurate nighttime sea fog detection using DNB data. Experimental results demonstrate that SEGAN achieves satisfactory performance, with probability of detection, false alarm rate, and critical success index reaching 0.8708, 0.0266, and 0.7395, respectively. Compared with the operational infrared detection algorithm, these metrics show improvements of 0.0632, 0.0287, and 0.1587. Notably, SEGAN excels at detecting sea fog obscured by thin cloud cover, a scenario where conventional infrared detection algorithms typically fail. SEGAN emphasizes semantic consistency in its output, endowing it with enhanced robustness across varying sea fog concentrations. Full article
(This article belongs to the Special Issue Advances in Sensor Design and Algorithm Development for Cloud Remote Sensing)
►▼ Show Figures

Figure 1

22 pages, 10283 KB  
Article
Outlier Correction in Remote Sensing Retrieval of Ocean Wave Wavelength and Application to Bathymetry
by Zhengwen Xu, Shouxian Zhu, Wenjing Zhang, Yanyan Kang and Xiangbai Wu
Remote Sens. 2025, 17(19), 3284; https://doi.org/10.3390/rs17193284 - 24 Sep 2025
Abstract
The extraction of ocean wave wavelengths from optical imagery via Fast Fourier Transform (FFT) exhibits significant potential for Wave-Derived Bathymetry (WDB). However, in practical applications, this method frequently produces anomalously large wavelength estimates. To date, there has been insufficient exploration into the mechanisms [...] Read more.
The extraction of ocean wave wavelengths from optical imagery via Fast Fourier Transform (FFT) exhibits significant potential for Wave-Derived Bathymetry (WDB). However, in practical applications, this method frequently produces anomalously large wavelength estimates. To date, there has been insufficient exploration into the mechanisms underlying image spectral leakage to low wavenumbers and its suppression strategies. This study investigates three plausible mechanisms contributing to spectral leakage in optical images and proposes a subimage-based preprocessing framework: prior to executing two-dimensional FFT, the remote sensing subimages employed for wavelength inversion undergo three sequential steps: (1) truncation of distorted pixel values using a Gaussian mixture model; (2) application of a polynomial detrending surface; (3) incorporation of a two-dimensional Hann window. Subsequently, the dominant wavenumber peak is localized in the power spectrum and converted to wavelength values. Water depth is then inverted using the linear dispersion equation, combined with wave periods derived from ERA5. Taking 2 m-resolution WorldView-2 imagery of Sanya Bay, China as a case study, 1024 m subimages are utilized, with validation conducted against chart-sounding data. Results demonstrate that the proportion of subimages with anomalous wavelengths is reduced from 18.9% to 3.3% (in contrast to 14.0%, 7.8%, and 16.6% when the three preprocessing steps are applied individually). Within the 0–20 m depth range, the water depth retrieval accuracy achieves a Mean Absolute Error (MAE) of 1.79 m; for the 20–40 m range, the MAE is 6.38 m. A sensitivity analysis of subimage sizes (512/1024/2048 m) reveals that the 1024 m subimage offers an optimal balance between accuracy and coverage. However, residual anomalous wavelengths persist in near-shore subimages, and errors still increase with increasing water depth. This method is both concise and effective, rendering it suitable for application in shallow-water WDB scenarios. Full article
(This article belongs to the Section Ocean Remote Sensing)
►▼ Show Figures

Figure 1

17 pages, 11436 KB  
Technical Note
Variation in SCM Supply Effects as Reflected by Coupling Relationship with Pycnocline
by Jie Yang, Yunzhao Han, Meng Hou and Lixing Fang
Remote Sens. 2025, 17(19), 3283; https://doi.org/10.3390/rs17193283 - 24 Sep 2025
Abstract
The subsurface chlorophyll maximum (SCM) is widely observed in the ocean and is often associated with phytoplankton biomass, where aggregated phytoplankton leads to increased chlorophyll concentrations in the water column. Pycnocline facilitates biomass accumulation by trapping nutrients and providing favorable physical conditions. However, [...] Read more.
The subsurface chlorophyll maximum (SCM) is widely observed in the ocean and is often associated with phytoplankton biomass, where aggregated phytoplankton leads to increased chlorophyll concentrations in the water column. Pycnocline facilitates biomass accumulation by trapping nutrients and providing favorable physical conditions. However, comprehensive studies remain lacking regarding the coupling mechanism between pycnocline and SCM and the extent to which this relationship affects SCM dynamics through biomass accumulation. To investigate the seasonal coupling between the pycnocline and SCM, we established a linear regression model and quantified their relationship using a coupling coefficient, which describes the seasonal transition of SCM in terms of biomass accumulation. The results were validated using BGC-Argo data. Our findings reveal that SCM and the pycnocline consistently exhibit periodic coupling patterns within seasonal cycles, and in the Indian Ocean and the northwestern Pacific, SCM is predominantly biomass-driven during seasons with strong pycnocline coupling (the coupling coefficient ranges between 0.5 and 0.7). In contrast, this coupling weakens significantly in oligotrophic regions (the coupling coefficient remained below 0.3 in more than half of the months studied), where SCM no longer exhibits a clear overlap with peaks in particulate backscattering (BBP). Full article
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-50
Previous Issue
Back to TopTop