Remote Sensing

16 pages, 6871 KB

Open AccessArticle

Investigation of Thermal Effects of Lakes on Their Adjacent Lands Across Tibetan Plateau Using Satellite Observation During 2000 to 2022

by Linan Guo, Wenbin Sun, Yanhong Wu, Junfeng Xiong and Jianing Jiang

Remote Sens. 2025, 17(19), 3314; https://doi.org/10.3390/rs17193314 (registering DOI) - 27 Sep 2025

Abstract

Understanding the regulatory effects of lakes on land surface temperature is critical for assessing regional climatological and ecological dynamics on the Tibetan Plateau (TP). This study investigates the spatiotemporal variability in the thermal effect of lakes across the TP from 2000 to 2022 [...] Read more.

Understanding the regulatory effects of lakes on land surface temperature is critical for assessing regional climatological and ecological dynamics on the Tibetan Plateau (TP). This study investigates the spatiotemporal variability in the thermal effect of lakes across the TP from 2000 to 2022 using the MODIS land surface temperature product and a model-based lake surface water temperature product. Our results show that the lake–land temperature difference (LLTD) within 10 km buffer zones surrounding lakes ranges from −2.8 °C to 3.4 °C. A declining trend in 79.2% of the lakes is detected during 2000–2022, with summer contributing most significantly to this decrease at a rate of −0.56 °C per decade. Assessments of the spatial extent of lake thermal effects show that the “warm island” effect in autumn (5.5 km) influences a larger area compared to the “cold island” effect in summer (1.3 km). Furthermore, southwestern lakes exhibit stronger warming intensities, while northwestern lakes show more pronounced cooling intensities. Correlation analyses indicate that lake thermal effects are significantly related to lake depth, freeze-up start date, and salinity. These findings highlight the importance of lake thermal regulation in heat balance changes and provide a foundation for further research into its climatic and ecological implications on the Tibetan Plateau. Full article

(This article belongs to the Special Issue Global Monitoring of Inland Water Using Remote Sensing and Artificial Intelligence)

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36 pages, 9276 KB

Open AccessArticle

Understanding Landslide Expression in SAR Backscatter Data: Global Study and Disaster Response Application

by Erin Lindsay, Alexandra Jarna Ganerød, Graziella Devoli, Johannes Reiche, Steinar Nordal and Regula Frauenfelder

Remote Sens. 2025, 17(19), 3313; https://doi.org/10.3390/rs17193313 (registering DOI) - 27 Sep 2025

Abstract

Cloud cover can delay landslide detection in optical satellite imagery for weeks, complicating disaster response. Synthetic Aperture Radar (SAR) backscatter imagery, which is widely used for monitoring floods and avalanches, remains underutilised for landslide detection due to a limited understanding of landslide signatures [...] Read more.

Cloud cover can delay landslide detection in optical satellite imagery for weeks, complicating disaster response. Synthetic Aperture Radar (SAR) backscatter imagery, which is widely used for monitoring floods and avalanches, remains underutilised for landslide detection due to a limited understanding of landslide signatures in SAR data. We developed a conceptual model of landslide expression in SAR backscatter (σ°) change images through iterative investigation of over 1000 landslides across 30 diverse study areas. Using multi-temporal composites and dense time series Sentinel-1 C-band SAR data, we identified characteristic patterns linked to land cover, terrain, and landslide material. The results showed either increased or decreased backscatter depending on environmental conditions, with reduced visibility in urban or mixed vegetation areas. Detection was also hindered by geometric distortions and snow cover. The diversity of landslide expression illustrates the need to consider local variability and multi-track (ascending and descending) satellite data in designing representative training datasets for automated detection models. The conceptual model was applied to three recent disaster events using the first post-event Sentinel-1 image, successfully identifying previously unknown landslides before optical imagery became available in two cases. This study provides a theoretical foundation for interpreting landslides in SAR imagery and demonstrates its utility for rapid landslide detection. The findings support further exploration of rapid landslides in SAR backscatter data and future development of automated detection models, offering a valuable tool for disaster response. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar Data Processing and Application (Second Edition))

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18 pages, 6145 KB

Open AccessArticle

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-K_60x, and the neural network emulator, RRTMG-K_NN, 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-K_60x, and the neural network emulator, RRTMG-K_NN, 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-K_NN 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-K_60x. In contrast, the infrequent application of RRTMG-K (RRTMG-K_60x) 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

Open AccessArticle

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

Open AccessArticle

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)

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27 pages, 3391 KB

Open AccessArticle

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

Open AccessArticle

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

Open AccessArticle

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

Open AccessArticle

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)

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21 pages, 7001 KB

Open AccessArticle

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)

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21 pages, 2807 KB

Open AccessArticle

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

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19 pages, 2814 KB

Open AccessArticle

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)

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24 pages, 7680 KB

Open AccessArticle

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)

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25 pages, 20535 KB

Open AccessArticle

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)

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29 pages, 23948 KB

Open AccessArticle

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)

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34 pages, 27487 KB

Open AccessArticle

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

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23 pages, 16110 KB

Open AccessArticle

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)

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24 pages, 8686 KB

Open AccessArticle

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))

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40 pages, 12558 KB

Open AccessArticle

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)

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