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Remote Sens., Volume 17, Issue 13 (July-1 2025) – 221 articles

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49 pages, 11337 KiB  
Review
A Systematic Review of Marine Habitat Mapping in the Central-Eastern Atlantic Archipelagos: Methodologies, Current Trends, and Knowledge Gaps
by Marcial Cosme De Esteban, Fernando Tuya, Ricardo Haroun and Francisco Otero-Ferrer
Remote Sens. 2025, 17(13), 2331; https://doi.org/10.3390/rs17132331 - 7 Jul 2025
Viewed by 164
Abstract
Mapping marine habitats is fundamental for biodiversity conservation and ecosystem-based management in oceanic regions under increasing anthropogenic and climatic pressures. In the context of global initiatives—such as marine protected area expansion and international agreements—habitat mapping has become mandatory for regional and global conservation [...] Read more.
Mapping marine habitats is fundamental for biodiversity conservation and ecosystem-based management in oceanic regions under increasing anthropogenic and climatic pressures. In the context of global initiatives—such as marine protected area expansion and international agreements—habitat mapping has become mandatory for regional and global conservation policies. It provides spatial data to delineate essential habitats, support connectivity analyses, and assess pressures, enabling ecosystem-based marine spatial planning aligned with EU directives (2008/56/EC; 2014/89/EU). Beyond biodiversity, macrophytes, rhodolith beds, and coral reefs deliver key ecosystem services—carbon sequestration, coastal protection, nursery functions, and fisheries support—essential to local socioeconomies. This systematic review (PRISMA guidelines) examined 69 peer-reviewed studies across Central-Eastern Atlantic archipelagos (Macaronesia: the Azores, Madeira, the Canaries, and Cabo Verde) and the Mid-Atlantic Ridge. We identified knowledge gaps, methodological trends, and key challenges, emphasizing the integration of cartographic, ecological, and technological approaches. Although methodologies diversified over time, the lack of survey standardization, limited ground truthing, and heterogeneous datasets constrained the production of high-resolution bionomic maps. Regional disparities persist in technology access and habitat coverage. The Azores showed the highest species richness (393), dominated by acoustic mapping in corals. Madeira was most advanced in the remote mapping of rhodoliths; the Canaries focused on shallow macrophytes with direct mapping; and Cabo Verde remains underrepresented. Harmonized protocols and regional cooperation are needed to improve data interoperability and predictive modeling. Full article
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23 pages, 10211 KiB  
Article
Integrating Sentinel-1 SAR and Machine Learning Models for Optimal Soil Moisture Sensor Placement at Catchment Scale
by Yi Xie, Guotao Cui, Kaifeng Zheng and Guoping Tang
Remote Sens. 2025, 17(13), 2330; https://doi.org/10.3390/rs17132330 - 7 Jul 2025
Viewed by 277
Abstract
Accurate calibration and validation of remote sensing soil moisture products critically depend on high-quality in situ measurements. However, effectively capturing representative soil moisture patterns across heterogeneous catchments using ground-based sensors remains a significant challenge. To address this, we propose a machine-learning-based framework for [...] Read more.
Accurate calibration and validation of remote sensing soil moisture products critically depend on high-quality in situ measurements. However, effectively capturing representative soil moisture patterns across heterogeneous catchments using ground-based sensors remains a significant challenge. To address this, we propose a machine-learning-based framework for optimizing soil moisture sensor network deployment at the catchment scale. The framework was validated using Sentinel-1 SAR-derived soil moisture data within a humid catchment in southern China. Results show that a network of nine optimally placed sensors minimized prediction errors (RMSE: 7.20%), outperforming both sparser and denser configurations. The optimized sensor network achieved a 52.45% reduction in RMSE compared to random placement. Moreover, the optimal number of sensors varied with seasonal dynamics: the wet season required 11 sensors due to increased precipitation-induced spatial variability, whereas the dry season could be adequately monitored with only six sensors. The proposed optimization approach offers a cost-effective strategy for collecting reliable in situ data, which is essential for improving the accuracy and applicability of remote sensing products in catchment-scale soil moisture monitoring. Full article
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28 pages, 48949 KiB  
Article
Effects of the October 2024 Storm over the Global Ionosphere
by Krishnendu Sekhar Paul, Haris Haralambous, Mefe Moses and Sharad C. Tripathi
Remote Sens. 2025, 17(13), 2329; https://doi.org/10.3390/rs17132329 - 7 Jul 2025
Viewed by 386
Abstract
The present study analyzes the global ionospheric response to the intense geomagnetic storm of 10–11 October 2024 (SYM—H minimum of −346 nT), using observations from COSMIC—2 and Swarm satellites, GNSS TEC, and Digisondes. Significant uplift of the F-region was observed across both Hemispheres [...] Read more.
The present study analyzes the global ionospheric response to the intense geomagnetic storm of 10–11 October 2024 (SYM—H minimum of −346 nT), using observations from COSMIC—2 and Swarm satellites, GNSS TEC, and Digisondes. Significant uplift of the F-region was observed across both Hemispheres on the dayside, primarily driven by equatorward thermospheric winds and prompt penetration electric fields (PPEFs). However, this uplift did not correspond with increases in foF2 due to enhanced molecular nitrogen-promoting recombination in sunlit regions and the F2 peak rising beyond the COSMIC—2 detection range. In contrast, in the Southern Hemisphere nightside ionosphere exhibited pronounced Ne depletion and low hmF2 values, attributed to G-conditions and thermospheric composition changes caused by storm-time circulation. Strong vertical plasma drifts exceeding 100 m/s were observed during both the main and recovery phases, particularly over Ascension Island, driven initially by southward IMF—Bz-induced PPEFs and later by disturbance dynamo electric fields (DDEFs) as IMF—Bz turned northward. Swarm data revealed a poleward expansion of the Equatorial Ionization Anomaly (EIA), with more pronounced effects in the Southern Hemisphere due to seasonal and longitudinal variations in ionospheric conductivity. Additionally, the storm excited Large-Scale Travelling Ionospheric Disturbances (LSTIDs), triggered by thermospheric perturbations and electrodynamic drivers, including PPEFs and DDEFs. These disturbances, along with enhanced westward thermospheric wind and altered zonal electric fields, modulated ionospheric irregularity intensity and distribution. The emergence of anti-Sq current systems further disrupted quiet-time electrodynamics, promoting global LSTID activity. Furthermore, storm-induced equatorial plasma bubbles (EPBs) were observed over Southeast Asia, initiated by enhanced PPEFs during the main phase and suppressed during recovery, consistent with super EPB development mechanisms. Full article
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27 pages, 7958 KiB  
Article
Spatiotemporal Dynamic Changes in Cropland and Multi-Scenario Simulation in the Yarlung Zangbo River Basin
by Mengni He, Yanguo Liu, Liwei Tan, Jingji Li, Ziqin Wang, Yafeng Lu, Wenxu Liu and Qi Tan
Remote Sens. 2025, 17(13), 2328; https://doi.org/10.3390/rs17132328 - 7 Jul 2025
Viewed by 230
Abstract
Cropland is crucial for food production, food security, and economic stability, especially in high-altitude Tibetan regions where it is limited. This study investigates the spatiotemporal changes and driving factors of cropland in the Yarlung Zangbo River Basin (YZRB) from 2000 to 2020. Using [...] Read more.
Cropland is crucial for food production, food security, and economic stability, especially in high-altitude Tibetan regions where it is limited. This study investigates the spatiotemporal changes and driving factors of cropland in the Yarlung Zangbo River Basin (YZRB) from 2000 to 2020. Using land use transfer matrices, center of gravity models, standard deviation ellipses, the Patch-generating Land Use Simulation (PLUS) model, and Partial Least Squares Structural Equation Modeling (PLS-SEM), it explores cropland dynamics and predicts land use for 2030. Results show the following: (1) Between 2000 and 2020, the area of cropland entering the basin exceeded that leaving, mainly concentrated in the middle and lower reaches, with a dynamic degree of 0.97%. The proportion of cropland increased from 1.28% in 2000 to 1.52% in 2020. (2) The center of gravity shifted northwest (2000–2005), southeast (2005–2015), and northwest again (2015–2020). (3) Factors like elevation, temperature, precipitation, population density, and GDP correlated with cropland changes. Natural factors positively affected cropland expansion, while socioeconomic and proximity factors indirectly inhibited it. (4) The 2030 cropland conservation scenario in the PLUS model ensures cropland security, ecological protection, and controlled construction land expansion, aligning with the Sustainable Development Goals. Targeted cropland conservation measures can effectively promote sustainable land use and ecological security in the Yarlung Zangbo River Basin. Full article
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20 pages, 13039 KiB  
Article
An Azimuth Ambiguity Suppression Method for SAR Based on Time-Frequency Joint Analysis
by Gangbing Zhou, Ze Yu, Xianxun Yao and Jindong Yu
Remote Sens. 2025, 17(13), 2327; https://doi.org/10.3390/rs17132327 - 7 Jul 2025
Viewed by 151
Abstract
Azimuth ambiguity caused by spectral aliasing severely degrades the quality of Synthetic Aperture Radar (SAR) images. To suppress azimuth ambiguity while preserving image details as much as possible, this paper proposes an azimuth ambiguity suppression method for SAR based on time-frequency joint analysis. [...] Read more.
Azimuth ambiguity caused by spectral aliasing severely degrades the quality of Synthetic Aperture Radar (SAR) images. To suppress azimuth ambiguity while preserving image details as much as possible, this paper proposes an azimuth ambiguity suppression method for SAR based on time-frequency joint analysis. By exploiting the distribution differences of ambiguous signals across different sub-spectra, the method locates azimuth ambiguity in the time domain through multi-sub-spectrum change detection and fusion, followed by ambiguity suppression in the azimuth time-frequency domain. Experimental results demonstrate that the proposed method effectively suppresses azimuth ambiguity while maintaining superior performance in preserving genuine targets. Full article
(This article belongs to the Special Issue Efficient Object Detection Based on Remote Sensing Images)
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21 pages, 5160 KiB  
Article
A Spatiotemporal Sequence Prediction Framework Based on Mask Reconstruction: Application to Short-Duration Precipitation Radar Echoes
by Zhi Yang, Changzheng Liu, Ping Mei and Lei Wang
Remote Sens. 2025, 17(13), 2326; https://doi.org/10.3390/rs17132326 - 7 Jul 2025
Viewed by 168
Abstract
Short-term precipitation forecasting is a core task in meteorological science, aiming to achieve accurate predictions by modeling the spatiotemporal evolution of radar echo sequences, thereby supporting meteorological services and disaster warning systems. However, existing spatiotemporal sequence prediction methods still struggle to disentangle complex [...] Read more.
Short-term precipitation forecasting is a core task in meteorological science, aiming to achieve accurate predictions by modeling the spatiotemporal evolution of radar echo sequences, thereby supporting meteorological services and disaster warning systems. However, existing spatiotemporal sequence prediction methods still struggle to disentangle complex spatiotemporal dependencies effectively and fail to capture the nonlinear chaotic characteristics of precipitation systems. This often results in ambiguous predictions, attenuation of echo intensity, and spatial localization errors. To address these challenges, this paper proposes a unified spatiotemporal sequence prediction framework based on spatiotemporal masking, which comprises two stages: self-supervised pre-training and task-oriented fine-tuning. During pre-training, the model learns global structural features of meteorological systems from sparse contexts by randomly masking local spatiotemporal regions of radar images. In the fine-tuning stage, considering the importance of the temporal dimension in short-term precipitation forecasting and the complex long-range dependencies in spatiotemporal evolution of precipitation systems, we design an RNN-based cyclic temporal mask self-encoder model (MAE-RNN) and a transformer-based spatiotemporal attention model (STMT). The former focuses on capturing short-term temporal dynamics, while the latter simultaneously models long-range dependencies across space and time via a self-attention mechanism, thereby avoiding the smoothing effects on high-frequency details that are typical of conventional convolutional or recurrent structures. The experimental results show that STMT improves 3.73% and 2.39% in CSI and HSS key indexes compared with the existing advanced models, and generates radar echo sequences that are closer to the real data in terms of air mass morphology evolution and reflection intensity grading. Full article
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19 pages, 7486 KiB  
Article
Advancing GNOS-R Soil Moisture Estimation: A Multi-Angle Retrieval Algorithm for FY-3E
by Xuerui Wu, Junming Xia, Weihua Bai and Yueqiang Sun
Remote Sens. 2025, 17(13), 2325; https://doi.org/10.3390/rs17132325 - 7 Jul 2025
Viewed by 162
Abstract
Surface soil moisture (SM) is a critical factor in hydrological modeling, agricultural management, and numerical weather forecasting. This paper presents a highly effective soil moisture retrieval algorithm developed for the FY-3E (FengYun-3E) GNOS-R (GNSS Occultation Sounder II-Reflectometry) instrument. The algorithm incorporates a first-order [...] Read more.
Surface soil moisture (SM) is a critical factor in hydrological modeling, agricultural management, and numerical weather forecasting. This paper presents a highly effective soil moisture retrieval algorithm developed for the FY-3E (FengYun-3E) GNOS-R (GNSS Occultation Sounder II-Reflectometry) instrument. The algorithm incorporates a first-order vegetation model that considers vegetation density and volume scattering. Utilizing multi-angle GNOS-R observations, the algorithm derives surface reflectivity, which is combined with ancillary data on opacity, vegetation water content, and soil moisture from SMAP (Soil Moisture Active Passive) to optimize the retrieval process. The algorithm has been specifically tailored for different surface conditions, including bare soil, areas with low vegetation, and densely vegetated regions. The algorithm directly incorporates the angle-dependence of observations, leading to enhanced retrieval accuracy. Additionally, a new approach parameterizes surface roughness as a function of angle, allowing for refined corrections in reflectivity measurements. For vegetated areas, the algorithm effectively isolates the soil surface signal by eliminating volume scattering and vegetation effects, enabling the accurate estimation of soil moisture. By leveraging multi-angle data, the algorithm achieves significantly improved retrieval accuracy, with root mean square errors of 0.0235, 0.0264, and 0.0191 (g/cm3) for bare, low-vegetation, and dense-vegetation areas, respectively. This innovative methodology offers robust global soil moisture estimation capabilities using the GNOS-R instrument, surpassing the accuracy of previous techniques. Full article
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35 pages, 4572 KiB  
Review
Land Use and Land Cover Products for Agricultural Mapping Applications in Brazil: Challenges and Limitations
by Priscilla Azevedo dos Santos, Marcos Adami, Michelle Cristina Araujo Picoli, Victor Hugo Rohden Prudente, Júlio César Dalla Mora Esquerdo, Gilberto Ribeiro de Queiroz, Cleverton Tiago Carneiro de Santana and Michel Eustáquio Dantas Chaves
Remote Sens. 2025, 17(13), 2324; https://doi.org/10.3390/rs17132324 - 7 Jul 2025
Viewed by 544
Abstract
Reliable remote sensing-based Land Use and Land Cover (LULC) information is crucial for assessing Earth’s surface activities. Brazil’s agricultural dynamics, including year-round cropping, multiple cropping, and regional climate variability, make LULC monitoring a highly challenging task. The country has thirteen remote sensing-based LULC [...] Read more.
Reliable remote sensing-based Land Use and Land Cover (LULC) information is crucial for assessing Earth’s surface activities. Brazil’s agricultural dynamics, including year-round cropping, multiple cropping, and regional climate variability, make LULC monitoring a highly challenging task. The country has thirteen remote sensing-based LULC products specifically tailored for this purpose. However, the differences and the results of these products have not yet been synthesized to provide coherent guidance in assessing their spatio-temporal agricultural dynamics and identifying promising approaches and issues that affect LULC analysis. This review represents the first comprehensive assessment of the advantages, challenges, and limitations, highlighting the main issues when dealing with contrasting LULC maps. These challenges include incompatibility, a lack of updates, non-systematic classification ontologies, and insufficient data to monitor Brazilian LULC information. The consequences include impacts on intercropping estimation, diminished representation or misrepresentation of croplands; temporal discontinuity; an insufficient number of classes for subannual cropping evaluation; and reduced compatibility, comparability, and spectral separability. The study provides insights into the use of these products as primary input data for remote sensing-based applications. Moreover, it provides prospects for enhancing existing mapping efforts or developing new national-level initiatives to represent the spatio-temporal variation of Brazilian agriculture. Full article
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22 pages, 2479 KiB  
Article
Principles of Correction for Long-Term Orbital Observations of Atmospheric Composition, Applied to AIRS v.6 CH4 and CO Data
by Vadim Rakitin, Eugenia Fedorova, Andrey Skorokhod, Natalia Kirillova, Natalia Pankratova and Nikolai Elansky
Remote Sens. 2025, 17(13), 2323; https://doi.org/10.3390/rs17132323 - 7 Jul 2025
Viewed by 131
Abstract
This study considers methods for assessing the quality of orbital observations, quantifying drift over time, and the application of correction methods to long-term series. AIRS v6 (IR-only) satellite methane (CH4) and carbon monoxide (CO) total column (TC) measurements were compared with [...] Read more.
This study considers methods for assessing the quality of orbital observations, quantifying drift over time, and the application of correction methods to long-term series. AIRS v6 (IR-only) satellite methane (CH4) and carbon monoxide (CO) total column (TC) measurements were compared with NDACC ground station data from 2003 to 2022. For CH4, negative trends were observed in the difference between satellite and ground measurements (AIRS-GR) at all 18 stations (mean drift: 1.69 × 1014 ± 0.31 × 1014 molecules/cm2 per day), suggesting a shift in the orbital spectrometer parameters is probable. The application of a dynamic correction based on this drift coefficient significantly improved the correlation with satellite data for both daily means and trends at all stations. In contrast, AIRS v6 CO measurements showed a strong initial correlation (R = 0.93 for the entire dataset, and R ~ 0.8–0.95 for separate stations) without systematic drift, i.e., the trends of AIRS-GR at individual sites were oppositely directed and statistically insignificant. Therefore, the AIRS v6 CO TC satellite product does not require additional correction within this method. The developed methodology for satellite data verification and correction is supposed to be universal and applicable to other long-term orbital observations. Full article
(This article belongs to the Special Issue Remote Sensing and Climate Pollutants)
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23 pages, 7965 KiB  
Article
A COSMIC-2-Based Global Mean TEC Model and Its Application to Calibrating IRI-2020 Global Ionospheric Maps
by Yuxiao Lei, Weitang Wang, Yibin Yao and Liang Zhang
Remote Sens. 2025, 17(13), 2322; https://doi.org/10.3390/rs17132322 - 7 Jul 2025
Viewed by 152
Abstract
While space weather indices (e.g., F10.7, Dst index) are commonly employed to characterize ionospheric activity levels, the Global Mean Electron Content (GMEC) provides a more direct and comprehensive indicator of the global ionospheric state. This metric demonstrates greater potential than space weather indices [...] Read more.
While space weather indices (e.g., F10.7, Dst index) are commonly employed to characterize ionospheric activity levels, the Global Mean Electron Content (GMEC) provides a more direct and comprehensive indicator of the global ionospheric state. This metric demonstrates greater potential than space weather indices for calibrating empirical ionospheric models such as IRI-2020. The COSMIC-2 constellation enables continuous, all-weather global ionospheric monitoring via radio occultation, unimpeded by land–sea distribution constraints, with over 8000 daily occultation events suitable for GMEC modeling. This study developed two lightweight GMEC models using COSMIC-2 data: (1) a POD GMEC model based on slant TEC (STEC) extracted from Level 1b podTc2 products and (2) a PROF GMEC model derived from vertical TEC (VTEC) calculated from electron density profiles (EDPs) in Level 2 ionPrf products. Both backpropagation neural network (BPNN)-based models generate hourly GMEC outputs as global spatial averages. Critically, GMEC serves as an essential intermediate step that addresses the challenges of utilizing spatially irregular occultation data by compressing COSMIC-2’s ionospheric information into an integrated metric. Building on this compressed representation, we implemented a convolutional neural network (CNN) that incorporates GMEC as an auxiliary feature to calibrate IRI-2020’s global ionospheric maps. This approach enables computationally efficient correction of systemic IRI TEC errors. Experimental results demonstrate (i) 48.5% higher accuracy in POD/PROF GMEC relative to IRI-2020 GMEC estimates, and (ii) the calibrated global IRI TEC model (designated GCIRI TEC) reduces errors by 50.15% during geomagnetically quiet periods and 28.5% during geomagnetic storms compared to the original IRI model. Full article
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18 pages, 2395 KiB  
Article
Theoretical Potential of TanSat-2 to Quantify China’s CH4 Emissions
by Sihong Zhu, Dongxu Yang, Liang Feng, Longfei Tian, Yi Liu, Junji Cao, Minqiang Zhou, Zhaonan Cai, Kai Wu and Paul I. Palmer
Remote Sens. 2025, 17(13), 2321; https://doi.org/10.3390/rs17132321 - 7 Jul 2025
Viewed by 172
Abstract
Satellite-based monitoring of atmospheric column-averaged dry-air mole fraction (XCH4) is essential for quantifying methane (CH4) emissions, yet uncharacterized spatially varying biases in XCH4 observations can cause misattribution in flux estimates. This study assesses the potential of the upcoming [...] Read more.
Satellite-based monitoring of atmospheric column-averaged dry-air mole fraction (XCH4) is essential for quantifying methane (CH4) emissions, yet uncharacterized spatially varying biases in XCH4 observations can cause misattribution in flux estimates. This study assesses the potential of the upcoming TanSat-2 satellite mission to estimate China’s CH4 emission using a series of Observing System Simulation Experiments (OSSEs) based on an Ensemble Kalman Filter (EnKF) inversion framework coupled with GEOS-Chem on a 0.5° × 0.625° grid, alongside an evaluation of current TROPOMI-based products against Total Carbon Column Observing Network (TCCON) observations. Assuming a target precision of 8 ppb, TanSat-2 could achieve an annual national emission estimate accuracy of 2.9% ± 4.2%, reducing prior uncertainty by 84%, with regional deviations below 5.0% across Northeast, Central, East, and Southwest China. In contrast, limited coverage in South China due to persistent cloud cover leads to a 26.1% discrepancy—also evident in pseudo TROPOMI OSSEs—highlighting the need for complementary ground-based monitoring strategies. Sensitivity analyses show that satellite retrieval biases strongly affect inversion robustness, reducing the accuracy in China’s total emission estimates by 5.8% for every 1 ppb increase in bias level across scenarios, particularly in Northeast, Central and East China. We recommend expanding ground-based XCH4 observations in these regions to support the correction of satellite-derived biases and improve the reliability of satellite-constrained inversion results. Full article
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16 pages, 5068 KiB  
Technical Note
VGOS Dual Linear Polarization Data Processing Techniques Applied to Differential Observation of Satellites
by Jiangying Gan, Fengchun Shu, Xuan He, Yidan Huang, Fengxian Tong and Yan Sun
Remote Sens. 2025, 17(13), 2319; https://doi.org/10.3390/rs17132319 - 7 Jul 2025
Viewed by 142
Abstract
The Very Long Baseline Interferometry Global Observing System (VGOS), a global network of stations equipped with small-diameter, fast-slewing antennas and broadband receivers, is primarily utilized for geodesy and astrometry. In China, the Shanghai and Urumqi VGOS stations have been developed to perform radio [...] Read more.
The Very Long Baseline Interferometry Global Observing System (VGOS), a global network of stations equipped with small-diameter, fast-slewing antennas and broadband receivers, is primarily utilized for geodesy and astrometry. In China, the Shanghai and Urumqi VGOS stations have been developed to perform radio source observation regularly. However, these VGOS stations have not yet been used to observe Earth satellites or deep-space probes. In addition, suitable systems for processing VGOS satellite data are unavailable. In this study, we explored a data processing pipeline and method suitable for VGOS data observed in the dual linear polarization mode and applied to the differential observation of satellites. We present the VGOS observations of the Chang’e 5 lunar orbiter as a pilot experiment for VGOS observations of Earth satellites to verify our processing pipeline. The interferometric fringes were obtained by the cross-correlation of Chang’e 5 lunar orbiter signals. The data analysis yielded a median delay precision of 0.16 ns with 30 s single-channel integration and a baseline closure delay standard deviation of 0.14 ns. The developed data processing pipeline can serve as a foundation for future Earth-orbiting satellite observations, potentially supporting space-tie satellite missions aimed at constructing the terrestrial reference frame (TRF). Full article
(This article belongs to the Special Issue Space Geodesy and Time Transfer: From Satellite to Science)
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24 pages, 5899 KiB  
Article
Unveiling Spatiotemporal Differences and Responsive Mechanisms of Seamless Hourly Ozone in China Using Machine Learning
by Jiachen Fan, Tijian Wang, Qingeng Wang, Mengmeng Li, Min Xie, Shu Li, Bingliang Zhuang and Ume Kalsoom
Remote Sens. 2025, 17(13), 2318; https://doi.org/10.3390/rs17132318 - 7 Jul 2025
Viewed by 195
Abstract
Surface ozone (O3) is a multifaceted threat that not only deteriorates the environment but also poses risks to human health. Here, we estimated the seamless hourly surface O3 in China using Extreme Gradient Boosting (XGBoost) with multisource data fusion to [...] Read more.
Surface ozone (O3) is a multifaceted threat that not only deteriorates the environment but also poses risks to human health. Here, we estimated the seamless hourly surface O3 in China using Extreme Gradient Boosting (XGBoost) with multisource data fusion to investigate spatiotemporal differences in O3 during multistage COVID-19, and the response of O3 variation to meteorology and emissions were explored using Shapley Additive Explanations (SHAP) and WRF-Chem. The results indicate that the optimized model demonstrated higher accuracy, with CV-R2 of 0.96–0.97 and RMSE of 4.58–5.00 μg/m3. Benefitting from the full coverage of the dataset, the underestimated O3 was corrected and hotspots of short-term O3 pollution events were successfully captured. O3 increased by 16.8% during the lockdown, with high values clustered in the north and west, attributed to the weakened urban NOx titration resulting from reduced emissions. During the control and regulation period, O3 levels declined year by year. O3 exhibited significant fluctuations in the Pearl River Delta but remained stable in western China, with both regions demonstrating high sensitivity to meteorological variability. Among these, solar radiation and temperature were the key meteorological factors. The seamless high-resolution O3 datasets will enable more insightful analyses regarding the spatiotemporal characterization and cause analysis. Full article
(This article belongs to the Section Atmospheric Remote Sensing)
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24 pages, 4777 KiB  
Article
Disagreements in Equivalent-Factor-Based Valuation of County-Level Ecosystem Services in China: Insights from Comparison Among Ten LULC Datasets
by Daiyi Song, Lizhou Wang, Yingge Wang, Bowen Zhao, Qi Jin and Jianxin Yang
Remote Sens. 2025, 17(13), 2320; https://doi.org/10.3390/rs17132320 - 6 Jul 2025
Viewed by 211
Abstract
Valuation of ecosystem services (ESs) is crucial for understanding the benefits provided by ecosystems and informing sustainable management and policy decisions related to ecosystem protection. This study explores the disagreements in ecosystem service value (ESV) at the county level across China in 2020 [...] Read more.
Valuation of ecosystem services (ESs) is crucial for understanding the benefits provided by ecosystems and informing sustainable management and policy decisions related to ecosystem protection. This study explores the disagreements in ecosystem service value (ESV) at the county level across China in 2020 by comparing ten land cover datasets of varying resolutions from 500 to 10 m, using the equivalent factor method. Significant disagreements in ESV estimates are identified, revealing spatial heterogeneity and large inconsistencies among estimates from different datasets, even with high spatial resolution (10 m). Across all counties, the typical discrepancy in ESV estimates between any two datasets reaches 3503 CNY/ha, and the ESV estimates for each county show an average coefficient of variation (CV) of 0.186 across the ten datasets, indicating considerable inconsistency attributable to dataset selection. The results highlight that ESV evaluations based on the CLCD, Globeland30, and GLC-FCS30 datasets demonstrate higher consistency and reliability, making them suitable for regional ecosystem service valuation. Both the landscape configurations and the area disparities of different land types have significant impacts on ESV disagreement. This study provides valuable insights into the applicability of different datasets for ESV evaluation, thereby enhancing the reliability of ESV assessments and supporting informed decision making in ecosystem management. Full article
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17 pages, 14349 KiB  
Article
The Western North Pacific Monsoon Dominates Basin-Scale Interannual Variations in Tropical Cyclone Frequency
by Xin Li, Jian Cao, Boyang Wang and Jiawei Feng
Remote Sens. 2025, 17(13), 2317; https://doi.org/10.3390/rs17132317 - 6 Jul 2025
Viewed by 144
Abstract
The monsoon is regarded as a key system influencing tropical cyclone (TC) activity over the Western North Pacific (WNP). However, the relationship between WNP TC frequency (TCF) and the monsoon across different timescales remains incompletely understood. This study explores the interannual-scale relationship between [...] Read more.
The monsoon is regarded as a key system influencing tropical cyclone (TC) activity over the Western North Pacific (WNP). However, the relationship between WNP TC frequency (TCF) and the monsoon across different timescales remains incompletely understood. This study explores the interannual-scale relationship between WNP TCF and the WNP summer monsoon over the period 1982–2020. We found that the interannual variation in basin-scale TCF is dominated by dynamic factors, particularly lower troposphere vorticity and middle troposphere ascending motion, which are driven by the WNP summer monsoon. Enhanced monsoonal precipitation over the WNP intensifies convective heating, which acts as a diabatic heat source and triggers a Rossby wave response to the west. This response generates anomalous lower troposphere cyclonic circulation and ascending motion in the main TC development region. In turn, the strengthened WNP summer monsoon circulation further amplifies precipitation, establishing positive feedback between atmospheric circulation and convection. This mechanism establishes dynamic conditions favorable for TC genesis, thereby dominating the basin-scale interannual variation in TCF. Full article
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23 pages, 15159 KiB  
Article
TBFH: A Total-Building-Focused Hybrid Dataset for Remote Sensing Image Building Detection
by Lin Yi, Feng Wang, Guangyao Zhou, Niangang Jiao, Minglin He, Jingxing Zhu and Hongjian You
Remote Sens. 2025, 17(13), 2316; https://doi.org/10.3390/rs17132316 - 6 Jul 2025
Viewed by 272
Abstract
Building extraction plays a crucial role in a variety of applications, including urban planning, high-precision 3D reconstruction, and environmental monitoring. In particular, the accurate detection of tall buildings is essential for reliable modeling and analysis. However, most existing building-detection methods are primarily trained [...] Read more.
Building extraction plays a crucial role in a variety of applications, including urban planning, high-precision 3D reconstruction, and environmental monitoring. In particular, the accurate detection of tall buildings is essential for reliable modeling and analysis. However, most existing building-detection methods are primarily trained on datasets dominated by low-rise structures, resulting in degraded performance when applied to complex urban scenes with high-rise buildings and severe occlusions. To address this limitation, we propose TBFH (Total-Building-Focused Hybrid), a novel dataset specifically designed for building detection in remote sensing imagery. TBFH comprises a diverse collection of tall buildings across various urban environments and is integrated with the publicly available WHU Building dataset to enable joint training. This hybrid strategy aims to enhance model robustness and generalization across varying urban morphologies. We also propose the KTC metric to quantitatively evaluate the structural integrity and shape fidelity of building segmentation results. We evaluated the effectiveness of TBFH on multiple state-of-the-art models, including UNet, UNetFormer, ABCNet, BANet, FCN, DeepLabV3, MANet, SegFormer, and DynamicVis. Our comparative experiments conducted on the Tall Building dataset, the WHU dataset, and TBFH demonstrated that models trained with TBFH significantly outperformed those trained on individual datasets, showing notable improvements in IoU, F1, and KTC scores as well as in the accuracy of building shape delineation. These findings underscore the critical importance of incorporating tall building-focused data to improve both detection accuracy and generalization performance. Full article
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28 pages, 35973 KiB  
Article
SFT-GAN: Sparse Fast Transformer Fusion Method Based on GAN for Remote Sensing Spatiotemporal Fusion
by Zhaoxu Ma, Wenxing Bao, Wei Feng, Xiaowu Zhang, Xuan Ma and Kewen Qu
Remote Sens. 2025, 17(13), 2315; https://doi.org/10.3390/rs17132315 - 5 Jul 2025
Viewed by 193
Abstract
Multi-source remote sensing spatiotemporal fusion aims to enhance the temporal continuity of high-spatial, low-temporal-resolution images. In recent years, deep learning-based spatiotemporal fusion methods have achieved significant progress in this field. However, existing methods face three major challenges. First, large differences in spatial resolution [...] Read more.
Multi-source remote sensing spatiotemporal fusion aims to enhance the temporal continuity of high-spatial, low-temporal-resolution images. In recent years, deep learning-based spatiotemporal fusion methods have achieved significant progress in this field. However, existing methods face three major challenges. First, large differences in spatial resolution among heterogeneous remote sensing images hinder the reconstruction of high-quality texture details. Second, most current deep learning-based methods prioritize spatial information while overlooking spectral information. Third, these methods often depend on complex network architectures, resulting in high computational costs. To address the aforementioned challenges, this article proposes a Sparse Fast Transformer fusion method based on Generative Adversarial Network (SFT-GAN). First, the method introduces a multi-scale feature extraction and fusion architecture to capture temporal variation features and spatial detail features across multiple scales. A channel attention mechanism is subsequently designed to integrate these heterogeneous features adaptively. Secondly, two information compensation modules are introduced: detail compensation module, which enhances high-frequency information to improve the fidelity of spatial details; spectral compensation module, which improves spectral fidelity by leveraging the intrinsic spectral correlation of the image. In addition, the proposed sparse fast transformer significantly reduces both the computational and memory complexity of the method. Experimental results on four publicly available benchmark datasets showed that the proposed SFT-GAN achieved the best performance compared with state-of-the-art methods in fusion accuracy while reducing computational cost by approximately 70%. Additional classification experiments further validated the practical effectiveness of SFT-GAN. Overall, this approach presents a new paradigm for balancing accuracy and efficiency in spatiotemporal fusion. Full article
(This article belongs to the Special Issue Remote Sensing Data Fusion and Applications (2nd Edition))
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23 pages, 3873 KiB  
Article
Coupling Machine Learning and Physically Based Hydrological Models for Reservoir-Based Streamflow Forecasting
by Benjun Jia and Wei Fang
Remote Sens. 2025, 17(13), 2314; https://doi.org/10.3390/rs17132314 - 5 Jul 2025
Viewed by 264
Abstract
High-accuracy streamflow forecasting with long lead times can help promote the efficient utilization of water resources. However, the construction of cascade reservoirs has allowed the evolution of natural continuous rivers into multi-block rivers. The existing streamflow forecasting methods fail to consider the impact [...] Read more.
High-accuracy streamflow forecasting with long lead times can help promote the efficient utilization of water resources. However, the construction of cascade reservoirs has allowed the evolution of natural continuous rivers into multi-block rivers. The existing streamflow forecasting methods fail to consider the impact of reservoir operation. Thus, a novel short-term streamflow forecasting method for multi-block watersheds was proposed by integrating machine learning and hydrological models. Firstly, based on IMERG precipitation, the forecast precipitation product’s error is corrected by the long short-term memory neural network (LSTM). Secondly, coupling convolutional LSTM (ConvLSTM) and LSTM, operation rules for cascade reservoirs are extracted. Thirdly, a short-term deterministic streamflow forecasting model was built for multi-block watersheds. Finally, according to the sources of forecasting errors, probabilistic streamflow forecasting models based on the Gaussian mixture model (GMM) were proposed, and their performances were compared. Taking the Yalong River as an example, the main results are as follows: (1) Deep learning models (ConvLSTM and LSTM) show good performance in forecast precipitation correction and reservoir operation rule extraction, contributing to streamflow forecasting accuracy. (2) The proposed streamflow deterministic forecasting method has good forecasting performance with NSE above 0.83 for the following 1–5 days. (3) The GMM model, using upstream evolutionary forecasted streamflow, interval forecasted streamflow, and downstream forecasted streamflow as the input–output combination, has good probabilistic forecasting performance and can adequately characterize the “non-normality” and “heteroskedasticity” of forecasting uncertainty. Full article
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31 pages, 20469 KiB  
Article
YOLO-SRMX: A Lightweight Model for Real-Time Object Detection on Unmanned Aerial Vehicles
by Shimin Weng, Han Wang, Jiashu Wang, Changming Xu and Ende Zhang
Remote Sens. 2025, 17(13), 2313; https://doi.org/10.3390/rs17132313 - 5 Jul 2025
Viewed by 316
Abstract
Unmanned Aerial Vehicles (UAVs) face a significant challenge in balancing high accuracy and high efficiency when performing real-time object detection tasks, especially amidst intricate backgrounds, diverse target scales, and stringent onboard computational resource constraints. To tackle these difficulties, this study introduces YOLO-SRMX, a [...] Read more.
Unmanned Aerial Vehicles (UAVs) face a significant challenge in balancing high accuracy and high efficiency when performing real-time object detection tasks, especially amidst intricate backgrounds, diverse target scales, and stringent onboard computational resource constraints. To tackle these difficulties, this study introduces YOLO-SRMX, a lightweight real-time object detection framework specifically designed for infrared imagery captured by UAVs. Firstly, the model utilizes ShuffleNetV2 as an efficient lightweight backbone and integrates the novel Multi-Scale Dilated Attention (MSDA) module. This strategy not only facilitates a substantial 46.4% reduction in parameter volume but also, through the flexible adaptation of receptive fields, boosts the model’s robustness and precision in multi-scale object recognition tasks. Secondly, within the neck network, multi-scale feature extraction is facilitated through the design of novel composite convolutions, ConvX and MConv, based on a “split–differentiate–concatenate” paradigm. Furthermore, the lightweight GhostConv is incorporated to reduce model complexity. By synthesizing these principles, a novel composite receptive field lightweight convolution, DRFAConvP, is proposed to further optimize multi-scale feature fusion efficiency and promote model lightweighting. Finally, the Wise-IoU loss function is adopted to replace the traditional bounding box loss. This is coupled with a dynamic non-monotonic focusing mechanism formulated using the concept of outlier degrees. This mechanism intelligently assigns elevated gradient weights to anchor boxes of moderate quality by assessing their relative outlier degree, while concurrently diminishing the gradient contributions from both high-quality and low-quality anchor boxes. Consequently, this approach enhances the model’s localization accuracy for small targets in complex scenes. Experimental evaluations on the HIT-UAV dataset corroborate that YOLO-SRMX achieves an mAP50 of 82.8%, representing a 7.81% improvement over the baseline YOLOv8s model; an F1 score of 80%, marking a 3.9% increase; and a substantial 65.3% reduction in computational cost (GFLOPs). YOLO-SRMX demonstrates an exceptional trade-off between detection accuracy and operational efficiency, thereby underscoring its considerable potential for efficient and precise object detection on resource-constrained UAV platforms. Full article
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39 pages, 18642 KiB  
Article
SDRFPT-Net: A Spectral Dual-Stream Recursive Fusion Network for Multispectral Object Detection
by Peida Zhou, Xiaoyong Sun, Bei Sun, Runze Guo, Zhaoyang Dang and Shaojing Su
Remote Sens. 2025, 17(13), 2312; https://doi.org/10.3390/rs17132312 - 5 Jul 2025
Viewed by 287
Abstract
Multispectral object detection faces challenges in effectively integrating complementary information from different modalities in complex environmental conditions. This paper proposes SDRFPT-Net (Spectral Dual-stream Recursive Fusion Perception Target Network), a novel architecture that integrates three key innovative modules: (1) the Spectral Hierarchical Perception Architecture [...] Read more.
Multispectral object detection faces challenges in effectively integrating complementary information from different modalities in complex environmental conditions. This paper proposes SDRFPT-Net (Spectral Dual-stream Recursive Fusion Perception Target Network), a novel architecture that integrates three key innovative modules: (1) the Spectral Hierarchical Perception Architecture (SHPA), which adopts a dual-stream separated structure with independently parameterized feature extraction paths for visible and infrared modalities; (2) the Spectral Recursive Fusion Module (SRFM), which combines hybrid attention mechanisms with recursive progressive fusion strategies to achieve deep feature interaction through parameter-sharing multi-round recursive processing; and (3) the Spectral Target Perception Enhancement Module (STPEM), which adaptively enhances target region representation and suppresses background interference. Extensive experiments on the VEDAI, FLIR-aligned, and LLVIP datasets demonstrate that SDRFPT-Net significantly outperforms state-of-the-art methods, with improvements of 2.5% in mAP50 and 5.4% in mAP50:95 on VEDAI, 11.5% in mAP50 on FLIR-aligned, and 9.5% in mAP50:95 on LLVIP. Ablation studies further validate the effectiveness of each proposed module. The proposed method provides an efficient and robust solution for multispectral object detection in remote sensing image interpretation, making it particularly suitable for all-weather monitoring applications from aerial and satellite platforms, as well as in intelligent surveillance and autonomous driving domains. Full article
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24 pages, 8603 KiB  
Article
Evaluating the Potential of Improving In-Season Potato Nitrogen Status Diagnosis Using Leaf Fluorescence Sensor as Compared with SPAD Meter
by Seiya Wakahara, Yuxin Miao, Dan Li, Jizong Zhang, Sanjay K. Gupta and Carl Rosen
Remote Sens. 2025, 17(13), 2311; https://doi.org/10.3390/rs17132311 - 5 Jul 2025
Viewed by 244
Abstract
The petiole nitrate–nitrogen concentration (PNNC) has been an industry standard indicator for in-season potato (Solanum tuberosum L.) nitrogen (N) status diagnosis. Leaf sensors can be used to predict the PNNC and other N status indicators non-destructively. The SPAD meter is a common [...] Read more.
The petiole nitrate–nitrogen concentration (PNNC) has been an industry standard indicator for in-season potato (Solanum tuberosum L.) nitrogen (N) status diagnosis. Leaf sensors can be used to predict the PNNC and other N status indicators non-destructively. The SPAD meter is a common leaf chlorophyll (Chl) meter, while the Dualex is a newer leaf fluorescence sensor. Limited research has been conducted to compare the two leaf sensors for potato N status assessment. Therefore, the objectives of this study were to (1) compare SPAD and Dualex for predicting potato N status indicators, and (2) evaluate the potential prediction improvement using multi-source data fusion. The plot-scale experiments were conducted in Becker, Minnesota, USA, in 2018, 2019, 2021, and 2023, involving different cultivars, N treatments, and irrigation rates. The results indicated that Dualex’s N balance index (NBI; Chl/Flav) always outperformed Dualex Chl but did not consistently perform better than the SPAD meter. All N status indicators were predicted with significantly higher accuracy with multi-source data fusion using machine learning models. A practical strategy was developed using a linear support vector regression model with SPAD, cultivar information, accumulated growing degree days, accumulated total moisture, and an as-applied N rate to predict the vine or whole-plant N nutrition index (NNI), achieving an R2 of 0.80–0.82, accuracy of 0.75–0.77, and Kappa statistic of 0.57–0.58 (near-substantial). Further research is needed to develop an easy-to-use application and corresponding in-season N recommendation strategy to facilitate practical on-farm applications. Full article
(This article belongs to the Special Issue Proximal and Remote Sensing for Precision Crop Management II)
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24 pages, 3003 KiB  
Article
Fault Geometry and Slip Distribution of the 2023 Jishishan Earthquake Based on Sentinel-1A and ALOS-2 Data
by Kaifeng Ma, Yang Liu, Qingfeng Hu, Jiuyuan Yang and Limei Wang
Remote Sens. 2025, 17(13), 2310; https://doi.org/10.3390/rs17132310 - 5 Jul 2025
Viewed by 243
Abstract
On 18 December 2023, a Mw 6.2 earthquake occurred in close proximity to Jishishan County, located on the northeastern edge of the Qinghai–Tibet Plateau. The event struck the structural intersection of the Haiyuan fault, Lajishan fault, and West Qinling fault, providing empirical [...] Read more.
On 18 December 2023, a Mw 6.2 earthquake occurred in close proximity to Jishishan County, located on the northeastern edge of the Qinghai–Tibet Plateau. The event struck the structural intersection of the Haiyuan fault, Lajishan fault, and West Qinling fault, providing empirical evidence for investigating the crustal compression mechanisms associated with the northeastward expansion of the Qinghai–Tibet Plateau. In this study, we successfully acquired a high-resolution coseismic deformation field of the earthquake by employing interferometric synthetic aperture radar (InSAR) technology. This was accomplished through the analysis of image data obtained from both the ascending and descending orbits of the Sentinel-1A satellite, as well as from the ascending orbit of the ALOS-2 satellite. Our findings indicate that the coseismic deformation is predominantly localized around the Lajishan fault zone, without leading to the development of a surface rupture zone. The maximum deformations recorded from the Sentinel-1A ascending and descending datasets are 7.5 cm and 7.7 cm, respectively, while the maximum deformation observed from the ALOS-2 ascending data reaches 10 cm. Geodetic inversion confirms that the seismogenic structure is a northeast-dipping thrust fault. The geometric parameters indicate a strike of 313° and a dip angle of 50°. The slip distribution model reveals that the rupture depth predominantly ranges between 5.7 and 15 km, with a maximum displacement of 0.47 m occurring at a depth of 9.6 km. By integrating the coseismic slip distribution and aftershock relocation, this study comprehensively elucidates the stress coupling mechanism between the mainshock and its subsequent aftershock sequence. Quantitative analysis indicates that aftershocks are primarily located within the stress enhancement zone, with an increase in stress ranging from 0.12 to 0.30 bar. It is crucial to highlight that the structural units, including the western segment of the northern margin fault of West Qinling, the eastern segment of the Daotanghe fault, the eastern segment of the Linxia fault, and both the northern and southern segment of Lajishan fault, exhibit characteristics indicative of continuous stress loading. This observation suggests a potential risk for fractures in these areas. Full article
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22 pages, 12185 KiB  
Article
Airborne Strapdown Gravity Survey of Sos Enattos Area (NE Sardinia, Italy): Insights into Geological and Geophysical Characterization of the Italian Candidate Site for the Einstein Telescope
by Filippo Muccini, Filippo Greco, Luca Cocchi, Maria Marsella, Antonio Zanutta, Alessandra Borghi, Matteo Cagnizi, Daniele Carbone, Mauro Coltelli, Danilo Contrafatto, Peppe Junior Valentino D’Aranno, Luca Frasca, Alfio Alex Messina, Luca Timoteo Mirabella, Monia Negusini and Eleonora Rivalta
Remote Sens. 2025, 17(13), 2309; https://doi.org/10.3390/rs17132309 - 5 Jul 2025
Viewed by 235
Abstract
Strapdown gravity systems are increasingly employed in airborne geophysical exploration and geodetic studies due to advantages such as ease of installation, wide dynamic range, and adaptability to various platforms, including airplanes, helicopters, and large drones. This study presents results from an airborne gravity [...] Read more.
Strapdown gravity systems are increasingly employed in airborne geophysical exploration and geodetic studies due to advantages such as ease of installation, wide dynamic range, and adaptability to various platforms, including airplanes, helicopters, and large drones. This study presents results from an airborne gravity survey conducted over the northeastern sector of Sardinia (Italy), using a high-resolution strapdown gravity ensuring an accuracy of approximately 1 mGal. Data were collected at an average altitude of 1800 m with a spatial resolution of 3.0 km. The survey focused on the Sos Enattos area near Lula (Nuoro province), a candidate site for the Einstein Telescope (ET), a third-generation gravitational wave observatory. The ideal site is required to be geologically and seismically stable with a well-characterized subsurface. To support this, we performed a new gravity survey to complement existing geological and seismic data aimed at characterizing the mid-to-shallow crustal structure of Sos Enattos. Results show that the strapdown system effectively detects gravity anomalies linked to crustal sources down to ~3.5 km, with particular emphasis within the 1–2 km depth range. Airborne gravity data reveal higher frequency anomalies than those resolved by the EGM2008 global gravity model and show good agreement with local terrestrial gravity data. Forward modeling of the gravity field suggests a crust dominated by alternating high-density metamorphic rocks and granitoid intrusions of the Variscan basement. These findings enhance the geophysical understanding of Sos Enattos and support its candidacy for the ET site. Full article
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18 pages, 1756 KiB  
Technical Note
Detection of Banana Diseases Based on Landsat-8 Data and Machine Learning
by Renata Retkute, Kathleen S. Crew, John E. Thomas and Christopher A. Gilligan
Remote Sens. 2025, 17(13), 2308; https://doi.org/10.3390/rs17132308 - 5 Jul 2025
Viewed by 302
Abstract
Banana is an important cash and food crop worldwide. Recent outbreaks of banana diseases are threatening the global banana industry and smallholder livelihoods. Remote sensing data offer the potential to detect the presence of disease, but formal analysis is needed to compare inferred [...] Read more.
Banana is an important cash and food crop worldwide. Recent outbreaks of banana diseases are threatening the global banana industry and smallholder livelihoods. Remote sensing data offer the potential to detect the presence of disease, but formal analysis is needed to compare inferred disease data with observed disease data. In this study, we present a novel remote-sensing-based framework that combines Landsat-8 imagery with meteorology-informed phenological models and machine learning to identify anomalies in banana crop health. Unlike prior studies, our approach integrates domain-specific crop phenology to enhance the specificity of anomaly detection. We used a pixel-level random forest (RF) model to predict 11 key vegetation indices (VIs) as a function of historical meteorological conditions, specifically daytime and nighttime temperature from MODIS and precipitation from NASA GES DISC. By training on periods of healthy crop growth, the RF model establishes expected VI values under disease-free conditions. Disease presence is then detected by quantifying the deviations between observed VIs from Landsat-8 imagery and these predicted healthy VI values. The model demonstrated robust predictive reliability in accounting for seasonal variations, with forecasting errors for all VIs remaining within 10% when applied to a disease-free control plantation. Applied to two documented outbreak cases, the results show strong spatial alignment between flagged anomalies and historical reports of banana bunchy top disease (BBTD) and Fusarium wilt Tropical Race 4 (TR4). Specifically, for BBTD in Australia, a strong correlation of 0.73 was observed between infection counts and the discrepancy between predicted and observed NDVI values at the pixel with the highest number of infections. Notably, VI declines preceded reported infection rises by approximately two months. For TR4 in Mozambique, the approach successfully tracked disease progression, revealing clear spatial spread patterns and correlations as high as 0.98 between VI anomalies and disease cases in some pixels. These findings support the potential of our method as a scalable early warning system for banana disease detection. Full article
(This article belongs to the Special Issue Plant Disease Detection and Recognition Using Remotely Sensed Data)
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27 pages, 4364 KiB  
Article
Mapping Soil Burn Severity and Crown Scorch Percentage with Sentinel-2 in Seasonally Dry Deciduous Oak and Pine Forests in Western Mexico
by Oscar Enrique Balcázar Medina, Enrique J. Jardel Peláez, Daniel José Vega-Nieva, Adrián Israel Silva-Cardoza and Ramón Cuevas Guzmán
Remote Sens. 2025, 17(13), 2307; https://doi.org/10.3390/rs17132307 - 5 Jul 2025
Viewed by 423
Abstract
There is a need to evaluate Sentinel-2 (S2) fire severity spectral indices (SFSIs) for predicting vegetation and soil burn severity for a variety of ecosystems. We evaluated the performance of 26 SFSIs across three fires in seasonally dry oak–pine forests in central-western Mexico. [...] Read more.
There is a need to evaluate Sentinel-2 (S2) fire severity spectral indices (SFSIs) for predicting vegetation and soil burn severity for a variety of ecosystems. We evaluated the performance of 26 SFSIs across three fires in seasonally dry oak–pine forests in central-western Mexico. The SFSIs were derived from composites of S2 multispectral images obtained with Google Earth Engine (GEE), processed using different techniques, for periods of 30, 60 and 90 days. Field verification was conducted through stratified random sampling by severity class on 100 circular plots of 707 m2, where immediate post-fire effects were evaluated for five strata, including the canopy scorch in overstory (OCS)—divided in canopy (CCS) and subcanopy (SCS)—understory (UCS) and soil burn severity (SBS). Best fits were obtained with relative, phenologically corrected indices of 60–90 days. For canopy scorch percentage prediction, the indices RBR3c and RBR5n, using NIR (bands 8 and 8a) and SWIR (band 12), provided the best accuracy (R2 = 0.82). SBS could be best mapped from RBR1c (using 11 and 12 bands) with relatively acceptable precision (R2 = 0.62). Our results support the feasibility to separately map OCS and SBS from S2, in relatively open oak–pine seasonally dry forests, potentially supporting post-fire management planning. Full article
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59 pages, 4824 KiB  
Review
Impacts of Climate Change on Oceans and Ocean-Based Solutions: A Comprehensive Review from the Deep Learning Perspective
by Xin Qiao, Ke Zhang and Weimin Huang
Remote Sens. 2025, 17(13), 2306; https://doi.org/10.3390/rs17132306 - 4 Jul 2025
Viewed by 242
Abstract
Climate change poses significant threats to oceans, leading to ocean acidification, sea level rise, and sea ice loss and so on. At the same time, oceans play a crucial role in climate change mitigation and adaptation, offering solutions such as renewable energy and [...] Read more.
Climate change poses significant threats to oceans, leading to ocean acidification, sea level rise, and sea ice loss and so on. At the same time, oceans play a crucial role in climate change mitigation and adaptation, offering solutions such as renewable energy and carbon sequestration. Moreover, the availability of diverse ocean data sources, both remote sensing observations and in situ measurements, provides unprecedented opportunities to monitor these processes. Remote sensing data, with its extensive spatial coverage and accessibility, forms the foundation for accurately capturing changes in ocean conditions and developing data-driven solutions. This review explores the dual relationship between climate change and oceans, focusing on the impacts of climate change on oceans and ocean-based strategies to combat these challenges. From the artificial intelligence perspective, this study systematically analyzes recent advances in applying deep learning techniques to understand changes in ocean physical properties and marine ecosystems, as well as to optimize ocean-based climate solutions. By evaluating existing methodologies and identifying knowledge gaps, this review highlights the pivotal role of deep learning in advancing ocean-related climate research, outlines existing current challenges, and provides insights into potential future directions. Full article
(This article belongs to the Section Ocean Remote Sensing)
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30 pages, 11197 KiB  
Article
Few-Shot Unsupervised Domain Adaptation Based on Refined Bi-Directional Prototypical Contrastive Learning for Cross-Scene Hyperspectral Image Classification
by Xuebin Tang, Hanyi Shi, Chunchao Li, Cheng Jiang, Xiaoxiong Zhang, Lingbin Zeng and Xiaolei Zhou
Remote Sens. 2025, 17(13), 2305; https://doi.org/10.3390/rs17132305 - 4 Jul 2025
Viewed by 300
Abstract
Hyperspectral image cross-scene classification (HSICC) tasks are confronted with tremendous challenges due to spectral shift phenomena across scenes and the tough work of obtaining labels. Unsupervised domain adaptation has proven its effectiveness in tackling this issue, but it has a fatal limitation of [...] Read more.
Hyperspectral image cross-scene classification (HSICC) tasks are confronted with tremendous challenges due to spectral shift phenomena across scenes and the tough work of obtaining labels. Unsupervised domain adaptation has proven its effectiveness in tackling this issue, but it has a fatal limitation of intending to narrow the disparity between source and target domains by utilizing fully labeled source data and unlabeled target data. However, it is costly even to attain labels from source domains in many cases, rendering sufficient labeling as used in prior work impractical. In this work, we investigate an extreme and realistic scenario where unsupervised domain adaptation methods encounter sparsely labeled source data when handling HSICC tasks, namely, few-shot unsupervised domain adaptation. We propose an end-to-end refined bi-directional prototypical contrastive learning (RBPCL) framework for overcoming the HSICC problem with only a few labeled samples in the source domain. RBPCL captures category-level semantic features of hyperspectral data and performs feature alignment through in-domain refined prototypical self-supervised learning and bi-directional cross-domain prototypical contrastive learning, respectively. Furthermore, our framework introduces the class-balanced multicentric dynamic prototype strategy to generate more robust and representative prototypes. To facilitate prototype contrastive learning, we employ a Siamese-style distance metric loss function to aggregate intra-class features while increasing the discrepancy of inter-class features. Finally, extensive experiments and ablation analysis implemented on two public cross-scene data pairs and three pairs of self-collected ultralow-altitude hyperspectral datasets under different illumination conditions verify the effectiveness of our method, which will further enhance the practicality of hyperspectral intelligent sensing technology. Full article
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24 pages, 7933 KiB  
Article
Multi-Temporal Dual Polarimetric SAR Crop Classification Based on Spatial Information Comprehensive Utilization
by Qiang Yin, Yuming Du, Fangfang Li, Yongsheng Zhou and Fan Zhang
Remote Sens. 2025, 17(13), 2304; https://doi.org/10.3390/rs17132304 - 4 Jul 2025
Viewed by 101
Abstract
Dual polarimetric SAR is capable of reflecting the biophysical and geometrical information of terrain with open access data availability. When it is combined with time-series observations, it can effectively capture the dynamic evolution of scattering characteristics of crops in different growth cycles. However, [...] Read more.
Dual polarimetric SAR is capable of reflecting the biophysical and geometrical information of terrain with open access data availability. When it is combined with time-series observations, it can effectively capture the dynamic evolution of scattering characteristics of crops in different growth cycles. However, the actual planting of crops often shows spatial dispersion, and the same crop may be dispersed in different plots, which fails to adequately consider the correlation information between dispersed plots of the same crop in spatial distribution. This study proposed a crop classification method based on multi-temporal dual polarimetric data, which considered the utilization of information between near and far spatial plots, by employing superpixel segmentation and a HyperGraph neural network, respectively. Firstly, the method utilized the dual polarimetric covariance matrix of multi-temporal data to perform superpixel segmentation on neighboring pixels, so that the segmented superpixel blocks were highly compatible with the actual plot shapes from a long-term period perspective. Then, a HyperGraph adjacency matrix was constructed, and a HyperGraph neural network (HGNN) was utilized to better learn the features of plots of the same crop that are distributed far from each other. The method fully utilizes the three dimensions of time, polarization and space information, which complement each other so as to effectively realize high-precision crop classification. The Sentinel-1 experimental results show that, under the optimal parameter settings, the classified accuracy of combined temporal superpixel scattering features using the HGNN was obviously improved, considering the near and far distance spatial correlations of crop types. Full article
(This article belongs to the Special Issue Cutting-Edge PolSAR Imaging Applications and Techniques)
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19 pages, 16060 KiB  
Article
Synergic Lidar Observations of Ozone Episodes and Transport During 2023 Summer AGES+ Campaign in NYC Region
by Dingdong Li, Yonghua Wu, Thomas Ely, Thomas Legbandt and Fred Moshary
Remote Sens. 2025, 17(13), 2303; https://doi.org/10.3390/rs17132303 - 4 Jul 2025
Viewed by 238
Abstract
We present coordinated observations from ozone Differential Absorption lidar (DIAL), aerosol lidar, and Doppler wind lidar at the City College of New York (CCNY) in northern Manhattan during the summer 2023 AGES+ campaigns across the New York City (NYC) region and Long Island [...] Read more.
We present coordinated observations from ozone Differential Absorption lidar (DIAL), aerosol lidar, and Doppler wind lidar at the City College of New York (CCNY) in northern Manhattan during the summer 2023 AGES+ campaigns across the New York City (NYC) region and Long Island Sound (LIS) areas. The results highlight significant ozone formation within the planetary boundary layer (PBL) and the concurrent transport of ozone/aerosol plumes aloft and mixing into the PBL during 26–28 July 2023. Especially, 26 July experienced the highest ozone concentration within the PBL during the three-day ozone episode despite having a lower temperature than the following two days. In addition, the onset of the afternoon sea breeze contributed to increased ozone levels in the PBL. A mobile ozone DIAL was also deployed at Columbia University’s Lamont–Doherty Earth Observatory (LDEO) in Palisades, NY, 29 km north of NYC, from 11 August to 8 September 2023. A notable high-ozone episode was observed by both ozone DIALs at the CCNY and the LDEO site during an unusual heatwave event in early September. On 7 September, the peak ozone concentration at the LDEO reached 120 ppb, exceeding the ozone levels observed in NYC. This enhancement was associated with urban plume transport, as indicated by wind lidar measurements, the HRRR (High-Resolution Rapid Refresh) model, and the Copernicus Sentinel-5 TROPOMI (TROPOspheric Monitoring Instrument) tropospheric column NO2 product. The results also show that, during both heatwave events, those days with slow southeast to southwest winds experienced significantly higher ozone pollution. Full article
(This article belongs to the Section Atmospheric Remote Sensing)
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25 pages, 67703 KiB  
Article
Robust Feature Matching of Multi-Illumination Lunar Orbiter Images Based on Crater Neighborhood Structure
by Bin Xie, Bin Liu, Kaichang Di, Wai-Chung Liu, Yuke Kou, Yutong Jia and Yifan Zhang
Remote Sens. 2025, 17(13), 2302; https://doi.org/10.3390/rs17132302 - 4 Jul 2025
Viewed by 141
Abstract
Lunar orbiter image matching is a critical process for achieving high-precision lunar mapping, positioning, and navigation. However, with the Moon’s weak-texture surface and rugged terrain, lunar orbiter images generally suffer from inconsistent lighting conditions and exhibit varying degrees of non-linear intensity distortion, which [...] Read more.
Lunar orbiter image matching is a critical process for achieving high-precision lunar mapping, positioning, and navigation. However, with the Moon’s weak-texture surface and rugged terrain, lunar orbiter images generally suffer from inconsistent lighting conditions and exhibit varying degrees of non-linear intensity distortion, which pose significant challenges to image traditional matching. This paper presents a robust feature matching method based on crater neighborhood structure, which is particularly robust to changes in illumination. The method integrates deep-learning based crater detection, Crater Neighborhood Structure features (CNSFs) construction, CNSF similarity-based matching, and outlier removal. To evaluate the effectiveness of the proposed method, we created an evaluation dataset, comprising Multi-illumination Lunar Orbiter Images (MiLOIs) from different latitudes (a total of 321 image pairs). And comparative experiments have been conducted using the proposed method and state-of-the-art image matching methods. The experimental results indicate that the proposed approach exhibits greater robustness and accuracy against variations in illumination. Full article
(This article belongs to the Special Issue Remote Sensing and Photogrammetry Applied to Deep Space Exploration)
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