Remote Sensing

34 pages, 5870 KB

Open AccessArticle

Difference-Gated Interaction and Change-Aware Cross-Temporal Fusion Network for Remote Sensing Image Change Detection

by Lele Li, Panpan Zheng, Liejun Wang and Yuqing Zhou

Remote Sens. 2026, 18(16), 2763; https://doi.org/10.3390/rs18162763 (registering DOI) - 15 Aug 2026

Remote sensing (RS) image change detection (CD) is crucial for environmental monitoring, urban planning, and disaster assessment. Despite recent advances, existing methods struggle to effectively exploit bitemporal difference information, leading to false alarms caused by illumination or seasonal variations. Furthermore, they often fail [...] Read more.

Remote sensing (RS) image change detection (CD) is crucial for environmental monitoring, urban planning, and disaster assessment. Despite recent advances, existing methods struggle to effectively exploit bitemporal difference information, leading to false alarms caused by illumination or seasonal variations. Furthermore, they often fail to fully leverage coarse predictions as explicit semantic priors, restricting their capability to detect small-scale change regions. To address these issues, we propose a Difference-Gated Interaction and Change-Aware Cross-Temporal Fusion Network (DCAFNet) within a unified coarse-to-fine framework. Specifically, a Difference-Gated Feature Interaction (DGFI) module generates change correlation gates based on difference magnitudes to suppress pseudo-changes while preserving genuine change signals, and a Change-Aware Cross-Temporal Fusion (CCTF) module leverages coarse predictions as semantic guidance for feature recalibration and employs cross-temporal attention with learnable adaptive fusion to recover subtle change regions. Extensive experiments on four benchmark datasets (LEVIR-CD, WHU-CD, CDD, and SYSU-CD) demonstrate that DCAFNet consistently outperforms ten state-of-the-art methods, with additional ablation studies confirming the effectiveness of each component. Full article

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

33 pages, 101379 KB

Open AccessArticle

Integrating Remote Sensing and Meteorological Time Series to Assess Rice Sheath Blight Habitat Suitability at Large-Scale: A Spatiotemporal Adaptive Framework

by Yujin Jing, Huiqin Ma, Rongfeng Cui, Jingcheng Zhang, Xianfeng Zhou, Zichao Jin and Dongmei Chen

Remote Sens. 2026, 18(16), 2762; https://doi.org/10.3390/rs18162762 (registering DOI) - 15 Aug 2026

Abstract

Precise spatiotemporal assessment of habitat suitability is essential for crop pest and disease risk warning and food security. However, most existing approaches focus on disease occurrence, overlook spatial heterogeneity and time series information, and therefore, struggle to capture the habitat dynamics from occurrence [...] Read more.

Precise spatiotemporal assessment of habitat suitability is essential for crop pest and disease risk warning and food security. However, most existing approaches focus on disease occurrence, overlook spatial heterogeneity and time series information, and therefore, struggle to capture the habitat dynamics from occurrence to epidemic. We propose a dynamic framework for rice sheath blight (RSB) habitat suitability assessment that integrates rice phenology and disease time series to reveal fine-grained intra-annual spatiotemporal variability via a spatiotemporally adaptive strategy beyond the reach of traditional static models. Remote sensing and meteorological time series data, together with RSB survey data and crowdsourced records from southern China, are integrated in this study. First, the study area is partitioned into sub-regions and sensitive time windows (STWs) based on climate and rice-cropping systems. MaxEnt, combined with natural breaks, is then used to assess RSB occurrence suitability and delineate multi-level suitable areas. Geographical and temporal weighted regression (GTWR) and the coefficient of variation (CV) are finally applied within moderate-to-high occurrence-suitability areas to reconstruct time series of intra-STW epidemic potential dynamics and quantify their temporal variation. Results indicate the optimal phenology-based scheme yields one STW for single-cropping sub-regions and three STWs for double- and mixed-cropping sub-regions. MaxEnt AUC ranges from 0.610 to 0.768, with natural-break thresholds at 0.312 and 0.473. GTWR produces generally robust intra-STW fits (most local R² > 0.4), and the CV highlights localized, time-varying high-fluctuation zones within occurrence-suitable areas that static maps do not reveal. Overall, our method extends crop disease habitat suitability assessment from a static paradigm to a spatiotemporally adaptive, dynamic one, providing useful habitat background constraints for monitoring, early warning, and forecasting of crop pests and diseases under complex cropping systems. Full article

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13 pages, 6277 KB

Open AccessTechnical Note

Case Study on Artificial Sea Fog Dispersal Effect Evaluation Based on Visibility Lidar

by Xu Zhou, Yuecheng Zheng, Xiaofeng Wang, Jiaxing Sun, Tixian Zeng, Ji Zhou, Jie Peng, Qun Ji, Shuxue Zhou and Jinlong Yuan

Remote Sens. 2026, 18(16), 2761; https://doi.org/10.3390/rs18162761 (registering DOI) - 15 Aug 2026

Abstract

Coastal warm fog poses a serious threat to the safety and efficiency of port shipping. However, its artificial dispersal and the effect evaluation of the dispersal remain a worldwide challenge. This paper conducted a field experiment of artificial fog dispersal using an unmanned [...] Read more.

Coastal warm fog poses a serious threat to the safety and efficiency of port shipping. However, its artificial dispersal and the effect evaluation of the dispersal remain a worldwide challenge. This paper conducted a field experiment of artificial fog dispersal using an unmanned aerial vehicle (UAV) to spray a new hygroscopic catalyst in Jinshan District, Shanghai, China. A traversing window method was proposed to quantitatively evaluate the dispersal effect of the catalyst on coastal warm fog using the RHI detection mode of a visibility lidar. The experimental results show that the operation period was a typical coastal radiation fog process with a stable meteorological background field. The near-surface wind was weak and the wind direction was constant southeast. The new composite hygroscopic catalyst had a certain effect on coastal warm fog dispersal, and its effect was closely related to the background fog concentration. The operation effect became prominent when the background fog concentration decreased. The maximum visibility improvement reached 456 m, and the peak effect appeared 2–3 min after the end of spraying. The best location for dispersal effect was observed below the operation position and in the downwind direction. The traversing window method proposed in this paper effectively reduces the contingency and spatial representativeness limitations of single-point evaluation. It can locate the optimal area of fog dispersal effect and quantify its intensity. This study provides technical support for the operational application of artificial fog dispersal in coastal area. Full article

(This article belongs to the Special Issue Remote Sensing with Radar or Lidar for Multi-Scale Atmospheric Observation)

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24 pages, 13299 KB

Open AccessArticle

BCNet: Boundary-Constrained Remote Sensing Change Detection Network Based on Vision Foundation Models

by Shenbo Liu, Dongxue Zhao, Huang He and Lijun Tang

Remote Sens. 2026, 18(16), 2760; https://doi.org/10.3390/rs18162760 (registering DOI) - 15 Aug 2026

Abstract

Limited by the diversity and complexity of real-world scenes, existing remote sensing change detection methods often suffer from insufficient fine-grained semantic understanding and blurred boundaries of change targets. To address these issues, this paper proposes a boundary-constrained remote sensing change detection network based [...] Read more.

Limited by the diversity and complexity of real-world scenes, existing remote sensing change detection methods often suffer from insufficient fine-grained semantic understanding and blurred boundaries of change targets. To address these issues, this paper proposes a boundary-constrained remote sensing change detection network based on vision foundation models (BCNet). BCNet employs a differential modeling approach and multi-branch guidance mechanism to design a differential detail enhancement module, amplifying fine-grained semantic information. Through cross-layer feature alignment, stepwise fusion, and edge-sensitive modeling, it constructs a multi-scale edge enhancement module that enhances perception of minute variations and edge details, fully leveraging the universal semantic representation capabilities of the vision foundation model. In addition, an edge feature constraint mechanism is introduced that applies dual guidance and supervision during the feature fusion and output stages. This mechanism achieves refined delineation of change region boundaries and significantly mitigates the issue of boundary blurring. Experimental results on four mainstream datasets, namely LEVIR-CD, WHU-CD, NJDS and MSRS-CD, demonstrate that BCNet outperforms 13 state-of-the-art methods in terms of key metrics including F1 and IoU. Against the best VFM-based baseline, BCNet obtains F1 score gains of 0.21%, 0.71%, 6.33% and 0.63% on the above four datasets. Specifically, the proposed method exhibits superior detection accuracy and edge detail preservation capabilities in complex regions. Full article

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

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28 pages, 14275 KB

Open AccessArticle

Energy Optimization-Based Segmentation and Extraction of 3D Sonar Point Clouds for Complex Underwater Structures

by Junchi Dong, Zilong Li, Hao Li, Shaobo Li and Yunlong Wu

Remote Sens. 2026, 18(16), 2759; https://doi.org/10.3390/rs18162759 (registering DOI) - 15 Aug 2026

Abstract

3D sonar is the primary technical means for the detection and health assessment of underwater structures. However, due to the harsh constraints of underwater imaging conditions, these point clouds experience severe noise interference and inconspicuous features. This complicates structural surface extraction and hinders [...] Read more.

3D sonar is the primary technical means for the detection and health assessment of underwater structures. However, due to the harsh constraints of underwater imaging conditions, these point clouds experience severe noise interference and inconspicuous features. This complicates structural surface extraction and hinders the engineering monitoring process. To address this challenge, we propose an object-based energy-optimization segmentation method for precise extraction. First, the Voxel Cloud Connectivity Segmentation (VCCS) algorithm transforms massive discrete point clouds into semantically coherent supervoxel objects, significantly reducing computational complexity. Next, a global energy optimization framework is constructed, integrating a data term that characterizes structural surfaces and a smoothness term based on spatial neighborhood constraints. Through a graph-cut optimization procedure, this model enables the joint extraction of conspicuous and inconspicuous surfaces. Finally, a region-growing algorithm with multi-attribute constraints effectively filters typical sonar noise. Experimental results demonstrate the method’s effectiveness, achieving an average F1-score of 89.19%. This approach maintains high segmentation accuracy and provides reliable technical support for underwater structure detection and assessment. Full article

(This article belongs to the Special Issue Remote Sensing for Maritime Monitoring)

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26 pages, 8482 KB

Open AccessArticle

Spatial-Temporal Analysis and Multi-Scenario Forecasting of Land Use and Net Primary Productivity in Qingdao

by Junze Xiao, Fengyi Li, Di Kong and Xiong Li

Remote Sens. 2026, 18(16), 2758; https://doi.org/10.3390/rs18162758 (registering DOI) - 15 Aug 2026

Abstract

To enhance regional carbon sequestration capacity and achieve carbon neutrality, it is crucial to accurately estimate the net primary productivity (NPP) of vegetation and its response to changes in land use/land cover (LULC). Based on remote sensing data, the study estimated the NPP [...] Read more.

To enhance regional carbon sequestration capacity and achieve carbon neutrality, it is crucial to accurately estimate the net primary productivity (NPP) of vegetation and its response to changes in land use/land cover (LULC). Based on remote sensing data, the study estimated the NPP of Qingdao, a typical coastal city in eastern China, from 1990 to 2020 using the CASA model and examined its spatiotemporal dynamics. The Geographic Detector was used in the study to measure the impact of anthropogenic and natural factors on variations in NPP. Additionally, the Markov–PLUS model was utilized to forecast future LULC patterns and NPP changes for 2030 under three development scenarios: natural development, cropland protection, and ecological protection. The results indicate considerable regional variation and an initial decrease followed by an increase in Qingdao’s NPP. It is distributed in a strip-like pattern from northwest to southeast, with the regions around Jiaozhou Bay showing the lowest levels. The changes in NPP are driven by multiple interacting factors; among these, LULC, DEM, and slope exhibit significant spatiotemporal correlations with NPP, with LULC playing a dominant role in this process. In addition, interaction analysis demonstrated that the synergistic effects between LULC and other factors constituted the principal driving force behind NPP dynamics, and the interactions between the two factors that contribute show bivariate or nonlinear enhancement effects. The ecological protection scenario produces the greatest NPP values among the three scenarios. Optimizing LULC may significantly increase carbon sequestration capability, as evident in the NPP forecast, which is higher than that of 2020 in all scenarios. This study demonstrates that the ability of vegetation to store carbon can be effectively enhanced through land-use optimization informed by remote sensing technologies. The results provide a scientific basis for adjusting LULC policies and conducting carbon-neutral spatial planning in coastal urban areas. Full article

(This article belongs to the Special Issue Remote Sensing-Guided Land-Use Optimization for Carbon Neutrality)

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27 pages, 1903 KB

Open AccessArticle

Hybrid TLS–Tachymetry Framework for Geometric Axis Validation of a Steel Lattice Transmission Tower

by Robert Gradka

Remote Sens. 2026, 18(16), 2757; https://doi.org/10.3390/rs18162757 (registering DOI) - 15 Aug 2026

Abstract

This study presents a hybrid geodetic validation framework for assessing the geometric consistency of the axis of a steel lattice transmission tower determined from terrestrial laser scanning (TLS) data using an independently established tachymetric reference. Unlike previous investigations that focused on the influence [...] Read more.

This study presents a hybrid geodetic validation framework for assessing the geometric consistency of the axis of a steel lattice transmission tower determined from terrestrial laser scanning (TLS) data using an independently established tachymetric reference. Unlike previous investigations that focused on the influence of TLS scanner characteristics, registration strategies, or internal consistency of TLS-derived axes, the proposed approach introduces an external geodetic reference, enabling direct external assessment of TLS-based geometric axis estimation. The reference axis was determined at fourteen height levels, while the TLS axis was estimated from horizontal cross-sections of a point cloud acquired from multiple scanning stations and registered using a cloud-to-cloud method without control points. To enable direct comparison, both datasets were transformed into a common reference system using a seven-parameter Helmert transformation. The transformation was applied solely to remove differences between the independent local coordinate systems prior to the geometric comparison. Axis consistency was evaluated using residual vectors and three-dimensional distances between corresponding points. The mean deviation was 0.031 m, the RMS value was 0.033 m, and the maximum deviation reached 0.078 m. Larger discrepancies occurred predominantly in the upper sections of the structure, in a pattern consistent with the combined influence of TLS registration uncertainty, non-uniform point-cloud coverage, and local geometric conditions. A comparison of TLS axis estimators (centroid, LS-R regression, and PCA) showed that PCA produced an RMS value close to that of the centroid estimator, whereas LS-R produced a higher RMS value; the maximum deviation was lowest for the centroid estimator and highest for PCA. Regression analysis revealed a statistically significant linear trend in the X direction (p = 0.019), indicating residual systematic geometric drift after coordinate-system integration. The obtained discrepancies should be interpreted in the context of a rapid engineering TLS workflow performed without registration targets or a control network, rather than as the intrinsic accuracy of the TLS instrument itself. The proposed hybrid validation framework provides an objective quality-control methodology for evaluating TLS-derived geometric axes against independent geodetic observations and may support reliability assessment of TLS-based inventories and deformation monitoring of slender engineering structures. Full article

(This article belongs to the Special Issue Laser Scanning in Environmental and Engineering Applications)

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24 pages, 35481 KB

Open AccessArticle

Context-Guided Discrimination Feature Learning in Color Space for Aircraft Detection in SAR Images

by Yu Zhang, Zhe Geng, Lujia Yao and Daiyin Zhu

Remote Sens. 2026, 18(16), 2756; https://doi.org/10.3390/rs18162756 (registering DOI) - 15 Aug 2026

Abstract

Aircraft often manifest as highly aspect/pose-sensitive, disjointed blobs made of pixels with fluctuating levels of brightness in classic grayscale SAR images, which makes the aircraft annotation task challenging even for human experts. As more and more high-resolution colorized SAR images acquired by the [...] Read more.

Aircraft often manifest as highly aspect/pose-sensitive, disjointed blobs made of pixels with fluctuating levels of brightness in classic grayscale SAR images, which makes the aircraft annotation task challenging even for human experts. As more and more high-resolution colorized SAR images acquired by the latest commercial imaging modes are released for open access, both the academia and the industry started to notice the benefits of color-coded SAR images. However, since the large-scale datasets in the area of SAR aircraft detection feature grayscale SAR images, research on discrimination feature learning in color space for SAR aircraft detection is very limited. To embrace the opportunity brought by the new generation of highly informative colored SAR images, we propose the Phase-Aware Clustering Enhanced Detector (PACE-Det), which consists of three main components: the Phase-Orientation Color Encoder (POCE) module, the core detection network, and the Multi-Space Clustering Constraint (MSCC) module. The front-end POCE module generates pseudo-color SAR images based on Phase Congruency (PC). The color representations are fed into the core detection network for feature extraction, where the phase-aware alignment loss is introduced in addition to the classification and regression losses in the standard object detection task. The initial predictions generated by the core detection network are further refined by the contextual information extracted by the post-processing MSCC module based on the image segmentation result in

L^{*} a^{*} b^{*}

color space, where anisotropic objects like aircraft and isotropic scatterers like impervious surfaces exhibit distinct spatial distributions and color features. Experiments based on the Composite SAR Aircraft Dataset (CSAD), which is constructed by fusing airport scenes with SAR aircraft target patches, show that the proposed PACE-Det achieves a

{mAP}_{75}

of 0.948 and

{mAP}_{90}

of 0.561, which are higher than many state-of-the-art networks. Full article

(This article belongs to the Special Issue Recent Advances in Synthetic Aperture Radar Imaging and Data Processing)

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27 pages, 3999 KB

Open AccessArticle

Mask and Contiguity-Constrained Subarray Design for SAR Antenna Arrays

by Tianxing Zhang, Haoxuan Qiao, Deqing Mao and Ye Yuan

Remote Sens. 2026, 18(16), 2755; https://doi.org/10.3390/rs18162755 (registering DOI) - 15 Aug 2026

Abstract

In high-resolution wide-swath (HRWS) spaceborne synthetic aperture radar (SAR) systems, clustered phased array (CPA) architectures alleviate hardware costs but face critical subarray synthesis challenges: manually prescribed pattern masks, redundant equality matching, and unrealizable crossover topologies. This paper proposes a mask- and contiguity-constrained power [...] Read more.

In high-resolution wide-swath (HRWS) spaceborne synthetic aperture radar (SAR) systems, clustered phased array (CPA) architectures alleviate hardware costs but face critical subarray synthesis challenges: manually prescribed pattern masks, redundant equality matching, and unrealizable crossover topologies. This paper proposes a mask- and contiguity-constrained power pattern matching method (MC-PMM) to address these limitations. First, a semidefinite programming (SDP) procedure constructs an SAR-metric-aware SDP-relaxed reference mask for the noise equivalent sigma zero (NESZ) and range ambiguity-to-signal ratio (RASR) requirements within the relaxed covariance space. Second, a dynamic programming (DP) strategy is integrated with a novel mask-constrained iterative projection method (Mask-IPM) to optimize physically contiguous partitions and weights. By transforming equality waveform fitting into mask-violation-based inequality matching, MC-PMM focuses restricted spatial degrees of freedom onto critical performance boundaries. Simulations on a 64-element array demonstrate that, at a subarray ratio of

3 / 4

, MC-PMM achieves a 100% crossover-free topology. Furthermore, relative to the conventional equality matching framework, it lowers the mean RASR by

24.50

dB, with a negligible worst-case NESZ degradation of under

0.4

dB. Full article

(This article belongs to the Topic Advanced Array Antenna Design and Signal Processing Techniques)

27 pages, 13326 KB

Open AccessArticle

Kinematic Mapping and Geomorphological Analysis of Rock Glaciers in the Pirin Mountains (Bulgaria)

by Flavius Sîrbu, Valentin Poncoș, Tazio Strozzi, Emil Gachev, Florina Ardelean and Alexandru Onaca

Remote Sens. 2026, 18(16), 2754; https://doi.org/10.3390/rs18162754 (registering DOI) - 15 Aug 2026

Abstract

Rock glaciers are critical indicators of periglacial environments and the spatial distribution of mountain permafrost. Given their complex deformation patterns and temporal variability, which may indicate progressive destabilization, a quantitative evaluation of their kinematic activity is critical from both climatological and geohazard perspectives. [...] Read more.

Rock glaciers are critical indicators of periglacial environments and the spatial distribution of mountain permafrost. Given their complex deformation patterns and temporal variability, which may indicate progressive destabilization, a quantitative evaluation of their kinematic activity is critical from both climatological and geohazard perspectives. This study applies Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR) to Sentinel-1 radar imagery on both ascending and descending orbits, in order to detect and map moving areas (MA) within the Pirin Mountains (Bulgaria). The primary objective of this study is to update the existing rock glacier inventory (RoGI) by integrating high-resolution Line-of-Sight (LOS) velocity data in accordance with the latest international standards established by the Rock Glacier Inventories and Kinematics (RGIK) standing committee. A secondary objective is to investigate the spatial relationships between the identified moving areas (MAs) and other surrounding geomorphological features (e.g., talus slopes), hence providing a wider context for slope dynamics and landform evolution. The results identified MAs with PSInSAR-derived Line-of-Sight (LOS) velocities reaching up to 10 cm yr⁻¹, which were subsequently classified according to RGIK kinematic categories. A substantial proportion of the detected moving areas occur outside mapped rock glacier boundaries and may reflect a range of geomorphological processes, including permafrost-related creep, talus creep, or other forms of slope deformation. The LOS velocity data were used to assess the activity status of 74 rock glacier units within the regional inventory, classifying 8 as transitional (velocity exceeding 1 cm yr⁻¹) and 66 as relict. Furthermore, we analyse the spatial distribution of these moving areas in relation to primary topographic variables, such as elevation, aspect, and slope. The results highlight the influence of topographic control factors and rock glacier dynamics and provide new insights into the distribution of active periglacial landforms and terrain potentially affected by permafrost in the Balkan Peninsula under changing climatic conditions. Full article

(This article belongs to the Special Issue Latest Improvements and Applications of Ground Deformation Monitoring Based on Remote Sensing Data (Second Edition))

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29 pages, 1724 KB

Open AccessArticle

UAV Remote Sensing Mission Scheduling for Vessel Emission Monitoring in Emission Control Areas

by Yunxiang Shu, Haoran Li, Qixiu Cheng and Shuaian Wang

Remote Sens. 2026, 18(16), 2753; https://doi.org/10.3390/rs18162753 (registering DOI) - 15 Aug 2026

Abstract

Maritime emissions in Emission Control Areas pose significant environmental and health concerns, and effective monitoring is essential for regulatory compliance. This paper addresses the scheduling of drones as a remote sensing platform for inspecting vessel emissions in these areas. A mixed-integer linear programming [...] Read more.

Maritime emissions in Emission Control Areas pose significant environmental and health concerns, and effective monitoring is essential for regulatory compliance. This paper addresses the scheduling of drones as a remote sensing platform for inspecting vessel emissions in these areas. A mixed-integer linear programming framework is proposed based on a time-expanded network representation. The model incorporates three practical features of the inspection environment. First, vessels are assumed to follow uniform linear motion within a narrow approach channel, enabling a closed-form expression for drone flying times. Second, inspection sub-windows are introduced to account for exhaust plume interference that may affect detection accuracy. Third, a time-dependent weighting scheme is adopted to encourage early inspections. The resulting optimisation model is solved directly using Gurobi. Computational experiments on instances with 30 vessels and a fleet of 15 drones demonstrate that the proposed formulation achieves an average optimality gap of 0.01%, with an average solution time of 5.91 s. Sensitivity analyses reveal that fleet size beyond 15 drones yields negligible additional benefit, while raising drone speed from 20 to 35 knots enables multi-vessel inspection tours and yields a relative increase of 85% in the number of vessels inspected. The results indicate that the proposed framework provides effective support for drone-based vessel inspection scheduling in Emission Control Areas. Full article

(This article belongs to the Special Issue Remote Sensing for Maritime Monitoring)

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30 pages, 54305 KB

Open AccessArticle

A Comprehensive Comparison of Seven Surface Air Temperature Reanalysis Products over Mainland China from Multiple Spatiotemporal and Geographical Perspectives

by Si Yan, Xin-Ming Zhu, Xue-Ying Li, Bo-Hui Tang, Yan-Ru Yu and Liang Gao

Remote Sens. 2026, 18(16), 2752; https://doi.org/10.3390/rs18162752 (registering DOI) - 15 Aug 2026

Abstract

Reanalysis surface air temperatures (SATs) offer significant advantages for monitoring climate change, but their performance has not been systematically evaluated across diverse perspectives, limiting studies on surface–atmosphere interactions. In this study, observations from approximately 2400 stations across China (2013–2018) were used as a [...] Read more.

Reanalysis surface air temperatures (SATs) offer significant advantages for monitoring climate change, but their performance has not been systematically evaluated across diverse perspectives, limiting studies on surface–atmosphere interactions. In this study, observations from approximately 2400 stations across China (2013–2018) were used as a reference to assess seven types of reanalysis SATs (CMA-RA/Land, ERA-Interim, ERA5, ERA5-Land, GLDAS, JRA55, MERRA2) from different perspectives, and the main factors affecting the evaluation results were also discussed. The results indicate that (1) the overall RMSEs of the seven SATs range from 2.06 to 2.85 K, with ERA5 presenting the highest accuracy (RMSE = 2.06 K, R² = 0.97) and JRA55 performing the worst (RMSE = 2.85 K, R² = 0.95); (2) overall, their performances vary across multiple time scales, spatial scales, and weather conditions, exhibiting poor performance in western China, in winter, in high-altitude areas, over forest land, and under cloudy skies; (3) by analyzing the influencing factors, we found that terrain correction and station quality are key sources of assessment errors, while the impact of interpolation methods can be disregarded. These results provide valuable references for the development and improvement of reanalysis data and can assist researchers to select appropriate SAT products in various geographical environment and weather conditions in China. Full article

(This article belongs to the Special Issue Land Surface Temperature Retrieval and Cross-Validation for Remote Sensing Applications)

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27 pages, 16065 KB

Open AccessArticle

Spatial Domain Mismatch Between Field Plots and GEDI Inflates Aboveground Biomass Model Accuracy in a Sudanian Savanna Woodland

by Ahmed M. M. Hasoba and Kornél Czimber

Remote Sens. 2026, 18(16), 2751; https://doi.org/10.3390/rs18162751 (registering DOI) - 14 Aug 2026

Abstract

Accurate estimation of aboveground biomass (AGB) in dryland savanna woodlands is constrained by sparse field data, which has motivated widespread fusion of field plots with spaceborne LiDAR reference data from the Global Ecosystem Dynamics Investigation (GEDI). Here, we show that such fusion can [...] Read more.

Accurate estimation of aboveground biomass (AGB) in dryland savanna woodlands is constrained by sparse field data, which has motivated widespread fusion of field plots with spaceborne LiDAR reference data from the Global Ecosystem Dynamics Investigation (GEDI). Here, we show that such fusion can substantially inflate apparent model accuracy when the two reference sources sample different spatial domains. Using 44 field plots from the Abu-Gadaf Natural Reserved Forest (AGNRF), Sudan, and 56 GEDI L4A footprints drawn from a 50 km buffer surrounding the reserve, we trained Random Forest (RF), Gradient Boosting (GB) and Classification and Regression Tree (CART) models on Sentinel-1, Sentinel-2, SRTM and Dynamic World predictors and evaluated them under 10-fold, 2 km block spatial cross-validation. The merged dataset yielded apparently moderate performance (RF: RMSE = 9.40 Mg ha⁻¹, R² = 0.33). However, GEDI-derived AGB was 2.1 times higher than field-measured AGB (18.71 vs. 8.89 Mg ha⁻¹; Kolmogorov–Smirnov D = 0.53, p < 0.001), and decomposing performance by source revealed that predictive skill within the field plot population was effectively absent (R² = 0.001–0.023). A classifier trained to discriminate data source from the predictor stack alone achieved 85% accuracy against a 56% baseline, quantile calibration removing the inter-source level difference reduced pooled R² from 0.33 to 0.13, and restricting GEDI footprints to within 20 km of the reserve reduced R² to 0.008. Apparent accuracy therefore derived largely from between-source separation rather than from structural prediction of AGB. We conclude that spatial cross-validation does not detect population heterogeneity arising from multi-source reference fusion, and that source-stratified validation is necessary. The AGB maps presented are interpreted as relative spatial patterns rather than validated absolute estimates. Full article

(This article belongs to the Section Forest Remote Sensing)

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33 pages, 9339 KB

Open AccessArticle

First Retrieval of Formic Acid from GOSAT-2 Thermal–Infrared Observations over Land

by Fengxin Xie, Ryoichi Imasu, Naoko Saitoh and Yu Someya

Remote Sens. 2026, 18(16), 2750; https://doi.org/10.3390/rs18162750 - 14 Aug 2026

Abstract

Formic acid (HCOOH), the most abundant carboxylic acid in the troposphere, modulates rainwater acidity, aerosol water uptake, and the oxidative capacity of remote atmospheres, yet its global budget remains poorly constrained. Herein, we present the first HCOOH total-column retrieval from thermal–infrared (TIR) measurements [...] Read more.

Formic acid (HCOOH), the most abundant carboxylic acid in the troposphere, modulates rainwater acidity, aerosol water uptake, and the oxidative capacity of remote atmospheres, yet its global budget remains poorly constrained. Herein, we present the first HCOOH total-column retrieval from thermal–infrared (TIR) measurements of the Thermal And Near-infrared Sensor for carbon Observation Fourier Transform Spectrometer-2 (TANSO-FTS-2) on board GOSAT-2, providing an early-afternoon observational perspective that complements existing morning low-Earth-orbit and geostationary HCOOH products. The Optimal Estimation retrieval sequentially fits the surface state, the atmospheric background (temperature, water vapor and ozone), and the HCOOH profile in a 1104–1109 cm

^{- 1}

microwindow centered on the

ν_{6}

Q-branch, with a radiance-ratio-scaled a priori that adapts to each scene. Averaging-kernel diagnostics concentrate the sensitivity in the 500–900 hPa layer with degrees of freedom for signal of approximately 1.05 under enhanced-emission conditions. For a 2019–2020 Australian bushfire case, including HCOOH in the state vector reduces the mean spectral residual from −0.327 K to 0.033 K. Independent evaluation against 113 time-coincident Toronto NDACC FTIR overpasses gives R = 0.95 and a zero-intercept slope of 2.12 for raw FTIR versus GOSAT-2. Applying the GOSAT-2 a priori and averaging kernel to the FTIR profiles changes the slope to 0.77 and reduces the RMSE to

0.23 \times 10^{16}

molec cm

^{- 2}

; this one-sided smoothing is treated only as a sensitivity diagnostic. Monthly global maps for December 2019 and June 2020 show cross-sensor consistency with the IASI/MetOp-B ANNI-HCOOH product at R = 0.83 and 0.76. Over East Asia during April–June 2023, GOSAT-2 correlates with FY-4B/GIIRS at R = 0.90 (April) and R = 0.65 (June), with coherent three-sensor daily variability. These satellite comparisons are treated as cross-sensor consistency assessments rather than independent validation. GOSAT-2 consistently reports lower columns, a sensitivity-limited tendency consistent with a priori dominance under weak signals, limited information content, a narrow retrieval window, and differences among retrieval frameworks. The current product is a first demonstration for cloud-free daytime land scenes; this domain defines its sampling scope and representativeness but is not interpreted as a direct cause of the lower columns. The product offers a traceable GOSAT-2 TIR observational constraint on tropospheric HCOOH for future multi-platform synergy. Full article

(This article belongs to the Special Issue Remote Sensing Data Refinement and Utilization for Advanced Atmospheric Observations)

43 pages, 13930 KB

Open AccessArticle

Bridging Individual-Tree and Stand-Scale Aboveground Biomass Estimation for Chinese Fir Using LiDAR and Machine Learning

by Yuanqing Zheng, Yinyin Zhao, Xiaodi Zhao, Huaqiang Du, Fangjie Mao, Li Chen, Hongyu Zhu, Zihao Huang, Kehan Mo and Xuejian Li

Remote Sens. 2026, 18(16), 2749; https://doi.org/10.3390/rs18162749 - 14 Aug 2026

Abstract

The accurate estimation of forest aboveground biomass (AGB) typically relies on extensive field surveys, which are highly time-consuming and cost-prohibitive. While unmanned aerial vehicle (UAV) Light Detection and Ranging (LiDAR) provides ultra-high point densities capable of reliable individual-tree analysis, its limited flight coverage [...] Read more.

The accurate estimation of forest aboveground biomass (AGB) typically relies on extensive field surveys, which are highly time-consuming and cost-prohibitive. While unmanned aerial vehicle (UAV) Light Detection and Ranging (LiDAR) provides ultra-high point densities capable of reliable individual-tree analysis, its limited flight coverage restricts large-scale applications. Conversely, regional airborne laser scanning (ALS) offers broad spatial coverage, but its relatively low point cloud density makes individual-tree level analysis unreliable. To bridge this scale and data gap, this study develops a scale-consistent framework that integrates UAV-LiDAR, three-dimensional simulation, multisource remote sensing, and machine learning for Chinese fir (Cunninghamia lanceolata) plantation AGB estimation. High-density UAV-LiDAR data were first used to construct individual-tree AGB models, and the predicted tree-level biomass was aggregated to generate spatially representative “agent plots” for stand-scale modeling. A three-dimensional (3D) radiative transfer simulation framework was further employed to reproduce airborne LiDAR observations under different point densities, enabling the evaluation of structural information loss caused by LiDAR sparsity. Structural features derived from simulated LiDAR and spectral information from Sentinel-2 imagery were integrated using the Tabular Prior-data Fitted Network (TabPFN). Model reliability was assessed through 10-fold spatial block cross-validation and Monte Carlo simulations, which quantified spatial generalization and uncertainty propagation from individual-tree estimation to stand-level prediction. Feature interpretation using SHapley Additive exPlanations (SHAP) revealed that the LiDAR-derived vertical canopy structure provided the primary constraints for biomass estimation, whereas Sentinel-2 shortwave infrared features supplied complementary information related to canopy conditions. The optimal TabPFN model achieved a stand-level accuracy of R² = 0.88 and RMSE = 9.23 Mg·ha⁻¹ using LiDAR combined with Sentinel-2 data. Uncertainty analysis further demonstrated the robustness of the proposed framework under propagated errors, highlighting its potential for scalable and reliable forest biomass estimation in data-limited subtropical ecosystems. Full article

27 pages, 17629 KB

Open AccessArticle

Characterization and Estimation of Evaporation Duct Strength Under Tropical Cyclone Conditions Using Stacking Ensemble Learning

by Jinzi Ma, Jian Wang, Cheng Yang, Wenlu Liu and Jiaying Shang

Remote Sens. 2026, 18(16), 2748; https://doi.org/10.3390/rs18162748 - 14 Aug 2026

Abstract

Tropospheric evaporation ducts can trap radio waves within a refractive layer, which may guide signals above 1 GHz, enabling beyond-line-of-sight transmission. This makes duct-assisted propagation attractive for maritime communications. The marine environment is characterized by complex hydrometeorological variability and frequent extremes, particularly tropical [...] Read more.

Tropospheric evaporation ducts can trap radio waves within a refractive layer, which may guide signals above 1 GHz, enabling beyond-line-of-sight transmission. This makes duct-assisted propagation attractive for maritime communications. The marine environment is characterized by complex hydrometeorological variability and frequent extremes, particularly tropical cyclones, which can perturb duct properties and degrade link reliability. This study develops a multivariate cyclone-aware nonlinear regression framework (CNRF) to estimate contemporaneous evaporation duct strength (EDS) by integrating high-resolution dropsonde observations with tropical-cyclone descriptors from the International Best Track Archive for Climate Stewardship (IBTrACS). The framework uses CatBoost, natural-gradient boosting (NGBoost), and a multilayer perceptron (MLP) as base learners, with a random forest (RF) serving as the second-stage nonlinear fusion model. Rather than relying solely on bulk physical parameterization, the framework aims to represent the nonlinear influence of tropical cyclone-related environmental factors on duct strength. Evaluated over 1996–2024, the CNRF attains a test-set R² of 0.791 and a root mean square error (RMSE) of 5.350 M-unit, corresponding to a 23.5% improvement in RMSE over the Naval Postgraduate School (NPS) numerical model. For Hurricane Fiona (2022), the model achieves an RMSE of 6.260 M-unit, and the inclusion of tropical cyclone descriptors improves RMSE by approximately 17.0% relative to a model that excludes tropical cyclone information. The proposed framework facilitates quantitative assessment of extreme-weather-driven duct variability and supports robust design and operation of duct-enabled maritime communication systems. Full article

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42 pages, 9616 KB

Open AccessArticle

A Photogrammetric Simulation Framework for Rockfall Change Detection with Statistically Validated Measurement Noise

by Riccardo Roncella, Abigail Watman, Davide Ettore Guccione, Klaus Thoeni and Anna Giacomini

Remote Sens. 2026, 18(16), 2747; https://doi.org/10.3390/rs18162747 - 14 Aug 2026

Abstract

Rockfalls are natural slope-instability phenomena that pose a significant hazard to infrastructure and human activity. In recent years, the increasing availability of high-resolution three-dimensional (3D) models acquired through photogrammetric techniques has enabled detailed pre-/post-event analyses of rock slopes. However, in this domain, the [...] Read more.

Rockfalls are natural slope-instability phenomena that pose a significant hazard to infrastructure and human activity. In recent years, the increasing availability of high-resolution three-dimensional (3D) models acquired through photogrammetric techniques has enabled detailed pre-/post-event analyses of rock slopes. However, in this domain, the availability of accurate ground truth for the quantitative evaluation of 3D change detection methods and for training machine-learning approaches aimed at recognising and volumetrically quantifying detachments on rock faces remains very limited. This work presents a simulator that, starting from a 3D model of a rock face, generates pre-/post-failure scenarios through controlled removal of rock blocks and produces photogrammetric acquisitions affected by realistic measurement noise. The pipeline emulates the main processing stages of the reconstruction workflow. Noise realism is validated and calibrated by comparing real and simulated data through a multi-indicator framework (marginal distribution, variogram, power spectrum, and multiscale roughness), integrated into a Mahalanobis-distance-based acceptance test with empirical thresholds derived from real measurements. Results from two pilot sites show that, after site-specific tuning of the simulator noise levels, the calibrated configurations reproduce the main magnitude and spatial-structure characteristics of the real noise, with stronger agreement for the fixed stereo-pair configuration and partial but still informative agreement for the more complex UAV-based case. Moreover, the simulator provides a controlled environment for benchmarking and sensitivity analyses of change detection methods. Full article

(This article belongs to the Topic Advanced Risk Assessment in Geotechnical Engineering)

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22 pages, 8044 KB

Open AccessArticle

Var-ANN Calibration of FY-3C VASS Temperature Profiles: Evaluation over the Tibetan Plateau and Application to WRF Precipitation Simulation

by Runze Zhao, Xiangde Xu, Tian Xian, Wenyue Cai, Shengjun Zhang, Zhiying Cai and Lin Chen

Remote Sens. 2026, 18(16), 2746; https://doi.org/10.3390/rs18162746 - 14 Aug 2026

Abstract

Accurate information on atmospheric temperature profiles is crucial for improving numerical weather prediction (NWP). However, the harsh environment of the Tibetan Plateau (TP) limits the availability of station observations, which thereby fail to meet the high spatial resolution required for NWP. In this [...] Read more.

Accurate information on atmospheric temperature profiles is crucial for improving numerical weather prediction (NWP). However, the harsh environment of the Tibetan Plateau (TP) limits the availability of station observations, which thereby fail to meet the high spatial resolution required for NWP. In this study, we present an integrated framework as an engineering refinement combining the variation method with an artificial neural network (Var-ANN) to calibrate temperature profiles obtained from the Vertical Atmosphere Sounding System (VASS) aboard the polar-orbiting satellite FY-3C. The variation method is first applied to construct a spatially consistent reference field from available station observations, and this field is then used as the training target for a back-propagation neural network that learns the empirical relationship between satellite brightness temperatures and corrected atmospheric temperature. The calibrated temperature profiles were evaluated against independent radiosonde observations and further tested through assimilation into the Weather Research and Forecasting (WRF) model for precipitation simulation over the TP. Results indicate that the Var-ANN calibration reduces the root-mean-square error (RMSE) by approximately 60% and the mean bias from approximately −5 °C to −0.7 °C relative to radiosonde observations. In two WRF case studies, the calibrated profiles show potential for improving precipitation forecast skill, although the limited sample size precludes robust conclusions about operational forecast improvements. The Var-ANN framework provides a practical approach for enhancing the utility of FY-3C VASS temperature products for NWP applications over data-sparse complex terrain. Full article

(This article belongs to the Special Issue AI-Driven Satellite Data for Global Environment Monitoring (Second Edition))

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29 pages, 12320 KB

Open AccessArticle

A Semi-Empirical Method for Estimating All-Sky Photosynthetically Active Radiation from Sentinel-2 for High-Resolution Land Surface Analysis

by Mustafa Serkan Isik, Leandro Parente, Lindsey Sloat, Josip Krizan, Karla Čmelar and Laerte Guimaraes Ferreira

Remote Sens. 2026, 18(16), 2745; https://doi.org/10.3390/rs18162745 - 14 Aug 2026

Abstract

Photosynthetically active radiation (PAR) is a fundamental driver of terrestrial photosynthesis and a key input for light use efficiency-based estimates of gross primary productivity (GPP). However, existing PAR products are typically designed for regional to global applications and often remain spatially mismatched with [...] Read more.

Photosynthetically active radiation (PAR) is a fundamental driver of terrestrial photosynthesis and a key input for light use efficiency-based estimates of gross primary productivity (GPP). However, existing PAR products are typically designed for regional to global applications and often remain spatially mismatched with the finer-resolution land surface variables now commonly derived from optical satellite observations. In this study, we present a semi-empirical framework for deriving daily clear-sky and all-sky PAR from Sentinel-2 Level-2A imagery. The approach combines solar geometry, daily extraterrestrial radiation, and simplified atmospheric transmittance parameterizations using Sentinel-2 aerosol, water vapor, and scene classification information to estimate clear-sky PAR, and further extends this formulation to all-sky conditions through a cloud-transmission factor derived from cloud probability to generate a spatially explicit PAR product aligned with Sentinel-2 observations. The resulting estimates are evaluated against flux tower observations from 172 AmeriFlux sites across North and South America for the period 2017–2024 and compared with MODIS MCD18, VIIRS VNP18, and CERES SYN1deg PAR products. The clear-sky Sentinel-2 formulation showed a moderate positive bias of 6.38 W m⁻², while the all-sky cloud adjustment reduced the mean bias to −1.44 W m⁻² with an RMSE of 23.53 W m⁻² and correlation of r = 0.87. The largest improvements occurred in spring and summer seasons, when atmospheric attenuation has the strongest influence on the clear-sky estimates. MODIS and CERES all-sky PAR products achieved lower overall errors with RMSE of 17.60 W m⁻² and 15.56 W m⁻², respectively, but at substantially coarser spatial resolution. The proposed framework therefore provides a practical high-resolution approximation of daily PAR that is spatially consistent with Sentinel-2 observations. Rather than replacing dedicated radiative transfer-based products, the method is intended to support analyses in which PAR needs to be evaluated together with Sentinel-2 bands, vegetation indices, and other Sentinel-2-derived variables within a common observational framework. Full article

(This article belongs to the Special Issue Monitoring and Managing Environmental Sustainability Using Remote Sensing (Second Edition))

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