Remote Sensing

22 pages, 3727 KB

Open AccessArticle

FPGA-Based Real-Time Deblurring and Enhancement for UAV-Captured Infrared Imagery

by Jianghua Cheng, Lehao Pan, Tong Liu, Bang Cheng and Yahui Cai

Remote Sens. 2025, 17(20), 3446; https://doi.org/10.3390/rs17203446 - 15 Oct 2025

In response to the inherent limitations of uncooled infrared imaging devices and the image degradation caused by UAV(Unmanned Aerial Vehicle) platform motion, resulting in low contrast and blurred details, a novel single-image blind deblurring and enhancement network is proposed for UAV infrared imagery. [...] Read more.

In response to the inherent limitations of uncooled infrared imaging devices and the image degradation caused by UAV(Unmanned Aerial Vehicle) platform motion, resulting in low contrast and blurred details, a novel single-image blind deblurring and enhancement network is proposed for UAV infrared imagery. This network achieves global blind deblurring and local feature enhancement, laying a foundation for subsequent high-level vision tasks. The proposed architecture comprises three key modules: feature extraction, feature fusion, and simulated diffusion. Furthermore, a region-specific pixel loss is introduced to strengthen local feature perception, while a progressive training strategy is adopted to optimize model performance. Experimental results on public infrared datasets demonstrate that the presented method outperforms state-of-the-art methods HCTIRdeblur, reducing parameter count by 18.4%, improving PSNR by 10.7%, and decreasing edge inference time by 25.6%. This work addresses critical challenges in UAV infrared image processing and offers a promising solution for real-world applications. Full article

(This article belongs to the Special Issue Advances in Deep Learning Approaches: UAV Data Analysis)

20 pages, 6948 KB

Open AccessArticle

Style Transfer from Sentinel-1 to Sentinel-2 for Fluvial Scenes with Multi-Modal and Multi-Temporal Image Fusion

by Patrice E. Carbonneau

Remote Sens. 2025, 17(20), 3445; https://doi.org/10.3390/rs17203445 - 15 Oct 2025

Abstract

Recently, there has been significant progress in the area of semantic classification of water bodies at global scales with deep learning. For the key purposes of water inventory and change detection, advanced deep learning classifiers such as UNets and Vision Transformers have been [...] Read more.

Recently, there has been significant progress in the area of semantic classification of water bodies at global scales with deep learning. For the key purposes of water inventory and change detection, advanced deep learning classifiers such as UNets and Vision Transformers have been shown to be both accurate and flexible when applied to large-scale, or even global, satellite image datasets from optical (e.g., Sentinel-2) and radar sensors (e.g., Sentinel-1). Most of this work is conducted with optical sensors, which usually have better image quality, but their obvious limitation is cloud cover, which is why radar imagery is an important complementary dataset. However, radar imagery is generally more sensitive to soil moisture than optical data. Furthermore, topography and wind-ripple effects can alter the reflected intensity of radar waves, which can induce errors in water classification models that fundamentally rely on the fact that water is darker than the surrounding landscape. In this paper, we develop a solution to the use of Sentinel-1 radar images for the semantic classification of water bodies that uses style transfer with multi-modal and multi-temporal image fusion. Instead of developing new semantic classification models that work directly on Sentinel-1 images, we develop a global style transfer model that produces synthetic Sentinel-2 images from Sentinel-1 input. The resulting synthetic Sentinel-2 imagery can then be classified with existing models. This has the advantage of obviating the need for large volumes of manually labeled Sentinel-1 water masks. Next, we show that fusing an 8-year cloud-free composite of the near-infrared band 8 of Sentinel-2 to the input Sentinel-1 image improves the classification performance. Style transfer models were trained and validated with global scale data covering the years 2017 to 2024, and include every month of the year. When tested against a global independent benchmark, S1S2-Water, the semantic classifications produced from our synthetic imagery show a marked improvement with the use of image fusion. When we use only Sentinel-1 data, we find an overall IoU (Intersection over Union) score of 0.70, but when we add image fusion, the overall IoU score rises to 0.93. Full article

(This article belongs to the Special Issue Multimodal Remote Sensing Data Fusion, Analysis and Application)

21 pages, 11447 KB

Open AccessArticle

Sensitivity Analysis of the Differential Atmospheric Transmission in Water Vapour Mixing Ratio Retrieval from Raman Lidar Measurements

by Arlett Díaz-Zurita, Víctor M. Naval-Hernández, David N. Whiteman, Onel Rodríguez-Navarro, Jorge Muñiz-Rosado, Daniel Pérez-Ramírez, Lucas Alados-Arboledas and Francisco Navas-Guzmán

Remote Sens. 2025, 17(20), 3444; https://doi.org/10.3390/rs17203444 - 15 Oct 2025

Abstract

This study assesses the effect of the differential atmospheric transmission term in Raman lidar water vapour mixing ratio retrievals. Such issue is evaluated for two optical configurations: the first is a vibrational–rotational Raman nitrogen (∼387 nm) and the second is a pure–rotational Raman [...] Read more.

This study assesses the effect of the differential atmospheric transmission term in Raman lidar water vapour mixing ratio retrievals. Such issue is evaluated for two optical configurations: the first is a vibrational–rotational Raman nitrogen (∼387 nm) and the second is a pure–rotational Raman molecular reference near 354 nm (nitrogen and oxygen). Both optical configurations use a vibrational–rotational water vapour channel at ∼408 nm. More than 300 aerosol profiles acquired by the University of Granada Raman lidar over the period 2010–2016 enabled the calculation of the aerosol contribution of the differential atmospheric transmission term, indicating that neglecting the total differential atmospheric transmission term can introduce systematic uncertainties in water vapour mixing ratio retrievals of approximately 5.1% and 15% (18% under high-aerosol conditions) at 6 km for the first and second configuration, respectively. Subsequently, in order to apply automatic differential transmission calculations, we developed a technique for estimating the aerosol contribution from sun photometer AOD measurements, yielding relative deviations in water vapour mixing ratio of 0.10% and 0.40% for ∼387 nm and ∼354 nm configurations when compared with cases where Raman lidar aerosol profiles were available. This approach transforms systematic uncertainties into random ones that can be reduced by increasing the number of measurements. Full article

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22 pages, 1668 KB

Open AccessArticle

Semantic Segmentation of High-Resolution Remote Sensing Images Based on RS³Mamba: An Investigation of the Extraction Algorithm for Rural Compound Utilization Status

by Xinyu Fang, Zhenbo Liu, Su’an Xie and Yunjian Ge

Remote Sens. 2025, 17(20), 3443; https://doi.org/10.3390/rs17203443 - 15 Oct 2025

Abstract

In this study, we utilize Gaofen-2 satellite remote sensing images to optimize and enhance the extraction of feature information from rural compounds, addressing key challenges in high-resolution remote sensing analysis: traditional methods struggle to effectively capture long-distance spatial dependencies for scattered rural compounds. [...] Read more.

In this study, we utilize Gaofen-2 satellite remote sensing images to optimize and enhance the extraction of feature information from rural compounds, addressing key challenges in high-resolution remote sensing analysis: traditional methods struggle to effectively capture long-distance spatial dependencies for scattered rural compounds. To this end, we implement the RS³Mamba+ deep learning model, which introduces the Mamba state space model (SSM) into its auxiliary branching—leveraging Mamba’s sequence modeling advantage to efficiently capture long-range spatial correlations of rural compounds, a critical capability for analyzing sparse rural buildings. This Mamba-assisted branch, combined with multi-directional selective scanning (SS2D) and the enhanced STEM network framework (replacing single 7 × 7 convolution with two-stage 3 × 3 convolutions to reduce information loss), works synergistically with a ResNet-based main branch for local feature extraction. We further introduce a multiscale attention feature fusion mechanism that optimizes feature extraction and fusion, enhances edge contour extraction accuracy in courtyards, and improves the recognition and differentiation of courtyards from regions with complex textures. The feature information of courtyard utilization status is finally extracted using empirical methods. A typical rural area in Weifang City, Shandong Province, is selected as the experimental sample area. Results show that the extraction accuracy reaches an average intersection over union (mIoU) of 79.64% and a Kappa coefficient of 0.7889, improving the F1 score by at least 8.12% and mIoU by 4.83% compared with models such as DeepLabv3+ and Transformer. The algorithm’s efficacy in mitigating false alarms triggered by shadows and intricate textures is particularly salient, underscoring its potential as a potent instrument for the extraction of rural vacancy rates. Full article

34 pages, 18916 KB

Open AccessArticle

Terrain Matters: A Focus+Context Visualization Approach for Landform-Based Remote Sensing Analysis of Agricultural Performance

by Roghayeh Heidari, Faramarz F. Samavati and Vincent Yeow Chieh Pang

Remote Sens. 2025, 17(20), 3442; https://doi.org/10.3390/rs17203442 - 15 Oct 2025

Abstract

Understanding spatial variability is central to precision agriculture, yet terrain features are often overlooked in remote sensing workflows that inform agronomic decision-making. This work introduces a terrain-aware visual analytics approach that integrates landform classification with crop performance analysis to better support field-level decisions. [...] Read more.

Understanding spatial variability is central to precision agriculture, yet terrain features are often overlooked in remote sensing workflows that inform agronomic decision-making. This work introduces a terrain-aware visual analytics approach that integrates landform classification with crop performance analysis to better support field-level decisions. Terrain features are an important contributor to yield variability, alongside environmental conditions, soil properties, and management practices. However, they are rarely integrated systematically into performance analysis and decision-making workflows—limiting the potential for terrain-aware insights in precision agriculture. Addressing this gap requires approaches that incorporate terrain attributes and landform classifications into agricultural performance analysis and management zone (MZ) delineation—ideally through visual analytics that offer interpretable insights beyond the constraints of purely data-driven methods. We introduce an interactive focus+context visualization tool that integrates multiple data layers—including terrain features, vegetation index–based performance metric, and management zones—into a unified, expressive view. The system leverages freely available remote sensing imagery and terrain data derived from Digital Elevation Models (DEMs) to evaluate crop performance and landform characteristics in support of agronomic analysis. The tool was applied to eleven agricultural fields across the Canadian Prairies under diverse environmental conditions. Fields were segmented into depressions, hilltops, and baseline areas, and crop performance was evaluated across these landform groups using the system’s interactive visualization and analytics. Depressions and hilltops consistently showed lower mean performance and higher variability (measured by coefficient of variation) compared to baseline regions, which covered an average of 82% of each field. We also subdivided baseline areas using slope and the Sediment Transport Index (STI) to investigate soil erosion effects, but field-level patterns were inconsistent and no systematic differences emerged across all sites. Expert evaluation confirmed the tool’s usability and its value for field-level decision support. Overall, the method enhances terrain-aware interpretation of remotely sensed data and contributes meaningfully to refining management zone delineation in precision agriculture. Full article

(This article belongs to the Special Issue Advances in Multi-Sensor Remote Sensing for Vegetation Monitoring)

28 pages, 4401 KB

Open AccessArticle

Uncovering Forest and Cropland Change with High-Resolution Data in a Biodiversity Hotspot, Madagascar

by Zy Harifidy Rakotoarimanana, Nobuhito Ohte and Zy Misa Harivelo Rakotoarimanana

Remote Sens. 2025, 17(20), 3441; https://doi.org/10.3390/rs17203441 - 15 Oct 2025

Abstract

The lack of reliable methods for cropland and forest monitoring remains a challenge in the Betsiboka basin and Ankarafantsika National Park (ANP), Madagascar. A key novelty of our study is the comparative analysis of multiple high-resolution datasets for 2017 and 2021 and future [...] Read more.

The lack of reliable methods for cropland and forest monitoring remains a challenge in the Betsiboka basin and Ankarafantsika National Park (ANP), Madagascar. A key novelty of our study is the comparative analysis of multiple high-resolution datasets for 2017 and 2021 and future projections under five Shared Socioeconomic Pathways (SSPs) from 2020 to 2100 using Google Earth Engine and Python. Results indicate that forest cover has remained below ~9% in the Betsiboka basin and above ~35% in ANP, while cropland stays under 7% in both areas. Inter-dataset agreement showed high overall accuracy (OA = 0.87–0.95), with stronger agreement in ANP (Kappa = 0.68–0.90). FROM-GLC10 and ESA performed best for cropland classification in Betsiboka, while Dynamic World and ESRI were most accurate for forest, particularly in ANP. Projections suggest that by 2100, forest area in Betsiboka may increase by +230% under SSP3 and +300% under SSP5, whereas ANP could see declines up to 39% under SSP1, −2.2% SSP5, and −1.4% SSP3. The predicted minor cropland increase across both regions suggests that forest expansion is unlikely to significantly constrain agricultural land, illustrating the potential for sustainable intensification and agroforestry to address food security challenges. Full article

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24 pages, 10966 KB

Open AccessArticle

UAV-Based Wellsite Reclamation Monitoring Using Transformer-Based Deep Learning on Multi-Seasonal LiDAR and Multispectral Data

by Dmytro Movchan, Zhouxin Xi, Angeline Van Dongen, Charumitha Selvaraj and Dani Degenhardt

Remote Sens. 2025, 17(20), 3440; https://doi.org/10.3390/rs17203440 - 15 Oct 2025

Abstract

Monitoring reclaimed wellsites in boreal forest environments requires accurate, scalable, and repeatable methods for assessing vegetation recovery. This study evaluates the use of uncrewed aerial vehicle (UAV)-based light detection and ranging (LiDAR) and multispectral (MS) imagery for individual tree detection, crown delineation, and [...] Read more.

Monitoring reclaimed wellsites in boreal forest environments requires accurate, scalable, and repeatable methods for assessing vegetation recovery. This study evaluates the use of uncrewed aerial vehicle (UAV)-based light detection and ranging (LiDAR) and multispectral (MS) imagery for individual tree detection, crown delineation, and classification across five reclaimed wellsites in Alberta, Canada. A deep learning workflow using 3D convolutional neural networks was applied to LiDAR and MS data collected in spring, summer, and autumn. Results show that LiDAR alone provided high accuracy for tree segmentation and height estimation, with a mean intersection over union (mIoU) of 0.94 for vegetation filtering and an F1-score of 0.82 for treetop detection. Incorporating MS data improved deciduous/coniferous classification, with the highest accuracy (mIoU = 0.88) achieved using all five spectral bands. Coniferous species were classified more accurately than deciduous species, and classification performance declined for trees shorter than 2 m. Spring conditions yielded the highest classification accuracy (mIoU = 0.93). Comparisons with ground measurements confirmed a strong correlation for tree height estimation (R² = 0.95; root mean square error = 0.40 m). Limitations of this technique included lower performance for short, multi-stemmed trees and deciduous species, particularly willow. This study demonstrates the value of integrating 3D structural and spectral data for monitoring forest recovery and supports the use of UAV remote sensing for scalable post-disturbance vegetation assessment. The trained models used in this study are publicly available through the TreeAIBox plugin to support further research and operational applications. Full article

(This article belongs to the Topic Advances in Sensor Data Fusion and AI for Environmental Monitoring)

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22 pages, 1977 KB

Open AccessArticle

Evaluation of the Partition of Global Solar Radiation into UVA, PAR, and NIR Components in a Rural Environment

by Lucía Moreno-Cuenca, Francisco Navas-Guzmán, Lionel Doppler and Inmaculada Foyo Moreno

Remote Sens. 2025, 17(20), 3439; https://doi.org/10.3390/rs17203439 - 15 Oct 2025

Abstract

Observational studies in several regions and our dataset indicate changes in global solar radiation (R_S); here, we analyze how atmospheric conditions modulate its spectral composition. This study investigates the effects of atmospheric conditions on the spectral composition of global solar radiation [...] Read more.

Observational studies in several regions and our dataset indicate changes in global solar radiation (R_S); here, we analyze how atmospheric conditions modulate its spectral composition. This study investigates the effects of atmospheric conditions on the spectral composition of global solar radiation (R_S) across different wavelength ranges: ultraviolet A (UVA), photosynthetically active radiation (PAR), and near-infrared radiation (NIR), using the ratios UVA/R_S, PAR/R_S, and NIR/R_S. A high-quality spectral irradiance dataset (300–1025 nm) covering eight years of observations from a representative rural site in Central Europe (Meteorological Observatory Lindenberg, Tauche, in North-East Germany) was used. The average values obtained for the ratios were 0.049 ± 0.010 for UVA/R_S, 0.433 ± 0.044 for PAR/R_S, and 0.259 ± 0.030 for NIR/R_S. Thus, the UVA range contributed approximately 5% to global radiation, PAR 43%, and NIR 26%. Strong correlations were found between each spectral component and R_S, with determination coefficients exceeding 0.90 in all cases, particularly for PAR. This suggests that, in the absence of direct spectral measurements, these components can be reliably estimated from R_S. A seasonal pattern was also identified, with maximum values in warmer months and minimum values in colder ones, most notably for PAR/R_S. In contrast, NIR/R_S exhibited an inverse pattern, likely influenced by atmospheric water vapor. A long-term decreasing trend in these ratios was also identified, being most pronounced in the UVA/R_S ratio. Additionally, atmospheric conditions significantly affected the spectral distribution of R_S, with UVA and PAR proportions increasing under specific conditions, while NIR remained more stable. Under overcast conditions, the ratios for shorter wavelengths (UVA and PAR) increased, indicating higher scattering effects, while NIR was less affected. Full article

(This article belongs to the Special Issue Remote Sensing of Solar Radiation Absorbed by Land Surfaces)

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30 pages, 7778 KB

Open AccessArticle

Diurnal Variation Characteristics of Precipitation in Summer Associated with Diverse Underlying Surfaces in the Arid Region of Eastern Xinjiang, Northwest China

by Abuduwaili Abulikemu, Zulipina Kadier, Lianmei Yang, Mamat Sawut, Junqiang Yao, Yong Zeng, Dawei An and Gang Yin

Remote Sens. 2025, 17(20), 3438; https://doi.org/10.3390/rs17203438 - 15 Oct 2025

Abstract

Investigating the diurnal variation characteristics of precipitation (DVCP) in Xinjiang, an arid region of Northwest China, is essential for improving water resource management and disaster mitigation strategies. This study examines the DVCP associated with diverse underlying surfaces in Eastern Xinjiang (EX)—one of the [...] Read more.

Investigating the diurnal variation characteristics of precipitation (DVCP) in Xinjiang, an arid region of Northwest China, is essential for improving water resource management and disaster mitigation strategies. This study examines the DVCP associated with diverse underlying surfaces in Eastern Xinjiang (EX)—one of the most arid regions in China—during summer (June–August) from 2015 to 2019, using hourly simulation data from the real-time forecasting system of Nanjing University (WRF_NJU). Evaluation against automatic weather station (AWS) observations indicates that WRF_NJU outperforms reanalysis (ERA5), satellite (CMORPH), and MESWEP datasets, demonstrating its reliability for regional precipitation analysis. Further investigation reveals that in the Turpan-Hami Basin (THB), below 1000 m above sea level (ASL), peaks in precipitation amount (PA), intensity (PI), and frequency (PF) occur at 06 local solar time (LST), whereas in mountainous areas above 3000 m ASL, these peaks are delayed until 13 LST. Analysis of the coefficient of variation (CV) shows that the most pronounced differences in DVCP between mountainous and basin regions are associated with PF and PI. Specifically, regions with high CV for PF are concentrated in the central to northern parts of the THB, while high CV for PI is found in the eastern Mid-Tianshan Mountains (MTM) and East Tianshan Mountains (ETM). Moreover, significant differences in DVCP are observed across land surface types: PA peaks over grasslands, forests, and water bodies occur around noon, whereas over impervious surfaces, croplands, and barren areas, they occur during the early morning hours. Full article

(This article belongs to the Special Issue Precipitation and Evapotranspiration Mechanisms in Drylands and Their Remote Sensing Retrieval & Simulation (Second Edition))

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26 pages, 19498 KB

Open AccessArticle

Estimation of Forest Aboveground Biomass in China Based on GEDI and Sentinel-2 Data: Quantitative Analysis of Optical Remote Sensing Saturation Effect and Terrain Compensation Mechanisms

by Jiarun Wang, Chengzhi Xiang and Ailin Liang

Remote Sens. 2025, 17(20), 3437; https://doi.org/10.3390/rs17203437 - 15 Oct 2025

Abstract

Forests store substantial amounts of aboveground biomass (AGB) and play a critical role in the global carbon cycle. Optical remote sensing offers long-term, large-scale monitoring capabilities; however, spectral saturation in high-biomass regions limits the accuracy of AGB estimation. Although radar and LiDAR data [...] Read more.

Forests store substantial amounts of aboveground biomass (AGB) and play a critical role in the global carbon cycle. Optical remote sensing offers long-term, large-scale monitoring capabilities; however, spectral saturation in high-biomass regions limits the accuracy of AGB estimation. Although radar and LiDAR data can mitigate the saturation problem, optical imagery remains irreplaceable for continuous, multi-decadal monitoring from regional to global scales. Nevertheless, quantitative analyses of nationwide optical saturation thresholds and compensation mechanisms are still lacking. In this study, we integrated high-accuracy AGB estimates from the Global Ecosystem Dynamics Investigation (GEDI) L4A product, Sentinel-2 optical imagery, and topographic variables to develop a 200 m resolution Light Gradient Boosting Machine (LightGBM) machine learning model for forests in China. Stratified error analysis, locally weighted scatterplot smoothing (LOWESS) curves, and SHapley Additive exPlanations (SHAP) were employed to quantify optical saturation thresholds and the compensatory effects of topographic features. Results showed that estimation accuracy declined markedly when AGB exceeded approximately 300 Mg·ha⁻¹. Red and red-edge bands saturated at around 80 Mg·ha⁻¹, while certain spectral indices delayed the threshold to 100–150 Mg·ha⁻¹. Topographic features maintained stable contributions below 300 Mg·ha⁻¹, providing critical compensation for AGB prediction in high-biomass areas. This study delivers a high-resolution national AGB dataset and a transferable analytical framework for saturation mechanisms, offering methodological insights for large-scale, long-term optical AGB monitoring. Full article

(This article belongs to the Special Issue Advances in Estimating Aboveground Biomass Based on Multi-source Remote Sensing Data)

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28 pages, 25651 KB

Open AccessArticle

Performance of Multi-Antenna GNSS Buoy and Co-Located Mooring Array Deployed Around Qianliyan Islet for Altimetry Satellite Calibration

by Bin Guan, Zhongmiao Sun, He Huang, Zhenhe Zhai, Xiaogang Liu, Jian Ma, Lingyong Huang, Zhiyong Huang, Mingda Ouyang, Mimi Zhang, Xiyu Xu and Lei Yang

Remote Sens. 2025, 17(20), 3436; https://doi.org/10.3390/rs17203436 - 15 Oct 2025

Abstract

To evaluate the prospects of multi-antenna GNSS buoy and mooring array in ocean altimetry satellite calibration, experiments are conducted in the ocean around Qianliyan islet in China’s Yellow Sea. The trials aim to validate the feasibility of establishing an ocean altimetry satellite calibration [...] Read more.

To evaluate the prospects of multi-antenna GNSS buoy and mooring array in ocean altimetry satellite calibration, experiments are conducted in the ocean around Qianliyan islet in China’s Yellow Sea. The trials aim to validate the feasibility of establishing an ocean altimetry satellite calibration site while assessing the performance of relevant calibration equipment. Utilizing one multi-antenna GNSS buoy system and one mooring array operating for over 20 days, the experiment incorporates continuous GNSS observation data from Qianliyan islet’s permanent station. Results reveal that high-frequency sea surface height (SSH) signals exhibit periods approaching or below 10 s, with the designed low-pass filter effectively attenuating these high-frequency components. Significant differences emerge in the power spectra of filtered SSH measurements between instruments: high-frequency signals detected by the mooring array demonstrate greater spectral concentration and lower signal intensity than those recorded by the GNSS buoy. Through multi-day synchronized observations, the height datum for mooring array SSH measurements is obtained, revealing average standard deviation of 2.76 cm in filtered SSH differences between platforms—validating both the system design and data processing methodology. This experiment successfully demonstrates the performance of calibration equipment, preliminarily verifies the effectiveness of ground-based calibration data processing techniques, and further confirms the technical viability of establishing an ocean altimetry satellite calibration site around Qianliyan islet. Full article

(This article belongs to the Special Issue Advancing Hydrological Monitoring and Prediction Through Multisource Geodetic Observations)

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22 pages, 3687 KB

Open AccessArticle

Spatial Sampling Uncertainty for MODIS Terra Land Surface Temperature Retrievals

by Claire E. Bulgin, Darren J. Ghent and Mike Perry

Remote Sens. 2025, 17(20), 3435; https://doi.org/10.3390/rs17203435 - 15 Oct 2025

Abstract

Land surface temperature (LST) data are often required at coarser resolutions than the native satellite data for user applications. LST products from infrared sensors are clear-sky only, and thus, coarsening such data introduces a sampling uncertainty where the target domain is not fully [...] Read more.

Land surface temperature (LST) data are often required at coarser resolutions than the native satellite data for user applications. LST products from infrared sensors are clear-sky only, and thus, coarsening such data introduces a sampling uncertainty where the target domain is not fully sampled. In this manuscript, we calculate sampling uncertainty as a function of clear-sky fraction for 0.01° products re-gridded to 0.05° and 0.1°. We find that sampling uncertainty is dependent on both the underlying land cover (biome) and the solar geometry at the time of the observation. The largest sampling uncertainties are seen for mixed pixels (encompassing a variety of biomes) at 0.05° resolution (0.98 K) and for urban pixels at 0.1° resolution (2.5 K). The spatial sampling uncertainty methodology presented here is applicable to any infrared LST products provided at these resolutions (from a native resolution of 0.01°/~1 km), irrespective of retrieval algorithm or satellite, provided that the uncertainty due to noise can be removed. Full article

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18 pages, 3535 KB

Open AccessArticle

UAV Based Weed Pressure Detection Through Relative Labelling

by Sebastiaan Verbesselt, Rembert Daems, Axel Willekens and Jonathan Van Beek

Remote Sens. 2025, 17(20), 3434; https://doi.org/10.3390/rs17203434 - 15 Oct 2025

Abstract

Agricultural management in Europe faces increasing pressure to reduce its environmental footprint. Implementing precision agriculture for weed management could offer a solution and minimize the use of chemical products. High spatial resolution imagery from real time kinematic (RTK) unmanned aerial vehicles (UAV) in [...] Read more.

Agricultural management in Europe faces increasing pressure to reduce its environmental footprint. Implementing precision agriculture for weed management could offer a solution and minimize the use of chemical products. High spatial resolution imagery from real time kinematic (RTK) unmanned aerial vehicles (UAV) in combination with supervised convolutional neural network (CNNs) models have proven successful in making location specific treatments. This site-specific advice limits the amount of herbicide applied to the field to areas that require action, thereby reducing the environmental impact and inputs for the farmer. To develop performant CNN models, there is a need for sufficient high-quality labelled data. To reduce the labelling effort and time, a new labelling method is proposed whereby image subsection pairs are labelled based on their relative differences in weed pressure to train a CNN ordinal regression model. The model is evaluated on detecting weed pressure in potato (Solanum tuberosum L.). Model performance was evaluated on different levels: pairwise accuracy, linearity (Pearson correlation coefficient), rank consistency (Spearman’s (r_s) and Kendal (τ) rank correlations coefficients) and binary accuracy. After hyperparameter tuning, a pairwise accuracy of 85.2%, significant linearity (r_s = 0.81) and significant rank consistency (r_s = 0.87 and τ = 0.69) were found. This suggests that the model is capable of correctly detecting the gradient in weed pressure for the dataset. A maximum binary accuracy and F1-score of 92% and 88% were found for the dataset after thresholding the predicted weed scores into weed versus non-weed images. The model architecture allows us to visualize the intermediate features of the last convolutional block. This allows data analysts to better evaluate if the model “sees” the features of interest (in this case weeds). The results indicate the potential of ordinal regression with relative labels as a fast, lightweight model that predicts weed pressure gradients. Experts have the freedom to decide which threshold value(s) can be used on predicted weed scores depending on the weed, crop and treatment that they want to use for flexible weed control management. Full article

(This article belongs to the Special Issue Remote Sensing and Machine Learning in Vegetation Biophysical Parameters Estimation (Second Edition))

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20 pages, 8158 KB

Open AccessArticle

Reconstructing Global Chlorophyll-a Concentration for the COCTS Aboard Chinese Ocean Color Satellites via the DINEOF Method

by Xiaomin Ye, Mingsen Lin, Bin Zou, Xiaomei Wang and Zhijia Lin

Remote Sens. 2025, 17(20), 3433; https://doi.org/10.3390/rs17203433 - 15 Oct 2025

Abstract

The chlorophyll-a (Chl-a) concentration, a critical parameter for characterizing marine primary productivity and ecological health, plays a vital role in providing ecological environment monitoring and climate change assessment while serving as a core retrieval product in ocean color remote sensing. Currently, more than [...] Read more.

The chlorophyll-a (Chl-a) concentration, a critical parameter for characterizing marine primary productivity and ecological health, plays a vital role in providing ecological environment monitoring and climate change assessment while serving as a core retrieval product in ocean color remote sensing. Currently, more than ten ocean color satellites operate globally, including China’s HY-1C, HY-1D and HY-1E satellites. However, significant spatial data gaps exist in Chl-a concentration retrieval from satellites because of cloud cover, sun-glint, and limitation of sensor swath. This study aimed to systematically enhance the spatiotemporal integrity of ocean monitoring data through multisource data merging and reconstruction techniques. We integrated Chl-a concentration datasets from four major sensor types—Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), Ocean and Land Color Instrument (OLCI), and Chinese Ocean Color and Temperature Scanner (COCTS)—and quantitatively evaluated their global coverage performance under different payload combinations. The key findings revealed that single-sensor 4-day continuous observation achieved effective coverage levels ranging from only 10.45–26.1%, while multi-sensor merging substantially increased coverage, namely, homogeneous payload merging provided 25.7% coverage for two MODIS satellites, 41.1% coverage for three VIIRS satellites, 24.8% coverage for two OLCI satellites, and 37.1% coverage for three COCTS satellites, with 10-payload merging increasing the coverage rate to 55.4%. Employing the Data Interpolating Empirical Orthogonal Functions (DINEOFS) algorithm, we successfully reconstructed data for China’s ocean color satellites. Validation against VIIRS reconstructions indicated high consistency (a mean relative error of 26% and a linear correlation coefficient of 0.93), whereas self-verification yielded a mean relative error of 27% and a linear correlation coefficient of 0.90. Case studies in Chinese offshore and adjacent waters, waters east of Mindanao Island and north of New Guinea, demonstrated the successful reconstruction of spatiotemporal Chl-a dynamics. The results demonstrated that China’s HY-1C, HY-1D, and HY-1E satellites enable daily global-scale Chl-a reconstruction. Full article

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29 pages, 6794 KB

Open AccessArticle

An Attitude Estimation Method for Space Targets Based on the Selection of Multi-View ISAR Image Sequences

by Junzhi Li, Xin Ning, Dou Sun and Rongzhen Du

Remote Sens. 2025, 17(20), 3432; https://doi.org/10.3390/rs17203432 - 14 Oct 2025

Abstract

Multi-view inverse synthetic aperture radar (ISAR) image sequences provide multi-dimensional observation information about space targets, enabling precise attitude estimation that is fundamental to both non-cooperative target monitoring and critical space operations including active debris removal and space collision avoidance. However, directly utilizing all [...] Read more.

Multi-view inverse synthetic aperture radar (ISAR) image sequences provide multi-dimensional observation information about space targets, enabling precise attitude estimation that is fundamental to both non-cooperative target monitoring and critical space operations including active debris removal and space collision avoidance. However, directly utilizing all images within an ISAR sequence for attitude estimation can result in a substantial data preprocessing workload and reduced algorithm efficiency. Given the inherent overlap and redundancy in the target information provided by these ISAR images, this paper proposes a novel space target attitude estimation method based on the selection of multi-view ISAR image sequences. The proposed method begins by establishing an ISAR imaging projection model, then characterizing the target information differences through variations in imaging plane normal, and proposing an image selection method based on the uniform sampling across elevation and azimuth angles of the imaging plane normal. On this basis, the method utilizes a high-resolution network (HRNet) to extract the feature points of typical components of the space target. This method enables simultaneous feature point extraction and matching association within ISAR images. The attitude estimation problem is subsequently modeled as an unconstrained optimization problem. Finally, the particle swarm optimization (PSO) algorithm is employed to solve this optimization problem, thereby achieving accurate attitude estimation of the space target. Experimental results demonstrate that the proposed methodology effectively filters image data, significantly reducing the number of images required while maintaining high attitude estimation accuracy. The method provides a more informative sequence than conventional selection strategies, and the tailored HRNet + PSO estimator resists performance degradation in sparse-data conditions, thereby ensuring robust overall performance. Full article

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22 pages, 6415 KB

Open AccessArticle

Post-Earthquake Damage and Recovery Assessment Using Nighttime Light Data: A Case Study of the Turkey–Syria Earthquake

by Jiaqi Yang, Shengbo Chen, Zibo Wang, Yaqi Zhang, Yuqiao Suo, Jinchen Zhu, Menghan Wu, Aonan Zhang and Qiqi Li

Remote Sens. 2025, 17(20), 3431; https://doi.org/10.3390/rs17203431 - 14 Oct 2025

Abstract

In recent years, the increasing frequency of global seismic events has imposed severe impacts on human society. Timely and accurate assessment of post-earthquake damage and recovery is essential for developing effective emergency response strategies and enhancing urban resilience. This study investigates 11 provinces [...] Read more.

In recent years, the increasing frequency of global seismic events has imposed severe impacts on human society. Timely and accurate assessment of post-earthquake damage and recovery is essential for developing effective emergency response strategies and enhancing urban resilience. This study investigates 11 provinces in Turkey affected by the February 2023 Turkey–Syria earthquake, conducting a multidimensional evaluation of disaster loss and recovery. For loss assessment, existing studies typically focus on changes in the total value of nighttime lights at the regional level, overlooking variations at the pixel scale. In this study, we introduce a pixel-level NTL loss metric, which provides finer-grained insights and helps interpret outcomes driven by spatial heterogeneity. For recovery assessment, we propose a Composite Nighttime Light Index (CNLI) that integrates multiple recovery-phase indicators into a single quantitative measure, thus capturing more information than a one-dimensional metric. To account for complex interrelationships among indicators, a Bayesian network is employed, which moves beyond the conventional independence assumption. Moreover, an information gain (IG) approach is applied to optimize indicator weights, minimizing subjectivity and avoiding abnormal weight distributions compared with traditional methods, thereby ensuring a more objective construction of the Resilience Index (RI). Results show that Sanliurfa, Kilis, and Hatay suffered the most severe damage; Kahramanmaras and Malatya exhibited the lowest CNLI values, while Hatay, Kilis, and Gaziantep showed higher CNLI values. In contrast, Gaziantep and Adana obtained the highest RI values. Since CNLI reflects actual recovery performance while RI characterizes inherent resilience, accordingly, effectively linking CNLI and RI establishes a dual-perspective and novel framework, the 11 provinces are classified into four categories, and differentiated recovery strategies are suggested. This study contributes a refined quantitative framework for post-earthquake loss and recovery assessment and provides scientific evidence to support emergency response and targeted reconstruction. Full article

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32 pages, 2564 KB

Open AccessArticle

Towards Reliable High-Resolution Satellite Products for the Monitoring of Chlorophyll-a and Suspended Particulate Matter in Optically Shallow Coastal Lagoons

by Samuel Martin, Philippe Bryère, Pierre Gernez, Pannimpullath Remanan Renosh and David Doxaran

Remote Sens. 2025, 17(20), 3430; https://doi.org/10.3390/rs17203430 - 14 Oct 2025

Abstract

Coastal lagoons are fragile and dynamic ecosystems that are particularly vulnerable to climate change and anthropogenic pressures such as urbanization and eutrophication. These vulnerabilities highlight the need for frequent and spatially extensive monitoring of water quality (WQ). While satellite remote sensing offers a [...] Read more.

Coastal lagoons are fragile and dynamic ecosystems that are particularly vulnerable to climate change and anthropogenic pressures such as urbanization and eutrophication. These vulnerabilities highlight the need for frequent and spatially extensive monitoring of water quality (WQ). While satellite remote sensing offers a valuable tool to support this effort, the optical complexity and shallow depths of lagoons pose major challenges for retrieving water column biogeochemical parameters such as chlorophyll-a ([chl-a]) and suspended particulate matter ([SPM]) concentrations. In this study, we develop and evaluate a robust satellite-based processing chain using Sentinel-2 MSI imagery over two French Mediterranean lagoon systems (Berre and Thau), supported by extensive in situ radiometric and biogeochemical datasets. Our approach includes the following: (i) a comparative assessment of six atmospheric correction (AC) processors, (ii) the development of an Optically Shallow Water Probability Algorithm (OSWPA), a new semi-empirical algorithm to estimate the probability of bottom contamination (BC), and (iii) the evaluation of several [chl-a] and [SPM] inversion algorithms. Results show that the Sen2Cor AC processor combined with a near-infrared similarity correction (NIR-SC) yields relative errors below 30% across all bands for retrieving remote-sensing reflectance R_rs(λ). OSWPA provides a spatially continuous and physically consistent alternative to binary BC masks. A new [chl-a] algorithm based on a near-infrared/blue R_rs ratio improves the retrieval accuracy while the 705 nm band appears to be the most suitable for retrieving [SPM] in optically shallow lagoons. This processing chain enables high-resolution WQ monitoring of two coastal lagoon systems and supports future large-scale assessments of ecological trends under increasing climate and anthropogenic stress. Full article

(This article belongs to the Section Ocean Remote Sensing)

24 pages, 8433 KB

Open AccessArticle

Global 0.1-Degree Monthly Mean Hourly Total Canopy Solar-Induced Chlorophyll Fluorescence Dataset Derived from Random Forest

by Yaojie Liu, Dayang Zhao, Yongguang Zhang and Zhaoying Zhang

Remote Sens. 2025, 17(20), 3429; https://doi.org/10.3390/rs17203429 - 14 Oct 2025

Abstract

Photosynthesis drives terrestrial carbon uptake, yet its diurnal dynamics remain poorly resolved due to the sparse availability of flux towers and the coarse spatial resolution of current satellite observations. Solar-induced chlorophyll fluorescence (SIF) provides a direct proxy of carbon uptake, but the existing [...] Read more.

Photosynthesis drives terrestrial carbon uptake, yet its diurnal dynamics remain poorly resolved due to the sparse availability of flux towers and the coarse spatial resolution of current satellite observations. Solar-induced chlorophyll fluorescence (SIF) provides a direct proxy of carbon uptake, but the existing global monthly mean diurnal total canopy SIF product is limited to 0.5° resolution. We developed a random forest-based downscaling framework to generate a global monthly mean hourly SIF dataset (SIF_{total_01}) at 0.1° resolution for 2000–2022. When validated against eddy-covariance-based gross primary productivity (GPP) data, SIF_{total_01} showed a strong correlation (R² = 0.81) and reduced root mean square error when compared with SIF_total (2.89→2.8 mW m⁻² nm⁻¹), providing notable gains in broadleaved forests (R²: 0.80→0.88 with a root mean square error of 2.32→1.81 mW m⁻² nm⁻¹). The SIF_{total_01} dataset revealed a distinct double-peak in the SIF_{total_01}–GPP slope, reflecting widespread afternoon depression of photosynthesis, with normalized slopes declining from 1.03 in the morning to 0.98 in the afternoon. Soil moisture modulated this depression pattern, as the afternoon–morning SIF_{total_01} difference increased from 0.02 to 0.10 mW m⁻² nm⁻¹ across dry to wet years. Under water stress, SIF yield was more sensitive than absorbed photosynthetic active radiation (APAR), with a doubling of the afternoon–morning SIF yield difference (0.5→1.1 10⁻³ nm⁻¹), while the afternoon–morning APAR difference showed a smaller change (−300→−180 kJ m⁻²). This study improves the potential for bridging observational gaps and constraining models offer valuable insights for fundamental and applied research in the analysis of ecosystem productivity, climate-carbon feedbacks, and vegetation stress. Full article

(This article belongs to the Special Issue Advances in the Remote Sensing of Solar-Induced Chlorophyll Fluorescence for Vegetation Stress)

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21 pages, 6020 KB

Open AccessArticle

Trees as Sensors: Estimating Wind Intensity Distribution During Hurricane Maria

by Vivaldi Rinaldi, Giovanny Motoa and Masoud Ghandehari

Remote Sens. 2025, 17(20), 3428; https://doi.org/10.3390/rs17203428 - 14 Oct 2025

Abstract

Hurricane Maria crossed Puerto Rico with winds as high as 250 km/h, resulting in widespread damages and loss of weather station data, thus limiting direct weather measurements of wind variability. Here, we identified more than 155 million trees to estimate the distribution of [...] Read more.

Hurricane Maria crossed Puerto Rico with winds as high as 250 km/h, resulting in widespread damages and loss of weather station data, thus limiting direct weather measurements of wind variability. Here, we identified more than 155 million trees to estimate the distribution of wind speed over 9000 km² of land from island-wide LiDAR point clouds collected before and after the hurricane. The point clouds were classified and rasterized into the canopy height model to perform individual tree identification and perform change detection analysis. Individual trees’ stem diameter at breast height were estimated using a function between delineated crown and extracted canopy height, validated using the records from Puerto Rico’s Forest Inventory 2003. The results indicate that approximately 35.7% of trees broke at the stem (below the canopy center) and 28.5% above the canopy center. Furthermore, we back-calculated the critical wind speed, or the minimum speed to cause breakage, at individual tree level this was performed by applying a mechanical model using the estimated diameter at breast height, the extrapolated breakage height, and pre-Hurricane Maria canopy height. Individual trees were then aggregated at 115 km² cells to summarize the critical wind speed distribution of each cell, based on the percentage of stem breakage. A vertical wind profile analysis was then applied to derive the hurricane wind distribution using the mean hourly wind speed 10 m above the canopy center. The estimated wind speed ranges from 250 km/h in the southeast at the landfall to 100 km/h in the southwest parts of the islands. Comparison of the modeled wind speed with the wind gust readings at the few remaining NOAA stations support the use of tree breakages to model the distribution of hurricane wind speed when ground readings are sparse. Full article

(This article belongs to the Section Environmental Remote Sensing)

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