Remote Sensing

17 pages, 4474 KiB

Open AccessArticle

Ground-Based LiDAR Analysis of Persistent Haze Pollution Events During Winter 2022 in Luohe City

by Wenyu Bai, Ran Dai, Chunmei Geng, Xinhua Wang, Nan Zhang, Jinbao Han and Wen Yang

Remote Sens. 2025, 17(5), 786; https://doi.org/10.3390/rs17050786 (registering DOI) - 24 Feb 2025

Aerosol transport flux LiDAR was used to observe heavy pollution events in Luohe City during January 2022 and was combined with monitoring data of ground meteorological parameters and conventional pollutants to analyze the vertical optical properties of aerosols, transport sources, and causes of [...] Read more.

Aerosol transport flux LiDAR was used to observe heavy pollution events in Luohe City during January 2022 and was combined with monitoring data of ground meteorological parameters and conventional pollutants to analyze the vertical optical properties of aerosols, transport sources, and causes of heavy pollution. Two pollution events (January 2nd–5th and 13th–20th, 2022) were effectively monitored and divided into four pollution phases according to PM_2.5 concentrations and relative humidity (RH). The results showed that all ground PM_2.5/PM₁₀ values were above 0.5 throughout the pollution, indicating a predominance of fine particulate matter. Analysis of the vertical distribution of aerosol flux LiDAR data showed that the inversion layer was distributed below 1 km; the vertical profile of extinction coefficient showed that all the pollution events were dominated by local emissions, while the contribution of regional transmission during the January 2nd to 5th was also quite prominent; kriging interpolation results showed that this pollution covered the most central and eastern regions of China during January 2022. The flux LiDAR monitoring results showed that there were three main transmission channels of PM_2.5: east (Zhoukou, Lu–Wan–Yu–Su junction), northeast (Lu–Yu junction), and southeast (YRD). The analysis of the clustered backward trajectories, potential source contribution function (PSCF), and concentration-weighted trajectory (CWT) models showed that the potential transmission sources of PM_2.5 were mainly in junction zones of Lu–Wan–Yu–Su as well as Shaanxi Province, with PSCF values above 0.7 and CWT values above 70 μg/m³. This study could provide a scientific basis for the prevention and control of local pollution. Full article

(This article belongs to the Special Issue Remote Sensing of Atmospheric Vertical Profile—Air Quality, Pollution and Aerosol Optical Properties)

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22 pages, 4474 KiB

Open AccessArticle

Advancing Stem Volume Estimation Using Multi-Platform LiDAR and Taper Model Integration for Precision Forestry

by Yongkyu Lee and Jungsoo Lee

Remote Sens. 2025, 17(5), 785; https://doi.org/10.3390/rs17050785 (registering DOI) - 24 Feb 2025

Abstract

Stem volume is a critical factor in managing and evaluating forest resources. At present, stem volume is commonly estimated indirectly by constructing a taper model that utilizes sampling, diameter at breast height (DBH), and tree height. However, these estimates are constrained by errors [...] Read more.

Stem volume is a critical factor in managing and evaluating forest resources. At present, stem volume is commonly estimated indirectly by constructing a taper model that utilizes sampling, diameter at breast height (DBH), and tree height. However, these estimates are constrained by errors arising from spatial and stand environment variations as well as uncertainties in height measurements. To address these issues, this study aimed to accurately estimate stem volume using light detection and ranging (LiDAR) technology, a key tool in modern precision forestry. LiDAR data were used to build comprehensive three-dimensional models of forests with multi-platform LiDAR systems. This approach allowed for precise measurements of tree heights and stem diameters at various heights, effectively mitigating the limitations of earlier measurement methods. Based on these data, a Kozak taper curve was developed at the individual tree level using LiDAR-derived tree height and diameter estimates. Integrating this curve with LiDAR data enabled a hybrid approach to estimating stem volume, facilitating the calculation of diameters at points not directly identifiable from LiDAR data alone. The proposed method was implemented and evaluated for two economically significant tree species in Korea: Pinus koraiensis and Larix kaempferi. The RMSE comparison between the taper curve-based approach and the hybrid volume estimation method showed that, for Pinus koraiensis, the RMSE was 0.11 m³ using the taper curve-based approach and 0.07 m³ for the hybrid method, while for Larix kaempferi, the RMSE was 0.13 m³ and 0.05 m³, respectively. The estimation error of the hybrid method was approximately half that of the taper curve-based approach. Consequently, the hybrid volume estimation method, which integrates LiDAR and the taper model, overcomes the limitations of conventional taper curve-based approaches and contributes to improving the accuracy of forest resource monitoring. Full article

(This article belongs to the Special Issue Remote Sensing-Assisted Forest Inventory Planning)

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21 pages, 45648 KiB

Open AccessArticle

A Big Data Approach for the Regional-Scale Spatial Pattern Analysis of Amazonian Palm Locations

by Matthew J. Drouillard and Anthony R. Cummings

Remote Sens. 2025, 17(5), 784; https://doi.org/10.3390/rs17050784 (registering DOI) - 24 Feb 2025

Abstract

Arecaceae (palms) are an important resource for indigenous communities as well as fauna populations across Amazonia. Understanding the spatial patterns and the environmental factors that determine the habitats of palms is of considerable interest to rainforest ecologists. Here, we utilize remotely sensed imagery [...] Read more.

Arecaceae (palms) are an important resource for indigenous communities as well as fauna populations across Amazonia. Understanding the spatial patterns and the environmental factors that determine the habitats of palms is of considerable interest to rainforest ecologists. Here, we utilize remotely sensed imagery in conjunction with topography and soil attribute data and employ a generalized cluster identification algorithm, Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), to study the underlying patterns of palms in two areas of Guyana, South America. The results of the HDBSCAN assessment were cross-validated with several point pattern analysis methods commonly used by ecologists (the quadrat test for complete spatial randomness, Morista Index, Ripley’s L-function, and the pair correlation function). A spatial logistic regression model was generated to understand the multivariate environmental influences driving the placement of cluster and outlier palms. Our results showed that palms are strongly clustered in the areas of interest and that the HDBSCAN’s clustering output correlates well with traditional analytical methods. The environmental factors influencing palm clusters or outliers, as determined by logistic regression, exhibit qualitative similarities to those identified in conventional ground-based palm surveys. These findings are promising for prospective research aiming to integrate remote flora identification techniques with traditional data collection studies. Full article

(This article belongs to the Special Issue Advancements in Environmental Remote Sensing and GIS)

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18 pages, 4555 KiB

Open AccessTechnical Note

GD-Det: Low-Data Object Detection in Foggy Scenarios for Unmanned Aerial Vehicle Imagery Using Re-Parameterization and Cross-Scale Gather-and-Distribute Mechanisms

by Rui Shi, Lili Zhang, Gaoxu Wang, Shutong Jia, Ning Zhang and Chensu Wang

Remote Sens. 2025, 17(5), 783; https://doi.org/10.3390/rs17050783 (registering DOI) - 24 Feb 2025

Abstract

Unmanned Aerial Vehicles (UAVs) play an extremely important role in real-time object detection for maritime emergency rescue missions. However, marine accidents often occur in low-visibility weather conditions, resulting in poor image quality and a lack of object detection samples, which significantly reduces detection [...] Read more.

Unmanned Aerial Vehicles (UAVs) play an extremely important role in real-time object detection for maritime emergency rescue missions. However, marine accidents often occur in low-visibility weather conditions, resulting in poor image quality and a lack of object detection samples, which significantly reduces detection accuracy. To tackle these issues, we propose GD-Det, a low-data object detection model with high accuracy, specifically designed to handle limited sample sizes and low-quality images. The model is primarily composed of three components: (i) A lightweight re-parameterization feature extraction module which integrates RepVGG blocks into multi-concat blocks to enhance the model’s spatial perception and feature diversity during training. Meanwhile, it reduces computational cost in the inference phase through the re-parameterization mechanism. (ii) A cross-scale gather-and-distribute pyramid module, which helps to augment the relationship representation of four-scale features via flexible skip fusion and distribution strategies. (iii) A decoupled prediction module with three branches is to implement classification and regression, enhancing detection accuracy by combining the prediction values from tri-level features. (iv) We also use a domain-adaptive training strategy with knowledge transfer to handle low-data issues. We conducted low-data training and comparison experiments using our constructed dataset AFO-fog. Our model achieved an overall detection accuracy of 84.8%, which is superior to other models. Full article

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4 pages, 1569 KiB

Open AccessCorrection

Correction: Berezowski et al. Comparison of Time-Lapse Ground-Penetrating Radar and Electrical Resistivity Tomography Surveys for Detecting Pig (Sus spp.) Cadaver Graves in an Australian Environment. Remote Sens. 2024, 16, 3498

by Victoria Berezowski, Xanthé Mallett, Dilan Seckiner, Isabella Crebert, Justin Ellis, Gabriel C. Rau and Ian Moffat

Remote Sens. 2025, 17(5), 782; https://doi.org/10.3390/rs17050782 (registering DOI) - 24 Feb 2025

Abstract

In the original publication [...] Full article

(This article belongs to the Special Issue Remote Sensing: 15th Anniversary)

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18 pages, 7616 KiB

Open AccessArticle

The Spatiotemporal Evolution and Driving Forces of the Urban Heat Island in Shijiazhuang

by Xia Zhang, Yue Liu, Ruohan Chen, Menglin Si, Ce Zhang, Yiran Tian and Guofei Shang

Remote Sens. 2025, 17(5), 781; https://doi.org/10.3390/rs17050781 (registering DOI) - 23 Feb 2025

Abstract

As a comprehensive reflection of the thermal characteristics of the urban environment, the urban heat island (UHI) effect has triggered a series of ecological and environmental issues. Existing studies on the UHI effect in Shijiazhuang, the capital of Hebei Province, China, have primarily [...] Read more.

As a comprehensive reflection of the thermal characteristics of the urban environment, the urban heat island (UHI) effect has triggered a series of ecological and environmental issues. Existing studies on the UHI effect in Shijiazhuang, the capital of Hebei Province, China, have primarily focused on spatial–temporal distribution characteristics and migration trends, with less focus on the influences of other contributing factors. This study focuses on Shijiazhuang city, using Landsat ETM+/OLI data from 2000 to 2020 to analyze the spatiotemporal traits of the UHI effect. The mono-window algorithm (MW) was used to retrieve land surface temperatures (LSTs), and the seasonal autoregressive integrated moving average (SARIMA) model was used to predict LST trends. Key factors such as the normalized difference vegetation index (NDVI), digital elevation model (DEM), population (POP), precipitation (PPT), impervious surface (IPS), potential evapotranspiration (PET), particulate matter 2.5 (PM2.5), and night light (NL) were analyzed using spatial autocorrelation to explore their dynamic relationship with the UHI. Specifically, a multi-scale analysis model was developed to search for the optimum urban spatial scale, enabling a comprehensive assessment of the spatiotemporal evolution and drivers of the UHI in Shijiazhuang. The UHI showed pronounced spatial clustering, expanding annually by 44.288 km², with a southeastward shift. Autumn exhibited the greatest reduction in UHI, while predictions suggested peak temperatures in summer 2027. According to the bivariate clustering analysis, the NDVI was the most influential factor in mitigating the UHI, while the IPS spatially showed the most significant enhancement in the UHI in the central urban areas. Other factors generally promoted the UHI after 2005. The multi-scale geographically weighted regression (MGWR) model was best fitted at a 3 km × 3 km scale. Considering the joint effects of multiple factors, the ranking of contributing factors to the model prediction is as follows: PET > DEM > NDVI > IPS > PPT > PM2.5 > NL > POP. The interactive effects, especially between the PET and DEM, reach a significant value of 0.72. These findings may address concerns regarding both future trends and mitigation indications for UHI variations in Shijiazhuang. Full article

(This article belongs to the Special Issue Remote Sensing of Land Surface Temperature: Retrieval, Modeling, and Applications)

19 pages, 4708 KiB

Open AccessArticle

An Improved Satellite ISAL Imaging Vibration Phase Compensation Algorithm Based on Prior Information and Adaptive Windowing

by Chenxuan Duan, Hongyuan Liu, Xiaona Wu, Jian Tang, Zhejun Feng and Changqing Cao

Remote Sens. 2025, 17(5), 780; https://doi.org/10.3390/rs17050780 (registering DOI) - 23 Feb 2025

Abstract

Spaceborne inverse synthetic aperture ladar (ISAL) can achieve high-resolution imaging of satellite targets. However, because the amplitudes of satellite microvibration are comparable to the ladar wavelength, the echoes will contain both space-variant and space-invariant phase errors. These errors will lead to azimuthal image [...] Read more.

Spaceborne inverse synthetic aperture ladar (ISAL) can achieve high-resolution imaging of satellite targets. However, because the amplitudes of satellite microvibration are comparable to the ladar wavelength, the echoes will contain both space-variant and space-invariant phase errors. These errors will lead to azimuthal image defocus and impede target analysis and identification. In this paper, we establish a phase error estimation model based on satellite vibration characteristics. Based on this model, we propose a vibration phase error compensation algorithm using prior information and adaptive windowing. Compared to conventional algorithms, this algorithm utilizes prior information to improve estimation accuracy while significantly reducing computational complexity. Furthermore, high-accuracy phase function estimation can be achieved through maximum likelihood estimation and adaptive window filtering, thereby enabling the compensation of vibration phase errors. Both simulation and real imaging experiments validate the effectiveness and robustness of the proposed algorithm. Full article

21 pages, 9315 KiB

Open AccessArticle

An Extension of Ozone Profile Retrievals from TROPOMI Based on the SAO2024 Algorithm

by Juseon Bak, Xiong Liu, Gonzalo González Abad and Kai Yang

Remote Sens. 2025, 17(5), 779; https://doi.org/10.3390/rs17050779 (registering DOI) - 23 Feb 2025

Abstract

We investigate the retrieval of ozone (O₃) profiles, with a particular focus on tropospheric O₃, from backscattered ultraviolet radiances measured by the TROPOspheric Monitoring Instrument (TROPOMI), using the UV2 (300–332 nm) and UV3 (305–400 nm) channels independently. An optimal [...] Read more.

We investigate the retrieval of ozone (O₃) profiles, with a particular focus on tropospheric O₃, from backscattered ultraviolet radiances measured by the TROPOspheric Monitoring Instrument (TROPOMI), using the UV2 (300–332 nm) and UV3 (305–400 nm) channels independently. An optimal estimation retrieval algorithm, originally developed for the Ozone Monitoring Instrument (OMI), was extended as a preliminary step toward integrating multiple satellite ozone profile datasets. The UV2 and UV3 channels exhibit distinct radiometric and wavelength calibration uncertainties, leading to inconsistencies in retrieval accuracy and convergence stability. A yearly “soft” calibration mitigates overestimation and cross-track-dependent biases (“stripes”) in tropospheric ozone retrievals, enhancing retrieval consistency between UV2 and UV3. Convergence stability is ensured by optimizing the measurement error constraints for each channel. It is shown that our research product outperforms the standard product (UV1 and UV2 combined) in capturing the seasonal and long-term variabilities of tropospheric ozone. An agreement between the retrieved tropospheric ozone and ozonesonde measurements is observed within 0–3 DU ± 5.5 DU (R = 0.75), which is better than that of the standard product by a factor of two. Despite lacking Hartley ozone information in UV2 and UV3, the retrieved stratospheric ozone columns have good agreement with ozonesondes (R = 0.96). Full article

(This article belongs to the Special Issue Satellite Observations for Particulate Matter and Gaseous Pollutants Research)

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29 pages, 6375 KiB

Open AccessArticle

The Importance of Humidity in the Afternoon Local-Scale Precipitation Intensity over Eastern China and Its Impacts on the Aerosol Effects

by Xinlei Tang, Qian Chen, Jianping Guo, Jing Yang, Zeyong Zou, Jinghua Chen and Yue Sun

Remote Sens. 2025, 17(5), 778; https://doi.org/10.3390/rs17050778 (registering DOI) - 23 Feb 2025

Abstract

Thermally driven local-scale precipitation (LSP) is an important type of summer precipitation over China, but the prestorm environmental conditions remain unclear. In order to investigate the major factors controlling the LSP intensity, the meteorological parameters preceding the occurrence of light and heavy afternoon [...] Read more.

Thermally driven local-scale precipitation (LSP) is an important type of summer precipitation over China, but the prestorm environmental conditions remain unclear. In order to investigate the major factors controlling the LSP intensity, the meteorological parameters preceding the occurrence of light and heavy afternoon LSP over Eastern China during 2018–2022 are examined using rain gauge, radiosonde sounding, and satellite observations. The temperature differences between heavy and light LSP events are relatively small, but heavy LSP events exhibit larger water vapor mixing ratios (Q_v) below a 5 km altitude than light LSP. With an almost identical vertical temperature distribution, an increment in Q_v increases the relative humidity (RH) in the lower troposphere. Furthermore, large eddy simulations with spectral bin microphysics are performed to investigate the impacts of humidity and aerosols on the LSP intensity. Increased low-level RH leads to larger mass concentrations of rain and graupel at the expense of cloud droplets due to enhanced drop collisions and the riming of ice particles, respectively, thereby reinforcing the LSP. However, an increased aerosol concentration leads to more cloud water but reduced rain water content, resulting mainly from suppressed drop collisions. The graupel mixing ratio exhibits a non-monotonic trend with aerosols, mostly contributed by riming. As a result, the LSP intensity first increases and then decreases with an increment in the aerosol concentration in both dry and humid air. Moreover, more aerosols lead to the humidification of the surrounding air due to the enhanced evaporation of cloud droplets, particularly under lower-RH conditions. These findings provide an enhanced understanding of the effects of covariations in humidity and aerosol concentrations on the afternoon LSP intensity over Eastern China. Full article

(This article belongs to the Special Issue Monitoring and Early Warning for Heavy Precipitation, Flash Floods and Waterlogging Disasters Using Remote Sensing)

26 pages, 10933 KiB

Open AccessArticle

Remote Sensing Identification and Analysis of Global Building Electrification (2012–2023)

by Shengya Ou, Mingquan Wu, Zheng Niu, Fang Chen, Jie Liu, Meng Wang and Dinghui Tian

Remote Sens. 2025, 17(5), 777; https://doi.org/10.3390/rs17050777 (registering DOI) - 23 Feb 2025

Abstract

The accurate collection of spatially distributed electrification data is considered of great importance for tracking progress toward target 7.1 of the sustainable development goals (SDGs) and the formulation of policy decisions on electricity access issues. However, the existing datasets face severe limitations in [...] Read more.

The accurate collection of spatially distributed electrification data is considered of great importance for tracking progress toward target 7.1 of the sustainable development goals (SDGs) and the formulation of policy decisions on electricity access issues. However, the existing datasets face severe limitations in terms of temporal discontinuity and restricted threshold selection. To effectively address these issues, in this work, an improved remote sensing method was proposed to monitor global building electrification. By integrating global land cover data, built-up area data, and annual NPP/VIIRS nighttime light images, a regional threshold method was used to identify electrified and unelectrified areas yearly, generating a global building electrification dataset for 2012–2023. Based on our analysis, we found the following: (1) The five assessment metrics of the product—Accuracy (0.9856), Precision (0.9734), Recall (0.9984), F1-score (0.9858), and Matthews Correlation Coefficient (0.9715)—all exceed 0.9, demonstrating that our method achieves high reliability in identifying electrified buildings. (2) In 2023, 91.88% of global building areas were electrified, with the unelectrified buildings being predominantly located in rural regions of developing countries. (3) Between 2012 and 2023, the global electrified building area increased by 2.4199 million km², with rural areas experiencing a faster growth rate than town areas. The annual reduction rate of unelectrified building area was 0.62%. However, to achieve universal electricity access by 2030, this rate must nearly double. (4) External factors such as the COVID-19 pandemic, extreme weather events, and armed conflicts significantly affect global electrification progress, with developing countries being particularly vulnerable. In our work, remote sensing methodologies and datasets for monitoring electrification trends were refined, and a detailed spatial representation of unelectrified areas worldwide was provided. Full article

(This article belongs to the Special Issue Big Earth Data in Support of the Sustainable Development Goals)

20 pages, 2403 KiB

Open AccessArticle

A Novel Dual-Branch Pansharpening Network with High-Frequency Component Enhancement and Multi-Scale Skip Connection

by Wei Huang, Yanyan Liu, Le Sun, Qiqiang Chen and Lu Gao

Remote Sens. 2025, 17(5), 776; https://doi.org/10.3390/rs17050776 (registering DOI) - 23 Feb 2025

Abstract

In recent years, the pansharpening methods based on deep learning show great advantages. However, these methods are still inadequate in considering the differences and correlations between multispectral (MS) and panchromatic (PAN) images. In response to the issue, we propose a novel dual-branch pansharpening [...] Read more.

In recent years, the pansharpening methods based on deep learning show great advantages. However, these methods are still inadequate in considering the differences and correlations between multispectral (MS) and panchromatic (PAN) images. In response to the issue, we propose a novel dual-branch pansharpening network with high-frequency component enhancement and a multi-scale skip connection. First, to enhance the correlations, the high-frequency branch consists of the high-frequency component enhancement module (HFCEM), which effectively enhances the high-frequency components through the multi-scale block (MSB), thereby obtaining the corresponding high-frequency weights to accurately capture the high-frequency information in MS and PAN images. Second, to address the differences, the low-frequency branch consists of the multi-scale skip connection module (MSSCM), which comprehensively captures the multi-scale features from coarse to fine through multi-scale convolution, and it effectively fuses these multilevel features through the designed skip connection mechanism to fully extract the low-frequency information from MS and PAN images. Finally, the qualitative and quantitative experiments are performed on the GaoFen-2, QuickBird, and WorldView-3 datasets. The results show that the proposed method outperforms the state-of-the-art pansharpening methods. Full article

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24 pages, 4587 KiB

Open AccessArticle

Structured Bayesian Super-Resolution Forward-Looking Imaging for Maneuvering Platforms Based on Enhanced Sparsity Model

by Yiheng Guo, Yujie Liang, Yi Liang and Xiangwei Sun

Remote Sens. 2025, 17(5), 775; https://doi.org/10.3390/rs17050775 (registering DOI) - 23 Feb 2025

Abstract

Sparse reconstruction-based imaging techniques can be utilized to solve forward-looking imaging problems with limited azimuth resolution. However, these methods perform well only under the traditional model for the platform with low speed, and the performance deteriorates for the maneuvering trajectory. In this paper, [...] Read more.

Sparse reconstruction-based imaging techniques can be utilized to solve forward-looking imaging problems with limited azimuth resolution. However, these methods perform well only under the traditional model for the platform with low speed, and the performance deteriorates for the maneuvering trajectory. In this paper, a structured Bayesian super-resolution forward-looking imaging algorithm for maneuvering platforms under an enhanced sparsity model is proposed. An enhanced sparsity model for maneuvering platforms is established to address the reconstruction problem, and a hierarchical Student-t (ST) prior is designed to model the distribution characteristics of the sparse imaging scene. To further leverage prior information about structural characteristics of the scatterings, coupled patterns among neighboring pixels are incorporated to construct a structured sparse prior. Finally, forward-looking imaging parameters are estimated using the expectation/maximization-based variational Bayesian inference. Numerical simulations validate the effectiveness of the proposed algorithm and the superiority over conventional methods based on pixel sparse assumptions in forward-looking scenes for maneuvering platforms. Full article

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26 pages, 9445 KiB

Open AccessArticle

Improving Wheat Yield Prediction with Multi-Source Remote Sensing Data and Machine Learning in Arid Regions

by Aamir Raza, Muhammad Adnan Shahid, Muhammad Zaman, Yuxin Miao, Yanbo Huang, Muhammad Safdar, Sheraz Maqbool and Nalain E. Muhammad

Remote Sens. 2025, 17(5), 774; https://doi.org/10.3390/rs17050774 (registering DOI) - 23 Feb 2025

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Abstract

Wheat (Triticum aestivum L.) is one of the world’s primary food crops, and timely and accurate yield prediction is essential for ensuring food security. There has been a growing use of remote sensing, climate data, and their combination to estimate yields, but [...] Read more.

Wheat (Triticum aestivum L.) is one of the world’s primary food crops, and timely and accurate yield prediction is essential for ensuring food security. There has been a growing use of remote sensing, climate data, and their combination to estimate yields, but the optimal indices and time window for wheat yield prediction in arid regions remain unclear. This study was conducted to (1) assess the performance of widely recognized remote sensing indices to predict wheat yield at different growth stages, (2) evaluate the predictive accuracy of different yield predictive machine learning models, (3) determine the appropriate growth period for wheat yield prediction in arid regions, and (4) evaluate the impact of climate parameters on model accuracy. The vegetation indices, widely recognized due to their proven effectiveness, used in this study include the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Atmospheric Resistance Vegetation Index (ARVI). Moreover, four machine learning models, viz. Decision Trees (DTs), Random Forest (RF), Gradient Boosting (GB), and Bagging Trees (BTs), were evaluated to assess their predictive accuracy for wheat yield in the arid region. The whole wheat growth period was divided into three time windows: tillering to grain filling (December 15–March), stem elongation to grain filling (January 15–March), and heading to grain filling (February–March 15). The model was evaluated and developed in the Google Earth Engine (GEE), combining climate and remote sensing data. The results showed that the RF model with ARVI could accurately predict wheat yield at the grain filling and the maturity stages in arid regions with an R² > 0.75 and yield error of less than 10%. The grain filling stage was identified as the optimal prediction window for wheat yield in arid regions. While RF with ARVI delivered the best results, GB with EVI showed slightly lower precision but still outperformed other models. It is concluded that combining multisource data and machine learning models is a promising approach for wheat yield prediction in arid regions. Full article

(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)

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26 pages, 12981 KiB

Open AccessArticle

A Geometric Calibration Method for Spaceborne Single-Photon Lasers That Integrates Laser Detectors and Corner Cube Retroreflectors

by Ren Liu, Junfeng Xie, Fan Mo, Xiaomeng Yang, Zhiyu Jiang and Liang Hong

Remote Sens. 2025, 17(5), 773; https://doi.org/10.3390/rs17050773 (registering DOI) - 23 Feb 2025

Abstract

Geometric calibration, as a crucial method for ensuring the precision of spaceborne single-photon laser point cloud data, has garnered significant attention. Nonetheless, prevailing geometric calibration methods are generally limited by inadequate precision or are unable to accommodate spaceborne lasers equipped with multiple payloads [...] Read more.

Geometric calibration, as a crucial method for ensuring the precision of spaceborne single-photon laser point cloud data, has garnered significant attention. Nonetheless, prevailing geometric calibration methods are generally limited by inadequate precision or are unable to accommodate spaceborne lasers equipped with multiple payloads on a single platform. To overcome these limitations, a novel geometric calibration method for spaceborne single-photon lasers that integrates laser detectors with corner cube retroreflectors (CCRs) is introduced in this study. The core concept of this method involves the use of triggered detectors to identify the laser footprint centerline (LFC). The geometric relationships between the triggered CCRs and the LFC are subsequently analyzed, and CCR data are incorporated to determine the coordinates of the nearest laser footprint centroids. These laser footprint centroids are then utilized as ground control points to perform the geometric calibration of the spaceborne single-photon laser. Finally, ATLAS observational data are used to simulate the geometric calibration process with detectors and CCRs, followed by conducting geometric calibration experiments with the gt2l and gt2r beams. The results demonstrate that the accuracy of the calibrated laser pointing angle is approximately 1 arcsec, and the ranging precision is better than 2.1 cm, which verifies the superiority and reliability of the proposed method. Furthermore, deployment strategies for detectors and CCRs are explored to provide feasible implementation plans for practical calibration. Notably, as this method only requires the positioning of laser footprint centroids using ground equipment for calibration, it provides exceptional calibration accuracy and is applicable to single-photon lasers across various satellite platforms. Full article

(This article belongs to the Special Issue Advances in High-Resolution Satellite Remote Sensing Image Processing and Classification)

21 pages, 15325 KiB

Open AccessArticle

Spatiotemporal Variations in Sea Ice Albedo: A Study of the Dynamics of Sea Ice Albedo in the Sea of Okhotsk

by Yingzhen Zhou, Wei Li, Nan Chen, Takenobu Toyota, Yongzhen Fan, Tomonori Tanikawa and Knut Stamnes

Remote Sens. 2025, 17(5), 772; https://doi.org/10.3390/rs17050772 (registering DOI) - 23 Feb 2025

Abstract

This study utilizes a novel albedo retrieval framework combining radiative transfer modeling with scientific machine learning (RTM-SciML) to investigate sea ice dynamics in the Sea of Okhotsk. By validating albedo estimates derived from the MODIS sensor against in situ pyranometer measurements near the [...] Read more.

This study utilizes a novel albedo retrieval framework combining radiative transfer modeling with scientific machine learning (RTM-SciML) to investigate sea ice dynamics in the Sea of Okhotsk. By validating albedo estimates derived from the MODIS sensor against in situ pyranometer measurements near the Hokkaido coast, we achieved a robust Pearson coefficient of 0.86 and an RMSE of 0.089 for all sea ice types, with even higher correlations for specific surfaces like snow-covered ice (Pearson-r = 0.89) and meltwater/open water (Pearson-r = 0.90). This confirms the framework’s efficacy across varying surface conditions. Cross-sensor comparisons between MODIS and the Second-Generation Global Imager (SGLI) further demonstrated its consistency, achieving an overall Pearson-r of 0.883 and RMSE of 0.036. Integrating these albedo estimates with sea ice concentration data from the Advanced Microwave Scanning Radiometer 2 (AMSR-2), we analyzed the complex role of the Sea of Okhotsk’s polynya systems and ice interactions in regional climate processes. Our results highlight the synergistic advantage of pairing optical sensors, like MODIS and SGLI, with microwave sensors, offering a more comprehensive understanding of evolving sea ice conditions and paving the way for future climate and cryosphere studies. Full article

(This article belongs to the Special Issue Monitoring Sea Ice Loss with Remote Sensing Techniques)

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23 pages, 24778 KiB

Open AccessArticle

Large-Scale Soil Organic Carbon Estimation via a Multisource Data Fusion Approach

by Eleni Kalopesa, Nikolaos Tziolas, Nikolaos L. Tsakiridis, José Lucas Safanelli, Tomislav Hengl and Jonathan Sanderman

Remote Sens. 2025, 17(5), 771; https://doi.org/10.3390/rs17050771 (registering DOI) - 23 Feb 2025

Abstract

This study presents a methodological framework for predicting soil organic carbon (SOC) using laboratory spectral recordings from a handheld near-infrared (NIR, 1350–2550 nm) device combined with open geospatial data derived from remote sensing sensors related to landform, climate, and vegetation. Initial experiments proved [...] Read more.

This study presents a methodological framework for predicting soil organic carbon (SOC) using laboratory spectral recordings from a handheld near-infrared (NIR, 1350–2550 nm) device combined with open geospatial data derived from remote sensing sensors related to landform, climate, and vegetation. Initial experiments proved the superiority of convolutional neural networks (CNNs) using only spectral data captured by the low-cost spectral devices reaching an

R^{2}

of 0.62, RMSE of 0.31 log-SOC, and an RPIQ of 1.87. Furthermore, the incorporation of geo-covariates with Neo-Spectra data substantially enhanced predictive capabilities, outperforming existing approaches. Although the CNN-derived spectral features had the greatest contribution to the model, the geo-covariates that were most informative to the model were primarily the rainfall data, the valley bottom flatness, and the snow probability. The results demonstrate that hybrid modeling approaches, particularly using CNNs to preprocess all features and fit prediction models with Extreme Gradient Boosting trees, CNN-XGBoost, significantly outperformed traditional machine learning methods, with a notable RMSE reduction, reaching an

R^{2}

of 0.72, and an RPIQ of 2.17. The findings of this study highlight the effectiveness of multimodal data integration and hybrid models in enhancing predictive accuracy for SOC assessments. Finally, the application of interpretable techniques elucidated the contributions of various climatic and topographical factors to predictions, as well as spectral information, underscoring the complex interactions affecting SOC variability. Full article

(This article belongs to the Special Issue Recent Advances in Remote Sensing of Soil Science)

27 pages, 7651 KiB

Open AccessArticle

Flood Mud Index (FMI): A Rapid and Effective Tool for Mapping Muddy Areas After Floods—The Valencia Case

by Emanuele Alcaras

Remote Sens. 2025, 17(5), 770; https://doi.org/10.3390/rs17050770 (registering DOI) - 23 Feb 2025

Viewed by 101

Abstract

Mapping flooded areas immediately after heavy rainfall is particularly challenging when sediment-laden floodwaters dominate the landscape. Traditional indices, such as the Normalized Difference Water Index (NDWI), are designed to detect water-covered areas but fail to identify muddy zones with high turbidity, which are [...] Read more.

Mapping flooded areas immediately after heavy rainfall is particularly challenging when sediment-laden floodwaters dominate the landscape. Traditional indices, such as the Normalized Difference Water Index (NDWI), are designed to detect water-covered areas but fail to identify muddy zones with high turbidity, which are common during extreme flood events. These muddy floodwaters often blend spectrally with surrounding land, leading to significant misclassifications. This study introduces the Flood Mud Index (FMI), a novel spectral index specifically developed to detect debris-laden flooded areas using only the red and blue bands. Landsat 8 imagery was utilized to validate the FMI, and its performance was evaluated through confusion matrices. The index achieved an overall accuracy of 97.86%, outperforming existing indices and demonstrating exceptional precision in delineating muddy floodplains. By relying solely on red and blue bands, the FMI is applicable to any platform equipped with RGB sensors, offering versatility for flood monitoring. Its compatibility with low-cost drones makes it especially valuable for rapid post-flood assessments, enabling immediate data collection even in scenarios with persistent cloud cover. The FMI addresses a critical gap in flood mapping, providing an effective tool for emergency response and management in sediment-rich environments. Full article

(This article belongs to the Special Issue Remote Sensing in Natural Hazard Exploration and Impact Assessment)

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21 pages, 7080 KiB

Open AccessArticle

AI-Driven Real-Time Monitoring of Ground-Nesting Birds: A Case Study on Curlew Detection Using YOLOv10

by Carl Chalmers, Paul Fergus, Serge Wich, Steven N. Longmore, Naomi Davies Walsh, Lee Oliver, James Warrington, Julieanne Quinlan and Katie Appleby

Remote Sens. 2025, 17(5), 769; https://doi.org/10.3390/rs17050769 (registering DOI) - 23 Feb 2025

Viewed by 125

Abstract

Effective monitoring of wildlife is critical for assessing biodiversity and ecosystem health as declines in key species often signal significant environmental changes. Birds, particularly ground-nesting species, serve as important ecological indicators due to their sensitivity to environmental pressures. Camera traps have become indispensable [...] Read more.

Effective monitoring of wildlife is critical for assessing biodiversity and ecosystem health as declines in key species often signal significant environmental changes. Birds, particularly ground-nesting species, serve as important ecological indicators due to their sensitivity to environmental pressures. Camera traps have become indispensable tools for monitoring nesting bird populations, enabling data collection across diverse habitats. However, the manual processing and analysis of such data are resource-intensive, often delaying the delivery of actionable conservation insights. This study presents an AI-driven approach for real-time species detection, focusing on the curlew (Numenius arquata), a ground-nesting bird experiencing significant population declines. A custom-trained YOLOv10 model was developed to detect and classify curlews and their chicks using 3/4G-enabled cameras linked to the Conservation AI platform. The system processes camera trap data in real time, significantly enhancing monitoring efficiency. Across 11 nesting sites in Wales, the model achieved high performance, with a sensitivity of 90.56%, specificity of 100%, and F1-score of 95.05% for curlew detections and a sensitivity of 92.35%, specificity of 100%, and F1-score of 96.03% for curlew chick detections. These results demonstrate the capability of AI-driven monitoring systems to deliver accurate, timely data for biodiversity assessments, facilitating early conservation interventions and advancing the use of technology in ecological research. Full article

(This article belongs to the Special Issue Remote Sensing Applications to Ecology: Opportunities and Challenges II)

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22 pages, 6239 KiB

Open AccessArticle

Fine-Grained Aircraft Recognition Based on Dynamic Feature Synthesis and Contrastive Learning

by Huiyao Wan, Pazlat Nurmamat, Jie Chen, Yice Cao, Shuai Wang, Yan Zhang and Zhixiang Huang

Remote Sens. 2025, 17(5), 768; https://doi.org/10.3390/rs17050768 (registering DOI) - 23 Feb 2025

Viewed by 122

Abstract

With the rapid development of deep learning, significant progress has been made in remote sensing image target detection. However, methods based on deep learning are confronted with several challenges: (1) the inherent limitations of activation functions and downsampling operations in convolutional networks lead [...] Read more.

With the rapid development of deep learning, significant progress has been made in remote sensing image target detection. However, methods based on deep learning are confronted with several challenges: (1) the inherent limitations of activation functions and downsampling operations in convolutional networks lead to frequency deviations and loss of local detail information, affecting fine-grained object recognition; (2) class imbalance and long-tail distributions further degrade the performance of minority categories; (3) large intra-class variations and small inter-class differences make it difficult for traditional deep learning methods to effectively extract fine-grained discriminative features. To address these issues, we propose a novel remote sensing aircraft recognition method. First, to mitigate the loss of local detail information, we introduce a learnable Gabor filter-based texture feature extractor, which enhances the discriminative feature representation of aircraft categories by capturing detailed texture information. Second, to tackle the long-tail distribution problem, we design a dynamic feature hallucination module that synthesizes diverse hallucinated samples, thereby improving the feature diversity of tail categories. Finally, to handle the challenge of large intra-class variations and small inter-class differences, we propose a contrastive learning module to enhance the spatial discriminative features of the targets. Extensive experiments on the large-scale fine-grained datasets FAIR1M and MAR20 demonstrate the effectiveness of our method, achieving detection accuracies of 53.56% and 89.72%, respectively, and surpassing state-of-the-art performance. The experimental results validate that our approach effectively addresses the key challenges in remote sensing aircraft recognition. Full article

(This article belongs to the Special Issue Efficient Object Detection Based on Remote Sensing Images)

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23 pages, 7532 KiB

Open AccessArticle

A Multipath Hemispherical Map with Strict Quality Control for Multipath Mitigation

by Houxiang Zhou, Xiaoya Wang, Shengjian Zhong, Kewei Xi and Hang Shen

Remote Sens. 2025, 17(5), 767; https://doi.org/10.3390/rs17050767 (registering DOI) - 23 Feb 2025

Viewed by 140

Abstract

The multipath effect is a critical factor that prevents the Global Navigation Satellite System (GNSS) from achieving millimeter-level positioning accuracy. A multipath hemispherical map (MHM) is a popular approach to achieving real-time multipath error mitigation. The premise of the constructed MHM model is [...] Read more.

The multipath effect is a critical factor that prevents the Global Navigation Satellite System (GNSS) from achieving millimeter-level positioning accuracy. A multipath hemispherical map (MHM) is a popular approach to achieving real-time multipath error mitigation. The premise of the constructed MHM model is that the residuals in the grid only contain multipath errors and noise without any outliers. However, when there are numerous obvious outliers in each grid, the traditional quality control method is unable to detect them effectively. Therefore, we propose a multipath hemispherical map with strict quality control (MHM-S) to mitigate multipath errors. This method first uses the maximum phase delay to eliminate obvious outliers. Then, the 3-sigma rule and F-test are applied to remove the remaining few outliers in the grid. After applying the proposed MHM-S method, the experimental results show that when the PRN20 satellite is affected by outliers, the standard deviation (STD) reduction rate of the MHM-S residuals is 12.03% compared with the residual STDs of the MHM model. In addition, we evaluate the capabilities of MHM-S with carrier phase observation (MHM-SC) and carrier phase and pseudo-range observation (MHM-SCP) models in multipath error mitigation. Especially in the east direction, the positioning accuracy of the MHM-SCP model is improved by 48% compared with the MHM-SC model. Full article

(This article belongs to the Topic GNSS Measurement Technique in Aerial Navigation)

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25 pages, 8505 KiB

Open AccessArticle

Mapping Groundwater Potential Zones in the Widyan Basin, Al Qassim, KSA: Analytical Hierarchy Process-Based Analysis Using Sentinel-2, ASTER-DEM, and Conventional Data

by Ragab A. El Sherbini, Hosni H. Ghazala, Mohammed A. Ahmed, Ismael M. Ibraheem, Hussain F. Al Ajmi and Mohamed A. Genedi

Remote Sens. 2025, 17(5), 766; https://doi.org/10.3390/rs17050766 (registering DOI) - 22 Feb 2025

Viewed by 283

Abstract

Groundwater availability in semi-arid regions like the Widyan Basin, the Kingdom of Saudi Arabia (KSA), is a critical challenge due to climatic, topographic, and hydrological variations. The accurate identification of groundwater zones is essential for sustainable development. Therefore, this study combines remote-sensing datasets [...] Read more.

Groundwater availability in semi-arid regions like the Widyan Basin, the Kingdom of Saudi Arabia (KSA), is a critical challenge due to climatic, topographic, and hydrological variations. The accurate identification of groundwater zones is essential for sustainable development. Therefore, this study combines remote-sensing datasets (Sentinel-2 and ASTER-DEM) with conventional data using Geographic Information System (GIS) and analytical hierarchy process (AHP) techniques to delineate groundwater potential zones (GWPZs). The basin’s geology includes Pre-Cambrian rock units of the Arabian Shield in the southwest and Cambrian–Ordovician units in the northeast, with the Saq Formation serving as the main groundwater aquifer. Six soil types were identified: Haplic and Calcic Yermosols, Calcaric Regosols, Cambic Arenosols, Orthic Solonchaks, and Lithosols. The topography varies from steep areas in the southwest and northwest to nearly flat terrain in the northeast. Hydrologically, the basin is divided into 28 sub-basins with four stream orders. Using GIS-based AHP and weighted overlay methods, the GWPZs were mapped, achieving a model consistency ratio of 0.0956. The zones were categorized as excellent (15.21%), good (40.85%), fair (43.94%), and poor (0%). The GWPZ model was validated by analyzing data from 48 water wells distributed in the study area. These wells range from fresh water to primary saline water, with water depths varying between 13.98 and 130 m. Nine wells—with an average total dissolved solids (TDS) value of 597.2 mg/L—fall within the excellent zone, twenty-one wells are categorized in the good zone, fifteen wells are classified in the fair zone, and the remaining wells fall into the poor zone, with TDS values reaching up to 2177 mg/L. The results indicate that the central zone of the study area is suitable for drilling new water wells. Full article

(This article belongs to the Special Issue Remote Sensing Approaches to Groundwater Resource Assessment and Sustainable Management)

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17 pages, 4036 KiB

Open AccessArticle

Doppler Shift Estimation Method for Frequency Diverse Array Radar Based on Graph Signal Processing

by Ningbo Xie, Haijun Wang, Kefei Liao, Shan Ouyang, Hanbo Chen and Qinlin Li

Remote Sens. 2025, 17(5), 765; https://doi.org/10.3390/rs17050765 (registering DOI) - 22 Feb 2025

Viewed by 186

Abstract

In this paper, a novel Doppler shift estimation method for frequency diverse array (FDA) radar based on graph signal processing (GSP) theory is proposed and investigated. First, a well-designed graph signal model for a monostatic linear FDA is formulated. Subsequently, spectral decomposition is [...] Read more.

In this paper, a novel Doppler shift estimation method for frequency diverse array (FDA) radar based on graph signal processing (GSP) theory is proposed and investigated. First, a well-designed graph signal model for a monostatic linear FDA is formulated. Subsequently, spectral decomposition is conducted on the constructed signal model utilizing graph Fourier transform (GFT) techniques, enabling the extraction of the target’s Doppler shift parameter through spectral peak search. A comprehensive series of simulation experiments demonstrates that the proposed method can achieve the accurate estimation of target parameters even under low signal-to-noise ratio (SNR) conditions. Furthermore, the proposed method exhibits superior performance compared to the MUSIC algorithm, offering enhanced resolution and estimation accuracy. Additionally, the method is highly amenable to parallel processing, significantly reducing the computational burden associated with traditional procedures. Full article

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25 pages, 6071 KiB

Open AccessArticle

A Multi-Scale Spatio-Temporal Fusion Network for Occluded Small Object Detection in Geiger-Mode Avalanche Photodiode LiDAR Systems

by Yuanxue Ding, Dakuan Du, Jianfeng Sun, Le Ma, Xianhui Yang, Rui He, Jie Lu and Yanchen Qu

Remote Sens. 2025, 17(5), 764; https://doi.org/10.3390/rs17050764 (registering DOI) - 22 Feb 2025

Viewed by 200

Abstract

The Geiger-Mode Avalanche Photodiode (Gm-APD) LiDAR system demonstrates high-precision detection capabilities over long distances. However, the detection of occluded small objects at long distances poses significant challenges, limiting its practical application. To address this issue, we propose a multi-scale spatio-temporal object detection network [...] Read more.

The Geiger-Mode Avalanche Photodiode (Gm-APD) LiDAR system demonstrates high-precision detection capabilities over long distances. However, the detection of occluded small objects at long distances poses significant challenges, limiting its practical application. To address this issue, we propose a multi-scale spatio-temporal object detection network (MSTOD-Net), designed to associate object information across different spatio-temporal scales for the effective detection of occluded small objects. Specifically, in the encoding stage, a dual-channel feature fusion framework is employed to process range and intensity images from consecutive time frames, facilitating the detection of occluded objects. Considering the significant differences between range and intensity images, a multi-scale context-aware (MSCA) module and a feature fusion (FF) module are incorporated to enable efficient cross-scale feature interaction and enhance small object detection. Additionally, an edge perception (EDGP) module is integrated into the network’s shallow layers to refine the edge details and enhance the information in unoccluded regions. In the decoding stage, feature maps from the encoder are upsampled and combined with multi-level fused features, and four prediction heads are employed to decode the object categories, confidence, widths and heights, and displacement offsets. The experimental results demonstrate that the MSTOD-Net achieves mAP₅₀ and mAR₅₀ scores of 96.4% and 96.9%, respectively, outperforming the state-of-the-art methods. Full article

(This article belongs to the Special Issue Remote Sensing Target Recognition and Detection: Theory and Applications)

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24 pages, 24497 KiB

Open AccessArticle

An Adaptive Feature Enhanced Gaussian Weighted Network for Hyperspectral Image Classification

by Fei Zhu, Cuiping Shi, Liguo Wang and Haizhu Pan

Remote Sens. 2025, 17(5), 763; https://doi.org/10.3390/rs17050763 (registering DOI) - 22 Feb 2025

Viewed by 98

Abstract

Recently, research on hyperspectral image classification (HSIC) methods has made significant progress. However, current models commonly only focus on the primary features, overlooking the valuable information contained in secondary features that can enhance the model’s learning capabilities. To address this issue, an adaptive [...] Read more.

Recently, research on hyperspectral image classification (HSIC) methods has made significant progress. However, current models commonly only focus on the primary features, overlooking the valuable information contained in secondary features that can enhance the model’s learning capabilities. To address this issue, an adaptive feature enhanced gaussian weighted network (AFGNet) is proposed in this paper. Firstly, an adaptive feature enhancement module (AFEM) was designed to evaluate the effectiveness of different features and enhance those that are more conducive to model learning. Secondly, a gaussian weighted feature fusion module (GWF2) was constructed to integrate local and global feature information effectively. Finally, a multi-head collaborative attention (MHCA) mechanism was proposed. MHCA enhances the feature extraction capability of the model for sequence data through direct interaction and global modeling. Extensive experiments were conducted on five challenging datasets. The experimental results demonstrate that the proposed method outperforms several SOTA methods. Full article

(This article belongs to the Special Issue Deep Learning for Spectral-Spatial Hyperspectral Image Classification)

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26 pages, 7288 KiB

Open AccessArticle

Correction Method for Thermal Deformation Line-of-Sight Errors of Low-Orbit Optical Payloads Under Unstable Illumination Conditions

by Yao Li, Xin Chen, Guangsen Liu and Peng Rao

Remote Sens. 2025, 17(5), 762; https://doi.org/10.3390/rs17050762 (registering DOI) - 22 Feb 2025

Viewed by 161

Abstract

Accurate optical axis pointing of optical payloads in low orbits is essential for sustained indication and high-precision positioning of motion targets. Owing to the short orbital period in low orbits and the influence of the sun, the incident light on the optical payloads [...] Read more.

Accurate optical axis pointing of optical payloads in low orbits is essential for sustained indication and high-precision positioning of motion targets. Owing to the short orbital period in low orbits and the influence of the sun, the incident light on the optical payloads and the space thermal environment undergo drastic and irregular changes over a short period. These changes cause optical distortions within the camera and variations in the installation matrix referenced for the satellite. Ultimately, these changes affect the imaging process of the camera and the line-of-sight (LOS) accuracy, greatly disadvantaging the high-precision pointing and positioning of space targets. In this paper, a correction method based on stellar observation data is proposed to address the LOS deviation issue of low-orbit optical payloads caused by space thermal deformation (STD). The proposed method innovatively utilizes the angle relationship between the solar vector, the satellite position vector, and the camera LOS vector as the correction parameters to characterize the thermal environment in which the payload operates. This method overcomes the irregularity and frequent correction requirements of LOS errors in low-orbit payloads. Experimental results showed that the mean absolute error of the camera LOS after the correction was 0.001096 rad, representing an 80.28% improvement over previous measurements, even reaching 99% improvement in the final mission. At a 95% confidence level, the correction errors for the final mission were consistently below 10⁻⁴ (2σ) rad in the right ascension and declination directions. Full article

(This article belongs to the Special Issue LEO-Augmented PNT Service)

26 pages, 38880 KiB

Open AccessArticle

The Impact of MERRA-2 and CAMS Aerosol Reanalysis Data on FengYun-4B Geostationary Interferometric Infrared Sounder Simulations

by Weiyi Peng, Fuzhong Weng and Chengzhi Ye

Remote Sens. 2025, 17(5), 761; https://doi.org/10.3390/rs17050761 (registering DOI) - 22 Feb 2025

Viewed by 245

Abstract

Aerosols significantly impact the brightness temperature (BT) in thermal infrared (IR) channels, and ignoring their effects can lead to relatively large observation-minus-background (OMB) bias in radiance calculations. The accuracy of aerosol datasets is essential for BT simulations and bias reduction. This study incorporated [...] Read more.

Aerosols significantly impact the brightness temperature (BT) in thermal infrared (IR) channels, and ignoring their effects can lead to relatively large observation-minus-background (OMB) bias in radiance calculations. The accuracy of aerosol datasets is essential for BT simulations and bias reduction. This study incorporated aerosol reanalysis datasets from the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) and Copernicus Atmosphere Monitoring Service (CAMS) into the Advanced Radiative Transfer Modeling System (ARMS) to compare their impacts on BT simulations from the Geostationary Interferometric Infrared Sounder (GIIRS) and their effectiveness in reducing OMB biases. The results showed that, for a sandstorm event on 10 April 2023, incorporating total aerosol data from the MERRA-2 improved the BT simulations by 0.56 K on average, surpassing CAMS’s 0.11 K improvement. Dust aerosols notably impacted the BT, with the MERRA-2 showing a 0.17 K improvement versus CAMS’s 0.06 K due to variations in the peak aerosol level, thickness, and column mass density. Improvements for sea salt and carbonaceous aerosols were concentrated in the South China Sea and Bay of Bengal, where the MERRA-2 outperformed CAMS. For sulfate aerosols, the MERRA-2 excelled in the Bohai Sea and southern Bay of Bengal, while CAMS was better in the northern Bay of Bengal. These findings provide guidance for aerosol assimilation and retrieval, emphasizing the importance of quality control and bias correction in data assimilation systems. Full article

(This article belongs to the Section Atmospheric Remote Sensing)

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23 pages, 29156 KiB

Open AccessArticle

U-MGA: A Multi-Module Unet Optimized with Multi-Scale Global Attention Mechanisms for Fine-Grained Segmentation of Cultivated Areas

by Yun Chen, Yiheng Xie, Weiyuan Yao, Yu Zhang, Xinhong Wang, Yanli Yang and Lingli Tang

Remote Sens. 2025, 17(5), 760; https://doi.org/10.3390/rs17050760 (registering DOI) - 22 Feb 2025

Viewed by 209

Abstract

Arable land is fundamental to agricultural production and a crucial component of ecosystems. However, its complex texture and distribution in remote sensing images make it susceptible to interference from other land cover types, such as water bodies, roads, and buildings, complicating accurate identification. [...] Read more.

Arable land is fundamental to agricultural production and a crucial component of ecosystems. However, its complex texture and distribution in remote sensing images make it susceptible to interference from other land cover types, such as water bodies, roads, and buildings, complicating accurate identification. Building on previous research, this study proposes an efficient and lightweight CNN-based network, U-MGA, to address the challenges of feature similarity between arable and non-arable areas, insufficient fine-grained feature extraction, and the underutilization of multi-scale information. Specifically, a Multi-Scale Adaptive Segmentation (MSAS) is designed during the feature extraction phase to provide multi-scale and multi-feature information, supporting the model’s feature reconstruction stage. In the reconstruction phase, the introduction of the Multi-Scale Contextual Module (MCM) and Group Aggregation Bridge (GAB) significantly enhances the efficiency and accuracy of multi-scale and fine-grained feature utilization. The experiments conducted on an arable land dataset based on GF-2 imagery and a publicly available dataset show that U-MGA outperforms mainstream networks (Unet, A2FPN, Segformer, FTUnetformer, DCSwin, and TransUnet) across six evaluation metrics (Overall Accuracy (OA), Precision, Recall, F1-score, Intersection-over-Union (IoU), and Kappa coefficient). Thus, this study provides an efficient and precise solution for the arable land recognition task, which is of significant importance for agricultural resource monitoring and ecological environmental protection. Full article

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20 pages, 96432 KiB

Open AccessArticle

Contrastive Feature Disentanglement via Physical Priors for Underwater Image Enhancement

by Fei Li, Li Wan, Jiangbin Zheng, Lu Wang and Yue Xi

Remote Sens. 2025, 17(5), 759; https://doi.org/10.3390/rs17050759 (registering DOI) - 22 Feb 2025

Viewed by 151

Abstract

Underwater image enhancement (UIE) serves as a fundamental preprocessing step in ocean remote sensing applications, encompassing marine life detection, archaeological surveying, and subsea resource exploration. However, UIE encounters substantial technical challenges due to the intricate physics of underwater light propagation and the inherent [...] Read more.

Underwater image enhancement (UIE) serves as a fundamental preprocessing step in ocean remote sensing applications, encompassing marine life detection, archaeological surveying, and subsea resource exploration. However, UIE encounters substantial technical challenges due to the intricate physics of underwater light propagation and the inherent homogeneity of aquatic environments. Images captured underwater are significantly degraded through wavelength-dependent absorption and scattering processes, resulting in color distortion, contrast degradation, and illumination irregularities. To address these challenges, we propose a contrastive feature disentanglement network (CFD-Net) that systematically addresses underwater image degradation. Our framework employs a multi-stream decomposition architecture with three specialized decoders to disentangle the latent feature space into components associated with degradation and those representing high-quality features. We incorporate hierarchical contrastive learning mechanisms to establish clear relationships between standard and degraded feature spaces, emphasizing intra-layer similarity and inter-layer exclusivity. Through the synergistic utilization of internal feature consistency and cross-component distinctiveness, our framework achieves robust feature extraction without explicit supervision. Compared to existing methods, our approach achieves a 12% higher UIQM score on the EUVP dataset and outperforms other state-of-the-art techniques on various evaluation metrics such as UCIQE, MUSIQ, and NIQE, both quantitatively and qualitatively. Full article

(This article belongs to the Special Issue Ocean Remote Sensing Based on Radar, Sonar and Optical Techniques)

17 pages, 6473 KiB

Open AccessCommunication

Terrestrial and Martian Paleo-Hydrologic Environment Systematic Comparison with ASI PRISMA and NASA CRISM Hyperspectral Instruments

by Angelo Zinzi, Paola Manzari, Veronica Camplone, Eleonora Ammannito, Giuseppe Sindoni, Francesco Zucca and Gianluca Polenta

Remote Sens. 2025, 17(5), 758; https://doi.org/10.3390/rs17050758 (registering DOI) - 22 Feb 2025

Viewed by 156

Abstract

The comparative analysis of hyperspectral data from different instruments can provide detailed information on the composition and geology of similar environments on different planets. This study aims to compare data acquired from the PRISMA satellite, used for Earth observation, with those collected by [...] Read more.

The comparative analysis of hyperspectral data from different instruments can provide detailed information on the composition and geology of similar environments on different planets. This study aims to compare data acquired from the PRISMA satellite, used for Earth observation, with those collected by the CRISM spectrometer onboard the Mars Reconnaissance Orbiter, orbiting Mars, in order to analyze the geological and mineralogical differences between the morphologies present on the two planets of interest. The comparison of these data will allow us to examine the mineralogical composition, highlighting the similarities and differences between the terrestrial and Martian environments. In particular, in this study, we present a method to refine the interpretation of spectral features of minerals commonly found in paleo-hydrological environments on Mars and identified also by field analysis of similar terrestrial sites, thus allowing us to improve the Martian sites’ characterization. Thanks to this approach, we have been able to find spectral similarities (e.g., band positions, band ratios) among specific Earth and Mars sites, thus demonstrating that it could be further expanded, by systematically using Earth-observation orbiting instruments to better characterize and constrain Martian spectral data. Full article

(This article belongs to the Special Issue Advances in Exploring the Moon, Mars, and Asteroids Based on In-Situ and Remote Sensing Measurements)

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27 pages, 11161 KiB

Open AccessArticle

Quantifying Tree Structural Change in an African Savanna by Utilizing Multi-Temporal TLS Data

by Tasiyiwa Priscilla Muumbe, Jussi Baade, Pasi Raumonen, Corli Coetsee, Jenia Singh and Christiane Schmullius

Remote Sens. 2025, 17(5), 757; https://doi.org/10.3390/rs17050757 (registering DOI) - 22 Feb 2025

Viewed by 157

Abstract

Structural changes in savanna trees vary spatially and temporally because of both biotic and abiotic drivers, as well as the complex interactions between them. Given this complexity, it is essential to monitor and quantify woody structural changes in savannas efficiently. We implemented a [...] Read more.

Structural changes in savanna trees vary spatially and temporally because of both biotic and abiotic drivers, as well as the complex interactions between them. Given this complexity, it is essential to monitor and quantify woody structural changes in savannas efficiently. We implemented a non-destructive approach based on Terrestrial Laser Scanning (TLS) and Quantitative Structure Models (QSMs) that offers the unique advantage of investigating changes in complex tree parameters, such as volume and branch length parameters that have not been previously reported for savanna trees. Leaf-off multi-scan TLS point clouds were acquired during the dry season, using a Riegl VZ1000 TLS, in September 2015 and October 2019 at the Skukuza flux tower in Kruger National Park, South Africa. These three-dimensional (3D) data covered an area of 15.2 ha with an average point density of 4270 points/m² (0.015°) and 1600 points/m² (0.025°) for the 2015 and 2019 clouds, respectively. Individual tree segmentation was applied on the two clouds using the comparative shortest-path algorithm in LiDAR 360(v5.4) software. We reconstructed optimized QSMs and assessed tree structural parameters such as Diameter at Breast Height (DBH), tree height, crown area, volume, and branch length at individual tree level. The DBH, tree height, crown area, and trunk volume showed significant positive correlations (R² > 0.80) between scanning periods regardless of the difference in the number of points of the matched trees. The opposite was observed for total and branch volume, total number of branches, and 1st-order branch length. As the difference in the point densities increased, the difference in the computed parameters also increased (R² < 0.63) for a high relative difference. A total of 45% of the trees present in 2015 were identified in 2019 as damaged/felled (75 trees), and the volume lost was estimated to be 83.4 m³. The results of our study showed that volume reconstruction algorithms such as TreeQSMs and high-resolution TLS datasets can be used successfully to quantify changes in the structure of savanna trees. The results of this study are key in understanding savanna ecology given its complex and dynamic nature and accurately quantifying the gains and losses that could arise from fire, drought, herbivory, and other abiotic and biotic disturbances. Full article

(This article belongs to the Special Issue Remote Sensing of Savannas and Woodlands II)

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Remote Sens., Volume 17, Issue 5 (March-1 2025) – 55 articles

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