Remote Sensing

20 pages, 1491 KiB

Open AccessArticle

Performance Analysis of Stellar Refraction Autonomous Navigation for Cross-Domain Vehicles

by Yuchang Xu, Yang Zhang, Xiaokang Wang, Guanbing Zhang, Guang Yang and Hong Yuan

Remote Sens. 2025, 17(14), 2367; https://doi.org/10.3390/rs17142367 (registering DOI) - 9 Jul 2025

Stellar refraction autonomous navigation provides a promising alternative for cross-domain vehicles, particularly in near-space environments where traditional inertial and satellite navigation methods face limitations. This study develops a stellar refraction navigation system that utilizes stellar refraction angle observations and the Implicit Unscented Kalman [...] Read more.

Stellar refraction autonomous navigation provides a promising alternative for cross-domain vehicles, particularly in near-space environments where traditional inertial and satellite navigation methods face limitations. This study develops a stellar refraction navigation system that utilizes stellar refraction angle observations and the Implicit Unscented Kalman Filter (IUKF) for state estimation. A representative orbit with altitudes ranging from 60 km to 200 km is designed to simulate cross-domain flight conditions. The navigation performance is analyzed under varying conditions, including orbital altitude, as well as star sensor design parameters, such as limiting magnitude, field of view (FOV) value, and measurement error, along with different sampling intervals. The simulation results show that increasing the limiting magnitude from 5 to 8 reduced the position error from 705.19 m to below 1 m, with optimal accuracy reaching 0.89 m when using a 20° × 20° field of view and a 3 s sampling interval. In addition, shorter sampling intervals improved accuracy and filter stability, while longer intervals introduced greater integration drift. When the sampling interval reached 100 s, position error grew to the kilometer level. These findings validate the feasibility of using stellar refraction for autonomous navigation in cross-domain scenarios and provide design guidance for optimizing star sensor configurations and sampling strategies in future near-space navigation systems. Full article

(This article belongs to the Special Issue Autonomous Space Navigation (Second Edition))

18 pages, 22954 KiB

Open AccessArticle

Spatiotemporal Analysis of Drought Variation from 2001 to 2023 in the China–Mongolia–Russia Transboundary Heilongjiang River Basin Based on ITVDI

by Weihao Zou, Juanle Wang, Congrong Li, Keming Yang, Denis Fetisov, Jiawei Jiang, Meng Liu and Yaping Liu

Remote Sens. 2025, 17(14), 2366; https://doi.org/10.3390/rs17142366 (registering DOI) - 9 Jul 2025

Abstract

Drought impacts agricultural production and regional sustainable development. Accordingly, timely and accurate drought monitoring is essential for ensuring food security in rain-fed agricultural regions. Alternating drought and flood events frequently occur in the Heilongjiang River Basin, the largest grain-producing area in Far East [...] Read more.

Drought impacts agricultural production and regional sustainable development. Accordingly, timely and accurate drought monitoring is essential for ensuring food security in rain-fed agricultural regions. Alternating drought and flood events frequently occur in the Heilongjiang River Basin, the largest grain-producing area in Far East Asia. However, spatiotemporal variability in drought is not well understood, in part owing to the limitations of the traditional Temperature Vegetation Dryness Index (TVDI). In this study, an Improved Temperature Vegetation Dryness Index (ITVDI) was developed by incorporating Digital Elevation Model data to correct land surface temperatures and introducing a constraint line method to replace the traditional linear regression for fitting dry–wet boundaries. Based on MODIS (Moderate-resolution Imaging Spectroradiometer) normalized vegetation index and land surface temperature products, the Heilongjiang River Basin, a cross-border basin between China, Mongolia, and Russia, exhibited pronounced spatiotemporal variability in drought conditions of the growing season from 2001 to 2023. Drought severity demonstrated clear geographical zonation, with a higher intensity in the western region and lower intensity in the eastern region. The Mongolian Plateau and grasslands were identified as drought hotspots. The Far East Asia forest belt was relatively humid, with an overall lower drought risk. The central region exhibited variation in drought characteristics. From the perspective of cross-national differences, the drought severity distribution in Northeast China and Inner Mongolia exhibits marked spatial heterogeneity. In Mongolia, regional drought levels exhibited a notable trend toward homogenization, with a higher proportion of extreme drought than in other areas. The overall drought risk in the Russian part of the basin was relatively low. A trend analysis indicated a general pattern of drought alleviation in western regions and intensification in eastern areas. Most regions showed relatively stable patterns, with few areas exhibiting significant changes, mainly surrounding cities such as Qiqihar, Daqing, Harbin, Changchun, and Amur Oblast. Regions with aggravation accounted for 52.29% of the total study area, while regions showing slight alleviation account for 35.58%. This study provides a scientific basis and data infrastructure for drought monitoring in transboundary watersheds and for ensuring agricultural production security. Full article

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24 pages, 4045 KiB

Open AccessArticle

Spatiotemporal Dynamics and Driving Factors of Soil Wind Erosion in Inner Mongolia, China

by Mei Yong, Batunacun, Chunxing Hai, An Chang, Yueming Chang, Yaxin Wang and Yunfeng Hu

Remote Sens. 2025, 17(14), 2365; https://doi.org/10.3390/rs17142365 (registering DOI) - 9 Jul 2025

Abstract

Wind erosion poses a major threat to ecosystem stability and land productivity in arid and semi-arid regions. Accurate identification of its spatiotemporal dynamics and underlying driving mechanisms is a critical prerequisite for effective risk forecasting and targeted erosion control. This study applied the [...] Read more.

Wind erosion poses a major threat to ecosystem stability and land productivity in arid and semi-arid regions. Accurate identification of its spatiotemporal dynamics and underlying driving mechanisms is a critical prerequisite for effective risk forecasting and targeted erosion control. This study applied the Revised Wind Erosion Equation (RWEQ) model to assess the spatial distribution, interannual variation, and seasonal dynamics of the Soil Wind Erosion Modulus (SWEM) across Inner Mongolia from 1990 to 2022. The GeoDetector model was further employed to quantify dominant drivers, key interactions, and high-risk zones via factor, interaction, and risk detection. The results showed that the average SWEM across the study period was 35.65 t·ha⁻¹·yr⁻¹ and showed a decreasing trend over time. However, localised increases were observed in the Horqin and Hulun Buir sandy lands and central grasslands. Wind erosion was most intense in spring (17.64 t·ha⁻¹·yr⁻¹) and weakest in summer (5.57 t·ha⁻¹·yr⁻¹). Gale days, NDVI, precipitation, and wind speed were identified as dominant drivers. Interaction detection revealed non-linear synergies between gale days and temperature (q = 0.40) and wind speed and temperature (q = 0.36), alongside a two-factor interaction between NDVI and precipitation (q = 0.19). Risk detection indicated that areas with gale days > 58, wind speed > 3.01 m/s, NDVI < 0.2, precipitation of 30.17–135.59 mm, and temperatures of 3.01–4.23 °C are highly erosion-prone. Management should prioritise these sensitive and intensifying areas by implementing site-specific strategies to enhance ecosystem resilience. Full article

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20 pages, 13493 KiB

Open AccessArticle

A General Model for Converting All-Wave Net Radiation at Instantaneous to Daily Scales Under Clear Sky

by Jiakun Han, Bo Jiang, Yu Zhao, Jianghai Peng, Shaopeng Li, Hui Liang, Xiuwan Yin and Yingping Chen

Remote Sens. 2025, 17(14), 2364; https://doi.org/10.3390/rs17142364 (registering DOI) - 9 Jul 2025

Abstract

Surface all-wave net radiation (

R_{n}

) is one of the essential parameters to describe surface radiative energy balance, and it is of great significance in scientific research and practical applications. Among various acquisition approaches, the estimation of

R_{n}

from satellite [...] Read more.

Surface all-wave net radiation (

R_{n}

) is one of the essential parameters to describe surface radiative energy balance, and it is of great significance in scientific research and practical applications. Among various acquisition approaches, the estimation of

R_{n}

from satellite data is gaining more and more attention. In order to obtain the daily

R_{n}

(

R_{n d}

) from the instantaneous satellite observations, a parameter

C_{d}

, which is defined as the ratio between the

R_{n}

at daily and at instantaneous under clear sky was proposed and has been widely applied. Inspired by the sinusoidal model, a new model for

C_{d}

estimation, namely New Model, was proposed based on the comprehensive clear-sky

R_{n}

measurements collected from 105 global sites in this study. Compared with existing models, New Model could estimate

C_{d}

at any moment during 9:30~14:30 h, only depending on the length of daytime. Against the measurements, New Model was evaluated by validating and comparing it with two popular existing models. The results demonstrated that the

R_{n d}

obtained by multiplying

C_{d}

from New Model had the best accuracy, yielding an overall

R^{2}

of 0.95, root mean square error (RMSE) of 14.07 Wm⁻², and Bias of −0.21 Wm⁻². Additionally, New Model performed relatively better over vegetated surfaces than over non- or less-vegetated surfaces with a relative RMSE (rRMSE) of 11.1% and 17.89%, respectively. Afterwards, the New Model

C_{d}

estimate was applied with MODIS data to calculate

R_{n d}

. After validation, the

R_{n d}

computed from

C_{d}

was much better than that from the sinusoidal model, especially for the case MODIS transiting only once in a day, with

R_{n d}

-validated

R^{2}

of 0.88 and 0.84, RMSEs of 19.60 and 27.70 Wm⁻², and Biases of −0.76 and 8.88 Wm⁻². Finally, more analysis on New Model further pointed out the robustness of this model under various conditions in terms of moments, land cover types, and geolocations, but the model is suggested to be applied at a time scale of 30 min. In summary, although the new

C_{d}

model only works for clear-sky, it has the strong potential to be used in estimating

R_{n d}

from satellite data, especially for those having fine spatial resolution but low temporal resolution. Full article

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

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30 pages, 9446 KiB

Open AccessArticle

Seasonal and Long-Term Water Regime Trends of Cheremsky Wetland: Analysis Based on Sentinel-2 Spectral Indices and Composite Indicator Development

by Oleksandr Melnyk and Ansgar Brunn

Remote Sens. 2025, 17(14), 2363; https://doi.org/10.3390/rs17142363 (registering DOI) - 9 Jul 2025

Abstract

Wetlands are critically important ecosystems, but their dynamics, especially in complex regions such as the Ukrainian Polissya, remain poorly understood. This study focuses on the Cheremsky Nature Reserve, an internationally important wetland, to assess long-term (2017–2024) seasonal (spring-summer) trends in water surface conditions. [...] Read more.

Wetlands are critically important ecosystems, but their dynamics, especially in complex regions such as the Ukrainian Polissya, remain poorly understood. This study focuses on the Cheremsky Nature Reserve, an internationally important wetland, to assess long-term (2017–2024) seasonal (spring-summer) trends in water surface conditions. Using Sentinel-2 data and the Google Earth Engine platform, 14 spectral water indices were calculated. Their temporal trends were analyzed using Sen’s method, mutual correlations, and principal component analysis (PCA) to identify the main patterns of variability. Based on the normalized trends and weights obtained from the first two principal components, an integral composite index (CI) for spring and summer seasons was developed. The results revealed seasonal differences in the behavior of the indices and their contribution to the principal components, as well as spatial differentiation of water regime trends within the reserve. The proposed CI allows for an integrated assessment of the long-term dynamics of the wetlands, which is important for the development of conservation and management strategies. Full article

33 pages, 2534 KiB

Open AccessArticle

Remotely Sensing Phytoplankton Size Structure in the Mediterranean Sea: Insights from In Situ Data and Temperature-Corrected Abundance-Based Models

by John A. Gittings, Eleni Livanou, Xuerong Sun, Robert J. W. Brewin, Stella Psarra, Manolis Mandalakis, Alexandra Peltekis, Annalisa Di Cicco, Vittorio E. Brando and Dionysios E. Raitsos

Remote Sens. 2025, 17(14), 2362; https://doi.org/10.3390/rs17142362 (registering DOI) - 9 Jul 2025

Abstract

Since the mid-1980s, the Mediterranean Sea’s surface and deeper layers have warmed at unprecedented rates, with recent projections identifying it as one of the regions most impacted by rising global temperatures. Metrics that characterize phytoplankton abundance, phenology and size structure are widely utilized [...] Read more.

Since the mid-1980s, the Mediterranean Sea’s surface and deeper layers have warmed at unprecedented rates, with recent projections identifying it as one of the regions most impacted by rising global temperatures. Metrics that characterize phytoplankton abundance, phenology and size structure are widely utilized as ecological indicators that enable a quantitative assessment of the status of marine ecosystems in response to environmental change. Here, using an extensive, updated in situ pigment dataset collated from numerous past research campaigns across the Mediterranean Sea, we re-parameterized an abundance-based phytoplankton size class model that infers Chl-a concentration in three phytoplankton size classes: pico- (<2 μm), nano- (2–20 μm) and micro-phytoplankton (>20 μm). Following recent advancements made within this category of size class models, we also incorporated information of sea surface temperature (SST) into the model parameterization. By tying model parameters to SST, the performance of the re-parameterized model was improved based on comparisons with concurrent, independent in situ measurements. Similarly, the application of the model to remotely sensed ocean color observations revealed strong agreement between satellite-derived estimates of phytoplankton size structure and in situ observations, with a performance comparable to the current regional operational datasets on size structure. The proposed conceptual regional model, parameterized with the most extended in situ pigment dataset available to date for the area, serves as a suitable foundation for long-term (1997–present) analyses on phytoplankton size structure and ecological indicators (i.e., phenology), ultimately linking higher trophic level responses to a changing Mediterranean Sea. Full article

(This article belongs to the Section Ocean Remote Sensing)

24 pages, 3937 KiB

Open AccessArticle

HyperTransXNet: Learning Both Global and Local Dynamics with a Dual Dynamic Token Mixer for Hyperspectral Image Classification

by Xin Dai, Zexi Li, Lin Li, Shuihua Xue, Xiaohui Huang and Xiaofei Yang

Remote Sens. 2025, 17(14), 2361; https://doi.org/10.3390/rs17142361 (registering DOI) - 9 Jul 2025

Abstract

Recent advances in hyperspectral image (HSI) classification have demonstrated the effectiveness of hybrid architectures that integrate convolutional neural networks (CNNs) and Transformers, leveraging CNNs for local feature extraction and Transformers for global dependency modeling. However, existing fusion approaches face three critical challenges: (1) [...] Read more.

Recent advances in hyperspectral image (HSI) classification have demonstrated the effectiveness of hybrid architectures that integrate convolutional neural networks (CNNs) and Transformers, leveraging CNNs for local feature extraction and Transformers for global dependency modeling. However, existing fusion approaches face three critical challenges: (1) insufficient synergy between spectral and spatial feature learning due to rigid coupling mechanisms; (2) high computational complexity resulting from redundant attention calculations; and (3) limited adaptability to spectral redundancy and noise in small-sample scenarios. To address these limitations, we propose HyperTransXNet, a novel CNN-Transformer hybrid architecture that incorporates adaptive spectral-spatial fusion. Specifically, the proposed HyperTransXNet comprises three key modules: (1) a Hybrid Spatial-Spectral Module (HSSM) that captures the refined local spectral-spatial features and models global spectral correlations by combining depth-wise dynamic convolution with frequency-domain attention; (2) a Mixture-of-Experts Routing (MoE-R) module that adaptively fuses multi-scale features by dynamically selecting optimal experts via Top-K sparse weights; and (3) a Spatial-Spectral Tokens Enhancer (SSTE) module that ensures causality-preserving interactions between spectral bands and spatial contexts. Extensive experiments on the Indian Pines, Houston 2013, and WHU-Hi-LongKou datasets demonstrate the superiority of HyperTransXNet. Full article

(This article belongs to the Special Issue AI-Driven Hyperspectral Remote Sensing of Atmosphere and Land)

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24 pages, 1295 KiB

Open AccessArticle

A Robust Tracking Method for Aerial Extended Targets with Space-Based Wideband Radar

by Linlin Fang, Yuxin Hu, Lihua Zhong and Lijia Huang

Remote Sens. 2025, 17(14), 2360; https://doi.org/10.3390/rs17142360 (registering DOI) - 9 Jul 2025

Abstract

Space-based radar systems offer significant advantages for air surveillance, including wide-area coverage and extended early-warning capabilities. The integrated design of detection and imaging in space-based wideband radar further enhances its accuracy. However, in the wideband tracking mode, large aircraft targets exhibit extended characteristics. [...] Read more.

Space-based radar systems offer significant advantages for air surveillance, including wide-area coverage and extended early-warning capabilities. The integrated design of detection and imaging in space-based wideband radar further enhances its accuracy. However, in the wideband tracking mode, large aircraft targets exhibit extended characteristics. Measurements from the same target cross multiple range resolution cells. Additionally, the nonlinear observation model and uncertain measurement noise characteristics under space-based long-distance observation substantially increase the tracking complexity. To address these challenges, we propose a robust aerial target tracking method for space-based wideband radar applications. First, we extend the observation model of the gamma Gaussian inverse Wishart probability hypothesis density filter to three-dimensional space by incorporating a spherical–radial cubature rule for improved nonlinear filtering. Second, variational Bayesian processing is integrated to enable the joint estimation of the target state and measurement noise parameters, and a recursive process is derived for both Gaussian and Student’s t-distributed measurement noise, enhancing the method’s robustness against noise uncertainty. Comprehensive simulations evaluating varying target extension parameters and noise conditions demonstrate that the proposed method achieves superior tracking accuracy and robustness. Full article

(This article belongs to the Special Issue Advances in Spaceborne SAR—Technology and Applications (Second Edition))

29 pages, 14590 KiB

Open AccessArticle

CAU²DNet: A Dual-Branch Deep Learning Network and a Dataset for Slum Recognition with Multi-Source Remote Sensing Data

by Xi Lyu, Chenyu Zhang, Lizhi Miao, Xiying Sun, Xinxin Zhou, Xinyi Yue, Zhongchang Sun and Yueyong Pang

Remote Sens. 2025, 17(14), 2359; https://doi.org/10.3390/rs17142359 (registering DOI) - 9 Jul 2025

Abstract

The efficient and precise identification of urban slums is a significant challenge for urban planning and sustainable development, as their morphological diversity and complex spatial distribution make it difficult to use traditional remote sensing inversion methods. Current deep learning (DL) methods mainly face [...] Read more.

The efficient and precise identification of urban slums is a significant challenge for urban planning and sustainable development, as their morphological diversity and complex spatial distribution make it difficult to use traditional remote sensing inversion methods. Current deep learning (DL) methods mainly face challenges such as limited receptive fields and insufficient sensitivity to spatial locations when integrating multi-source remote sensing data, and high-quality datasets that integrate multi-spectral and geoscientific indicators to support them are scarce. In response to these issues, this study proposes a DL model (coordinate-attentive U²-DeepLab network [CAU²DNet]) that integrates multi-source remote sensing data. The model integrates the multi-scale feature extraction capability of U²-Net with the global receptive field advantage of DeepLabV3+ through a dual-branch architecture. Thereafter, the spatial semantic perception capability is enhanced by introducing the CoordAttention mechanism, and ConvNextV2 is adopted to optimize the backbone network of the DeepLabV3+ branch, thereby improving the modeling capability of low-resolution geoscientific features. The two branches adopt a decision-level fusion mechanism for feature fusion, which means that the results of each are weighted and summed using learnable weights to obtain the final output feature map. Furthermore, this study constructs the São Paulo slums dataset for model training due to the lack of a multi-spectral slum dataset. This dataset covers 7978 samples of 512 × 512 pixels, integrating high-resolution RGB images, Normalized Difference Vegetation Index (NDVI)/Modified Normalized Difference Water Index (MNDWI) geoscientific indicators, and POI infrastructure data, which can significantly enrich multi-source slum remote sensing data. Experiments have shown that CAU²DNet achieves an intersection over union (IoU) of 0.6372 and an F1 score of 77.97% on the São Paulo slums dataset, indicating a significant improvement in accuracy over the baseline model. The ablation experiments verify that the improvements made in this study have resulted in a 16.12% increase in precision. Moreover, CAU²DNet also achieved the best results in all metrics during the cross-domain testing on the WHU building dataset, further confirming the model’s generalizability. Full article

(This article belongs to the Special Issue Target Detection, Recognition, Tracking, and Positioning Using Remote Sensing and AI Techniques)

20 pages, 14490 KiB

Open AccessArticle

Estimation of Forest Aboveground Biomass Using Sentinel-1/2 Synergized with Extrapolated Parameters from LiDAR Data and Analysis of Its Ecological Driving Factors

by Xu Xu, Jingyu Yang, Shanze Qi, Yue Ma, Wei Liu, Luanxin Li, Xiaoqiang Lu and Yan Liu

Remote Sens. 2025, 17(14), 2358; https://doi.org/10.3390/rs17142358 (registering DOI) - 9 Jul 2025

Abstract

Accurate estimation of forest aboveground biomass (AGB) and understanding its ecological drivers are vital for carbon monitoring and sustainable forest management. However, AGB estimation using remote sensing is hindered by signal saturation in high-biomass areas and insufficient attention to ecological structural factors. Focusing [...] Read more.

Accurate estimation of forest aboveground biomass (AGB) and understanding its ecological drivers are vital for carbon monitoring and sustainable forest management. However, AGB estimation using remote sensing is hindered by signal saturation in high-biomass areas and insufficient attention to ecological structural factors. Focusing on Guangdong Province, this study proposes a novel approach that spatially extrapolates airborne LiDAR-derived Forest structural parameters and integrates them with Sentinel-1/2 data to construct an AGB prediction model. Results show that incorporating structural parameters significantly reduces saturation effects, improving prediction accuracy and AGB maximum range in high-AGB regions (R² from 0.724 to 0.811; RMSE = 10.64 Mg/ha; max AGB > 180 Mg/ha). Using multi-scale geographically weighted regression (MGWR), we further examined the spatial influence of forest type, age structure, and species mixture. Forest age showed a strong positive correlation with AGB in over 95% of the area, particularly in mountainous and hilly regions (coefficients up to 1.23). Species mixture had positive effects in 87.7% of the region, especially in the north and parts of the south. Natural forests consistently exhibited higher AGB than plantations, with differences amplifying at later successional stages. Highly mixed natural forests showed faster biomass accumulation and higher steady-state AGB, highlighting the regulatory role of structural complexity and successional maturity. This study not only mitigates remote sensing saturation issues but also deepens understanding of spatial and ecological drivers of AGB, offering theoretical and technical support for targeted carbon stock assessment and forest management strategies. Full article

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23 pages, 11755 KiB

Open AccessArticle

The Optimal Estimation Model for Soil Salinization Based on the FOD-CNN Spectral Index

by Jicun Yang, Bing Guo and Rui Zhang

Remote Sens. 2025, 17(14), 2357; https://doi.org/10.3390/rs17142357 (registering DOI) - 9 Jul 2025

Abstract

Globally, diverse regions are experiencing significant salinization, yet research leveraging two-dimensional spectral indices derived from fractional-order differentiated hyperspectral data remains relatively scarce. Given that the Yellow River Delta exemplifies a severely salinized area, this study employs it as a case study to advance [...] Read more.

Globally, diverse regions are experiencing significant salinization, yet research leveraging two-dimensional spectral indices derived from fractional-order differentiated hyperspectral data remains relatively scarce. Given that the Yellow River Delta exemplifies a severely salinized area, this study employs it as a case study to advance salinization monitoring by integrating fractional-order differentiation with two-dimensional spectral indices. Compared to fractional-order differentiation (FOD) and deep learning models, integer-order differentiation and traditional detection models suffer from lower accuracy. Therefore, a two-dimensional spectral index was constructed to identify sensitive parameters. Modeling methods such as Convolutional Neural Networks (CNNs), Partial Least Squares Regression (PLSR), and Random Forest (RF) were employed to predict soil salinity. The results show that FOD effectively emphasizes gradual changes in spectral curve transformations, significantly improving the correlation between spectral indices and soil salinity. The 1.6-order NDI spectral index (1244 nm, 2081 nm) showed the highest correlation with soil salinity, with a coefficient of 0.9, followed by the 1.6-order RI spectral index (2242 nm, 1208 nm), with a correlation coefficient of 0.882. The CNN model yielded the highest inversion accuracy. Compared to the PLSR and RF models, the CNN model increased the RPD of the prediction set by 0.710 and 1.721 and improved the R² by 0.057 and 0.272, while reducing the RMSE by 0.145 g/kg and 1.470 g/kg. This study provides support for monitoring salinization in the Yellow River Delta. Full article

(This article belongs to the Special Issue Remote Sensing of Soil Moisture and the Dynamics of Soil–Vegetation Systems)

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24 pages, 4465 KiB

Open AccessArticle

A Deep Learning-Based Echo Extrapolation Method by Fusing Radar Mosaic and RMAPS-NOW Data

by Shanhao Wang, Zhiqun Hu, Fuzeng Wang, Ruiting Liu, Lirong Wang and Jiexin Chen

Remote Sens. 2025, 17(14), 2356; https://doi.org/10.3390/rs17142356 (registering DOI) - 9 Jul 2025

Abstract

Radar echo extrapolation is a critical forecasting tool in the field of meteorology, playing an especially vital role in nowcasting and weather modification operations. In recent years, spatiotemporal sequence prediction models based on deep learning have garnered significant attention and achieved notable progress [...] Read more.

Radar echo extrapolation is a critical forecasting tool in the field of meteorology, playing an especially vital role in nowcasting and weather modification operations. In recent years, spatiotemporal sequence prediction models based on deep learning have garnered significant attention and achieved notable progress in radar echo extrapolation. However, most of these extrapolation network architectures are built upon convolutional neural networks, using radar echo images as input. Typically, radar echo intensity values ranging from −5 to 70 dBZ with a resolution of 5 dBZ are converted into 0–255 grayscale images from pseudo-color representations, which inevitably results in the loss of important echo details. Furthermore, as the extrapolation time increases, the smoothing effect inherent to convolution operations leads to increasingly blurred predictions. To address the algorithmic limitations of deep learning-based echo extrapolation models, this study introduces three major improvements: (1) A Deep Convolutional Generative Adversarial Network (DCGAN) is integrated into the ConvLSTM-based extrapolation model to construct a DCGAN-enhanced architecture, significantly improving the quality of radar echo extrapolation; (2) Considering that the evolution of radar echoes is closely related to the surrounding meteorological environment, the study incorporates specific physical variable products from the initial zero-hour field of RMAPS-NOW (the Rapid-update Multiscale Analysis and Prediction System—NOWcasting subsystem), developed by the Institute of Urban Meteorology, China. These variables are encoded jointly with high-resolution (0.5 dB) radar mosaic data to form multiple radar cells as input. A multi-channel radar echo extrapolation network architecture (MR-DCGAN) is then designed based on the DCGAN framework; (3) Since radar echo decay becomes more prominent over longer extrapolation horizons, this study departs from previous approaches that use a single model to extrapolate 120 min. Instead, it customizes time-specific loss functions for spatiotemporal attenuation correction and independently trains 20 separate models to achieve the full 120 min extrapolation. The dataset consists of radar composite reflectivity mosaics over North China within the range of 116.10–117.50°E and 37.77–38.77°N, collected from June to September during 2018–2022. A total of 39,000 data samples were matched with the initial zero-hour fields from RMAPS-NOW, with 80% (31,200 samples) used for training and 20% (7800 samples) for testing. Based on the ConvLSTM and the proposed MR-DCGAN architecture, 20 extrapolation models were trained using four different input encoding strategies. The models were evaluated using the Critical Success Index (CSI), Probability of Detection (POD), and False Alarm Ratio (FAR). Compared to the baseline ConvLSTM-based extrapolation model without physical variables, the models trained with the MR-DCGAN architecture achieved, on average, 18.59%, 8.76%, and 11.28% higher CSI values, 19.46%, 19.21%, and 19.18% higher POD values, and 19.85%, 11.48%, and 9.88% lower FAR values under the 20 dBZ, 30 dBZ, and 35 dBZ reflectivity thresholds, respectively. Among all tested configurations, the model that incorporated three physical variables—relative humidity (rh), u-wind, and v-wind—demonstrated the best overall performance across various thresholds, with CSI and POD values improving by an average of 16.75% and 24.75%, respectively, and FAR reduced by 15.36%. Moreover, the SSIM of the MR-DCGAN models demonstrates a more gradual decline and maintains higher overall values, indicating superior capability in preserving echo structural features. Meanwhile, the comparative experiments demonstrate that the MR-DCGAN (u, v + rh) model outperforms the MR-ConvLSTM (u, v + rh) model in terms of evaluation metrics. In summary, the model trained with the MR-DCGAN architecture effectively enhances the accuracy of radar echo extrapolation. Full article

(This article belongs to the Special Issue Advance of Radar Meteorology and Hydrology II)

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20 pages, 3185 KiB

Open AccessArticle

Radiative Transfer Model-Integrated Approach for Hyperspectral Simulation of Mixed Soil-Vegetation Scenarios and Soil Organic Carbon Estimation

by Asmaa Abdelbaki, Robert Milewski, Mohammadmehdi Saberioon, Katja Berger, José A. M. Demattê and Sabine Chabrillat

Remote Sens. 2025, 17(14), 2355; https://doi.org/10.3390/rs17142355 (registering DOI) - 9 Jul 2025

Abstract

Soils serve as critical carbon reservoirs, playing an essential role in climate change mitigation and agricultural sustainability. Accurate soil property determination relies on soil spectral reflectance data from Earth observation (EO), but current vegetation models often oversimplify soil conditions. This study introduces a [...] Read more.

Soils serve as critical carbon reservoirs, playing an essential role in climate change mitigation and agricultural sustainability. Accurate soil property determination relies on soil spectral reflectance data from Earth observation (EO), but current vegetation models often oversimplify soil conditions. This study introduces a novel approach that combines radiative transfer models (RTMs) with open-access soil spectral libraries to address this challenge. Focusing on conditions of low soil moisture content (SMC), photosynthetic vegetation (PV), and non-photosynthetic vegetation (NPV), the coupled Marmit–Leaf–Canopy (MLC) model is used to simulate early crop growth stages. The MLC model, which integrates MARMIT and PRO4SAIL2, enables the generation of mixed soil–vegetation scenarios. A simulated EO disturbed soil spectral library (DSSL) was created, significantly expanding the EU LUCAS cropland soil spectral library. A 1D convolutional neural network (1D-CNN) was trained on this database to predict Soil Organic Carbon (SOC) content. The results demonstrated relatively high SOC prediction accuracy compared to previous approaches that rely only on RTMs and/or machine learning approaches. Incorporating soil moisture content significantly improved performance over bare soil alone, yielding an R² of 0.86 and RMSE of 4.05 g/kg, compared to R² = 0.71 and RMSE = 6.01 g/kg for bare soil. Adding PV slightly reduced accuracy (R² = 0.71, RMSE = 6.31 g/kg), while the inclusion of NPV alongside moisture led to modest improvement (R² = 0.74, RMSE = 5.84 g/kg). The most comprehensive model, incorporating bare soil, SMC, PV, and NPV, achieved a balanced performance (R² = 0.76, RMSE = 5.49 g/kg), highlighting the importance of accounting for all surface components in SOC estimation. While further validation with additional scenarios and SOC prediction methods is needed, these findings demonstrate, for the first time, using radiative-transfer simulations of mixed vegetation-soil-water environments, that an EO-DSSL approach enhances machine learning-based SOC modeling from EO data, improving SOC mapping accuracy. This innovative framework could significantly improve global-scale SOC predictions, supporting the design of next-generation EO products for more accurate carbon monitoring. Full article

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30 pages, 19719 KiB

Open AccessArticle

DMA-Net: Dynamic Morphology-Aware Segmentation Network for Remote Sensing Images

by Chao Deng, Haojian Liang, Xiao Qin and Shaohua Wang

Remote Sens. 2025, 17(14), 2354; https://doi.org/10.3390/rs17142354 (registering DOI) - 9 Jul 2025

Abstract

Semantic segmentation of remote sensing imagery is a pivotal task for intelligent interpretation, with critical applications in urban monitoring, resource management, and disaster assessment. Recent advancements in deep learning have significantly improved RS image segmentation, particularly through the use of convolutional neural networks, [...] Read more.

Semantic segmentation of remote sensing imagery is a pivotal task for intelligent interpretation, with critical applications in urban monitoring, resource management, and disaster assessment. Recent advancements in deep learning have significantly improved RS image segmentation, particularly through the use of convolutional neural networks, which demonstrate remarkable proficiency in local feature extraction. However, due to the inherent locality of convolutional operations, prevailing methodologies frequently encounter challenges in capturing long-range dependencies, thereby constraining their comprehensive semantic comprehension. Moreover, the preprocessing of high-resolution remote sensing images by dividing them into sub-images disrupts spatial continuity, further complicating the balance between local feature extraction and global context modeling. To address these limitations, we propose DMA-Net, a Dynamic Morphology-Aware Segmentation Network built on an encoder–decoder architecture. The proposed framework incorporates three primary parts: a Multi-Axis Vision Transformer (MaxViT) encoder achieves a balance between local feature extraction and global context modeling through multi-axis self-attention mechanisms; a Hierarchy Attention Decoder (HA-Decoder) enhanced with Hierarchy Convolutional Groups (HCG) for precise recovery of fine-grained spatial details; and a Channel and Spatial Attention Bridge (CSA-Bridge) to mitigate the encoder–decoder semantic gap while amplifying discriminative feature representations. Extensive experimentation has been conducted to demonstrate the state-of-the-art performance of DMA-Net, which has been shown to achieve 87.31% mIoU on Potsdam, 83.23% on Vaihingen, and 54.23% on LoveDA, thereby surpassing existing methods. Full article

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27 pages, 7955 KiB

Open AccessArticle

Land Surface Condition-Driven Emissivity Variation and Its Impact on Diurnal Land Surface Temperature Retrieval Uncertainty

by Lijuan Wang, Ping Yue, Yang Yang, Sha Sha, Die Hu, Xueyuan Ren, Xiaoping Wang, Hui Han and Xiaoyu Jiang

Remote Sens. 2025, 17(14), 2353; https://doi.org/10.3390/rs17142353 (registering DOI) - 9 Jul 2025

Abstract

Land surface emissivity (LSE) is the most critical factor affecting land surface temperature (LST) retrieval. Understanding its variation characteristics is essential, as this knowledge provides fundamental prior constraints for the LST retrieval process. This study utilizes thermal infrared emissivity and hyperspectral data collected [...] Read more.

Land surface emissivity (LSE) is the most critical factor affecting land surface temperature (LST) retrieval. Understanding its variation characteristics is essential, as this knowledge provides fundamental prior constraints for the LST retrieval process. This study utilizes thermal infrared emissivity and hyperspectral data collected from diverse underlying surfaces from 2017 to 2024 to analyze LSE variation characteristics across different surface types, spectral bands, and temporal scales. Key influencing factors are quantified to establish empirical relationships between LSE dynamics and environmental variables. Furthermore, the impact of LSE models on diurnal LST retrieval accuracy is systematically evaluated through comparative experiments, emphasizing the necessity of integrating time-dependent LSE corrections into radiative transfer equations. The results indicate that LSE in the 8–11 µm band is highly sensitive to surface composition, with distinct dual-valley absorption features observed between 8 and 9.5 µm across different soil types, highlighting spectral variability. The 9.6 µm LSE exhibits strong sensitivity to crop growth dynamics, characterized by pronounced absorption valleys linked to vegetation biochemical properties. Beyond soil composition, LSE is significantly influenced by soil moisture, temperature, and vegetation coverage, emphasizing the need for multi-factor parameterization. LSE demonstrates typical diurnal variations, with an amplitude reaching an order of magnitude of 0.01, driven by thermal inertia and environmental interactions. A diurnal LSE retrieval model, integrating time-averaged LSE and diurnal perturbations, was developed based on underlying surface characteristics. This model reduced the root mean square error (RMSE) of LST retrieved from geostationary satellites from 6.02 °C to 2.97 °C, significantly enhancing retrieval accuracy. These findings deepen the understanding of LSE characteristics and provide a scientific basis for refining LST/LSE separation algorithms in thermal infrared remote sensing and for optimizing LSE parameterization schemes in land surface process models for climate and hydrological simulations. Full article

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

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24 pages, 32355 KiB

Open AccessArticle

Evaluating UAV LiDAR and Field Spectroscopy for Estimating Residual Dry Matter Across Conservation Grazing Lands

by Bruce Markman, H. Scott Butterfield, Janet Franklin, Lloyd Coulter, Moses Katkowski and Daniel Sousa

Remote Sens. 2025, 17(14), 2352; https://doi.org/10.3390/rs17142352 (registering DOI) - 9 Jul 2025

Abstract

Residual dry matter (RDM) is a term used in rangeland management to describe the non-photosynthetic plant material left on the soil surface at the end of the growing season. RDM measurements are used by agencies and conservation entities for managing grazing and fire [...] Read more.

Residual dry matter (RDM) is a term used in rangeland management to describe the non-photosynthetic plant material left on the soil surface at the end of the growing season. RDM measurements are used by agencies and conservation entities for managing grazing and fire fuels. Measuring the RDM using traditional methods is labor-intensive, costly, and subjective, making consistent sampling challenging. Previous studies have assessed the use of multispectral remote sensing to estimate the RDM, but with limited success across space and time. The existing approaches may be improved through the use of spectroscopic (hyperspectral) sensors, capable of capturing the cellulose and lignin present in dry grass, as well as Unmanned Aerial Vehicle (UAV)-mounted Light Detection and Ranging (LiDAR) sensors, capable of capturing centimeter-scale 3D vegetation structures. Here, we evaluate the relationships between the RDM and spectral and LiDAR data across the Jack and Laura Dangermond Preserve (Santa Barbara County, CA, USA), which uses grazing and prescribed fire for rangeland management. The spectral indices did not correlate with the RDM (R² < 0.1), likely due to complete areal coverage with dense grass. The LiDAR canopy height models performed better for all the samples (R² = 0.37), with much stronger performance (R² = 0.81) when using a stratified model to predict the RDM in plots with predominantly standing (as opposed to laying) vegetation. This study demonstrates the potential of UAV LiDAR for direct RDM quantification where vegetation is standing upright, which could help improve RDM mapping and management for rangelands in California and beyond. Full article

(This article belongs to the Special Issue Integration of Remote Sensing and GIS to Forest and Grassland Ecosystem Monitoring)

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21 pages, 4409 KiB

Open AccessArticle

Differences in Time Comparison and Positioning of BDS-3 PPP-B2b Signal Broadcast Through GEO

by Hongjiao Ma, Jinming Yang, Xiaolong Guan, Jianfeng Wu and Huabing Wu

Remote Sens. 2025, 17(14), 2351; https://doi.org/10.3390/rs17142351 (registering DOI) - 9 Jul 2025

Abstract

The BeiDou-3 Navigation Satellite System (BDS-3) precise point positioning (PPP) service through the B2b signal (PPP-B2b) leverages precise correction data disseminated by satellites to eliminate or mitigate key error sources, including satellite orbit errors, clock biases, and ionospheric delays, thereby enabling high-precision timing [...] Read more.

The BeiDou-3 Navigation Satellite System (BDS-3) precise point positioning (PPP) service through the B2b signal (PPP-B2b) leverages precise correction data disseminated by satellites to eliminate or mitigate key error sources, including satellite orbit errors, clock biases, and ionospheric delays, thereby enabling high-precision timing and positioning. This paper investigates the disparities in time comparison and positioning capabilities associated with the PPP-B2b signals transmitted by the BDS-3 Geostationary Earth Orbit (GEO) satellites (C59 and C61). Three stations in the Asia–Pacific region were selected to establish two time comparison links. The study evaluated the time transfer accuracy of PPP-B2b signals by analyzing orbit and clock corrections from BDS-3 GEO satellites C59 and C61. Using multi-GNSS final products (GBM post-ephemeris) as a reference, the performance of PPP-B2b-based time comparison was assessed. The results indicate that while both satellites achieve comparable time transfer accuracy, C59 demonstrates superior stability and availability compared to C61. Additionally, five stations from the International GNSS Service (IGS) and the International GNSS Monitoring and Assessment System (iGMAS) were selected to assess the positioning accuracy of PPP-B2b corrections transmitted by BDS-3 GEO satellites C59 and C61. Using IGS/iGMAS weekly solution positioning results as a reference, the analysis demonstrates that PPP-B2b enables centimeter-level static positioning and decimeter-level simulated kinematic positioning. Furthermore, C59 achieves higher positioning accuracy than C61. Full article

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21 pages, 4829 KiB

Open AccessArticle

Quantification of MODIS Land Surface Temperature Downscaled by Machine Learning Algorithms

by Qi Su, Xiangchen Meng, Lin Sun and Zhongqiang Guo

Remote Sens. 2025, 17(14), 2350; https://doi.org/10.3390/rs17142350 (registering DOI) - 9 Jul 2025

Abstract

Land Surface Temperature (LST) is essential for understanding the interactions between the land surface and the atmosphere. This study presents a comprehensive evaluation of machine learning (ML)-based downscaling algorithms to enhance the spatial resolution of MODIS LST data from 960 m to 30 [...] Read more.

Land Surface Temperature (LST) is essential for understanding the interactions between the land surface and the atmosphere. This study presents a comprehensive evaluation of machine learning (ML)-based downscaling algorithms to enhance the spatial resolution of MODIS LST data from 960 m to 30 m, leveraging auxiliary variables including vegetation indices, terrain parameters, and land surface reflectance. By establishing non-linear relationships between LST and predictive variables through eXtreme Gradient Boosting (XGBoost) and Random Forest (RF) algorithms, the proposed framework was rigorously validated using in situ measurements across China’s Heihe River Basin. Comparative analyses demonstrated that integrating multiple vegetation indices (e.g., NDVI, SAVI) with terrain factors yielded superior accuracy compared to factors utilizing land surface reflectance or excessive variable combinations. While slope and aspect parameters marginally improved accuracy in mountainous regions, including them degraded performance in flat terrain. Notably, land surface reflectance proved to be ineffective in snow/ice-covered areas, highlighting the need for specialized treatment in cryospheric environments. This work provides a reference for LST downscaling, with significant implications for environmental monitoring and urban heat island investigations. Full article

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

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23 pages, 4200 KiB

Open AccessArticle

Thermal Multi-Sensor Assessment of the Spatial Sampling Behavior of Urban Landscapes Using 2D Turbulence Indicators

by Gabriel I. Cotlier, Drazen Skokovic, Juan Carlos Jimenez and José Antonio Sobrino

Remote Sens. 2025, 17(14), 2349; https://doi.org/10.3390/rs17142349 (registering DOI) - 9 Jul 2025

Abstract

Understanding spatial variations in land surface temperature (LST) is critical for analyzing urban climate dynamics, especially within the framework of two-dimensional (2D) turbulence theory. This study assesses the spatial sampling behavior of urban thermal fields across eight metropolitan areas, encompassing diverse morphologies, surface [...] Read more.

Understanding spatial variations in land surface temperature (LST) is critical for analyzing urban climate dynamics, especially within the framework of two-dimensional (2D) turbulence theory. This study assesses the spatial sampling behavior of urban thermal fields across eight metropolitan areas, encompassing diverse morphologies, surface materials, and Köppen–Geiger climate zones. We analyzed thermal infrared (TIR) imagery from two remote sensing platforms—MODIS (1 km) and Landsat (30 m)—to evaluate resolution-dependent turbulence indicators such as spectral slopes and breakpoints. Power spectral analysis revealed systematic divergences across spatial scales. Landsat exhibited more negative breakpoint values, indicating a greater ability to capture fine-scale thermal heterogeneity tied to vegetation, buildings, and surface cover. MODIS, in contrast, emphasized broader thermal gradients, suitable for regional-scale assessments. Seasonal differences reinforced the turbulence framework: summer spectra displayed steeper, more variable slopes, reflecting increased thermal activity and surface–atmosphere decoupling. Despite occasional agreement between sensors, spectral metrics remain inherently resolution-dependent. MODIS is better suited for macro-scale thermal structures, while Landsat provides detailed insights into intra-urban processes. Our findings confirm that 2D turbulence indicators are not fully scale-invariant and vary with sensor resolution, season, and urban form. This multi-sensor comparison offers a framework for interpreting LST data in support of climate adaptation, urban design, and remote sensing integration. Full article

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