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Hazard Susceptibility Mapping with Machine and Deep Learning: A Literature Review
Bio-Optical Variability of W-Iberian Coastal Waters
Spectroscopic Phenological Characterization of Mangrove Communities
Automatic Mapping of Flooded Areas Through Sentinel-2 Data: RST-FLOOD for the Emilia Romagna (Italy) 2023 Event
Global Trends in Soil Organic Carbon Estimation: Remote Sensing and Machine Learning Synergies Explored via BERTopic Model

Journal Description

Remote Sensing

Remote Sensing is an international, peer-reviewed, open access journal about the science and application of remote sensing technology, and is published semimonthly online by MDPI. The Remote Sensing Society of Japan (RSSJ) and the Japan Society of Photogrammetry and Remote Sensing (JSPRS) are affiliated with Remote Sensing, and their members receive a discount on the article processing charge.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, SCIE (Web of Science), Ei Compendex, PubAg, GeoRef, Astrophysics Data System, Inspec, dblp, and other databases.
Journal Rank: JCR - Q1 (Geosciences, Multidisciplinary) / CiteScore - Q1 (General Earth and Planetary Sciences)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 24.7 days after submission; acceptance to publication is undertaken in 2.8 days (median values for papers published in this journal in the first half of 2024).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Companion journal: Geomatics

Impact Factor: 4.2 (2023); 5-Year Impact Factor: 4.9 (2023)

Imprint Information Journal Flyer Open Access ISSN: 2072-4292

Latest Articles

20 pages, 2331 KiB

Open AccessArticle

Reconstruction of Hourly Gap-Free Sea Surface Skin Temperature from Multi-Sensors

by Qianguang Tu, Zengzhou Hao, Dong Liu, Bangyi Tao, Liangliang Shi and Yunwei Yan

Remote Sens. 2024, 16(22), 4268; https://doi.org/10.3390/rs16224268 (registering DOI) - 15 Nov 2024

The sea surface skin temperature (SST_skin) is of critical importance with regard to air–sea interactions and marine carbon circulation. At present, no single remote sensor is capable of providing a gap-free SST_skin. The use of data fusion techniques is [...] Read more.

The sea surface skin temperature (SST_skin) is of critical importance with regard to air–sea interactions and marine carbon circulation. At present, no single remote sensor is capable of providing a gap-free SST_skin. The use of data fusion techniques is therefore essential for the purpose of filling these gaps. The extant fusion methodologies frequently fail to account for the influence of depth disparities and the diurnal variability of sea surface temperatures (SSTs) retrieved from multi-sensors. We have developed a novel approach that integrates depth and diurnal corrections and employs advanced data fusion techniques to generate hourly gap-free SST datasets. The General Ocean Turbulence Model (GOTM) is employed to model the diurnal variability of the SST profile, incorporating depth and diurnal corrections. Subsequently, the corrected SSTs at the same observed time and depth are blended using the Markov method and the remaining data gaps are filled with optimal interpolation. The overall precision of the hourly gap-free SST_skin generated demonstrates a mean bias of −0.14 °C and a root mean square error of 0.57 °C, which is comparable to the precision of satellite observations. The hourly gap-free SST_skin is vital for improving our comprehension of air–sea interactions and monitoring critical oceanographic processes with high-frequency variability. Full article

(This article belongs to the Special Issue Advances in Retrieval, Operationalization, Monitoring and Application of Sea Surface Temperature III)

17 pages, 9635 KiB

Open AccessArticle

Damage Detection and Segmentation in Disaster Environments Using Combined YOLO and Deeplab

by So-Hyeon Jo, Joo Woo, Chang Ho Kang and Sun Young Kim

Remote Sens. 2024, 16(22), 4267; https://doi.org/10.3390/rs16224267 (registering DOI) - 15 Nov 2024

Building damage due to various causes occurs frequently and has risk factors that can cause additional collapses. However, it is difficult to accurately identify objects in complex structural sites because of inaccessible situations and image noise. In conventional approaches, close-up images have been [...] Read more.

Building damage due to various causes occurs frequently and has risk factors that can cause additional collapses. However, it is difficult to accurately identify objects in complex structural sites because of inaccessible situations and image noise. In conventional approaches, close-up images have been used to detect and segment damage images such as cracks. In this study, the method of using a deep learning model is proposed for the rapid determination and analysis of multiple damage types, such as cracks and concrete rubble, in disaster sites. Through the proposed method, it is possible to perform analysis by receiving image information from a robot explorer instead of a human, and it is possible to detect and segment damage information even when the damaged point is photographed at a distance. To accomplish this goal, damage information is detected and segmented using YOLOv7 and Deeplabv2. Damage information is quickly detected through YOLOv7, and semantic segmentation is performed using Deeplabv2 based on the bounding box information obtained through YOLOv7. By using images with various resolutions and senses of distance for training, damage information can be effectively detected not only at short distances but also at long distances. When comparing the results, depending on how YOLOv7 and Deeplabv2 were used, they returned better scores than the comparison model, with a Recall of 0.731, Precision of 0.843, F1 of 0.770, and mIoU of 0.638, and had the lowest standard deviation. Full article

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19 pages, 3200 KiB

Open AccessArticle

High-Resolution Remotely Sensed Evidence Shows Solar Thermal Power Plant Increases Grassland Growth on the Tibetan Plateau

by Naijing Liu, Huaiwu Peng, Zhenshi Zhang, Yujin Li, Kai Zhang, Yuehan Guo, Yuzheng Cui, Yingsha Jiang, Wenxiang Gao and Donghai Wu

Remote Sens. 2024, 16(22), 4266; https://doi.org/10.3390/rs16224266 (registering DOI) - 15 Nov 2024

Solar energy plays a crucial role in mitigating greenhouse gas emissions in the context of global climate change. However, its deployment for green electricity generation can significantly influence regional climate and vegetation dynamics. While prior studies have examined the impacts of solar power [...] Read more.

Solar energy plays a crucial role in mitigating greenhouse gas emissions in the context of global climate change. However, its deployment for green electricity generation can significantly influence regional climate and vegetation dynamics. While prior studies have examined the impacts of solar power plants on vegetation, the accuracy of these assessments has often been constrained by the availability of publicly accessible multispectral, high-resolution remotely sensed imagery. Given the abundant solar energy resources and the ecological significance of the Tibetan Plateau, a thorough evaluation of the vegetation effects associated with solar power installations is warranted. In this study, we utilize sub-meter resolution imagery from the GF-2 satellite to reconstruct the fractional vegetation cover (FVC) at the Gonghe solar thermal power plant through image classification, in situ sampling, and sliding window techniques. We then quantify the plant’s impact on FVC by comparing data from the pre-installation and post-installation periods. Our findings indicate that the Gonghe solar thermal power plant is associated with a 0.02 increase in FVC compared to a surrounding control region (p < 0.05), representing a 12.5% increase relative to the pre-installation period. Notably, the enhancement in FVC is more pronounced in the outer ring areas than near the central tower. The observed enhancement in vegetation growth at the Gonghe plant suggests potential ecological and carbon storage benefits resulting from solar power plant establishment on the Tibetan Plateau. These findings underscore the necessity of evaluating the climate and ecological impacts of renewable energy facilities during the planning and design phases to ensure a harmonious balance between clean energy development and local ecological integrity. Full article

(This article belongs to the Special Issue Remote Sensing of Mountain and Plateau Vegetation)

27 pages, 8254 KiB

Open AccessArticle

Small Object Detection in UAV Remote Sensing Images Based on Intra-Group Multi-Scale Fusion Attention and Adaptive Weighted Feature Fusion Mechanism

by Zhe Yuan, Jianglei Gong, Baolong Guo, Chao Wang, Nannan Liao, Jiawei Song and Qiming Wu

Remote Sens. 2024, 16(22), 4265; https://doi.org/10.3390/rs16224265 (registering DOI) - 15 Nov 2024

In view of the issues of missed and false detections encountered in small object detection for UAV remote sensing images, and the inadequacy of existing algorithms in terms of complexity and generalization ability, we propose a small object detection model named IA-YOLOv8 in [...] Read more.

In view of the issues of missed and false detections encountered in small object detection for UAV remote sensing images, and the inadequacy of existing algorithms in terms of complexity and generalization ability, we propose a small object detection model named IA-YOLOv8 in this paper. This model integrates the intra-group multi-scale fusion attention mechanism and the adaptive weighted feature fusion approach. In the feature extraction phase, the model employs a hybrid pooling strategy that combines Avg and Max pooling to replace the single Max pooling operation used in the original SPPF framework. Such modifications enhance the model’s ability to capture the minute features of small objects. In addition, an adaptive feature fusion module is introduced, which is capable of automatically adjusting the weights based on the significance and contribution of features at different scales to improve the detection sensitivity for small objects. Simultaneously, a lightweight intra-group multi-scale fusion attention module is implemented, which aims to effectively mitigate background interference and enhance the saliency of small objects. Experimental results indicate that the proposed IA-YOLOv8 model has a parameter quantity of 10.9 MB, attaining an average precision (mAP) value of 42.1% on the Visdrone2019 test set, an mAP value of 82.3% on the DIOR test set, and an mAP value of 39.8% on the AI-TOD test set. All these results outperform the existing detection algorithms, demonstrating the superior performance of the IA-YOLOv8 model in the task of small object detection for UAV remote sensing. Full article

(This article belongs to the Special Issue Remote Sensing Image Classification and Semantic Segmentation (Second Edition))

11 pages, 11841 KiB

Open AccessArticle

Deep Learning Model Size Performance Evaluation for Lightning Whistler Detection on Arase Satellite Dataset

by I Made Agus Dwi Suarjaya, Desy Purnami Singgih Putri, Yuji Tanaka, Fajar Purnama, I Putu Agung Bayupati, Linawati, Yoshiya Kasahara, Shoya Matsuda, Yoshizumi Miyoshi and Iku Shinohara

Remote Sens. 2024, 16(22), 4264; https://doi.org/10.3390/rs16224264 (registering DOI) - 15 Nov 2024

The plasmasphere within Earth’s magnetosphere plays a crucial role in space physics, with its electron density distribution being pivotal and strongly influenced by solar activity. Very Low Frequency (VLF) waves, including whistlers, provide valuable insights into this distribution, making the study of their [...] Read more.

The plasmasphere within Earth’s magnetosphere plays a crucial role in space physics, with its electron density distribution being pivotal and strongly influenced by solar activity. Very Low Frequency (VLF) waves, including whistlers, provide valuable insights into this distribution, making the study of their propagation through the plasmasphere essential for predicting space weather impacts on various technologies. In this study, we evaluate the performance of different deep learning model sizes for lightning whistler detection using the YOLO (You Only Look Once) architecture. To achieve this, we transformed the entirety of raw data from the Arase (ERG) Satellite for August 2017 into 2736 images, which were then used to train the models. Our approach involves exposing the models to spectrogram diagrams—visual representations of the frequency content of signals—derived from the Arase Satellite’s WFC (WaveForm Capture) subsystem, with a focus on analyzing whistler-mode plasma waves. We experimented with various model sizes, adjusting epochs, and conducted performance analysis using a partial set of labeled data. The testing phase confirmed the effectiveness of the models, with YOLOv5n emerging as the optimal choice due to its compact size (3.7 MB) and impressive detection speed, making it suitable for resource-constrained applications. Despite challenges such as image quality and the detection of smaller whistlers, YOLOv5n demonstrated commendable accuracy in identifying scenarios with simple shapes, thereby contributing to a deeper understanding of whistlers’ impact on Earth’s magnetosphere and fulfilling the core objectives of this study. Full article

(This article belongs to the Special Issue Artificial Intelligence Algorithm for Remote Sensing Imagery Processing (4th Edition))

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22 pages, 20030 KiB

Open AccessArticle

Methodology for Object-Level Change Detection in Post-Earthquake Building Damage Assessment Based on Remote Sensing Images: OCD-BDA

by Zhengtao Xie, Zifan Zhou, Xinhao He, Yuguang Fu, Jiancheng Gu and Jiandong Zhang

Remote Sens. 2024, 16(22), 4263; https://doi.org/10.3390/rs16224263 (registering DOI) - 15 Nov 2024

Remote sensing and computer vision technologies are increasingly leveraged for rapid post-disaster building damage assessment, becoming a crucial and practical approach. In this context, the accuracy of employing various AI models in pixel-level change detection methods is significantly dependent on the consistency between [...] Read more.

Remote sensing and computer vision technologies are increasingly leveraged for rapid post-disaster building damage assessment, becoming a crucial and practical approach. In this context, the accuracy of employing various AI models in pixel-level change detection methods is significantly dependent on the consistency between pre- and post-disaster building images, particularly regarding variations in resolution, viewing angle, and lighting conditions; in object-level feature recognition methods, the low richness of semantic details of damaged buildings in images leads to a poor detection accuracy. This paper proposes a novel method, OCD-BDA (Object-Level Change Detection for Post-Disaster Building Damage Assessment), as an alternative to pixel-level change detection and object-level feature recognition methods. Inspired by human cognitive processes, this method incorporates three key steps: an efficient sample acquisition for object localization, labeling via HGC (Hierarchical and Gaussian Clustering), and model training and prediction for classification. Furthermore, this study establishes a change detection dataset based on Google Earth imagery of regions in Hatay Province before and after the Turkish earthquake. This dataset is characterized by pixel inconsistency and significant differences in photographic angles and lighting conditions between pre- and post-disaster images, making it a valuable test dataset for other studies. As a result, in the experiments of comparative generalization capabilities, OCD-BDA demonstrated a significant improvement, achieving an accuracy of 71%, which is twice that of the second-ranking method. Moreover, OCD-BDA exhibits superior performance in tasks with small sample amounts and rapid training time. With only 1% of the training samples, it achieves a prediction accuracy exceeding that of traditional transfer learning methods with 60% of samples. Additionally, it completes assessments across a large disaster area (450 km²) with 93% accuracy in under 23 min. Full article

(This article belongs to the Section AI Remote Sensing)

16 pages, 17000 KiB

Open AccessTechnical Note

Quasi-Linear Convective Systems in Catalonia Detected Through Radar and Lightning Data

by Tomeu Rigo and Carme Farnell

Remote Sens. 2024, 16(22), 4262; https://doi.org/10.3390/rs16224262 - 15 Nov 2024

Quasi-Linear Convective Systems (QLCSs) are a type of Mesoscale Convective System characterised by their linear shape and association with severe weather phenomena (such as hail, tornadoes, or wind gusts). This study deals with the application of a technique that consists of combinations of [...] Read more.

Quasi-Linear Convective Systems (QLCSs) are a type of Mesoscale Convective System characterised by their linear shape and association with severe weather phenomena (such as hail, tornadoes, or wind gusts). This study deals with the application of a technique that consists of combinations of radar and lightning data to identify QLCS in Catalonia (the northeast region of the Iberian Peninsula) and the surrounding areas. Even with the limitation of reduced coverage, the technique has revealed efficiency in identifying the systems affecting the region of interest. Concretely, we have detected twenty cases for 2013–2023, significantly less than for other parts of Central Europe but similar to the global values for the whole continent and the United States of America. All cases occurred during the warm season and are divided into diurnal and nocturnal cases with different behaviours. Full article

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22 pages, 10867 KiB

Open AccessArticle

Modeling the Land Surface Phenological Responses of Dominant Miombo Tree Species to Climate Variability in Western Tanzania

by Siwa E. Nkya, Deo D. Shirima, Robert N. Masolele, Henrik Hedenas and August B. Temu

Remote Sens. 2024, 16(22), 4261; https://doi.org/10.3390/rs16224261 - 15 Nov 2024

Species-level phenology models are essential for predicting shifts in tree species under climate change. This study quantified phenological differences among dominant miombo tree species and modeled seasonal variability using climate variables. We used TIMESAT version 3.3 software and the Savitzky–Golay filter to derive [...] Read more.

Species-level phenology models are essential for predicting shifts in tree species under climate change. This study quantified phenological differences among dominant miombo tree species and modeled seasonal variability using climate variables. We used TIMESAT version 3.3 software and the Savitzky–Golay filter to derive phenology metrics from bi-monthly PlanetScope Normalized Difference Vegetation Index (NDVI) data from 2017 to 2024. A repeated measures Analysis of Variance (ANOVA) assessed differences in phenology metrics between species, while a regression analysis modeled the Start of Season (SOS) and End of Season (EOS). The results show significant seasonal and species-level variations in phenology. Brachystegia spiciformis differed from other species in EOS, Length of Season (LOS), base value, and peak value. Surface solar radiation and skin temperature one month before SOS were key predictors of SOS, with an adjusted R-squared of 0.90 and a Root Mean Square Error (RMSE) of 13.47 for Brachystegia spiciformis. SOS also strongly predicted EOS, with an adjusted R-squared of 1 and an RMSE of 3.01 for Brachystegia spiciformis, indicating a shift in the growth cycle of tree species due to seasonal variability. These models provide valuable insights into potential phenological shifts in miombo species due to climate change. Full article

(This article belongs to the Special Issue Advances in Detecting and Understanding Land Surface Phenology)

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21 pages, 14998 KiB

Open AccessArticle

Anti-Maneuvering Repeater Jamming Using Up- and Down-Chirp Modulation in Spaceborne Synthetic Aperture Radar

by Yu Sha, Xiaoxiao Feng, Tingcun Wei, Jiang Du and Weidong Yu

Remote Sens. 2024, 16(22), 4260; https://doi.org/10.3390/rs16224260 - 15 Nov 2024

With the continuous development of synthetic aperture radar (SAR) jamming technology, low-power maneuvering repeater jammers are now flexible and can be located on multiple unmanned aerial vehicles (UAVs) and unmanned vehicles (UVs) at the same time, which greatly increases the difficulty of the [...] Read more.

With the continuous development of synthetic aperture radar (SAR) jamming technology, low-power maneuvering repeater jammers are now flexible and can be located on multiple unmanned aerial vehicles (UAVs) and unmanned vehicles (UVs) at the same time, which greatly increases the difficulty of the anti-maneuvering repeater jamming method for spaceborne SAR. Due to the low-power transmission, the locations of the low-power repeater jammers and the protected areas in the imaged swath are relatively close in distance, while the transmission delay of the jamming is approximately equal to the pulse repetition interval (PRI). According to this phenomenon, an anti-maneuvering repeater jamming method using up- and down-chirp modulation is proposed in this paper. After alternately transmitting up- and down-chirp modulation signals, echoes of the jamming and the protected area are recorded in the same location within the echo-receiving window and are related to different chirp modulations. To remove the jamming echoes, de-chirping and frequency filtering are adopted after echo data segmentation. With jamming interference removal using frequency notch filtering, parts of the spectra corresponding to the desired echoes of the imaged swath are simultaneously removed. To recover the unwanted removed range spectra, linear prediction is introduced to improve the focusing quality. Finally, simulation results on both point and distributed targets validate the proposed anti-maneuvering repeater jamming method by using up- and down-chirp modulation. Full article

(This article belongs to the Section Satellite Missions for Earth and Planetary Exploration)

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16 pages, 6021 KiB

Open AccessTechnical Note

The Spacecraft Parabolic Antenna Payload Orientation Estimation Method Based on the Step Effect of Measured Radar Cross Section Sequences

by Junzhi Li and Xin Ning

Remote Sens. 2024, 16(22), 4259; https://doi.org/10.3390/rs16224259 - 15 Nov 2024

The analysis and processing of active radar image information is an important method for determining the payload orientation of non-cooperative targets. However, a challenge for spacecraft carrying parabolic antenna payloads is that active radar imaging is susceptible to defocus, making it difficult to [...] Read more.

The analysis and processing of active radar image information is an important method for determining the payload orientation of non-cooperative targets. However, a challenge for spacecraft carrying parabolic antenna payloads is that active radar imaging is susceptible to defocus, making it difficult to achieve a reliable estimate of the orientation of such payloads. As such, this paper proposes a method for estimating the orientation of spacecraft parabolic antenna payloads based on radar-measured radar cross section (RCS) sequence data. By utilizing the step effect produced when the ground-based radar observes the parabolic antenna payload, the mathematical model for estimating the orientation of the parabolic antenna payload is established through the analysis of the geometric relationship between the radar observation vector and the antenna payload orientation in the orbital coordinate system. This method employs an optimized model and particle swarm optimization to achieve the pointing estimation of the parabolic antenna payload. The simulation results demonstrate that the proposed algorithm can accurately estimate the pointing direction of the antenna payload, with the maximum error being better than three degrees, and it has good robustness. The results of real data processing further verify the effectiveness of the algorithm. Full article

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21 pages, 9120 KiB

Open AccessArticle

Differentiating Cheatgrass and Medusahead Phenological Characteristics in Western United States Rangelands

by Trenton D. Benedict, Stephen P. Boyte and Devendra Dahal

Remote Sens. 2024, 16(22), 4258; https://doi.org/10.3390/rs16224258 - 15 Nov 2024

Expansions in the extent and infestation levels of exotic annual grass (EAG) within the rangelands of the western United States are well documented. Land managers are tasked with developing plans to limit EAG spread and prevent irreversible ecosystem deterioration. The most common EAG [...] Read more.

Expansions in the extent and infestation levels of exotic annual grass (EAG) within the rangelands of the western United States are well documented. Land managers are tasked with developing plans to limit EAG spread and prevent irreversible ecosystem deterioration. The most common EAG species and the subject of extensive study is Bromus tectorum (cheatgrass). Cheatgrass has spread rapidly in western rangelands since its initial invasion more than 100 years ago. Another concerning aggressive EAG, Taeniatherum caput-medusae (medusahead), is also commonly found in some of these areas. To control the spread of EAGs, researchers have investigated applying several control methods during different developmental stages of cheatgrass and medusahead. These control strategies require accurate maps of the timing and spatial patterns of the developmental stages to apply mitigation strategies in the correct areas at the right time. In this study, we developed annual phenological datasets for cheatgrass and medusahead with two objectives. The first objective was to determine if cheatgrass and medusahead can be differentiated at 30 m resolution using their phenological differences. The second objective was to establish an annual phenology metric regression tree model used to map the growing seasons of cheatgrass and medusahead. Harmonized Landsat and Sentinel-2 (HLS)-derived predicted weekly cloud-free 30 m normalized difference vegetation index (NDVI) images were used to develop these metric maps. The result of this effort was maps that identify the start and end of sustained growing season time for cheatgrass and medusahead at 30 m for the Snake River Plain and Northern Basin and Range ecoregions. These phenological datasets also identify the start and end-of-season NDVI values, along with maximum NDVI throughout the study period. These metrics may be utilized to characterize annual growth patterns for cheatgrass and medusahead. This approach can be utilized to plan time-sensitive control measures such as herbicide applications or cattle grazing. Full article

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15 pages, 6823 KiB

Open AccessTechnical Note

Investigating Tropical Cyclone Warm Core and Boundary Layer Structures with Constellation Observing System for Meteorology, Ionosphere, and Climate 2 Radio Occultation Data

by Xiaoxu Qi, Shengpeng Yang and Li He

Remote Sens. 2024, 16(22), 4257; https://doi.org/10.3390/rs16224257 - 15 Nov 2024

The Constellation Observing System for Meteorology, Ionosphere, and Climate 2 (COSMIC-2) collects data covering latitudes primarily between 40 degrees north and south, providing abundant data for tropical cyclone (TC) research. The radio occultation data provide valuable information on the boundary layer. However, quality [...] Read more.

The Constellation Observing System for Meteorology, Ionosphere, and Climate 2 (COSMIC-2) collects data covering latitudes primarily between 40 degrees north and south, providing abundant data for tropical cyclone (TC) research. The radio occultation data provide valuable information on the boundary layer. However, quality control of the data within the boundary layer remains a challenging issue. The aim of this study is to obtain a more accurate COSMIC-2 radio occultation (RO) dataset through quality control (QC) and use this dataset to validate warm core structures and explore the planetary boundary layer (PBL) structures of TCs. In this study, COSMIC-2 data are used to analyze the distribution of the relative local spectral width (LSW) and the confidence parameter characterizing the random error of the bending angle. An LSW less than 20% is set as a data QC threshold, and the warm core and PBL composite structures of TCs at three intensities in the Northwest Pacific Ocean are investigated. We reproduce the warm core intensity and warm core height characteristics of TCs. In the radial direction of the typhoon eyewall, the impact height of the PBL increases from 3.45 km to 4 km, with the tropopause ranging from 160 hPa to 100 hPa. At the bottom of the troposphere, the variations in the positive and negative bias between the RO-detected and background field bending angles correspond well to the PBL heights, and the variations in the positive bias between the RO-detected and background field refractivity reach 14%. This research provides an effective QC method and reveals that the bending angle is sensitive to the PBL height. Full article

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25 pages, 2899 KiB

Open AccessArticle

Learning Omni-Dimensional Spatio-Temporal Dependencies for Millimeter-Wave Radar Perception

by Hang Yan, Yongji Li, Luping Wang and Shichao Chen

Remote Sens. 2024, 16(22), 4256; https://doi.org/10.3390/rs16224256 - 15 Nov 2024

Reliable environmental perception capabilities are a prerequisite for achieving autonomous driving. Cameras and LiDAR are sensitive to illumination and weather conditions, while millimeter-wave radar avoids these issues. Existing models rely heavily on image-based approaches, which may not be able to fully characterize radar [...] Read more.

Reliable environmental perception capabilities are a prerequisite for achieving autonomous driving. Cameras and LiDAR are sensitive to illumination and weather conditions, while millimeter-wave radar avoids these issues. Existing models rely heavily on image-based approaches, which may not be able to fully characterize radar sensor data or efficiently further utilize them for perception tasks. This paper rethinks the approach to modeling radar signals and proposes a novel U-shaped multilayer perceptron network (U-MLPNet) that aims to enhance the learning of omni-dimensional spatio-temporal dependencies. Our method involves innovative signal processing techniques, including a 3D CNN for spatio-temporal feature extraction and an encoder–decoder framework with cross-shaped receptive fields specifically designed to capture the sparse and non-uniform characteristics of radar signals. We conducted extensive experiments using a diverse dataset of urban driving scenarios to characterize the sensor’s performance in multi-view semantic segmentation and object detection tasks. Experiments showed that U-MLPNet achieves competitive performance against state-of-the-art (SOTA) methods, improving the mAP by 3.0% and mDice by 2.7% in RD segmentation and AR and AP by 1.77% and 2.03%, respectively, in object detection. These improvements signify an advancement in radar-based perception for autonomous vehicles, potentially enhancing their reliability and safety across diverse driving conditions. Full article

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24 pages, 18018 KiB

Open AccessArticle

Analysis of Land Surface Performance Differences and Uncertainty in Multiple Versions of MODIS LST Products

by Ruoyi Zhao, Wenping Yu, Xiangyi Deng, Yajun Huang, Wen Yang and Wei Zhou

Remote Sens. 2024, 16(22), 4255; https://doi.org/10.3390/rs16224255 - 15 Nov 2024

Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) products are essential data sources for global and regional climate change research. Currently, several versions of the MODIS LST product have been released, yet the performance differences and uncertainties they introduce in land surface [...] Read more.

Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) products are essential data sources for global and regional climate change research. Currently, several versions of the MODIS LST product have been released, yet the performance differences and uncertainties they introduce in land surface studies remain insufficiently addressed. To bridge this gap, this study focuses on four distinct versions of the LST product: MxD11A1 Collection 5 (C5), Collection 6 (C6), Collection 6.1 (C6.1), and MxD21A1 Collection 6.1 (MxD21). The spatial resolution of all product generations is 1 km, and the temporal resolution is 0.5 days. This study provides a comprehensive analysis of the errors arising from different generations of these products in various land surface process studies. The error assessment includes cross-comparisons between product versions and evaluations of the absolute errors generated. Absolute errors in evaluation data were collected from 13 surface sites within the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) project during the period 2013–2018. Cross-validation results show that the largest difference between C5 and C6.1 occurs over bare land, with an RMSE of approximately 1.45 K, while there is no significant change between C6 and C6.1. MOD21 shows considerable variation compared to C6.1 at night across different land cover types, with RMSE over cropland exceeding 2 K. The temperature difference between MOD21 and C6.1 is more pronounced at night (2.01 K) than during the day (0.30 K). Validation results based on temperature indicate that C5 has greater uncertainty compared to C6, especially over bare land, where errors are 2.06 K and 1.06 K, respectively. Furthermore, MxD21 demonstrates significant day–night performance discrepancies, with an average bias of 0.10 K at night, while daytime errors over bare land can reach 2 K, potentially influenced by atmospheric conditions. Based on the research in this paper, it is possible to clarify the performance of different versions of MODIS products, reflecting the appropriateness of their past applications; on the other hand, it is recommended to prioritize the use of the MxD11A1 C6 and C6.1 products for monitoring and applications in bare soil areas to ensure higher accuracy. Furthermore, for day and night monitoring, it may be beneficial to alternate between the MxD11A1 and MxD21A1 products to fully leverage their respective advantages and enhance overall monitoring effectiveness. Full article

(This article belongs to the Special Issue Remote Sensing for Land Surface Temperature and Related Applications)

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23 pages, 22588 KiB

Open AccessArticle

Monitoring Dissolved Organic Carbon Concentration and Flux in the Qiantang Riverine System Using Sentinel-2 Satellite Images

by Yujia Yan, Xianqiang He, Yan Bai, Jinsong Liu, Palanisamy Shanmugame, Yaqi Zhao, Xuan Zhang, Zhihong Wang, Yifan Zhang and Fang Gong

Remote Sens. 2024, 16(22), 4254; https://doi.org/10.3390/rs16224254 - 15 Nov 2024

Real-time monitoring of riverine-dissolved organic carbon (DOC) and its controlling factors is critical for formulating strategies regarding the river basin and marginal seas pollution prevention and control. In this study, we established a linear regression formulation that relates the permanganate index (COD_Mn [...] Read more.

Real-time monitoring of riverine-dissolved organic carbon (DOC) and its controlling factors is critical for formulating strategies regarding the river basin and marginal seas pollution prevention and control. In this study, we established a linear regression formulation that relates the permanganate index (COD_Mn) to the DOC concentration based on in situ measurements collected on five field surveys in 2023–2024. This regression formulation was used on a large number of data collected from automatic monitoring stations in the Qiantang River area to construct a daily quasi-in situ database of DOC concentration. By combining the quasi-in situ DOC data and Sentinel-2 measurements, an enhanced algorithm for empirical DOC estimation was developed (R² = 0.66) using the extreme gradient boosting (XGBoost) method and its spatial and temporal variations in the Qiantang River were analyzed from 2016 to 2023. Spatially, the main stream of the Qiantang River exhibited an overall decreasing and increasing trend influenced by population density, economic development, and pollutant discharge in the basin area, and the temporal distribution of DOC was controlled by meteorological conditions. The DOC contents had the highest in summer, primarily due to high rainfall and leaching. The inter-annual variation in DOC concentration was influenced by the total annual runoff volumes, with a minimum level of 2.24 mg L⁻¹ in 2023 and a maximum level of 2.45 mg L⁻¹ in 2019. The monthly DOC fluxes ranged from 6.3 to 13.8 × 10⁴ t, with the highest values coinciding with the maximum river discharge volumes in June and July. The DOC levels in the Qiantang River remained relatively high in recent years (2016–2023). This study enables the concerned stakeholders and researchers to better understand carbon transportation and its dynamics in the Qiantang River and its coastal areas. Full article

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18 pages, 19260 KiB

Open AccessArticle

Refraction-Aware Structure from Motion for Airborne Bathymetry

by Alexandros Makris, Vassilis C. Nicodemou, Evangelos Alevizos, Iason Oikonomidis, Dimitrios D. Alexakis and Anastasios Roussos

Remote Sens. 2024, 16(22), 4253; https://doi.org/10.3390/rs16224253 - 15 Nov 2024

In this work, we introduce the first pipeline that combines a refraction-aware structure from motion (SfM) method with a deep learning model specifically designed for airborne bathymetry. We accurately estimate the 3D positions of the submerged points by integrating refraction geometry within the [...] Read more.

In this work, we introduce the first pipeline that combines a refraction-aware structure from motion (SfM) method with a deep learning model specifically designed for airborne bathymetry. We accurately estimate the 3D positions of the submerged points by integrating refraction geometry within the SfM optimization problem. This way, no refraction correction as post-processing is required. Experiments with simulated data that approach real-world capturing conditions demonstrate that SfM with refraction correction is extremely accurate, with submillimeter errors. We integrate our refraction-aware SfM within a deep learning framework that also takes into account radiometrical information, developing a combined spectral and geometry-based approach, with further improvements in accuracy and robustness to different seafloor types, both textured and textureless. We conducted experiments with real-world data at two locations in the southern Mediterranean Sea, with varying seafloor types, which demonstrate the benefits of refraction correction for the deep learning framework. We made our refraction-aware SfM open source, providing researchers in airborne bathymetry with a practical tool to apply SfM in shallow water areas. Full article

(This article belongs to the Special Issue Advanced Application of Artificial Intelligence and Machine Vision in Remote Sensing (Third Edition))

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26 pages, 13283 KiB

Open AccessArticle

Reconstruction of 30 m Land Cover in the Qilian Mountains from 1980 to 1990 Based on Super-Resolution Generative Adversarial Networks

by Xiaoya Wang, Bo Zhong, Kai Ao, Bailin Du, Longfei Hu, He Cai, Yang Qiao, Junjun Wu, Aixia Yang, Shanlong Wu and Qinhuo Liu

Remote Sens. 2024, 16(22), 4252; https://doi.org/10.3390/rs16224252 - 14 Nov 2024

Long time series of annual land cover with fine spatio-temporal resolutions play a crucial role in studying environmental climate change, biophysical modeling, carbon cycling models, and land management. Despite a strong consistency exhibited by several publicly available medium to fine resolution global land [...] Read more.

Long time series of annual land cover with fine spatio-temporal resolutions play a crucial role in studying environmental climate change, biophysical modeling, carbon cycling models, and land management. Despite a strong consistency exhibited by several publicly available medium to fine resolution global land cover datasets, significant discrepancies exist at the regional scale; moreover, only every 5/10 year land cover were available. Consequently, high-quality annual land cover datasets before 2000 are unavailable in China. In this study, we proposed a deep learning-based method by integrating multiple remote sensing data from different platforms with historical high spatial resolution land cover datasets (CNLUCC) to derive the 30 m annual land cover maps from 1980 to 1990 for Qilian Mountain. First, the super-resolution generative adversarial network models for upscaling the 5.5 km AVHRR NDVI to 250 m were established by employing the AVHRR and MODIS NDVI data with the same year as input, and the early time series AVHRR NDVI data were subsequently upscaled to 250 m through the above models. Second, the breaks for the additive seasonal and trend (BFAST) change detection algorithm was applied to the upscaled time series NDVI data to detect the change time of different land cover types. Third, the CNLUCC data in 1980 and 1990 were updated to annual land cover datasets from 1980 to 1990 and the annual mapping results provided insights into the dynamic processes of urbanization, deforestation, water bodies, and farmland from 1980 to 1990. Finally, comprehensive analysis and validation were carried out for evaluation and an overall accuracy of 77.26% for the land cover product in 1986 was achieved. Full article

(This article belongs to the Special Issue Surface Radiative Transfer: Modeling, Inversion, and Applications)

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28 pages, 3209 KiB

Open AccessArticle

DESAT: A Distance-Enhanced Strip Attention Transformer for Remote Sensing Image Super-Resolution

by Yujie Mao, Guojin He, Guizhou Wang, Ranyu Yin, Yan Peng and Bin Guan

Remote Sens. 2024, 16(22), 4251; https://doi.org/10.3390/rs16224251 - 14 Nov 2024

Transformer-based methods have demonstrated impressive performance in image super-resolution tasks. However, when applied to large-scale Earth observation images, the existing transformers encounter two significant challenges: (1) insufficient consideration of spatial correlation between adjacent ground objects; and (2) performance bottlenecks due to the underutilization [...] Read more.

Transformer-based methods have demonstrated impressive performance in image super-resolution tasks. However, when applied to large-scale Earth observation images, the existing transformers encounter two significant challenges: (1) insufficient consideration of spatial correlation between adjacent ground objects; and (2) performance bottlenecks due to the underutilization of the upsample module. To address these issues, we propose a novel distance-enhanced strip attention transformer (DESAT). The DESAT integrates distance priors, easily obtainable from remote sensing images, into the strip window self-attention mechanism to capture spatial correlations more effectively. To further enhance the transfer of deep features into high-resolution outputs, we designed an attention-enhanced upsample block, which combines the pixel shuffle layer with an attention-based upsample branch implemented through the overlapping window self-attention mechanism. Additionally, to better simulate real-world scenarios, we constructed a new cross-sensor super-resolution dataset using Gaofen-6 satellite imagery. Extensive experiments on both simulated and real-world remote sensing datasets demonstrate that the DESAT outperforms state-of-the-art models by up to 1.17 dB along with superior qualitative results. Furthermore, the DESAT achieves more competitive performance in real-world tasks, effectively balancing spatial detail reconstruction and spectral transform, making it highly suitable for practical remote sensing super-resolution applications. Full article

(This article belongs to the Special Issue Deep Learning for Remote Sensing Image Enhancement)

18 pages, 4574 KiB

Open AccessArticle

Spatio-Temporal Generalization of VIS-NIR-SWIR Spectral Models for Nitrogen Prediction in Sugarcane Leaves

by Carlos Augusto Alves Cardoso Silva, Rodnei Rizzo, Marcelo Andrade da Silva, Matheus Luís Caron and Peterson Ricardo Fiorio

Remote Sens. 2024, 16(22), 4250; https://doi.org/10.3390/rs16224250 - 14 Nov 2024

Nitrogen fertilization is a challenging task that usually requires intensive use of resources, such as fertilizers, management and water. This study explored the potential of VIS-NIR-SWIR remote sensing for quantifying leaf nitrogen content (LNC) in sugarcane from different regions and vegetative stages. Conducted [...] Read more.

Nitrogen fertilization is a challenging task that usually requires intensive use of resources, such as fertilizers, management and water. This study explored the potential of VIS-NIR-SWIR remote sensing for quantifying leaf nitrogen content (LNC) in sugarcane from different regions and vegetative stages. Conducted in three regions of São Paulo, Brazil (Jaú, Piracicaba and Santa Maria), the research involved three experiments, one per location. The spectral data were obtained at 140, 170, 200, 230 and 260 days after cutting (DAC). From the hyperspectral data, clustering analysis was performed to identify the patterns between the spectral bands for each region where the spectral readings were made, using the Partitioning Around Medoids (PAM) algorithm. Then, the LNC values were used to generate spectral models using Partial Least Squares Regression (PLSR). Subsequently, the generalization of the models was tested with the leave-one-date-out cross-validation (LOOCV) technique. The results showed that although the variation in leaf N was small, the sensor demonstrated the ability to detect these variations. Furthermore, it was possible to determine the influence of N concentrations on the leaf spectra and how this impacted cluster formation. It was observed that the greater the average variation in N content in each cluster, the better defined and denser the groups formed were. The best time to quantify N concentrations was at 140 DAC (R² = 0.90 and RMSE = 0.74 g kg⁻¹). From LOOCV, the areas with sandier soil texture presented a lower model performance compared to areas with clayey soil, with R² < 0.54. The spatial generalization of the models recorded the best performance at 140 DAC (R² = 0.69, RMSE = 1.18 g kg⁻¹ and dr = 0.61), decreasing in accuracy at the crop-maturation stage (260 DAC), R² of 0.05, RMSE of 1.73 g kg⁻¹ and dr of 0.38. Although the technique needs further studies to be improved, our results demonstrated potential, which tends to provide support and benefits for the quantification of nutrients in sugarcane in the long term. Full article

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

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18 pages, 12063 KiB

Open AccessArticle

Deformation Monitoring and Analysis of Beichuan National Earthquake Ruins Museum Based on Time Series InSAR Processing

by Jing Fan, Weihong Wang, Jialun Cai, Zhouhang Wu, Xiaomeng Wang, Hui Feng, Yitong Yao, Hongyao Xiang and Xinlong Luo

Remote Sens. 2024, 16(22), 4249; https://doi.org/10.3390/rs16224249 - 14 Nov 2024

Since the Wenchuan earthquake in 2008, Old Beichuan County-town has experienced significant subsidence due to the disruption of the geological environment and the concurrent increase in precipitation. The ongoing land surface deformation poses a threat to the preservation and utilization of the Beichuan [...] Read more.

Since the Wenchuan earthquake in 2008, Old Beichuan County-town has experienced significant subsidence due to the disruption of the geological environment and the concurrent increase in precipitation. The ongoing land surface deformation poses a threat to the preservation and utilization of the Beichuan National Earthquake Ruins Museum (BNERM), as well as to the safety of urban residents’ lives. However, the evolutionary characteristics of surface deformation in these areas remain largely unexplored. Here, we focused on the BNERM control zone and employed the small-baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technique to accurately measure land surface deformation and its spatiotemporal changes. Subsequently, we integrated this data with land cover types and precipitation to investigate the driving factors of deformation. The results indicate a slight overall elevation increase in the study area from June 2015 to May 2023, with deformation rates varying between −35.2 mm/year and 22.9 mm/year. Additionally, four unstable slopes were identified within the BNERM control zone. Our analysis indicates that surface deformation in the study area is closely linked to changes in land cover types and precipitation, exhibiting a seasonal cumulative pattern, and active geological activity may also be a cause of deformation. This study provides invaluable insights into the surface deformation characteristics of the BNERM and can serve as a scientific foundation for the protection of earthquake ruins, risk assessment, early warning, and disaster prevention measures. Full article

(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)

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