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 23 days after submission; acceptance to publication is undertaken in 2.7 days (median values for papers published in this journal in the second half of 2023).
- 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:
5.0 (2022);
5-Year Impact Factor:
5.6 (2022)
Latest Articles
Overestimated Fog-Top Entrainment in WRF Simulation Leading to Unrealistic Dissipation of Sea Fog: A Case Study
Remote Sens. 2024, 16(10), 1656; https://doi.org/10.3390/rs16101656 (registering DOI) - 07 May 2024
Abstract
Entrainment at the top of the planetary boundary layer (PBL) is of significant importance because it controls the upward growth of the PBL height. An option called ysu_topdown_pblmix, which provides a parameterization of fog-top entrainment, has been proposed for valley fog modeling and
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Entrainment at the top of the planetary boundary layer (PBL) is of significant importance because it controls the upward growth of the PBL height. An option called ysu_topdown_pblmix, which provides a parameterization of fog-top entrainment, has been proposed for valley fog modeling and introduced into the YSU (Yonsei University) PBL scheme in the Weather Research and Forecasting (WRF) model. However, enabling this option in simulations of sea fog over the Yellow Sea typically results in unrealistic dissipation near the fog bottom and even within the entire fog layer. In this study, we theoretically examine the composition of the option ysu_topdown_pblmix, and then argue that one term in this option might be redundant for sea-fog modeling. The fog-top variables are employed in this term to determine the basic entrainment in the dry PBL, which is already parameterized by the surface variables in the original YSU PBL scheme. This term likely leads to an overestimation of the fog-top entrainment rate, so we refer to it as redundant. To explore the connection between the redundant term and unrealistic dissipation, a widespread sea-fog episode over the Yellow Sea is employed as a case study based on the WRF model. The simulation results clearly attribute the unrealistic dissipation to the extra entrainment rate that the redundant term induces. Fog-top entrainment is unexpectedly overestimated due to this extra entrainment rate, resulting in a significantly drier and warmer bias within the interior of sea fog. When sea fog develops and reaches a temperature lower than the sea surface, the sea surface functions as a warming source to heat the fog bottom jointly with the downward heat flux brought by the fog-top entrainment, leading the dissipation to initially occur near the fog bottom and then gradually expand upwards. We suggest a straightforward method to modify the option ysu_topdown_pblmix for sea-fog modeling that eliminates the redundant term. The improvement effect of this method was supported by the results of sensitivity tests. However, more sea-fog cases are required to validate the modification method.
Full article
(This article belongs to the Special Issue Severe Weather Observations and Meteorology Modeling Development Using Remote Sensing)
Open AccessTechnical Note
Hyperspectral Imaging Spectroscopy for Non-Destructive Determination of Grape Berry Total Soluble Solids and Titratable Acidity
by
Hongyi Lyu, Miles Grafton, Thiagarajah Ramilan, Matthew Irwin and Eduardo Sandoval
Remote Sens. 2024, 16(10), 1655; https://doi.org/10.3390/rs16101655 (registering DOI) - 07 May 2024
Abstract
Wine grape quality heavily influences the price received for a product. Hyperspectral imaging has the potential to provide a non-destructive technique for predicting various enological parameters. This study aims to explore the feasibility of applying hyperspectral imaging to measure the total soluble solids
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Wine grape quality heavily influences the price received for a product. Hyperspectral imaging has the potential to provide a non-destructive technique for predicting various enological parameters. This study aims to explore the feasibility of applying hyperspectral imaging to measure the total soluble solids (TSS) and titratable acidity (TA) in wine grape berries. A normalized difference spectral index (NDSI) spectral preprocessing method was built and compared with the conventional preprocessing method: multiplicative scatter correction and Savitzky–Golay smoothing (MSC+SG). Different machine learning models were built to examine the performance of the preprocessing methods. The results show that the NDSI preprocessing method demonstrated better performance than the MSC+SG preprocessing method in different classification models, with the best model correctly classifying 93.8% of the TSS and 84.4% of the TA. In addition, the TSS can be predicted with moderate performance using support vector regression (SVR) and MSC+SG preprocessing with a root mean squared error (RMSE) of 0.523 °Brix and a coefficient of determination (R2) of 0.622, and the TA can be predicted with moderate performance using SVR and NDSI preprocessing (RMSE = 0.19%, R2 = 0.525). This study demonstrates that hyperspectral imaging data and NDSI preprocessing have the potential to be a method for grading wine grapes for producing quality wines.
Full article
(This article belongs to the Special Issue Image Change Detection Research in Remote Sensing II)
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Open AccessArticle
Disaggregating National Statistical Data to Assess the Sub-National SDG 6.4.2 Water Stress Indicator by Integrating Satellite Observations and Model Data
by
Jing Lu and Li Jia
Remote Sens. 2024, 16(10), 1654; https://doi.org/10.3390/rs16101654 - 07 May 2024
Abstract
Ensuring the sustainable management of water and sanitation for all is the primary goal of Sustainable Development Goal 6 (SDG 6). SDG indicator 6.4.2 (level of water stress) is critical for monitoring the progress toward SDG 6. The assessment of the SDG indicator
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Ensuring the sustainable management of water and sanitation for all is the primary goal of Sustainable Development Goal 6 (SDG 6). SDG indicator 6.4.2 (level of water stress) is critical for monitoring the progress toward SDG 6. The assessment of the SDG indicator 6.4.2 is currently based on statistical data at the national scale, i.e., one value for one country, which cannot reflect spatial variability in water stress in a country. The lack of data at sub-national scales limits the assessment of water stress in sub-national regions. This study developed a method of disaggregating national statistical renewable water resources (TRWR) and freshwater withdrawals (TFWW) to estimate the SDG 6.4.2 water stress indicator at a sub-national scale by combining satellite remote sensing data and model simulated data. Remote sensing (RS)-based precipitation (P); the difference between precipitation and evapotranspiration (P-ET); and the difference between precipitation, evapotranspiration, terrestrial water storage change (P-ET-dS), and model-simulated naturized runoff and withdrawal water use were used as spatial and temporal surrogates to disaggregate the national-scale statistics of TRWR and TFWW to the grid scale. Gridded TRWR and TFWW can be used to calculate the water stress of any interest regions. Disaggregated TRWR, TFWW, and water stress estimation were validated at three different spatial scales, from major river basins and provinces to prefectures in China, by comparing the corresponding statistical data. The results show that the disaggregation for TRWR is generally better than for TFWW, and the overall accuracy for water stress estimation can reach up to 91%. The temporal evolution of disaggregated variables also showed good consistency with statistical time series data. The RS-based P-ET and P-ET-dS have great potential for disaggregating TRWR at different spatiotemporal scales, with no obvious differences with the results using the model simulation as a surrogate for the disaggregation of SDG indicator 6.4.2. The disaggregation accuracy can be further improved when the sub-regional statistical data of TRWR and TFWW are applied to the disaggregation approach.
Full article
Open AccessArticle
Zero-Shot Sketch-Based Remote-Sensing Image Retrieval Based on Multi-Level and Attention-Guided Tokenization
by
Bo Yang, Chen Wang, Xiaoshuang Ma, Beiping Song, Zhuang Liu and Fangde Sun
Remote Sens. 2024, 16(10), 1653; https://doi.org/10.3390/rs16101653 - 07 May 2024
Abstract
Effectively and efficiently retrieving images from remote-sensing databases is a critical challenge in the realm of remote-sensing big data. Utilizing hand-drawn sketches as retrieval inputs offers intuitive and user-friendly advantages, yet the potential of multi-level feature integration from sketches remains underexplored, leading to
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Effectively and efficiently retrieving images from remote-sensing databases is a critical challenge in the realm of remote-sensing big data. Utilizing hand-drawn sketches as retrieval inputs offers intuitive and user-friendly advantages, yet the potential of multi-level feature integration from sketches remains underexplored, leading to suboptimal retrieval performance. To address this gap, our study introduces a novel zero-shot, sketch-based retrieval method for remote-sensing images, leveraging multi-level feature extraction, self-attention-guided tokenization and filtering, and cross-modality attention update. This approach employs only vision information and does not require semantic knowledge concerning the sketch and image. It starts by employing multi-level self-attention guided feature extraction to tokenize the query sketches, as well as self-attention feature extraction to tokenize the candidate images. It then employs cross-attention mechanisms to establish token correspondence between these two modalities, facilitating the computation of sketch-to-image similarity. Our method significantly outperforms existing sketch-based remote-sensing image retrieval techniques, as evidenced by tests on multiple datasets. Notably, it also exhibits robust zero-shot learning capabilities in handling unseen categories and strong domain adaptation capabilities in handling unseen novel remote-sensing data. The method’s scalability can be further enhanced by the pre-calculation of retrieval tokens for all candidate images in a database. This research underscores the significant potential of multi-level, attention-guided tokenization in cross-modal remote-sensing image retrieval. For broader accessibility and research facilitation, we have made the code and dataset used in this study publicly available online.
Full article
(This article belongs to the Special Issue Advanced Artificial Intelligence for Remote Sensing: Methodology and Applications)
Open AccessArticle
Identification of Dominant Species and Their Distributions on an Uninhabited Island Based on Unmanned Aerial Vehicles (UAVs) and Machine Learning Models
by
Jinfeng Wu, Kesheng Huang, Youhao Luo, Xiaoze Long, Chuying Yu, Hong Xiong and Jianhui Du
Remote Sens. 2024, 16(10), 1652; https://doi.org/10.3390/rs16101652 - 07 May 2024
Abstract
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Comprehensive vegetation surveys are crucial for species selection and layout during the restoration of degraded island ecosystems. However, due to the poor accessibility of uninhabited islands, traditional quadrat surveys are time-consuming and labor-intensive, and it is challenging to fully identify the specific species
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Comprehensive vegetation surveys are crucial for species selection and layout during the restoration of degraded island ecosystems. However, due to the poor accessibility of uninhabited islands, traditional quadrat surveys are time-consuming and labor-intensive, and it is challenging to fully identify the specific species and their spatial distributions. With miniaturized sensors and strong accessibility, high spatial and temporal resolution, Unmanned Aerial Vehicles (UAVs) have been extensively implemented for vegetation surveys. By collecting UAVs multispectral images and conducting field quadrat surveys on Anyu Island, we employ four machine learning models, namely Gradient Boosting Decision Tree (GBDT), Support Vector Machine (SVM), Random Forest (RF) and Multiple Classifier Systems (MCS). We aim to identify the dominant species and analyze their spatial distributions according to spectral characteristics, vegetation index, topographic factors, texture features, and canopy heights. The results indicate that SVM model achieves the highest (88.55%) overall accuracy (OA) (kappa coefficient = 0.87), while MCS model does not significantly improve it as expected. Acacia confusa has the highest OA among 7 dominant species, reaching 97.67%. Besides the spectral characteristics, the inclusion of topographic factors and texture features in the SVM model can significantly improve the OA of dominant species. By contrast, the vegetation index, particularly the canopy height even reduces it. The dominant species exhibit significant zonal distributions with distance from the coastline on the Anyu Island (p < 0.001). Our study provides an effective and universal path to identify and map the dominant species and is helpful to manage and restore the degraded vegetation on uninhabited islands.
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Open AccessArticle
Fault Detection and Interactive Multiple Models Optimization Algorithm Based on Factor Graph Navigation System
by
Shouyi Wang, Qinghua Zeng, Chen Shao, Fangdong Li and Jianye Liu
Remote Sens. 2024, 16(10), 1651; https://doi.org/10.3390/rs16101651 - 07 May 2024
Abstract
Accurate and stable positioning is significant for vehicle navigation systems, especially in complex urban environments. However, urban canyons and dynamic interference make vehicle sensors prone to disturbance, leading to vehicle positioning errors and even failures. To address these issues, an adaptive loosely coupled
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Accurate and stable positioning is significant for vehicle navigation systems, especially in complex urban environments. However, urban canyons and dynamic interference make vehicle sensors prone to disturbance, leading to vehicle positioning errors and even failures. To address these issues, an adaptive loosely coupled IMU/GNSS/LiDAR integrated navigation system based on factor graph optimization with sensor weight optimization and fault detection is proposed. First, the factor nodes and system framework are constructed based on error models of sensors, and the optimization method principle is derived. Second, the interactive multiple-model algorithm based on factor graph optimization (IMMFGO) is utilized to calculate and adjust sensor weights for global optimization, which will reduce the impact of disturbed sensors. Finally, a multi-stage fault detection, isolation, and recovery (MSFDIR) strategy is implemented based on the IMMFGO results and IMU pre-integration measurements, which can detect significant sensor faults and optimize the system structure. Vehicle experiments show that our IMMFGO method generally obtains better performance in positioning accuracy by 23.7% compared to adaptive factor graph optimization (AFGO) methods, and the MSFDIR strategy possesses the capability of fault sensor detection, which provides an essential reference for multi-source vehicle navigation systems in urban canyons.
Full article
(This article belongs to the Section Engineering Remote Sensing)
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Open AccessArticle
Development of an Integrated Urban Flood Model and Its Application in a Concave-Down Overpass Area
by
Yuna Yan, Han Zhang, Na Zhang and Chuhan Feng
Remote Sens. 2024, 16(10), 1650; https://doi.org/10.3390/rs16101650 - 07 May 2024
Abstract
Urban floods caused by extreme rainstorm events have increased in recent decades, particularly in concave-down bridge zones. To simulate urban flooding processes accurately, an integrated urban flood model (IUFM) was constructed by coupling a distributed urban surface runoff model based on the cellular
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Urban floods caused by extreme rainstorm events have increased in recent decades, particularly in concave-down bridge zones. To simulate urban flooding processes accurately, an integrated urban flood model (IUFM) was constructed by coupling a distributed urban surface runoff model based on the cellular automata framework (CA-DUSRM), a widely used pipe convergence module in the storm water management model (SWMM), with an inundation module that describes the overflow expansion process associated with terrain and land-cover. The IUFM was used in a case study of the Anhua Bridge (a typical concave-down overpass) study area in Beijing, China. The spatial-temporal variations in flood depth modeled by the IUFM were verified to be reliable by comparison with actual measurements and other simulations. The validated IUFM was used to obtain temporal variations in flood range, depth, and volume under four rainstorm scenarios (return periods of 3-year, 10-year, 50-year, and 100-year). The results showed that the surface runoff process, overflow from drainage networks, and overflow expansion process could affect the flooding status by changing the composition and spatial configuration of pervious or impervious patches, drainage capacity, and underlying surface characteristics (such as terrain and land-cover). Overall, although the simulation results from the IUFM contain uncertainties from the model structures and inputs, the IUFM is an effective tool that can provide accurate and timely information to prevent and control urban flood disasters and provide decision-making support for long-term storm water management and sponge city construction.
Full article
(This article belongs to the Special Issue Flood Monitoring, Modelling, Forecasting and Analysis with Remote Sensing Tools)
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Open AccessArticle
Terrace Extraction Method Based on Remote Sensing and a Novel Deep Learning Framework
by
Yinghai Zhao, Jiawei Zou, Suhong Liu and Yun Xie
Remote Sens. 2024, 16(9), 1649; https://doi.org/10.3390/rs16091649 - 06 May 2024
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Terraces, farmlands built along hillside contours, are common anthropogenically designed landscapes. Terraces control soil and water loss and improve land productivity; therefore, obtaining their spatial distribution is necessary for soil and water conservation and agricultural production. Spatial information of large-scale terraces can be
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Terraces, farmlands built along hillside contours, are common anthropogenically designed landscapes. Terraces control soil and water loss and improve land productivity; therefore, obtaining their spatial distribution is necessary for soil and water conservation and agricultural production. Spatial information of large-scale terraces can be obtained using satellite images and through deep learning. However, when extracting terraces, accurately segmenting the boundaries of terraces and identifying small terraces in diverse scenarios continues to be challenging. To solve this problem, we combined two deep learning modules, ANB-LN and DFB, to produce a new deep learning framework (NLDF-Net) for terrace extraction using remote sensing images. The model first extracted the features of the terraces through the coding area to obtain abstract semantic features, and then gradually recovered the original size through the decoding area using feature fusion. In addition, we constructed a terrace dataset (the HRT-set) for Guangdong Province and conducted a series of comparative experiments on this dataset using the new framework. The experimental results show that our framework had the best extraction effect compared to those of other deep learning methods. This framework provides a method and reference for extracting ground objects using remote sensing images.
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Open AccessArticle
Mapping the Continuous Cover of Invasive Noxious Weed Species Using Sentinel-2 Imagery and a Novel Convolutional Neural Regression Network
by
Fei Xing, Ru An, Xulin Guo and Xiaoji Shen
Remote Sens. 2024, 16(9), 1648; https://doi.org/10.3390/rs16091648 - 06 May 2024
Abstract
Invasive noxious weed species (INWS) are typical poisonous plants and forbs that are considered an increasing threat to the native alpine grassland ecosystems in the Qinghai–Tibetan Plateau (QTP). Accurate knowledge of the continuous cover of INWS across complex alpine grassland ecosystems over a
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Invasive noxious weed species (INWS) are typical poisonous plants and forbs that are considered an increasing threat to the native alpine grassland ecosystems in the Qinghai–Tibetan Plateau (QTP). Accurate knowledge of the continuous cover of INWS across complex alpine grassland ecosystems over a large scale is required for their control and management. However, the cooccurrence of INWS and native grass species results in highly heterogeneous grass communities and generates mixed pixels detected by remote sensors, which causes uncertainty in classification. The continuous coverage of INWS at the pixel level has not yet been achieved. In this study, objective 1 was to test the capability of Senginel-2 imagery at estimating continuous INWS cover across complex alpine grasslands over a large scale and objective 2 was to assess the performance of the state-of-the-art convolutional neural network-based regression (CNNR) model in estimating continuous INWS cover. Therefore, a novel CNNR model and a random forest regression (RFR) model were evaluated for estimating INWS continuous cover using Sentinel-2 imagery. INWS continuous cover was estimated directly from Sentinel-2 imagery with an R2 ranging from 0.88 to 0.93 using the CNNR model. The RFR model combined with multiple features had a comparable accuracy, which was slightly lower than that of the CNNR model, with an R2 of approximately 0.85. Twelve green band-, red-edge band-, and near-infrared band-related features had important contributions to the RFR model. Our results demonstrate that the CNNR model performs well when estimating INWS continuous cover directly from Sentinel-2 imagery, and the RFR model combined with multiple features derived from the Sentinel-2 imager can also be used for INWS continuous cover mapping. Sentinel-2 imagery is suitable for mapping continuous INWS cover across complex alpine grasslands over a large scale. Our research provides information for the advanced mapping of the continuous cover of invasive species across complex grassland ecosystems or, more widely, terrestrial ecosystems over large spatial areas using remote sensors such as Sentinel-2.
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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Open AccessArticle
High Resolution Ranging with Small Sample Number under Low SNR Utilizing RIP-OMCS Strategy and AHRC l1 Minimization for Laser Radar
by
Min Xue, Mengdao Xing, Yuexin Gao, Jixiang Fu, Zhixin Wu and Wangshuo Tang
Remote Sens. 2024, 16(9), 1647; https://doi.org/10.3390/rs16091647 - 06 May 2024
Abstract
This manuscript presents a novel scheme to achieve high-resolution laser-radar ranging with a small sample number under low signal-to-noise ratio (SNR) conditions. To reduce the sample number, the Restricted Isometry Property-based optimal multi-channel coprime-sampling (RIP-OMCS) strategy is established. In the RIP-OMCS strategy, the
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This manuscript presents a novel scheme to achieve high-resolution laser-radar ranging with a small sample number under low signal-to-noise ratio (SNR) conditions. To reduce the sample number, the Restricted Isometry Property-based optimal multi-channel coprime-sampling (RIP-OMCS) strategy is established. In the RIP-OMCS strategy, the data collected across multiple channels with very low coprime-sampling rates can record accurate range information on each target. Further, the asynchronous problem caused by channel sampling-time errors is considered. The sampling-time errors are estimated using the cross-correlation function. After canceling the asynchronous problem, the data collected by multiple channels are then merged into non-uniform sampled signals. Using data combination, target-range estimation is converted into an optimization problem of sparse representation consisting of a non-uniform Fourier dictionary. This optimization problem is solved using adaptive hybrid re-weighted constraint (AHRC) l1 minimization. Two constraints are formed from statistical attributes of the targets and clutter. Moreover, as the detailed characteristics of the target, clutter, and noise are unknown before the solution, the two constraints can be adaptively modified, which guarantees that l1 minimization obtains the high-resolution range profile and accurate distance of all targets under a low SNR. Our experiments confirmed the effectiveness of the proposed method.
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(This article belongs to the Topic Radar Signal and Data Processing with Applications)
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Open AccessArticle
Remote Detection of Geothermal Alteration Using Airborne Light Detection and Ranging Return Intensity
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Yan Restu Freski, Christoph Hecker, Mark van der Meijde and Agung Setianto
Remote Sens. 2024, 16(9), 1646; https://doi.org/10.3390/rs16091646 - 05 May 2024
Abstract
The remote detection of hydrothermally altered grounds in geothermal exploration demands datasets capable of reliably detecting key outcrops with fine spatial resolution. While optical thermal or radar-based datasets have resolution limitations, airborne LiDAR offers point-based detection through its LiDAR return intensity (LRI) values,
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The remote detection of hydrothermally altered grounds in geothermal exploration demands datasets capable of reliably detecting key outcrops with fine spatial resolution. While optical thermal or radar-based datasets have resolution limitations, airborne LiDAR offers point-based detection through its LiDAR return intensity (LRI) values, serving as a proxy for surface reflectivity. Despite this potential, few studies have explored LRI value variations in the context of hydrothermal alteration and their utility in distinguishing altered from unaltered rocks. Although the link between alteration degree and LRI values has been established under laboratory conditions, this relationship has yet to be demonstrated in airborne data. This study investigates the applicability of laboratory results to airborne LRI data for alteration detection. Utilising LRI data from an airborne LiDAR point cloud (wavelength 1064 nm, density 12 points per square metre) acquired over a prospective geothermal area in Bajawa, Indonesia, where rock sampling for a related laboratory study took place, we compare the airborne LRI values within each ground sampling area of a 3 m radius (due to hand-held GPS uncertainty) with laboratory LRI values of corresponding rock samples. Our findings reveal distinguishable differences between strongly altered and unaltered samples, with LRI discrepancies of approximately ~28 for airborne data and ~12 for laboratory data. Furthermore, the relative trends of airborne and laboratory-based LRI data concerning alteration degree exhibit striking similarity. These consistent results for alteration degree in laboratory and airborne data mark a significant step towards LRI-based alteration mapping from airborne platforms.
Full article
(This article belongs to the Special Issue Surface and Sub-surface Geological Remote Sensing at Regional Mapping Scales)
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Open AccessArticle
Analysis of Atmospheric Boundary Layer Characteristics on Different Underlying Surfaces of the Eastern Tibetan Plateau in Summer
by
Xiaohang Wen, Jie Ma and Mei Chen
Remote Sens. 2024, 16(9), 1645; https://doi.org/10.3390/rs16091645 - 05 May 2024
Abstract
The atmospheric boundary layer is a key region for human activities and the interaction of various layers and is an important channel for the transportation of momentum, heat, and various substances between the free atmosphere and the surface, which has a significant impact
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The atmospheric boundary layer is a key region for human activities and the interaction of various layers and is an important channel for the transportation of momentum, heat, and various substances between the free atmosphere and the surface, which has a significant impact on the development of weather and climate change. During the Second Tibetan Plateau Scientific Expedition and Research Program (STEP) in June 2022, utilizing the comprehensive stereoscopic observation experiment of the “Plateau Low Vortex Network”, this study analyzed the variation characteristics and influencing factors of the atmospheric boundary layer height (ABLH) at three stations with different underlying surface types on the Qinghai–Tibet Plateau (QTP): Qumalai Station (grassland), Southeast Tibet Observation and Research Station for the Alpine Environment (SETORS, forest), and Sieshan Station (cropland). The analysis utilized sounding observation data, microwave radiometer data, and ERA5 reanalysis data. The results revealed that the temperature differences between the sounding observation data and microwave radiometer data were minor at the three stations, with a notable temperature inversion phenomenon observed at Sieshan Station. Regarding water vapor density, the differences between the sounding observation data and microwave radiometer data were relatively small at Sieshan Station. The relative humidity increased with height at Sieshan Station, whereas it increased and then decreased with height at SETORS and Qumalai Station. The ABLH at all sites reached its maximum value around noon, approximately 1500 m, and exhibited mostly convective boundary layer (CBL) characteristics. During the night, the ABLH mostly showed a stable boundary layer (SBL) pattern, with heights around 250 m. In summer, latent heat flux (LE) and sensible heat flux (H) in the eastern plateau were generally lower than those in the western plateau except at 20:00, where they were higher. Vertical velocity (w) in the eastern plateau was greater than in the western plateau. Among Sieshan Station and SETORS, LE, and H had the most significant impact on ABLH, while at Qumalai Station, ABLH was more influenced by surface long-wave radiation (Rlu). These four influencing factors showed a positive correlation with ABLH. The impact of different underlying surface types on ABLH primarily manifests in surface temperature variations, solar radiation intensity, vegetation cover, and terrain. Grasslands typically exhibit a larger range of ABLH variations, while the ABLH in forests and mountainous cropland areas is relatively stable.
Full article
(This article belongs to the Special Issue Land-Atmosphere Interactions and Effects on the Climate of the Tibetan Plateau and Surrounding Regions III)
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Open AccessArticle
A Technique for SAR Significant Wave Height Retrieval Using Azimuthal Cut-Off Wavelength Based on Machine Learning
by
Shaijie Leng, Mengyu Hao, Weizeng Shao, Armando Marino and Xingwei Jiang
Remote Sens. 2024, 16(9), 1644; https://doi.org/10.3390/rs16091644 - 05 May 2024
Abstract
This study introduces a new machine learning-based algorithm for the retrieving significant wave height (SWH) using synthetic aperture radar (SAR) images. This algorithm is based on the azimuthal cut-off wavelength and was developed in quad-polarized stripmap (QPS) mode in coastal waters. The collected
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This study introduces a new machine learning-based algorithm for the retrieving significant wave height (SWH) using synthetic aperture radar (SAR) images. This algorithm is based on the azimuthal cut-off wavelength and was developed in quad-polarized stripmap (QPS) mode in coastal waters. The collected images are collocated with a wave simulation from the numeric model, called WAVEWATCH-III (WW3), and the current speed from the HYbrid Coordinate Ocean Model (HYCOM). The sea surface wind is retrieved from the image at the vertical–vertical polarization channel, using the geophysical model function (GMF) CSARMOD-GF. The results of the algorithm were validated against the measurements obtained from the Haiyang-2B (HY-2B) scatterometer, yielding a root mean squared error (RMSE) of 1.99 m/s with a 0.82 correlation (COR) and 0.27 scatter index of wind speed. It was found that the SWH depends on the wind speed and azimuthal cut-off wavelength. However, the current speed has less of an influence on azimuthal cut-off wavelength. Following this rationale, four widely known machine learning methods were employed that take the SAR-derived azimuthal cut-off wavelength, wind speed, and radar incidence angle as inputs and then output the SWH. The validation result shows that the SAR-derived SWH by eXtreme Gradient Boosting (XGBoost) against the HY-2B altimeter products has a 0.34 m RMSE with a 0.97 COR and a 0.07 bias, which is better than the results obtained using an existing algorithm (i.e., a 1.10 m RMSE with a 0.77 COR and a 0.44 bias) and the other three machine learning methods (i.e., >a 0.58 m RMSE with a < 0.95 COR), i.e., convolutional neural networks (CNNs), Support Vector Regression (SVR) and the ridge regression model (RR). As a result, XGBoost is a highly efficient approach for GF-3 wave retrieval at the regular sea state.
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(This article belongs to the Section Ocean Remote Sensing)
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Open AccessArticle
Estimating Urban Forests Biomass with LiDAR by Using Deep Learning Foundation Models
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Hanzhang Liu, Chao Mou, Jiateng Yuan, Zhibo Chen, Liheng Zhong and Xiaohui Cui
Remote Sens. 2024, 16(9), 1643; https://doi.org/10.3390/rs16091643 - 05 May 2024
Abstract
Accurately estimating vegetation biomass in urban forested areas is of great interest to researchers as it is a key indicator of the carbon sequestration capacity necessary for cities to achieve carbon neutrality. The emerging vegetation biomass estimation methods that use AI technologies with
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Accurately estimating vegetation biomass in urban forested areas is of great interest to researchers as it is a key indicator of the carbon sequestration capacity necessary for cities to achieve carbon neutrality. The emerging vegetation biomass estimation methods that use AI technologies with remote sensing images often suffer from arge estimating errors due to the diversity of vegetation and the complex three-dimensional terrain environment in urban ares. However, the high resolution of Light Detection and Ranging (i.e., LiDAR) data provides an opportunity to accurately describe the complex 3D scenes of urban forests, thereby improving estimation accuracy. Additionally, deep earning foundation models have widely succeeded in the industry, and show great potential promise to estimate vegetation biomass through processing complex and arge amounts of urban LiDAR data efficiently and accurately. In this study, we propose an efficient and accurate method called 3D-CiLBE (3DCity Long-term Biomass Estimation) to estimate urban vegetation biomass by utilizing advanced deep earning foundation models. In the 3D-CiLBE method, the Segment Anything Model (i.e., SAM) was used to segment single wood information from a arge amount of complex urban LiDAR data. Then, we modified the Contrastive Language–Image Pre-training (i.e., CLIP) model to identify the species of the wood so that the classic anisotropic growth equation can be used to estimate biomass. Finally, we utilized the Informer model to predict the biomass in the ong term. We evaluate it in eight urban areas across the United States. In the task of identifying urban greening areas, the 3D-CiLBE achieves optimal performance with a mean Intersection over Union (i.e., mIoU) of 0.94. Additionally, for vegetation classification, 3D-CiLBE achieves an optimal recognition accuracy of 92.72%. The estimation of urban vegetation biomass using 3D-CiLBE achieves a Mean Square Error of 0.045 kg/m2, reducing the error by up to 8.2% compared to 2D methods. The MSE for biomass prediction by 3D-CiLBE was 0.06kg/m2 smaller on average than the inear regression model. Therefore, the experimental results indicate that the 3D-CiLBE method can accurately estimate urban vegetation biomass and has potential for practical application.
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(This article belongs to the Special Issue Earth Observation in Forest Biophysical/Biochemical Parameter Retrieval-II)
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Open AccessArticle
Miniaturizing Hyperspectral Lidar System Employing Integrated Optical Filters
by
Haibin Sun, Yicheng Wang, Zhipei Sun, Shaowei Wang, Shengli Sun, Jianxin Jia, Changhui Jiang, Peilun Hu, Haima Yang, Xing Yang, Mika Karjalnen, Juha Hyyppä and Yuwei Chen
Remote Sens. 2024, 16(9), 1642; https://doi.org/10.3390/rs16091642 - 04 May 2024
Abstract
Hyperspectral LiDAR (HSL) has been utilized as an efficacious technique in object classification and recognition based on its unique capability to obtain ranges and spectra synchronously. Different kinds of HSL prototypes with varied structures have been promoted and measured its performance. However, almost
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Hyperspectral LiDAR (HSL) has been utilized as an efficacious technique in object classification and recognition based on its unique capability to obtain ranges and spectra synchronously. Different kinds of HSL prototypes with varied structures have been promoted and measured its performance. However, almost all of these HSL prototypes employ complex and large spectroscopic devices, such as an Acousto-Optic Tunable Filter and Liquid-Crystal Tunable Filter, which makes this HSL system bulky and expensive, and then hinders its extensive application in many fields. In this paper, a smart and smaller spectroscopic component, an intergraded optical filter (IOF), is promoted to miniaturize these HSL systems. The system calibration, range precision, and spectral profile experiments were carried out to test the HSL prototype. Although the IOF employed here only covered a wavelength range of 699–758 nm with a six-channel passband and showed a transmittance of less than 50%, the HSL prototype showed excellent performance in ranging and spectral profile collecting. The spectral profiles collected are well in accordance with those acquired based on the AOTF. The spectral profiles of the fruits, vegetables, plants, and ore samples collected by the HSL based on an IOF can effectively reveal the status of the plants, the component materials, and ore species. Finally, we also showed the integrated design of the HSL based on a three-dimensional IOF and combined with a detector. The performance and designs of this HSL system based on an IOF show great potential for miniaturizing in some specific applications.
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(This article belongs to the Special Issue Remote Sensing and Lidar Data for Forest Monitoring)
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Open AccessArticle
MVT: Multi-Vision Transformer for Event-Based Small Target Detection
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Shilong Jing, Hengyi Lv, Yuchen Zhao, Hailong Liu and Ming Sun
Remote Sens. 2024, 16(9), 1641; https://doi.org/10.3390/rs16091641 - 04 May 2024
Abstract
Object detection in remote sensing plays a crucial role in various ground identification tasks. However, due to the limited feature information contained within small targets, which are more susceptible to being buried by complex backgrounds, especially in extreme environments (e.g., low-light, motion-blur scenes).
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Object detection in remote sensing plays a crucial role in various ground identification tasks. However, due to the limited feature information contained within small targets, which are more susceptible to being buried by complex backgrounds, especially in extreme environments (e.g., low-light, motion-blur scenes). Meanwhile, event cameras offer a unique paradigm with high temporal resolution and wide dynamic range for object detection. These advantages enable event cameras without being limited by the intensity of light, to perform better in challenging conditions compared to traditional cameras. In this work, we introduce the Multi-Vision Transformer (MVT), which comprises three efficiently designed components: the downsampling module, the Channel Spatial Attention (CSA) module, and the Global Spatial Attention (GSA) module. This architecture simultaneously considers short-term and long-term dependencies in semantic information, resulting in improved performance for small object detection. Additionally, we propose Cross Deformable Attention (CDA), which progressively fuses high-level and low-level features instead of considering all scales at each layer, thereby reducing the computational complexity of multi-scale features. Nevertheless, due to the scarcity of event camera remote sensing datasets, we provide the Event Object Detection (EOD) dataset, which is the first dataset that includes various extreme scenarios specifically introduced for remote sensing using event cameras. Moreover, we conducted experiments on the EOD dataset and two typical unmanned aerial vehicle remote sensing datasets (VisDrone2019 and UAVDT Dataset). The comprehensive results demonstrate that the proposed MVT-Net achieves a promising and competitive performance.
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(This article belongs to the Special Issue Remote Sensing of Target Object Detection and Identification II)
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Open AccessArticle
Space–Air–Ground–Sea Integrated Network with Federated Learning
by
Hao Zhao, Fei Ji, Yan Wang, Kexing Yao and Fangjiong Chen
Remote Sens. 2024, 16(9), 1640; https://doi.org/10.3390/rs16091640 - 04 May 2024
Abstract
A space–air–ground–sea integrated network (SAGSIN) is a promising heterogeneous network framework for the next generation mobile communications. Moreover, federated learning (FL), as a widely used distributed intelligence approach, can improve advanced network performance. In view of the combination and cooperation of SAGSINs and
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A space–air–ground–sea integrated network (SAGSIN) is a promising heterogeneous network framework for the next generation mobile communications. Moreover, federated learning (FL), as a widely used distributed intelligence approach, can improve advanced network performance. In view of the combination and cooperation of SAGSINs and FL, an FL-based SAGSIN framework faces a number of unprecedented challenges, not only from the communication aspect but also on the security and privacy side. Motivated by these observations, in this article, we first give a detailed state-of-the-art review of recent progress and ongoing research works on FL-based SAGSINs. Then, the challenges of FL-based SAGSINs are discussed. After that, for different service demands, basic applications are introduced with their benefits and functions. In addition, two case studies are proposed, in order to improve SAGSINs’ communication efficiency under a significant communication latency difference and to protect user-level privacy for SAGSIN participants, respectively. Simulation results show the effectiveness of the proposed algorithms. Moreover, future trends of FL-based SAGSINs are discussed.
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(This article belongs to the Special Issue Space-Air-Ground-Ocean Integrated Sensing and Information Transmission)
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Open AccessArticle
Urban Land Surface Temperature Downscaling in Chicago: Addressing Ethnic Inequality and Gentrification
by
Jangho Lee, Max Berkelhammer, Matthew D. Wilson, Natalie Love and Ralph Cintron
Remote Sens. 2024, 16(9), 1639; https://doi.org/10.3390/rs16091639 - 04 May 2024
Abstract
In this study, we developed a XGBoost-based algorithm to downscale 2 km-resolution land surface temperature (LST) data from the GOES satellite to a finer 70 m resolution, using ancillary variables including NDVI, NDBI, and DEM. This method demonstrated a superior performance over the
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In this study, we developed a XGBoost-based algorithm to downscale 2 km-resolution land surface temperature (LST) data from the GOES satellite to a finer 70 m resolution, using ancillary variables including NDVI, NDBI, and DEM. This method demonstrated a superior performance over the conventional TsHARP technique, achieving a reduced RMSE of 1.90 °C, compared to 2.51 °C with TsHARP. Our approach utilizes the geostationary GOES satellite data alongside high-resolution ECOSTRESS data, enabling hourly LST downscaling to 70 m—a significant advancement over previous methodologies that typically measure LST only once daily. Applying these high-resolution LST data, we examined the hottest days in Chicago and their correlation with ethnic inequality. Our analysis indicated that Hispanic/Latino communities endure the highest LSTs, with a maximum LST that is 1.5 °C higher in blocks predominantly inhabited by Hispanic/Latino residents compared to those predominantly occupied by White residents. This study highlights the intersection of urban development, ethnic inequality, and environmental inequities, emphasizing the need for targeted urban planning to mitigate these disparities. The enhanced spatial and temporal resolution of our LST data provides deeper insights into diurnal temperature variations, crucial for understanding and addressing the urban heat distribution and its impact on vulnerable communities.
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(This article belongs to the Special Issue Remote Sensing for Land Surface Temperature and Related Applications)
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Spatial and Temporal Evolution of Precipitation in the Bahr el Ghazal River Basin, Africa
by
Jinyu Meng, Zengchuan Dong, Guobin Fu, Shengnan Zhu, Yiqing Shao, Shujun Wu and Zhuozheng Li
Remote Sens. 2024, 16(9), 1638; https://doi.org/10.3390/rs16091638 - 03 May 2024
Abstract
Accurate and punctual precipitation data are fundamental to understanding regional hydrology and are a critical reference point for regional flood control. The aims of this study are to evaluate the performance of three widely used precipitation datasets—CRU TS, ERA5, and NCEP—as potential alternatives
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Accurate and punctual precipitation data are fundamental to understanding regional hydrology and are a critical reference point for regional flood control. The aims of this study are to evaluate the performance of three widely used precipitation datasets—CRU TS, ERA5, and NCEP—as potential alternatives for hydrological applications in the Bahr el Ghazal River Basin in South Sudan, Africa. This includes examining the spatial and temporal evolution of regional precipitation using relatively accurate precipitation datasets. The findings indicate that CRU TS is the best precipitation dataset in the Bahr el Ghazal Basin. The spatial and temporal distributions of precipitation from CRU TS reveal that precipitation in the Bahr el Ghazal Basin has a clear wet season, with June–August accounting for half of the annual precipitation and peaking in July and August. The long-term annual total precipitation exhibits a gradual increasing trend from the north to the south, with the southwestern part of the Basin having the largest percentage of wet season precipitation. Notably, the Bahr el Ghazal Basin witnessed a significant precipitation shift in 1967, followed by an increasing trend. Moreover, the spatial and temporal precipitation evolutions reveal an ongoing risk of flooding in the lower part of the Basin; therefore, increased engineering counter-measures might be needed for effective flood prevention.
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(This article belongs to the Special Issue Advances in Remote Sensing to Understand Hydrological and Meteorological Extreme Events)
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Temporal Variations in Land Surface Temperature within an Urban Ecosystem: A Comprehensive Assessment of Land Use and Land Cover Change in Kharkiv, Ukraine
by
Gareth Rees, Liliia Hebryn-Baidy and Vadym Belenok
Remote Sens. 2024, 16(9), 1637; https://doi.org/10.3390/rs16091637 - 03 May 2024
Abstract
Remote sensing technologies are critical for analyzing the escalating impacts of global climate change and increasing urbanization, providing vital insights into land surface temperature (LST), land use and cover (LULC) changes, and the identification of urban heat island (UHI) and surface urban heat
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Remote sensing technologies are critical for analyzing the escalating impacts of global climate change and increasing urbanization, providing vital insights into land surface temperature (LST), land use and cover (LULC) changes, and the identification of urban heat island (UHI) and surface urban heat island (SUHI) phenomena. This research focuses on the nexus between LULC alterations and variations in LST and air temperature (Tair), with a specific emphasis on the intensified SUHI effect in Kharkiv, Ukraine. Employing an integrated approach, this study analyzes time-series data from Landsat and MODIS satellites, alongside Tair climate records, utilizing machine learning techniques and linear regression analysis. Key findings indicate a statistically significant upward trend in Tair and LST during the summer months from 1984 to 2023, with a notable positive correlation between Tair and LST across both datasets. MODIS data exhibit a stronger correlation (R2 = 0.879) compared to Landsat (R2 = 0.663). The application of a supervised classification through Random Forest algorithms and vegetation indices on LULC data reveals significant alterations: a 70.3% increase in urban land and a decrement in vegetative cover comprising a 15.5% reduction in dense vegetation and a 62.9% decrease in sparse vegetation. Change detection analysis elucidates a 24.6% conversion of sparse vegetation into urban land, underscoring a pronounced trajectory towards urbanization. Temporal and seasonal LST variations across different LULC classes were analyzed using kernel density estimation (KDE) and boxplot analysis. Urban areas and sparse vegetation had the smallest average LST fluctuations, at 2.09 °C and 2.16 °C, respectively, but recorded the most extreme LST values. Water and dense vegetation classes exhibited slightly larger fluctuations of 2.30 °C and 2.24 °C, with the bare land class showing the highest fluctuation 2.46 °C, but fewer extremes. Quantitative analysis with the application of Kolmogorov-Smirnov tests across various LULC classes substantiated the normality of LST distributions p > 0.05 for both monthly and annual datasets. Conversely, the Shapiro-Wilk test validated the normal distribution hypothesis exclusively for monthly data, indicating deviations from normality in the annual data. Thresholded LST classifies urban and bare lands as the warmest classes at 39.51 °C and 38.20 °C, respectively, and classifies water at 35.96 °C, dense vegetation at 35.52 °C, and sparse vegetation 37.71 °C as the coldest, which is a trend that is consistent annually and monthly. The analysis of SUHI effects demonstrates an increasing trend in UHI intensity, with statistical trends indicating a growth in average SUHI values over time. This comprehensive study underscores the critical role of remote sensing in understanding and addressing the impacts of climate change and urbanization on local and global climates, emphasizing the need for sustainable urban planning and green infrastructure to mitigate UHI effects.
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(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)
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