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Benchmarking Under- and Above-Canopy Laser Scanning Solutions for Deriving Stem Attributes in Varying Forest Conditions
Highlighting the Use of UAV to Increase the Resilience of Native Hawaiian Coastal Cultural Heritage
LUCA: A Sentinel-1 SAR-Based Global Forest Land Use Change Alert
Four Years of Atmospheric Boundary Layer Height Retrievals Using COSMIC-2 Satellite Data
Remote Sensing of Forests in Bavaria: A Review

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

23 pages, 16575 KiB

Open AccessArticle

Remote Sensing of Floodwater-Induced Subsurface Halite Dissolution in a Salt Karst System, with Implications for Landscape Evolution: The Western Shores of the Dead Sea

by Gidon Baer, Ittai Gavrieli, Iyad Swaed and Ran N. Nof

Remote Sens. 2024, 16(17), 3294; https://doi.org/10.3390/rs16173294 - 4 Sep 2024

We study the interrelations between salt karst and landscape evolution at the Ze’elim and Hever alluvial fans, Dead Sea (DS), Israel, in an attempt to characterize the ongoing surface and subsurface processes and identify future trends. Using light detection and ranging, interferometric synthetic [...] Read more.

We study the interrelations between salt karst and landscape evolution at the Ze’elim and Hever alluvial fans, Dead Sea (DS), Israel, in an attempt to characterize the ongoing surface and subsurface processes and identify future trends. Using light detection and ranging, interferometric synthetic aperture radar, drone photography, time-lapse cameras, and direct measurements of floodwater levels, we document floodwater recharge through riverbed sinkholes, subsurface salt dissolution, groundwater flow, and brine discharge at shoreline sinkholes during the years 2011–2023. At the Ze’elim fan, most of the surface floodwater drains into streambed sinkholes and discharges at shoreline sinkholes, whereas at the Hever fan, only a small fraction of the floodwater drains into sinkholes, while the majority flows downstream to the DS. This difference is attributed to the low-gradient stream profiles in Ze’elim, which enable water accumulation and recharge in sinkholes and their surrounding depressions, in contrast with the higher-gradient Hever profiles, which yield high-energy floods capable of carrying coarse gravel that eventually fill the sinkholes. The rapid drainage of floodwater into sinkholes also involves slope failure due to pore-pressure drop and cohesion loss within hours after each drainage event. Surface subsidence lineaments detected by InSAR indicate the presence of subsurface dissolution channels between recharge and discharge sites in the two fans and in the nearby Lynch straits. Subsidence and streambed sinkholes occur in most other fans and streams that flow to the DS; however, with the exception of Ze’elim, all other streams show only minor or no recharge along their course. This is due to either the high-gradient profiles, the gravelly sediments, the limited floods, or the lack of conditions for sinkhole development in the other streambeds. Thus, understanding the factors that govern the flood-related karst formation is of great importance for predicting landscape evolution in the DS region and elsewhere and for sinkhole hazard assessment. Full article

(This article belongs to the Special Issue Remote Sensing of the Dead Sea Region)

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21 pages, 16056 KiB

Open AccessArticle

The Remote Effects of Typhoons on the Cold Filaments in the Southwestern South China Sea

by Zezheng Zhao, Shengmu Yang, Huipeng Wang, Taikang Yuan and Kaijun Ren

Remote Sens. 2024, 16(17), 3293; https://doi.org/10.3390/rs16173293 - 4 Sep 2024

Cold filaments (CFs) in the southwestern South China Sea (SCS) impact local hydrodynamics and the ecological environment. In this study, the effects of typhoons passing over the northern SCS on CFs are investigated using multi-source observational and reanalysis data. Statistical analysis of CF [...] Read more.

Cold filaments (CFs) in the southwestern South China Sea (SCS) impact local hydrodynamics and the ecological environment. In this study, the effects of typhoons passing over the northern SCS on CFs are investigated using multi-source observational and reanalysis data. Statistical analysis of CF responses to typhoons over the past 24 years shows that during typhoon periods in the northern SCS, the CFs are intensified. We further analyze the remote effect of typhoons on the CF during Typhoon Kalmaegi in 2014, which caused a sea surface temperature (SST) drop of more than 3 °C in the CF region. The strengthened along-coast wind induced strong upwelling off the Vietnam coast. The maximum vertical velocity in the CF reaches three times the usual value. Meanwhile, influenced by the peripheral wind field of Kalmaegi, cold coastal water accumulated at the CF region due to the shafting of the offshore current. Both strong offshore currents and coastal upwellings enhanced the intensity of the CF. These findings demonstrate that typhoons not only directly affect ocean dynamic processes along their path but also present significant remote influences on coastal dynamics in other regions. This study enhances the understanding of CF evolution and sea–air interactions during extreme events. Full article

(This article belongs to the Special Issue Linking Upper Ocean Dynamics with Extreme Weather and Climate Events over the Ocean (Second Edition))

34 pages, 17617 KiB

Open AccessArticle

Integration of a Mobile Laser Scanning System with a Forest Harvester for Accurate Localization and Tree Stem Measurements

by Tamás Faitli, Eric Hyyppä, Heikki Hyyti, Teemu Hakala, Harri Kaartinen, Antero Kukko, Jesse Muhojoki and Juha Hyyppä

Remote Sens. 2024, 16(17), 3292; https://doi.org/10.3390/rs16173292 - 4 Sep 2024

Automating forest machines to optimize the forest value chain requires the ability to map the surroundings of the machine and to conduct accurate measurements of nearby trees. In the near-to-medium term, integrating a forest harvester with a mobile laser scanner system may have [...] Read more.

Automating forest machines to optimize the forest value chain requires the ability to map the surroundings of the machine and to conduct accurate measurements of nearby trees. In the near-to-medium term, integrating a forest harvester with a mobile laser scanner system may have multiple applications, including real-time assistance of the harvester operator using laser-scanner-derived tree measurements and the collection of vast amounts of training data for large-scale airborne laser scanning-based surveys at the individual tree level. In this work, we present a comprehensive processing flow for a mobile laser scanning (MLS) system mounted on a forest harvester starting from the localization of the harvester under the forest canopy followed by accurate and automatic estimation of tree attributes, such as diameter at breast height (DBH) and stem curve. To evaluate our processing flow, we recorded and processed MLS data from a commercial thinning operation on three test strips with a total driven length ranging from 270 to 447 m in a managed Finnish spruce forest stand containing a total of 658 reference trees within a distance of 15 m from the harvester trajectory. Localization reference was obtained by a robotic total station, while reference tree attributes were derived using a high-quality handheld laser scanning system. As some applications of harvester-based MLS require real-time capabilities while others do not, we investigated the positioning accuracy both for real-time localization of the harvester and after the optimization of the full trajectory. In the real-time positioning mode, the absolute localization error was on average 2.44 m, while the corresponding error after the full optimization was 0.21 m. Applying our automatic stem diameter estimation algorithm for the constructed point clouds, we measured DBH and stem curve with a root-mean-square error (RMSE) of 3.2 cm and 3.6 cm, respectively, while detecting approximately 90% of the reference trees with

DBH > 20

cm that were located within 15 m from the harvester trajectory. To achieve these results, we demonstrated a distance-adjusted bias correction method mitigating diameter estimation errors caused by the high beam divergence of the laser scanner used. Full article

(This article belongs to the Special Issue Remote Sensing and Smart Forestry II)

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27 pages, 17955 KiB

Open AccessArticle

Characterization of Complex Rock Mass Discontinuities from LiDAR Point Clouds

by Yanan Liu, Weihua Hua, Qihao Chen and Xiuguo Liu

Remote Sens. 2024, 16(17), 3291; https://doi.org/10.3390/rs16173291 - 4 Sep 2024

The distribution and development of rock mass discontinuities in 3D space control the deformation and failure characteristics of the rock mass, which in turn affect the strength, permeability, and stability of rock masses. Therefore, it is essential to accurately and efficiently characterize these [...] Read more.

The distribution and development of rock mass discontinuities in 3D space control the deformation and failure characteristics of the rock mass, which in turn affect the strength, permeability, and stability of rock masses. Therefore, it is essential to accurately and efficiently characterize these discontinuities. Light Detection and Ranging (LiDAR) now allows for fast and precise 3D data collection, which supports the creation of new methods for characterizing rock mass discontinuities. However, uneven density distribution and local surface undulations can limit the accuracy of discontinuity characterization. To address this, we propose a method for characterizing complex rock mass discontinuities based on laser point cloud data. This method is capable of processing datasets with varying densities and can reduce over-segmentation in non-planar areas. The suggested approach involves a five-stage process that includes: (1) adaptive resampling of point cloud data based on density comparison; (2) normal vector calculation using Principal Component Analysis (PCA); (3) identifying non-planar areas using a watershed-like algorithm, and determine the main discontinuity sets using Multi-threshold Mean Shift (MTMS); (4) identify single discontinuity clusters using Density-Based Spatial Clustering of Applications with Noise (DBSCAN); (5) fitting discontinuity planes with Random Sample Consensus (RANSAC) and determining discontinuity orientations using analytic geometry. This method was applied to three rock slope datasets and compared with previous research results and manual measurement results. The results indicate that this method can effectively reduce over-segmentation and the characterization results have high accuracy. Full article

(This article belongs to the Special Issue Machine Learning and Remote/Proximal Sensing for Rock Mass Characterization and Slope Analyses)

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28 pages, 4219 KiB

Open AccessReview

Delving into the Potential of Deep Learning Algorithms for Point Cloud Segmentation at Organ Level in Plant Phenotyping

by Kai Xie, Jianzhong Zhu, He Ren, Yinghua Wang, Wanneng Yang, Gang Chen, Chengda Lin and Ruifang Zhai

Remote Sens. 2024, 16(17), 3290; https://doi.org/10.3390/rs16173290 - 4 Sep 2024

Three-dimensional point clouds, as an advanced imaging technique, enable researchers to capture plant traits more precisely and comprehensively. The task of plant segmentation is crucial in plant phenotyping, yet current methods face limitations in computational cost, accuracy, and high-throughput capabilities. Consequently, many researchers [...] Read more.

Three-dimensional point clouds, as an advanced imaging technique, enable researchers to capture plant traits more precisely and comprehensively. The task of plant segmentation is crucial in plant phenotyping, yet current methods face limitations in computational cost, accuracy, and high-throughput capabilities. Consequently, many researchers have adopted 3D point cloud technology for organ-level segmentation, extending beyond manual and 2D visual measurement methods. However, analyzing plant phenotypic traits using 3D point cloud technology is influenced by various factors such as data acquisition environment, sensors, research subjects, and model selection. Although the existing literature has summarized the application of this technology in plant phenotyping, there has been a lack of in-depth comparison and analysis at the algorithm model level. This paper evaluates the segmentation performance of various deep learning models on point clouds collected or generated under different scenarios. These methods include outdoor real planting scenarios and indoor controlled environments, employing both active and passive acquisition methods. Nine classical point cloud segmentation models were comprehensively evaluated: PointNet, PointNet++, PointMLP, DGCNN, PointCNN, PAConv, CurveNet, Point Transformer (PT), and Stratified Transformer (ST). The results indicate that ST achieved optimal performance across almost all environments and sensors, albeit at a significant computational cost. The transformer architecture for points has demonstrated considerable advantages over traditional feature extractors by accommodating features over longer ranges. Additionally, PAConv constructs weight matrices in a data-driven manner, enabling better adaptation to various scales of plant organs. Finally, a thorough analysis and discussion of the models were conducted from multiple perspectives, including model construction, data collection environments, and platforms. Full article

(This article belongs to the Section Remote Sensing Image Processing)

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22 pages, 14082 KiB

Open AccessArticle

A Robust SAR-Optical Heterologous Image Registration Method Based on Region-Adaptive Keypoint Selection

by Keke Zhang, Anxi Yu, Wenhao Tong and Zhen Dong

Remote Sens. 2024, 16(17), 3289; https://doi.org/10.3390/rs16173289 (registering DOI) - 4 Sep 2024

The differences in sensor imaging mechanisms, observation angles, and scattering characteristics of terrestrial objects significantly limit the registration performance of synthetic aperture radar (SAR) and optical heterologous images. Traditional methods particularly struggle in weak feature regions, such as harbors and islands with substantial [...] Read more.

The differences in sensor imaging mechanisms, observation angles, and scattering characteristics of terrestrial objects significantly limit the registration performance of synthetic aperture radar (SAR) and optical heterologous images. Traditional methods particularly struggle in weak feature regions, such as harbors and islands with substantial water coverage, as well as in desolate areas like deserts. This paper introduces a robust heterologous image registration technique based on region-adaptive keypoint selection that integrates image texture features, targeting two pivotal aspects: feature point extraction and matching point screening. Initially, a dual threshold criterion based on block region information entropy and variance products effectively identifies weak feature regions. Subsequently, it constructs feature descriptors to generate similarity maps, combining histogram parameter skewness with non-maximum suppression (NMS) to enhance matching point accuracy. Extensive experiments have been conducted on conventional SAR-optical datasets and typical SAR-optical images with different weak feature regions to assess the method’s performance. The findings indicate that this method successfully removes outliers in weak feature regions and completes the registration task of SAR and optical images with weak feature regions. Full article

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19 pages, 20474 KiB

Open AccessArticle

Evaluation of Urban Microscopic Nighttime Light Environment Based on the Coupling Observation of Remote Sensing and UAV Observation

by Baogang Zhang, Ming Liu, Ruicong Li, Jie Liu, Lie Feng, Han Zhang, Weili Jiao and Liang Lang

Remote Sens. 2024, 16(17), 3288; https://doi.org/10.3390/rs16173288 - 4 Sep 2024

The urban canopy refers to the spatial area at the average height range of urban structures. The light environment of the urban canopy not only influences the ecological conditions of the canopy layer region but also serves as an indicator of the upward [...] Read more.

The urban canopy refers to the spatial area at the average height range of urban structures. The light environment of the urban canopy not only influences the ecological conditions of the canopy layer region but also serves as an indicator of the upward light influx of artificial nighttime light in the urban environment. Previous research on urban nighttime light environment mainly focused on the urban surface layer and urban night sky layer, lacking attention to the urban canopy layer. This study observes the urban canopy layer with the flight and photography functions of an unmanned aerial vehicle (UAV) and combines color band remote sensing data with ground measurement data to explore the relationship between the three levels of the urban nighttime light environment. Furthermore, a three–dimensional observation method is established for urban nighttime light environments based on a combination of three observation methods. The research results indicate that there is a good correlation between drone aerial photography data and remote sensing data (R² = 0.717), as well as between ground–measured data and remote sensing data (R² = 0.876). It also shows that UAV images can serve as a new path for the observation of urban canopy nighttime light environments because of the accuracy and reliability of UAV aerial data. Meanwhile, the combination of UAV photography, ground measurement, and remote sensing data provides a new method for the monitoring and control of urban nighttime light pollution. Full article

(This article belongs to the Special Issue Nighttime Light Remote Sensing Products for Urban Applications)

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25 pages, 4712 KiB

Open AccessArticle

Improving Angle-Only Orbit Determination Accuracy for Earth–Moon Libration Orbits Using a Neural-Network-Based Approach

by Zhe Zhang, Yishuai Shi and Zuoxiu Zheng

Remote Sens. 2024, 16(17), 3287; https://doi.org/10.3390/rs16173287 - 4 Sep 2024

In the realm of precision space applications, improving the accuracy of orbit determination (OD) is a crucial and demanding task, primarily because of the presence of measurement noise. To address this issue, a novel machine learning method based on bidirectional long short-term memory [...] Read more.

In the realm of precision space applications, improving the accuracy of orbit determination (OD) is a crucial and demanding task, primarily because of the presence of measurement noise. To address this issue, a novel machine learning method based on bidirectional long short-term memory (BiLSTM) is proposed in this research. In particular, the proposed method aims to improve the OD accuracy of Earth–Moon Libration orbits with angle-only measurements. The proposed BiLSTM network is designed to detect inaccurate measurements during an OD process, which is achieved by incorporating the least square method (LSM) as a basic estimation approach. The structure, inputs, and outputs of the modified BiLSTM network are meticulously crafted for the detection of inaccurate measurements. Following the detection of inaccurate measurements, a compensating strategy is devised to modify these detection results and thereby reduce their negative impact on OD accuracy. The modified measurements are then used to obtain a more accurate OD solution. The proposed method is applied to solve the OD problem of a 4:1 synodic resonant near-rectilinear halo orbit around the Earth–Moon L2 point. The training results reveal that the bidirectional network structure outperforms the regular unidirectional structures in terms of detection accuracy. Numerical simulations show that the proposed method can reduce the estimated error by approximately 10%. The proposed method holds significant potential for future missions in cislunar space. Full article

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

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19 pages, 15208 KiB

Open AccessArticle

Analysis of the Influence of Refraction-Parameter Deviation on Underwater Stereo-Vision Measurement with Flat Refraction Interface

by Guanqing Li, Shengxiang Huang, Zhi Yin, Nanshan Zheng and Kefei Zhang

Remote Sens. 2024, 16(17), 3286; https://doi.org/10.3390/rs16173286 - 4 Sep 2024

There has been substantial research on multi-medium visual measurement in fields such as underwater three-dimensional reconstruction and underwater structure monitoring. Addressing the issue where traditional air-based visual-measurement models fail due to refraction when light passes through different media, numerous studies have established refraction-imaging [...] Read more.

There has been substantial research on multi-medium visual measurement in fields such as underwater three-dimensional reconstruction and underwater structure monitoring. Addressing the issue where traditional air-based visual-measurement models fail due to refraction when light passes through different media, numerous studies have established refraction-imaging models based on the actual geometry of light refraction to compensate for the effects of refraction on cross-media imaging. However, the calibration of refraction parameters inevitably contains errors, leading to deviations in these parameters. To analyze the impact of refraction-parameter deviations on measurements in underwater structure visual navigation, this paper develops a dual-media stereo-vision measurement simulation model and conducts comprehensive simulation experiments. The results indicate that to achieve high-precision underwater-measurement outcomes, the calibration method for refraction parameters, the distribution of the targets in the field of view, and the distance of the target from the camera must all be meticulously designed. These findings provide guidance for the construction of underwater stereo-vision measurement systems, the calibration of refraction parameters, underwater experiments, and practical applications. Full article

(This article belongs to the Special Issue High-Precision and High-Reliability Positioning, Navigation, and Timing: Opportunities and Challenges)

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20 pages, 10219 KiB

Open AccessArticle

Climatology of Polar Stratospheric Clouds Derived from CALIPSO and SLIMCAT

by Douwang Li, Zhe Wang, Shun Li, Jiankai Zhang and Wuhu Feng

Remote Sens. 2024, 16(17), 3285; https://doi.org/10.3390/rs16173285 - 4 Sep 2024

Polar stratospheric clouds (PSCs) play a crucial role in ozone depletion in the polar stratosphere. In this study, the space-based PSCs record from CALISPO and an offline three-dimensional chemical transport model (SLIMCAT) are used to analyze the PSCs in the Arctic and the [...] Read more.

Polar stratospheric clouds (PSCs) play a crucial role in ozone depletion in the polar stratosphere. In this study, the space-based PSCs record from CALISPO and an offline three-dimensional chemical transport model (SLIMCAT) are used to analyze the PSCs in the Arctic and the Antarctic for the period 2006−2021. Observations indicate that the seasonal evolution of the Antarctic PSC area is similar from year to year. In contrast, the Arctic PSCs show large differences in seasonal variations of coverage and duration in different years. The SLIMCAT simulations effectively capture the seasonal and interannual variations of PSCs. However, the simulated PSC areas are larger than CALIPSO observations, which can be attributed to the relatively high instrumental detection threshold of CALIPSO. SLIMCAT can capture the zonal asymmetry of PSCs in both the Antarctic and Arctic, and it can reproduce a more accurate spatial distribution of PSCs when the PSC coverage area is larger. In addition, accurate simulation of HNO₃ is important for PSC simulation. Because the simulation of denitrification processes is poor in SLIMCAT, which uses the thermodynamic equilibrium PSC scheme, the PSCs modeled by SLIMCAT are located at higher altitudes compared to the observation in the Antarctic, where the denitrification processes are strong. In contrast, for ice PSCs of which HNO₃ is not required in calculations and the Arctic where denitrification is weak, the simulated PSC at different altitudes closely matches the observations. Full article

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24 pages, 6313 KiB

Open AccessArticle

Lightweight Ship Detection Network for SAR Range-Compressed Domain

by Xiangdong Tan, Xiangguang Leng, Zhongzhen Sun, Ru Luo, Kefeng Ji and Gangyao Kuang

Remote Sens. 2024, 16(17), 3284; https://doi.org/10.3390/rs16173284 - 4 Sep 2024

The utilization of Synthetic Aperture Radar (SAR) for real-time ship detection proves highly advantageous in the supervision and monitoring of maritime activities. Ship detection in the range-compressed domain of SAR rather than in fully focused SAR imagery can significantly reduce the time and [...] Read more.

The utilization of Synthetic Aperture Radar (SAR) for real-time ship detection proves highly advantageous in the supervision and monitoring of maritime activities. Ship detection in the range-compressed domain of SAR rather than in fully focused SAR imagery can significantly reduce the time and computational resources required for complete SAR imaging, enabling lightweight real-time ship detection methods to be implemented on an airborne or spaceborne SAR platform. However, there is a lack of lightweight ship detection methods specifically designed for the SAR range-compressed domain. In this paper, we propose Fast Range-Compressed Detection (FastRCDet), a novel lightweight network for ship detection in the SAR range-compressed domain. Firstly, to address the distinctive geometric characteristics of the SAR range-compressed domain, we propose a Lightweight Adaptive Network (LANet) as the backbone of the network. We introduce Arbitrary Kernel Convolution (AKConv) as a fundamental component, which enables the flexible adjustment of the receptive field shape and better adaptation to the large scale and aspect ratio characteristics of ships in the range-compressed domain. Secondly, to enhance the efficiency and simplicity of the network model further, we propose an innovative Multi-Scale Fusion Head (MSFH) module directly integrated after the backbone, eliminating the need for a neck module. This module effectively integrates features at various scales to more accurately capture detailed information about the target. Thirdly, to further enhance the network’s adaptability to ships in the range-compressed domain, we propose a novel Direction IoU (DIoU) loss function that leverages angle cost to control the convergence direction of predicted bounding boxes, thereby improving detection accuracy. Experimental results on a publicly available dataset demonstrate that FastRCDet achieves significant reductions in parameters and computational complexity compared to mainstream networks without compromising detection performance in SAR range-compressed images. FastRCDet achieves a low parameter of 2.49 M and a high detection speed of 38.02 frames per second (FPS), surpassing existing lightweight detection methods in terms of both model size and processing rate. Simultaneously, it attains an average accuracy (AP) of 77.12% in terms of its detection performance. This method provides a baseline in lightweight network design for SAR ship detection in the range-compressed domain and offers practical implications for resource-constrained embedded platforms. Full article

(This article belongs to the Special Issue SAR-Based Signal Processing and Target Recognition (Second Edition))

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17 pages, 2777 KiB

Open AccessArticle

Comparing Satellite Soil Moisture Products Using In Situ Observations Over an Instrumented Experimental Basin in Romania

by Sofia Ortenzi, Corrado Cencetti, Florentina-Iuliana Mincu, Gianina Neculau, Viorel Chendeş, Luca Ciabatta, Christian Massari and Lucio Di Matteo

Remote Sens. 2024, 16(17), 3283; https://doi.org/10.3390/rs16173283 - 4 Sep 2024

This study assessed the performance of different remotely sensed soil moisture products with in situ observations; six profile probes for the water content monitoring were selected, operating during 2016–2021 from the Voineşti Experimental Basin in the Romanian Subcarpathian region. The reliability of satellite [...] Read more.

This study assessed the performance of different remotely sensed soil moisture products with in situ observations; six profile probes for the water content monitoring were selected, operating during 2016–2021 from the Voineşti Experimental Basin in the Romanian Subcarpathian region. The reliability of satellite observations has been analyzed on both single ground-based observation points and spatialized information, considering near-surface and root-zone soil moisture data. The physics-based index (HCI) and some statistical tests widely used in inter-comparison analyses have been computed. The study of HCI highlighted that the SMAP SP_L4_SM products have shown the best performances considering the near-surface and root-zone data evaluations. The comparison of SWI1km observations with in situ data produced good results for single-point and spatialized soil moisture estimations acquired at different depths over the experimental basin. The SSM1km and SMAP L2_SM_SP products exhibited the lowest performances. The results contribute to the validation of satellite products of surface and root-zone soil moisture in the Subcarpathian region, helping to provide information in an area not monitored by the International Soil Moisture Network. The findings offer valuable insights into evaluating the performance of satellite soil moisture products in the Romanian region. Full article

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27 pages, 10427 KiB

Open AccessArticle

UMMFF: Unsupervised Multimodal Multilevel Feature Fusion Network for Hyperspectral Image Super-Resolution

by Zhongmin Jiang, Mengyao Chen and Wenju Wang

Remote Sens. 2024, 16(17), 3282; https://doi.org/10.3390/rs16173282 - 4 Sep 2024

Due to the inadequacy in utilizing complementary information from different modalities and the biased estimation of degraded parameters, the unsupervised hyperspectral super-resolution algorithm suffers from low precision and limited applicability. To address this issue, this paper proposes an approach for hyperspectral image super-resolution, [...] Read more.

Due to the inadequacy in utilizing complementary information from different modalities and the biased estimation of degraded parameters, the unsupervised hyperspectral super-resolution algorithm suffers from low precision and limited applicability. To address this issue, this paper proposes an approach for hyperspectral image super-resolution, namely, the Unsupervised Multimodal Multilevel Feature Fusion network (UMMFF). The proposed approach employs a gated cross-retention module to learn shared patterns among different modalities. This module effectively eliminates the intermodal differences while preserving spatial–spectral correlations, thereby facilitating information interaction. A multilevel spatial–channel attention and parallel fusion decoder are constructed to extract features at three levels (low, medium, and high), enriching the information of the multimodal images. Additionally, an independent prior-based implicit neural representation blind estimation network is designed to accurately estimate the degraded parameters. The utilization of UMMFF on the “Washington DC”, Salinas, and Botswana datasets exhibited a superior performance compared to existing state-of-the-art methods in terms of primary performance metrics such as PSNR and ERGAS, and the PSNR values improved by 18.03%, 8.55%, and 5.70%, respectively, while the ERGAS values decreased by 50.00%, 75.39%, and 53.27%, respectively. The experimental results indicate that UMMFF demonstrates excellent algorithm adaptability, resulting in high-precision reconstruction outcomes. Full article

(This article belongs to the Special Issue Image Enhancement and Fusion Techniques in Remote Sensing)

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26 pages, 10864 KiB

Open AccessArticle

Near-Real-Time Long-Strip Geometric Processing without GCPs for Agile Push-Frame Imaging of LuoJia3-01 Satellite

by Rongfan Dai, Mi Wang and Zhao Ye

Remote Sens. 2024, 16(17), 3281; https://doi.org/10.3390/rs16173281 - 4 Sep 2024

Long-strip imaging is an important way of improving the coverage and acquisition efficiency of remote sensing satellite data. During the agile maneuver imaging process of the satellite, the LuoJia3-01 satellite can obtain a sequence of array long-strip images with a certain degree of [...] Read more.

Long-strip imaging is an important way of improving the coverage and acquisition efficiency of remote sensing satellite data. During the agile maneuver imaging process of the satellite, the LuoJia3-01 satellite can obtain a sequence of array long-strip images with a certain degree of overlap. Limited by the relative accuracy of satellite attitude, there will be relative misalignment between the sequence frame images, requiring high-precision geometric processing to meet the requirements of large-area remote sensing applications. Therefore, this study proposes a new method for the geometric correction of long-strip images without ground control points (GCPs) through GPU acceleration. Firstly, through the relative orientation of sequence images, the relative geometric errors between the images are corrected frame-by-frame. Then, block perspective transformation and image point densified filling (IPDF) direct mapping processing are carried out, mapping the sequence images frame-by-frame onto the stitched image. In this way, the geometric correction and image stitching of the sequence frame images are completed simultaneously. Finally, computationally intensive steps, such as point matching, coordinate transformation, and grayscale interpolation, are processed in parallel using GPU to further enhance the program’s execution efficiency. The experimental results show that the method proposed in this study achieves a stitching accuracy of less than 0.3 pixels for the geometrically corrected long-strip images, an internal geometric accuracy of less than 1.5 pixels, and an average processing time of less than 1.5 s per frame, meeting the requirements for high-precision near-real-time processing applications. Full article

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

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19 pages, 5864 KiB

Open AccessArticle

Combination of Multiple Variables and Machine Learning for Regional Cropland Water and Carbon Fluxes Estimation: A Case Study in the Haihe River Basin

by Minghan Cheng, Kaihua Liu, Zhangxin Liu, Junzeng Xu, Zhengxian Zhang and Chengming Sun

Remote Sens. 2024, 16(17), 3280; https://doi.org/10.3390/rs16173280 - 4 Sep 2024

Understanding the water and carbon cycles within terrestrial ecosystems is crucial for effective monitoring and management of regional water resources and the ecological environment. However, physical models like the SEB- and LUE-based ones can be complex and demand extensive input data. In our [...] Read more.

Understanding the water and carbon cycles within terrestrial ecosystems is crucial for effective monitoring and management of regional water resources and the ecological environment. However, physical models like the SEB- and LUE-based ones can be complex and demand extensive input data. In our study, we leveraged multiple variables (vegetation growth, surface moisture, radiative energy, and other relative variables) as inputs for various regression algorithms, including Multiple Linear Regression (MLR), Random Forest Regression (RFR), and Backpropagation Neural Network (BPNN), to estimate water (ET) and carbon fluxes (NEE) in the Haihe River Basin, and compared the estimated results with the observations from six eddy covariance flux towers. We aimed to (1) assess the impacts of different input variables on the accuracy of ET and NEE estimations, (2) compare the accuracy of the three regression methods, including three machine learning algorithms and Multiple Linear Regression, and (3) evaluate the performance of ET and NEE estimation models across various regions. The key findings include: (1) Increasing the number of input variables typically improved the accuracy of ET and NEE estimations. (2) RFR proved to be the most accurate for both ET and NEE estimations among the three regression algorithms. Of these, the four types of variables used together with RFR resulted in the best accuracy for ET (R² of 0.81 and an RMSE of 1.13 mm) and NEE (R² of 0.83 and an RMSE of 2.83 gC/m²) estimations. (3) Vegetation growth variables (i.e., VIs) are the most important inputs for ET and NEE estimation. (4) The proposed ET and NEE estimation models exhibited some variation in accuracy across different validation sites. Despite these variations, the accuracy levels across all six validation sites remained relatively high. Overall, this study lays the groundwork for an efficient approach to agricultural water resources and ecosystem monitoring and management. Full article

(This article belongs to the Topic Carbon-Energy-Water Nexus in Global Energy Transition)

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17 pages, 4965 KiB

Open AccessArticle

Identifying Habitat Productivity Thresholds to Assess the Effects of Drought on a Specialist Folivore

by Ivan Kotzur, Ben D. Moore, Chris Meakin, Maldwyn J. Evans and Kara N. Youngentob

Remote Sens. 2024, 16(17), 3279; https://doi.org/10.3390/rs16173279 - 4 Sep 2024

Climate change has altered the frequency and severity of extreme weather, which can affect vegetation condition and habitat quality for wildlife. Declines in vegetation productivity during droughts and heatwaves can negatively impact animals that depend on vegetation for water and nutrition. We used [...] Read more.

Climate change has altered the frequency and severity of extreme weather, which can affect vegetation condition and habitat quality for wildlife. Declines in vegetation productivity during droughts and heatwaves can negatively impact animals that depend on vegetation for water and nutrition. We used the normalised difference vegetation index (NDVI) to look at relationships between vegetation productivity and the presence of koalas (Phascolarctos cinereus) in potential habitat throughout much of their range. Using a large, long-term koala presence dataset, we tested the hypothesis that locations where koalas had been observed would exhibit higher NDVI values than a random, representative sample from the same vegetation group. We also identified the minimum NDVI threshold at which koalas occurred across time for each vegetation group and compared these to the minimum NDVI values across potential koala habitat before and during the Millennium Drought, one of the worst recorded in Australia. Additionally, we investigated whether vegetation above the minimum NDVI thresholds was significantly closer to perennial water than unsuitable vegetation. We found that koalas tend to occur at locations with higher NDVI than average for all vegetation groups. Prior to the drought, 49% of potential koala habitat maintained a minimum NDVI above the koalas’ threshold, equating to 190,227 km², which declined to 166,746 km² during drought (i.e., a 12% reduction). We also found that unsuitable vegetation tended to occur farther from perennial water than suitable vegetation for some vegetation groups. Areas that remained above the NDVI thresholds during the drought should be considered potential refugia for populations during an event of similar magnitude and could indicate future habitat extent. Full article

(This article belongs to the Section Ecological Remote Sensing)

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18 pages, 232655 KiB

Open AccessArticle

SFA-Net: Semantic Feature Adjustment Network for Remote Sensing Image Segmentation

by Gyutae Hwang, Jiwoo Jeong and Sang Jun Lee

Remote Sens. 2024, 16(17), 3278; https://doi.org/10.3390/rs16173278 - 3 Sep 2024

Advances in deep learning and computer vision techniques have made impacts in the field of remote sensing, enabling efficient data analysis for applications such as land cover classification and change detection. Convolutional neural networks (CNNs) and transformer architectures have been utilized in visual [...] Read more.

Advances in deep learning and computer vision techniques have made impacts in the field of remote sensing, enabling efficient data analysis for applications such as land cover classification and change detection. Convolutional neural networks (CNNs) and transformer architectures have been utilized in visual perception algorithms due to their effectiveness in analyzing local features and global context. In this paper, we propose a hybrid transformer architecture that consists of a CNN-based encoder and transformer-based decoder. We propose a feature adjustment module that refines the multiscale feature maps extracted from an EfficientNet backbone network. The adjusted feature maps are integrated into the transformer-based decoder to perform the semantic segmentation of the remote sensing images. This paper refers to the proposed encoder–decoder architecture as a semantic feature adjustment network (SFA-Net). To demonstrate the effectiveness of the SFA-Net, experiments were thoroughly conducted with four public benchmark datasets, including the UAVid, ISPRS Potsdam, ISPRS Vaihingen, and LoveDA datasets. The proposed model achieved state-of-the-art accuracy on the UAVid, ISPRS Vaihingen, and LoveDA datasets for the segmentation of the remote sensing images. On the ISPRS Potsdam dataset, our method achieved comparable accuracy to the latest model while reducing the number of trainable parameters from 113.8 M to 10.7 M. Full article

(This article belongs to the Special Issue Deep Learning for Remote Sensing and Geodata)

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25 pages, 23009 KiB

Open AccessArticle

Exploring Reinforced Class Separability and Discriminative Representations for SAR Target Open Set Recognition

by Fei Gao, Xin Luo, Rongling Lang, Jun Wang, Jinping Sun and Amir Hussain

Remote Sens. 2024, 16(17), 3277; https://doi.org/10.3390/rs16173277 - 3 Sep 2024

Current synthetic aperture radar (SAR) automatic target recognition (ATR) algorithms primarily operate under the closed-set assumption, implying that all target classes have been previously learned during the training phase. However, in open scenarios, they may encounter target classes absent from the training set, [...] Read more.

Current synthetic aperture radar (SAR) automatic target recognition (ATR) algorithms primarily operate under the closed-set assumption, implying that all target classes have been previously learned during the training phase. However, in open scenarios, they may encounter target classes absent from the training set, thereby necessitating an open set recognition (OSR) challenge for SAR-ATR. The crux of OSR lies in establishing distinct decision boundaries between known and unknown classes to mitigate confusion among different classes. To address this issue, we introduce a novel framework termed reinforced class separability for SAR target open set recognition (RCS-OSR), which focuses on optimizing prototype distribution and enhancing the discriminability of features. First, to capture discriminative features, a cross-modal causal features enhancement module (CMCFE) is proposed to strengthen the expression of causal regions. Subsequently, regularized intra-class compactness loss (RIC-Loss) and intra-class relationship aware consistency loss (IRC-Loss) are devised to optimize the embedding space. In conjunction with joint supervised training using cross-entropy loss, RCS-OSR can effectively reduce empirical classification risk and open space risk simultaneously. Moreover, a class-aware OSR classifier with adaptive thresholding is designed to leverage the differences between different classes. Consequently, our method can construct distinct decision boundaries between known and unknown classes to simultaneously classify known classes and identify unknown classes in open scenarios. Extensive experiments conducted on the MSTAR dataset demonstrate the effectiveness and superiority of our method in various OSR tasks. Full article

(This article belongs to the Special Issue Target Recognition and Detection Based on High Resolution Radar Images)

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21 pages, 13602 KiB

Open AccessArticle

Analysis of Nearshore Near-Inertial Oscillations Using Numerical Simulation with Data Assimilation in the Pearl River Estuary of the South China Sea

by Zihao Jiang, Chunlei Wei, Fan Yang and Jun Wei

Remote Sens. 2024, 16(17), 3276; https://doi.org/10.3390/rs16173276 - 3 Sep 2024

The High-Frequency (HF) radar network has become an effective method for detecting coastal currents. In this study, we confirmed the effectiveness of the HF radar measurements by comparing with the Acoustic Doppler Current Profiler (ADCP) and explore the possibility of assimilating radar data [...] Read more.

The High-Frequency (HF) radar network has become an effective method for detecting coastal currents. In this study, we confirmed the effectiveness of the HF radar measurements by comparing with the Acoustic Doppler Current Profiler (ADCP) and explore the possibility of assimilating radar data into a regional coastal ocean model. A regional high-resolution model with resolution of 10 m was first built in the Pearl River Estuary (PRE). However, analysis of the Hovmöller diagrams from the model simulations in this study indicated a significant deficiency in representing Near-Inertial Oscillations (NIOs) in the PRE, particularly in the east–west direction, despite including wind fields in the input data, during the week from 3 to 8 August 2022. To overcome the model deficiency, we conducted a set of assimilation experiments and performed sensitivity analyses. The results of sensitivity experiments indicate that the model exhibits a sufficient capacity to replicate NIOs after assimilation, lasting approximately 5–6 days. To further analyze the reasons for the decay in the magnitude of the NIOs, data from the three ADCP stations were compared with model results by applying the momentum equation. The assimilated vertical diffusion term outperforms the unassimilated model in representing NIOs. These findings highlight the importance of the vertical diffusion term for simulating NIOs and the data assimilation in improving the model’s representation of physical processes. Full article

(This article belongs to the Section Ocean Remote Sensing)

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16 pages, 1550 KiB

Open AccessArticle

A New Data Fusion Method for GNSS/INS Integration Based on Weighted Multiple Criteria

by Chen Jiang, Qiuzhao Zhang and Dongbao Zhao

Remote Sens. 2024, 16(17), 3275; https://doi.org/10.3390/rs16173275 - 3 Sep 2024

The standard Kalman filter and most of its enhancements are typically designed based on the criterion that minimizes the mean squared error, with little discussion of multiple criteria in the positioning and navigation fields. Therefore, a novel data fusion method that takes into [...] Read more.

The standard Kalman filter and most of its enhancements are typically designed based on the criterion that minimizes the mean squared error, with little discussion of multiple criteria in the positioning and navigation fields. Therefore, a novel data fusion method that takes into account weighted multiple criteria is proposed in this paper, implementing a filtering algorithm based on integrated criteria with different weights determined by a weight adjustment factor. The proposed algorithm and conventional filtering algorithms were utilized for data fusion in GNSS/INS integration. Experiments were conducted using actual data collected from an urban environment. Comparative analysis revealed that, when utilizing the proposed algorithm, the precision of the position, velocity, and attitude of the tested land vehicle could be improved by approximately 24%, 48%, and 35%, respectively. Furthermore, a series of filtering algorithms with different weight adjustment factors was performed to test their influence on the filtering. The application of the proposed algorithm should be accompanied by an appropriate weight adjustment factor. Full article

(This article belongs to the Special Issue Data Fusion Methods and AI Technologies for Resilient PNT in Challenging Observation Areas)

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