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 21.1 days after submission; acceptance to publication is undertaken in 2.6 days (median values for papers published in this journal in the first 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
A Novel Building Extraction Network via Multi-Scale Foreground Modeling and Gated Boundary Refinement
Remote Sens. 2023, 15(24), 5638; https://doi.org/10.3390/rs15245638 - 05 Dec 2023
Abstract
Deep learning-based methods for building extraction from remote sensing images have been widely applied in fields such as land management and urban planning. However, extracting buildings from remote sensing images commonly faces challenges due to specific shooting angles. First, there exists a foreground–background
[...] Read more.
Deep learning-based methods for building extraction from remote sensing images have been widely applied in fields such as land management and urban planning. However, extracting buildings from remote sensing images commonly faces challenges due to specific shooting angles. First, there exists a foreground–background imbalance issue, and the model excessively learns features unrelated to buildings, resulting in performance degradation and propagative interference. Second, buildings have complex boundary information, while conventional network architectures fail to capture fine boundaries. In this paper, we designed a multi-task U-shaped network (BFL-Net) to solve these problems. This network enhances the expression of the foreground and boundary features in the prediction results through foreground learning and boundary refinement, respectively. Specifically, the Foreground Mining Module (FMM) utilizes the relationship between buildings and multi-scale scene spaces to explicitly model, extract, and learn foreground features, which can enhance foreground and related contextual features. The Dense Dilated Convolutional Residual Block (DDCResBlock) and the Dual Gate Boundary Refinement Module (DGBRM) individually process the diverted regular stream and boundary stream. The former can effectively expand the receptive field, and the latter utilizes spatial and channel gates to activate boundary features in low-level feature maps, helping the network refine boundaries. The predictions of the network for the building, foreground, and boundary are respectively supervised by ground truth. The experimental results on the WHU Building Aerial Imagery and Massachusetts Buildings Datasets show that the IoU scores of BFL-Net are 91.37% and 74.50%, respectively, surpassing state-of-the-art models.
Full article
(This article belongs to the Special Issue Deep Learning Meets Remote Sensing for Earth Observation and Monitoring)
►
Show Figures
Open AccessArticle
Correcting for Mobile X-Band Weather Radar Tilt Using Solar Interference
Remote Sens. 2023, 15(24), 5637; https://doi.org/10.3390/rs15245637 - 05 Dec 2023
Abstract
Precise knowledge of the antenna pointing direction is a key facet to ensure the accuracy of observations from scanning weather radars. The sun is an often-used reference point to aid accurate alignment of weather radar systems and is particularly useful when observed as
[...] Read more.
Precise knowledge of the antenna pointing direction is a key facet to ensure the accuracy of observations from scanning weather radars. The sun is an often-used reference point to aid accurate alignment of weather radar systems and is particularly useful when observed as interference during normal scanning operations. In this study, we combine two online solar interference approaches to determine the pointing accuracy of an X-band mobile weather radar system deployed for 26 months in northern England (54.517°N, 3.615°W). During the deployment, several shifts in the tilt of the radar system are diagnosed between site visits. One extended period of time (>11 months) is shown to have a changing tilt that is independent of human intervention. To verify the corrections derived from this combined approach, quantitative precipitation estimates (QPEs) from the radar system are compared to surface observations: an approach that takes advantage of the variations in the magnitude of partial beam blockage corrections required due to tilting of the radar system close to mountainous terrain. The observed improvements in QPE performance after correction support the use of the derived tilt corrections for further applications using the corrected dataset. Finally, recommendations for future deployments are made, with particular focus on higher latitudes where solar interference spikes show more seasonality than those at mid-latitudes.
Full article
(This article belongs to the Section Atmospheric Remote Sensing)
►▼
Show Figures

Figure 1
Open AccessArticle
Efficient 3D Frequency Semi-Airborne Electromagnetic Modeling Based on Domain Decomposition
Remote Sens. 2023, 15(24), 5636; https://doi.org/10.3390/rs15245636 - 05 Dec 2023
Abstract
Landslides are common geological hazards that often result in significant casualties and economic losses. Due to their occurrence in complex terrain areas, conventional geophysical techniques face challenges in early detection and warning of landslides. Semi-airborne electromagnetic (SAEM) technology, utilizing unmanned aerial platforms for
[...] Read more.
Landslides are common geological hazards that often result in significant casualties and economic losses. Due to their occurrence in complex terrain areas, conventional geophysical techniques face challenges in early detection and warning of landslides. Semi-airborne electromagnetic (SAEM) technology, utilizing unmanned aerial platforms for rapid unmanned remote sensing, can overcome the influence of complex terrain and serve as an effective approach for landslide detection and monitoring. In response to the low computational efficiency of conventional semi-airborne EM 3D forward modeling, this study proposes an efficient forward modeling method. To handle arbitrarily complex topography and geologic structures, the unstructured tetrahedron mesh is adopted to discretize the earth. Based on the vector finite element formula, the Dual–Primal Finite Element Tearing and Interconnecting (FETI-DP) method is further applied to enhance modeling efficiency. It obtains a reduced order subsystem and avoids directly solving huge overall linear equations by converting the entirety problem into the interface problem. We check our algorithm by comparing it with 1D semi-analytical solutions and the conventional finite element method. The numerical experiments reveal that the FETI-DP method utilities less memory and exhibits higher computation efficiency than the conventional methods. Additionally, we compare the electromagnetic responses with the topography model and flat earth model. The comparison results indicate that the effect of topography cannot be ignored. Further, we analyze the characteristic of electromagnetic responses when the thickness of the aquifer changes in a landslide area. We demonstrate the effectiveness of the 3D SAEM method for landslide detection and monitoring.
Full article
(This article belongs to the Special Issue Multi-Data Applied to Near-Surface Geophysics)
Open AccessArticle
Performance Analysis of Artificial Intelligence Approaches for LEMP Classification
Remote Sens. 2023, 15(24), 5635; https://doi.org/10.3390/rs15245635 - 05 Dec 2023
Abstract
Lightning Electromagnetic Pulses, or LEMPs, propagate in the Earth–ionosphere waveguide and can be detected remotely by ground-based lightning electric field sensors. LEMPs produced by different types of lightning processes have different signatures. A single thunderstorm can produce thousands of LEMPs, which makes their
[...] Read more.
Lightning Electromagnetic Pulses, or LEMPs, propagate in the Earth–ionosphere waveguide and can be detected remotely by ground-based lightning electric field sensors. LEMPs produced by different types of lightning processes have different signatures. A single thunderstorm can produce thousands of LEMPs, which makes their classification virtually impossible to carry out manually. The lightning classification is important to distinguish the types of thunderstorms and to know their severity. Lightning type is also related to aerosol concentration and can reveal wildfires. Artificial Intelligence (AI) is a good approach to recognizing patterns and dealing with huge datasets. AI is the general denomination for different Machine Learning Algorithms (MLAs) including deep learning and others. The constant improvements in the AI field show us that most of the Lightning Location Systems (LLS) will soon incorporate those techniques to improve their performance in the lightning-type classification task. In this study, we assess the performance of different MLAs, including a SVM (Support Vector Machine), MLP (Multi-Layer Perceptron), FCN (Fully Convolutional Network), and Residual Neural Network (ResNet) in the task of LEMP classification. We also address different aspects of the dataset that can interfere with the classification problem, including data balance, noise level, and LEMP recorded length.
Full article
(This article belongs to the Special Issue Advances in Instrumentation and Algorithms for Atmospheric Electricity Applications)
►▼
Show Figures

Figure 1
Open AccessArticle
Comparison of Supervised Learning and Changepoint Detection for Insect Detection in Lidar Data
Remote Sens. 2023, 15(24), 5634; https://doi.org/10.3390/rs15245634 - 05 Dec 2023
Abstract
Concerns about decreases in insect population and biodiversity, in addition to the need for monitoring insects in agriculture and disease control, have led to an increased need for automated, non-invasive monitoring techniques. To this end, entomological lidar systems have been developed and successfully
[...] Read more.
Concerns about decreases in insect population and biodiversity, in addition to the need for monitoring insects in agriculture and disease control, have led to an increased need for automated, non-invasive monitoring techniques. To this end, entomological lidar systems have been developed and successfully used for detecting and classifying insects. However, the data produced by these lidar systems create several problems from a data analysis standpoint: the data can contain millions of observations, very few observations contain insects, and the background environment is non-stationary. This study compares the insect-detection performance of various supervised machine learning and unsupervised changepoint detection algorithms and provides commentary on the relative strengths of each method. We found that the supervised methods generally perform better than the changepoint detection methods, at the cost of needing labeled data. The supervised learning method with the highest Matthew’s Correlation Coefficient score on the testing set correctly identified 99.5% of the insect-containing images and 83.7% of the non-insect images; similarly, the best changepoint detection method correctly identified 83.2% of the insect-containing images and 84.2% of the non-insect images. Our results show that both types of methods can reduce the need for manual data analysis.
Full article
(This article belongs to the Special Issue Machine Learning and GeoAI for Remote Sensing Environmental Monitoring)
►▼
Show Figures

Figure 1
Open AccessArticle
Multi-Scale Remote Sensing Assessment of Ecological Environment Quality and Its Driving Factors in Watersheds: A Case Study of Huashan Creek Watershed in China
Remote Sens. 2023, 15(24), 5633; https://doi.org/10.3390/rs15245633 - 05 Dec 2023
Abstract
The Huashan Creek watershed is the largest water source and the main production area of honeydew in Pinghe County, whose extensive cultivation of honeydew has exacerbated soil and water pollution. However, the spatial application of remote sensing ecological index (RSEI) in this watershed
[...] Read more.
The Huashan Creek watershed is the largest water source and the main production area of honeydew in Pinghe County, whose extensive cultivation of honeydew has exacerbated soil and water pollution. However, the spatial application of remote sensing ecological index (RSEI) in this watershed and key driving factors are not clear considering the applicability of data quality and the diversity of methodological scales. To explore the RSEI and driving factors at distinct scales in Huashan Creek watershed, this study constructed the RSEI based on the environmental balance matrix at seven scales in 2020, revealed its spatial response characteristics at different scales, and analyzed the key drivers. The results show that the 240 m grid as well as rural and watershed scale convergence analyses satisfy the assessment of RSEI, whose Moran indexes are 0.558, 0.595, and 0.146, respectively. The RSEIs at different scales have significant spatial aggregation characteristics, but the overall status is moderate. The central town–riparian area with poor RSEI contrasts with the western mountainous area, which has comparatively better quality. Population has a major influence on RSEI at multiple scales (0.8), with elevation and patch index acting significantly at the village and grid scales, respectively. These findings help to identify the spatial distribution of quality and control mechanisms of RSEI in the Huashan Creek watershed and provide new insights into key scales and drivers of ecological restoration practices in the watershed.
Full article
(This article belongs to the Special Issue Remote Sensing for Geology and Mapping)
►▼
Show Figures

Figure 1
Open AccessArticle
Remote Sensing Analysis for Vegetation Assessment of a Large-Scale Constructed Wetland Treating Produced Water Polluted with Oil Hydrocarbons
Remote Sens. 2023, 15(24), 5632; https://doi.org/10.3390/rs15245632 - 05 Dec 2023
Abstract
The identification and assessment of plant stress using wetland satellite images is a major task in remote sensing. In this study, one of the largest constructed wetlands (CWs) in the world, located in the Sultanate of Oman, was examined, assessed, and evaluated using
[...] Read more.
The identification and assessment of plant stress using wetland satellite images is a major task in remote sensing. In this study, one of the largest constructed wetlands (CWs) in the world, located in the Sultanate of Oman, was examined, assessed, and evaluated using remote sensor data from Sentinel-2. This CW system treats produced water generated during oil exploration activities in a desert environment; thus, CW vegetation is subjected to stress induced by oil hydrocarbons and water salinity. This study examined the plant stress and detected changes between the years of 2017 and 2019. Sentinel satellite images were evaluated for vegetation status extraction. The Normalized Difference Vegetation Index (NDVI), Modified Soil-Adjusted Vegetation Index (MSAVI), and Normalized Difference Salinity Index (NDSI) were used to evaluate the vegetation change. The results showed a comprehensive mapping identification of the plant stress and water flow parameter factors including oil in water contamination (OIW), dissolved oxygen (DO), water temperature (WT), and water conductivity (COND). Among the three indices, it was found that the NDVI showed a very good correlation with all parameters in both years with average R2 = 0.78, 0.67, 0.75, and 0.60 for OIW, DO, WT, and COND, respectively. The same trend was found for MSAVI but with R2 = 0.59, 0.48, 0.55, and 0.56 for OIW, DO, WT, and COND, respectively. This shows that the NDVI performed better than the MSAVI in evaluating the water flow parameters. On the other hand, the NDSI showed a strong correlation with one flow parameter, that is, water conductivity, especially at the outlet cells of the CW with R2 = 0.86 and 0.82 for winter time and summer time, respectively. The synchronization and correlation between the water flow parameters and remote sensing vegetation indices in this study lead to a new approach to large-scale landscape wetland monitoring that improves and helps predict any degradation or stress on vegetation growth. Furthermore, the results of this work can help decision makers potentially modify the wetland design and water flow path to improve future expansion phases. The mapping of such a critical and massive industrial CW should consider the use of high spatial resolution sensors where identifications and classifications are further improved. In summary, this research demonstrates that it is feasible to estimate vegetation stress within the constructed wetland using remote sensing techniques across extensive regions when an ample dataset comprising field data, satellite imagery, and supporting information is accessible.
Full article
(This article belongs to the Section Ecological Remote Sensing)
►▼
Show Figures

Figure 1
Open AccessArticle
SiameseNet Based Fine-Grained Semantic Change Detection for High Resolution Remote Sensing Images
Remote Sens. 2023, 15(24), 5631; https://doi.org/10.3390/rs15245631 - 05 Dec 2023
Abstract
Change detection in high resolution (HR) remote sensing images faces more challenges than in low resolution images because of the variations of land features, which prompts this research on faster and more accurate change detection methods. We propose a pixel-level semantic change detection
[...] Read more.
Change detection in high resolution (HR) remote sensing images faces more challenges than in low resolution images because of the variations of land features, which prompts this research on faster and more accurate change detection methods. We propose a pixel-level semantic change detection method to solve the fine-grained semantic change detection for HR remote sensing image pairs, which takes one lightweight semantic segmentation network (LightNet), using the parameter-sharing SiameseNet, as the architecture to carry out pixel-level semantic segmentations for the dual-temporal image pairs and achieve pixel-level change detection based directly on semantic comparison. LightNet consists of four long–short branches, each including lightweight dilated residual blocks and an information enhancement module. The feature information is transmitted, fused, and enhanced among the four branches, where two large-scale feature maps are fused and then enhanced via the channel information enhancement module. The two small-scale feature maps are fused and then enhanced via a spatial information enhancement module, and the four upsampling feature maps are finally concatenated to form the input of the Softmax. We used high resolution remote sensing images of Lake Erhai in Yunnan Province in China, collected by GF-2, to make one dataset with a fine-grained semantic label and a dual-temporal image-pair label to train our model, and the experiments demonstrate the superiority of our method and the accuracy of LightNet; the pixel-level semantic change detection methods are up to 89% and 86%, respectively.
Full article
(This article belongs to the Special Issue Multisource Remote Sensing Image Interpretation and Application)
►▼
Show Figures

Figure 1
Open AccessArticle
A Machine-Learning-Based Study on All-Day Cloud Classification Using Himawari-8 Infrared Data
Remote Sens. 2023, 15(24), 5630; https://doi.org/10.3390/rs15245630 - 05 Dec 2023
Abstract
Clouds are diverse and complex, making accurate cloud type identification vital in improving the accuracy of weather forecasting and the effectiveness of climate monitoring. However, current cloud classification research has largely focused on daytime data. The lack of visible light data at night
[...] Read more.
Clouds are diverse and complex, making accurate cloud type identification vital in improving the accuracy of weather forecasting and the effectiveness of climate monitoring. However, current cloud classification research has largely focused on daytime data. The lack of visible light data at night presents challenges in characterizing nocturnal cloud attributes, leading to difficulties in achieving continuous all-day cloud classification results. This study proposed an all-day infrared cloud classification model (AInfraredCCM) based on XGBoost. Initially, the latitude/longitude, 10 infrared channels, and 5 brightness temperature differences of the Himawari-8 satellite were selected as input features. Then, 1,314,275 samples were collected from the Himawari-8 full-disk data and cloud classification was conducted using the CPR/CALIOP merged cloud type product as training data. The key cloud types included cirrus, deep convective, altostratus, altocumulus, nimbostratus, stratocumulus, stratus, and cumulus. The cloud classification model achieved an overall accuracy of 86.22%, along with precision, recall, and F1-score values of 0.88, 0.84, and 0.86, respectively. The practicality of this model was validated across all-day temporal, daytime/nighttime, and seasonal scenarios. The results showed that the AInfraredCCM consistently performed well across various time periods and seasons, confirming its temporal applicability. In conclusion, this study presents an all-day cloud classification approach to obtain comprehensive cloud information for continuous weather monitoring, ultimately enhancing weather prediction accuracy and climate monitoring.
Full article
(This article belongs to the Special Issue Analysis of Satellite Cloud Images via Deep Learning Techniques)
►▼
Show Figures

Figure 1
Open AccessArticle
Effect of Grassland Fires on Dust Storms in Dornod Aimag, Mongolia
Remote Sens. 2023, 15(24), 5629; https://doi.org/10.3390/rs15245629 - 05 Dec 2023
Abstract
Grassland fires and dust weather in Mongolia can trigger major cascading disasters. Grassland fires from autumn to the following spring can indirectly affect dust weather occurrence in the spring by affecting land surface vegetation cover. In this paper, we selected the aimag (province)
[...] Read more.
Grassland fires and dust weather in Mongolia can trigger major cascading disasters. Grassland fires from autumn to the following spring can indirectly affect dust weather occurrence in the spring by affecting land surface vegetation cover. In this paper, we selected the aimag (province) of Dornod, Mongolia, a typical temperate grassland area, as the study area. The study aims to (1) analyze the spatiotemporal patterns of grassland fire and dust weather in the past 22 years, as well as the effect of grassland fire on dust weather and to (2) explore in depth the mechanisms of the effects of grassland fire on dust weather. To achieve these goals, we utilize high-resolution satellite burned-area data and Synop dust data. In general, grassland fire and dust weather occurrence clearly varied spatiotemporally across the study area. Grassland fires are typically more frequent in spring and autumn, and dust weather is mainly concentrated in spring. Cumulative grassland fires (both days and burned area) from autumn to the following spring affected the spring cumulative dust weather days significantly, especially the spring cumulative dust storm days. Analysis of the mechanism of the effect of grassland fire on dust storms showed that abundant summer precipitation resulted in higher vegetation cover and more accumulated fuel from autumn to April of the following spring. Consequently, the cumulative grassland fire days were higher, and the cumulative burned area was larger during the period, leading to a significant increase in cumulative dust storm days in May of the spring. In Mongolia, grassland fires are often caused by human factors. The findings of the present study could facilitate the crafting of measures to prevent and reduce grassland fires and indirectly minimize dust weather frequency to protect the ecological environment and promote sustainable development.
Full article
(This article belongs to the Special Issue Awareness of Natural Hazards in the Context of Climate Change Using Remote Sensing Techniques)
►▼
Show Figures

Figure 1
Open AccessArticle
Reshaping Leaf-Level Reflectance Data for Plant Species Discrimination: Exploring Image Shape’s Impact on Deep Learning Results
Remote Sens. 2023, 15(24), 5628; https://doi.org/10.3390/rs15245628 - 05 Dec 2023
Abstract
The application of hyperspectral imagery coupled with deep learning shows vast promise in plant species discrimination. Reshaping one-dimensional (1D) leaf-level reflectance data (LLRD) into two-dimensional (2D) grayscale images as convolutional neural network (CNN) model input demonstrated marked effectiveness in plant species distinction. However,
[...] Read more.
The application of hyperspectral imagery coupled with deep learning shows vast promise in plant species discrimination. Reshaping one-dimensional (1D) leaf-level reflectance data (LLRD) into two-dimensional (2D) grayscale images as convolutional neural network (CNN) model input demonstrated marked effectiveness in plant species distinction. However, the impact of the image shape on CNN model performance remained unexplored. This study addressed this by reshaping data into fifteen distinct rectangular formats and creating nine CNN models to examine the effect of image structure. Results demonstrated that irrespective of CNN model structure, elongated narrow images yielded superior species identification results. The ‘l’-shaped images at 225 × 9 pixels outperformed other configurations based on 93.95% accuracy, 94.55% precision, and 0.94 F1 score. Furthermore, ‘l’-shaped hyperspectral images consistently produced high classification precision across species. The results suggest this image shape boosts robust predictive performance, paving the way for enhancing leaf trait estimation and proposing a practical solution for pixel-level categorization within hyperspectral imagery (HSIs).
Full article
(This article belongs to the Special Issue Computational Intelligence in Hyperspectral Remote Sensing)
►▼
Show Figures

Graphical abstract
Open AccessArticle
Measuring the Multi-Scale Landscape Pattern of China’s Largest Archipelago from a Dual-3D Perspective Based on Remote Sensing
Remote Sens. 2023, 15(24), 5627; https://doi.org/10.3390/rs15245627 - 05 Dec 2023
Abstract
►▼
Show Figures
Measuring the landscape pattern from a three-dimensional perspective is of great significance for comprehensively revealing the complex spatial characteristics of island ecosystems. However, the archipelago composed of rocky islands has received little attention as its three-dimensional landscape characteristics are difficult to quantify. This
[...] Read more.
Measuring the landscape pattern from a three-dimensional perspective is of great significance for comprehensively revealing the complex spatial characteristics of island ecosystems. However, the archipelago composed of rocky islands has received little attention as its three-dimensional landscape characteristics are difficult to quantify. This study took the largest archipelago in China, the Zhoushan Archipelago, as the study area and constructed an island landscape pattern evaluation model from a dual-three-dimensional (dual-3D) perspective. The model divided the island into upper and lower layers, namely the surface landscape based on topography and the landscape elements above the surface (i.e., vegetation and buildings), and then evaluated their landscape patterns from a three-dimensional perspective, respectively. The landscape pattern model based on a dual-3D perspective and multiple scales achieved excellent results in the archipelago. First, the island landscape pattern was evaluated from three-dimensional perspectives, including human interference, landscape fragmentation, vegetation space, and building space. Second, landscape indices such as the human interference three-dimensional index (HITI), the landscape fragmentation three-dimensional index (LFTI), the vegetation three-dimensional index (VTI), and the building three-dimensional index (BTI) established at multiple spatial scales revealed spatial heterogeneity within and between islands. Environmental factors such as elevation, slope, and island area exhibited significant correlations with them. There were significant differences in landscape pattern indices between the two-dimensional (2D) and the three-dimensional (3D) perspectives, and high values were mainly distributed in areas with significant topographic changes and larger islands. In addition, as the evaluation unit increased, the landscape indices increased, and HITI became more responsive to the transitions from 2D to 3D, while LFTI was the opposite. Therefore, the multiscale landscape pattern measurement of China’s largest archipelago based on high-resolution remote sensing was carried out from three-dimensional perspectives to accurately reveal the spatial heterogeneity.
Full article

Figure 1
Open AccessArticle
Diffractive Sail-Based Displaced Orbits for High-Latitude Environment Monitoring
Remote Sens. 2023, 15(24), 5626; https://doi.org/10.3390/rs15245626 - 05 Dec 2023
Abstract
This paper analyzes the possibility of maintaining a circular displaced non-Keplerian orbit around the Sun by means of a Sun-facing diffractive sail. With the goal of monitoring the Earth’s high-latitude regions, the spacecraft is required to track its displaced orbit at an angular
[...] Read more.
This paper analyzes the possibility of maintaining a circular displaced non-Keplerian orbit around the Sun by means of a Sun-facing diffractive sail. With the goal of monitoring the Earth’s high-latitude regions, the spacecraft is required to track its displaced orbit at an angular velocity equal to the mean motion of the planet. In doing so, the spacecraft keeps a constant average phase shift with respect to Earth’s angular position along its orbit, allowing the objectives of the scientific mission to be achieved. The diffractive sail, recently proposed by Swartzlander and chosen in this paper as the spacecraft’s primary propulsion system, is a special photonic solar sail in which the membrane film is covered by an advanced diffractive metamaterial. In particular, a Sun-facing diffractive sail with a grating at normal incidence generates radial and transverse thrust components of equal magnitude; that is, the thrust vector is tilted 45 degrees from the Sun-spacecraft line. This peculiarity enables the diffractive sail to maintain a family of circular displaced non-Keplerian orbits, each of which is characterized by unique values of radius and a lightness number for an assigned value of spacecraft displacement relative to the Ecliptic. A comparison with the ideal reflecting sail shows that the diffractive sail performs better because for the same overall spacecraft mass, the latter needs about less surface area exposed to the Sun. Finally, this paper discusses the classical stability problem, assuming an error in orbit insertion of the diffractive sail-based spacecraft. In this context, extensive numerical simulations show that such displaced orbits are marginally stable.
Full article
(This article belongs to the Section Satellite Missions for Earth and Planetary Exploration)
►▼
Show Figures

Figure 1
Open AccessArticle
Spatio-Temporal Dynamics of Total Suspended Sediments in the Belize Coastal Lagoon
by
, , , , , , , , , , , , , , , and
Remote Sens. 2023, 15(23), 5625; https://doi.org/10.3390/rs15235625 - 04 Dec 2023
Abstract
Increased tourism in Belize over the last decade and the growth of the local population have led to coastal development and infrastructure expansion. Land use alteration and anthropogenic activity may change the sediment and nutrient loads in coastal systems, which can negatively affect
[...] Read more.
Increased tourism in Belize over the last decade and the growth of the local population have led to coastal development and infrastructure expansion. Land use alteration and anthropogenic activity may change the sediment and nutrient loads in coastal systems, which can negatively affect ecosystems via mechanisms such as reducing photosynthetically active radiation fields, smothering sessile habitats, and stimulating eutrophication events. Accurate monitoring and prediction of water quality parameters such as Total Suspended Sediments (TSS), are essential in order to understand the influence of land-based changes, climate, and human activities on the coastal systems and devise strategies to mitigate negative impacts. This study implements machine learning algorithms such as Random Forests (RF), Extreme Gradient Boosting (XGB), and Deep Neural Networks (DNN) to estimate TSS using Sentinel-2 reflectance data in the Belize Coastal Lagoon (BCL) and validates the results using TSS data collected in situ. DNN performed the best and estimated TSS with a testing R2 of 0.89. Time-series analysis was also performed on the BCL’s TSS trends using Bayesian Changepoint Detection (BCD) methods to flag anomalously high TSS spatio-temporally, which may be caused by dredging events. Having such a framework can ease the near-real-time monitoring of water quality in Belize, help track the TSS dynamics for anomalies, and aid in meeting and maintaining the sustainable goals for Belize.
Full article
(This article belongs to the Section Ocean Remote Sensing)
►▼
Show Figures

Figure 1
Open AccessArticle
Weather Radar Parameter Estimation Based on Frequency Domain Processing: Technical Details and Performance Evaluation
Remote Sens. 2023, 15(23), 5624; https://doi.org/10.3390/rs15235624 - 04 Dec 2023
Abstract
Parameter estimation is important in weather radar signal processing. Time-domain processing (TDP) and frequency-domain processing (FDP) are two basic parameter estimation methods used in the weather radar field. TDP is widely used in operational weather radars because of its high efficiency and robustness;
[...] Read more.
Parameter estimation is important in weather radar signal processing. Time-domain processing (TDP) and frequency-domain processing (FDP) are two basic parameter estimation methods used in the weather radar field. TDP is widely used in operational weather radars because of its high efficiency and robustness; however, it must be assumed that the received signal has a symmetrical or Gaussian power spectrum, which limits its performance. FDP does not require assumptions about its power spectrum model and has a seamless connection to spectrum analysis; however, its application performance has not been fully validated to ensure its robustness in an operational environment. In this study, we introduce several technical details in FDP, including window function selection, aliasing correction, and noise correction. Additionally, we evaluate the performance of FDP and compare the performance of FDP and TDP based on simulated and measured weather in-phase/quadrature (I/Q) data. The results show that FDP has potential for operational applications; however, further improvements are required, e.g., windowing processing for signals mixed with severe clutter.
Full article
(This article belongs to the Special Issue Synergetic Remote Sensing of Clouds and Precipitation II)
►▼
Show Figures

Graphical abstract
Open AccessArticle
Grassland Chlorophyll Content Estimation from Drone Hyperspectral Images Combined with Fractional-Order Derivative
Remote Sens. 2023, 15(23), 5623; https://doi.org/10.3390/rs15235623 - 04 Dec 2023
Abstract
Chlorophyll plays a critical role in assessing the photosynthetic capacity and health of grasslands. However, existing studies on the hyperspectral inversion of chlorophyll have mainly focused on field crops, leading to limited accuracy when applied to natural grasslands due to their complex canopy
[...] Read more.
Chlorophyll plays a critical role in assessing the photosynthetic capacity and health of grasslands. However, existing studies on the hyperspectral inversion of chlorophyll have mainly focused on field crops, leading to limited accuracy when applied to natural grasslands due to their complex canopy structures and species diversity. This study aims to address this challenge by extrapolating the measured leaf chlorophyll to the canopy level using the green vegetation coverage approach. Additionally, fractional-order derivative (FOD) methods are employed to enhance the sensitivity of hyperspectral data to chlorophyll. Several FOD spectral indices are developed to minimize interference from factors such as bare soil and hay, resulting in improved chlorophyll estimation accuracy. The study utilizes partial least squares regression (PLSR) and support vector machine regression (SVR) to construct inversion models based on full-band FOD, two-band FOD spectral indices, and their combination. Through comparative analysis, the optimal model for estimating grassland chlorophyll content is determined, yielding an R2 value of 0.808, RMSE value of 1.720, and RPD value of 2.347.
Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Vegetation Traits Retrieval Based on Hyperspectral Data Analysis)
►▼
Show Figures

Graphical abstract
Open AccessArticle
Comparison of GPM IMERG Version 06 Final Run Products and Its Latest Version 07 Precipitation Products across Scales: Similarities, Differences and Improvements
Remote Sens. 2023, 15(23), 5622; https://doi.org/10.3390/rs15235622 - 04 Dec 2023
Abstract
Precipitation is an essential element in earth system research, which greatly benefits from the emergence of Satellite Precipitation Products (SPPs). Therefore, assessment of the accuracy of the SPPs is necessary both scientifically and practically. The Integrated Multi-Satellite Retrievals for GPM (IMERG) is one
[...] Read more.
Precipitation is an essential element in earth system research, which greatly benefits from the emergence of Satellite Precipitation Products (SPPs). Therefore, assessment of the accuracy of the SPPs is necessary both scientifically and practically. The Integrated Multi-Satellite Retrievals for GPM (IMERG) is one of the most widely used SPPs in the scientific community. However, there is a lack of comprehensive evaluation for the performance of the newly released IMERG Version 07, which is essential for determining its effectiveness and reliability in precipitation estimation. In this study, we compare the IMERG V07 Final Run (V07_FR) with its predecessor IMERG V06_FR across scales from January 2016 to December 2020 over the globe (cross-compare their similarities and differences) and a focused study on mainland China (validate against 2481 rain gauges). The results show that: (1) Globally, the annual mean precipitation of V07_FR increases 2.2% compared to V06_FR over land but decreases 5.8% over the ocean. The two SPPs further exhibit great differences as indicated by the Critical Success Index (CSI = 0.64) and the Root Mean Squared Difference (RMSD = 3.42 mm/day) as compared to V06_FR to V07_FR. (2) Over mainland China, V06_FR and V07_FR detect comparable precipitation annually. However, the Probability of Detection (POD) improves by 5.0%, and the RMSD decreases by 3.7% when analyzed by grid cells. Further, the POD (+0%~+6.1%) and CSI (+0%~+8.8%) increase and the RMSD (−11.1%~0%) decreases regardless of the sub-regions. (3) Under extreme rainfall rates, V07_FR measures 4.5% lower extreme rainfall rates than V06_FR across mainland China. But V07_FR tends to detect more accurate extreme precipitation at both daily and event scales. These results can be of value for further SPP development, application in climatological and hydrological modeling, and risk analysis.
Full article
(This article belongs to the Section Environmental Remote Sensing)
►▼
Show Figures

Figure 1
Open AccessArticle
Evaluating Feature Selection Methods and Machine Learning Algorithms for Mapping Mangrove Forests Using Optical and Synthetic Aperture Radar Data
Remote Sens. 2023, 15(23), 5621; https://doi.org/10.3390/rs15235621 - 04 Dec 2023
Abstract
Mangrove forests, mostly found in the intertidal zone, are among the highest-productivity ecosystems and have great ecological and economic value. The accurate mapping of mangrove forests is essential for the scientific management and restoration of mangrove ecosystems. However, it is still challenging to
[...] Read more.
Mangrove forests, mostly found in the intertidal zone, are among the highest-productivity ecosystems and have great ecological and economic value. The accurate mapping of mangrove forests is essential for the scientific management and restoration of mangrove ecosystems. However, it is still challenging to perform the rapid and accurate information mapping of mangrove forests due to the complexity of mangrove forests themselves and their environments. Utilizing multi-source remote sensing data is an effective approach to address this challenge. Feature extraction and selection, as well as the selection of classification models, are crucial for accurate mangrove mapping using multi-source remote sensing data. This study constructs multi-source feature sets based on optical (Sentinel-2) and SAR (synthetic aperture radar) (C-band: Sentinel-1; L-band: ALOS-2) remote sensing data, aiming to compare the impact of three feature selection methods (RFS, random forest; ERT, extremely randomized tree; MIC, maximal information coefficient) and four machine learning algorithms (DT, decision tree; RF, random forest; XGBoost, extreme gradient boosting; LightGBM, light gradient-boosting machine) on classification accuracy, identify sensitive feature variables that contribute to mangrove mapping, and formulate a classification framework for accurately recognizing mangrove forests. The experimental results demonstrated that using the feature combination selected via the ERT method could obtain higher accuracy with fewer features compared to other methods. Among the feature combinations, the visible bands, shortwave infrared bands, and the vegetation indices constructed from these bands contributed the greatest to the classification accuracy. The classification performance of optical data was significantly better than SAR data in terms of data sources. The combination of optical and SAR data could improve the accuracy of mangrove mapping to a certain extent (0.33% to 4.67%), which is essential for the research of mangrove mapping in a larger area. The XGBoost classification model performed optimally in mangrove mapping, with the highest overall accuracy of 95.00% among all the classification models. The results of the study show that combining optical and SAR remote sensing data with the ERT feature selection method and XGBoost classification model has great potential for accurate mangrove mapping at a regional scale, which is important for mangrove restoration and protection and provides a reliable database for mangrove scientific management.
Full article
(This article belongs to the Special Issue GIS and Remote Sensing in Ocean and Coastal Ecology)
►▼
Show Figures

Graphical abstract
Open AccessArticle
Learn by Yourself: A Feature-Augmented Self-Distillation Convolutional Neural Network for Remote Sensing Scene Image Classification
Remote Sens. 2023, 15(23), 5620; https://doi.org/10.3390/rs15235620 - 04 Dec 2023
Abstract
In recent years, with the rapid development of deep learning technology, great progress has been made in remote sensing scene image classification. Compared with natural images, remote sensing scene images are usually more complex, with high inter-class similarity and large intra-class differences, which
[...] Read more.
In recent years, with the rapid development of deep learning technology, great progress has been made in remote sensing scene image classification. Compared with natural images, remote sensing scene images are usually more complex, with high inter-class similarity and large intra-class differences, which makes it difficult for commonly used networks to effectively learn the features of remote sensing scene images. In addition, most existing methods adopt hard labels to supervise the network model, which makes the model prone to losing fine-grained information of ground objects. In order to solve these problems, a feature-augmented self-distilled convolutional neural network (FASDNet) is proposed. First, ResNet34 is adopted as the backbone network to extract multi-level features of images. Next, a feature augmentation pyramid module (FAPM) is designed to extract and fuse multi-level feature information. Then, auxiliary branches are constructed to provide additional supervision information. The self-distillation method is utilized between the feature augmentation pyramid module and the backbone network, as well as between the backbone network and auxiliary branches. Finally, the proposed model is jointly supervised using feature distillation loss, logits distillation loss, and cross-entropy loss. A lot of experiments are conducted on four widely used remote sensing scene image datasets, and the experimental results show that the proposed method is superior to some state-ot-the-art classification methods.
Full article
(This article belongs to the Special Issue Knowledge-Driven and/or Data-Driven Methods for Remote Sensing Image Processing)
►▼
Show Figures

Figure 1
Open AccessArticle
Fusing Ascending and Descending Time-Series SAR Images with Dual-Polarized Pixel Attention UNet for Landslide Recognition
by
and
Remote Sens. 2023, 15(23), 5619; https://doi.org/10.3390/rs15235619 - 04 Dec 2023
Abstract
Conducting landslide recognition research holds notable practical significance for disaster management. In response to the challenges posed by noise, information redundancy, and geometric distortions in single-orbit SAR imagery during landslide recognition, this study proposes a dual-polarization SAR image landslide recognition approach that combines
[...] Read more.
Conducting landslide recognition research holds notable practical significance for disaster management. In response to the challenges posed by noise, information redundancy, and geometric distortions in single-orbit SAR imagery during landslide recognition, this study proposes a dual-polarization SAR image landslide recognition approach that combines ascending and descending time-series information while considering polarization channel details to enhance the accuracy of landslide identification. The results demonstrate notable improvements in landslide recognition accuracy using the ascending and descending fusion strategy compared to single-orbit data, with F1 scores increasing by 5.19% and 8.82% in Hokkaido and Papua New Guinea, respectively. Additionally, utilizing time-series imagery in Group 2 as opposed to using only pre- and post-event images in Group 4 leads to F1 score improvements of 6.94% and 9.23% in Hokkaido and Papua New Guinea, respectively, confirming the effectiveness of time-series information in enhancing landslide recognition accuracy. Furthermore, employing dual-polarization strategies in Group 4 relative to single-polarization Groups 5 and 6 results in peak F1 score increases of 7.46% and 12.07% in Hokkaido and Papua New Guinea, respectively, demonstrating the feasibility of dual-polarization strategies. However, due to limitations in Sentinel-1 imagery resolution and terrain complexities, omissions and false alarms may arise near landslide edges. The improvements achieved in this study hold critical implications for landslide disaster assessment and provide valuable insights for further enhancing landslide recognition capabilities.
Full article
(This article belongs to the Topic Landslides and Natural Resources)
►▼
Show Figures

Figure 1

Journal Menu
► ▼ Journal Menu-
- Remote Sensing Home
- Aims & Scope
- Editorial Board
- Reviewer Board
- Topical Advisory Panel
- Photography Exhibition
- Instructions for Authors
- Special Issues
- Topics
- Sections & Collections
- Article Processing Charge
- Indexing & Archiving
- Editor’s Choice Articles
- Most Cited & Viewed
- Journal Statistics
- Journal History
- Journal Awards
- Society Collaborations
- Conferences
- Editorial Office
Journal Browser
► ▼ Journal BrowserHighly Accessed Articles
Latest Books
E-Mail Alert
News
Topics
Topic in
Applied Sciences, Inventions, JMSE, Oceans, Remote Sensing, Sensors
Ship Dynamics, Stability and Safety
Topic Editors: Zaojian Zou, Weilin LuoDeadline: 20 December 2023
Topic in
AI, Electronics, IoT, JSAN, Remote Sensing, Sensors
Machine Learning in Internet of Things
Topic Editors: Dawid Połap, Robertas Damasevicius, Hafiz Tayyab RaufDeadline: 31 December 2023
Topic in
AI, Applied Sciences, Electronics, IJGI, Remote Sensing, Robotics, Sensors
Artificial Intelligence in Navigation
Topic Editors: Arpad Barsi, Niclas Zeller, Eliseo ClementiniDeadline: 20 January 2024
Topic in
Energies, Geosciences, Land, Remote Sensing, Water
Urban Hydrogeology Research
Topic Editors: C. Radu Gogu, Oana LucaDeadline: 31 January 2024

Conferences
Special Issues
Special Issue in
Remote Sensing
Remote Sensing of Forest and Wetland Hydrology
Guest Editor: Sudhanshu Sekhar PandaDeadline: 15 December 2023
Special Issue in
Remote Sensing
Applications of Laser Scanning in Urban Environment
Guest Editors: Henrique Lorenzo, Pedro Arias-SánchezDeadline: 20 December 2023
Special Issue in
Remote Sensing
Use of Remote Sensing in Valuation of Blue Carbon and Its Co-benefits
Guest Editor: Gail L. ChmuraDeadline: 31 December 2023
Special Issue in
Remote Sensing
Remote Sensing of Urban Forests and Landscape Ecology
Guest Editors: Ivan Pilaš, Mateo Gašparović, Damir KlobučarDeadline: 1 January 2024
Topical Collections
Topical Collection in
Remote Sensing
Google Earth Engine Applications
Collection Editors: Lalit Kumar, Onisimo Mutanga
Topical Collection in
Remote Sensing
Feature Papers for Section Environmental Remote Sensing
Collection Editor: Magaly Koch
Topical Collection in
Remote Sensing
Discovering A More Diverse Remote Sensing Discipline
Collection Editors: Karen Joyce, Meghan Halabisky, Cristina Gómez, Michelle Kalamandeen, Gopika Suresh, Kate C. Fickas
Topical Collection in
Remote Sensing
Current, Planned, and Future Satellite Missions: Guidelines for Data Exploitation by the Remote Sensing Community
Collection Editors: Jose Moreno, Magaly Koch, Robert Wang