Journal Description
Remote Sensing
Remote Sensing
is an international, peer-reviewed, open access journal about the science and application of remote sensing technology, and is published semimonthly online by MDPI. The Remote Sensing Society of Japan (RSSJ) and the Japan Society of Photogrammetry and Remote Sensing (JSPRS) are affiliated with Remote Sensing, and their members receive a discount on the article processing charge.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), Ei Compendex, PubAg, GeoRef, Astrophysics Data System, Inspec, dblp, and other databases.
- Journal Rank: JCR - Q1 (Geosciences, Multidisciplinary) / CiteScore - Q1 (General Earth and Planetary Sciences)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 23 days after submission; acceptance to publication is undertaken in 2.7 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Companion journal: Geomatics
Impact Factor:
5.0 (2022);
5-Year Impact Factor:
5.6 (2022)
Latest Articles
The Retrieval of Ground NDVI (Normalized Difference Vegetation Index) Data Consistent with Remote-Sensing Observations
Remote Sens. 2024, 16(7), 1212; https://doi.org/10.3390/rs16071212 (registering DOI) - 29 Mar 2024
Abstract
The Normalized Difference Vegetation Index (NDVI) is widely used for monitoring vegetation status, as accurate and reliable NDVI time series are crucial for understanding the relationship between environmental conditions, vegetation health, and productivity. Ground digital cameras have been recognized as important potential data
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The Normalized Difference Vegetation Index (NDVI) is widely used for monitoring vegetation status, as accurate and reliable NDVI time series are crucial for understanding the relationship between environmental conditions, vegetation health, and productivity. Ground digital cameras have been recognized as important potential data sources for validating remote-sensing NDVI products. However, differences in the spectral characteristics and imaging methods between sensors onboard satellites and ground digital cameras hinder direct consistency analyses, thereby limiting the quantitative application of camera-based observations. To address this limitation and meet the needs of vegetation monitoring research and remote-sensing NDVI validation, this study implements a novel NDVI camera. The proposed camera incorporates narrowband dual-pass filters designed to precisely separate red and near-infrared (NIR) spectral bands, which are aligned with the configuration of sensors onboard satellites. Through software-controlled imaging parameters, the camera captures the real radiance of vegetation reflection, ensuring the acquisition of accurate NDVI values while preserving the evolving trends of the vegetation status. The performance of this NDVI camera was evaluated using a hyperspectral spectrometer in the Hulunbuir Grassland over a period of 93 days. The results demonstrate distinct seasonal characteristics in the camera-derived NDVI time series using the Green Chromatic Coordinate (GCC) index. Moreover, in comparison to the GCC index, the camera’s NDVI values exhibit greater consistency with those obtained from the hyperspectral spectrometer, with a mean deviation of 0.04, and a relative root mean square error of 9.68%. This indicates that the narrowband NDVI, compared to traditional color indices like the GCC index, has a stronger ability to accurately capture vegetation changes. Cross-validation using the NDVI results from the camera and the PlanetScope satellite further confirms the potential of the camera-derived NDVI data for consistency analyses with remote sensing-based NDVI products, thus highlighting the potential of camera observations for quantitative applications The research findings emphasize that the novel NDVI camera, based on a narrowband spectral design, not only enables the acquisition of real vegetation index (VI) values but also facilitates the direct validation of vegetation remote-sensing NDVI products.
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Open AccessArticle
Gaussian Process Regression Hybrid Models for the Top-of-Atmosphere Retrieval of Vegetation Traits Applied to PRISMA and EnMAP Imagery
by
Ana B. Pascual-Venteo, Jose L. Garcia, Katja Berger, José Estévez, Jorge Vicent, Adrián Pérez-Suay, Shari Van Wittenberghe and Jochem Verrelst
Remote Sens. 2024, 16(7), 1211; https://doi.org/10.3390/rs16071211 (registering DOI) - 29 Mar 2024
Abstract
The continuous monitoring of the terrestrial Earth system by a growing number of optical satellite missions provides valuable insights into vegetation and cropland characteristics. Satellite missions typically provide different levels of data, such as level 1 top-of-atmosphere (TOA) radiance and level 2 bottom-of-atmosphere
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The continuous monitoring of the terrestrial Earth system by a growing number of optical satellite missions provides valuable insights into vegetation and cropland characteristics. Satellite missions typically provide different levels of data, such as level 1 top-of-atmosphere (TOA) radiance and level 2 bottom-of-atmosphere (BOA) reflectance products. Exploiting TOA radiance data directly offers the advantage of bypassing the complex atmospheric correction step, where errors can propagate and compromise the subsequent retrieval process. Therefore, the objective of our study was to develop models capable of retrieving vegetation traits directly from TOA radiance data from imaging spectroscopy satellite missions. To achieve this, we constructed hybrid models based on radiative transfer model (RTM) simulated data, thereby employing the vegetation SCOPE RTM coupled with the atmosphere LibRadtran RTM in conjunction with Gaussian process regression (GPR). The retrieval evaluation focused on vegetation canopy traits, including the leaf area index (LAI), canopy chlorophyll content (CCC), canopy water content (CWC), the fraction of absorbed photosynthetically active radiation (FAPAR), and the fraction of vegetation cover (FVC). Employing band settings from the upcoming Copernicus Hyperspectral Imaging Mission (CHIME), two types of hybrid GPR models were assessed: (1) one trained at level 1 (L1) using TOA radiance data and (2) one trained at level 2 (L2) using BOA reflectance data. Both the TOA- and BOA-based GPR models were validated against in situ data with corresponding hyperspectral data obtained from field campaigns. The TOA-based hybrid GPR models revealed a range of performance from moderate to optimal results, thus reaching = 0.92 (LAI), = 0.72 (CCC) and 0.68 (CWC), = 0.94 (FAPAR), and = 0.95 (FVC). To demonstrate the models’ applicability, the TOA- and BOA-based GPR models were subsequently applied to imagery from the scientific precursor missions PRISMA and EnMAP. The resulting trait maps showed sufficient consistency between the TOA- and BOA-based models, with relative errors between and ( between 0.68 and 0.97). Altogether, these findings illuminate the path for the development and enhancement of machine learning hybrid models for the estimation of vegetation traits directly tailored at the TOA level.
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(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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Open AccessArticle
Satellite-Based Background Aerosol Optical Depth Determination via Global Statistical Analysis of Multiple Lognormal Distribution
by
Qi-Xiang Chen, Chun-Lin Huang, Shi-Kui Dong and Kai-Feng Lin
Remote Sens. 2024, 16(7), 1210; https://doi.org/10.3390/rs16071210 (registering DOI) - 29 Mar 2024
Abstract
Determining background aerosol optical depth threshold value (BAOD) is critical to aerosol type identification and air pollution control. This study presents a statistical method to select the best BAOD threshold value using the VIIRS DB AOD products at 1 × 1 degree resolution
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Determining background aerosol optical depth threshold value (BAOD) is critical to aerosol type identification and air pollution control. This study presents a statistical method to select the best BAOD threshold value using the VIIRS DB AOD products at 1 × 1 degree resolution from 2012 to 2019 as a major testbed. A series of multiple lognormal distributions with 1 to 5 peaks are firstly applied to fit the AOD histogram at each grid point, and the distribution with the highest correlation coefficient (R) gives preliminary estimations of BAOD, which is defined as either the intersection point of the first two normal distribution curves when having multiple peaks, or the midpoint between the peak AOD and the first AOD with non-zero probability when the mono peak is the best fit. Then, the lowest 1st to 100th percentile AOD distributions are compared with the preliminary BAOD distribution on a global scale. The final BAOD is obtained from the best cutoff percentile AOD distributions with the lowest bias compared with preliminary BAOD. Results show that the lowest 30th percentile AOD is the best estimation of BAOD for different AOD datasets and different seasons. Analysis of aerosol chemical information from MERRA-2 further supports this selection. Based on the BAOD, we updated the VIIRS aerosol type classification scheme, and the results show that the updated scheme is able to achieve reliable detection of aerosol type change in low aerosol loading conditions.
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(This article belongs to the Special Issue Aerosol and Atmospheric Correction)
Open AccessArticle
Iterative Signal Detection and Channel Estimation with Superimposed Training Sequences for Underwater Acoustic Information Transmission in Time-Varying Environments
by
Lin Li, Xiao Han and Wei Ge
Remote Sens. 2024, 16(7), 1209; https://doi.org/10.3390/rs16071209 (registering DOI) - 29 Mar 2024
Abstract
Underwater signal processing is primarily based on sound waves because of the unique properties of water. However, the slow speed and limited bandwidth of sound introduce numerous challenges, including pronounced time-varying characteristics and significant multipath effects. This paper explores a channel estimation method
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Underwater signal processing is primarily based on sound waves because of the unique properties of water. However, the slow speed and limited bandwidth of sound introduce numerous challenges, including pronounced time-varying characteristics and significant multipath effects. This paper explores a channel estimation method utilizing superimposed training sequences. Compared with conventional schemes, this method offers higher spectral efficiency and better adaptability to time-varying channels owing to its temporal traversal. To ensure success in this scheme, it is crucial to obtain time-varying channel estimation and data detection at low SNRs given that superimposed training sequences consume power resources. To achieve this goal, we initially employ coarse channel estimation utilizing superimposed training sequences. Subsequently, we employ approximate message passing algorithms based on the estimated channels for data detection, followed by iterative channel estimation and equalization based on estimated symbols. We devise an approximate message passing channel estimation method grounded on a Gaussian mixture model and refine its hyperparameters through the expectation maximization algorithm. Then, we refine the channel information based on time correlation by employing an autoregressive hidden Markov model. Lastly, we perform numerical simulations of communication systems by utilizing a time-varying channel toolbox to simulate time-varying channels, and we validate the feasibility of the proposed communication system using experimental field data.
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(This article belongs to the Special Issue Space-Air-Ground-Ocean Integrated Sensing and Information Transmission)
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Open AccessArticle
Random Forest Classifier for Cloud Clearing of the Operational TROPOMI XCH4 Product
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Tobias Borsdorff, Mari C. Martinez-Velarte, Maarten Sneep, Mark ter Linden and Jochen Landgraf
Remote Sens. 2024, 16(7), 1208; https://doi.org/10.3390/rs16071208 - 29 Mar 2024
Abstract
The TROPOMI XCH4 data product requires rigorous cloud filtering to achieve a product accuracy of <1%. To this end, operational XCH4 data processing has been based on SUOMI-NPP VIIRS cloud observations. However, SUOMI-NPP is nearing the end of its operational life
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The TROPOMI XCH4 data product requires rigorous cloud filtering to achieve a product accuracy of <1%. To this end, operational XCH4 data processing has been based on SUOMI-NPP VIIRS cloud observations. However, SUOMI-NPP is nearing the end of its operational life and has encountered malfunctions in 2022 and 2023. In this study, we introduce a novel machine learning cloud-clearing approach based on a random forest classifier (RFC). The RFC is trained on collocated TROPOMI and SUOMI-NPP VIIRS data to emulate VIIRS-like cloud clearing. After training, cloud masking requires only TROPOMI data, and so becomes operationally independent of SUOMI-NPP. We demonstrate the RFC approach by applying cloud clearing to operational TROPOMI XCH4 data for August 2022, a period in which VIIRS was not operational. For validation, we analyze the TROPOMI XCH4 data at 12 TCCON stations. Comparison of cloud clearing using the RFC and the original VIIRS method reveals excellent agreement with a similar station-to-station bias (−7.4 ppb versus −5.6 ppb), a similar standard deviation of the station-to-station bias (11.6 ppb versus 12 ppb), and the same Pearson correlation coefficient of 0.9. Remarkably, the RFC cloud clearing provides a slightly higher volume of data (2182 versus 2035 daily means) and appears to have fewer outliers. Since 21 November 2023, the RFC approach is part of the operational processing chain of the European Space Agency (ESA). For now, the default practice is to utilize SNPP-VIIRS when accessible. Only in cases where VIIRS data are unavailable do we resort to the RFC cloud mask.
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(This article belongs to the Section Atmospheric Remote Sensing)
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Open AccessArticle
A Framework Based on LIDs and Storage Pumping Stations for Urban Waterlogging
by
Huayue Li, Qinghua Luan, Jiahong Liu, Cheng Gao and Hong Zhou
Remote Sens. 2024, 16(7), 1207; https://doi.org/10.3390/rs16071207 - 29 Mar 2024
Abstract
Climate change has resulted in an increase in extreme rainstorm events, posing the challenges of urban waterlogging and runoff pollution. Low Impact Development (LID) is widely used to address the issues above, but its effectiveness is unknown in mountainous areas. Due to a
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Climate change has resulted in an increase in extreme rainstorm events, posing the challenges of urban waterlogging and runoff pollution. Low Impact Development (LID) is widely used to address the issues above, but its effectiveness is unknown in mountainous areas. Due to a flash flood and high flood peak, storage pumping stations are also needed to drain. Thus, a framework composed of storage pumping stations and Low Impact Developments (LIDs) was proposed based on the topography and the regional upstream and downstream relationships. The water quantity in this framework is applied to YI County in Hebei Province, China. The results showed that individual LIDs effectively reduced runoff volume, with the implementation area being more crucial than the location. Combining storage pumping stations with LIDs significantly reduces peak outflow and delays it by 5 to 51 min. The combined downstream implementation of storage pumping stations and LIDs yielded the most effective results. These findings offer important insights and management strategies for controlling waterlogging in mountainous cities of developing countries.
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(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment II)
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Open AccessTechnical Note
Estimation of Daily Ground Level Air Pollution in Italian Municipalities with Machine Learning Models Using Sentinel-5P and ERA5 Data
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Alessandro Fania, Alfonso Monaco, Ester Pantaleo, Tommaso Maggipinto, Loredana Bellantuono, Roberto Cilli, Antonio Lacalamita, Marianna La Rocca, Sabina Tangaro, Nicola Amoroso and Roberto Bellotti
Remote Sens. 2024, 16(7), 1206; https://doi.org/10.3390/rs16071206 - 29 Mar 2024
Abstract
Recent years have witnessed an increasing interest in air pollutants and their effects on human health. More generally, it has become evident how human, animal and environmental health are deeply interconnected within a One Health framework. Ground level air monitoring stations are sparse
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Recent years have witnessed an increasing interest in air pollutants and their effects on human health. More generally, it has become evident how human, animal and environmental health are deeply interconnected within a One Health framework. Ground level air monitoring stations are sparse and thus have limited coverage due to high costs. Satellite and reanalysis data represent an alternative with high spatio-temporal resolution. The idea of this work is to build an Artificial Intelligence model for the estimation of surface-level daily concentrations of air pollutants over the entire Italian territory using satellite, climate reanalysis, geographical and social data. As ground truth we use data from the monitoring stations of the Regional Environmental Protection Agency (ARPA) covering the period 2019–2022 at municipal level. The analysis compares different models and applies an Explainable Artificial Intelligence approach to evaluate the role of individual features in the model. The best model reaches an average of 0.84 ± 0.01 and MAE of 5.00 ± 0.01 g/m3 across all pollutants which compare well with the body of literature. The XAI analysis highlights the pivotal role of satellite and climate reanalysis data. Our work can facilitate One Health surveys and help researchers and policy makers.
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(This article belongs to the Topic Accessing and Analyzing Air Quality and Atmospheric Environment)
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Open AccessArticle
Prediction of Sea Surface Temperature Using U-Net Based Model
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Jing Ren, Changying Wang, Ling Sun, Baoxiang Huang, Deyu Zhang, Jiadong Mu and Jianqiang Wu
Remote Sens. 2024, 16(7), 1205; https://doi.org/10.3390/rs16071205 - 29 Mar 2024
Abstract
Sea surface temperature (SST) is a key parameter in ocean hydrology. Currently, existing SST prediction methods fail to fully utilize the potential spatial correlation between variables. To address this challenge, we propose a spatiotenporal UNet (ST-UNet) model based on the UNet model. In
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Sea surface temperature (SST) is a key parameter in ocean hydrology. Currently, existing SST prediction methods fail to fully utilize the potential spatial correlation between variables. To address this challenge, we propose a spatiotenporal UNet (ST-UNet) model based on the UNet model. In particular, in the encoding phase of ST-UNet, we use parallel convolution with different kernel sizes to efficiently extract spatial features, and use ConvLSTM to capture temporal features based on the utilization of spatial features. Atrous Spatial Pyramid Pooling (ASPP) module is placed at the bottleneck of the network to further incorporate the multi-scale features, allowing the spatial features to be fully utilized. The final prediction is then generated in the decoding stage using parallel convolution with different kernel sizes similar to the encoding stage. We conducted a series of experiments on the Bohai Sea and Yellow Sea SST data set, as well as the South China Sea SST data set, using SST data from the past 35 days to predict SST data for 1, 3, and 7 days in the future. The model was trained using data spanning from 2010 to 2021, with data from 2022 being utilized to assess the model’s predictive performance. The experimental results show that the model proposed in this research paper achieves excellent results at different prediction scales in both sea areas, and the model consistently outperforms other methods. Specifically, in the Bohai Sea and Yellow Sea sea areas, when the prediction scales are 1, 3, and 7 days, the MAE of ST-UNet outperforms the best results of the other three compared models by 17%, 12%, and 2%, and the MSE by 16%, 18%, and 9%, respectively. In the South China Sea, when the prediction ranges are 1, 3, and 7 days, the MAE of ST-UNet is 27%, 18%, and 3% higher than the best of the other three compared models, and the MSE is 46%, 39%, and 16% higher, respectively. Our results highlight the effectiveness of the ST-UNet model in capturing spatial correlations and accurately predicting SST. The proposed model is expected to improve marine hydrographic studies.
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(This article belongs to the Special Issue Artificial Intelligence and Big Data for Oceanography)
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Modelling Floodplain Vegetation Response to Climate Change, Using the Soil and Water Assessment Tool (SWAT) Model Simulated LAI, Applying Different GCM’s Future Climate Data and MODIS LAI Data
by
Newton Muhury, Armando Apan and Tek Maraseni
Remote Sens. 2024, 16(7), 1204; https://doi.org/10.3390/rs16071204 - 29 Mar 2024
Abstract
Scientists widely agree that anthropogenically driven climate change significantly impacts vegetation growth, particularly in floodplain areas, by altering river flow and flood regimes. This impact will accelerate in the future, according to climate change projections. For example, in Australia, climate change has been
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Scientists widely agree that anthropogenically driven climate change significantly impacts vegetation growth, particularly in floodplain areas, by altering river flow and flood regimes. This impact will accelerate in the future, according to climate change projections. For example, in Australia, climate change has been attributed to a decrease in winter precipitation in the range of 56% to 72.9% and an increase in summer from 11% to 27%, according to different climate scenarios. This research attempts to understand vegetation responses to climate change variability at the floodplain level. Further, this study is an effort to enlighten our understanding of temporal climate change impacts under different climate scenarios. To achieve these aims, a semi-distributed hydrological model was applied at a sub-catchment level to simulate the Leaf Area Index (LAI). The model was simulated against future time series of climate data according to Global Climate Model (GCM) projections. The time series data underwent a non-parametric Mann–Kendall test to detect trends and assess the magnitude of change. To quantify the model’s performance, calibration and validation were conducted against the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI. The calibration and validation results show Nash–Sutcliffe efficiency (NSE) values of 0.85 and 0.78, respectively, suggesting the model’s performance is very good. The modeling results reveal that the rainfall pattern fluctuates under climate projections within the study site, in which vegetation tends to be more vibrant during the warmer seasons. Moreover, the modeling results highlighted increases in the average projected future winter temperatures, which can help vegetation growth during winter. The results of this study may be employed for sustainable floodplain management, restoration, land-use planning, and policymaking, and help floodplain communities better prepare for and respond to changing flood patterns and related challenges under a future changing climate.
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(This article belongs to the Special Issue Analysis of Groundwater and Total Water Storage Changes Using GRACE Observations II)
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Open AccessArticle
Weakly Supervised Object Detection for Remote Sensing Images via Progressive Image-Level and Instance-Level Feature Refinement
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Shangdong Zheng, Zebin Wu, Yang Xu and Zhihui Wei
Remote Sens. 2024, 16(7), 1203; https://doi.org/10.3390/rs16071203 - 29 Mar 2024
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Weakly supervised object detection (WSOD) aims to predict a set of bounding boxes and corresponding category labels for instances with only image-level supervisions. Compared with fully supervised object detection, WSOD in remote sensing images (RSIs) is much more challenging due to the vast
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Weakly supervised object detection (WSOD) aims to predict a set of bounding boxes and corresponding category labels for instances with only image-level supervisions. Compared with fully supervised object detection, WSOD in remote sensing images (RSIs) is much more challenging due to the vast foreground-related context regions. In this paper, we propose a progressive image-level and instance-level feature refinement network to address the problems of missing detection and part domination for WSOD in RSIs. Firstly, we propose a multi-label attention mining loss (MAML)-guided image-level feature refinement branch to effectively allocate the computational resources towards the most informative part of images. With the supervision of MAML, all latent instances in images are emphasized. However, image-level feature refinement further expands responsive gaps between the informative part and other sub-optimal informative ones, which results in exacerbating the problem of part domination. In order to alleviate the above-mentioned limitation, we further construct an instance-level feature refinement branch to re-balance the contributions of different adjacent candidate bounding boxes according to the detection task. An instance selection loss (ISL) is proposed to progressively boost the representation of salient regions by exploring supervision from the network itself. Finally, we integrate the image-level and instance-level feature refinement branches into a complete network and the proposed MAML and ISL functions are merged with class classification and box regression to optimize the whole WSOD network in an end-to-end training fashion. We conduct experiments on two popular WSOD datasets, NWPU VHR-10.v2 and DIOR. All the experimental results demonstrate that our method achieves a competitive performance compared with other state-of-the-art approaches.
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Open AccessArticle
Impact of Traffic Flow Rate on the Accuracy of Short-Term Prediction of Origin-Destination Matrix in Urban Transportation Networks
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Renata Żochowska and Teresa Pamuła
Remote Sens. 2024, 16(7), 1202; https://doi.org/10.3390/rs16071202 - 29 Mar 2024
Abstract
Information about spatial distribution (OD flows) is a key element in traffic management systems in urban transport networks that enables efficient traffic control and decisions to redirect traffic to less congested sections of the network in emergencies. With the development of modern techniques
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Information about spatial distribution (OD flows) is a key element in traffic management systems in urban transport networks that enables efficient traffic control and decisions to redirect traffic to less congested sections of the network in emergencies. With the development of modern techniques of remote sensing, more and more advanced methods are used to measure traffic and determine OD flows. However, they may produce results with different levels of errors caused by various factors. The article examines the impact of traffic volume and its variability on the error values of short-term prediction of the OD matrix in the urban network. The OD flows were determined using a deep learning network based on data obtained from video remote sensing devices. These data were recorded at earlier intervals concerning the forecasting time. The extent to which there is a correlation between the size of OD flows and the prediction error was examined. The most frequently used measure of prediction accuracy, i.e., MAPE (mean absolute percentage error), was considered. The analysis carried out made it possible to determine the ranges of traffic flow rate for which the MAPE stabilizes at the level of approximately 6%. A set of video remote sensing devices was used to collect spatiotemporal data. They were located at the entrances and exits from the study area on important roads of a medium-sized city in Poland. The conclusions obtained may be helpful in further research on improving methods to determine OD matrices and estimate their reliability. This, in turn, involves the development of more precise methods that allow for reliable traffic forecasting and improve the efficiency of traffic management in urban areas.
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(This article belongs to the Special Issue Remote Sensing Advances in Urban Traffic Monitoring)
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Open AccessArticle
Automatic Martian Polar Ice Cap Extraction Algorithm for Remote Sensing Data and Analysis of Their Spatiotemporal Variation Characteristics
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Weiye Xu, Zhulin Chen, Huifang Zhang, Kun Jia, Degyi Yangzom, Xiang Zhao, Yunjun Yao and Xiaotong Zhang
Remote Sens. 2024, 16(7), 1201; https://doi.org/10.3390/rs16071201 - 29 Mar 2024
Abstract
The detection of Martian polar ice cap change patterns is important for understanding their effects on driving Mars’s global water cycle and for regulating atmospheric circulation. However, current Martian ice cap identification using optical remote sensing data mainly relies on visual interpretation, which
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The detection of Martian polar ice cap change patterns is important for understanding their effects on driving Mars’s global water cycle and for regulating atmospheric circulation. However, current Martian ice cap identification using optical remote sensing data mainly relies on visual interpretation, which makes it difficult to quickly extract ice caps from multiple images and analyze their fine-scale spatiotemporal variation characteristics. Therefore, this study proposes an automatic Martian polar ice cap extraction algorithm for remote sensing data and analyzes the dynamic change characteristics of the Martian North Pole ice cap using time-series data. First, the automatic Martian ice cap segmentation algorithm was developed based on the ice cap features of high reflectance in the blue band and low saturation in the RGB band. Second, the Martian North Pole ice cap was extracted for the three Martian years MY25, 26, and 28 using Mars Orbiter Camera (MOC) Mars Daily Global Maps (MDGMs) data, which had better spatiotemporal continuity to analyze its variation characteristics. Lastly, the spatiotemporal variation characteristics of the ice cap and the driving factors of ice cap ablation were explored for the three aforementioned Martian years. The results indicated that the proposed automatic ice cap extraction algorithm had good performance, and the classification accuracy exceeded 93%. The ice cap ablation boundary retreat rates and spatiotemporal distributions were similar for the three years, with approximately 105 km2 of ice cap ablation for every one degree of areocentric longitude of the Sun (Ls). The main driving factor of ice cap ablation was solar radiation, which was mainly related to Ls. In addition, elevation had a different effect on ice cap ablation at different Ls in the same latitude area near the ablation boundary of the ice cap.
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(This article belongs to the Special Issue Advances in Remote Sensing of Mars: Geomorphological Research and Environmental Assessment)
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Open AccessArticle
Influences of Climate Variability on Land Use and Land Cover Change in Rural South Africa
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Buster Percy Mogonong, Wayne Twine, Gregor Timothy Feig, Helga Van der Merwe and Jolene T. Fisher
Remote Sens. 2024, 16(7), 1200; https://doi.org/10.3390/rs16071200 - 29 Mar 2024
Abstract
Changes in land use and land cover over space and time are an indication of biophysical, socio-economic, and political dynamics. In rural communities, land-based livelihood strategies such as agriculture are crucial for sustaining livelihoods in terms of food provision and as a source
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Changes in land use and land cover over space and time are an indication of biophysical, socio-economic, and political dynamics. In rural communities, land-based livelihood strategies such as agriculture are crucial for sustaining livelihoods in terms of food provision and as a source of local employment and income. In recent years, African studies have documented an overall decline in the extent of small-scale crop farming, with many crop fields left abandoned. This study uses rural areas in three former apartheid homelands in South Africa as a case study to quantify patterns and trends in the overall land cover change and small-scale agricultural lands related to changes in climate over a 38-year period. Random forest classification was applied on the Landsat imagery to detect land use and land cover change, achieving an overall accuracy of above 80%. Rainfall and temperature anomalies, as well as the Standardized Precipitation Evapotranspiration Index (SPEI) were used as climate proxies to assess the influence of climate variability on crop farming, as the systems investigated rely completely on rainfall. Agricultural land declined from 107.5 km2 to 49.5 km2 in Umhlabuyalingana; 54 km2 to 1.6 km2 in Joe Morolong; and 254.6 km2 to 7.4 km2 in Mangaung between 1984 and 2022. Declines in cropland cover, precipitation, and the SPEI were highly correlated. We argue that climatic variability influences crop farming activities; however, this could be one factor in a suite of drivers that interact together to influence the cropping practices in rural areas.
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(This article belongs to the Special Issue Remote Sensing for Land Change Science: Looking at Land Surface as a Coupled Human-Environment System)
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Open AccessArticle
Assessing the Potential of Multi-Temporal Conditional Generative Adversarial Networks in SAR-to-Optical Image Translation for Early-Stage Crop Monitoring
by
Geun-Ho Kwak and No-Wook Park
Remote Sens. 2024, 16(7), 1199; https://doi.org/10.3390/rs16071199 - 29 Mar 2024
Abstract
The incomplete construction of optical image time series caused by cloud contamination is one of the major limitations facing the application of optical satellite images in crop monitoring. Thus, the construction of a complete optical image time series via image reconstruction of cloud-contaminated
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The incomplete construction of optical image time series caused by cloud contamination is one of the major limitations facing the application of optical satellite images in crop monitoring. Thus, the construction of a complete optical image time series via image reconstruction of cloud-contaminated regions is essential for thematic mapping in croplands. This study investigates the potential of multi-temporal conditional generative adversarial networks (MTcGANs) that use a single synthetic aperture radar (SAR) image acquired on a prediction date and a pair of SAR and optical images acquired on a reference date in the context of early-stage crop monitoring. MTcGAN has an advantage over conventional SAR-to-optical image translation methods as it allows input data of various compositions. As the prediction performance of MTcGAN depends on the input data composition, the variations in the prediction performance should be assessed for different input data combination cases. Such an assessment was performed through experiments using Sentinel-1 and -2 images acquired in the US Corn Belt. MTcGAN outperformed existing SAR-to-optical image translation methods, including Pix2Pix and supervised CycleGAN (S-CycleGAN), in cases representing various input compositions. In particular, MTcGAN was substantially superior when there was little change in crop vitality between the reference and prediction dates. For the SWIR1 band, the root mean square error of MTcGAN (0.021) for corn was significantly improved by 54.4% and 50.0% compared to Pix2Pix (0.046) and S-CycleGAN (0.042), respectively. Even when there were large changes in crop vitality, the prediction accuracy of MTcGAN was more than twice that of Pix2Pix and S-CycleGAN. Without considering the temporal intervals between input image acquisition dates, MTcGAN was found to be beneficial when crops were visually distinct in both SAR and optical images. These experimental results demonstrate the potential of MTcGAN in SAR-to-optical image translation for crop monitoring during the early growth stage and can serve as a guideline for selecting appropriate input images for MTcGAN.
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(This article belongs to the Special Issue Satellite Image Processing and Object Recognition for Agriculture and Food Security Applications)
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Open AccessArticle
Enhancement of Small Ship Detection Using Polarimetric Combination from Sentinel−1 Imagery
by
Dae-Woon Shin, Chan-Su Yang and Sree Juwel Kumar Chowdhury
Remote Sens. 2024, 16(7), 1198; https://doi.org/10.3390/rs16071198 (registering DOI) - 29 Mar 2024
Abstract
Speckle noise and the spatial resolution of the Sentinel−1 Synthetic Aperture Radar (SAR) image can cause significant difficulties in the detection of small objects, such as small ships. Therefore, in this study, the Polarimetric Combination-based Ship Detection (PCSD) approach is proposed for enhancing
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Speckle noise and the spatial resolution of the Sentinel−1 Synthetic Aperture Radar (SAR) image can cause significant difficulties in the detection of small objects, such as small ships. Therefore, in this study, the Polarimetric Combination-based Ship Detection (PCSD) approach is proposed for enhancing small ship detection performance, which combines three different characteristics of polarization: newVH, enhanced VH, and enhanced VV. Employing the Radar Cross Section (RCS) value in three stages, the newVH was utilized to detect Automatic Identification System (AIS) -ships and small ships. In the first step, the adaptive threshold (AT) method was applied to newVH with a high RCS condition (>−10.36 (dB)) for detecting AIS-ships. Secondly, the first small ship target was detected with the maximum suppression of false alarms by using the AT with a middle RCS condition (>−16.98 (dB)). In the third step, a candidate group was identified by applying a condition to the RCS values (>−23.01 (dB)), where both small ships and speckle noise were present simultaneously. Subsequently, the enhanced VH and VV polarizations were employed, and an optimized threshold value was selected for each polarization to detect the second small ship while eliminating noise pixels. Finally, the results were evaluated using the AIS and small fishing vessel tracking system (V-Pass) based on the detected ship positions and ship lengths. The average matching results from 26 scenes in 2022 indicated a matching rate of over 86.67% for AIS-ships. Regarding small ships, the detection performance of PCSD was 42.27%, which was over twice as accurate as the previous Constant False Alarm Rate (CFAR) ship detection model. As a result, PCSD enhanced the detection rate of small ships while maintaining the capacity for detecting AIS-equipped ships.
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(This article belongs to the Section Ocean Remote Sensing)
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Open AccessArticle
High-Precision Time Difference of Arrival Estimation Method Based on Phase Measurement
by
Jihao Xin, Xuyang Ge, Yuan Zhang, Xingdong Liang, Hang Li, Linghao Wu, Jiashuo Wei and Xiangxi Bu
Remote Sens. 2024, 16(7), 1197; https://doi.org/10.3390/rs16071197 (registering DOI) - 29 Mar 2024
Abstract
In unmanned aerial vehicle (UAV)-based time difference of arrival (TDOA) positioning technique, baselines are limited due to communication constraints. In this case, the accuracy is highly sensitive to the TDOA measurements’ error. This article primarily addresses the problem of short-baseline high-precision time synchronization
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In unmanned aerial vehicle (UAV)-based time difference of arrival (TDOA) positioning technique, baselines are limited due to communication constraints. In this case, the accuracy is highly sensitive to the TDOA measurements’ error. This article primarily addresses the problem of short-baseline high-precision time synchronization and TDOA measurement. We conducted a detailed analysis of error models in TDOA systems, considering both the time and phase measurement. We utilize the frequency division wireless phase synchronization technique in TDOA systems. Building upon this synchronization scheme, we propose a novel time delay estimation method that relies on phase measurements based on the integer least squares method. The performance of this method is demonstrated through Monte Carlo simulations and outdoor experiments. The standard deviations of synchronization and TDOA measurements in experiments are 1.12 ps and 1.66 ps, respectively. Furthermore, the circular error probable (CEP) accuracy is improved from 0.33%R to 0.02%R, offering support for the practical application of distributed short-baseline high-precision passive location techniques.
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(This article belongs to the Special Issue Unconventional Drone-Based Surveying 2nd Edition)
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Open AccessArticle
Trans-Boundary Dust Transport of Dust Storms in Northern China: A Study Utilizing Ground-Based Lidar Network and CALIPSO Satellite
by
Zhisheng Zhang, Zhiqiang Kuang, Caixia Yu, Decheng Wu, Qibing Shi, Shuai Zhang, Zhenzhu Wang and Dong Liu
Remote Sens. 2024, 16(7), 1196; https://doi.org/10.3390/rs16071196 (registering DOI) - 29 Mar 2024
Abstract
During 14–16 March 2021, a large-scale dust storm event occurred in the northern region of China, and it was considered the most intense event in the past decade. This study employs observation data for PM2.5 and PM10 from the air quality monitoring station,
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During 14–16 March 2021, a large-scale dust storm event occurred in the northern region of China, and it was considered the most intense event in the past decade. This study employs observation data for PM2.5 and PM10 from the air quality monitoring station, the HYSPLIT model, ground-based polarized Lidar networks, AGRI payload data from Fengyun satellites and CALIPSO satellite Lidar data to jointly explore and scrutinize the three-dimensional spatial and temporal characteristics of aerosol transport. Firstly, by integrating meteorological data for PM2.5 and PM10, the air quality is assessed across six stations within the Lidar network during the dust storm. Secondly, employing a backward trajectory tracking model, the study elucidates sources of dust at the Lidar network sites. Thirdly, deploying a newly devised portable infrared 1064 nm Lidar and a pulsed 532 nm Lidar, a ground-based Lidar observation network is established for vertical probing of transboundary dust transport within the observed region. Finally, by incorporating cloud imagery from Fengyun satellites and CALIPSO satellite Lidar data, this study revealed the classification of dust and the height distribution of dust layers at pertinent sites within the Lidar observation network. The findings affirm that the eastward movement and southward compression of the intensifying Mongolian cyclone led to severe dust storm weather in western and southern Mongolia, as well as Inner Mongolia, further transporting dust into northern, northwestern, and northeastern parts of China. This dust event wielded a substantial impact on a broad expanse in northern China, manifesting in localized dust storms in Inner Mongolia, Beijing, Gansu, and surrounding areas. In essence, the dust emanated from the deserts in Mongolia and northwest China, encompassing both deserts and the Gobi region. The amalgamation of ground-based and spaceborne Lidar observations conclusively establishes that the distribution height of dust in the source region ranged from 3 to 5 km. Influenced by high-pressure systems, the protracted transport of dust over extensive distances prompted a gradual reduction in its distribution height owing to sedimentation. The comprehensive analysis of pertinent research data and information collectively affirms the precision and efficacy of the three-dimensional aerosol monitoring conducted by the ground-based Lidar network within the region.
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(This article belongs to the Special Issue Remote Sensing of Particulate Matter, Its Components and Air Pollution Assessment)
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Open AccessArticle
Modulations of the South China Sea Ocean Circulation by the Summer Monsoon Intraseasonal Oscillation Inferred from Satellite Observations
by
Zhiyuan Hu, Keiwei Lyu and Jianyu Hu
Remote Sens. 2024, 16(7), 1195; https://doi.org/10.3390/rs16071195 (registering DOI) - 29 Mar 2024
Abstract
The South China Sea (SCS) displays remarkable responses and feedback to the summer monsoon intraseasonal oscillation (ISO). This study investigates how the SCS summer ocean circulation responds to the monsoon ISO based on weekly satellite data. In summer, the largest amplitudes for intraseasonal
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The South China Sea (SCS) displays remarkable responses and feedback to the summer monsoon intraseasonal oscillation (ISO). This study investigates how the SCS summer ocean circulation responds to the monsoon ISO based on weekly satellite data. In summer, the largest amplitudes for intraseasonal (30–90 days) sea surface height variations in the SCS occur around the northeastward offshore current off southeast Vietnam between a north–south eddy dipole. Our results show that such strong intraseasonal sea surface height variations are mainly caused by the alternate enhancement of the two eddies of the eddy dipole. Specifically, in response to the intraseasonal intensification of southwesterly winds, the northern cyclonic eddy of the eddy dipole strengthens within 1–2 weeks, and its southern boundary tends to be more southerly. Afterwards, as the wind-driven southern anticyclonic gyre spins up, the southern anticyclonic eddy gradually intensifies and expands its northern boundary northward, while the northern cyclonic eddy weakens and retreats northward. Besides the local wind forcing, westward propagations of the eastern boundary-originated sea surface height anomalies, which exhibit latitude-dependent features that are consistent with the linear Rossby wave theory, play an important role in ocean dynamical adjustments to the monsoon ISO, especially in the southern SCS. Case studies further confirm our findings and indicate that understanding this wind-driven process makes the ocean more predictable on short-term timescales.
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(This article belongs to the Special Issue Remote Sensing Applications in Ocean Observation II)
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Non-Linear PSInSAR Analysis of Deformation Patterns in Islamabad/Rawalpindi Region: Unveiling Tectonics and Earthquake-Driven Changes
by
Zeeshan Afzal, Timo Balz and Aamir Asghar
Remote Sens. 2024, 16(7), 1194; https://doi.org/10.3390/rs16071194 (registering DOI) - 29 Mar 2024
Abstract
The standard Permanent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR) technique, which is commonly used for surface motion analysis, assumes linear deformation velocities. While effective for monitoring urban subsidence over short periods, it falls short when dealing with non-linear, earthquake-related deformations over extended timeframes.
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The standard Permanent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR) technique, which is commonly used for surface motion analysis, assumes linear deformation velocities. While effective for monitoring urban subsidence over short periods, it falls short when dealing with non-linear, earthquake-related deformations over extended timeframes. To address this limitation, we use a non-linear PSInSAR technique, which is an enhancement of PSInSAR, to identify non-linear deformation patterns. We processed Sentinel-1A images from ascending and descending orbits in the Islamabad/Rawalpindi region from December 2015 to January 2023 using non-linear PSInSAR. By calculating the differences in deformation, we analyzed surface movements and assessed the impact of the 2017 earthquake on urban areas. Our findings reveal that the earthquake significantly increased the deformation in ascending and descending orbit tracks, with an average deformation of up to 70 mm/yr and a line-of-sight movement of up to 30 mm/yr. Our observations indicate that the deformation is directed towards the line of sight in the north and south of the deformed area, suggesting subsidence between the two uplifting faults, potentially linked to a concealed fault line along the deformation zone boundary. This contradicts previous arguments, suggesting that water extraction is the leading cause of deformation. Our analysis with non-linear PSInSAR demonstrates that tectonics play a significant role in deformation, providing valuable insights into tectonic-activity-induced deformations in urban areas over the long term.
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(This article belongs to the Section Environmental Remote Sensing)
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Open AccessReview
Review of GNSS-R Technology for Soil Moisture Inversion
by
Changzhi Yang, Kebiao Mao, Zhonghua Guo, Jiancheng Shi, Sayed M. Bateni and Zijin Yuan
Remote Sens. 2024, 16(7), 1193; https://doi.org/10.3390/rs16071193 - 28 Mar 2024
Abstract
Soil moisture (SM) is an important parameter in water cycle research. Rapid and accurate monitoring of SM is critical for hydrological and agricultural applications, such as flood detection and drought characterization. The Global Navigation Satellite System (GNSS) uses L-band microwave signals as carriers,
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Soil moisture (SM) is an important parameter in water cycle research. Rapid and accurate monitoring of SM is critical for hydrological and agricultural applications, such as flood detection and drought characterization. The Global Navigation Satellite System (GNSS) uses L-band microwave signals as carriers, which are particularly sensitive to SM and suitable for monitoring it. In recent years, with the development of Global Navigation Satellite System–Reflectometry (GNSS-R) technology and data analysis methods, many studies have been conducted on GNSS-R SM monitoring, which has further enriched the research content. However, current GNSS-R SM inversion methods mainly rely on auxiliary data to reduce the impact of non-target parameters on the accuracy of inversion results, which limits the practical application and widespread promotion of GNSS-R SM monitoring. In order to promote further development in GNSS-R SM inversion research, this paper aims to comprehensively review the current status and principles of GNSS-R SM inversion methods. It also aims to identify the problems and future research directions of existing research, providing a reference for researchers. Firstly, it introduces the characteristics, usage scenarios, and research status of different GNSS-R SM observation platforms. Then, it explains the mechanisms and modeling methods of various GNSS-R SM inversion research methods. Finally, it highlights the shortcomings of existing research and proposes future research directions, including the introduction of transfer learning (TL), construction of small models based on spatiotemporal analysis and spatial feature fusion, and further promoting downscaling research.
Full article
(This article belongs to the Section Environmental Remote Sensing)
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