Journal Description
Remote Sensing
Remote Sensing
is a 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 19.7 days after submission; acceptance to publication is undertaken in 2.9 days (median values for papers published in this journal in the second half of 2022).
- 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:
5.349 (2021);
5-Year Impact Factor:
5.786 (2021)
Latest Articles
Analysis of Spatial and Temporal Variation in Water Coverage in the Sub-Lakes of Poyang Lake Based on Multi-Source Remote Sensing
Remote Sens. 2023, 15(11), 2788; https://doi.org/10.3390/rs15112788 (registering DOI) - 26 May 2023
Abstract
As the largest freshwater lake in China, Poyang Lake is an internationally important wetland and the largest migratory bird habitat in Asia. Many sub-lakes distributed in the lake basin are seasonal lakes, which have a significant impact on hydro-ecological processes and are susceptible
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As the largest freshwater lake in China, Poyang Lake is an internationally important wetland and the largest migratory bird habitat in Asia. Many sub-lakes distributed in the lake basin are seasonal lakes, which have a significant impact on hydro-ecological processes and are susceptible to various changes. In this study, using multi-source remote sensing data, a continuous time-series construction method of water coverage suitable in Poyang Lake was developed. That method combined the downscaling of the MNDWI (modified normalized difference water index) with the ISODATA (iterative self-organizing data analysis technique algorithm), and its accuracy can be up to 97% in the months when Landsat 8 is available or 87% when it is unavailable. Based on that method, the increasing variation in water coverage was observed in the sub-lakes of Poyang Lake during 2013–2020 to be within a range of 200–690 km2 normally. The center of the sub-lakes always remained inundated (>80% inundation frequency), while the surrounding areas were probably kept dry for seven months (except for June to September). The dominant influencing factors of water coverage variations were different in different hydrological periods (wet season and dry–wet season: discharge; dry season: temperature and wind speed; wet–dry season: temperature and precipitation). In addition, “returning farmland to lakes” affected the increase in the water area in the sub-lakes. This study is helpful for the management of water resources and the protection of migratory birds in the Poyang Lake region.
Full article
(This article belongs to the Special Issue Monitoring Terrestrial Water Resource Using Multiple Satellite Sensors)
Open AccessArticle
Line Scan Hyperspectral Imaging Framework for Open Source Low-Cost Platforms
Remote Sens. 2023, 15(11), 2787; https://doi.org/10.3390/rs15112787 (registering DOI) - 26 May 2023
Abstract
With advancements in computer processing power and deep learning techniques, hyperspectral imaging is continually being explored for improved sensing applications in various fields. However, the high cost associated with such imaging platforms impedes their widespread use in spite of the availability of the
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With advancements in computer processing power and deep learning techniques, hyperspectral imaging is continually being explored for improved sensing applications in various fields. However, the high cost associated with such imaging platforms impedes their widespread use in spite of the availability of the needed processing power. In this paper, we develop a novel theoretical framework required for an open source ultra-low-cost hyperspectral imaging platform based on the line scan method suitable for remote sensing applications. Then, we demonstrate the design and fabrication of an open source platform using consumer-grade commercial off-the-shelf components that are both affordable and easily accessible to researchers and users. At the heart of the optical system is a consumer-grade spectroscope along with a basic galvanometer mirror that is widely used in laser scanning devices. The utilized pushbroom scanning method provides a very high spectral resolution of 2.8 nm, as tested against commercial spectral sensors. Since the resolution is limited by the slit width of the spectroscope, we also provide a deconvolution method for the line scan in order to improve the monochromatic spatial resolution. Finally, we provide a cost-effective testing method for the hyperspectral imaging platform where the results validate both the spectral and spatial performances of the platform.
Full article
Open AccessArticle
Surface Soil Moisture Retrieval of China Using Multi-Source Data and Ensemble Learning
Remote Sens. 2023, 15(11), 2786; https://doi.org/10.3390/rs15112786 (registering DOI) - 26 May 2023
Abstract
Large-scale surface soil moisture (SSM) distribution is very necessary for agricultural drought monitoring, water resource management, and climate change research. However, the current large-scale SSM products have relatively coarse spatial resolution, which limits their application. In this study, we estimate the 1 km
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Large-scale surface soil moisture (SSM) distribution is very necessary for agricultural drought monitoring, water resource management, and climate change research. However, the current large-scale SSM products have relatively coarse spatial resolution, which limits their application. In this study, we estimate the 1 km daily SSM in China based on ensemble learning using a multi-source data set including in situ soil moisture measurements from 2980 meteorological stations, MODIS Surface Reflectance products, SMAP (Soil Moisture Active Passive) soil moisture products, ERA5-Land dataset, SRTM DEM and soil texture. Among them, in situ measurements are used as independent variables, and other data are used as dependent variables. In order to improve the spatio-temporal completeness of SSM, the missing value in SMAP soil moisture products were reconstructed using the Discrete Cosine Transformation-penalized Partial Least Square (DCT-PLS) method to provide spatially complete background field information for soil moisture retrieval. The results show that the reconstructed soil moisture value has high quality, and the DCT-PLS method can fully utilize the three-dimensional spatiotemporal information to fill the data gaps. Subsequently, the performance of four ensemble learning models of random forest (RF), extremely randomized trees (ERT), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM) for soil moisture retrieval was evaluated. The LightGBM outperformed the other three machine learning models, with a correlation coefficient (R2) of 0.88, a bias of 0.0004 m³/m³, and an unbiased root mean square error (ubRMSE) of 0.0366 m³/m³. The high correlation between the in situ soil moisture and the predicted values at each meteorological station further indicate that LightGBM can well capture the temporal variation of soil moisture. Finally, the model was used to map the 1 km daily SSM in China on the first day of each month from May to October 2018. This study can provide some reference and help for future long-term daily 1 km surface soil moisture mapping in China.
Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Ecohydrology)
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Open AccessArticle
An Automatic Method for Rice Mapping Based on Phenological Features with Sentinel-1 Time-Series Images
by
, , , , , , and
Remote Sens. 2023, 15(11), 2785; https://doi.org/10.3390/rs15112785 (registering DOI) - 26 May 2023
Abstract
Rice is one of the most important staple foods in the world, feeding more than 50% of the global population. However, rice is also a significant emitter of greenhouse gases and plays a role in global climate change. As a result, quickly and
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Rice is one of the most important staple foods in the world, feeding more than 50% of the global population. However, rice is also a significant emitter of greenhouse gases and plays a role in global climate change. As a result, quickly and accurately obtaining the rice mapping is crucial for ensuring global food security and mitigating global warming. In this study, we proposed an automated rice mapping method called automated rice mapping using V-shaped phenological features of rice (Auto-RMVPF) based on the time-series Sentinel-1A images, which are composed of four main steps. First, the dynamic threshold method automatically extracts abundant rice samples by flooding signals. Second, the second-order difference method automatically extracts the phenological period of rice based on the scattering feature of rice samples. Then, the key “V” feature of the VH backscatter time series, which rises before and after rice transplanting due to flooding, is used for rice mapping. Finally, the farmland mask is extracted to avoid interference from non-farmland features on the rice map, and the median filter is applied to remove noise from the rice map and obtain the final spatial distribution of rice. The results show that the Auto-RMVPF method not only can automatically obtain abundant rice samples but also can extract the accurate phenological period of rice. At the same time, the accuracy of rice mapping is also satisfactory, with an overall accuracy is more than 95% and an score of over 0.91. The overall accuracy of the Auto-RMVPF method is improved by 2.8–12.2% compared with support vector machine (SVM) with an overall accuracy of 89.9% (25 training samples) and 92.2% (124 training samples), random forest (RF) with an overall accuracy of 82.8% (25 training samples) and 88.3% (124 training samples), and automated rice mapping using synthetic aperture radar flooding signals (ARM-SARFS) with an overall accuracy of 89.9%. Altogether, these experimental results suggest that the Auto-RMVPF method has broad prospects for automatic rice mapping, especially for mountainous regions where ground samples are often not easily accessible.
Full article
(This article belongs to the Section Biogeosciences Remote Sensing)
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Open AccessArticle
An ISAR and Visible Image Fusion Algorithm Based on Adaptive Guided Multi-Layer Side Window Box Filter Decomposition
by
, , , , , , , and
Remote Sens. 2023, 15(11), 2784; https://doi.org/10.3390/rs15112784 (registering DOI) - 26 May 2023
Abstract
Traditional image fusion techniques generally use symmetrical methods to extract features from different sources of images. However, these conventional approaches do not resolve the information domain discrepancy from multiple sources, resulting in the incompleteness of fusion. To solve the problem, we propose an
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Traditional image fusion techniques generally use symmetrical methods to extract features from different sources of images. However, these conventional approaches do not resolve the information domain discrepancy from multiple sources, resulting in the incompleteness of fusion. To solve the problem, we propose an asymmetric decomposition method. Firstly, an information abundance discrimination method is used to sort images into detailed and coarse categories. Then, different decomposition methods are proposed to extract features at different scales. Next, different fusion strategies are adopted for different scale features, including sum fusion, variance-based transformation, integrated fusion, and energy-based fusion. Finally, the fusion result is obtained through summation, retaining vital features from both images. Eight fusion metrics and two datasets containing registered visible, ISAR, and infrared images were adopted to evaluate the performance of the proposed method. The experimental results demonstrate that the proposed asymmetric decomposition method could preserve more details than the symmetric one, and performed better in both objective and subjective evaluations compared with the fifteen state-of-the-art fusion methods. These findings can inspire researchers to consider a new asymmetric fusion framework that can adapt to the differences in information richness of the images, and promote the development of fusion technology.
Full article
(This article belongs to the Special Issue Advances and Challenges on Multisource Remote Sensing Image Fusion: Datasets, New Technologies, and Applications)
Open AccessArticle
Geometric Configuration Design and Fast Imaging for Multistatic Forward-Looking SAR Based on Wavenumber Spectrum Formation Approach
Remote Sens. 2023, 15(11), 2783; https://doi.org/10.3390/rs15112783 (registering DOI) - 26 May 2023
Abstract
Multistatic forward-looking synthetic aperture radar (Mu-FLSAR) has the potential of high-resolution imaging with short synthetic aperture time, which can improve the transmitter’s survivability, by coherently fusing simultaneously observed measurements of multiple receivers. However, the combined performance of the multiple measurements strictly depends on
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Multistatic forward-looking synthetic aperture radar (Mu-FLSAR) has the potential of high-resolution imaging with short synthetic aperture time, which can improve the transmitter’s survivability, by coherently fusing simultaneously observed measurements of multiple receivers. However, the combined performance of the multiple measurements strictly depends on an appropriate geometric configuration among the transmitter and receivers because the forward-looking application limits the flight directions of receivers. In this paper, to design a geometric configuration for Mu-FLSAR, a wavenumber spectrum formation (WSF) approach is proposed based on the projection relationship between the wavenumber support regions (WSRs) and geometric configuration parameters. On the one hand, the projected pattern of multiple WSRs is deduced, and the relationship between multiple WSRs and the point spread function (PSF) is analyzed. Based on the geometric feature of the kernel WSR, which is formed by the transmitter and the master receiver, and the relationship between the geometric features and the geometric configuration parameters, including synthetic aperture time and azimuthal angle, a WSF method is proposed to visually and quickly deduce the geometric parameter of the salve receivers. On the other hand, based on the designed geometric configuration of Mu-FLSAR, a wavenumber-dependent fast polar format algorithm (WF-PFA) is proposed to efficiently reconstruct the targets relying on the geometric features of WSRs. Simulation results verify the proposed method.
Full article
(This article belongs to the Topic Radar Signal and Data Processing with Applications)
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Open AccessArticle
Current Status and Challenges of BDS Satellite Precise Orbit Products: From a View of Independent SLR Validation
Remote Sens. 2023, 15(11), 2782; https://doi.org/10.3390/rs15112782 (registering DOI) - 26 May 2023
Abstract
As an essential infrastructure that provides positioning, navigation, and timing services, China constructed the BeiDou Navigation Satellite System (BDS). The last BDS satellite was launched in June 2020, which represents the completion of BDS. BDS’s constellation consists of Medium Earth Orbit (MEO), Inclined
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As an essential infrastructure that provides positioning, navigation, and timing services, China constructed the BeiDou Navigation Satellite System (BDS). The last BDS satellite was launched in June 2020, which represents the completion of BDS. BDS’s constellation consists of Medium Earth Orbit (MEO), Inclined Geosynchronous Orbit (IGSO), and Geostationary Orbit satellites. The precise modeling of non-conservative forces for BDS satellites is a challenging task. As an independent observation, Satellite Laser Ranging (SLR) is an important validation method of GNSS orbit modeling. In this paper, we validated the precise orbit products of different Analysis Centers (ACs) by using SLR observations, focusing on the BDS orbit modeling. By comparing BDS precise orbit products generated by four ACs with respect to SLR observations for the period of February 2017 to March 2021, we proved that an obvious satellite signature effect exists in the SLR residuals of BDS observed by multi-photon stations. The result indicates that multi-photon stations have a root mean square (RMS) of SLR residuals about 5 mm lower than that of single-photon detectors. The slope of SLR residuals with regard to nadir angle of IGSO satellites for single-photon and multi-photon stations is −2.0 and −2.5 mm/deg, respectively, while the slope of MEO satellites for these stations is about −0.6 to −0.3 and −1.0 to −0.4 mm/deg, respectively. To assess the effect of non-conservative force modeling, we selected seven high-performing stations, including five single-photon and two multi-photon stations. By comparing the SLR residuals of four ACs’ orbits, we analyzed the effect of the solutions of orbit processing, especially solar radiation pressure (SRP) models. We found that some centers may have modeling defects, including BDS-3 orbits of the Deutsches GeoForschungsZentrum and BDS-2 orbits of the European Space Agency, inferred from the large RMS of SLR residuals. Modeling the SRP of BDS satellites is challenging, while an appropriate prior box-wing model can improve the accuracy of SRP modeling and provide a more stable performance.
Full article
(This article belongs to the Special Issue Precision Orbit Determination of Satellites)
Open AccessArticle
A GNSS Spoofing Detection and Direction-Finding Method Based on Low-Cost Commercial Board Components
by
, , , , , , , , and
Remote Sens. 2023, 15(11), 2781; https://doi.org/10.3390/rs15112781 (registering DOI) - 26 May 2023
Abstract
The Global Navigation Satellite System (GNSS) is vulnerable to deliberate spoofing signal attacks. Once the user wrongly locks on the spoofing signal, the wrong position, velocity, and time (PVT) information will be calculated, which will harm the user. GNSS spoofing signals are difficult
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The Global Navigation Satellite System (GNSS) is vulnerable to deliberate spoofing signal attacks. Once the user wrongly locks on the spoofing signal, the wrong position, velocity, and time (PVT) information will be calculated, which will harm the user. GNSS spoofing signals are difficult to carry out spoofing attacks in the direction of arrival (DOA) of the real signal, so the spoofing detection method based on DOA is very effective. On the basis of identifying spoofing signals, accurate DOA information of the signal can be further used to locate the spoofer. At present, the existing DOA monitoring methods for spoofing signals are mainly based on dedicated antenna arrays and receivers, which are costly and difficult to upgrade and are not conducive to large-scale deployment, upgrade, and maintenance. This paper proposes a spoofing detection and direction-finding method based on a low-cost commercial GNSS board component (including an antenna). Based on the traditional principle of using a multi-antenna carrier phase to solve DOA, this paper innovatively solves the following problems: the poor direction-finding accuracy caused by the unstable phase center of low-cost commercial antennas, the low success rate of spoofing detection in a multipath environment, and the inconsistent sampling time among multiple low-cost commercial GNSS boards. Moreover, the corresponding prototype equipment for spoofing detection and direction-finding is developed. The measured results show that it can effectively detect spoofing signals in open environments. Under a certain false alarm rate, the detection success rate can reach 100%, and the typical direction-finding accuracy can reach .
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(This article belongs to the Special Issue Satellite Navigation and Signal Processing)
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Open AccessArticle
Changes in Carbon Dioxide Balance Associated with Land Use and Land Cover in Brazilian Legal Amazon Based on Remotely Sensed Imagery
by
, , , , , , , , , and
Remote Sens. 2023, 15(11), 2780; https://doi.org/10.3390/rs15112780 (registering DOI) - 26 May 2023
Abstract
The Amazon region comprises the largest tropical forest on the planet and is responsible for absorbing huge amounts of CO2 from the atmosphere. However, changes in land use and cover have contributed to an increase in greenhouse gas emissions, especially CO2
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The Amazon region comprises the largest tropical forest on the planet and is responsible for absorbing huge amounts of CO2 from the atmosphere. However, changes in land use and cover have contributed to an increase in greenhouse gas emissions, especially CO2, and in endangered indigenous lands and protected areas in the region. The objective of this study was to detect changes in CO2 emissions and removals associated with land use and land cover changes in the Brazilian Legal Amazon (BLA) through the analysis of multispectral satellite images from 2009 to 2019. The Gross Primary Production (GPP) and CO2Flux variables were estimated by the MODIS sensor onboard Terra and Aqua satellite, representing carbon absorption by vegetation during the photosynthesis process. Atmospheric CO2 concentration was estimated from the GOSAT satellite. The variables GPP and CO2Flux showed the effective flux of carbon in the BLA to atmosphere, which were weakly correlated with precipitation (r = 0.191 and 0.133). The forest absorbed 211.05 TgC annually but, due to its partial conversion to other land uses, the loss of 135,922.34 km2 of forest area resulted in 5.82 TgC less carbon being absorbed. Pasture and agriculture, which comprise the main land conversions, increased by 100,340.39 km2 and absorbed 1.32 and 3.19 TgC less, and emitted close to twice more, than forest in these areas. Atmospheric CO2 concentrations increased from 2.2 to 2.8 ppm annually in BLA, with hotspots observed in the southeast Amazonia, and CO2 capture by GPP showed an increase over the years, mainly after 2013, in the north and west of the BLA. This study brings to light the carbon dynamics, by GPP and CO2Flux models, as related to the land use and land cover in one of the biggest world carbon reservoirs, the Amazon, which is also important to fulfillment of international agreements signed by Brazil to reduce greenhouse gas emissions and for biodiversity conservation and other ecosystem services in the region.
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(This article belongs to the Special Issue Remote Sensing of the Amazon Region)
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Open AccessArticle
Absorbing Aerosol Optical Properties and Radiative Effects on Near-Surface Photochemistry in East Asia
by
, , , , , , , , , , , and
Remote Sens. 2023, 15(11), 2779; https://doi.org/10.3390/rs15112779 (registering DOI) - 26 May 2023
Abstract
Absorbing aerosols have significant influences on tropospheric photochemistry and regional climate change. Here, the direct radiative effects of absorbing aerosols at the major AERONET sites in East Asia and corresponding impacts on near-surface photochemical processes were quantified by employing a radiation transfer model.
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Absorbing aerosols have significant influences on tropospheric photochemistry and regional climate change. Here, the direct radiative effects of absorbing aerosols at the major AERONET sites in East Asia and corresponding impacts on near-surface photochemical processes were quantified by employing a radiation transfer model. The average annual aerosol optical depth (AOD) of sites in China, Korea, and Japan was 1.15, 1.02 and 0.94, respectively, and the corresponding proportion of absorbing aerosol optical depth (AAOD) was 8.61%, 6.69%, and 6.49%, respectively. The influence of absorbing aerosol on ultraviolet (UV) radiation mainly focused on UV-A band (315–400 nm). Under the influence of such radiative effect, the annual mean near-surface J[NO2] (J[O1D]) of sites in China, Korea, and Japan decreased by 16.95% (22.42%), 9.61% (13.55%), and 9.63% (13.79%), respectively. In Beijing–Tianjin–Hebei (BTH) and Yangtze River Delta (YRD) region, the annual average AOD was 1.48 and 1.29, and the AAOD was 0.14 and 0.13, respectively. The UV radiative forcing caused by aerosols dominated by black carbon (BC-dominated aerosols) on the surface was −3.19 and −2.98 W m−2, respectively, accounting for about 40% of the total aerosol radiative forcing, indicating that the reduction efficiency of BC-dominated aerosols on solar radiation was higher than that of other types of aerosols. The annual mean J[NO2] (J[O1D]) decreased by 14.90% (20.53%) and 13.71% (18.20%) due to the BC-dominated aerosols. The daily maximum photolysis rate usually occurred near noon due to the diurnal variation of solar zenith angle and, thus, the daily average photolysis rate decreased by 2–3% higher than that average during 10:00–14:00.
Full article
(This article belongs to the Special Issue Observation and Impact Evaluation of Atmospheric Pollution by Remote Sensing)
Open AccessArticle
Spatiotemporal Variation of Hourly Scale Extreme Rainstorms in the Huang-Huai-Hai Plain and Its Impact on NDVI
Remote Sens. 2023, 15(11), 2778; https://doi.org/10.3390/rs15112778 (registering DOI) - 26 May 2023
Abstract
This paper utilizes high-resolution ERA5 hourly data from 1980 to 2020 and long-term normalized difference vegetation index (NDVI) time series obtained from remote sensing and applies trend analysis, correlation analysis, lag analysis, and other methods to study the spatiotemporal characteristics of extreme rainfall
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This paper utilizes high-resolution ERA5 hourly data from 1980 to 2020 and long-term normalized difference vegetation index (NDVI) time series obtained from remote sensing and applies trend analysis, correlation analysis, lag analysis, and other methods to study the spatiotemporal characteristics of extreme rainfall at daily and hourly scales in the Huang-Huai-Hai Plain. The paper explores the NDVI’s variability and its relationship with extreme hourly precipitation and analyzes the main factors affecting it. The study made the following observations: (1) The extreme daily precipitation in the Huang-Huai-Hai Plain shows a decreasing trend, with a 13.6 mm/yr reduction rate. In contrast, the proportion of extreme rainfall to total precipitation generally exceeds 20%, and the intensity of extreme rain has gradually increased. The spatial distribution pattern of extreme rainfall follows the distribution pattern of China’s rain belts, with the terrain being an important influencing factor. The high-incidence areas for extreme rainfall are the Huaihe River region and the Shandong Peninsula. (2) The observed significant increase in hourly extreme precipitation events in the Shandong and Henan provinces of the Huang-Huai-Hai Plain has led to an increased risk of flooding, while the corresponding events in the northwest region of the Plain have exhibited a gradual weakening trend over time. (3) The extreme hourly precipitation in the Huang-Huai-Hai plain shows a frequent and scattered pattern, with decreasing intensity over time. Extreme precipitation mainly occurs in the first half of the night, especially between 19:00 and 21:00, with extreme hourly rainfall intensity fluctuating between 0.2 and 0.25 and the proportion of rainfall to total precipitation reaching as high as 10%. The spatial distribution of extreme hourly rainstorms during the peak period (19:00–21:00) exhibits a high rainfall volume, intensity, and frequency pattern in the eastern region, while the western part exhibits low rainfall volume, intensity, and frequency. (4) The incidence of extremely heavy rainfall in an hour has exhibited a more significant increase compared to extreme daily events in the Huang-Huai-Hai Plain, primarily in the form of backward-type precipitation. Hourly extreme precipitation events in the Huang-Huai-Hai Plain are affected by terrain and land use/cover change (LUCC), with the micro-topography of hilly areas leading to a concentrated distribution of precipitation and LUCC suppressing extreme precipitation events in arid climates. (5) At the ten-day scale, the spatial distribution of the NDVI shows a gradually increasing trend from northwest to southeast, with the highest NDVI value reaching up to 0.6 in the southern part of the study area. For extreme hourly precipitation, there is no significant change observed at the multi-year ten-day scale; while the NDVI in the northern and central parts of the Huang-Huai-Hai Plain shows a significant decreasing trend, in contrast, it presents a significant increasing trend in the southern region. (6) Finally, the correlation between NDVI at the ten-day scale and extreme hourly precipitation exhibits a decreasing pattern from north to south, with a correlation coefficient decreasing from 0.48 to 0.08. The lagged correlation analysis of extreme hourly rainfall and NDVI for one, two, and three ten-day periods shows that the lagged effect of extreme hourly precipitation on NDVI is negligible. Analyzing the correlation between extreme hourly rainfall and NDVI for different months, the impact of extreme hourly precipitation on NDVI is predominantly negative, except for June, which shows a positive correlation (0.35), passing the significance test. This study offers a scientific foundation for enhancing disaster warning accuracy and timeliness and strengthening the research on disaster reduction techniques.
Full article
(This article belongs to the Special Issue Understanding the Meteorological Environment in Arid Regions through the Integrative Analyses of Remote Sensing, Ground Observational Stations and Numerical Models)
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Open AccessArticle
Describing Polyps Behavior of a Deep-Sea Gorgonian, Placogorgia sp., Using a Deep-Learning Approach
by
, , , , , , , , , , , , and
Remote Sens. 2023, 15(11), 2777; https://doi.org/10.3390/rs15112777 (registering DOI) - 26 May 2023
Abstract
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Gorgonians play a fundamental role in the deep sea (below 200 m depth), composing three-dimensional habitats that are characterized by a high associated biodiversity and playing an important part in biogeochemical cycles. Here we describe the use of a benthic lander to monitoring
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Gorgonians play a fundamental role in the deep sea (below 200 m depth), composing three-dimensional habitats that are characterized by a high associated biodiversity and playing an important part in biogeochemical cycles. Here we describe the use of a benthic lander to monitoring polyps activity, used as a proxy of gorgonian feeding activity of three colonies of Placogorgia sp. Images cover a period of 22 days with a temporal resolution of 30 min. In addition, this seafloor observatory is instrumented with oceanographic sensors that allows continuous monitoring of the hydrographic conditions in the site. Deep-learning is used for automatic detection of the state of the polyps registered in the images. More than 1000 images of 3 large specimens of gorgonians are analyzed, annotating polyps as extended or retracted, using the semantic segmentation algorithm ConvNeXt. The segmentation results are used to describe the feeding patterns of this species. Placogorgia sp. shows a daily pattern of feeding conduct, depending on the hours of day and night. Using a Singular Spectrum Analysis approach, feeding activity is related to currents dynamics and Acoustic Doppler Current Profile (ADCP) return signal intensity, as proxy of suspended matter, achieving a linear correlation of 0.35 and 0.11 respectively. This is the first time that the behavior of the Placogorgia polyps, directly related to their feeding process, is described.
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Open AccessArticle
Mapping Small Watercourses from DEMs with Deep Learning—Exploring the Causes of False Predictions
Remote Sens. 2023, 15(11), 2776; https://doi.org/10.3390/rs15112776 (registering DOI) - 26 May 2023
Abstract
Vector datasets of small watercourses, such as rivulets, streams, and ditches, are important for many visualization and analysis use cases. Mapping small watercourses with traditional methods is laborious and costly. Convolutional neural networks (CNNs) are state-of-the-art computer vision methods that have been shown
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Vector datasets of small watercourses, such as rivulets, streams, and ditches, are important for many visualization and analysis use cases. Mapping small watercourses with traditional methods is laborious and costly. Convolutional neural networks (CNNs) are state-of-the-art computer vision methods that have been shown to be effective for extracting geospatial features, including small watercourses, from LiDAR point clouds, digital elevation models (DEMs), and aerial images. However, the cause of the false predictions by machine-learning models is often not thoroughly explored, and thus the impact of the results on the process of producing accurate datasets is not well understood. We digitized a highly accurate and complete dataset of small watercourses from a study area in Finland. We then developed a process based on a CNN that can be used to extract small watercourses from DEMs. We tested and validated the performance of the network with different input data layers, and their combinations to determine the best-performing layer. We analyzed the false predictions to gain an understanding of their nature. We also trained models where watercourses with high levels of uncertainty were removed from the training sets and compared the results to training models with all watercourses in the training set. The results show that the DEM was the best-performing layer and that combinations of layers provided worse results. Major causes of false predictions were shown to be boundary errors with an offset between the prediction and labeled data, as well as errors of omission by watercourses with high levels of uncertainty. Removing features with the highest level of uncertainty from the labeled dataset increased the overall f1-score but reduced the recall of the remaining features. Additional research is required to determine if the results remain similar to other CNN methods.
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(This article belongs to the Section AI Remote Sensing)
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Open AccessArticle
Mapping Malaria Vector Habitats in West Africa: Drone Imagery and Deep Learning Analysis for Targeted Vector Surveillance
by
, , , , , , , , , , , , , , and
Remote Sens. 2023, 15(11), 2775; https://doi.org/10.3390/rs15112775 (registering DOI) - 26 May 2023
Abstract
Disease control programs are needed to identify the breeding sites of mosquitoes, which transmit malaria and other diseases, in order to target interventions and identify environmental risk factors. The increasing availability of very-high-resolution drone data provides new opportunities to find and characterize these
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Disease control programs are needed to identify the breeding sites of mosquitoes, which transmit malaria and other diseases, in order to target interventions and identify environmental risk factors. The increasing availability of very-high-resolution drone data provides new opportunities to find and characterize these vector breeding sites. Within this study, drone images from two malaria-endemic regions in Burkina Faso and Côte d’Ivoire were assembled and labeled using open-source tools. We developed and applied a workflow using region-of-interest-based and deep learning methods to identify land cover types associated with vector breeding sites from very-high-resolution natural color imagery. Analysis methods were assessed using cross-validation and achieved maximum Dice coefficients of 0.68 and 0.75 for vegetated and non-vegetated water bodies, respectively. This classifier consistently identified the presence of other land cover types associated with the breeding sites, obtaining Dice coefficients of 0.88 for tillage and crops, 0.87 for buildings and 0.71 for roads. This study establishes a framework for developing deep learning approaches to identify vector breeding sites and highlights the need to evaluate how results will be used by control programs.
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(This article belongs to the Special Issue Remote Sensing and Infectious Diseases)
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Open AccessTechnical Note
Spatial-Temporal Changes of Abarkuh Playa Landform from Sentinel-1 Time Series Data
Remote Sens. 2023, 15(11), 2774; https://doi.org/10.3390/rs15112774 (registering DOI) - 26 May 2023
Abstract
Playas, as the flattest landforms in semiarid and arid regions, are extremely sensitive to climate changes, such as changes in the hydrologic regime of the landscape. The changes in these landforms due to irrigation, anthropogenic activities, and climate change could be a source
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Playas, as the flattest landforms in semiarid and arid regions, are extremely sensitive to climate changes, such as changes in the hydrologic regime of the landscape. The changes in these landforms due to irrigation, anthropogenic activities, and climate change could be a source of disasters. In this study, we explored the spatial-temporal changes of the Abarkuh Playa in Central Iran using the time series of the Sentinel-1 backscatter dataset in the three scales. Our results showed that the western area of the Abarkuh Playa has been changed to other landforms with different characteristics, which is clear from all backscatter maps. The spatial-temporal analysis of the time series of backscatter data using the independent component analysis and time series of precipitation revealed that the backscatter variations were associated with direct rainfall across the playa and the surface was reacting to changes in the soil moisture content. The results of the power scale showed that the boundary of the playa could successfully be recognized as the oscillating pattern from other landforms in the study area. Moreover, the spatial-temporal analysis of backscatter in the power scale showed that different polarizations could reveal different patterns of surface changes for the playa.
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(This article belongs to the Special Issue Analysis of SAR/InSAR Data in Geoscience)
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Open AccessArticle
Monitoring the Impact of Heat Damage on Summer Maize on the Huanghuaihai Plain, China
Remote Sens. 2023, 15(11), 2773; https://doi.org/10.3390/rs15112773 (registering DOI) - 26 May 2023
Abstract
As an important food crop, summer maize is widely planted all over the world. Monitoring its growth and output is of great significance for world food security. With the trend of global warming and deterioration, the frequency of high temperature and heat damage
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As an important food crop, summer maize is widely planted all over the world. Monitoring its growth and output is of great significance for world food security. With the trend of global warming and deterioration, the frequency of high temperature and heat damage affecting summer corn has been increasing in the past ten years. Therefore, there is an increasing demand for monitoring the high temperature and heat damage of summer maize. At present, there are nearly a hundred indices or methods for research on high temperature and heat damage. However, research based on the vegetation index cannot fully describe the damage caused by high-temperature thermal damage, and there is an obvious asynchrony effect. Research based on hyperspectral remote sensing has many inconveniences in data acquisition and complex physical model construction. Therefore, this study uses remote sensing data, including MODIS surface reflection data, MODIS land surface temperature products, as well as ground observation data and statistical data, combined with multiple remote sensing indices and land surface temperature, to construct a remote sensing index, LSHDI (land surface heat damage index). The LSHDI first searches for a location with the worst vegetation growth conditions in the three-dimensional feature space based on the LST (land surface temperature), the normalized difference vegetation index (NDVI), and the land surface water index (LSWI). Then, it calculates the distance between each point and this location to measure the degree of vegetation affected by high temperature and heat damage. Finally, because there is no reliable disaster verification dataset that has been published at present, this study uses soil moisture as a reference to explain the performance and stability of the LSHDI. The results showed that their coefficient of determination was above 0.5 and reached a significance level of 0.01. The LSHDI can well-reflect the high temperature and heat damage of land surface vegetation and can provide important data support and references for agricultural management departments.
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(This article belongs to the Special Issue Advances in Remote Sensing for Monitoring and Characterising Vegetation Responses to Changing and Extreme Climatic Conditions)
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Open AccessArticle
GNSS Real-Time Warning Technology for Expansive Soil Landslide—A Case in Ningming Demonstration Area
Remote Sens. 2023, 15(11), 2772; https://doi.org/10.3390/rs15112772 (registering DOI) - 26 May 2023
Abstract
Efficient monitoring and early warning are the preconditions of realizing expansive soil landslide hazard prevention and control. Previous early warning of expansive soil landslides was evaluated through soil sampling experiments to analyze the stability coefficient. However, the existing methods lack timeliness and ignore
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Efficient monitoring and early warning are the preconditions of realizing expansive soil landslide hazard prevention and control. Previous early warning of expansive soil landslides was evaluated through soil sampling experiments to analyze the stability coefficient. However, the existing methods lack timeliness and ignore the inconsistent deformation characteristics of different parts of the landslide mass. There are still difficulties in the dynamic numerical early warning of landslides at multiple points. Considering that the degradation of expansive soil landslides’ strength is directly reflected by surface displacement, for the Ningming expansive soil demonstration area and based on the GNSS shallow real-time displacement monitoring sequence, a landslide early-warning method based on the GNSS displacement rate combined with the GNSS displacement tangent angle model was proposed, and we thus designed early-warning thresholds for different warning levels. Combined with multi-source data such as soil moisture, soil pressure, and rainfall, the feasibility of accurate early warning of expansive soil landslides based on GNSS real-time surface displacement was verified. The proposed method does not require numerical calculation of internal stress and achieved two successful early warnings of landslides in the test area, which has a certain promotional value.
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(This article belongs to the Topic Recent Advances in PNT Technology with GNSS as the Core and Its Application in Emerging Fields)
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Open AccessArticle
Magnetopause Detection under Low Solar Wind Density Based on Deep Learning
Remote Sens. 2023, 15(11), 2771; https://doi.org/10.3390/rs15112771 (registering DOI) - 26 May 2023
Abstract
Extracting the peak value of the X-ray signal in the original magnetopause detection method of soft X-ray imaging (SXI) for the SMILE satellite is problematic because of the unclear interface of the magnetosphere system under low solar wind density and the short integration
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Extracting the peak value of the X-ray signal in the original magnetopause detection method of soft X-ray imaging (SXI) for the SMILE satellite is problematic because of the unclear interface of the magnetosphere system under low solar wind density and the short integration time. Herein, we propose a segmentation algorithm for soft X-ray images based on depth learning, we construct an SXI simulation dataset, and we segment the magnetospheric system by learning the spatial structure characteristics of the magnetospheric system image. Then, we extract the maximum position of the X-ray signal and calculate the spatial configuration of the magnetopause using the tangent fitting approach. Under a uniform universe condition, we achieved a pixel accuracy of the maximum position of the photon number detected by the network as high as 90.94% and contained the position error of the sunset point of the 3D magnetopause below 0.2 RE. This result demonstrates that the proposed method can detect the peak photon number of magnetospheric soft X-ray images with low solar wind density. As such, its use improves the segmentation accuracy of magnetospheric soft X-ray images and reduces the imaging time requirements of the input image.
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(This article belongs to the Special Issue Space Weather: Observations and Modeling of the Near Earth Environment II)
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Open AccessTechnical Note
Identifying PM2.5-Related Health Burden in the Context of the Integrated Development of Urban Agglomeration Using Remote Sensing and GEMM Model
by
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Remote Sens. 2023, 15(11), 2770; https://doi.org/10.3390/rs15112770 (registering DOI) - 26 May 2023
Abstract
Integrated development of urban agglomeration is emerging as the main pattern of China’s new modernization. Yet, atmospheric pollution continues to have an adverse impact on public health, challenging efforts to promote coordinated regional development. To better understand the interaction between atmospheric pollution-related health
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Integrated development of urban agglomeration is emerging as the main pattern of China’s new modernization. Yet, atmospheric pollution continues to have an adverse impact on public health, challenging efforts to promote coordinated regional development. To better understand the interaction between atmospheric pollution-related health burdens and urbanization, this study employed deep learning technology to obtain high-resolution satellite-derived PM2.5 concentration data across the Yangtze River Delta (YRD) region. Using the Global Exposure Mortality Model (GEMM), this study estimated premature mortality resulting from long-term exposure to PM2.5 and innovatively incorporated exposure factors to improve accuracy. Results indicated that while PM2.5 concentrations decreased by 16.13% from 2015 to 2019, the region still experienced 239,000 premature mortalities in 2019, with notable disparities among cities of different economic levels and sizes. Furthermore, it was found through correlation analysis that residential density and GDP per capita were highly associated with premature mortality. In conclusion, these findings highlight the continuing challenge of achieving equitable effectiveness of joint air pollution control across regions in the context of integrated development of urban agglomeration.
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(This article belongs to the Special Issue Remote Sensing for Environmental Health: From Fine-Scale Measurement towards Dynamic Exposure Assessment)
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Open AccessArticle
Generation of High Temporal Resolution Full-Coverage Aerosol Optical Depth Based on Remote Sensing and Reanalysis Data
Remote Sens. 2023, 15(11), 2769; https://doi.org/10.3390/rs15112769 (registering DOI) - 26 May 2023
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Aerosol Optical Depth (AOD) is a crucial physical parameter used to measure the radiative and scattering properties of the atmosphere. Obtaining full-coverage AOD measurements is essential for a thorough understanding of its impact on climate and air quality. However, satellite-based AOD products can
[...] Read more.
Aerosol Optical Depth (AOD) is a crucial physical parameter used to measure the radiative and scattering properties of the atmosphere. Obtaining full-coverage AOD measurements is essential for a thorough understanding of its impact on climate and air quality. However, satellite-based AOD products can be affected by abnormal weather conditions and high reflectance surfaces, leading to gaps in spatial coverage. To address this issue, we propose a satellite-based AOD filling method based on hourly level-3 Himawari-8 AOD products. In this study, the optimal model with a mean bias error (MBE) less than 0.01 and a root-mean-square error (RMSE) less than 0.1 in most land cover types was selected to generate the full-coverage AOD. The generated full-coverage AOD was validated against in situ measurements from the AERONET sites and compared with the performance of Himawari-8 AOD and MERRA-2 AOD over the AERONET sites. The validation results indicate that the accuracy of full-coverage AOD is comparable to that of the Advanced Himawari Imager (AHI) AOD, and for other land cover types (excluding barren land), the accuracy of full-coverage AOD is superior to that of MERRA-2 AOD. To investigate the practical application of full-coverage AOD, we utilized it as an input parameter to perform radiative transfer simulations in northwestern and southern China. The validation results showed that the simulated at-sensor radiance based on full-coverage AOD was in good agreement with the at-sensor radiance observations from MODIS. These results indicate that complete and accurate measurements of AOD have considerable potential for application in the simulation of at-sensor radiance and other related topics.
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