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发布日期: 2022-1-20
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Remote Sensing | January-2 2022 - Browse Articles

get_app subject View online as: Abstract Page Full-Text HTML Open Access Article High-Throughput Legume Seed Phenotyping Using a Handheld 3D Laser Scanner by Xia Huang , Shunyi Zheng and Ningning Zhu Remote Sens. 2022 , 14 (2), 431; https://doi.org/10.3390/rs14020431 - 17 Jan 2022 Viewed by 94 Abstract High-throughput phenotyping involves many samples and diverse trait types. For the goal of automatic measurement and batch data processing, a novel method for high-throughput legume seed phenotyping is proposed. A pipeline of automatic data acquisition and processing, including point cloud acquisition, single-seed extraction, [...] Read more. High-throughput phenotyping involves many samples and diverse trait types. For the goal of automatic measurement and batch data processing, a novel method for high-throughput legume seed phenotyping is proposed. A pipeline of automatic data acquisition and processing, including point cloud acquisition, single-seed extraction, pose normalization, three-dimensional (3D) reconstruction, and trait estimation, is proposed. First, a handheld laser scanner is used to obtain the legume seed point clouds in batches. Second, a combined segmentation method using the RANSAC method, the Euclidean segmentation method, and the dimensionality of the features is proposed to conduct single-seed extraction. Third, a coordinate rotation method based on PCA and the table normal is proposed to conduct pose normalization. Fourth, a fast symmetry-based 3D reconstruction method is built to reconstruct a 3D model of the single seed, and the Poisson surface reconstruction method is used for surface reconstruction. Finally, 34 traits, including 11 morphological traits, 11 scale factors, and 12 shape factors, are automatically calculated. A total of 2500 samples of five kinds of legume seeds are measured. Experimental results show that the average accuracies of scanning and segmentation are 99.52% and 100%, respectively. The overall average reconstruction error is 0.014 mm. The average morphological trait measurement accuracy is submillimeter, and the average relative percentage error is within 3%. The proposed method provides a feasible method of batch data acquisition and processing, which will facilitate the automation in high-throughput legume seed phenotyping. Full article (This article belongs to the Special Issue 3D Modelling and Mapping for Precision Agriculture ) ? ▼ Show Figures Figure 1 get_app subject View online as: Abstract Page Full-Text HTML Open Access Article Automatic Filtering of Lidar Building Point Cloud in Case of Trees Associated to Building Roof by Fayez Tarsha Kurdi , Zahra Gharineiat , Glenn Campbell , Mohammad Awrangjeb and Emon Kumar Dey Remote Sens. 2022 , 14 (2), 430; https://doi.org/10.3390/rs14020430 - 17 Jan 2022 Viewed by 126 Abstract This paper suggests a new algorithm for automatic building point cloud filtering based on the Z coordinate histogram. This operation aims to select the roof class points from the building point cloud, and the suggested algorithm considers the general case where high trees [...] Read more. This paper suggests a new algorithm for automatic building point cloud filtering based on the Z coordinate histogram. This operation aims to select the roof class points from the building point cloud, and the suggested algorithm considers the general case where high trees are associated with the building roof. The Z coordinate histogram is analyzed in order to divide the building point cloud into three zones: the surrounding terrain and low vegetation, the facades, and the tree crowns and/or the roof points. This operation allows the elimination of the first two classes which represent an obstacle toward distinguishing between the roof and the tree points. The analysis of the normal vectors, in addition to the change of curvature factor of the roof class leads to recognizing the high tree crown points. The suggested approach was tested on five datasets with different point densities and urban typology. Regarding the results’ accuracy quantification, the average values of the correctness, the completeness, and the quality indices are used. Their values are, respectively, equal to 97.9%, 97.6%, and 95.6%. These results confirm the high efficacy of the suggested approach. Full article (This article belongs to the Special Issue New Tools or Trends for Large-Scale Mapping and 3D Modelling ) ? ▼ Show Figures Graphical abstract get_app subject View online as: Abstract Page Full-Text HTML Open Access Article Towards a Multimodal Representation: Claudia Octavia’s Bequeathal by Sara Gonizzi Barsanti , Santiago Lillo Giner and Adriana Rossi Remote Sens. 2022 , 14 (2), 429; https://doi.org/10.3390/rs14020429 (registering?DOI) - 17 Jan 2022 Viewed by 52 Abstract Through a non-contact survey methodology, based on image-based techniques, the authors digitally ‘build’ a three-dimensional hypothesis of a monumental complex carved on a first-century AC marble tombstone. Guided by the mathematical rationality recognised in the artefact, the paper illustrates the reasons for the [...] Read more. Through a non-contact survey methodology, based on image-based techniques, the authors digitally ‘build’ a three-dimensional hypothesis of a monumental complex carved on a first-century AC marble tombstone. Guided by the mathematical rationality recognised in the artefact, the paper illustrates the reasons for the reconstructive choices and then proposes a reflection on the architectural contents. The ultimate goal focuses on the potential use of the digital product, which, thanks to and by virtue of the use of dedicated platforms, promotes strategies that include identity values by superimposing technical, social, and economic aspects. The setting up of collaborative spaces programmed with different strategies can effectively support the cognitive experience by verifying the possibility of “remedying” contents that, in our case, direct the study, dissemination, and protection of cultural heritage according to the most recent UNESCO recommendations. Full article (This article belongs to the Special Issue 3D Virtual Reconstruction for Cultural Heritage ) ? ▼ Show Figures Graphical abstract get_app subject View online as: Abstract Page Full-Text HTML Open Access Article Assessing Vegetation Decline Due to Pollution from Solid Waste Management by a Multitemporal Remote Sensing Approach by Giuseppe Mancino , Rodolfo Console , Michele Greco , Chiara Iacovino , Maria Lucia Trivigno and Antonio Falciano Remote Sens. 2022 , 14 (2), 428; https://doi.org/10.3390/rs14020428 - 17 Jan 2022 Viewed by 83 Abstract Nowadays, the huge production of Municipal Solid Waste (MSW) is one of the most strongly felt environmental issues. Consequently, the European Union (EU) delivers laws and regulations for better waste management, identifying the essential requirements for waste disposal operations and the characteristics that [...] Read more. Nowadays, the huge production of Municipal Solid Waste (MSW) is one of the most strongly felt environmental issues. Consequently, the European Union (EU) delivers laws and regulations for better waste management, identifying the essential requirements for waste disposal operations and the characteristics that make waste hazardous to human health and the environment. In Italy, environmental regulations define, among other things, the characteristics of sites to be classified as “potentially contaminated”. From this perspective, the Basilicata region is currently one of the Italian regions with the highest number of potentially polluted sites in proportion to the number of inhabitants. This research aimed to identify the possible effects of potentially toxic element (PTE) pollution due to waste disposal activities in three “potentially contaminated” sites in southern Italy. The area was affected by a release of inorganic pollutants with values over the thresholds ruled by national/European legislation. Potential physiological efficiency variations of vegetation were analyzed through the multitemporal processing of satellite images. Landsat 5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager (OLI) images were used to calculate the trend in the Normalized Difference Vegetation Index (NDVI) over the years. The multitemporal trends were analyzed using the median of the non-parametric Theil–Sen estimator. Finally, the Mann–Kendall test was applied to evaluate trend significance featuring areas according to the contamination effects on investigated vegetation. The applied procedure led to the exclusion of significant effects on vegetation due to PTEs. Thus, waste disposal activities during previous years do not seem to have significantly affected vegetation around targeted sites. Full article ? ▼ Show Figures Figure 1 get_app subject View online as: Abstract Page Full-Text HTML Open Access Article Multiscale Object Detection in Remote Sensing Images Combined with Multi-Receptive-Field Features and Relation-Connected Attention by Jiahang Liu , Donghao Yang and Fei Hu Remote Sens. 2022 , 14 (2), 427; https://doi.org/10.3390/rs14020427 - 17 Jan 2022 Viewed by 85 Abstract Object detection is an important task of remote sensing applications. In recent years, with the development of deep convolutional neural networks, object detection in remote sensing images has made great improvements. However, the large variation of object scales and complex scenarios will seriously [...] Read more. Object detection is an important task of remote sensing applications. In recent years, with the development of deep convolutional neural networks, object detection in remote sensing images has made great improvements. However, the large variation of object scales and complex scenarios will seriously affect the performance of the detectors. To solve these problems, a novel object detection algorithm based on multi-receptive-field features and relation-connected attention is proposed for remote sensing images to achieve more accurate detection results. Specifically, we propose a multi-receptive-field feature extraction module with dilated convolution to aggregate the context information of different receptive fields. This achieves a strong capability of feature representation, which can effectively adapt to the scale changes of objects, either due to various object scales or different resolutions. Then, a relation-connected attention module based on relation modeling is constructed to automatically select and refine the features, which combines global and local attention to make the features more discriminative and can effectively improve the robustness of the detector. We designed these two modules as plug-and-play blocks and integrated them into the framework of Faster R-CNN to verify our method. The experimental results on NWPU VHR-10 and HRSC2016 datasets demonstrate that these two modules can effectively improve the performance of basic deep CNNs, and the proposed method can achieve better results of multiscale object detection in complex backgrounds. Full article (This article belongs to the Section AI Remote Sensing ) ? ▼ Show Figures Graphical abstract get_app subject View online as: Abstract Page Full-Text HTML Open Access Article A Novel Tropical Cyclone Size Estimation Model Based on a Convolutional Neural Network Using Geostationary Satellite Imagery by You-Hyun Baek , Il-Ju Moon , Jungho Im and Juhyun Lee Remote Sens. 2022 , 14 (2), 426; https://doi.org/10.3390/rs14020426 - 17 Jan 2022 Viewed by 65 Abstract A novel tropical cyclone (TC) size estimation model (TC-SEM) in the western North Pacific was developed based on a convolutional neural network (CNN) using geostationary satellite infrared (IR) images. The proposed TC-SEM was tested using three CNN schemes: a single-task regression model that [...] Read more. A novel tropical cyclone (TC) size estimation model (TC-SEM) in the western North Pacific was developed based on a convolutional neural network (CNN) using geostationary satellite infrared (IR) images. The proposed TC-SEM was tested using three CNN schemes: a single-task regression model that separately estimated the radius of maximum wind (RMW) and the radius of 34 kt wind (R34) of the TC, a multi-task regression model that estimated the RMW and R34 simultaneously, and a multi-task regression model using best-track TC intensity information. For model training, validation, and testing, 29,730, 2505, and 11,624 geostationary satellite images of the region around the center of the TC, respectively, were used, each containing four IR bands: short-wavelength IR (3.7 ?m), water vapor (6.7 ?m), IR1 (10.8 ?m), and IR2 (12.0 ?m). The results showed that the multi-task model performed better than the single-task model due to knowledge sharing and its ability to solve multiple interrelated tasks simultaneously. The inclusion of TC intensity information in the multi-task model further improved the performance of the RMW and R34 estimations, with correlations (mean absolute errors) of 0.95 (2.05 nmi) and 0.93 (9.77 nmi), respectively, which represent significant improvements over the performance of existing linear regression statistical methods. The results suggested that this CNN model using geostationary satellite images may be a powerful tool for estimating TC sizes in operational TC forecasts. Full article ? ▼ Show Figures Figure 1 get_app subject View online as: Abstract Page Full-Text HTML Open Access Article A Fusion-Based Defogging Algorithm by Ting Chen , Mengni Liu , Tao Gao , Peng Cheng , Shaohui Mei and Yonghui Li Remote Sens. 2022 , 14 (2), 425; https://doi.org/10.3390/rs14020425 - 17 Jan 2022 Viewed by 56 Abstract To solve the problem that traditional dark channel is not suitable for a large sky area and can easyily distort defogged images, we propose a novel fusion-based defogging algorithm. Firstly, an improved remote sensing image segmentation algorithm is introduced to mix the dark [...] Read more. To solve the problem that traditional dark channel is not suitable for a large sky area and can easyily distort defogged images, we propose a novel fusion-based defogging algorithm. Firstly, an improved remote sensing image segmentation algorithm is introduced to mix the dark channel. Secondly, we establish a dark-light channel fusion model to calculate the atmospheric light map. Furthermore, in order to refine the transmittance image without reducing restoration quality, the grayscale image corresponding to the original image is selected as a guide image. Meanwhile, we optimize the set value of the defogging intensity parameter ω in the transmission estimation formula as the value of atmospheric light. Finally, a brightness/color compensation model based on visual perception is generated for image correction. Experimental results demonstrate that the proposed algorithm achieves superior performance on UAV foggy images in both subjective and objective evaluation, which verifies the effectiveness of the proposed algorithm. Full article ? ▼ Show Figures Figure 1 get_app subject View online as: Abstract Page Full-Text HTML Open Access Article Underground Morphological Detection of Ground Fissures in Collapsible Loess Area Based on Three-Dimensional Laser Scanning Technology by Yibo He , Zhenqi Hu , Yaokun Fu , Kun Yang , Rui Wang , Guomou Shi , Zhanjie Feng , Qirang Yang and Liang Yu Remote Sens. 2022 , 14 (2), 424; https://doi.org/10.3390/rs14020424 - 17 Jan 2022 Viewed by 62 Abstract Underground coal mining inevitably causes ground fissures, especially permanent cracks that cannot be closed at the boundary of the working face. Studying the underground three-dimensional morphology of the permanent cracks allows one to accurately constrain the formation and development of the ground fissures. [...] Read more. Underground coal mining inevitably causes ground fissures, especially permanent cracks that cannot be closed at the boundary of the working face. Studying the underground three-dimensional morphology of the permanent cracks allows one to accurately constrain the formation and development of the ground fissures. This information will contribute to reducing mine disasters and is also a prerequisites to avoid environmental pollution. We selected the Zhangjiamao coal mine (China), which is situated in a collapsible loess area, as a case study for deciphering the formation of permanent cracks. After injecting gypsum slurry into the mine, a three-dimensional model of the ground fissures is obtained by three-dimensional (3D) laser scanner technology that records the 3D underground morphology. Integrating the geological context of a collapsible loess area, the characteristics and main processes of the ground fissure development are constrained: (1) The width of the ground fissure decreases to 0 with increasing depth and is strongly affected by the soil composition. (2) Along the vertical extension direction, the ground fissures are generally inclined to the inner-side of the working face, but the direction remains uncertain at different depths. (3) The transverse propagation direction of the ground fissure becomes more complex with increasing depth. (4) Under the influence of soil texture and water, loose soil fills the bottom of the ground fissure, thus affecting the underground 3D morphology. Full article ? ▼ Show Figures Graphical abstract attachment Supplementary material: Supplementary File 1 (ZIP, 262 KiB) get_app subject View online as: Abstract Page Full-Text HTML Open Access Article Evaluation of SMOS L4 Sea Surface Salinity Product in the Western Iberian Coast by Beatriz Biguino , Estrella Olmedo , Afonso Ferreira , Nuno Zacarias , Luísa Lamas , Luciane Favareto , Carla Palma , Carlos Borges , Ana Teles-Machado , Joaquim Dias , Paola Castellanos and Ana C. Brito Remote Sens. 2022 , 14 (2), 423; https://doi.org/10.3390/rs14020423 - 17 Jan 2022 Viewed by 63 Abstract Salinity is one of the oldest parameters being measured in oceanography and one of the most important to study in the context of climate change. However, its quantification by satellite remote sensing has been a relatively recent achievement. Currently, after over ten years [...] Read more. Salinity is one of the oldest parameters being measured in oceanography and one of the most important to study in the context of climate change. However, its quantification by satellite remote sensing has been a relatively recent achievement. Currently, after over ten years of data gathering, there are still many challenges in quantifying salinity from space, especially when it is intended for coastal environments study. That is mainly due to the spatial resolution of the available products. Recently, a new higher resolution (5 km) L4 SMOS sea surface salinity (SSS) product was developed by the Barcelona Expert Center (BEC). In this study, the quality of this product was tested along the Western Iberian Coast through its comparison with in situ observations and modelled salinity estimates (CMEMS IBI Ocean Reanalysis system). Moreover, several parameters such as the temperature and depth of in situ measurements were tested to identify the variables or processes that induced higher errors in the product or influenced its performance. Lastly, a seasonal and interannual analysis was conducted considering data between 2011 to 2019 to test the product as a potential tool for long-term studies. The results obtained in the present analysis showed a high potential of using the L4 BEC SSS SMOS product in extended temporal and spatial analyses along the Portuguese coast. A good correlation between the satellite and the in situ datasets was observed, and the satellite dataset showed lower errors in retrieving coastal salinities than the oceanic model. Overall, the distance to the coast and the closest rivers were the factors that most influenced the quality of the product. The present analysis showed that great progress has been made in deriving coastal salinity over the years and that the SMOS SSS product is a valuable contribution to worldwide climatological studies. In addition, these results reinforce the need to continue developing satellite remote sensing products as a global and cost-effective methodology for long-term studies. Full article (This article belongs to the Special Issue Moving Forward on Remote Sensing of Sea Surface Salinity ) ? ▼ Show Figures Figure 1 get_app subject View online as: Abstract Page Full-Text HTML Open Access Article 3D Modeling of Urban Area Based on Oblique UAS Images—An End-to-End Pipeline by Valeria-Ersilia Oniga , Ana-Ioana Breaban , Norbert Pfeifer and Maximilian Diac Remote Sens. 2022 , 14 (2), 422; https://doi.org/10.3390/rs14020422 - 17 Jan 2022 Viewed by 84 Abstract 3D modelling of urban areas is an attractive and active research topic, as 3D digital models of cities are becoming increasingly common for urban management as a consequence of the constantly growing number of people living in cities. Viewed as a digital representation [...] Read more. 3D modelling of urban areas is an attractive and active research topic, as 3D digital models of cities are becoming increasingly common for urban management as a consequence of the constantly growing number of people living in cities. Viewed as a digital representation of the Earth’s surface, an urban area modeled in 3D includes objects such as buildings, trees, vegetation and other anthropogenic structures, highlighting the buildings as the most prominent category. A city’s 3D model can be created based on different data sources, especially LiDAR or photogrammetric point clouds. This paper’s aim is to provide an end-to-end pipeline for 3D building modeling based on oblique UAS images only, the result being a parametrized 3D model with the Open Geospatial Consortium (OGC) CityGML standard, Level of Detail 2 (LOD2). For this purpose, a flight over an urban area of about 20.6 ha has been taken with a low-cost UAS, i.e., a DJI Phantom 4 Pro Professional (P4P), at 100 m height. The resulting UAS point cloud with the best scenario, i.e., 45 Ground Control Points (GCP), has been processed as follows: filtering to extract the ground points using two algorithms, CSF and terrain-mark; classification, using two methods, based on attributes only and a random forest machine learning algorithm; segmentation using local homogeneity implemented into Opals software; plane creation based on a region-growing algorithm; and plane editing and 3D model reconstruction based on piece-wise intersection of planar faces. The classification performed with ~35% training data and 31 attributes showed that the Visible-band difference vegetation index (VDVI) is a key attribute and 77% of the data was classified using only five attributes. The global accuracy for each modeled building through the workflow proposed in this study was around 0.15 m, so it can be concluded that the proposed pipeline is reliable. Full article (This article belongs to the Special Issue Photogrammetry and Remote Sensing in Environmental and Engineering Applications ) ? ▼ Show Figures Figure 1 get_app subject View online as: Abstract Page Full-Text HTML Open Access Article An Accurate Digital Subsidence Model for Deformation Detection of Coal Mining Areas Using a UAV-Based LiDAR by Junliang Zheng , Wanqiang Yao , Xiaohu Lin , Bolin Ma and Lingxiao Bai Remote Sens. 2022 , 14 (2), 421; https://doi.org/10.3390/rs14020421 - 17 Jan 2022 Viewed by 56 Abstract Coal mine surface subsidence detection determines the damage degree of coal mining, which is of great importance for the mitigation of hazards and property loss. Therefore, it is very important to detect deformation during coal mining. Currently, there are many methods used to [...] Read more. Coal mine surface subsidence detection determines the damage degree of coal mining, which is of great importance for the mitigation of hazards and property loss. Therefore, it is very important to detect deformation during coal mining. Currently, there are many methods used to detect deformations in coal mining areas. However, with most of them, the accuracy is difficult to guarantee in mountainous areas, especially for shallow seam mining, which has the characteristics of active, rapid, and high-intensity surface subsidence. In response to these problems, we made a digital subsidence model (DSuM) for deformation detection in coal mining areas based on airborne light detection and ranging (LiDAR). First, the entire point cloud of the study area was obtained by coarse to fine registration. Second, noise points were removed by multi-scale morphological filtering, and the progressive triangulation filtering classification (PTFC) algorithm was used to obtain the ground point cloud. Third, the DEM was generated from the clean ground point cloud, and an accurate DSuM was obtained through multiple periods of DEM difference calculations. Then, data mining was conducted based on the DSuM to obtain parameters such as the maximum surface subsidence value, a subsidence contour map, the subsidence area, and the subsidence boundary angle. Finally, the accuracy of the DSuM was analyzed through a comparison with ground checkpoints (GCPs). The results show that the proposed method can achieve centimeter-level accuracy, which makes the data a good reference for mining safety considerations and subsequent restoration of the ecological environment. Full article (This article belongs to the Special Issue Techniques and Applications of UAV-Based Photogrammetric 3D Mapping ) ? ▼ Show Figures Figure 1 get_app subject View online as: Abstract Page Full-Text HTML Open Access Article Small Object Detection Method Based on Adaptive Spatial Parallel Convolution and Fast Multi-Scale Fusion by Guanqiu Qi , Yuanchuan Zhang , Kunpeng Wang , Neal Mazur , Yang Liu and Devanshi Malaviya Remote Sens. 2022 , 14 (2), 420; https://doi.org/10.3390/rs14020420 - 17 Jan 2022 Viewed by 58 Abstract As one type of object detection, small object detection has been widely used in daily-life-related applications with many real-time requirements, such as autopilot and navigation. Although deep-learning-based object detection methods have achieved great success in recent years, they are not effective in small [...] Read more. As one type of object detection, small object detection has been widely used in daily-life-related applications with many real-time requirements, such as autopilot and navigation. Although deep-learning-based object detection methods have achieved great success in recent years, they are not effective in small object detection and most of them cannot achieve real-time processing. Therefore, this paper proposes a single-stage small object detection network (SODNet) that integrates the specialized feature extraction and information fusion techniques. An adaptively spatial parallel convolution module (ASPConv) is proposed to alleviate the lack of spatial information for target objects and adaptively obtain the corresponding spatial information through multi-scale receptive fields, thereby improving the feature extraction ability. Additionally, a split-fusion sub-module (SF) is proposed to effectively reduce the time complexity of ASPConv. A fast multi-scale fusion module (FMF) is proposed to alleviate the insufficient fusion of both semantic and spatial information. FMF uses two fast upsampling operators to first unify the resolution of the multi-scale feature maps extracted by the network and then fuse them, thereby effectively improving the small object detection ability. Comparative experimental results prove that the proposed method considerably improves the accuracy of small object detection on multiple benchmark datasets and achieves a high real-time performance. Full article (This article belongs to the Special Issue Moving Object Detection and Control Using Remote Sensing and Artificial Intelligence ) ? ▼ Show Figures Graphical abstract attachment Supplementary material: Supplementary File 1 (ZIP, 1217 KiB) get_app subject View online as: Abstract Page Full-Text HTML Open Access Article Variations of Urban NO 2 Pollution during the COVID-19 Outbreak and Post-Epidemic Era in China: A Synthesis of Remote Sensing and In Situ Measurements by Chunhui Zhao , Chengxin Zhang , Jinan Lin , Shuntian Wang , Hanyang Liu , Hongyu Wu and Cheng Liu Remote Sens. 2022 , 14 (2), 419; https://doi.org/10.3390/rs14020419 - 17 Jan 2022 Viewed by 80 Abstract Since the COVID-19 outbreak in 2020, China’s air pollution has been significantly affected by control measures on industrial production and human activities. In this study, we analyzed the temporal variations of NO 2 concentrations during the COVID-19 lockdown and post-epidemic era in 11 [...] Read more. Since the COVID-19 outbreak in 2020, China’s air pollution has been significantly affected by control measures on industrial production and human activities. In this study, we analyzed the temporal variations of NO 2 concentrations during the COVID-19 lockdown and post-epidemic era in 11 Chinese megacities by using satellite and ground-based remote sensing as well as in situ measurements. The average satellite tropospheric vertical column density (TVCD) of NO 2 by TROPOMI decreased by 39.2–71.93% during the 15 days after Chinese New Year when the lockdown was at its most rigorous compared to that of 2019, while the in situ NO 2 concentration measured by China National Environmental Monitoring Centre (CNEMC) decreased by 42.53–69.81% for these cities. Such differences between both measurements were further investigated by using ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) remote sensing of NO 2 vertical profiles. For instance, in Beijing, MAX-DOAS NO 2 showed a decrease of 14.19% (versus 18.63% by in situ) at the ground surface, and 36.24% (versus 36.25% by satellite) for the total tropospheric column. Thus, vertical discrepancies of atmospheric NO 2 can largely explain the differences between satellite and in situ NO 2 variations. In the post-epidemic era of 2021, satellite NO 2 TVCD and in situ NO 2 concentrations decreased by 10.42–64.96% and 1.05–34.99% compared to 2019, respectively, possibly related to the reduction of the transportation industry. This study reveals the changes of China’s urban NO 2 pollution in the post-epidemic era and indicates that COVID-19 had a profound impact on human social activities and industrial production. Full article (This article belongs to the Special Issue Optical and Laser Remote Sensing of Atmospheric Composition ) ? ▼ Show Figures Figure 1 get_app subject View online as: Abstract Page Full-Text HTML Open Access Article Estimating Boundary Layer Height from LiDAR Data under Complex Atmospheric Conditions Using Machine Learning by Zhenxing Liu , Jianhua Chang , Hongxu Li , Sicheng Chen and Tengfei Dai Remote Sens. 2022 , 14 (2), 418; https://doi.org/10.3390/rs14020418 - 17 Jan 2022 Viewed by 54 Abstract Reliable estimation of the atmospheric boundary layer height (ABLH) is critical for a range of meteorological applications, including air quality assessment and weather forecasting. Several algorithms have been proposed to detect ABLH from aerosol LiDAR backscatter data. However, most of these focus on [...] Read more. Reliable estimation of the atmospheric boundary layer height (ABLH) is critical for a range of meteorological applications, including air quality assessment and weather forecasting. Several algorithms have been proposed to detect ABLH from aerosol LiDAR backscatter data. However, most of these focus on cloud-free conditions or use other ancillary instruments due to strong interference from clouds or residual layer aerosols. In this paper, a machine learning method named the Mahalanobis transform K-near-means (MKnm) algorithm is first proposed to derive ABLH under complex atmospheric conditions using only LiDAR-based instruments. It was applied to the micro pulse LiDAR data obtained at the Southern Great Plains site of the Atmospheric Radiation Measurement (ARM) program. The diurnal cycles of ABLH from cloudy weather were detected by using the gradient method (GM), wavelet covariance transform method (WM), K-means, and MKnm. Meanwhile, the ABLH obtained by these four methods under cloud or residual layer conditions based on micropulse LiDAR data were compared with the reference height retrieved from radiosonde data. The results show that MKnm was good at tracking the diurnal variation of ABLH, and the ABLHs obtained by it have remarkable correlation coefficients and smaller mean absolute error and mean deviation with the radiosonde-derived ABLHs than those measured by other three methods. We conclude that MKnm is a promising algorithm to estimate ABLH under cloud or residual layer conditions. Full article (This article belongs to the Topic Recent Progress in Aerosol Remote Sensing and Products ) ? ▼ Show Figures Figure 1 get_app subject View online as: Abstract Page Full-Text HTML Open Access Article The Impacts of a Large Water Transfer Project on a Waterbird Community in the Receiving Dam: A Case Study of Miyun Reservoir, China by Waner Liang , Jialin Lei , Bingshu Ren , Ranxing Cao , Zhixu Yang , Niri Wu and Yifei Jia Remote Sens. 2022 , 14 (2), 417; https://doi.org/10.3390/rs14020417 - 17 Jan 2022 Viewed by 61 Abstract As natural wetlands are degrading worldwide, artificial wetlands can operate as a substitute to provide waterbirds with refuge, but they cannot replace natural wetlands. Reservoirs, one of the most common artificial wetlands in China, can be of great importance to waterbirds. Miyun reservoir [...] Read more. As natural wetlands are degrading worldwide, artificial wetlands can operate as a substitute to provide waterbirds with refuge, but they cannot replace natural wetlands. Reservoirs, one of the most common artificial wetlands in China, can be of great importance to waterbirds. Miyun reservoir in Beijing, China, has undergone a process similar to a natural lake being constructed in a reservoir. In this study, we surveyed waterbird community composition and evaluated the corresponding land cover and land use change with satellite and digital elevation model images of both before and after the water level change. The results showed that in all modelled scenarios, when the water level rises, agricultural lands suffer the greatest loss, with wetlands and forests following. The water level rise also caused a decrease in shallow water areas and a decline in the number and diversity of waterbird communities, as the components shifted from a shallow-water preferring group (waders, geese and dabbling ducks) to a deep-water preferring group (most diving ducks, gulls and terns). Miyun reservoir ceased to be an important waterbird habitat in China and is no longer an important stopover site for white-naped cranes. A similar process is likely to occur when a natural lake is constructed in a reservoir. Therefore, we suggest that policymakers consider the needs of waterbirds when constructing or managing reservoirs. Full article (This article belongs to the Special Issue Application of Remote Sensing in Migratory Birds Conservation ) ? ▼ Show Figures Graphical abstract get_app subject View online as: Abstract Page Full-Text HTML Open Access Article Hybrid Compact Polarimetric SAR Calibration Considering the Amplitude and Phase Coefficients Inconsistency by Wentao Hou , Fengjun Zhao , Xiuqing Liu , Dacheng Liu , Yonghui Han , Yao Gao and Robert Wang Remote Sens. 2022 , 14 (2), 416; https://doi.org/10.3390/rs14020416 - 17 Jan 2022 Viewed by 42 Abstract Calibration using corner reflectors is an effective way to estimate the distortion parameters of hybrid compact polarimetric (HCP) synthetic aperture radar (SAR) systems. However, the existing literature lacks a discussion on the inconsistency of the amplitude and phase coefficients between measured scattering vectors [...] Read more. Calibration using corner reflectors is an effective way to estimate the distortion parameters of hybrid compact polarimetric (HCP) synthetic aperture radar (SAR) systems. However, the existing literature lacks a discussion on the inconsistency of the amplitude and phase coefficients between measured scattering vectors of different corner reflectors. In response to this problem, this paper first proves that this inconsistency will seriously deteriorate the estimation accuracy of polarimetric distortion parameters. Based on the optimization algorithm, two calibration schemes for simultaneously estimating the traditional distortion parameters and the amplitude/phase coefficients are proposed while ignoring crosstalk (ICT) and considering crosstalk (CCT). In the process of distortion parameter estimation, the idea of “optimizing while compensating” is adopted to eliminate the problem of uneven echo intensity. Simulation results show that both schemes can eliminate the influence of the inconsistency of amplitude and phase coefficients, and estimate distortion parameters accurately. When the received crosstalk level is lower than ?30 dB, the ICT scheme can accurately estimate polarimetric distortion parameters. The CCT scheme has a wider application range of crosstalk and can work well when the crosstalk level is lower than ?20 dB, but it also has a higher requirement for the signal-to-clutter ratio (SCR). When SCR is greater than 35 dB, the CCT scheme yields higher estimation accuracy than the ICT scheme. In addition, the effectiveness of the calibration schemes is verified based on the L-band measured data acquired by the Aerospace Information Research Institute, Chinese Academy of Sciences. Full article (This article belongs to the Section Remote Sensing Image Processing ) ? ▼ Show Figures Figure 1 get_app subject View online as: Abstract Page Full-Text HTML Open Access Article Leaf Area Index Estimation of Pergola-Trained Vineyards in Arid Regions Based on UAV RGB and Multispectral Data Using Machine Learning Methods by Osman Ilniyaz , Alishir Kurban and Qingyun Du Remote Sens. 2022 , 14 (2), 415; https://doi.org/10.3390/rs14020415 - 17 Jan 2022 Viewed by 72 Abstract The leaf area index (LAI), a valuable variable for assessing vine vigor, reflects nutrient concentrations in vineyards and assists in precise management, including fertilization, improving yield, quality, and vineyard uniformity. Although some vegetation indices (VIs) have been successfully used to assess LAI variations, [...] Read more. The leaf area index (LAI), a valuable variable for assessing vine vigor, reflects nutrient concentrations in vineyards and assists in precise management, including fertilization, improving yield, quality, and vineyard uniformity. Although some vegetation indices (VIs) have been successfully used to assess LAI variations, they are unsuitable for vineyards of different types and structures. By calibrating the light extinction coefficient of a digital photography algorithm for proximal LAI measurements, this study aimed to develop VI-LAI models for pergola-trained vineyards based on high-resolution RGB and multispectral images captured by an unmanned aerial vehicle (UAV). The models were developed by comparing five machine learning (ML) methods, and a robust ensemble model was proposed using the five models as base learners. The results showed that the ensemble model outperformed the base models. The highest R 2 and lowest RMSE values that were obtained using the best combination of VIs with multispectral data were 0.899 and 0.434, respectively; those obtained using the RGB data were 0.825 and 0.547, respectively. By improving the results by feature selection, ML methods performed better with multispectral data than with RGB images, and better with higher spatial resolution data than with lower resolution data. LAI variations can be monitored efficiently and accurately for large areas of pergola-trained vineyards using this framework. Full article (This article belongs to the Special Issue Applications of Remote Data Capture Systems in Agriculture and Vegetation ) ? ▼ Show Figures Figure 1 get_app subject View online as: Abstract Page Full-Text HTML Open Access Article Tomographic Inversion Methods for Retrieving the Tropospheric Water Vapor Content Based on the NDSA Measurement Approach by Agnese Mazzinghi , Fabrizio Cuccoli , Fabrizio Argenti , Arjan Feta and Luca Facheris Remote Sens. 2022 , 14 (2), 414; https://doi.org/10.3390/rs14020414 - 17 Jan 2022 Viewed by 52 .

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