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Volume 14
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Remote Sens., Volume 14, Issue 19 (October-1 2022) – 381 articles

Cover Story (view full-size image): Introduction: Many works develop algorithms based on in situ reflectance for various reasons, but when the algorithms developed from in situ reflectances are applied to satellite imagery, their performance also depends on the errors in water reflectance retrieval after atmospheric correction (AC). Therefore, this article is the second part of a previous study to complete the applicability step, and the validation process has been conducted for both the estimated remote sensing reflectances from four AC processors (C2RCC, C2X, C2XC and Polymer) and water quality algorithms. The best results after the algorithm validation were obtained using the remote sensing reflectance of the Polymer and C2XC; the ACs with the best validation results correlate to the in situ and remote sensing reflectance data for each band. View this paper
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12 pages, 3492 KiB  
Technical Note
Comparison and Analysis of Stellar Occultation Simulation Results and SABER-Satellite-Measured Data in Near Space
by Mingchen Sun, Xiang Dong, Qinglin Zhu, Xuan Cheng, Hongguang Wang and Jiaji Wu
Remote Sens. 2022, 14(19), 5065; https://doi.org/10.3390/rs14195065 - 10 Oct 2022
Cited by 2 | Viewed by 1348
Abstract
In this study, we analyze the accuracy of the stellar occultation technique to detect the oxygen number density and temperature in near space. Based on the validation of the algorithm related to stellar occultation using a single wavelength of 762 nm, the simulation [...] Read more.
In this study, we analyze the accuracy of the stellar occultation technique to detect the oxygen number density and temperature in near space. Based on the validation of the algorithm related to stellar occultation using a single wavelength of 762 nm, the simulation and inversion are performed using the oxygen absorption A-band, and the results are compared with SABER observations to calculate the deviation. Then, the distribution of the detection accuracy with wavelength, latitude, and altitude is analyzed. The results show that the radiant transmittance of the basic observation varies significantly with wavelength and altitude, and it is not sensitive to a change of latitude. The inversion results of each wavelength at different latitudes can be combined, and it can be seen that the 754–769 nm band is preferred for oxygen and temperature detection. Therefore, analyzing the accuracy results of the specific wavelength 757.84 nm at different latitudes, the temperature accuracy can reach 0.1 K in the stratosphere at both low and high latitudes and 0.6–34 K at middle latitudes. The temperature detection accuracy in the mesosphere at each latitude reaches about a dozen K. The deviation of the inversion results at middle latitudes is larger in the thermosphere, and at the other two latitudes, it is about a few dozen K. From the analysis of relative deviation, excluding the deviation of 95–100 km, the deviation of other altitudes is within the ideal range, and the minimum can reach 0. The accuracy of the oxygen number density increases with latitude, and the relative deviation of the middle and high latitudes is around 10–20%. Based on the above results, it is concluded that the technique of starlight occultation exhibits high accuracy for detecting atmospheric parameters in the near space region, and the results lay the technical foundation for the independent development of stellar occultation. Full article
(This article belongs to the Special Issue Advances in Infrared Observation of Earth's Atmosphere)
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21 pages, 6261 KiB  
Article
PODD: A Dual-Task Detection for Greenhouse Extraction Based on Deep Learning
by Junning Feng, Dongliang Wang, Fan Yang, Jing Huang, Minghao Wang, Mengfan Tao and Wei Chen
Remote Sens. 2022, 14(19), 5064; https://doi.org/10.3390/rs14195064 - 10 Oct 2022
Cited by 3 | Viewed by 2054
Abstract
The rapid boom of the global population is causing more severe food supply problems. To deal with these problems, the agricultural greenhouse is an effective way to increase agricultural production within a limited space. To better guide agricultural activities and respond to future [...] Read more.
The rapid boom of the global population is causing more severe food supply problems. To deal with these problems, the agricultural greenhouse is an effective way to increase agricultural production within a limited space. To better guide agricultural activities and respond to future food crises, it is important to obtain both the agricultural greenhouse area and quantity distribution. In this study, a novel dual-task algorithm called Pixel-based and Object-based Dual-task Detection (PODD) that combines object detection and semantic segmentation is proposed to estimate the quantity and extract the area of agricultural greenhouses based on RGB remote sensing images. This algorithm obtains the quantity of agricultural greenhouses based on the improved You Only Look Once X (YOLOX) network structure, which is embedded with Convolutional Block Attention Module (CBAM) and Adaptive Spatial Feature Fusion (ASFF). The introduction of CBAM can make up for the lack of expression ability of its feature extraction layer to retain more important feature information. Adding the ASFF module can make full use of the features in different scales to increase the precision. This algorithm obtains the area of agricultural greenhouses based on the DeeplabV3+ neural network using ResNet-101 as a feature extraction network, which not only effectively reduces hole and plaque issues but also extracts edge details. Experimental results show that the mAP and F1-score of the improved YOLOX network reach 97.65% and 97.50%, 1.50% and 2.59% higher than the original YOLOX solution. At the same time, the accuracy and mIoU of the DeeplabV3+ network reach 99.2% and 95.8%, 0.5% and 2.5% higher than the UNet solution. All of the metrics in the dual-task algorithm reach 95% and even higher. Proving that the PODD algorithm could be useful for agricultural greenhouse automatic extraction (both quantity and area) in large areas to guide agricultural policymaking. Full article
(This article belongs to the Special Issue Deep and Machine Learning Applications in Remote Sensing Data to Monitor and Manage Crops Using Precision Agriculture Systems)
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25 pages, 7465 KiB  
Article
Target Detection Method of UAV Aerial Imagery Based on Improved YOLOv5
by Xudong Luo, Yiquan Wu and Feiyue Wang
Remote Sens. 2022, 14(19), 5063; https://doi.org/10.3390/rs14195063 - 10 Oct 2022
Cited by 28 | Viewed by 3910
Abstract
Due to the advantages of small size, lightweight, and simple operation, the unmanned aerial vehicle (UAV) has been widely used, and it is also becoming increasingly convenient to capture high-resolution aerial images in a variety of environments. Existing target-detection methods for UAV aerial [...] Read more.
Due to the advantages of small size, lightweight, and simple operation, the unmanned aerial vehicle (UAV) has been widely used, and it is also becoming increasingly convenient to capture high-resolution aerial images in a variety of environments. Existing target-detection methods for UAV aerial images lack outstanding performance in the face of challenges such as small targets, dense arrangement, sparse distribution, and a complex background. In response to the above problems, some improvements on the basis of YOLOv5l have been made by us. Specifically, three feature-extraction modules are proposed, using asymmetric convolutions. They are named the Asymmetric ResNet (ASResNet) module, Asymmetric Enhanced Feature Extraction (AEFE) module, and Asymmetric Res2Net (ASRes2Net) module, respectively. According to the respective characteristics of the above three modules, the residual blocks in different positions in the backbone of YOLOv5 were replaced accordingly. An Improved Efficient Channel Attention (IECA) module was added after Focus, and Group Spatial Pyramid Pooling (GSPP) was used to replace the Spatial Pyramid Pooling (SPP) module. In addition, the K-Means++ algorithm was used to obtain more accurate anchor boxes, and the new EIOU-NMS method was used to improve the postprocessing ability of the model. Finally, ablation experiments, comparative experiments, and visualization of results were performed on five datasets, namely CIFAR-10, PASCAL VOC, VEDAI, VisDrone 2019, and Forklift. The effectiveness of the improved strategies and the superiority of the proposed method (YOLO-UAV) were verified. Compared with YOLOv5l, the backbone of the proposed method increased the top-one accuracy of the classification task by 7.20% on the CIFAR-10 dataset. The mean average precision (mAP) of the proposed method on the four object-detection datasets was improved by 5.39%, 5.79%, 4.46%, and 8.90%, respectively. Full article
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38 pages, 138821 KiB  
Article
A Multi-Temporal Network for Improving Semantic Segmentation of Large-Scale Landsat Imagery
by Xuan Yang, Bing Zhang, Zhengchao Chen, Yongqing Bai and Pan Chen
Remote Sens. 2022, 14(19), 5062; https://doi.org/10.3390/rs14195062 - 10 Oct 2022
Cited by 1 | Viewed by 1902
Abstract
With the development of deep learning, semantic segmentation technology has gradually become the mainstream technical method in large-scale multi-temporal landcover classification. Large-scale and multi-temporal are the two significant characteristics of Landsat imagery. However, the mainstream single-temporal semantic segmentation network lacks the constraints and [...] Read more.
With the development of deep learning, semantic segmentation technology has gradually become the mainstream technical method in large-scale multi-temporal landcover classification. Large-scale and multi-temporal are the two significant characteristics of Landsat imagery. However, the mainstream single-temporal semantic segmentation network lacks the constraints and assistance of pre-temporal information, resulting in unstable results, poor generalization ability, and inconsistency with the actual situation in the multi-temporal classification results. In this paper, we propose a multi-temporal network that introduces pre-temporal information as prior constrained auxiliary knowledge. We propose an element-wise weighting block module to improve the fine-grainedness of feature optimization. We propose a chained deduced classification strategy to improve multi-temporal classification’s stability and generalization ability. We label the large-scale multi-temporal Landsat landcover classification dataset with an overall classification accuracy of over 90%. Through extensive experiments, compared with the mainstream semantic segmentation methods, our proposed multi-temporal network achieves state-of-the-art performance with good robustness and generalization ability. Full article
(This article belongs to the Special Issue Land Use Classification with GIS and Remote Sensing Data Based on AI Technology)
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21 pages, 4364 KiB  
Article
Habitat Prediction of Northwest Pacific Saury Based on Multi-Source Heterogeneous Remote Sensing Data Fusion
by Yanling Han, Junyan Guo, Zhenling Ma, Jing Wang, Ruyan Zhou, Yun Zhang, Zhonghua Hong and Haiyan Pan
Remote Sens. 2022, 14(19), 5061; https://doi.org/10.3390/rs14195061 - 10 Oct 2022
Cited by 4 | Viewed by 1492
Abstract
Accurate habitat prediction is important to improve fishing efficiency. Most of the current habitat-prediction studies use the single-source datasets and the sequence model based on single-source datasets, which, to a certain extent, limits the further improvement of prediction accuracy. In this paper, we [...] Read more.
Accurate habitat prediction is important to improve fishing efficiency. Most of the current habitat-prediction studies use the single-source datasets and the sequence model based on single-source datasets, which, to a certain extent, limits the further improvement of prediction accuracy. In this paper, we propose a habitat-prediction method based on the multi-source heterogeneous remote-sensing data fusion, using product-level remote-sensing data and L1B-level original remote-sensing data. We designed a heterogeneous data feature extraction model based on a Convolution Neural Network (CNN) and Long and Short-Term Memory network (LSTM), and we designed a decision-fusion model based on multi-source heterogeneous data feature extraction. In the habitat prediction for the Northwest Pacific Saury, the mean R2 of the model reaches 0.9901 and the RMSE decreases to 0.01588 in the model validation experiment. It is significantly better than the results of other models, with the single datasets as input. Moreover, the model performs well in the generalization experiment because we limited the prediction error to less than 8%. Compared with the single-source sequence network model in the existing literature, the proposed method in this paper solves the problem of ineffective fusion caused by the differences in the structure and size of heterogeneous data through multilevel feature fusion and decision fusion, and it deeply explores the features of remote-sensing fishery data with different data structures and sizes. It can effectively improve the accuracy of fishery prediction, proving the feasibility and advancement of using multi-source remote-sensing data for habitat prediction. It also provides new methods and ideas for future research in the field of habitat prediction. Full article
(This article belongs to the Special Issue Remote Sensing in Intelligent Maritime Research)
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25 pages, 8841 KiB  
Article
A Fast Registration Method for Optical and SAR Images Based on SRAWG Feature Description
by Zhengbin Wang, Anxi Yu, Ben Zhang, Zhen Dong and Xing Chen
Remote Sens. 2022, 14(19), 5060; https://doi.org/10.3390/rs14195060 - 10 Oct 2022
Cited by 2 | Viewed by 1763
Abstract
Due to differences in synthetic aperture radar (SAR) and optical imaging modes, there is a considerable degree of nonlinear intensity difference (NID) and geometric difference between the two images. The SAR image is also accompanied by strong multiplicative speckle noise. These phenomena lead [...] Read more.
Due to differences in synthetic aperture radar (SAR) and optical imaging modes, there is a considerable degree of nonlinear intensity difference (NID) and geometric difference between the two images. The SAR image is also accompanied by strong multiplicative speckle noise. These phenomena lead to what is known as a challenging task to register optical and SAR images. With the development of remote sensing technology, both optical and SAR images equipped with sensor positioning parameters can be roughly registered according to geographic coordinates in advance. However, due to the inaccuracy of sensor parameters, the relative positioning accuracy is still as high as tens or even hundreds of pixels. This paper proposes a fast co-registration method including 3D dense feature description based on a single-scale Sobel and the ratio of exponentially weighted averages (ROEWA) combined with the angle-weighted gradient (SRAWG), overlapping template merging, and non-maxima suppressed template search. In order to more accurately describe the structural features of the image, the single-scale Sobel and ROEWA operators are used to calculate the gradients of optical and SAR images, respectively. On this basis, the 3 × 3 neighborhood angle-weighted gradients of each pixel are fused to form a pixel-wise 3D dense feature description. Aiming at the repeated feature description in the overlapping template and the multi-peak problem on the search surface, this paper adopts the template search strategy of overlapping template merging and non-maximum suppression. The registration results obtained on seven pairs of test images show that the proposed method has significant advantages over state-of-the-art methods in terms of comprehensive registration accuracy and efficiency. Full article
(This article belongs to the Special Issue New Advancements in Remote Sensing Image Processing)
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19 pages, 18039 KiB  
Article
Activity and Kinematics of Two Adjacent Freeze–Thaw-Related Landslides Revealed by Multisource Remote Sensing of Qilian Mountain
by Jie Chen, Jing Zhang, Tonghua Wu, Junming Hao, Xiaodong Wu, Xuyan Ma, Xiaofan Zhu, Peiqing Lou and Lina Zhang
Remote Sens. 2022, 14(19), 5059; https://doi.org/10.3390/rs14195059 - 10 Oct 2022
Cited by 4 | Viewed by 1790
Abstract
The increase in temperatures and changing precipitation patterns resulting from climate change are accelerating the occurrence and development of landslides in cold regions, especially in permafrost environments. Although the boundary regions between permafrost and seasonally frozen ground are very sensitive to climate warming, [...] Read more.
The increase in temperatures and changing precipitation patterns resulting from climate change are accelerating the occurrence and development of landslides in cold regions, especially in permafrost environments. Although the boundary regions between permafrost and seasonally frozen ground are very sensitive to climate warming, slope failures and their kinematics remain barely characterized or understood in these regions. Here, we apply multisource remote sensing and field investigation to study the activity and kinematics of two adjacent landslides (hereafter referred to as “twin landslides”) along the Datong River in the Qilian Mountains of the Qinghai-Tibet Plateau. After failure, there is no obvious change in the area corresponding to the twin landslides. Based on InSAR measurements derived from ALOS PALSAR-1 and -2, we observe significant downslope movements of up to 15 mm/day within the twin landslides and up to 5 mm/day in their surrounding slopes. We show that the downslope movements exhibit distinct seasonality; during the late thaw and early freeze season, a mean velocity of about 4 mm/day is observed, while during the late freeze and early thaw season the downslope velocity is nearly inactive. The pronounced seasonality of downslope movements during both pre- and post-failure stages suggest that the occurrence and development of the twin landslide are strongly influenced by freeze–thaw processes. Based on meteorological data, we infer that the occurrence of twin landslides are related to extensive precipitation and warm winters. Based on risk assessment, InSAR measurements, and field investigation, we infer that new slope failure or collapse may occur in the near future, which will probably block the Datong River and cause catastrophic disasters. Our study provides new insight into the failure mechanisms of slopes at the boundaries of permafrost and seasonally frozen ground. Full article
(This article belongs to the Special Issue Remote Sensing for Natural Hazards Assessment and Control)
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24 pages, 6350 KiB  
Article
Spatial Sampling and Grouping Information Entropy Strategy Based on Kernel Fuzzy C-Means Clustering Method for Hyperspectral Band Selection
by Zhou Zhang, Degang Wang, Xu Sun, Lina Zhuang, Rong Liu and Li Ni
Remote Sens. 2022, 14(19), 5058; https://doi.org/10.3390/rs14195058 - 10 Oct 2022
Cited by 8 | Viewed by 1497
Abstract
The high spectral resolution of hyperspectral images (HSIs) provides rich information but causes data redundancy, which imposes a computational burden on practical applications. Band selection methods can select a subset of HSI without changing the main information to reduce the spectral dimension. Clustering-based [...] Read more.
The high spectral resolution of hyperspectral images (HSIs) provides rich information but causes data redundancy, which imposes a computational burden on practical applications. Band selection methods can select a subset of HSI without changing the main information to reduce the spectral dimension. Clustering-based methods can reduce band correlation significantly, but traditional clustering methods are mostly hard clustering and are not accurate enough to partition the bands. An unsupervised band selection method based on fuzzy c-means clustering (FCM) was introduced to tackle this problem. However, FCM can easily obtain the local optimal solution and take a long time to process high-dimensional data. Hence, this work applies kernel function and a sampling strategy to reduce calculation time, and information entropy is used to initialize the cluster center. A kernel FCM algorithm based on spatial sampling and a grouping information entropy strategy is proposed and called SSGIE-KFCM. This method not only optimizes the calculation process and reduces the amount of computation data, accelerating the calculation efficiency, but also adopts grouping information entropy to improve the probability of obtaining a global optimal solution. Classification experiments on two public HSI datasets show that: (1) The classification performance of the whole band can be achieved or even exceeded by using only a small number of bands to achieve the purpose of dimensionality reduction. (2) The classification accuracy can be improved compared with the FCM method. (3) With the introduction of sampling strategy and kernel function, the computational speed is at least 24 times faster than that of FCM. It has been proven that the SSGIE-KFCM method can significantly reduce the amount of HSI while retaining the primary information of the original data, which further promotes the research and application of HSI in the remote sensing area. Full article
(This article belongs to the Special Issue Pattern Recognition in Hyperspectral Remote Sensing)
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25 pages, 12071 KiB  
Article
Characteristics of Snow Depth and Snow Phenology in the High Latitudes and High Altitudes of the Northern Hemisphere from 1988 to 2018
by Shanna Yue, Tao Che, Liyun Dai, Lin Xiao and Jie Deng
Remote Sens. 2022, 14(19), 5057; https://doi.org/10.3390/rs14195057 - 10 Oct 2022
Cited by 6 | Viewed by 1660
Abstract
Snow cover is an important part of the Earth’s surface and its changes affect local and even global climates due to the high albedo and heat insulation. However, it is difficult to directly compare the results of previous studies on changes in snow [...] Read more.
Snow cover is an important part of the Earth’s surface and its changes affect local and even global climates due to the high albedo and heat insulation. However, it is difficult to directly compare the results of previous studies on changes in snow cover in the Northern Hemisphere mainland (NH) due to the use of different datasets, research methods, or study periods, and a lack comparison in terms of the differences and similarities at high latitudes and high altitudes. By using snow depth datasets, we analyzed the spatio-temporal distributions and variations in snow depth (SD) and snow phenology (SP) in the NH and nine typical areas. This study revealed that SD in the NH generally decreased significantly (p < 0.01) from 1988 to 2018, with a rate of −0.55 cm/decade. Changes in SD were insignificant at high altitudes, but significant decreases were found at high latitudes. With regard to SP, the snow cover onset day (SCOD) advanced in 31.57% of the NH and was delayed in 21.10% of the NH. In typical areas such as the Rocky Mountains, the West Siberian Plain, and the Central Siberian Plateau, the SCOD presented significant advancing trends, while a significant delay was the trend observed in the Eastern European Plain. The snow cover end day (SCED) advanced in 37.29% of the NH and was delayed in 14.77% of the NH. Negative SCED trends were found in most typical areas. The snow cover duration (SCD) and snow season length (SSL) showed significant positive trends in the Rocky Mountains, while significant negative trends were found in the Qinghai–Tibet Plateau. The results of this comprehensive comparison showed that most typical areas were characterized by decreased SD, advanced SCOD and SCED, and insignificantly increasing SCD and SSL trends. The SCD and SSL values were similar at high latitudes, while the SSL value was larger than the SCD value at high altitudes. The SD exhibited similar interannual fluctuation characteristics as the SCD and SSL in each typical area. The SCD and SSL increased (decreased) with advanced (delayed) SCODs. Full article
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24 pages, 12495 KiB  
Article
Noise Parameter Estimation Two-Stage Network for Single Infrared Dim Small Target Image Destriping
by Teliang Wang, Qian Yin, Fanzhi Cao, Miao Li, Zaiping Lin and Wei An
Remote Sens. 2022, 14(19), 5056; https://doi.org/10.3390/rs14195056 - 10 Oct 2022
Cited by 4 | Viewed by 1405
Abstract
The existing nonuniformity correction methods generally have the defects of image blur, artifacts, image over-smoothing, and nonuniform residuals. It is difficult for these methods to meet the requirements of image enhancement in various complex application scenarios. In particular, when these methods are applied [...] Read more.
The existing nonuniformity correction methods generally have the defects of image blur, artifacts, image over-smoothing, and nonuniform residuals. It is difficult for these methods to meet the requirements of image enhancement in various complex application scenarios. In particular, when these methods are applied to dim small target images, they may remove dim small targets as noise points due to the image over-smoothing. This paper draws on the idea of a residual network and proposes a two-stage learning network based on the imaging mechanism of an infrared line-scan system. We adopt a multi-scale feature extraction unit and design a gain correction sub-network and an offset correction sub-network, respectively. Then, we pre-train the two sub-networks independently. Finally, we cascade the two sub-networks into a two-stage network and train it. The experimental results show that the PSNR gain of our method can reach more than 15 dB, and it can achieve excellent performance in different backgrounds and different intensities of nonuniform noise. Moreover, our method can avoid losing texture details or dim small targets after effectively removing nonuniform noise. Full article
(This article belongs to the Special Issue Advanced Artificial Intelligence Algorithm for the Analysis of Remote Sensing Images)
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18 pages, 4081 KiB  
Article
An Aerial and Ground Multi-Agent Cooperative Location Framework in GNSS-Challenged Environments
by Haoyuan Xu, Chaochen Wang, Yuming Bo, Changhui Jiang, Yanxi Liu, Shijie Yang and Weisong Lai
Remote Sens. 2022, 14(19), 5055; https://doi.org/10.3390/rs14195055 - 10 Oct 2022
Cited by 1 | Viewed by 2264
Abstract
In order to realize the cooperative localization of multi-unmanned platforms in the GNSS-denied environment, this paper proposes a collaborative SLAM (simultaneous localization and mapping, SLAM) framework based on image feature point matching. Without GNSS, a single unmanned platform UGV and UAV (unmanned ground [...] Read more.
In order to realize the cooperative localization of multi-unmanned platforms in the GNSS-denied environment, this paper proposes a collaborative SLAM (simultaneous localization and mapping, SLAM) framework based on image feature point matching. Without GNSS, a single unmanned platform UGV and UAV (unmanned ground vehicle, UGV; unmanned aerial vehicle, UAV) equipped with vision and IMU (inertial measurement unit, IMU) sensors can exchange information through data communication to jointly build a three-dimensional visual point map, and determine the relative position of each other through visual-based position re- identification and PnP (Perspective-n-Points, PnP) methods. When any agent can receive reliable GNSS signals, GNSS positioning information will greatly improve the positioning accuracy without changing the positioning algorithm framework. In order to achieve this function, we designed a set of two-stage position estimation algorithms. In the first stage, we used the modified ORB-SLAM3 algorithm for position estimation by fusing visual and IMU information. In the second stage, we integrated GNSS positioning and cooperative positioning information using the factor graph optimization (FGO) algorithm. Our framework consists of an UGV as the central server node and three UAVs carried by the UGV, that will collaborate on space exploration missions. Finally, we simulated the influence of different visibility and lighting conditions on the framework function on the virtual simulation experiment platform built based on ROS (robot operating system, ROS) and Unity3D. The accuracy of the cooperative localization algorithm and the single platform localization algorithm was evaluated. In the two cases of GNSS-denied and GNSS-challenged, the error of co-location reduced by 15.5% and 19.7%, respectively, compared with single-platform independent positioning. Full article
(This article belongs to the Special Issue Advanced Technologies for Position and Navigation under GNSS Signal Challenging or Denied Environments)
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18 pages, 3125 KiB  
Article
UAV Remote Sensing Prediction Method of Winter Wheat Yield Based on the Fused Features of Crop and Soil
by Zezhong Tian, Yao Zhang, Kaidi Liu, Zhenhai Li, Minzan Li, Haiyang Zhang and Jiangmei Wu
Remote Sens. 2022, 14(19), 5054; https://doi.org/10.3390/rs14195054 - 10 Oct 2022
Cited by 2 | Viewed by 1767
Abstract
The early and accurate acquisition of crop yields is of great significance for maintaining food market stability and ensuring global food security. Unmanned aerial vehicle (UAV) remote sensing offers the possibility of predicting crop yields with its advantages of flexibility and high resolution. [...] Read more.
The early and accurate acquisition of crop yields is of great significance for maintaining food market stability and ensuring global food security. Unmanned aerial vehicle (UAV) remote sensing offers the possibility of predicting crop yields with its advantages of flexibility and high resolution. However, most of the existing remote sensing yield estimation studies focused solely on crops but did not fully consider the influence of soil on yield formation. As an integrated system, the status of crop and soil together determines the final yield. Compared to crop-only yield prediction, the approach that additionally considers soil background information will effectively improve the accuracy and reduce bias in the results. In this study, a novel method for segmenting crop and soil spectral images based on different vegetation coverage is first proposed, in which pixels of crop and soil can be accurately identified by determining the discriminant value Q. On the basis of extracting crop and soil waveband’s information by individual pixel, an innovative approach, projected non-negative matrix factorization based on good point set and matrix cross fusion (PNMF-MCF), was developed to effectively extract and fuse the yield-related features of crop and soil. The experimental results on winter wheat show that the proposed segmentation method can accurately distinguish crop and soil pixels under complex soil background of four different growth periods. Compared with the single reflectance of crop or soil and the simple combination of crop and soil reflectance, the fused yield features spectral matrix FP obtained with PNMF−MCF achieved the best performance in yield prediction at the flowering, flag leaf and pustulation stages, with R2 higher than 0.7 in these three stages. Especially at the flowering stage, the yield prediction model based on PNMF-MCF had the highest R2 with 0.8516 and the lowest RMSE with 0.0744 kg/m2. Correlation analysis with key biochemical parameters (nitrogen and carbon, pigments and biomass) of yield formation showed that the flowering stage was the most vigorous season for photosynthesis and the most critical stage for yield prediction. This study provides a new perspective and complete framework for high-precision crop yield forecasting using UAV remote sensing technology. Full article
(This article belongs to the Topic Applications of Big Data and Machine Learning in Smart Agriculture)
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38 pages, 19049 KiB  
Article
A Field-Data-Aided Comparison of Three 10 m Land Cover Products in Southeast Asia
by Yaxin Ding, Xiaomei Yang, Zhihua Wang, Dongjie Fu, He Li, Dan Meng, Xiaowei Zeng and Junyao Zhang
Remote Sens. 2022, 14(19), 5053; https://doi.org/10.3390/rs14195053 - 10 Oct 2022
Cited by 9 | Viewed by 2046
Abstract
To study global and regional environment protection and sustainable development and also to optimize mapping methods, it is of great significance to compare three existing 10 m resolution global land cover products in terms of accuracy: FROM-GLC10, the ESRI 2020 land cover product [...] Read more.
To study global and regional environment protection and sustainable development and also to optimize mapping methods, it is of great significance to compare three existing 10 m resolution global land cover products in terms of accuracy: FROM-GLC10, the ESRI 2020 land cover product (ESRI2020), and the European Space Agency world cover 2020 product (ESA2020). However, most previous validations lack field collection points in large regions, especially in Southeast Asia, which has a cloudy and rainy climate, creating many difficulties in land cover mapping. In 2018 and 2019, we conducted a 56-day field investigation in Southeast Asia and collected 3326 points from different places. By combining these points and 14,808 other manual densification points in a stratified random sampling, we assessed the accuracy of the three land cover products in Southeast Asia. We also compared the impacts of the different classification standards, the different sample methods, and the different spatial distributions of the sample points. The results show that in Southeast Asia, (1) the mean overall accuracies of the FROM-GLC10, ESRI2020, and ESA2020 products are 75.43%, 79.99%, and 81.11%, respectively; (2) all three products perform well in croplands, forests, and built-up areas; ESRI2020 and ESA2020 perform well in water, but only ESA2020 performs well in grasslands; and (3) all three products perform badly in shrublands, wetlands, or bare land, as both the PA and the UA are lower than 50%. We recommend ESA2020 as the first choice for Southeast Asia’s land cover because of its high overall accuracy. FROM-GLC10 also has an advantage over the other two in some classes, such as croplands and water in the UA aspect and the built-up area in the PA aspect. Extracting the individual classes from the three products according to the research goals would be the best practice. Full article
(This article belongs to the Section Environmental Remote Sensing)
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22 pages, 2934 KiB  
Review
Big Geospatial Data and Data-Driven Methods for Urban Dengue Risk Forecasting: A Review
by Zhichao Li and Jinwei Dong
Remote Sens. 2022, 14(19), 5052; https://doi.org/10.3390/rs14195052 - 10 Oct 2022
Cited by 6 | Viewed by 3284
Abstract
With advancements in big geospatial data and artificial intelligence, multi-source data and diverse data-driven methods have become common in dengue risk prediction. Understanding the current state of data and models in dengue risk prediction enables the implementation of efficient and accurate prediction in [...] Read more.
With advancements in big geospatial data and artificial intelligence, multi-source data and diverse data-driven methods have become common in dengue risk prediction. Understanding the current state of data and models in dengue risk prediction enables the implementation of efficient and accurate prediction in the future. Focusing on predictors, data sources, spatial and temporal scales, data-driven methods, and model evaluation, we performed a literature review based on 53 journal and conference papers published from 2018 to the present and concluded the following. (1) The predominant predictors include local climate conditions, historical dengue cases, vegetation indices, human mobility, population, internet search indices, social media indices, landscape, time index, and extreme weather events. (2) They are mainly derived from the official meteorological agency satellite-based datasets, public websites, department of health services and national electronic diseases surveillance systems, official statistics, and public transport datasets. (3) Country-level, province/state-level, city-level, district-level, and neighborhood-level are used as spatial scales, and the city-level scale received the most attention. The temporal scales include yearly, monthly, weekly, and daily, and both monthly and weekly are the most popular options. (4) Most studies define dengue risk forecasting as a regression task, and a few studies define it as a classification task. Data-driven methods can be categorized into single models, ensemble learning, and hybrid learning, with single models being further subdivided into time series, machine learning, and deep learning models. (5) Model evaluation concentrates primarily on the quantification of the difference/correlation between time-series observations and predicted values, the ability of models to determine whether a dengue outbreak occurs or not, and model uncertainty. Finally, we highlighted the importance of big geospatial data, data cloud computing, and other deep learning models in future dengue risk forecasting. Full article
(This article belongs to the Special Issue Applications of Remote Sensing for One Health)
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8 pages, 1503 KiB  
Technical Note
Backscattering Analysis Utilizing Relaxed Hierarchical Equivalent Source Algorithm (RHESA) for Scatterers in Vegetation Medium
by Syabeela Syahali, Hong-Tat Ewe, Gobi Vetharatnam and Li-Jun Jiang
Remote Sens. 2022, 14(19), 5051; https://doi.org/10.3390/rs14195051 - 10 Oct 2022
Cited by 1 | Viewed by 973
Abstract
The backscattering cross section of cylindrical and elliptical disk-shaped scatterers was investigated in this study, utilising a new numerical solution method called the relaxed hierarchical equivalent source algorithm (RHESA). The results were compared with the backscattering cross section of similar cases, using analytical [...] Read more.
The backscattering cross section of cylindrical and elliptical disk-shaped scatterers was investigated in this study, utilising a new numerical solution method called the relaxed hierarchical equivalent source algorithm (RHESA). The results were compared with the backscattering cross section of similar cases, using analytical method validation from literature. The objective of this research was to look into the possibility of replacing analytical methods with the RHESA in volume scattering calculations, and integrating it into modelling the backscattering of layers of dense media for microwave remote sensing in vegetation; as RHESA provides the freedom to model any shape of scatterer, as opposed to the limited shapes available of scatterers in analytical method models. The results demonstrated a good match, indicating that the RHESA may be a good fit for modelling more complicated media, such as vegetation, in future studies. Full article
(This article belongs to the Special Issue Remote Sensing for Mountain Vegetation and Snow Cover)
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21 pages, 11055 KiB  
Article
Factors Influencing Seasonal Changes in Inundation of the Daliyaboyi Oasis, Lower Keriya River Valley, Central Tarim Basin, China
by Jinhua Wang, Feng Zhang, Guangming Luo, Yuchuan Guo, Jianghua Zheng, Shixin Wu, Dawei Wang, Suhong Liu and Qingdong Shi
Remote Sens. 2022, 14(19), 5050; https://doi.org/10.3390/rs14195050 - 10 Oct 2022
Cited by 3 | Viewed by 1542
Abstract
The ecological water diversion project (EWDP) in the Tarim River Basin, China, aims to allocate more surface water to downstream reaches to restore the degraded ecosystems. However, seasonal changes in ecological water diversion; the factors (natural and anthropogenic) controlling the ecological water diversion, [...] Read more.
The ecological water diversion project (EWDP) in the Tarim River Basin, China, aims to allocate more surface water to downstream reaches to restore the degraded ecosystems. However, seasonal changes in ecological water diversion; the factors (natural and anthropogenic) controlling the ecological water diversion, whether the seasonal delivery of water temporally corresponded to the vegetation’s seasonal water demands; and the benefits of the ecological water diversion through overflowing surface water irrigation are unclear. To address the above issues, this study examines the intra-annual changes and its influencing factors in ecological water diversion (inundation) in the Daliyaboyi Oasis in the lower Keriya River valley within the Tarim Basin, discusses whether the seasonal delivery of water temporally corresponded to the vegetation’s seasonal water demands, and assesses the ecological benefits of overflowing surface water irrigation. Inundation was quantified by digitizing monthly changes in the inundated area from 2000 to 2018 in the oasis using 184 Landsat images. The results demonstrate that seasonal changes in the inundated area varied significantly, with maximum peaks occurring in February and August; a period of minimal inundation occurred in May. Differences in the July/August peak (i.e., July or August) in inundation dominated the inter-annual variations in the inundated area over the 19-year study period. The two peaks in the inundation area were temporally consistent with the vegetation’s seasonal water demand. Local residents have used ecological water to irrigate vegetation in different parts of the oasis during different seasons, an approach that expanded the inundated area. The February peak in the inundated area is closely linked to elevated downstream groundwater levels and the melting of ice along the river. The August peak is related to a peak in runoff from headwater areas. The minimum May value is correlated to a relatively low value in upstream runoff and an increase in agricultural water demand. Thus, natural factors control the intra-annual and inter-annual variations in the inundated area. Humans changed the spatial distribution of the inundated area and enhanced the water’s ecological benefits, but did not alter the correlation between peak periods of inundation and vegetation water demand. The results from this study improve our understanding of the benefits of the EWDP in the Tarim River Basin. Full article
(This article belongs to the Special Issue Wetland Monitoring Using Remote Sensing)
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20 pages, 11950 KiB  
Article
Research on Co-Channel Interference Cancellation for Underwater Acoustic MIMO Communications
by Yuehai Zhou, Feng Tong and Xiaoyu Yang
Remote Sens. 2022, 14(19), 5049; https://doi.org/10.3390/rs14195049 - 10 Oct 2022
Cited by 5 | Viewed by 1604
Abstract
Multiple-input–multiple-output (MIMO) communication systems utilize multiple transmitters to send different pieces of information in parallel. This offers a promising way to communicate at a high data rate over bandwidth-limited underwater acoustic channels. However, underwater acoustic MIMO communication not only suffers from serious inter-symbol [...] Read more.
Multiple-input–multiple-output (MIMO) communication systems utilize multiple transmitters to send different pieces of information in parallel. This offers a promising way to communicate at a high data rate over bandwidth-limited underwater acoustic channels. However, underwater acoustic MIMO communication not only suffers from serious inter-symbol interference, but also critical co-channel interference (CoI), both of which degrade the communication performance. In this paper, we propose a new framework for underwater acoustic MIMO communications. The proposed framework consists of a CoI-cancellation-based channel estimation method and channel-estimation-based decision feedback equalizer (CE-DFE) with CoI cancellation functionalities for underwater acoustic MIMO communication. We introduce a new channel estimation model that projects the received signal to a specific subspace where the interference is free; therefore, the CoI is cancelled. We also introduce a CE-DFE with CoI cancellation by appending some filters from traditional CE-DFE. In addition, the traditional direct adaptive decision feedback equalization (DA-DFE) method and the proposed method are compared in terms of communication performance and computational complexity. Finally, the sea trial experiment demonstrates the effectiveness and merits of the proposed method. The proposed method achieves a more than 1 dB of output SNR over traditional DA-DFE, and is less sensitive to parameters. The proposed method provides a new approach to the design of robust underwater acoustic MODEM. Full article
(This article belongs to the Special Issue Underwater Communication and Networking)
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18 pages, 15642 KiB  
Article
A New Ship Detection Algorithm in Optical Remote Sensing Images Based on Improved R3Det
by Jianfeng Li, Zongfeng Li, Mingxu Chen, Yongling Wang and Qinghua Luo
Remote Sens. 2022, 14(19), 5048; https://doi.org/10.3390/rs14195048 - 10 Oct 2022
Cited by 5 | Viewed by 2058
Abstract
The task of ship target detection based on remote sensing images has attracted more and more attention because of its important value in civil and military fields. To solve the problem of low accuracy in ship target detection in optical remote sensing ship [...] Read more.
The task of ship target detection based on remote sensing images has attracted more and more attention because of its important value in civil and military fields. To solve the problem of low accuracy in ship target detection in optical remote sensing ship images due to complex scenes and large-target-scale differences, an improved R3Det algorithm is proposed in this paper. On the basis of R3Det, a feature pyramid network (FPN) structure is replaced by a search architecture-based feature pyramid network (NAS FPN) so that the network can adaptively learn and select the feature combination update and enrich the multiscale feature information. After the feature extraction network, a shallow feature is added to the context information enhancement (COT) module to supplement the small target semantic information. An efficient channel attention (ECA) module is added to make the network gather in the target area. The improved algorithm is applied to the ship data in the remote sensing image data set FAIR1M. The effectiveness of the improved model in a complex environment and for small target detection is verified through comparison experiments with R3Det and other models. Full article
(This article belongs to the Special Issue Deep Learning Based Target Detection and Recognition in Remote Sensing Images)
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15 pages, 2442 KiB  
Article
An Application of Hyperspectral Image Clustering Based on Texture-Aware Superpixel Technique in Deep Sea
by Panjian Ye, Chenhua Han, Qizhong Zhang, Farong Gao, Zhangyi Yang and Guanghai Wu
Remote Sens. 2022, 14(19), 5047; https://doi.org/10.3390/rs14195047 - 10 Oct 2022
Cited by 2 | Viewed by 1335
Abstract
This paper aims to study the application of hyperspectral technology in the classification of deep-sea manganese nodules. Considering the spectral spatial variation of hyperspectral images, the difficulty of label acquisition, and the inability to guarantee stable illumination in deep-sea environments. This paper proposes [...] Read more.
This paper aims to study the application of hyperspectral technology in the classification of deep-sea manganese nodules. Considering the spectral spatial variation of hyperspectral images, the difficulty of label acquisition, and the inability to guarantee stable illumination in deep-sea environments. This paper proposes a local binary pattern manifold superpixel-based fuzzy clustering method (LMSLIC-FCM). Firstly, we introduce a uniform local binary pattern (ULBP) to design a superpixel algorithm (LMSLIC) that is insensitive to illumination and has texture perception. Secondly, the weighted feature and the mean feature are fused as the representative features of superpixels. Finally, it is fused with fuzzy clustering method (FCM) to obtain a superpixel-based clustering algorithm LMSLIC-FCM. To verify the feasibility of LMSLIC-FCM on deep-sea manganese nodule data, the experiments were conducted on three different types of manganese nodule data. The average identification rate of LMSLIC-FCM reached 83.8%, and the average true positive rate reached 93.3%, which was preferable to the previous algorithms. Therefore, LMSLIC-FCM is effective in the classification of manganese nodules. Full article
(This article belongs to the Special Issue Deep Learning for the Analysis of Multi-/Hyperspectral Images)
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18 pages, 5908 KiB  
Article
DSNUNet: An Improved Forest Change Detection Network by Combining Sentinel-1 and Sentinel-2 Images
by Jiawei Jiang, Yuanjun Xing, Wei Wei, Enping Yan, Jun Xiang and Dengkui Mo
Remote Sens. 2022, 14(19), 5046; https://doi.org/10.3390/rs14195046 - 10 Oct 2022
Cited by 4 | Viewed by 2445
Abstract
The use of remote sensing images to detect forest changes is of great significance for forest resource management. With the development and implementation of deep learning algorithms in change detection, a large number of models have been designed to detect changes in multi-phase [...] Read more.
The use of remote sensing images to detect forest changes is of great significance for forest resource management. With the development and implementation of deep learning algorithms in change detection, a large number of models have been designed to detect changes in multi-phase remote sensing images. Although synthetic aperture radar (SAR) data have strong potential for application in forest change detection tasks, most existing deep learning-based models have been designed for optical imagery. Therefore, to effectively combine optical and SAR data in forest change detection, this paper proposes a double Siamese branch-based change detection network called DSNUNet. DSNUNet uses two sets of feature branches to extract features from dual-phase optical and SAR images and employs shared weights to combine features into groups. In the proposed DSNUNet, different feature extraction branch widths were used to compensate for a difference in the amount of information between optical and SAR images. The proposed DSNUNet was validated by experiments on the manually annotated forest change detection dataset. According to the obtained results, the proposed method outperformed other change detection methods, achieving an F1-score of 76.40%. In addition, different combinations of width between feature extraction branches were analyzed in this study. The results revealed an optimal performance of the model at initial channel numbers of the optical imaging branch and SAR image branch of 32 and 8, respectively. The prediction results demonstrated the effectiveness of the proposed method in accurately predicting forest changes and suppressing cloud interferences to some extent. Full article
(This article belongs to the Special Issue Remote Sensing and Smart Forestry)
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21 pages, 5419 KiB  
Article
Rice Yield Prediction and Model Interpretation Based on Satellite and Climatic Indicators Using a Transformer Method
by Yuanyuan Liu, Shaoqiang Wang, Jinghua Chen, Bin Chen, Xiaobo Wang, Dongze Hao and Leigang Sun
Remote Sens. 2022, 14(19), 5045; https://doi.org/10.3390/rs14195045 - 10 Oct 2022
Cited by 14 | Viewed by 3600
Abstract
As the second largest rice producer, India contributes about 20% of the world’s rice production. Timely, accurate, and reliable rice yield prediction in India is crucial for global food security and health issues. Deep learning models have achieved excellent performances in predicting crop [...] Read more.
As the second largest rice producer, India contributes about 20% of the world’s rice production. Timely, accurate, and reliable rice yield prediction in India is crucial for global food security and health issues. Deep learning models have achieved excellent performances in predicting crop yield. However, the interpretation of deep learning models is still rare. In this study, we proposed a transformer-based model, Informer, to predict rice yield across the Indian Indo-Gangetic Plains by integrating time-series satellite data, environmental variables, and rice yield records from 2001 to 2016. The results showed that Informer had better performance (R2 = 0.81, RMSE = 0.41 t/ha) than four other machine learning and deep learning models for end-of-season prediction. For within-season prediction, the Informer model could achieve stable performances (R2 ≈ 0.78) after late September, which indicated that the optimal prediction could be achieved 2 months before rice maturity. In addition, we interpreted the prediction models by evaluating the input feature importance and analyzing hidden features. The evaluation of feature importance indicated that NIRV was the most critical factor, while intervals 6 (mid-August) and 12 (mid-November) were the key periods for rice yield prediction. The hidden feature analysis demonstrated that the attention-based long short-term memory (AtLSTM) model accumulated the information of each growth period, while the Informer model focused on the information around intervals 5 to 6 (August) and 11 to 12 (November). Our findings provided a reliable and simple framework for crop yield prediction and a new perspective for explaining the internal mechanism of deep learning models. Full article
(This article belongs to the Topic Applications of Big Data and Machine Learning in Smart Agriculture)
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2 pages, 415 KiB  
Correction
Correction: Chen et al. High-Precision Stand Age Data Facilitate the Estimation of Rubber Plantation Biomass: A Case Study of Hainan Island, China. Remote Sens. 2020, 12, 3853
by Bangqian Chen, Ting Yun, Jun Ma, Weili Kou, Hailiang Li, Chuan Yang, Xiangming Xiao, Xian Zhang, Rui Sun, Guishui Xie and Zhixiang Wu
Remote Sens. 2022, 14(19), 5044; https://doi.org/10.3390/rs14195044 - 10 Oct 2022
Viewed by 879
Abstract
In the original article [...] Full article
(This article belongs to the Special Issue Forest Biomass and Carbon Observation with Remote Sensing)
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19 pages, 614 KiB  
Article
An Oblique Projection-Based Beamforming Method for Coherent Signals Receiving
by Yumei Guo, Qiang Li, Linrang Zhang, Juan Zhang and Zhanye Chen
Remote Sens. 2022, 14(19), 5043; https://doi.org/10.3390/rs14195043 - 09 Oct 2022
Cited by 1 | Viewed by 1411
Abstract
Within a complex sea or ground surface background, multipath signals are strongly correlated or even completely coherent, which leads to signal cancellation when conventional optimal beamforming is performed. Aiming at the above problem, a coherent signal-receiving algorithm is proposed based on oblique projection [...] Read more.
Within a complex sea or ground surface background, multipath signals are strongly correlated or even completely coherent, which leads to signal cancellation when conventional optimal beamforming is performed. Aiming at the above problem, a coherent signal-receiving algorithm is proposed based on oblique projection technology in this paper. The direction of arrival (DOA) of incident signals is estimated firstly by the space smoothing-based MUSIC method. The composite steering vector of multipath coherent signals is then obtained utilizing the oblique projection matrix constructed with the estimated angles. The weight vector is thereby derived with the minimum variance distortionless response criteria. The proposed oblique projection-based beamformer can receive the multipath coherent signals effectively. Moreover, the proposed beamformer is more robust and converges to optimal beamformer rapidly without aperture loss. The theoretical analysis and simulation verify the validity and superiority of the proposed coherent signal beamformer. Full article
(This article belongs to the Special Issue Microwave Passive Remote Sensing of Sea Surface Temperature, Salinity and Wind Vector)
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18 pages, 20784 KiB  
Article
Prediction of Radar Echo Space-Time Sequence Based on Improving TrajGRU Deep-Learning Model
by Qiangyu Zeng, Haoran Li, Tao Zhang, Jianxin He, Fugui Zhang, Hao Wang, Zhipeng Qing, Qiu Yu and Bangyue Shen
Remote Sens. 2022, 14(19), 5042; https://doi.org/10.3390/rs14195042 - 09 Oct 2022
Cited by 5 | Viewed by 2310
Abstract
Nowcasting of severe convective precipitation is of great importance in meteorological disaster prevention. Radar echo extrapolation is an effective method for short-term precipitation nowcasting. The traditional radar echo extrapolation methods lack the utilization of radar historical data as well as overlooking the nonlinear [...] Read more.
Nowcasting of severe convective precipitation is of great importance in meteorological disaster prevention. Radar echo extrapolation is an effective method for short-term precipitation nowcasting. The traditional radar echo extrapolation methods lack the utilization of radar historical data as well as overlooking the nonlinear motion of small- to medium-sized convective systems in radar echoes. To solve this, we propose a deep-learning model combining CNN and RNN. The model T-UNet proposed in this paper uses an efficient convolutional neural network of UNet architecture with a residual network, where the encoder and decoder networks are connected by nested dense skip paths, while a TrajGRU recurrent neural network is added at each layer, to achieve the perceptual capability of time series. The quantitative statistical evaluation showed that the use of T-UNet could improve the nowcasting skill (CSI score, HSS score) by a maximum of 10.57% and 7.80% over a 60 min prediction cycle. Further evaluation showed that T-UNet also improved the prediction accuracy and prediction performance in the strong echo region. Full article
(This article belongs to the Special Issue Radar Remote Sensing: Retrieval Algorithms and Applications for Characterizing Precipitation)
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19 pages, 12258 KiB  
Article
Spatiotemporal Variation in Vegetation Growth Status and Its Response to Climate in the Three-River Headwaters Region, China
by Chenyang He, Feng Yan, Yanjiao Wang and Qi Lu
Remote Sens. 2022, 14(19), 5041; https://doi.org/10.3390/rs14195041 - 09 Oct 2022
Cited by 6 | Viewed by 1588
Abstract
The Three-River Headwaters Region (TRHR), located in the hinterland of the Qinghai–Tibet Plateau (QTP), is an important water-conservation and ecological-function reserve in China. Studies of the growth of vegetation in the TRHR and its response to climate under the background of global warming [...] Read more.
The Three-River Headwaters Region (TRHR), located in the hinterland of the Qinghai–Tibet Plateau (QTP), is an important water-conservation and ecological-function reserve in China. Studies of the growth of vegetation in the TRHR and its response to climate under the background of global warming are of great relevance for ecological protection of the QTP. In this study, based on MOD13Q1 Enhanced Vegetation Index (EVI) data and ERA5-Land climate data, the ensemble empirical mode decomposition method, random forest algorithm, and Hurst exponent were used to detect the spatiotemporal dynamics and response to climate change in TRHR vegetation during 2000–2021. The results indicated the following. (1) Comparatively, the condition of vegetation growth was better in 2021, 2010, and 2018 and poorer in 2015, 2003, and 2008. The EVI gradually decreased from the southeast to the northwest, and the area of improved vegetation growth was larger than the area of degraded vegetation growth. (2) The area of zones with either monotonous greening or monotonous browning of vegetation was 30.30% and 6.30%, respectively, and the trend of reversed vegetation change occurred in 63.40% of the areas. The area of future degradation of vegetation in the TRHR was larger than the area of future improvement, and the risk of vegetation degradation was higher. (3) Precipitation and soil temperature are the main and secondary driving factors of vegetation change in the TRHR, respectively. Warming and humidification of the QTP climate play major roles in the improvement of vegetation growth in the TRHR. Full article
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16 pages, 4651 KiB  
Article
Identifying a Leading Predictor of Arctic Stratospheric Ozone for April Precipitation in Eastern North America
by Xuan Ma, Fei Xie, Xiaosong Chen, Lei Wang and Guanyu Yang
Remote Sens. 2022, 14(19), 5040; https://doi.org/10.3390/rs14195040 - 09 Oct 2022
Cited by 1 | Viewed by 1320
Abstract
An analysis of the relationship between changes in Arctic stratospheric ozone (ASO) and precipitation in eastern North America (38°–54°N, 65°–87°W; PENA) was performed using observational and reanalysis data coupled with the Whole Atmosphere Community Climate Model version 4 (WACCM4). We found that March [...] Read more.
An analysis of the relationship between changes in Arctic stratospheric ozone (ASO) and precipitation in eastern North America (38°–54°N, 65°–87°W; PENA) was performed using observational and reanalysis data coupled with the Whole Atmosphere Community Climate Model version 4 (WACCM4). We found that March ASO exhibits a strong correlation with PENA in April, indicating that the one-month leading ASO exerts a potentially strong impact on April PENA. Changes in tropospheric circulation over the North Pacific and North America can be influenced by ASO anomalies via stratosphere–troposphere interactions. Increased ASO typically results in the transport of drier, colder air from northwest to eastern North America and suppresses local convective activity by enhancing regional downwelling. These conditions lead to a decrease in regional atmospheric water vapor content (1000–600 hPa). Abnormally high ASO may therefore suppress precipitation, whereas abnormally low ASO serves to enhance precipitation, and the finding is supported by WACCM4 simulations incorporating these ASO anomaly signals. We also present an ASO-based statistical linear model for predicting April PENA. Results confirm that the linear model reproduces April PENA for both training and testing periods, based on March ASO, demonstrating the reliability and stability of this linear model. This study verifies that ASO is a viable predictor for projecting April PENA and thus improving forecasts of regional seasonal precipitation. Full article
(This article belongs to the Special Issue Effects of Stratosphere-Troposphere-Land-Ocean Interaction on the Atmospheric Environment and Ecosystem)
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16 pages, 9806 KiB  
Article
Analysis of Diurnal Evolution of Cloud Properties and Convection Tracking over the South China Coastal Area
by Xinyue Wang, Hironobu Iwabuchi and Jean-Baptiste Courbot
Remote Sens. 2022, 14(19), 5039; https://doi.org/10.3390/rs14195039 - 09 Oct 2022
Viewed by 1231
Abstract
Different diurnal rainfall cycles occur over the offshore and inland regions of the South China coastal area (SCCA). Inspired by these findings, in this study, we investigated the diurnal evolution features of cloud systems and cloud properties inside such systems for both the [...] Read more.
Different diurnal rainfall cycles occur over the offshore and inland regions of the South China coastal area (SCCA). Inspired by these findings, in this study, we investigated the diurnal evolution features of cloud systems and cloud properties inside such systems for both the SCCA offshore and inland regions, using cloud data retrieved from a recently developed deep neural network model. Rainy day data for June 2017 revealed that the ice cloud optical thickness and top height reached their peak intensities at noon (~12 local standard time (LST)) over the offshore region, approximately 2 h later than the rainfall peak. Over the inland region, cloud and rainfall peaks simultaneously appeared from ~18 to 20 LST. When further examining the cloud-amount variation of different ice-cloud types, we found a clear diurnal oscillation in the medium-thick cloud amount over the offshore region, while for the inland region, this cloud type had no obvious diurnal peak, showing a low cloud amount throughout the day. This phenomenon suggests different inner structures and intensities between offshore and inland convections. To better elucidate the convection features over different regions, a tracking algorithm was applied to obtain various parameters, such as size, number, and duration of mesoscale convective systems. The strongest convections, which lasted over 12 h, tended to be abundant over the offshore region from ~03 to 12 LST, and an inland to offshore migration at ~03 LST was facilitated by the beneficial meteorological conditions observed at 113–116˚E, 20.5–22.5˚N. Full article
(This article belongs to the Special Issue Advances in Mesoscale Meteorology and Precipitation Monitoring and Processes Using Remote Sensing Observations and Technologies)
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12 pages, 722 KiB  
Article
Assessment of Material Layers in Building Walls Using GeoRadar
by Ildar Gilmutdinov, Ingrid Schlögel, Alois Hinterleitner, Peter Wonka and Michael Wimmer
Remote Sens. 2022, 14(19), 5038; https://doi.org/10.3390/rs14195038 - 09 Oct 2022
Cited by 1 | Viewed by 1444
Abstract
Assessing the structure of a building with non-invasive methods is an important problem. One of the possible approaches is to use GeoRadar to examine wall structures by analyzing the data obtained from the scans. However, so far, the obtained data have to be [...] Read more.
Assessing the structure of a building with non-invasive methods is an important problem. One of the possible approaches is to use GeoRadar to examine wall structures by analyzing the data obtained from the scans. However, so far, the obtained data have to be assessed manually, relying on the experience of the user in interpreting GPR radargrams. We propose a data-driven approach to evaluate the material composition of a wall from its GPR radargrams. In order to generate training data, we use gprMax to model the scanning process. Using simulation data, we use a convolutional neural network to predict the thicknesses and dielectric properties of walls per layer. We evaluate the generalization abilities of the trained model on the data collected from real buildings. Full article
(This article belongs to the Special Issue Radar Techniques for Structures Characterization and Monitoring)
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18 pages, 12232 KiB  
Article
Gradient Boosting and Linear Regression for Estimating Coastal Bathymetry Based on Sentinel-2 Images
by Fahim Abdul Gafoor, Maryam R. Al-Shehhi, Chung-Suk Cho and Hosni Ghedira
Remote Sens. 2022, 14(19), 5037; https://doi.org/10.3390/rs14195037 - 09 Oct 2022
Cited by 9 | Viewed by 2351
Abstract
Thousands of vessels travel around the world every day, making the safety, efficiency, and optimization of marine transportation essential. Therefore, the knowledge of bathymetry is crucial for a variety of maritime applications, such as shipping and navigation. Maritime applications have benefited from recent [...] Read more.
Thousands of vessels travel around the world every day, making the safety, efficiency, and optimization of marine transportation essential. Therefore, the knowledge of bathymetry is crucial for a variety of maritime applications, such as shipping and navigation. Maritime applications have benefited from recent advancements in satellite navigation technology, which can utilize multi-spectral bands for retrieving information on water depth. As part of these efforts, this study combined deep learning techniques with satellite observations in order to improve the estimation of satellite-based bathymetry. The objective of this study is to develop a new method for estimating coastal bathymetry using Sentinel-2 images. Sentinel-2 was used here due to its high spatial resolution, which is desirable for bathymetry maps, as well as its visible bands, which are useful for estimating bathymetry. The conventional linear model approach using the satellite-derived bathymetry (SDB) ratio (green to blue) was applied, and a new four-band ratio using the four visible bands of Sentienl-2 was proposed. In addition, three atmospheric correction models, Sen2Cor, ALOCITE, and C2RCC, were evaluated, and Sen2Cor was found to be the most effective model. Gradient boosting was also applied in this study to both the conventional band ratio and the proposed FVBR ratio. Compared to the green to blue ratio, the proposed ratio FVBR performed better, with R2 exceeding 0.8 when applied to 12 snapshots between January and December. The gradient boosting method was also found to provide better estimates of bathymetry than linear regression. According to findings of this study, the chlorophyll-a (Chl-a) concentration, sediments, and atmospheric dust do not affect the estimated bathymetry. However, tidal oscillations were found to be a significant factor affecting satellite estimates of bathymetry. Full article
(This article belongs to the Special Issue Satellite Derived Bathymetry for Coastal Mapping)
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18 pages, 17320 KiB  
Article
Permafrost Early Deformation Signals before the Norilsk Oil Tank Collapse in Russia
by Peng Zhang, Yan Chen, Youhua Ran and Yunping Chen
Remote Sens. 2022, 14(19), 5036; https://doi.org/10.3390/rs14195036 - 09 Oct 2022
Cited by 2 | Viewed by 1847
Abstract
Despite the profound roles of surface deformation monitoring techniques in observing permafrost surface stability, predetermining the approximate location and time of possibly occurring severe permafrost degradation before applying these techniques is extremely necessary, but has received little attention. Taking the oil tank collapse [...] Read more.
Despite the profound roles of surface deformation monitoring techniques in observing permafrost surface stability, predetermining the approximate location and time of possibly occurring severe permafrost degradation before applying these techniques is extremely necessary, but has received little attention. Taking the oil tank collapse accident in the Norilsk region as a case, we explored this concern by analyzing the permafrost deformation mechanisms and determining early surface deformation signals. Regarding this case, we firstly applied the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique to obtain its permafrost surface deformation rate, then utilized a sine model to decompose its interannual deformation and seasonal deformation, and finally compared the relationship between the topographic slope and deformation rate. Based on experimental results, we reveal that when the annual average temperature continuously increases at a rate of 2 °C/year for 2∼3 consecutive years, permafrost areas with relatively large topographic slopes (>15°) are more prone to severe surface deformation during the summer thaw period. Therefore, this paper suggests that permafrost areas with large topographic slopes (>15°) should be taken as the key surveillance areas, and that the appropriate monitoring time for employing surface deformation monitoring techniques should be the summer thawing period after a continuous increase in annual average temperature at a rate of 2 °C/year for 2∼3 years. Full article
(This article belongs to the Special Issue Remote Sensing of the Cryosphere)
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