Remote Sensing

41 pages, 2368 KiB

Open AccessEditor’s ChoiceArticle

Fast, Efficient, and Viable Compressed Sensing, Low-Rank, and Robust Principle Component Analysis Algorithms for Radar Signal Processing

by Reinhard Panhuber

Remote Sens. 2023, 15(8), 2216; https://doi.org/10.3390/rs15082216 - 21 Apr 2023

Cited by 1 | Viewed by 1514

Abstract

Modern radar signal processing techniques make strong use of compressed sensing, affine rank minimization, and robust principle component analysis. The corresponding reconstruction algorithms should fulfill the following desired properties: complex valued, viable in the sense of not requiring parameters that are unknown in [...] Read more.

Modern radar signal processing techniques make strong use of compressed sensing, affine rank minimization, and robust principle component analysis. The corresponding reconstruction algorithms should fulfill the following desired properties: complex valued, viable in the sense of not requiring parameters that are unknown in practice, fast convergence, low computational complexity, and high reconstruction performance. Although a plethora of reconstruction algorithms are available in the literature, these generally do not meet all of the aforementioned desired properties together. In this paper, a set of algorithms fulfilling these conditions is presented. The desired requirements are met by a combination of turbo-message-passing algorithms and smoothed

ℓ_{0}

-refinements. Their performance is evaluated by use of extensive numerical simulations and compared with popular conventional algorithms. Full article

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27 pages, 20567 KiB

Open AccessArticle

Fast Factorized Backprojection Algorithm in Orthogonal Elliptical Coordinate System for Ocean Scenes Imaging Using Geosynchronous Spaceborne–Airborne VHF UWB Bistatic SAR

by Xiao Hu, Hongtu Xie, Lin Zhang, Jun Hu, Jinfeng He, Shiliang Yi, Hejun Jiang and Kai Xie

Remote Sens. 2023, 15(8), 2215; https://doi.org/10.3390/rs15082215 - 21 Apr 2023

Cited by 8 | Viewed by 1597

Abstract

Geosynchronous (GEO) spaceborne–airborne very high-frequency ultra-wideband bistatic synthetic aperture radar (VHF UWB BiSAR) can conduct high-resolution and wide-swath imaging for ocean scenes. However, GEO spaceborne–airborne VHF UWB BiSAR imaging faces some challenges such as the geometric configuration, huge amount of echo data, serious [...] Read more.

Geosynchronous (GEO) spaceborne–airborne very high-frequency ultra-wideband bistatic synthetic aperture radar (VHF UWB BiSAR) can conduct high-resolution and wide-swath imaging for ocean scenes. However, GEO spaceborne–airborne VHF UWB BiSAR imaging faces some challenges such as the geometric configuration, huge amount of echo data, serious range–azimuth coupling, large spatial variance, and complex motion error, which increases the difficulty of the high-efficiency and high-precision imaging. In this paper, we present an improved bistatic fast factorization backprojection (FFBP) algorithm for ocean scene imaging using the GEO satellite-unmanned aerial vehicle (GEO-UAV) VHF UWB BiSAR, which can solve the above issues with high efficiency and high precision. This method reconstructs the subimages in the orthogonal elliptical polar (OEP) coordinate system based on the GEO satellite and UAV trajectories as well as the location of the imaged scene, which can further reduce the computational burden. First, the imaging geometry and signal model of the GEO-UAV VHF UWB BiSAR are established, and the construction of the OEP coordinate system and the subaperture imaging method are proposed. Moreover, the Nyquist sampling requirements for the subimages in the OEP coordinate system are derived from the range error perspective, which can offer a near-optimum tradeoff between precision and efficiency. In addition, the superiority of the OEP coordinate system is analyzed, which demonstrates that the angular dimensional sampling rate of the subimages is significantly reduced. Finally, the implementation processes and computational burden of the proposed algorithm are provided, and the speed-up factor of the proposed FFBP algorithm compared with the BP algorithm is derived and discussed. Experimental results of ideal point targets and natural ocean scenes demonstrate the correctness and effectiveness of the proposed algorithm, which can achieve near-optimal imaging performance with a low computational burden. Full article

(This article belongs to the Special Issue Radar Signal Processing and Imaging for Ocean Remote Sensing)

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28 pages, 36592 KiB

Open AccessArticle

An Interferogram Re-Flattening Method for InSAR Based on Local Residual Fringe Removal and Adaptively Adjusted Windows

by Di Zhuang, Lamei Zhang and Bin Zou

Remote Sens. 2023, 15(8), 2214; https://doi.org/10.3390/rs15082214 - 21 Apr 2023

Cited by 1 | Viewed by 1194

Abstract

InSAR technology uses the geometry between antennas and targets to obtain DEM and deformation; therefore, accurate orbit information, which can provide reliable geometry, is the prerequisite for InSAR processing. However, the orbit information provided by some satellites may be inaccurate. Further, this inaccuracy [...] Read more.

InSAR technology uses the geometry between antennas and targets to obtain DEM and deformation; therefore, accurate orbit information, which can provide reliable geometry, is the prerequisite for InSAR processing. However, the orbit information provided by some satellites may be inaccurate. Further, this inaccuracy will be reflected in the interferogram and will be difficult to remove, finally resulting in incorrect results. More importantly, it was found that the residual fringes caused by inaccurate orbit information vary unevenly throughout the whole image and cannot be completely removed by the existing refinement and re-flattening methods. Therefore, an interferogram re-flattening method based on local residual fringe removal and adaptively adjusted windows was proposed in this paper, with the aim being to remove the unevenly varying residual fringes. There are two innovative advantages of the proposed method. One advantage is that the method aims at the global inhomogeneity of residual fringes; the idea of combining local processing and residual fringe removal was proposed to ensure the residual fringes in the whole image can be removed. The other is that an adaptively adjusted local flattening window was designed to ensure that the residual fringes within the local window can be removed cleanly. Three sets of GaoFen-3 data and one pair of Sentinle-1A data were used for experiments. The re-flattening process shows that the local flattening and the adjustment of the local window are absolutely essential to the clean removal of time-varying and uneven residual fringes. The generated DEM and the estimated building heights are used to indirectly reflect the performance of re-flattening methods. The final results show that compared with mature refinement and re-flattening methods, the DEMs based on the proposed method are more accurate, which reflects that the proposed method has a better performance in the removal of time-varying and uneven residual fringes. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar: Calibration, Analysis, and Application)

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18 pages, 3883 KiB

Open AccessEditor’s ChoiceArticle

Methods of Analyzing the Error and Rectifying the Calibration of a Solar Tracking System for High-Precision Solar Tracking in Orbit

by Yingqiu Shao, Zhanfeng Li, Xiaohu Yang, Yu Huang, Bo Li, Guanyu Lin and Jifeng Li

Remote Sens. 2023, 15(8), 2213; https://doi.org/10.3390/rs15082213 - 21 Apr 2023

Cited by 2 | Viewed by 1062

Abstract

Reliability is the most critical characteristic of space missions, for example in capturing and tracking moving targets. To this end, two methods are designed to track sunlight using solar remote-sensing instruments (SRSIs). The primary method is to use the offset angles of the [...] Read more.

Reliability is the most critical characteristic of space missions, for example in capturing and tracking moving targets. To this end, two methods are designed to track sunlight using solar remote-sensing instruments (SRSIs). The primary method is to use the offset angles of the guide mirror for closed-loop tracking, while the alternative method is to use the sunlight angles, calculated from the satellite attitude, solar vector, and mechanical installation correction parameters, for open-loop tracking. By comprehensively analyzing the error and rectifying the calibration of the solar tracking system, we demonstrate that the absolute value of the azimuth tracking precision is less than 0.0121° and the pitch is less than 0.0037° with the primary method. Correspondingly, they are 0.0992° and 0.0960° with the alternative method. These precisions meet the requirements of SRSIs. In addition, recalibration due to mechanical vibration during the satellite’s launch may invalidate the above methods, leading to the failure of SRSIs. Hence, we propose a dedicated injection parameter strategy to rectify the sunlight angles to capture and track the sunlight successfully. The stable and effective results in the ultraviolet to near-infrared spectrum validate the SRSI’s high-precision sunlight tracking performance. Furthermore, the above methods can also be applied to all orbital inclinations and may provide a solution for capturing and tracking moving targets. Full article

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27 pages, 1518 KiB

Open AccessArticle

TPENAS: A Two-Phase Evolutionary Neural Architecture Search for Remote Sensing Image Classification

by Lei Ao, Kaiyuan Feng, Kai Sheng, Hongyu Zhao, Xin He and Zigang Chen

Remote Sens. 2023, 15(8), 2212; https://doi.org/10.3390/rs15082212 - 21 Apr 2023

Cited by 5 | Viewed by 1580

Abstract

The application of deep learning in remote sensing image classification has been paid more and more attention by industry and academia. However, manually designed remote sensing image classification models based on convolutional neural networks usually require sophisticated expert knowledge. Moreover, it is notoriously [...] Read more.

The application of deep learning in remote sensing image classification has been paid more and more attention by industry and academia. However, manually designed remote sensing image classification models based on convolutional neural networks usually require sophisticated expert knowledge. Moreover, it is notoriously difficult to design a model with both high classification accuracy and few parameters. Recently, neural architecture search (NAS) has emerged as an effective method that can greatly reduce the heavy burden of manually designing models. However, it remains a challenge to search for a classification model with high classification accuracy and few parameters in the huge search space. To tackle this challenge, we propose TPENAS, a two-phase evolutionary neural architecture search framework, which optimizes the model using computational intelligence techniques in two search phases. In the first search phase, TPENAS searches for the optimal depth of the model. In the second search phase, TPENAS searches for the structure of the model from the perspective of the whole model. Experiments on three open benchmark datasets demonstrate that our proposed TPENAS outperforms the state-of-the-art baselines in both classification accuracy and reducing parameters. Full article

(This article belongs to the Topic Computational Intelligence in Remote Sensing)

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16 pages, 14094 KiB

Open AccessArticle

Remote Sensing Image Compression Based on the Multiple Prior Information

by Chuan Fu and Bo Du

Remote Sens. 2023, 15(8), 2211; https://doi.org/10.3390/rs15082211 - 21 Apr 2023

Cited by 2 | Viewed by 1550

Abstract

Learned image compression has achieved a series of breakthroughs for nature images, but there is little literature focusing on high-resolution remote sensing image (HRRSI) datasets. This paper focuses on designing a learned lossy image compression framework for compressing HRRSIs. Considering the local and [...] Read more.

Learned image compression has achieved a series of breakthroughs for nature images, but there is little literature focusing on high-resolution remote sensing image (HRRSI) datasets. This paper focuses on designing a learned lossy image compression framework for compressing HRRSIs. Considering the local and non-local redundancy contained in HRRSI, a mixed hyperprior network is designed to explore both the local and non-local redundancy in order to improve the accuracy of entropy estimation. In detail, a transformer-based hyperprior and a CNN-based hyperprior are fused for entropy estimation. Furthermore, to reduce the mismatch between training and testing, a three-stage training strategy is introduced to refine the network. In this training strategy, the entire network is first trained, and then some sub-networks are fixed while the others are trained. To evaluate the effectiveness of the proposed compression algorithm, the experiments are conducted on an HRRSI dataset. The results show that the proposed algorithm achieves comparable or better compression performance than some traditional and learned image compression algorithms, such as Joint Photographic Experts Group (JPEG) and JPEG2000. At a similar or lower bitrate, the proposed algorithm is about 2 dB higher than the PSNR value of JPEG2000. Full article

(This article belongs to the Special Issue AI-Based Obstacle Detection and Avoidance in Remote Sensing Images)

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16 pages, 6089 KiB

Open AccessArticle

The Ocean Surface Current in the East China Sea Computed by the Geostationary Ocean Color Imager Satellite

by Youzhi Ma, Wenbin Yin, Zheng Guo and Jiliang Xuan

Remote Sens. 2023, 15(8), 2210; https://doi.org/10.3390/rs15082210 - 21 Apr 2023

Cited by 3 | Viewed by 1874

Abstract

High-frequency observations of surface current field data over large areas and long time series are imperative for comprehending sea-air interaction and ocean dynamics. Nonetheless, neither in situ observations nor polar-orbiting satellites can fulfill the requirements necessary for such observations. In recent years, geostationary [...] Read more.

High-frequency observations of surface current field data over large areas and long time series are imperative for comprehending sea-air interaction and ocean dynamics. Nonetheless, neither in situ observations nor polar-orbiting satellites can fulfill the requirements necessary for such observations. In recent years, geostationary satellite data with ultra-high temporal resolution have been increasingly utilized for the computation of surface flow fields. In this paper, the surface flow field in the East China Sea is estimated using maximum cross-correlation, which is the most widely used flow field computation algorithm, based on the total suspended solids (TSS) data acquired from the Geostationary Ocean Color Imager satellite. The inversion results were compared with the modeled tidal current data and the measured tidal elevation data for verification. The results of the verification demonstrated that the mean deviation of the long semiaxis of the tidal ellipse of the inverted M₂ tide is 0.0335 m/s, the mean deviation of the short semiaxis is 0.0276 m/s, and the mean deviation of the tilt angle is 6.89°. Moreover, the spatially averaged flow velocity corresponds with the observed pattern of tidal elevation changes, thus showcasing the field’s significant reliability. Afterward, we calculated the sea surface current fields in the East China Sea for the years 2013 to 2019 and created distribution maps for both climatology and seasonality. The resulting current charts provide an intuitive display of the spatial structure and seasonal variations in the East China Sea circulation. Lastly, we performed a diagnostic analysis on the surface TSS variation mechanism in the frontal zone along the Zhejiang coast, utilizing inverted flow data collected on 3 August 2013, which had a high spatial coverage and complete time series. Our analysis revealed that the intraday variation in TSS in the local surface layer was primarily influenced by tide-induced vertical mixing. The research findings of this article not only provide valuable data support for the study of local ocean dynamics but also verify the reliability of short-period surface flow inversion of high-turbidity waters near the coast using geostationary satellites. Full article

(This article belongs to the Special Issue Recent Advancements in Remote Sensing for Ocean Current)

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22 pages, 8060 KiB

Open AccessArticle

A Hybrid Chlorophyll a Estimation Method for Oligotrophic and Mesotrophic Reservoirs Based on Optical Water Classification

by Xiaoyan Dang, Jun Du, Chao Wang, Fangfang Zhang, Lin Wu, Jiping Liu, Zheng Wang, Xu Yang and Jingxu Wang

Remote Sens. 2023, 15(8), 2209; https://doi.org/10.3390/rs15082209 - 21 Apr 2023

Cited by 1 | Viewed by 1564

Abstract

Low- and medium-resolution satellites have been a relatively mature platform for inland eutrophic water classification and chlorophyll a concentration (Chl-a) retrieval algorithms. However, for oligotrophic and mesotrophic waters in small- and medium-sized reservoirs, problems of low satellite resolution, insufficient water sampling, and higher [...] Read more.

Low- and medium-resolution satellites have been a relatively mature platform for inland eutrophic water classification and chlorophyll a concentration (Chl-a) retrieval algorithms. However, for oligotrophic and mesotrophic waters in small- and medium-sized reservoirs, problems of low satellite resolution, insufficient water sampling, and higher uncertainty in retrieval accuracy exist. In this paper, a hybrid Chl-a estimation method based on spectral characteristics (i.e., remote sensing reflectance (Rrs)) classification was developed for oligotrophic and mesotrophic waters using high-resolution satellite Sentinel-2 (A and B) data. First, 99 samples and quasi-synchronous Sentinel-2 satellite data were collected from four small- and medium-sized reservoirs in central China, and the usability of the Sentinel-2 Rrs data in inland oligotrophic and mesotrophic waters was verified by accurate atmospheric correction. Second, a new optical classification method was constructed based on different water characteristics to classify waters into clear water, phytoplankton-dominated water, and water dominated by phytoplankton and suspended matter together using the thresholds of Rrs490/Rrs560 and Rrs665/Rrs560. The proposed method has a higher classification accuracy compared to other classification methods, and the band-ratio algorithm is simpler and more effective for satellite sensors without NIR bands. Third, given the sensitivity of the empirical method to water variability and the ease of development and implementation, a nonlinear least squares fitted one-dimensional nonlinear function was established based on the selection of the best-fitting spectral indices for different optical water types (OWTs) and compared with other Chl-a estimation algorithms. The validation results showed that the hybrid two-band method had the highest accuracy with squared correlation coefficient, root mean squared difference, mean absolute percentage error, and bias of 0.85, 2.93, 32.42%, and −0.75 mg/m³, respectively, and the results of the residual values further validated the applicability and reliability of the model. Finally, the performance of the classification and estimation algorithms on the four reservoirs was evaluated to obtain images mapping the Chl-a in the reservoirs. In conclusion, this study improves the accuracy of Chl-a estimation for oligotrophic and mesotrophic waters by combining a new classification algorithm with a two-band hybrid model, which is an important contribution to solving the problem of low resolution and high uncertainty in the retrieval of Chl-a in oligotrophic and mesotrophic waters in small- and medium-sized reservoirs and has the potential to be applied to other optically similar oligotrophic and mesotrophic lakes and reservoirs using similar spectrally satellite sensors. Full article

(This article belongs to the Special Issue Advanced Studies in Monitoring Inland Waters through Remote Sensing Techniques)

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16 pages, 20205 KiB

Open AccessArticle

Multi-Scale and Context-Aware Framework for Flood Segmentation in Post-Disaster High Resolution Aerial Images

by Sultan Daud Khan and Saleh Basalamah

Remote Sens. 2023, 15(8), 2208; https://doi.org/10.3390/rs15082208 - 21 Apr 2023

Cited by 2 | Viewed by 1670

Abstract

Floods are the most frequent natural disasters, occurring almost every year around the globe. To mitigate the damage caused by a flood, it is important to timely assess the magnitude of the damage and efficiently conduct rescue operations, deploy security personnel and allocate [...] Read more.

Floods are the most frequent natural disasters, occurring almost every year around the globe. To mitigate the damage caused by a flood, it is important to timely assess the magnitude of the damage and efficiently conduct rescue operations, deploy security personnel and allocate resources to the affected areas. To efficiently respond to the natural disaster, it is very crucial to swiftly obtain accurate information, which is hard to obtain during a post-flood crisis. Generally, high resolution satellite images are predominantly used to obtain post-disaster information. Recently, deep learning models have achieved superior performance in extracting high-level semantic information from satellite images. However, due to the loss of multi-scale and global contextual features, existing deep learning models still face challenges in extracting complete and uninterrupted results. In this work, we proposed a novel deep learning semantic segmentation model that reduces the loss of multi-scale features and enhances global context awareness. Generally, the proposed framework consists of three modules, encoder, decoder and bridge, combined in a popular U-shaped scheme. The encoder and decoder modules of the framework introduce Res-inception units to obtain reliable multi-scale features and employ a bridge module (between the encoder and decoder) to capture global context. To demonstrate the effectiveness of the proposed framework, we perform an evaluation using a publicly available challenging dataset, FloodNet. Furthermore, we compare the performance of the proposed framework with other reference methods. We compare the proposed framework with recent reference models. Quantitative and qualitative results show that the proposed framework outperforms other reference models by an obvious margin. Full article

(This article belongs to the Special Issue Deep Learning Meets Remote Sensing for Earth Observation and Monitoring)

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12 pages, 1715 KiB

Open AccessCommunication

Photosynthetically Active Radiation and Foliage Clumping Improve Satellite-Based NIRv Estimates of Gross Primary Production

by Iolanda Filella, Adrià Descals, Manuela Balzarolo, Gaofei Yin, Aleixandre Verger, Hongliang Fang and Josep Peñuelas

Remote Sens. 2023, 15(8), 2207; https://doi.org/10.3390/rs15082207 - 21 Apr 2023

Cited by 1 | Viewed by 1777

Abstract

Monitoring gross primary production (GPP) is necessary for quantifying the terrestrial carbon balance. The near-infrared reflectance of vegetation (NIRv) has been proven to be a good predictor of GPP. Given that radiation powers photosynthesis, we hypothesized that (i) the addition of photosynthetic photon [...] Read more.

Monitoring gross primary production (GPP) is necessary for quantifying the terrestrial carbon balance. The near-infrared reflectance of vegetation (NIRv) has been proven to be a good predictor of GPP. Given that radiation powers photosynthesis, we hypothesized that (i) the addition of photosynthetic photon flux density (PPFD) information to NIRv would improve estimates of GPP and that (ii) a further improvement would be obtained by incorporating the estimates of radiation distribution in the canopy provided by the foliar clumping index (CI). Thus, we used GPP data from FLUXNET sites to test these possible improvements by comparing the performance of a model based solely on NIRv with two other models, one combining NIRv and PPFD and the other combining NIRv, PPFD and the CI of each vegetation cover type. We tested the performance of these models for different types of vegetation cover, at various latitudes and over the different seasons. Our results demonstrate that the addition of daily radiation information and the clumping index for each vegetation cover type to the NIRv improves its ability to estimate GPP. The improvement was related to foliage organization, given that the foliar distribution in the canopy (CI) affects radiation distribution and use and that radiation drives productivity. Evergreen needleleaf forests are the vegetation cover type with the greatest improvement in GPP estimation after the addition of CI information, likely as a result of their greater radiation constraints. Vegetation type was more determinant of the sensitivity to PPFD changes than latitude or seasonality. We advocate for the incorporation of PPFD and CI into NIRv algorithms and GPP models to improve GPP estimates. Full article

(This article belongs to the Special Issue Remote Sensing Applications for the Biosphere)

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18 pages, 6981 KiB

Open AccessArticle

Combined GPR and Self-Potential Techniques for Monitoring Steel Rebar Corrosion in Reinforced Concrete Structures: A Laboratory Study

by Giacomo Fornasari, Luigi Capozzoli and Enzo Rizzo

Remote Sens. 2023, 15(8), 2206; https://doi.org/10.3390/rs15082206 - 21 Apr 2023

Cited by 1 | Viewed by 2219

Abstract

Steel rebar corrosion is one of the main causes of the deterioration of engineering reinforced structures. Steel rebar in concrete is normally in a non-corroding, passive condition, but these conditions are not always achieved in practice, due to which corrosion of rebars takes [...] Read more.

Steel rebar corrosion is one of the main causes of the deterioration of engineering reinforced structures. Steel rebar in concrete is normally in a non-corroding, passive condition, but these conditions are not always achieved in practice, due to which corrosion of rebars takes place. This degradation has physical consequences, such as decreased ultimate strength and serviceability of engineering concrete structures. This work describes a laboratory test where GPR and SP geophysical techniques were used to detect and monitor the corrosion phenomena. The laboratory tests have been performed with several reinforced concrete samples. The concrete samples were partially submerged in water with a 5% sodium chloride (NaCl) solution. Therefore, an accelerated corrosion phenomenon has been produced by a direct current (DC) power supply along the rebar. The geophysical measurements were performed with a 2.0 GHz centre frequency GPR antenna along several parallel lines on the samples, always being the radar line perpendicular to the rebar axis. The GPR A-scan amplitude signals were elaborated with the Hilbert Transform approach, observing the envelope variations due to the progress of the steel rebar corrosion in each concrete sample. Moreover, Self-Potential acquisitions were carried out on the surface of the concrete sample at the beginning and end of the experiments. Each technique provided specific information, but a data integration method used in the operating system will further improve the overall quality of diagnosis. The collected data were used for an integrated detection approach useful to observe the corrosion evolution along the reinforcement bar. These first laboratory results highlight how the GPR should give a quantitative contribution to the deterioration of reinforced concrete structure. Full article

(This article belongs to the Special Issue Sensing, Processing and Data Fusion for Non-destructive Testing and Earth Observation)

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17 pages, 8483 KiB

Open AccessArticle

Optimizing Observation Plans for Identifying Faxon Fir (Abies fargesii var. Faxoniana) Using Monthly Unmanned Aerial Vehicle Imagery

by Weibo Shi, Xiaohan Liao, Jia Sun, Zhengjian Zhang, Dongliang Wang, Shaoqiang Wang, Wenqiu Qu, Hongbo He, Huping Ye, Huanyin Yue and Torbern Tagesson

Remote Sens. 2023, 15(8), 2205; https://doi.org/10.3390/rs15082205 - 21 Apr 2023

Cited by 1 | Viewed by 1434

Abstract

Faxon fir (Abies fargesii var. faxoniana), as a dominant tree species in the subalpine coniferous forest of Southwest China, has strict requirements regarding the temperature and humidity of the growing environment. Therefore, the dynamic and continuous monitoring of Faxon fir distribution [...] Read more.

Faxon fir (Abies fargesii var. faxoniana), as a dominant tree species in the subalpine coniferous forest of Southwest China, has strict requirements regarding the temperature and humidity of the growing environment. Therefore, the dynamic and continuous monitoring of Faxon fir distribution is very important to protect this highly sensitive ecological environment. Here, we combined unmanned aerial vehicle (UAV) imagery and convolutional neural networks (CNNs) to identify Faxon fir and explored the identification capabilities of multispectral (five bands) and red-green-blue (RGB) imagery under different months. For a case study area in Wanglang Nature Reserve, Southwest China, we acquired monthly RGB and multispectral images on six occasions over the growing season. We found that the accuracy of RGB imagery varied considerably (the highest intersection over union (IoU), 83.72%, was in April and the lowest, 76.81%, was in June), while the accuracy of multispectral imagery was consistently high (IoU > 81%). In April and October, the accuracy of the RGB imagery was slightly higher than that of multispectral imagery, but for the other months, multispectral imagery was more accurate (IoU was nearly 6% higher than those of the RGB imagery for June). Adding vegetation indices (VIs) improved the accuracy of the RGB models during summer, but there was still a gap to the multispectral model. Hence, our results indicate that the optimized time of the year for identifying Faxon fir using UAV imagery is during the peak of the growing season when using a multispectral imagery. During the non-growing season, RGB imagery was no worse or even slightly better than multispectral imagery for Faxon fir identification. Our study can provide guidance for optimizing observation plans regarding data collection time and UAV loads and could further help enhance the utility of UAVs in forestry and ecological research. Full article

(This article belongs to the Special Issue Vegetation Biophysical Variables and Remote Sensing Applications)

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31 pages, 14747 KiB

Open AccessArticle

Mapping and Influencing the Mechanism of CO₂ Emissions from Building Operations Integrated Multi-Source Remote Sensing Data

by You Zhao, Yuan Zhou, Chenchen Jiang and Jinnan Wu

Remote Sens. 2023, 15(8), 2204; https://doi.org/10.3390/rs15082204 - 21 Apr 2023

Viewed by 1673

Abstract

Urbanization has led to rapid growth in energy consumption and CO₂ emissions in the building sector. Building operation emissions (BCEs) are a major part of emissions in the building life cycle. Existing studies have attempted to estimate fine-scale BCEs using remote sensing [...] Read more.

Urbanization has led to rapid growth in energy consumption and CO₂ emissions in the building sector. Building operation emissions (BCEs) are a major part of emissions in the building life cycle. Existing studies have attempted to estimate fine-scale BCEs using remote sensing data. However, there is still a lack of research on estimating long-term BCEs by integrating multi-source remote sensing data and applications in different regions. We selected the Beijing–Tianjin–Hebei (BTH) urban agglomeration and the National Capital Region of Japan (NCRJ) as research areas for this study. We also built multiple linear regression (MLR) models between prefecture-level BCEs and multi-source remote sensing data. The prefecture-level BCEs were downscaled to grid scale at a 1 km² resolution. The estimation results verify the method’s difference and accuracy at different development stages. The multi-scale BCEs showed a continuous growth trend in the BTH urban agglomeration and a significant downward trend in the NCRJ. The decrease in energy intensity and population density were the main factors contributing to the negative growth of BCEs, whereas GDP per capita and urban expansion significantly promoted it. Through our methods and analyses, we contribute to the study of estimating greenhouse gas emissions with remote sensing and exploring the environmental impact of urban growth. Full article

(This article belongs to the Special Issue Remote Sensing Applications for Interaction between Urbanization and Eco-Environment)

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20 pages, 8340 KiB

Open AccessEditor’s ChoiceArticle

Analysis of Spatial and Temporal Criteria for Altimeter Collocation of Significant Wave Height and Wind Speed Data in Deep Waters

by Ricardo M. Campos

Remote Sens. 2023, 15(8), 2203; https://doi.org/10.3390/rs15082203 - 21 Apr 2023

Cited by 1 | Viewed by 1413

Abstract

This paper investigates the spatial and temporal variability of significant wave height (Hs) and wind speed (U10) using altimeter data from the Australian Ocean Data Network (AODN) and buoy data from the National Data Buoy Center (NDBC). The main goal is to evaluate [...] Read more.

This paper investigates the spatial and temporal variability of significant wave height (Hs) and wind speed (U10) using altimeter data from the Australian Ocean Data Network (AODN) and buoy data from the National Data Buoy Center (NDBC). The main goal is to evaluate spatial and temporal criteria for collocating altimeter data to fixed-point positions and to provide practical guidance on altimeter collocation in deep waters. The results show that a temporal criterion of 30 min and a spatial criterion between 25 km and 50 km produce the best results for altimeter collocation, in close agreement with buoy data. Applying a 25 km criterion leads to slightly better error metrics but at the cost of fewer matchups, whereas using 50 km augments the resulting collocated dataset while keeping the differences to buoy measurements very low. Furthermore, the study demonstrates that using the single closest altimeter record to the buoy position leads to worse results compared to the collocation method based on temporal and spatial averaging. The final validation of altimeter data against buoy observations shows an RMSD of 0.21 m, scatter index of 0.09, and correlation coefficient of 0.98 for Hs, confirming the optimal choice of temporal and spatial criteria employed and the high quality of the calibrated AODN altimeter dataset. Full article

(This article belongs to the Special Issue Remote Sensing of Wave Fields under Extreme Weather Conditions (in Tropical and Extra-Tropical Cyclones and Polar Lows))

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19 pages, 14731 KiB

Open AccessArticle

Quantitative Assessment of Apple Mosaic Disease Severity Based on Hyperspectral Images and Chlorophyll Content

by Yanfu Liu, Yu Zhang, Danyao Jiang, Zijuan Zhang and Qingrui Chang

Remote Sens. 2023, 15(8), 2202; https://doi.org/10.3390/rs15082202 - 21 Apr 2023

Cited by 13 | Viewed by 2017

Abstract

The infection of Apple mosaic virus (ApMV) can severely damage the cellular structure of apple leaves, leading to a decrease in leaf chlorophyll content (LCC) and reduced fruit yield. In this study, we propose a novel method that utilizes hyperspectral imaging (HSI) technology [...] Read more.

The infection of Apple mosaic virus (ApMV) can severely damage the cellular structure of apple leaves, leading to a decrease in leaf chlorophyll content (LCC) and reduced fruit yield. In this study, we propose a novel method that utilizes hyperspectral imaging (HSI) technology to non-destructively monitor ApMV-infected apple leaves and predict LCC as a quantitative indicator of disease severity. LCC data were collected from 360 ApMV-infected leaves, and optimal wavelengths were selected using competitive adaptive reweighted sampling algorithms. A high-precision LCC inversion model was constructed based on Boosting and Stacking strategies, with a validation set

R_{v}^{2}

of 0.9644, outperforming traditional ensemble learning models. The model was used to invert the LCC distribution image and calculate the average and coefficient of variation (CV) of LCC for each leaf. Our findings indicate that the average and CV of LCC were highly correlated with disease severity, and their combination with sensitive wavelengths enabled the accurate identification of disease severity (validation set overall accuracy = 98.89%). Our approach considers the role of plant chemical composition and provides a comprehensive evaluation of disease severity at the leaf scale. Overall, our study presents an effective way to monitor and evaluate the health status of apple leaves, offering a quantifiable index of disease severity that can aid in disease prevention and control. Full article

(This article belongs to the Special Issue Application of Hyperspectral Imagery in Precision Agriculture)

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22 pages, 5175 KiB

Open AccessArticle

Climate-Adaptive Potential Crops Selection in Vulnerable Agricultural Lands Adjacent to the Jamuna River Basin of Bangladesh Using Remote Sensing and a Fuzzy Expert System

by Kazi Faiz Alam and Tofael Ahamed

Remote Sens. 2023, 15(8), 2201; https://doi.org/10.3390/rs15082201 - 21 Apr 2023

Cited by 1 | Viewed by 1566

Abstract

Agricultural crop production was affected worldwide due to the variability of weather causing floods or droughts. In climate change impacts, flood becomes the most devastating in deltaic regions due to the inundation of crops within a short period of time. Therefore, the aim [...] Read more.

Agricultural crop production was affected worldwide due to the variability of weather causing floods or droughts. In climate change impacts, flood becomes the most devastating in deltaic regions due to the inundation of crops within a short period of time. Therefore, the aim of this study was to propose climate-adaptive crops that are suitable for the flood inundation in risk-prone areas of Bangladesh. The research area included two districts adjacent to the Jamuna River in Bangladesh, covering an area of 5489 km², and these districts were classified as highly to moderately vulnerable due to inundation by flood water during the seasonal monsoon time. In this study, first, an inundation vulnerability map was prepared from the multicriteria analysis by applying a fuzzy expert system in the GIS environment using satellite remote sensing datasets. Among the analyzed area, 42.3% was found to be highly to moderately vulnerable, 42.1% was marginally vulnerable and 15.6% was not vulnerable to inundation. Second, the most vulnerable areas for flooding were identified from the previous major flood events and cropping practices based on the crop calendar. Based on the crop adaptation suitability analysis, two cash crops, sugarcane and jute, were recommended for cultivation during major flooding durations. Finally, a land suitability analysis was conducted through multicriteria analysis applying a fuzzy expert system. According to our analysis, 28.6% of the land was highly suitable, 27.9% was moderately suitable, 19.7% was marginally suitable and 23.6% of the land was not suitable for sugarcane and jute cultivation in the vulnerable areas. The inundation vulnerability and suitability analysis proposed two crops, sugarcane and jute, as potential candidates for climate-adaptive selection in risk-prone areas. Full article

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23 pages, 35922 KiB

Open AccessArticle

MUREN: MUltistage Recursive Enhanced Network for Coal-Fired Power Plant Detection

by Shuai Yuan, Juepeng Zheng, Lixian Zhang, Runmin Dong, Ray C. C. Cheung and Haohuan Fu

Remote Sens. 2023, 15(8), 2200; https://doi.org/10.3390/rs15082200 - 21 Apr 2023

Cited by 1 | Viewed by 1063

Abstract

The accurate detection of coal-fired power plants (CFPPs) is meaningful for environmental protection, while challenging. The CFPP is a complex combination of multiple components with varying layouts, unlike clearly defined single objects, such as vehicles. CFPPs are typically located in industrial districts with [...] Read more.

The accurate detection of coal-fired power plants (CFPPs) is meaningful for environmental protection, while challenging. The CFPP is a complex combination of multiple components with varying layouts, unlike clearly defined single objects, such as vehicles. CFPPs are typically located in industrial districts with similar backgrounds, further complicating the detection task. To address this issue, we propose a MUltistage Recursive Enhanced Detection Network (MUREN) for accurate and efficient CFPP detection. The effectiveness of MUREN lies in the following: First, we design a symmetrically enhanced module, including a spatial-enhanced subnetwork (SEN) and a channel-enhanced subnetwork (CEN). SEN learns the spatial relationships to obtain spatial context information. CEN provides adaptive channel recalibration, restraining noise disturbance and highlighting CFPP features. Second, we use a recursive construction set on top of feature pyramid networks to receive features more than once, strengthening feature learning for relatively small CFPPs. We conduct comparative and ablation experiments in two datasets and apply MUREN to the Pearl River Delta region in Guangdong province for CFPP detection. The comparative experiment results show that MUREN improves the mAP by 5.98% compared with the baseline method and outperforms by 4.57–21.38% the existing cutting-edge detection methods, which indicates the promising potential of MUREN in large-scale CFPP detection scenarios. Full article

(This article belongs to the Special Issue Artificial Intelligence-Driven Methods for Remote Sensing Target and Object Detection)

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10 pages, 2827 KiB

Open AccessTechnical Note

Blind Spots Analysis of Magnetic Tensor Localization Method

by Lei Xu, Xianyuan Huang, Zhonghua Dai, Fuli Yuan, Xu Wang and Jinyu Fan

Remote Sens. 2023, 15(8), 2199; https://doi.org/10.3390/rs15082199 - 21 Apr 2023

Viewed by 979

Abstract

In order to compare and analyze the positioning efficiency of the magnetic tensor location method, this paper studies the blind spots of the magnetic tensor location method. By constructing two magnetic tensor localization models, the localization principles of the single-point magnetic tensor localization [...] Read more.

In order to compare and analyze the positioning efficiency of the magnetic tensor location method, this paper studies the blind spots of the magnetic tensor location method. By constructing two magnetic tensor localization models, the localization principles of the single-point magnetic tensor localization method (STLM) and the two-point magnetic tensor linear localization method (TTLM) are analyzed. Furthermore, the eigenvalue analysis method is studied to analyze the blind spots of STLM, and the spherical analysis method is proposed to analyze the blind spots of TTLM. The results show that when the direction of any measuring point is perpendicular to the direction of the target magnetic moment, blind spots of STLM appear. However, TTLM still has good positioning performance in the blind spot. Full article

(This article belongs to the Special Issue Satellite Missions for Magnetic Field Analysis)

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19 pages, 16851 KiB

Open AccessArticle

Dynamic Effects of Atmosphere over and around the Tibetan Plateau on the Sustained Drought in Southwest China from 2009 to 2014

by Yiwei Ye, Rongxiang Tian and Zhan Jin

Remote Sens. 2023, 15(8), 2198; https://doi.org/10.3390/rs15082198 - 21 Apr 2023

Cited by 1 | Viewed by 1229

Abstract

The two westerly branches have a significant impact on the climate of the area on the eastern side of the Tibetan Plateau when flowing around it. A continuous drought event in Southwest China from the winter of 2009 to the spring of 2014 [...] Read more.

The two westerly branches have a significant impact on the climate of the area on the eastern side of the Tibetan Plateau when flowing around it. A continuous drought event in Southwest China from the winter of 2009 to the spring of 2014 caused huge economic losses. This research focuses on the dynamic field anomalies over the Tibetan Plateau during this event using statistical analysis, attempts to decipher its mechanism on drought in Southwest China, and provides a regression model. We established that the anticyclone and downdraft over the Tibetan Plateau were weaker than usual during the drought, which would reduce the southward cold airflow on the northeast of the Tibetan Plateau and strengthen the west wind from dry central Asia on the south of the plateau. As a result, a larger area of the southwest region in China was controlled by the warm and dry air mass, which was acting against precipitation. The results will be of reference value to the drought forecast for Southwest China, and also encourage further research about how the Tibetan Plateau influence the climate on its eastern side. Full article

(This article belongs to the Special Issue Land-Atmosphere Interactions and Effects on the Climate of the Tibetan Plateau and Surrounding Regions II)

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16 pages, 20442 KiB

Open AccessArticle

Geomatic Data Fusion for 3D Tree Modeling: The Case Study of Monumental Chestnut Trees

by Mattia Balestra, Enrico Tonelli, Alessandro Vitali, Carlo Urbinati, Emanuele Frontoni and Roberto Pierdicca

Remote Sens. 2023, 15(8), 2197; https://doi.org/10.3390/rs15082197 - 21 Apr 2023

Cited by 6 | Viewed by 2192

Abstract

In recent years, advancements in remote and proximal sensing technology have driven innovation in environmental and land surveys. The integration of various geomatics devices, such as reflex and UAVs equipped with RGB cameras and mobile laser scanners (MLS), allows detailed and precise surveys [...] Read more.

In recent years, advancements in remote and proximal sensing technology have driven innovation in environmental and land surveys. The integration of various geomatics devices, such as reflex and UAVs equipped with RGB cameras and mobile laser scanners (MLS), allows detailed and precise surveys of monumental trees. With these data fusion method, we reconstructed three monumental 3D tree models, allowing the computation of tree metric variables such as diameter at breast height (DBH), total height (TH), crown basal area (CBA), crown volume (CV) and wood volume (WV), even providing information on the tree shape and its overall conditions. We processed the point clouds in software such as CloudCompare, 3D Forest, R and MATLAB, whereas the photogrammetric processing was conducted with Agisoft Metashape. Three-dimensional tree models enhance accessibility to the data and allow for a wide range of potential applications, including the development of a tree information model (TIM), providing detailed data for monitoring tree health, growth, biomass and carbon sequestration. The encouraging results provide a basis for extending the virtualization of these monumental trees to a larger scale for conservation and monitoring. Full article

(This article belongs to the Special Issue 3D Modelling and Mapping for Precision Agriculture)

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24 pages, 8835 KiB

Open AccessArticle

Monitoring and Forecasting Green Tide in the Yellow Sea Using Satellite Imagery

by Shuwen Xu, Tan Yu, Jinmeng Xu, Xishan Pan, Weizeng Shao, Juncheng Zuo and Yang Yu

Remote Sens. 2023, 15(8), 2196; https://doi.org/10.3390/rs15082196 - 21 Apr 2023

Viewed by 1657

Abstract

This paper proposes a semi-automatic green tide extraction method based on the NDVI to extract Yellow Sea green tides from 2008 to 2022 using remote sensing (RS) images from multiple satellites: GF-1, Landsat 5 TM, Landsat 8 OLI_TIRS, HJ-1A/B, HY-1C, and MODIS. The [...] Read more.

This paper proposes a semi-automatic green tide extraction method based on the NDVI to extract Yellow Sea green tides from 2008 to 2022 using remote sensing (RS) images from multiple satellites: GF-1, Landsat 5 TM, Landsat 8 OLI_TIRS, HJ-1A/B, HY-1C, and MODIS. The results of the accuracy assessment based on three indicators: Precision, Recall, and F1-score, showed that our extraction method can be applied to the images of most satellites and different environments. We traced the source of the Yellow Sea green tide to Jiangsu Subei shoal and the southeastern Yellow Sea and earliest advanced the tracing time to early April. The Gompertz and Logistic growth curve models were selected to predict and monitor the extent and duration of the Yellow Sea green tide, and uncertainty for the predicted growth curve was estimated. The prediction for 2022 was that its start and dissipation dates were expected to be June 1 and August 15, respectively, and the accumulative cover area was expected to be approximately 1190.90–1191.21 km². Full article

(This article belongs to the Special Issue Advanced Applications of Remote Sensing in Monitoring Marine Environment)

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17 pages, 3621 KiB

Open AccessArticle

A Spectral Library Study of Mixtures of Common Lunar Minerals and Glass

by Xiaoyi Hu, Te Jiang, Pei Ma, Hao Zhang, Paul Lucey and Menghua Zhu

Remote Sens. 2023, 15(8), 2195; https://doi.org/10.3390/rs15082195 - 21 Apr 2023

Cited by 3 | Viewed by 1691

Abstract

Reflectance spectroscopy is a powerful tool to remotely identify the mineral and chemical compositions of the lunar regolith. The lunar soils contain silicate minerals with prominent absorption features and glasses with much less distinctive spectral features. The accuracy of mineral abundance retrieval may [...] Read more.

Reflectance spectroscopy is a powerful tool to remotely identify the mineral and chemical compositions of the lunar regolith. The lunar soils contain silicate minerals with prominent absorption features and glasses with much less distinctive spectral features. The accuracy of mineral abundance retrieval may be affected by the presence of glasses. In this work, we construct a spectral library of mixtures of major lunar-type minerals and synthetic glasses with varying relative abundances and test their performance on mineral abundance retrievals. By matching the library spectra with the spectra of mineral mixtures with known abundances, we found that the accuracy of mineral abundance retrieval can be improved by including glass as an endmember. Although our method cannot identify the abundance of glasses quantitatively, the presence or absence of glasses in the mixtures can be decisively determined. Full article

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19 pages, 21341 KiB

Open AccessArticle

A Phase Difference Measurement Method for Integrated Optical Interferometric Imagers

by Jialiang Chen, Qinghua Yu, Ben Ge, Chuang Zhang, Yan He and Shengli Sun

Remote Sens. 2023, 15(8), 2194; https://doi.org/10.3390/rs15082194 - 21 Apr 2023

Viewed by 1612

Abstract

Interferometric imagers based on integrated optics have the advantages of miniaturization and low cost compared with traditional telescope imaging systems and are expected to be applied in the field of space target detection. Phase measurement of the complex coherence factor is crucial for [...] Read more.

Interferometric imagers based on integrated optics have the advantages of miniaturization and low cost compared with traditional telescope imaging systems and are expected to be applied in the field of space target detection. Phase measurement of the complex coherence factor is crucial for the image reconstruction of interferometric imaging technology. This study discovers the effect of the phase of the complex coherence factor on the extrema of the interference fringes in the interferometric imager and proposes a method for calculating the phase difference of the complex coherence factor of two interference signals by comparing the extrema of the interferometric fringes in the area of approximate linear change in the envelope shape to obtain the phase information required for imaging. Experiments using two interferometric signals with a phase difference of

π

were conducted to verify the validity and feasibility of the phase difference measurement method. Compared with the existing phase measurement methods, this method does not need to calibrate the position of the zero optical path difference and can be applied to the integrated optical interferometric imager using a single-mode fiber, which also allows the imager to work in a more flexible way. The theoretical phase measurement accuracy of this method is higher than 0.05

π

, which meets the image reconstruction requirements. Full article

(This article belongs to the Special Issue Laser and Optical Remote Sensing for Planetary Exploration)

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20 pages, 11363 KiB

Open AccessArticle

An Earth Observation Task Representation Model Supporting Dynamic Demand for Flood Disaster Monitoring and Management

by Zhongguo Zhao, Chuli Hu, Ke Wang, Yixiao Zhang, Zhangyan Xu and Xuan Ding

Remote Sens. 2023, 15(8), 2193; https://doi.org/10.3390/rs15082193 - 21 Apr 2023

Cited by 1 | Viewed by 1730

Abstract

A comprehensive, accurate, and timely expression of earth observation (EO) tasks is the primary prerequisite for the response to and the emergency monitoring of disasters, especially floods. However, the existing information model does not fully satisfy the demand for a fine-grain observation expression [...] Read more.

A comprehensive, accurate, and timely expression of earth observation (EO) tasks is the primary prerequisite for the response to and the emergency monitoring of disasters, especially floods. However, the existing information model does not fully satisfy the demand for a fine-grain observation expression of EO task, which results in the absence of task process management. The current study proposed an EO task representation model based on meta-object facility to address this problem. The model not only describes the static information of a task, but it also defines the dynamics of an observation task by introducing a functional metamodel. This metamodel describes the full life cycle of a task; it comprises five process methods: birth, separation, combination, updating, and extinction. An earth observation task modeling and management prototype system (EO-TMMS) for conducting a remote sensing satellite sensor observation task representation experiment on flooding was developed. In accordance with the results, the proposed model can describe various EO tasks demands and the full life cycle process of an EO task. Compared with other typical observation task information models, the proposed model satisfies the dynamic and fine-grain process representation of EO tasks, which can improve the efficiency of EO sensor utilization. Full article

(This article belongs to the Special Issue Advances in Earth Observation to Improve Flood Disaster Monitoring and Management)

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27 pages, 8754 KiB

Open AccessArticle

Multiscale Entropy-Based Surface Complexity Analysis for Land Cover Image Semantic Segmentation

by Lianfa Li, Zhiping Zhu and Chengyi Wang

Remote Sens. 2023, 15(8), 2192; https://doi.org/10.3390/rs15082192 - 21 Apr 2023

Viewed by 1665

Abstract

Recognizing and classifying natural or artificial geo-objects under complex geo-scenes using remotely sensed data remains a significant challenge due to the heterogeneity in their spatial distribution and sampling bias. In this study, we propose a deep learning method of surface complexity analysis based [...] Read more.

Recognizing and classifying natural or artificial geo-objects under complex geo-scenes using remotely sensed data remains a significant challenge due to the heterogeneity in their spatial distribution and sampling bias. In this study, we propose a deep learning method of surface complexity analysis based on multiscale entropy. This method can be used to reduce sampling bias and preserve entropy-based invariance in learning for the semantic segmentation of land use and land cover (LULC) images. Our quantitative models effectively identified and extracted local surface complexity scores, demonstrating their broad applicability. We tested our method using the Gaofen-2 image dataset in mainland China and accurately estimated multiscale complexity. A downstream evaluation revealed that our approach achieved similar or better performance compared to several representative state-of-the-art deep learning methods. This highlights the innovative and significant contribution of our entropy-based complexity analysis and its applicability in improving LULC semantic segmentations through optimal stratified sampling and constrained optimization, which can also potentially be used to enhance semantic segmentation under complex geo-scenes using other machine learning methods. Full article

(This article belongs to the Section Remote Sensing Image Processing)

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22 pages, 13555 KiB

Open AccessArticle

Effects of Directional Wave Spectra on the Modeling of Ocean Radar Backscatter at Various Azimuth Angles by a Modified Two-Scale Method

by Qiushuang Yan, Yuqi Wu, Chenqing Fan, Junmin Meng, Tianran Song and Jie Zhang

Remote Sens. 2023, 15(8), 2191; https://doi.org/10.3390/rs15082191 - 20 Apr 2023

Viewed by 1531

Abstract

Knowledge of the ocean backscatter at various azimuth angles is critical to the radar detection of the ocean environment. In this study, the modified two-scale model (TSM), which introduces a correction term in the conventional TSM, is improved based on the empirical model, [...] Read more.

Knowledge of the ocean backscatter at various azimuth angles is critical to the radar detection of the ocean environment. In this study, the modified two-scale model (TSM), which introduces a correction term in the conventional TSM, is improved based on the empirical model, CMOD5.n. Then, the influences of different directional wave spectra on the prediction of azimuthal behavior of ocean radar backscatter are investigated by comparing the simulated results with CMOD5.n and the Advanced Scatterometer (ASCAT) measurements. The results show that the overall performance of the single spectra of D, A, E, and H18 and the composite spectra of AH18 and AEH18 in predicting ocean backscatter are different at different wind speeds and incidence angles. Generally, the AH18 spectrum has better performance at low and moderate wind speeds, while the A spectrum works better at high wind speed. Nevertheless, the wave spectra have little effect on the prediction of the azimuthal fluctuation of scattering, which is highly dependent on the directional spreading function. The relative patterns of azimuthal undulation produced by different spreading functions are rather different at different wind speeds, but similar under different incidence angles. The Gaussian spreading function generally has better performance in predicting the azimuthal fluctuation of scattering. Full article

(This article belongs to the Section Ocean Remote Sensing)

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17 pages, 4605 KiB

Open AccessArticle

Epoch-Wise Estimation and Analysis of GNSS Receiver DCB under High and Low Solar Activity Conditions

by Xiao Zhang, Linyuan Xia, Hong Lin and Qianxia Li

Remote Sens. 2023, 15(8), 2190; https://doi.org/10.3390/rs15082190 - 20 Apr 2023

Cited by 1 | Viewed by 1241

Abstract

Differential code bias (DCB) is one of the main errors involved in ionospheric total electron content (TEC) retrieval using a global navigation satellite system (GNSS). It is typically assumed to be constant over time. However, this assumption is not always valid because receiver [...] Read more.

Differential code bias (DCB) is one of the main errors involved in ionospheric total electron content (TEC) retrieval using a global navigation satellite system (GNSS). It is typically assumed to be constant over time. However, this assumption is not always valid because receiver DCBs have long been known to exhibit apparent intraday variations. In this paper, a combined method is introduced to estimate the epoch-wise receiver DCB, which is divided into two parts: the receiver DCB at the initial epoch and its change with respect to the initial value. In the study, this method was proved feasible by subsequent experiments and was applied to analyze the possible reason for the intraday receiver DCB characteristics of 200 International GNSS Service (IGS) stations in 2014 (high solar activity) and 2017 (low solar activity). The results show that the proportion of intraday receiver DCB stability less than 1 ns increased from 72.5% in 2014 to 87% in 2017, mainly owing to the replacement of the receiver hardware in stations. Meanwhile, the intraday receiver DCB estimates in summer generally exhibited more instability than those in other seasons. Although more than 90% of the stations maintained an intraday receiver DCB stability within 2 ns, substantial variations with a peak-to-peak range of 5.78 ns were detected for certain stations, yielding an impact of almost 13.84 TECU on the TEC estimates. Moreover, the intraday variability of the receiver DCBs is related to the receiver environment temperature. Their correlation coefficient (greater than 0.5 in our analyzed case) increases with the temperature. By contrast, the receiver firmware version does not exert a great impact on the intraday variation characteristics of the receiver DCB in this case. Full article

(This article belongs to the Special Issue Beidou/GNSS Positioning, Navigation and Timing: Methods and Technology)

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37 pages, 11631 KiB

Open AccessArticle

Determination of Accurate Dynamic Topography for the Baltic Sea Using Satellite Altimetry and a Marine Geoid Model

by Majid Mostafavi, Nicole Delpeche-Ellmann, Artu Ellmann and Vahidreza Jahanmard

Remote Sens. 2023, 15(8), 2189; https://doi.org/10.3390/rs15082189 - 20 Apr 2023

Cited by 4 | Viewed by 1641

Abstract

Accurate determination of dynamic topography (DT) is expected to quantify a realistic sea surface with respect to its vertical datum and in identifying sub-mesoscale features of ocean dynamics. This study explores a method that derives DT by using satellite altimetry (SA) in conjunction [...] Read more.

Accurate determination of dynamic topography (DT) is expected to quantify a realistic sea surface with respect to its vertical datum and in identifying sub-mesoscale features of ocean dynamics. This study explores a method that derives DT by using satellite altimetry (SA) in conjunction with a high-resolution marine geoid model. To assess the method, DT was computed using along-track SA from Sentinel- 3A (S3A), Sentinel-3B (S3B), and Jason-3 (JA3), then compared with DT derived from a tide-gauge-corrected hydrodynamic model (HDM) for the period 2017–2019 over the Baltic Sea. Comparison of SA-derived DT and corrected HDM showed average discrepancies in the range of ±20 cm, with root mean square errors of 9 cm (for S3B) and 6 cm (for S3A and JA6) and a standard deviation between 2 and 16 cm. Inter-comparisons between data sources and multi-mission SA over the Baltic Sea also potentially identified certain persistent and semi-persistent problematic areas that are either associated with deficiencies in the geoid, tide gauge, HDM, and SA or a combination of all of these. In addition, it was observed that SA data have the potential to show a more realistic (detailed) variation of DT compared to HDM, which tended to generate only a smooth (low-pass) surface and underestimate DT. Full article

(This article belongs to the Special Issue Satellite Altimetry: Technology and Application in Geodesy)

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19 pages, 3922 KiB

Open AccessArticle

Improving the Detection Accuracy of Underwater Obstacles Based on a Novel Combined Method of Support Vector Regression and Gravity Gradient

by Tengda Fu, Wei Zheng, Zhaowei Li, Yifan Shen, Huizhong Zhu and Aigong Xu

Remote Sens. 2023, 15(8), 2188; https://doi.org/10.3390/rs15082188 - 20 Apr 2023

Viewed by 1533

Abstract

Underwater gravity gradient detection techniques are conducive to ensuring the safety of submersible sailing. In order to improve the accuracy of underwater obstacle detection based on gravity gradient detection technology, this paper studies the gravity gradient underwater obstacle detection method based on the [...] Read more.

Underwater gravity gradient detection techniques are conducive to ensuring the safety of submersible sailing. In order to improve the accuracy of underwater obstacle detection based on gravity gradient detection technology, this paper studies the gravity gradient underwater obstacle detection method based on the combined support vector regression (SVR) algorithm. First, the gravity gradient difference ratio (GGDR) equation, which is only related to the obstacle’s position, is obtained based on the gravity gradient equation by using the difference and ratio methods. Aiming at solving the shortcomings of the GGDR equation based on Newton–Raphson method (NRM), combined with SVR algorithm, a novel SVR–gravity gradient joint method (SGJM) is proposed. Second, the differential ratio dataset is constructed by simulating the gravity gradient data generated by obstacles, and the obstacle location model is trained using SVR. Four measuring lines were selected to verify the SVR-based positioning model. The verification results show that the mean absolute error of the new method in the x, y, and z directions is less than 5.39 m, the root-mean-square error is less than 7.58 m, and the relative error is less than 4% at a distance of less than 500 m. These evaluation metrics validate the reliability of the novel SGJM-based detection of underwater obstacles. Third, comparative experiments based on the novel SGJM and traditional NRM were carried out. The experimental results show that the positioning accuracy of x and z directions in the obstacle’s position calculation based on the novel SGJM is improved by 88% and 85%, respectively. Full article

(This article belongs to the Special Issue Remote Sensing in Space Geodesy and Cartography Methods II)

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21 pages, 98313 KiB

Open AccessArticle

Hybrid BBO-DE Optimized SPAARCTree Ensemble for Landslide Susceptibility Mapping

by Duc Anh Hoang, Hung Van Le, Dong Van Pham, Pham Viet Hoa and Dieu Tien Bui

Remote Sens. 2023, 15(8), 2187; https://doi.org/10.3390/rs15082187 - 20 Apr 2023

Viewed by 1389

Abstract

This paper presents a new hybrid ensemble modeling method called BBO-DE-STreeEns for land-slide susceptibility mapping in Than Uyen district, Vietnam. The method uses subbagging and random subspacing to generate subdatasets for constituent classifiers of the ensemble model, and a split-point and attribute reduced [...] Read more.

This paper presents a new hybrid ensemble modeling method called BBO-DE-STreeEns for land-slide susceptibility mapping in Than Uyen district, Vietnam. The method uses subbagging and random subspacing to generate subdatasets for constituent classifiers of the ensemble model, and a split-point and attribute reduced classifier (SPAARC) decision tree algorithm to build each classifier. To optimize hyperparameters of the ensemble model, a hybridization of biogeography-based optimization (BBO) and differential evolution (DE) algorithms is adopted. The land-slide database for the study area includes 114 landslide locations, 114 non-landslide locations, and ten influencing factors: elevation, slope, curvature, aspect, relief amplitude, soil type, geology, distance to faults, distance to roads, and distance to rivers. The database was used to build and verify the BBO-DE-StreeEns model, and standard statistical metrics, namely, positive predictive value (PPV), negative predictive value (NPV), sensitivity (Sen), specificity (Spe), accuracy (Acc), Fscore, Cohen’s Kappa, and the area under the ROC curve (AUC), were calculated to evaluate prediction power. Logistic regression, multi-layer perceptron neural network, support vector machine, and SPAARC were used as benchmark models. The results show that the proposed model outperforms the benchmarks with a high prediction power (PPV = 90.3%, NPV = 83.8%, Sen = 82.4%, Spe = 91.2%, Acc = 86.8%, Fscore = 0.862, Kappa = 0.735, and AUC = 0.940). Therefore, the BBO-DE-StreeEns method is a promising tool for landslide susceptibility mapping. Full article

(This article belongs to the Special Issue Assessing Natural Hazards through Advanced Machine Learning Methods and Remote Sensing Technology II)

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