Remote Sensing

22 pages, 6259 KiB

Open AccessArticle

A Hypered Deep-Learning-Based Model of Hyperspectral Images Generation and Classification for Imbalanced Data

by Hasan A. H. Naji, Tianfeng Li, Qingji Xue and Xindong Duan

Remote Sens. 2022, 14(24), 6406; https://doi.org/10.3390/rs14246406 - 19 Dec 2022

Cited by 6 | Viewed by 2616

Recently, hyperspectral image (HSI) classification has become a hot topic in the geographical images research area. Sufficient samples are required for image classes to properly train classification models. However, a class imbalance problem has emerged in hyperspectral image (HSI) datasets as some classes [...] Read more.

Recently, hyperspectral image (HSI) classification has become a hot topic in the geographical images research area. Sufficient samples are required for image classes to properly train classification models. However, a class imbalance problem has emerged in hyperspectral image (HSI) datasets as some classes do not have enough samples for training, and some classes have many samples. Therefore, the performance of classifiers is likely to be biased toward the classes with the largest samples, and this can lead to a decrease in the classification accuracy. Therefore, a new deep-learning-based model is proposed for hyperspectral images generation and classification of imbalanced data. Firstly, the spectral features are extracted by a 1D convolutional neural network, whereas a 2D convolutional neural network extracts the spatial features and the extracted spatial features and spectral features are catenated into a stacked spatial–spectral feature vector. Secondly, an autoencoder model was developed to generate synthetic images for minority classes, and the image samples were balanced. The GAN model is applied to determine the synthetic images from the real ones and then enhancing the classification performance. Finally, the balanced datasets are fed to a 2D CNN model for performing classification and validating the efficiency of the proposed model. Our model and the state-of-the-art classifiers are evaluated by four open-access HSI datasets. The results showed that the proposed approach can generate better quality samples for rebalancing datasets, which in turn noticeably enhances the classification performance compared to the existing classification models. Full article

(This article belongs to the Special Issue Hyperspectral Images Processing and Classification Using Artificial Intelligence (AI) Techniques)

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17 pages, 5154 KiB

Open AccessArticle

Surface Albedo and Snowline Altitude Estimation Using Optical Satellite Imagery and In Situ Measurements in Muz Taw Glacier, Sawir Mountains

by Fengchen Yu, Puyu Wang and Hongliang Li

Remote Sens. 2022, 14(24), 6405; https://doi.org/10.3390/rs14246405 - 19 Dec 2022

Cited by 3 | Viewed by 2224

Abstract

Glacier surface albedo strongly affects glacier mass balance by controlling the glacier surface energy budget. As an indicator of the equilibrium line altitude (ELA), the glacier snowline altitude (SLA) at the end of the melt season can reflect variations in the glacier mass [...] Read more.

Glacier surface albedo strongly affects glacier mass balance by controlling the glacier surface energy budget. As an indicator of the equilibrium line altitude (ELA), the glacier snowline altitude (SLA) at the end of the melt season can reflect variations in the glacier mass balance. Therefore, it is extremely crucial to investigate the changes of glacier surface albedo and glacier SLA for calculating and evaluating glacier mass loss. In this study, from 2011 to 2021, the surface albedo of the Muz Taw Glacier was derived from Landsat images with a spatial resolution of 30 m and from the Moderate Resolution Imaging Spectroradiometer albedo products (MOD10A1) with a temporal resolution of 1 day, which was verified through the albedo measured by the Automatic Weather Station (AWS) installed in the glacier. Moreover, the glacier SLA was determined based on the variation in the surface albedo, with the altitude change along the glacier main flowline derived from the Landsat image at the end of the melt season. The correlation coefficient of >0.7, with a risk of error lower than 5%, between the surface albedo retrieved from remote sensing images and the in situ measurement data indicated that the method of deriving the glacier surface albedo by the remote sensing method was reliable. The annual average albedo showed a slight upward trend (0.24%) from 2011 to 2021. A unimodal seasonal variation in albedo was demonstrated, with the downward trend from January to August and the upward trend from August to December. The spatial distribution of the albedo was not entirely dependent on altitude due to the dramatic effects of the topography and glacier surface conditions. The average SLA was 3446 m a.s.l., with a variation of 160 m from 2011 to 2021. The correlation analysis between the glacier SLA and annual mean temperature/annual precipitation demonstrated that the variations of the average SLA on the Muz Taw Glacier was primarily affected by the air temperature. This study improved our understanding of the ablation process and mechanism of the Muz Taw Glacier. Full article

(This article belongs to the Special Issue The Cryosphere Observations Based on Using Remote Sensing Techniques II)

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24 pages, 6412 KiB

Open AccessArticle

Regional Satellite Algorithms to Estimate Chlorophyll-a and Total Suspended Matter Concentrations in Vembanad Lake

by Varunan Theenathayalan, Shubha Sathyendranath, Gemma Kulk, Nandini Menon, Grinson George, Anas Abdulaziz, Nick Selmes, Robert J. W. Brewin, Anju Rajendran, Sara Xavier and Trevor Platt

Remote Sens. 2022, 14(24), 6404; https://doi.org/10.3390/rs14246404 - 19 Dec 2022

Cited by 5 | Viewed by 4336

Abstract

A growing coastal population is leading to increased anthropogenic pollution that greatly affects coastal and inland water bodies, especially in the tropics. The Sustainable Development Goal-14, ‘Life below water’ emphasises the importance of conservation and sustainable use of the ocean and its resources. [...] Read more.

A growing coastal population is leading to increased anthropogenic pollution that greatly affects coastal and inland water bodies, especially in the tropics. The Sustainable Development Goal-14, ‘Life below water’ emphasises the importance of conservation and sustainable use of the ocean and its resources. Pollution management practices often include monitoring of water quality using in situ observations of chlorophyll-a (chl-a) and total suspended matter (TSM). Satellite technology, including the MultiSpectral Instrument (MSI) sensor onboard Sentinel-2, enables the continuous monitoring of these variables in inland waters at high spatial and temporal resolutions. To improve the monitoring of water quality in the tropical Vembanad-Kol-Wetland (VKW) system, situated on the southwest coast of India, we present two regionally tuned satellite algorithms developed to estimate chl-a and TSM concentrations. The new algorithms estimate the chl-a and TSM concentrations from the simulated reflectance values as a function of the inherent optical properties using a forward modelling approach. The model was parameterised using the National Aeronautics and Space Administration (NASA) bio-Optical Marine Algorithm Dataset (NOMAD) and in situ measurements collected in the VKW system. To assess model performance, results were compared with in situ measurements of chl-a and TSM and other existing satellite-based models of chl-a and TSM. For satellite application, two different atmospheric correction methods (ACOLITE and POLYMER) were tested and satellite matchups were used to validate the new chl-a and TSM algorithms following standard validation procedures. The results demonstrated that the new algorithms were in good agreement with in situ observations and outperform existing chl-a and TSM algorithms. The new regional satellite algorithms can be used to monitor water quality within the VKW system to support the sustainable management under natural (cyclones, floods, rainfall, and tsunami) and anthropogenic pressures (industrial effluents, agricultural practices, recreational activities, construction, and demolishing concrete structures) and help achieve Sustainable Development Goal 14. Full article

(This article belongs to the Section Ocean Remote Sensing)

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2 pages, 174 KiB

Open AccessCorrection

Correction: Jin et al. Influence of the Nocturnal Effect on the Estimated Global CO₂ Flux. Remote Sens. 2022, 14, 3192

by Rui Jin, Tan Yu, Bangyi Tao, Weizeng Shao, Song Hu and Yongliang Wei

Remote Sens. 2022, 14(24), 6403; https://doi.org/10.3390/rs14246403 - 19 Dec 2022

Viewed by 1388

Abstract

We believe that several sentences in the description of the source (sink) changes of CO₂ are prone to ambiguity and are not particularly well presented [...] Full article

(This article belongs to the Special Issue Advances on Land–Ocean Heat Fluxes Using Remote Sensing)

16 pages, 4280 KiB

Open AccessTechnical Note

Load Estimation Based Dynamic Access Protocol for Satellite Internet of Things

by Mingchuan Yang, Guanchang Xue, Botao Liu, Yupu Yang and Yanyong Su

Remote Sens. 2022, 14(24), 6402; https://doi.org/10.3390/rs14246402 - 19 Dec 2022

Cited by 2 | Viewed by 1727

Abstract

In recent years, the Internet of Things (IoT) industry has become a research hotspot. With the advancement of satellite technology, the satellite Internet of Things is further developed along with a new generation of information technology and commercial markets. However, existing random access [...] Read more.

In recent years, the Internet of Things (IoT) industry has become a research hotspot. With the advancement of satellite technology, the satellite Internet of Things is further developed along with a new generation of information technology and commercial markets. However, existing random access protocols cannot cope with the access of a large number of sensors and short burst transmissions. The current satellite Internet of Things application scenarios are divided into two categories, one has only sensor nodes and no sink nodes, and the other has sink nodes. A time-slot random access protocol based on Walsh code is proposed for the satellite Internet-of-Things scenario with sink nodes. In this paper, the load estimation algorithm is used to reduce the resource occupancy rate in the case of medium and low load, and a dynamic Walsh code slot random access protocol is proposed to select the appropriate Walsh code length and frame length h. The simulation results show that the slotted random access protocol based on Walsh code can effectively improve the throughput of the system under high load. The introduction of load estimation in the case of medium and low load can effectively reduce the resource utilization of the system, and ensure that the performance of the access protocol based on Walsh codes does not deteriorate. However, in the case of high load, a large resource overhead is still required to ensure the access performance of the system. Full article

(This article belongs to the Special Issue Satellite and UAV for Internet of Things (IoT))

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16 pages, 2059 KiB

Open AccessArticle

Effects of Inter- and Intra-Specific Interactions on Moose Habitat Selection Limited by Temperature

by Heng Bao, Penghui Zhai, Dusu Wen, Weihua Zhang, Ye Li, Feifei Yang, Xin Liang, Fan Yang, Nathan J. Roberts, Yanchun Xu and Guangshun Jiang

Remote Sens. 2022, 14(24), 6401; https://doi.org/10.3390/rs14246401 - 19 Dec 2022

Cited by 2 | Viewed by 2155

Abstract

Habitat selection and daily activity patterns of large herbivores might be affected by inter- and intra-specific interaction, changes of spatial scale, and seasonal temperature. To reveal what factors were driving the habitat selection of moose, we collected moose (Alces alces) and [...] Read more.

Habitat selection and daily activity patterns of large herbivores might be affected by inter- and intra-specific interaction, changes of spatial scale, and seasonal temperature. To reveal what factors were driving the habitat selection of moose, we collected moose (Alces alces) and roe deer (Capreolus pygargus bedfordi) occurrence data, analyzed the multi-scale habitat selection and daily activity patterns of moose, and quantified the effects of spatial heterogeneity distribution of temperature, as well as the occurrence of roe deer on these habitat selection processes. Our results suggested that moose and roe deer distribution spatially overlap and that moose habitat selection is especially sensitive to landscape variables at large scales. We also found that the activity patterns of both sexes of moose had a degree of temporal separation with roe deer. In the snow-free season, temperatures drove moose habitat selection to be limited by threshold temperatures of 17 °C; in the snowy season, there were no similar temperature driving patterns, due to the severe cold environment. The daily activity patterns of moose showed seasonal change, and were more active at dawn and nightfall to avoid heat pressure during the snow-free season, but more active in the daytime for cold adaptation to the snow season. Consequently, this study provides new insights on how the comprehensive effects of environmental change and inter- and intra- specific relationships influence the habitat selection and daily activity patterns of moose and other heat sensitive animals with global warming. Full article

(This article belongs to the Special Issue Camera Trapping for Animal Ecology and Conservation)

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20 pages, 2770 KiB

Open AccessArticle

Spatial Downscaling of NPP-VIIRS Nighttime Light Data Using Multiscale Geographically Weighted Regression and Multi-Source Variables

by Shangqin Liu, Xizhi Zhao, Fuhao Zhang, Agen Qiu, Liujia Chen, Jing Huang, Song Chen and Shu Zhang

Remote Sens. 2022, 14(24), 6400; https://doi.org/10.3390/rs14246400 - 19 Dec 2022

Cited by 10 | Viewed by 3304

Abstract

Remote sensing images of nighttime lights (NTL) were successfully used at global and regional scales for various applications, including studies on population, politics, economics, and environmental protection. The Suomi National Polar-orbiting Partnership with the Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) NTL data has [...] Read more.

Remote sensing images of nighttime lights (NTL) were successfully used at global and regional scales for various applications, including studies on population, politics, economics, and environmental protection. The Suomi National Polar-orbiting Partnership with the Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) NTL data has the advantages of high temporal resolution, long coverage time series, and wide spatial range. The spatial resolution of the monthly and annual composite data of NPP-VIIRS NTL is only 500 m, which hinders studies requiring higher resolution. We propose a multi-source spatial variable and Multiscale Geographically Weighted Regression (MGWR)-based method to achieve the downscaling of NPP-VIIRS NTL data. An MGWR downscaling framework was implemented to obtain NTL data at 120 m resolution based on auxiliary data representing socioeconomic or physical geographic attributes. The downscaled NTL data were validated against LuoJia1-01 imagery based on the coefficient of determination (R²) and the root-mean-square error (RMSE). The results suggested that the spatial resolution of the data was enhanced after downscaling, and the MGWR-based downscaling results demonstrated higher R² (R² = 0.9141) and lower RMSE than those of Geographically Weighted Regression and Random Forest-based algorithms. Additionally, MGWR can reveal the different relationships between multiple auxiliary and NTL data. Therefore, this study demonstrates that the spatial resolution of NPP-VIIRS NTL data is improved from 500 m to 120 m upon downscaling, thereby facilitating NTL-based applications. Full article

(This article belongs to the Special Issue Recent Advances of Urban Development Scenarios Simulation Using Remote Sensing and GIS)

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20 pages, 3377 KiB

Open AccessArticle

An In-Depth Assessment of the Drivers Changing China’s Crop Production Using an LMDI Decomposition Approach

by Yuqiao Long, Wenbin Wu, Joost Wellens, Gilles Colinet and Jeroen Meersmans

Remote Sens. 2022, 14(24), 6399; https://doi.org/10.3390/rs14246399 - 19 Dec 2022

Cited by 3 | Viewed by 2233

Abstract

Over the last decades, growing crop production across China has had far-reaching consequences for both the environment and human welfare. One of the emerging questions is “how to meet the growing food demand in China?” In essence, the consensus is that the best [...] Read more.

Over the last decades, growing crop production across China has had far-reaching consequences for both the environment and human welfare. One of the emerging questions is “how to meet the growing food demand in China?” In essence, the consensus is that the best way forward would be to increase crop yield rather than further extend the current cropland area. However, assessing progress in crop production is challenging as it is driven by multiple factors. To date, there are no studies to determine how multiple factors affect the crop production increase, considering both intensive farming (using yield and multiple cropping index) and large-scale farming (using mean parcel size and number of parcels). Using the Logarithmic-Mean-Divisia-Index (LMDI) decomposition method combined with statistical data and land cover data (GlobeLand30), we assess the contribution of intensive farming and large-scale farming changes to crop production dynamics at the national and county scale. Despite a negative contribution from MPS (mean parcel size, ), national crop production increased due to positive contributions from yield (), MCI (multiple cropping index, ), as well as NP (number of parcels, ). This allowed China to meet the growing national crop demand. We further find that large differences across regions persist over time. For most counties, the increase in crop production is a consequence of improved yields. However, in the North China Plain, NP is another important factor leading to crop production improvement. On the other hand, regions witnessing a decrease in crop production (e.g., the southeast coastal area of China) were characterized by a remarkable decrease in yield and MCI. Our detailed analyses of crop production provide accurate estimates and therefore can guide policymakers in addressing food security issues. Specifically, besides stabilizing yield and maintaining the total NP, it would be advantageous for crop production to increase the mean parcel size and MCI through land consolidation and financial assistance for land transfer and advanced agricultural infrastructure. Full article

(This article belongs to the Special Issue Monitoring Agricultural Land-Use Change and Land-Use Intensity Ⅱ)

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21 pages, 4938 KiB

Open AccessArticle

Drought Monitoring and Performance Evaluation Based on Machine Learning Fusion of Multi-Source Remote Sensing Drought Factors

by Yangyang Zhao, Jiahua Zhang, Yun Bai, Sha Zhang, Shanshan Yang, Malak Henchiri, Ayalkibet Mekonnen Seka and Lkhagvadorj Nanzad

Remote Sens. 2022, 14(24), 6398; https://doi.org/10.3390/rs14246398 - 19 Dec 2022

Cited by 22 | Viewed by 6083

Abstract

Drought is an extremely dangerous natural hazard that causes water crises, crop yield reduction, and ecosystem fires. Researchers have developed many drought indices based on ground-based climate data and various remote sensing data. Ground-based drought indices are more accurate but limited in coverage; [...] Read more.

Drought is an extremely dangerous natural hazard that causes water crises, crop yield reduction, and ecosystem fires. Researchers have developed many drought indices based on ground-based climate data and various remote sensing data. Ground-based drought indices are more accurate but limited in coverage; while the remote sensing drought indices cover larger areas but have poor accuracy. Applying data-driven models to fuse multi-source remote sensing data for reproducing composite drought index may help fill this gap and better monitor drought in terms of spatial resolution. Machine learning methods can effectively analyze the hierarchical and non-linear relationships between the independent and dependent variables, resulting in better performance compared with traditional linear regression models. In this study, seven drought impact factors from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensor, Global Precipitation Measurement Mission (GPM), and Global Land Data Assimilation System (GLDAS) were used to reproduce the standard precipitation evapotranspiration index (SPEI) for Shandong province, China, from 2002 to 2020. Three machine learning methods, namely bias-corrected random forest (BRF), extreme gradient boosting (XGBoost), and support vector machines (SVM) were applied as regression models. Then, the best model was used to construct the spatial distribution of SPEI. The results show that the BRF outperforms XGBoost and SVM in SPEI estimation. The BRF model can effectively monitor drought conditions in areas without ground observation data. The BRF model provides comprehensive drought information by producing a spatial distribution of SPEI, which provides reliability for the BRF model to be applied in drought monitoring. Full article

(This article belongs to the Special Issue Recent Advances in Drought Risk Assessment, Monitoring, and Forecasting)

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20 pages, 5220 KiB

Open AccessArticle

Development and Assessment of Seasonal Rainfall Forecasting Models for the Bani and the Senegal Basins by Identifying the Best Predictive Teleconnection

by Luis Balcázar, Khalidou M. Bâ, Carlos Díaz-Delgado, Miguel A. Gómez-Albores, Gabriel Gaona and Saula Minga-León

Remote Sens. 2022, 14(24), 6397; https://doi.org/10.3390/rs14246397 - 19 Dec 2022

Viewed by 2404

Abstract

The high variability of rainfall in the Sahel region causes droughts and floods that affect millions of people every year. Several rainfall forecasting models have been proposed, but the results still need to be improved. In this study, linear, polynomial, and exponential models [...] Read more.

The high variability of rainfall in the Sahel region causes droughts and floods that affect millions of people every year. Several rainfall forecasting models have been proposed, but the results still need to be improved. In this study, linear, polynomial, and exponential models are developed to forecast rainfall in the Bani and Senegal River basins. All three models use Atlantic sea surface temperature (SST). A fourth algorithm using stepwise regression was also developed for the precipitation estimates over these two basins. The stepwise regression algorithm uses SST with covariates, mean sea level pressure (MSLP), relative humidity (RHUM), and five El Niño indices. The explanatory variables SST, RHUM, and MSLP were selected based on principal component analysis (PCA) and cluster analysis to find the homogeneous region of the Atlantic with the greatest predictive ability. PERSIANN-CDR rainfall data were used as the dependent variable. Models were developed for each pixel of 0.25° × 0.25° spatial resolution. The second-order polynomial model with a lag of about 11 months outperforms all other models and explains 87% of the variance in precipitation over the two watersheds. Nash–Sutcliffe efficiency (NSE) values were between 0.751 and 0.926 for the Bani River basin and from 0.175 to 0.915 for the Senegal River basin, for which the lowest values are found in the driest area (Sahara). Results showed that the North Atlantic SST shows a more robust teleconnection with precipitation dynamics in both basins. Full article

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22 pages, 5581 KiB

Open AccessArticle

An Improved Exponential Model Considering a Spectrally Effective Moisture Threshold for Proximal Hyperspectral Reflectance Simulation and Soil Salinity Estimation

by Xi Huang, Tiecheng Bai, Huade Guan, Xiayong Wei, Yali Wang and Xiaomin Mao

Remote Sens. 2022, 14(24), 6396; https://doi.org/10.3390/rs14246396 - 18 Dec 2022

Cited by 1 | Viewed by 2330

Abstract

Soil salinization has become one of the main factors restricting sustainable development of agriculture. Field spectrometry provides a quick way to predict the soil salinization. However, soil moisture content (SMC) seriously interferes with the spectral information of saline soil in arid areas. It [...] Read more.

Soil salinization has become one of the main factors restricting sustainable development of agriculture. Field spectrometry provides a quick way to predict the soil salinization. However, soil moisture content (SMC) seriously interferes with the spectral information of saline soil in arid areas. It is vital to establish a model that is insensitive to SMC for potential in situ field applications. The soil spectral reflectance exponential model (SSREM) has been widely employed for reflectance simulation and SSC inversion. However, its reliability for saline soils with high SMC has not been verified yet. Based on hyperspectral remote sensing data (400~1000 nm) on 459 saline soil samples in Shiyang River Basin of Northwest China, we investigated the role of SMC and SSC in soil spectral reflectance from 29 October 2020 to 22 January 2021. Targeted at saline soils, soil spectral moisture threshold (MT) was introduced to improve the SSREM toward a modified spectral reflectance exponential model (MT-SSREM). The bands that are sensitive to SSC but not sensitive to SMC were obtained based on a method of correlation analysis between original spectra, four kinds of spectral data, and SSC. SSREM and MT-SSREM were finally applied to inversely estimate SSC. Results show that wavelengths at 658~660, 671~685, 938 nm were suitable for SSC estimation. Furthermore, although SSREM was able to simulate the spectral reflectance of most saline soils, its simulation accuracy was low for saline soil samples with high SMC (SMC > MT(i),

400 nm \leq i \leq 1000 nm

), while MT-SSREM performed well over the whole range of SMC. The simulated spectral reflectance from MT-SSREM agreed well with the measured reflectance, with the R² being generally larger than 0.9 and RMSE being less than 0.1. More importantly, MT-SSREM performed substantially better than SSREM for SSC estimation; in the statistical performance of the former case, R² was in range of 0.60~0.66, RMSE was in range of 0.29~0.33 dS m⁻¹; in the latter case, R² was in range of 0.10~0.16, RMSE was in the range of 0.26~0.29 dS m⁻¹. MT-SSREM proposed in this study thus provides a new direction for estimating hyperspectral reflectance and SSC under various soil moisture conditions at wavelengths from 400 to 1000 nm. It also provides an approach for SSC and SMC mapping in salinization regions by incorporating remote sensing data, such as GF-5. Full article

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

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23 pages, 35978 KiB

Open AccessArticle

Comprehensive Evaluation of Data-Related Factors on BDS-3 B1I + B2b Real-Time PPP/INS Tightly Coupled Integration

by Junyao Kan, Zhouzheng Gao, Qiaozhuang Xu, Ruohua Lan, Jie Lv and Cheng Yang

Remote Sens. 2022, 14(24), 6395; https://doi.org/10.3390/rs14246395 - 18 Dec 2022

Cited by 2 | Viewed by 1886

Abstract

Owing to the developments of satellite-based and network-based real-time satellite precise products, the Precise Point Positioning (PPP) technique has been applied far and wide, especially since the PPP-B2b service was provided by the third-generation BeiDou Navigation Satellite System (BDS-3). However, satellite outages during [...] Read more.

Owing to the developments of satellite-based and network-based real-time satellite precise products, the Precise Point Positioning (PPP) technique has been applied far and wide, especially since the PPP-B2b service was provided by the third-generation BeiDou Navigation Satellite System (BDS-3). However, satellite outages during dynamic application lead to significant degradation of the accuracy and continuity of PPP. A generally used method is integrating PPP with Inertial Measurement Units (IMUs) to enhance positioning performance. Previous works on this topic are usually based on IMU data at a high sampling rate and are mostly implemented in post-processing mode. This paper will carry out a compressive assessment of the impacts of different types of precise satellite products (real-time products from the CAS, DLR, GFZ, WHU, and the final one from GFZ), Doppler observations, and different sampling rates of IMU data on the performance of the tightly coupled integration of the BDS-3 B1I/B2b and the Inertial Navigation System (INS). Results based on a group of on-board experimental data illustrate that (1) the positioning accuracy with products supplied by the CAS and WHU are roughly consistent with those using the final products; (2) the Doppler observations can effectively improve the accuracies of velocity, attitude, and vertical position at the initial epochs and during the reconvergence periods, but have invisible influences on the overall positioning, velocity, and attitude determination; and (3) the impact of IMU data interval on the performance of PPP/INS tightly coupled integration is insignificant when there are enough available satellites. However, the divergent speed of position is visibly affected by the IMU sampling rate during satellite outage periods. Full article

(This article belongs to the Special Issue Advances in Beidou/GNSS High Precision Positioning and Navigation)

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21 pages, 29314 KiB

Open AccessArticle

Integration of Terrestrial Laser Scanner (TLS) and Ground Penetrating Radar (GPR) to Characterize the Three-Dimensional (3D) Geometry of the Maoyaba Segment of the Litang Fault, Southeastern Tibetan Plateau

by Di Zhang, Zhonghai Wu, Danni Shi, Jiacun Li and Yan Lu

Remote Sens. 2022, 14(24), 6394; https://doi.org/10.3390/rs14246394 - 18 Dec 2022

Cited by 6 | Viewed by 2393

Abstract

High-resolution topographic and stratigraphic datasets have been increasing applied in active fault investigation and seismic hazard assessment. There is a need for the comprehensive analysis of active faults on the basis of the correlating geomorphologic features and stratigraphic data. The integration of TLS [...] Read more.

High-resolution topographic and stratigraphic datasets have been increasing applied in active fault investigation and seismic hazard assessment. There is a need for the comprehensive analysis of active faults on the basis of the correlating geomorphologic features and stratigraphic data. The integration of TLS and GPR was adopted to characterize the 3D geometry of the fault on the Maoyaba segment of Litang fault. The TLS was used to obtain the high-resolution topographic data for establishing the 3D surficial model of the fault. The 2D 250 MHz and 500 MHz GPR profiles were carried out to image the shallow geometry of the fault along four survey lines. In addition, the 3D GPR survey was performed by ten 2D 500 MHz GPR profiles with 1 m spacing. From the 2D and 3D GPR results, a wedge-shaped deformation zone of the electromagnetic wave was clearly found on the GPR profiles, and it was considered to be the main fault zone with a small graben structure. Three faults were identified on the main fault zone, and fault F₁ and F₃ were the boundary faults, while the fault F₂ was the secondary fault. The subsurface geometry of the fault on the GPR interpreted results is consistent with the geomorphologic features of the TLS-derived data, and it indicates that the Maoyaba fault is a typical, normal fault. For reducing the environmental disruption and economic losses, GPR was the most optimal method for detecting the subsurface structures of active faults in the Litang fault with a non-destructive and cost-effective fashion. The 3D surface and subsurface geometry of the fault was interpreted from the integrated data of TLS and GPR. The fusion data also offers the chance for the subsurface structures of active faults on the GPR profiles to be better understood with its corresponding superficial features. The study results demonstrate that the integration of TLS and GPR has the capability to obtain the high-resolution micro geomorphology and shallow geometry of active faults on the Maoyaba segment of the Litang fault, and it also provides a future prospect for the integration of TLS and GPR, and is valuable for active fault investigation and seismic hazard assessment, especially in the Qinghai-Tibet Plateau area. Full article

(This article belongs to the Special Issue Remote Sensing in Earthquake, Tectonics and Seismic Hazards)

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18 pages, 9447 KiB

Open AccessArticle

Locating Earth Disturbances Using the SDR Earth Imager

by Radwan Sharif, Suleyman Gokhun Tanyer, Stephen Harrison, William Junor, Peter Driessen and Rodney Herring

Remote Sens. 2022, 14(24), 6393; https://doi.org/10.3390/rs14246393 - 18 Dec 2022

Cited by 2 | Viewed by 2091

Abstract

The Radio Wave Phase Imager uses monitoring and recording concepts, such as Software Defined Radio (SDR), to image Earth’s atmosphere. The Long Wavelength Array (LWA), New Mexico Observatory is considered a high-resolution camera that obtains phase information about Earth and space disturbances; therefore, [...] Read more.

The Radio Wave Phase Imager uses monitoring and recording concepts, such as Software Defined Radio (SDR), to image Earth’s atmosphere. The Long Wavelength Array (LWA), New Mexico Observatory is considered a high-resolution camera that obtains phase information about Earth and space disturbances; therefore, it was employed to capture radio signals reflected from Earth’s F ionization layer. Phase information reveals and measures the properties of waves that exist in the ionization layer. These waves represent terrestrial and solar Earth disturbances, such as power losses from power generating and distribution stations. Two LWA locations were used to capture the ionization layer waves, including University of New Mexico’s Long Wavelength Array’s LWA-1 and LWA-SV. Two locations of the measurements showed wavevector directions of disturbances, whereas the intersection of wavevectors determined the source of the disturbance. The research described here focused on measuring the ionization layer wave’s phase shifts, frequencies, and wavevectors. This novel approach is a significant contribution to determine the source of any disturbance. Full article

(This article belongs to the Special Issue Monitoring and Reconstruction of Key Parameters for Ionospheric Weather Using Ground and LEO Based GNSS Data)

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23 pages, 7691 KiB

Open AccessArticle

Quality Assessment of FY-3D/MERSI-II Thermal Infrared Brightness Temperature Data from the Arctic Region: Application to Ice Surface Temperature Inversion

by Haihua Chen, Xin Meng, Lele Li and Kun Ni

Remote Sens. 2022, 14(24), 6392; https://doi.org/10.3390/rs14246392 - 18 Dec 2022

Cited by 4 | Viewed by 2450

Abstract

The Arctic region plays an important role in the global climate system. To promote the application of Medium Resolution Spectral Imager-II (MERSI-II) data in the ice surface temperature (IST) inversion, we used the thermal infrared channels (channels 24 and 25) of the MERSI-II [...] Read more.

The Arctic region plays an important role in the global climate system. To promote the application of Medium Resolution Spectral Imager-II (MERSI-II) data in the ice surface temperature (IST) inversion, we used the thermal infrared channels (channels 24 and 25) of the MERSI-II onboard Chinese FY-3D satellite and the thermal infrared channels (channels 31 and 32) of the Earth Observing System (EOS) Moderate-Resolution Imaging Spectroradiometer (MODIS) onboard the National Aeronautical and Space Administration (NASA) Aqua satellite for data analysis. Using the Observation–Observation cross-calibration algorithm to cross-calibrate the MERSI and MODIS thermal infrared brightness temperature (T_b) data in the Arctic, channel 24 and 25 data from the FY-3D/MERSI-II on Arctic ice were evaluated. The thermal infrared T_b data of the MERSI-II were used to retrieve the IST via the split-window algorithm. In this study, the correlation coefficients of the thermal infrared channel T_b data between the MERSI and MODIS were >0.95, the mean bias was −0.5501–0.1262 K, and the standard deviation (Std) was <1.3582 K. After linear fitting, the MERSI-II thermal infrared T_b data were closer to the MODIS data, and the bias range of the 11 μm and 12 μm channels was −0.0214–0.0119 K and the Std was <1.2987 K. These results indicate that the quality of the MERSI-II data is comparable to that of the MODIS data, so that can be used for application to IST inversion. When using the MERSI thermal infrared T_b data after calibration to retrieve the IST, the results of the MERSI and MODIS IST were more consistent. By comparing the IST retrieved from the MERSI thermal infrared calibrated T_b data with MODIS MYD29 product, the mean bias was −0.0612–0.0423 °C and the Std was <1.3988 °C. Using the MERSI thermal infrared T_b data after calibration is better than that before calibration for retrieving the IST. When comparing the Arctic ocean sea and ice surface temperature reprocessed data (L4 SST/IST) with the IST data retrieved from MERSI, the bias was 0.9891–2.7510 °C, and the Std was <3.5774 °C. Full article

(This article belongs to the Special Issue Remote Sensing of Polar Sea Ice)

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17 pages, 5238 KiB

Open AccessArticle

The Lidargrammetric Model Deformation Method for Altimetric UAV-ALS Data Enhancement

by Antoni Rzonca and Mariusz Twardowski

Remote Sens. 2022, 14(24), 6391; https://doi.org/10.3390/rs14246391 - 17 Dec 2022

Cited by 2 | Viewed by 1905

Abstract

The altimetric accuracy of aerial laser scanning (ALS) data is one of the most important issues of ALS data processing. In this paper, the authors present a previously unknown, yet simple and efficient method for altimetric enhancement of ALS data based on the [...] Read more.

The altimetric accuracy of aerial laser scanning (ALS) data is one of the most important issues of ALS data processing. In this paper, the authors present a previously unknown, yet simple and efficient method for altimetric enhancement of ALS data based on the concept of lidargrammetry. The generally known photogrammetric theory of stereo model deformations caused by relative orientation parameters errors of stereopair was applied for the continuous correction of lidar data based on ground control points. The preliminary findings suggest that the method is correct, efficient and precise, whilst the correction of the point cloud is continuous. The theory of the method and its implementation within the research software are presented in the text. Several tests were performed on synthetic and real data. The most significant results are presented and discussed in the article together with a discussion of the potential of lidargrammetry, and the main directions of future research are also mapped out. These results confirm that the research gap in the area of altimetric enhancement of ALS data without additional trajectory data is resolved in this study. Full article

(This article belongs to the Special Issue Photogrammetry and Remote Sensing in Environmental and Engineering Applications)

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21 pages, 17801 KiB

Open AccessArticle

Validating the Crop Identification Capability of the Spectral Variance at Key Stages (SVKS) Computed via an Object Self-Reference Combined Algorithm

by Hailan Zhao, Jihua Meng, Tingting Shi, Xiaobo Zhang, Yanan Wang, Xiangjiang Luo, Zhenxin Lin and Xinyan You

Remote Sens. 2022, 14(24), 6390; https://doi.org/10.3390/rs14246390 - 17 Dec 2022

Cited by 1 | Viewed by 1861

Abstract

Crop-distribution information constitutes the premise of precise management for crop cultivation. Euclidean distance and spectral angle mapper algorithms (ED and SAM) mostly use the spectral similarity and difference metric (SSDM) to determine the spectral variance associated with the spatial location for crop distribution [...] Read more.

Crop-distribution information constitutes the premise of precise management for crop cultivation. Euclidean distance and spectral angle mapper algorithms (ED and SAM) mostly use the spectral similarity and difference metric (SSDM) to determine the spectral variance associated with the spatial location for crop distribution acquisition. These methods are relatively insensitive to spectral shape or amplitude variation and must reconstruct a reference curve representing the entire class, possibly resulting in notable indeterminacy in the ultimate results. Few studies utilize these methods to compute the spectral variance associated with time and to define a new index for crop identification—namely, the spectral variance at key stages (SVKS)—even though this temporal spectral characteristic could be helpful for crop identification. To integrate the advantages of sensibility and avoid reconstructing the reference curve, an object self-reference combined algorithm comprising ED and SAM (CES) was proposed to compute SVKS. To objectively validate the crop-identification capability of SVKS-CES (SVKS computed via CES), SVKS-ED (SVKS computed via ED), SVKS-SAM (SVKS computed via SAM), and five spectral index (SI) types were selected for comparison in an example of maize identification. The results indicated that SVKS-CES ranges can characterize greater interclass spectral separability and attained better identification accuracy compared to other identification indexes. In particular, SVKS-CES2 provided the greatest interclass spectral separability and the best PA (92.73%), UA (100.00%), and OA (98.30%) in maize identification. Compared to the performance of the SI, SVKS attained greater interclass spectral separability, but more non-maize fields were incorrectly identified as maize fields via SVKS usage. Owning to the accuracy-improvement capability of SVKS-CES, the omission and commission errors were obviously reduced via the combined utilization of SVKS-CES and SI. The findings suggest that SVKS-CES application is expected to further spread in crop identification. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Vegetation Classification)

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19 pages, 5691 KiB

Open AccessArticle

A Multi-Source Data Fusion Method to Improve the Accuracy of Precipitation Products: A Machine Learning Algorithm

by Mazen E. Assiri and Salman Qureshi

Remote Sens. 2022, 14(24), 6389; https://doi.org/10.3390/rs14246389 - 17 Dec 2022

Cited by 4 | Viewed by 3083

Abstract

In recent decades, several products have been proposed for estimating precipitation amounts. However, due to the complexity of climatic conditions, topography, etc., providing more accurate and stable precipitation products is of great importance. Therefore, the purpose of this study was to develop a [...] Read more.

In recent decades, several products have been proposed for estimating precipitation amounts. However, due to the complexity of climatic conditions, topography, etc., providing more accurate and stable precipitation products is of great importance. Therefore, the purpose of this study was to develop a multi-source data fusion method to improve the accuracy of precipitation products. In this study, data from 14 existing precipitation products, a digital elevation model (DEM), land surface temperature (LST) and soil water index (SWI) and precipitation data recorded at 256 gauge stations in Saudi Arabia were used. In the first step, the accuracy of existing precipitation products was assessed. In the second step, the importance degree of various independent variables, such as precipitation interpolation maps obtained from gauge stations, elevation, LST and SWI in improving the accuracy of precipitation modelling, was evaluated. Finally, to produce a precipitation product with higher accuracy, information obtained from independent variables were combined using a machine learning algorithm. Random forest regression with 150 trees was used as a machine learning algorithm. The highest and lowest degree of importance in the production of precipitation maps based on the proposed method was for existing precipitation products and surface characteristics, respectively. The importance degree of surface properties including SWI, DEM and LST were 65%, 22% and 13%, respectively. The products of IMERGFinal (9.7), TRMM3B43 (10.6), PRECL (11.5), GSMaP-Gauge (12.5), and CHIRPS (13.0 mm/mo) had the lowest RMSE values. The KGE values of these products in precipitation estimation were 0.56, 0.48, 0.52, 0.44 and 0.37, respectively. The RMSE and KGE values of the proposed precipitation product were 6.6 mm/mo and 0.75, respectively, which indicated the higher accuracy of this product compared to existing precipitation products. The results of this study showed that the fusion of information obtained from different existing precipitation products improved the accuracy of precipitation estimation. 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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20 pages, 11794 KiB

Open AccessArticle

A Robust Adaptive Filtering Algorithm for GNSS Single-Frequency RTK of Smartphone

by Yuxing Li, Jinzhong Mi, Yantian Xu, Bo Li, Dingxuan Jiang and Weifeng Liu

Remote Sens. 2022, 14(24), 6388; https://doi.org/10.3390/rs14246388 - 17 Dec 2022

Cited by 9 | Viewed by 2516

Abstract

In this paper, a single-frequency real-time kinematic positioning (RTK) robust adaptive Kalman filtering algorithm is proposed in order to realize real-time dynamic high-precision positioning of smartphone global navigation satellite systems (GNSSs). A robust model is established by using the quartile method to dynamically [...] Read more.

In this paper, a single-frequency real-time kinematic positioning (RTK) robust adaptive Kalman filtering algorithm is proposed in order to realize real-time dynamic high-precision positioning of smartphone global navigation satellite systems (GNSSs). A robust model is established by using the quartile method to dynamically determine the threshold value and eliminate the gross error of observation. The Institute of Geodesy and Geophysics Ⅲ (IGG Ⅲ) weight function is used to construct the position and speed classification adaptive factors to weaken the impact of state mutation errors. Based on the analysis of the measured data of Xiaomi 8 and Huawei P40 smartphones, simulated dynamic tests show that the overall accuracy of the Xiaomi 8 is improved by more than 85% with the proposed robust RTK algorithm, and the overall positioning error is less than 0.5 m in both open and sheltered environments. The overall accuracy of the Huawei P40 is improved by more than 25%. Furthermore, the overall positioning accuracy is better than 0.3 m in open environments, and about 0.8 m in blocked situations. Dynamic experiments show that the use of the robust adaptive RTK algorithm improves the full-time solution planar positioning accuracy of the Xiaomi 8 by more than 15%. In addition, the planar positioning accuracy under open and occluded conditions is 0.8 m and 1.5 m, respectively, and the overall positioning accuracy of key nodes whose movement state exhibits major changes improves by more than 20%. Full article

(This article belongs to the Special Issue Smartphone-Derived GNSS Measurements Characterization for Precise Positioning and Navigation Applications)

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25 pages, 9501 KiB

Open AccessArticle

Simulation of the Use of Variance Component Estimation in Relative Weighting of Inter-Satellite Links and GNSS Measurements

by Tomasz Kur and Tomasz Liwosz

Remote Sens. 2022, 14(24), 6387; https://doi.org/10.3390/rs14246387 - 17 Dec 2022

Cited by 5 | Viewed by 1722

Abstract

Inter-satellite links (ISLs) can improve the performance of the Global Navigation Satellite System (GNSS) in terms of precise orbit determination, communication, and data-exchange capabilities. This research aimed to evaluate a simulation-based processing strategy involving the exploitation of ISLs in orbit determination of Galileo [...] Read more.

Inter-satellite links (ISLs) can improve the performance of the Global Navigation Satellite System (GNSS) in terms of precise orbit determination, communication, and data-exchange capabilities. This research aimed to evaluate a simulation-based processing strategy involving the exploitation of ISLs in orbit determination of Galileo satellites, which are not equipped with operational ISLs. The performance of the estimation process is first tested based on relative weighting coefficients obtained with methods of variance component estimation (VCE) varying in the complexity of the calculations. Inclusion of biases in the ISL measurements allows evaluation of the processing strategy and assessment of the impact of three different sets of ground stations: 44 and 16 stations distributed globally and 16 located in Europe. The results indicate that using different VCE approaches might lower orbit errors by up to 20% with a negligible impact on clock estimation. Depending on the applied ISL connectivity scheme, ISL range bias can be estimated with RMS between 10% to 30% of initial bias values. The accuracy of bias estimation may be associated with weighting approach and the number of ground stations. The results of this study show how introducing VCE with various simulation parameters into the processing chain might increase the accuracy of the orbit estimation. Full article

(This article belongs to the Special Issue Precise Orbit Determination with GNSS)

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22 pages, 10515 KiB

Open AccessArticle

Ore-Waste Discrimination Using Supervised and Unsupervised Classification of Hyperspectral Images

by Mehdi Abdolmaleki, Mariano Consens and Kamran Esmaeili

Remote Sens. 2022, 14(24), 6386; https://doi.org/10.3390/rs14246386 - 17 Dec 2022

Cited by 12 | Viewed by 3023

Abstract

Ore and waste discrimination is essential for optimizing exploitation and minimizing ore dilution in a mining operation. The conventional ore/waste discrimination approach relies on the interpretation of ore control by geologists, which is subjective, time-consuming, and can cause safety hazards. Hyperspectral remote sensing [...] Read more.

Ore and waste discrimination is essential for optimizing exploitation and minimizing ore dilution in a mining operation. The conventional ore/waste discrimination approach relies on the interpretation of ore control by geologists, which is subjective, time-consuming, and can cause safety hazards. Hyperspectral remote sensing can be used as an alternative approach for ore/waste discrimination. The focus of this study is to investigate the application of hyperspectral remote sensing and deep learning (DL) for real-time ore and waste classification. Hyperspectral images of several meters of drill core samples from a silver ore deposit labeled by a site geologist as ore and waste material were used to train and test the models. A DL model was trained on the labels generated by a spectral angle mapper (SAM) machine learning technique. The performance on ore/waste discrimination of three classifiers (supervised DL and SAM, and unsupervised k-means clustering) was evaluated using Rand Error and Pixel Error as disagreement analysis and accuracy assessment indices. The results showed that the DL method outperformed the other two techniques. The performance of the DL model reached 0.89, 0.95, 0.89, and 0.91, respectively, on overall accuracy, precision, recall, and F1 score, which indicate the strong capability of the DL model in ore and waste discrimination. An integrated hyperspectral imaging and DL technique has strong potential to be used for practical and efficient discrimination of ore and waste in a near real-time manner. Full article

(This article belongs to the Special Issue Advances in Hyperspectral Remote Sensing: Methods and Applications)

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25 pages, 11338 KiB

Open AccessArticle

Automatic Calibration between Multi-Lines LiDAR and Visible Light Camera Based on Edge Refinement and Virtual Mask Matching

by Chengkai Chen, Jinhui Lan, Haoting Liu, Shuai Chen and Xiaohan Wang

Remote Sens. 2022, 14(24), 6385; https://doi.org/10.3390/rs14246385 - 17 Dec 2022

Cited by 2 | Viewed by 2281

Abstract

To assist in the implementation of a fine 3D terrain reconstruction of the scene in remote sensing applications, an automatic joint calibration method between light detection and ranging (LiDAR) and visible light camera based on edge points refinement and virtual mask matching is [...] Read more.

To assist in the implementation of a fine 3D terrain reconstruction of the scene in remote sensing applications, an automatic joint calibration method between light detection and ranging (LiDAR) and visible light camera based on edge points refinement and virtual mask matching is proposed in this paper. The proposed method is used to solve the problem of inaccurate edge estimation of LiDAR with different horizontal angle resolutions and low calibration efficiency. First, we design a novel calibration target, adding four hollow rectangles for fully automatic locating of the calibration target and increasing the number of corner points. Second, an edge refinement strategy based on background point clouds is proposed to estimate the target edge more accurately. Third, a two-step method of automatically matching between the calibration target in 3D point clouds and the 2D image is proposed. Through this method, i.e., locating firstly and then fine processing, corner points can be automatically obtained, which can greatly reduce the manual operation. Finally, a joint optimization equation is established to optimize the camera’s intrinsic and extrinsic parameters of LiDAR and camera. According to our experiments, we prove the accuracy and robustness of the proposed method through projection and data consistency verifications. The accuracy can be improved by at least 15.0% when testing on the comparable traditional methods. The final results verify that our method is applicable to LiDAR with large horizontal angle resolutions. Full article

(This article belongs to the Special Issue Pattern Recognition in Remote Sensing)

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18 pages, 4264 KiB

Open AccessEditor’s ChoiceArticle

Sentinel-1 Backscatter Time Series for Characterization of Evapotranspiration Dynamics over Temperate Coniferous Forests

by Marlin M. Mueller, Clémence Dubois, Thomas Jagdhuber, Florian M. Hellwig, Carsten Pathe, Christiane Schmullius and Susan Steele-Dunne

Remote Sens. 2022, 14(24), 6384; https://doi.org/10.3390/rs14246384 - 16 Dec 2022

Cited by 4 | Viewed by 2969

Abstract

Forests’ ecosystems are an essential part of the global carbon cycle with vast carbon storage potential. These systems are currently under external pressures showing increasing change due to climate change. A better understanding of the biophysical properties of forests is, therefore, of paramount [...] Read more.

Forests’ ecosystems are an essential part of the global carbon cycle with vast carbon storage potential. These systems are currently under external pressures showing increasing change due to climate change. A better understanding of the biophysical properties of forests is, therefore, of paramount importance for research and monitoring purposes. While there are many biophysical properties, the focus of this study is on the in-depth analysis of the connection between the C-band Copernicus Sentinel-1 SAR backscatter and evapotranspiration (ET) estimates based on in situ meteorological data and the FAO-based Penman–Monteith equation as well as the well-established global terrestrial ET product from the Terra and Aqua MODIS sensors. The analysis was performed in the Free State of Thuringia, central Germany, over coniferous forests within an area of 2452 km², considering a 5-year time series (June 2016–July 2021) of 6- to 12-day Sentinel-1 backscatter acquisitions/observations, daily in situ meteorological measurements of four weather stations as well as an 8-day composite of ET products of the MODIS sensors. Correlation analyses of the three datasets were implemented independently for each of the microwave sensor’s acquisition parameters, ascending and descending overpass direction and co- or cross-polarization, investigating different time series seasonality filters. The Sentinel-1 backscatter and both ET time series datasets show a similar multiannual seasonally fluctuating behavior with increasing values in the spring, peaks in the summer, decreases in the autumn and troughs in the winter months. The backscatter difference between summer and winter reaches over 1.5 dB, while the evapotranspiration difference reaches 8 mm/day for the in situ measurements and 300 kg/m²/8-day for the MODIS product. The best correlation between the Sentinel-1 backscatter and both ET products is achieved in the ascending overpass direction, with datasets acquired in the late afternoon, and reaches an R²-value of over 0.8. The correlation for the descending overpass direction reaches values of up to 0.6. These results suggest that the SAR backscatter signal of coniferous forests is sensitive to the biophysical property evapotranspiration under some scenarios. Full article

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19 pages, 4111 KiB

Open AccessArticle

A Novel GB-SAR System Based on TD-MIMO for High-Precision Bridge Vibration Monitoring

by Zexi Zhang, Zhiyong Suo, Feng Tian, Lin Qi, Haihong Tao and Zhenfang Li

Remote Sens. 2022, 14(24), 6383; https://doi.org/10.3390/rs14246383 - 16 Dec 2022

Cited by 3 | Viewed by 2277

Abstract

Ground-based synthetic aperture radar (GB-SAR) is a highly effective technique that is widely used in landslide and bridge deformation monitoring. GB-SAR based on multiple input multiple output (MIMO) technology can achieve high accuracy and real-time detection performance. In this paper, a novel method [...] Read more.

Ground-based synthetic aperture radar (GB-SAR) is a highly effective technique that is widely used in landslide and bridge deformation monitoring. GB-SAR based on multiple input multiple output (MIMO) technology can achieve high accuracy and real-time detection performance. In this paper, a novel method is proposed to design transmitting and receiving array elements, which increases the minimum spacing of the antenna by sacrificing several equivalent phase centers. In MIMO arrays, the minimum antenna spacing in the azimuth direction is doubled, which increases the variety of antenna options for this design. To improve the accuracy of the system, a new method is proposed to estimate channel phase errors, amplitude errors, and position errors. The position error is decomposed into three directions with one compensated by the phase error and two estimated by the strong point. Finally, we validate the accuracy of the system and our error estimation method through simulations and experiments. The results prove that the GB-SAR system performs well in bridge deformation and vibration monitoring with the proposed method. Full article

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17 pages, 2769 KiB

Open AccessArticle

Mapping Dwellings in IDP/Refugee Settlements Using Deep Learning

by Omid Ghorbanzadeh, Alessandro Crivellari, Dirk Tiede, Pedram Ghamisi and Stefan Lang

Remote Sens. 2022, 14(24), 6382; https://doi.org/10.3390/rs14246382 - 16 Dec 2022

Cited by 1 | Viewed by 3406

Abstract

The improvement in computer vision, sensor quality, and remote sensing data availability makes satellite imagery increasingly useful for studying human settlements. Several challenges remain to be overcome for some types of settlements, particularly for internally displaced populations (IDPs) and refugee camps. Refugee-dwelling footprints [...] Read more.

The improvement in computer vision, sensor quality, and remote sensing data availability makes satellite imagery increasingly useful for studying human settlements. Several challenges remain to be overcome for some types of settlements, particularly for internally displaced populations (IDPs) and refugee camps. Refugee-dwelling footprints and detailed information derived from satellite imagery are critical for a variety of applications, including humanitarian aid during disasters or conflicts. Nevertheless, extracting dwellings remains difficult due to their differing sizes, shapes, and location variations. In this study, we use U-Net and residual U-Net to deal with dwelling classification in a refugee camp in northern Cameroon, Africa. Specifically, two semantic segmentation networks are adapted and applied. A limited number of randomly divided sample patches is used to train and test the networks based on a single image of the WorldView-3 satellite. Our accuracy assessment was conducted using four different dwelling categories for classification purposes, using metrics such as Precision, Recall, F1, and Kappa coefficient. As a result, F1 ranges from 81% to over 99% and approximately 88.1% to 99.5% based on the U-Net and the residual U-Net, respectively. Full article

(This article belongs to the Special Issue Advanced Application of Artificial Intelligence and Machine Vision in Remote Sensing II)

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17 pages, 3502 KiB

Open AccessArticle

Evaluation of Hybrid Wavelet Models for Regional Drought Forecasting

by Gilbert Hinge, Jay Piplodiya, Ashutosh Sharma, Mohamed A. Hamouda and Mohamed M. Mohamed

Remote Sens. 2022, 14(24), 6381; https://doi.org/10.3390/rs14246381 - 16 Dec 2022

Cited by 11 | Viewed by 2279

Abstract

Drought forecasting is essential for risk management and preparedness of drought mitigation measures. The present study aims to evaluate the effectiveness of the proposed hybrid technique for regional drought forecasting. Multiple Linear Regression (MLR), Artificial Neural Network (ANN), and two wavelet techniques, namely, [...] Read more.

Drought forecasting is essential for risk management and preparedness of drought mitigation measures. The present study aims to evaluate the effectiveness of the proposed hybrid technique for regional drought forecasting. Multiple Linear Regression (MLR), Artificial Neural Network (ANN), and two wavelet techniques, namely, Discrete Wavelet Transform (DWT) and Wavelet Packet Transform (WPT), were evaluated in drought forecasting up to a lead time of six months. Standard error metrics were used to select optimal model parameters, such as number of inputs, number of hidden neurons, level of decomposition, and number of mother wavelets. Additionally, the performance of various mother wavelets, including the Haar wavelet (db1) and 19 Daubechies wavelets (db1 to db20), were evaluated. The results indicated that the ANN model produced better forecasts than the MLR model, whereas the hybrid models outperformed both ANN and MLR models, which failed to predict the SPI values for a lead time greater than two months. The performance of all the models was found to improve as the timescale increased from 3 to 12 months. However, all the models’ performances deteriorated as the lead time increased. The hybrid WPT-MLR was the best model for the study area. The findings indicated that a hybrid WPT-MLR model could be used for drought early warning systems in the study area. Full article

(This article belongs to the Special Issue Recent Advances in Drought Risk Assessment, Monitoring, and Forecasting)

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18 pages, 6547 KiB

Open AccessArticle

Estimation of Vertical Phase Center Offset and Phase Center Variations for BDS-3 B1CB2a Signals

by Shichao Xie, Guanwen Huang, Le Wang, Xingyuan Yan and Zhiwei Qin

Remote Sens. 2022, 14(24), 6380; https://doi.org/10.3390/rs14246380 - 16 Dec 2022

Cited by 1 | Viewed by 1978

Abstract

The BeiDou Global Satellite Navigation System (BDS-3) broadcast newly developed B1C and B2a signals. To provide a better service for global users, the vertical phase center offset (PCO) and phase center variation (PCV) are estimated for the B1C/B2a ionospheric-free linear combination of the [...] Read more.

The BeiDou Global Satellite Navigation System (BDS-3) broadcast newly developed B1C and B2a signals. To provide a better service for global users, the vertical phase center offset (PCO) and phase center variation (PCV) are estimated for the B1C/B2a ionospheric-free linear combination of the BDS-3 inclined geostationary orbit (IGSO) and medium earth orbit (MEO) satellites in this study. And considering the traditional PCC estimation method needs two Precise orbit determination (POD) processing, based on the correlation between PCO z-offset and PCV, the theoretical analysis and experimental comparison have been made to discuss whether the POD procedure for the PCO estimation can be omitted. The estimated z-offset time series revealed the inadequacy of the solar radiation pressure (SRP) model for the IGSO satellites and the MEO satellites with Pseudo Random Noise code (PRN) C45 and C46. The PCVraws estimated by the traditional method and the PCO estimation omitted method have the same characteristic. The final PCO z-offsets and PCVs calculated by the two schemes agreed very well with differences can be harmlessly ignored, which confirmed that the PCO estimation can be safely omitted to save computation time. The PCC model proposed in this study has been compared with the Test and Assessment Research Center of China Satellite Navigation Office (TARC/CSNO) released model, the qualities of the orbits and BDS-only precise point positioning (PPP) solutions of the new model both show improvements, except for the IGSO orbits. The analysis of the IGSO orbits further verifies the SRP model is not suitable for the IGSO satellites. Full article

(This article belongs to the Special Issue Precision Orbit Determination of Satellites)

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18 pages, 13513 KiB

Open AccessArticle

Study on Radiative Flux of Road Resolution during Winter Based on Local Weather and Topography

by Hyuk-Gi Kwon, Hojin Yang and Chaeyeon Yi

Remote Sens. 2022, 14(24), 6379; https://doi.org/10.3390/rs14246379 - 16 Dec 2022

Cited by 1 | Viewed by 1819

Abstract

Large-scale traffic accidents caused by black ice on roads have increased rapidly; hence, there is an urgent need to prepare safety measures for their prevention. Here, we used local weather road observations and the linkage between weather prediction and a radiation flux model [...] Read more.

Large-scale traffic accidents caused by black ice on roads have increased rapidly; hence, there is an urgent need to prepare safety measures for their prevention. Here, we used local weather road observations and the linkage between weather prediction and a radiation flux model (LDAPS-SOLWEIG) to calculate prediction information regarding habitual shade areas, sky view factor (SVF), and downward shortwave radiative flux by road direction and lane. Using the LDAPS-SOLWEIG model system, a set of real-time weather prediction data (temperature, humidity, wind speed, and insolation at 1.5 km resolution) was applied, and 5 m resolution radiative flux prediction data, with road resolution blocked by local weather and topography, were calculated. We found that the habitual shaded area can be divided by the direction and lane of the road according to the height and shape of the terrain around the road. The downward shortwave radiation flux data from local meteorological observation data and that calculated from the LDAPS-SOLWEIG model system were compared. When road-freezing occurred on a case day, the RMSE was 20.41 W·m⁻², MB was −5.04 W·m⁻², and r was 0.78. The calculated information, habitual shaded area, and SVF can highlight road sections vulnerable to winter freezing and can be helpful in the special management of these areas. Full article

(This article belongs to the Special Issue New Challenges in Solar Radiation, Modeling and Remote Sensing)

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19 pages, 4788 KiB

Open AccessArticle

Three-Dimensional Mapping on Lightning Discharge Processes Using Two VHF Broadband Interferometers

by Zhuling Sun, Xiushu Qie, Mingyuan Liu, Rubin Jiang and Hongbo Zhang

Remote Sens. 2022, 14(24), 6378; https://doi.org/10.3390/rs14246378 - 16 Dec 2022

Cited by 4 | Viewed by 1992

Abstract

Lightning Very-high-frequency (VHF) broadband interferometer has become an effective approach to map lightning channels in two dimensions with high time resolution. This paper reports an approach to mapping lightning channels in three dimensions (3D) using two simultaneous interferometers separated by about 10 km. [...] Read more.

Lightning Very-high-frequency (VHF) broadband interferometer has become an effective approach to map lightning channels in two dimensions with high time resolution. This paper reports an approach to mapping lightning channels in three dimensions (3D) using two simultaneous interferometers separated by about 10 km. A 3D mapping algorithm was developed based on the triangular intersection method considering the location accuracy of both interferometers and the arrival time of lightning VHF radiation. Simulation results reveal that the horizontal and vertical location errors within 10 km of the center of the two stations are less than 500 m and 700 m, respectively. The 3D development of an intra-cloud (IC) lightning flash and a negative cloud-to-ground (-CG) lightning flash with two different ground terminations in the same thunderstorm are reconstructed, and the extension direction and speed of lightning channels are estimated consequently. Both IC and CG flash discharges showed a two-layer structure in the cloud with discharges occurring in the upper positive charge region and the lower negative charge region, and two horizontally separated positive charge regions were involved in the two flashes. The average distance of the CG ground terminations between the interferometer results and the CG location system was about 448 m. Although disadvantages may still exist in 3D real-time location compared with the lightning mapping array system working with the principle of the time of arrival, interferometry with two or more stations has the advantage of lower station number and is feasible in regions with poor installation conditions, such as heavy-radio-frequency-noise regions or regions that are difficult for the long-baseline location system. Full article

(This article belongs to the Special Issue Advances in Instrumentation and Algorithms for Atmospheric Electricity Applications)

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18 pages, 5878 KiB

Open AccessArticle

Possible Overestimation of Nitrogen Dioxide Outgassing during the Beirut 2020 Explosion

by Ashraf Farahat, Nayla El-Kork, Ramesh P. Singh and Feng Jing

Remote Sens. 2022, 14(24), 6377; https://doi.org/10.3390/rs14246377 - 16 Dec 2022

Viewed by 2227

Abstract

On 4 August 2020, a strong explosion occurred near the Beirut seaport, Lebanon and killed more than 200 people and damaged numerous buildings in the vicinity. As Amonium Nitrate (AN) caused the explosion, many studies claimed the release of large amounts of NO [...] Read more.

On 4 August 2020, a strong explosion occurred near the Beirut seaport, Lebanon and killed more than 200 people and damaged numerous buildings in the vicinity. As Amonium Nitrate (AN) caused the explosion, many studies claimed the release of large amounts of NO₂ in the atmosphere may have resulted in a health hazard in Beirut and the vicinity. In order to reasonably evaluate the significance of NO₂ amounts released in the atmosphere, it is important to investigate the spatio-temporal distribution of NO₂ during and after the blast and compare it to the average day-to-day background emissions from vehicle and ship traffic in Beirut. In the present study, we use Sentinel-5 TROPOMI data to study NO₂ emissions in the atmosphere close to the affected area prior, during, and after the Beirut explosion (28 July–8 August 2020). Analysis shows an increase in NO₂ concentrations over Beirut up to about 1.8 mol/m² one day after the explosion that was gradually dissipated in about 4 days. Seven days before the blast (on 28 July 2020) NO₂ concentration was, however, observed to be up to about 4.3 mol/m² over Beirut, which is mostly attributed to vehicle emissions in Lebanon, ships passing by the Beirut seaport and possibly the militant activities in Syria during 20–26 July. It is found that the Beirut blast caused a temporarily and spatially limited increase in NO₂. The blast mostly affected the coastal areas in Lebanon, while it did not have much effect on inland regions. TROPOMI data are also analyzed for the Greater Cairo Area (GCA), Suez Canal, Egypt, and in Nicosia, Cyprus to confirm the effect of human activities, vehicles, and ship traffic on NO₂ emissions in relatively high and relatively low populated zones. Full article

(This article belongs to the Section Atmospheric Remote Sensing)

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Remote Sens., Volume 14, Issue 24 (December-2 2022) – 222 articles

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