Next Issue
Volume 14, October-1
Previous Issue
Volume 14, September-1
 
 
remotesensing-logo

Journal Browser

Journal Browser

Remote Sens., Volume 14, Issue 18 (September-2 2022) – 257 articles

Cover Story (view full-size image): The Global Satellite Mapping of Precipitation (GSMaP) provides multi-satellite precipitation products using multiple passive microwave (PMW) imagers/sounders. Different specifications of the instruments and different precipitation estimation algorithms cause inconsistent estimates for each satellite sensor. To mitigate the discrepancy of GSMaP estimates among PMW sensors, the Method of Microwave Rainfall Normalization (MMN) is implemented in its latest version (V05), released in December 2021. The basic idea of the MMN is to calibrate target PMW sensors with reference sensors using the cumulative distribution function of the precipitation rate in the last 30 days. Differences in the frequency of occurrence of rainfall intensity is investigated using an MMN table. The monthly mean rainfall and mean bias error are improved. View this paper
  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.
Order results
Result details
Section
Select all
Export citation of selected articles as:
20 pages, 10842 KiB  
Article
Study and Prediction of Surface Deformation Characteristics of Different Vegetation Types in the Permafrost Zone of Linzhi, Tibet
by Xiaoci Wang, Qiang Yu, Jun Ma, Linzhe Yang, Wei Liu and Jianzheng Li
Remote Sens. 2022, 14(18), 4684; https://doi.org/10.3390/rs14184684 - 19 Sep 2022
Cited by 2 | Viewed by 1947
Abstract
Permafrost and alpine vegetation are widely distributed in Tibet, which is a sensitive area for global climate change. In this study, we inverted the surface deformation from 22 May 2018 to 9 October 2021 in a rectangular area within the city of Linzhi, [...] Read more.
Permafrost and alpine vegetation are widely distributed in Tibet, which is a sensitive area for global climate change. In this study, we inverted the surface deformation from 22 May 2018 to 9 October 2021 in a rectangular area within the city of Linzhi, Tibet, using the Sentinel1-A data and two time-series interferometric system aperture radar (InSAR) techniques. Then, the significant features of surface deformation were analyzed separately according to different vegetation types. Finally, multiple machine learning methods were used to predict future surface deformation, and the results were compared to obtain the model with the highest prediction accuracy. This study aims to provide a scientific reference and decision basis for global ecological security and sustainable development. The results showed that the surface deformation rate in the study area was basically between ±10 mm/a, and the cumulative surface deformation was basically between ±35 mm. The surface deformation of grassland, meadow, coniferous forest, and alpine vegetation were all significantly correlated with NDVI, and the effect of alpine vegetation, coniferous forest, and grassland on permafrost was stronger than that of the meadow. The prediction accuracy of the Holt–Winters model was higher than that of Holt′s model and the ARIMA model; it was expected that the ground surface would keep rising in the next two months, and the ground surface deformation of alpine vegetation and the coniferous forest was relatively small. The above studies indicated that the surface deformation in the Tibetan permafrost region was relatively stable under the conditions of alpine vegetation and coniferous forest. Future-related ecological construction needs to pay more attention to permafrost areas under grassland and meadow conditions, which are prone to surface deformation and affect the stability of ecosystems. Full article
Show Figures

Graphical abstract

16 pages, 1533 KiB  
Article
Effects of Anthropogenic Emission Control and Meteorology Changes on the Inter-Annual Variations of PM2.5–AOD Relationship in China
by Ling Qi, Haotian Zheng, Dian Ding and Shuxiao Wang
Remote Sens. 2022, 14(18), 4683; https://doi.org/10.3390/rs14184683 - 19 Sep 2022
Cited by 3 | Viewed by 1507
Abstract
We identified controlling factors of the inter-annual variations of surface PM2.5–aerosol optical depth (AOD) relationship in China from 2006 to 2017 using a nested 3D chemical transport model—GEOS-Chem. We separated the contributions from anthropogenic emission control and meteorological changes by fixing [...] Read more.
We identified controlling factors of the inter-annual variations of surface PM2.5–aerosol optical depth (AOD) relationship in China from 2006 to 2017 using a nested 3D chemical transport model—GEOS-Chem. We separated the contributions from anthropogenic emission control and meteorological changes by fixing meteorology at the 2009 level and fixing anthropogenic emissions at the 2006 level, respectively. Both observations and model show significant downward trends of PM2.5/AOD ratio (η, p < 0.01) in the North China Plain (NCP), the Yangtze River Delta (YRD) and the Pearl River Delta (PRD) in 2006–2017. The model suggests that the downward trends are mainly attributed to anthropogenic emission control. PM2.5 concentration reduces faster at the surface than aloft due to the closeness of surface PM2.5 to emission sources. The Pearson correlation coefficient of surface PM2.5 and AOD (rPM-AOD) shows strong inter-annual variations (±27%) but no statistically significant trends in the three regions. The inter-annual variations of rPM-AOD are mainly determined by meteorology changes. Except for the well-known effects from relative humidity, planetary boundary layer height and wind speed, we find that temperature, tropopause pressure, surface pressure and atmospheric instability are also important meteorological elements that have a strong correlation with inter-annual variations of rPM-AOD in different seasons. This study suggests that as the PM2.5–AOD relationship weakens with reduction of anthropogenic emissions, validity of future retrieval of surface PM2.5 using satellite AOD should be carefully evaluated. Full article
(This article belongs to the Section Atmospheric Remote Sensing)
Show Figures

Figure 1

18 pages, 6287 KiB  
Article
SSML: Spectral-Spatial Mutual-Learning-Based Framework for Hyperspectral Pansharpening
by Xianlin Peng, Yihao Fu, Shenglin Peng, Kai Ma, Lu Liu and Jun Wang
Remote Sens. 2022, 14(18), 4682; https://doi.org/10.3390/rs14184682 - 19 Sep 2022
Viewed by 1804
Abstract
This paper considers problems associated with the large size of the hyperspectral pansharpening network and difficulties associated with learning its spatial-spectral features. We propose a deep mutual-learning-based framework (SSML) for spectral-spatial information mining and hyperspectral pansharpening. In this framework, a deep mutual-learning mechanism [...] Read more.
This paper considers problems associated with the large size of the hyperspectral pansharpening network and difficulties associated with learning its spatial-spectral features. We propose a deep mutual-learning-based framework (SSML) for spectral-spatial information mining and hyperspectral pansharpening. In this framework, a deep mutual-learning mechanism is introduced to learn spatial and spectral features from each other through information transmission, which achieves better fusion results without entering too many parameters. The proposed SSML framework consists of two separate networks for learning spectral and spatial features of HSIs and panchromatic images (PANs). A hybrid loss function containing constrained spectral and spatial information is designed to enforce mutual learning between the two networks. In addition, a mutual-learning strategy is used to balance the spectral and spatial feature learning to improve the performance of the SSML path compared to the original. Extensive experimental results demonstrated the effectiveness of the mutual-learning mechanism and the proposed hybrid loss function for hyperspectral pan-sharpening. Furthermore, a typical deep-learning method was used to confirm the proposed framework’s capacity for generalization. Ideal performance was observed in all cases. Moreover, multiple experiments analysing the parameters used showed that the proposed method achieved better fusion results without adding too many parameters. Thus, the proposed SSML represents a promising framework for hyperspectral pansharpening. Full article
(This article belongs to the Special Issue Remote Sensing and Machine Learning of Signal and Image Processing)
Show Figures

Figure 1

19 pages, 8667 KiB  
Article
Decoupled Object-Independent Image Features for Fine Phasing of Segmented Mirrors Using Deep Learning
by Yirui Wang, Chunyue Zhang, Liang Guo, Shuyan Xu and Guohao Ju
Remote Sens. 2022, 14(18), 4681; https://doi.org/10.3390/rs14184681 - 19 Sep 2022
Cited by 1 | Viewed by 1690
Abstract
A segmented primary mirror is very important for extra-large astronomical telescopes, in order to detect the phase error between segmented mirrors. Traditional iterative algorithms are hard to detect co−phasing aberrations in real time due to the long-time iterative process. Deep learning has shown [...] Read more.
A segmented primary mirror is very important for extra-large astronomical telescopes, in order to detect the phase error between segmented mirrors. Traditional iterative algorithms are hard to detect co−phasing aberrations in real time due to the long-time iterative process. Deep learning has shown large potential in wavefront sensing, and it gradually focuses on detecting piston error. However, the current methods based on deep learning are mainly applied to coarse phase sensing, and only consider the detection of piston error with no tip/tilt errors, which is inconsistent with reality. In this paper, by innovatively designing the form of pupil mask, and further updating the OTF in the frequency domain, we obtain a new decoupled independent feature image that can simultaneously detect the piston error and tilt/tilt error of all sub-mirrors, which is effectively decoupled, and eliminates the dependence of the data set on the imaging object. Then, the Bi−GRU network is used to recover phase error information with high accuracy from the feature image proposed in this paper. The network’s detection accuracy ability is verified under single wavelength and broadband spectrum in simulation. This paper demonstrates that co−phasing errors can be accurately decoupled and extracted by the new feature image we proposed and will contribute to the fine phasing accuracy and practicability of the extended scenes for the segmented telescopes. Full article
Show Figures

Figure 1

24 pages, 7060 KiB  
Article
Multiscale Ground Validation of Satellite and Reanalysis Precipitation Products over Diverse Climatic and Topographic Conditions
by Muhammad Umer Nadeem, Abdulnoor A. J. Ghanim, Muhammad Naveed Anjum, Donghui Shangguan, Ghulam Rasool, Muhammad Irfan, Usama Muhammad Niazi and Sharjeel Hassan
Remote Sens. 2022, 14(18), 4680; https://doi.org/10.3390/rs14184680 - 19 Sep 2022
Cited by 12 | Viewed by 2066
Abstract
The validity of two reanalysis (ERA5 and MEERA2) and seven satellite-based (CHIRPS, IMERG, PERSIANN-CCS, PERSIANN-CDR, PERSIANN-PDIR, PERSIANN, and TRMM) precipitation products was assessed in relation to the observations of in situ weather stations installed in different topographical and climatic regions of Pakistan. From [...] Read more.
The validity of two reanalysis (ERA5 and MEERA2) and seven satellite-based (CHIRPS, IMERG, PERSIANN-CCS, PERSIANN-CDR, PERSIANN-PDIR, PERSIANN, and TRMM) precipitation products was assessed in relation to the observations of in situ weather stations installed in different topographical and climatic regions of Pakistan. From 2010 to 2018, all precipitation products were evaluated on daily, monthly, seasonal, and annual bases at a point-to-pixel scale and over the entire spatial domain. The accuracy of the products was evaluated using commonly used evaluation and categorical indices, including Root Mean Square Error (RMSE), Correlation Coefficient (CC), Bias, Relative Bias (rBias), Critical Success Index (CSI), Success Ratio (SR) Probability of Detection (POD), and False Alarm Ratio (FAR). The results show that: (1) Over the entire country, the spatio-temporal distribution of observed precipitation could be represented by IMERG and TRMM products. (2) All products (reanalysis and SPPs) demonstrated good agreement with the reference data at the monthly scale compared to the daily data (CC > 0.7 at monthly scale). (3) All other products were outperformed by IMERG and TRMM in terms of their capacity to detect precipitation events throughout the year, regardless of the season (i.e., winter, spring, summer, and autumn). Furthermore, both products (IMERG and TRMM) consistently depicted the incidence of precipitation events across Pakistan’s various topography and climatic regimes. (4) Generally, CHIRPS and ERA5 products showed moderate performances in the plan areas. PERSIANN, PERSIANN-CCS, PDIR, PERSIANN-CDR, and MEERA2 products were uncertain to detect the occurrence and precipitation over the higher intensities and altitudes. Considering the finding of this assessment, we recommend the use of daily and monthly estimates of the IMERG product for hydro climatic studies in Pakistan. Full article
(This article belongs to the Special Issue Remote Sensing of Clouds and Precipitation at Multiple Scales II)
Show Figures

Graphical abstract

22 pages, 5128 KiB  
Article
Inter-Calibration and Statistical Validation of Topside Ionosphere Electron Density Observations Made by CSES-01 Mission
by Alessio Pignalberi, Michael Pezzopane, Igino Coco, Mirko Piersanti, Fabio Giannattasio, Paola De Michelis, Roberta Tozzi and Giuseppe Consolini
Remote Sens. 2022, 14(18), 4679; https://doi.org/10.3390/rs14184679 - 19 Sep 2022
Cited by 8 | Viewed by 1670
Abstract
The China Seismo-Electromagnetic Satellite (CSES-01) provides in situ electron density (Ne) observations through Langmuir probes (LPs) in the topside ionosphere since February 2018. CSES-01 is a sun-synchronous satellite probing the ionosphere around two fixed local times (LTs), 14 LT in the [...] Read more.
The China Seismo-Electromagnetic Satellite (CSES-01) provides in situ electron density (Ne) observations through Langmuir probes (LPs) in the topside ionosphere since February 2018. CSES-01 is a sun-synchronous satellite probing the ionosphere around two fixed local times (LTs), 14 LT in the daytime sector and 02 LT in the night-time sector, at an altitude of about 500 km. Previous studies evidenced that CSES-01 seems to underestimate Ne measurements with respect to those acquired by similar satellites or obtained from different instruments. To overcome this issue, we calibrated CSES-01 LP Ne observations through Swarm B satellite data, which flies approximately at CSES-01 altitude. As a first step, Swarm B LP Ne observations were calibrated through Faceplate (FP) Ne observations from the same satellite. Such calibration allowed solving the Ne overestimation made by Swarm LP during nighttime for low solar activity. Then, the calibrated Swarm B LP Ne observations were used to calibrate CSES-01 Ne observations on a statistical basis. Finally, the goodness of the proposed calibration procedure was statistically assessed through a comparison with Ne observations by incoherent scatter radars (ISRs) located at Jicamarca, Arecibo, and Millstone Hill. The proposed calibration procedure allowed solving the CSES-01 Ne underestimation issue for both daytime and nighttime sectors and brought CSES-01 Ne observations in agreement with corresponding ones measured by Swarm B, ISRs, and with those modelled by the International Reference Ionosphere (IRI). This is a first fundamental step towards a possible future inclusion of CSES-01 Ne observations in the dataset underlying IRI for the purpose of improving the description of the topside ionosphere made by IRI. Full article
Show Figures

Figure 1

14 pages, 4079 KiB  
Article
Analysis of GNSS-Derived Tropospheric Zenith Non-Hydrostatic Delay Anomaly during Sandstorms in Northern China on 15th March 2021
by Maosheng Zhou, Jinyun Guo, Xin Liu, Rui Hou and Xin Jin
Remote Sens. 2022, 14(18), 4678; https://doi.org/10.3390/rs14184678 - 19 Sep 2022
Cited by 5 | Viewed by 1827
Abstract
On the 15th of March 2021, the strongest sandstorm in a decade occurred in northern China, and had a great adverse impact on the natural environment and human health in northern China. Real-time monitoring of dust storms is becoming increasingly important. In order [...] Read more.
On the 15th of March 2021, the strongest sandstorm in a decade occurred in northern China, and had a great adverse impact on the natural environment and human health in northern China. Real-time monitoring of dust storms is becoming increasingly important. In order to effectively analyze the non-hydrostatic delay (ZNHD) anomaly during a sandstorm, the method based on GNSS-derived tropospheric ZNHD residual to monitor the sandstorm is proposed at the same time. We studied the relationship between ZNHD/PWV and PM10/PM2.5 in Beijing, Changchun, Pingliang and Zhongwei before and after sandstorms. The ZNHD time series was then decomposed by singular spectrum analysis (SSA) and the residuals were obtained. The relationship between the GNSS-derived ZNHD residual and PM10 was analyzed. The results show that the impact of the sandstorm on PM10 is greater than that on PM2.5. Before the sandstorm, the correlation between PM10 and ZNHD was low, less than 0.25. When the sandstorm occurred, the correlation between PM10 and ZNHD increased significantly, and the maximum was greater than 0.7. When the sandstorm ended, the correlation between PM10 and ZNHD decreased significantly. Through the relationship between the ZNHD residual and PM10, it can be found that when the peak-to-peak values of the ZNHD residual are all above 80 mm, sandstorms may occur. But Rainfall, snowfall, haze and other abnormal weather can also lead to ZNHD anomalies. Full article
(This article belongs to the Special Issue Remote Sensing in Space Geodesy and Cartography Methods)
Show Figures

Figure 1

12 pages, 2921 KiB  
Communication
Land Use and Land Cover Influence on Sentinel-2 Aerosol Optical Depth below City Scales over Beijing
by Yue Yang, Jan Cermak, Kangzhuo Yang, Eva Pauli and Yunping Chen
Remote Sens. 2022, 14(18), 4677; https://doi.org/10.3390/rs14184677 - 19 Sep 2022
Cited by 3 | Viewed by 1926
Abstract
Atmospheric aerosols can impact human health, necessitating the understanding of their distribution determinants, especially in urban areas. The study discusses the relationships between five major land cover types and aerosol optical depth (AOD) within a city combining the high-resolution satellite-derived AOD products (derived [...] Read more.
Atmospheric aerosols can impact human health, necessitating the understanding of their distribution determinants, especially in urban areas. The study discusses the relationships between five major land cover types and aerosol optical depth (AOD) within a city combining the high-resolution satellite-derived AOD products (derived from Sentinel-2) and land cover products (60 m and 100 m, respectively) for Beijing and its surroundings from 2017 to 2019. Contribution analysis is performed to quantitatively evaluate the influences of land cover on regional AOD over the study area. Patterns of aerosol distribution remarkably vary in time and space. Statistics of seasonal average AOD peak in spring and then progressively decline from summer through autumn to winter. High AOD values coincide with a low normalized difference vegetation index (NDVI) and a high normalized difference built-up index (NDBI). Urban and built-up land is a major contributor to regional AOD in the study area, especially in spring; forest and grassland always reduce AOD. Anthropogenic activities have a non-negligible influence on AOD and can even reverse the contribution of a land cover type to aerosols. Insights of the study promote the comprehension of the impacts of land cover on aerosols and air pollution and contribute to the planning of land use within a city. Full article
(This article belongs to the Topic Recent Progress in Aerosol Remote Sensing and Products)
Show Figures

Figure 1

37 pages, 40019 KiB  
Article
Triangle Distance IoU Loss, Attention-Weighted Feature Pyramid Network, and Rotated-SARShip Dataset for Arbitrary-Oriented SAR Ship Detection
by Zhijing Xu, Rui Gao, Kan Huang and Qihui Xu
Remote Sens. 2022, 14(18), 4676; https://doi.org/10.3390/rs14184676 - 19 Sep 2022
Cited by 11 | Viewed by 2713
Abstract
In synthetic aperture radar (SAR) images, ship targets are characterized by varying scales, large aspect ratios, dense arrangements, and arbitrary orientations. Current horizontal and rotation detectors fail to accurately recognize and locate ships due to the limitations of loss function, network structure, and [...] Read more.
In synthetic aperture radar (SAR) images, ship targets are characterized by varying scales, large aspect ratios, dense arrangements, and arbitrary orientations. Current horizontal and rotation detectors fail to accurately recognize and locate ships due to the limitations of loss function, network structure, and training data. To overcome the challenge, we propose a unified framework combining triangle distance IoU loss (TDIoU loss), an attention-weighted feature pyramid network (AW-FPN), and a Rotated-SARShip dataset (RSSD) for arbitrary-oriented SAR ship detection. First, we propose a TDIoU loss as an effective solution to the loss-metric inconsistency and boundary discontinuity in rotated bounding box regression. Unlike recently released approximate rotational IoU losses, we derive a differentiable rotational IoU algorithm to enable back-propagation of the IoU loss layer, and we design a novel penalty term based on triangle distance to generate a more precise bounding box while accelerating convergence. Secondly, considering the shortage of feature fusion networks in connection pathways and fusion methods, AW-FPN combines multiple skip-scale connections and attention-weighted feature fusion (AWF) mechanism, enabling high-quality semantic interactions and soft feature selections between features of different resolutions and scales. Finally, to address the limitations of existing SAR ship datasets, such as insufficient samples, small image sizes, and improper annotations, we construct a challenging RSSD to facilitate research on rotated ship detection in complex SAR scenes. As a plug-and-play scheme, our TDIoU loss and AW-FPN can be easily embedded into existing rotation detectors with stable performance improvements. Experiments show that our approach achieves 89.18% and 95.16% AP on two SAR image datasets, RSSD and SSDD, respectively, and 90.71% AP on the aerial image dataset, HRSC2016, significantly outperforming the state-of-the-art methods. Full article
(This article belongs to the Special Issue Synthetic Aperture Radar (SAR) Meets Deep Learning)
Show Figures

Graphical abstract

16 pages, 9517 KiB  
Article
Building Floorplan Reconstruction Based on Integer Linear Programming
by Qiting Wang, Zunjie Zhu, Ruolin Chen, Wei Xia and Chenggang Yan
Remote Sens. 2022, 14(18), 4675; https://doi.org/10.3390/rs14184675 - 19 Sep 2022
Cited by 4 | Viewed by 1777
Abstract
The reconstruction of the floorplan for a building requires the creation of a two-dimensional floorplan from a 3D model. This task is widely employed in interior design and decoration. In reality, the structures of indoor environments are complex with much clutter and occlusions, [...] Read more.
The reconstruction of the floorplan for a building requires the creation of a two-dimensional floorplan from a 3D model. This task is widely employed in interior design and decoration. In reality, the structures of indoor environments are complex with much clutter and occlusions, making it difficult to reconstruct a complete and accurate floorplan. It is well known that a suitable dataset is a key point to drive an effective algorithm, while existing datasets of floorplan reconstruction are synthetic and small. Without reliable accumulations of real datasets, the robustness of methods to real scene reconstruction is weakened. In this paper, we first annotate a large-scale realistic benchmark, which contains RGBD image sequences and 3D models of 80 indoor scenes with more than 10,000 square meters. We also introduce a framework for the floorplan reconstruction with mesh-based point cloud normalization. The loose-Manhattan constraint is performed in our optimization process, and the optimal floorplan is reconstructed via constraint integer programming. The experimental results on public and our own datasets demonstrate that the proposed method outperforms FloorNet and Floor-SP. Full article
Show Figures

Figure 1

17 pages, 5219 KiB  
Article
High-Accuracy Clock Offsets Estimation Strategy of BDS-3 Using Multi-Source Observations
by Jianhua Yang, Chengpan Tang, Sanshi Zhou, Yezhi Song, Jinhuo Liu, Yu Xiang, Yuchen Liu, Qiuning Tian, Yufei Yang, Zuo Yang and Xiaogong Hu
Remote Sens. 2022, 14(18), 4674; https://doi.org/10.3390/rs14184674 - 19 Sep 2022
Cited by 3 | Viewed by 1588
Abstract
Satellite clock offsets are the critical parameters for The Global Navigation Satellite Systems (GNSSs) to provide position and timing (PNT) service. Unlike other GNSSs, BDS-3 uses the two-way superimposition strategy to measure satellite clock offsets. However, affected by some deficiencies of the two-way [...] Read more.
Satellite clock offsets are the critical parameters for The Global Navigation Satellite Systems (GNSSs) to provide position and timing (PNT) service. Unlike other GNSSs, BDS-3 uses the two-way superimposition strategy to measure satellite clock offsets. However, affected by some deficiencies of the two-way superimposition strategy, the accuracy of BDS-3 clock offsets parameters is 1.29 ns (RMS), which is the main bottleneck for BDS-3 to improve its space signal accuracy. After analyzing problems in the clock offsets measurement process of BDS-3, the paper proposes a new strategy to real-time estimate high-accuracy satellite clock offsets. The clock offsets estimated by the new strategy show a good consistency with GBM clock offsets. The averaged STD of their differences in MEO is 0.14 ns, and the clock offsets estimated by the new strategy present less fluctuation in the 1-day fitting residuals. Applying the new clock offsets to prediction, BDS-3 can reduce its clock offsets errors from 1.05 ns to 0.29 ns (RMS), about 72%. The above results indicate that the new clock offsets estimated strategy can improve the accuracy of clock offsets parameters of BDS-3 effectively. Full article
Show Figures

Graphical abstract

17 pages, 6296 KiB  
Article
Assessment of Galileo FOC + IOV Signals and Geometry-Based Single-Epoch Resolution of Quad-Frequency Carrier Ambiguities
by Chunyang Liu, Chao Liu, Jian Wang, Xingwang Zhao, Jian Chen and Ya Fan
Remote Sens. 2022, 14(18), 4673; https://doi.org/10.3390/rs14184673 - 19 Sep 2022
Viewed by 1541
Abstract
Galileo can independently provide navigation and positioning services globally. Galileo satellites transmit quad-frequency E1, E5a, E5b, and E5 signals, which can benefit the integer ambiguity rapid resolution. Firstly, the qualities of Galileo signals from Carrier-to-Noise (C/N0), Multipath Combination (MPC), and pseudo-range and phase [...] Read more.
Galileo can independently provide navigation and positioning services globally. Galileo satellites transmit quad-frequency E1, E5a, E5b, and E5 signals, which can benefit the integer ambiguity rapid resolution. Firstly, the qualities of Galileo signals from Carrier-to-Noise (C/N0), Multipath Combination (MPC), and pseudo-range and phase noise using the ultra-short baseline were evaluated. The experimental results indicated that the Galileo E5 signal has the highest C/N0, while the C/N0 of other signals is lower and almost equal. In terms of MPC, the Galileo E1 was the most severe followed by E5a and E5b, and the MPC of E5 is less severe. As for the precision of un-differenced observations, the carrier phase and pseudo-range observations of Galileo E5 had higher accuracy than those of Galileo E5a, E5b, and E1. Secondly, the quad-frequency observations allowed for various linear combinations of different frequencies, which provides some feasibility for improving the performance of ambiguity resolution. Assuming that the phase noise σΔΦ = 0.01 m and the first-order ionosphere σΔI1 = 1 m, the total noise of the Extra-Wide-Lane (EWL) combination observation ((0, 0, 1, −1) and (0, −1, 1, 0)) and Very-Wide-Lane (VWL) combination observation ((0, −2, 1, 1), (0, −3, 2, 1)) are still less than 0.5 cycles. Finally, a geometry-based quad-frequency carrier ambiguities (GB-QCAR) method was developed, and all different options of linear combinations were investigated systematically from the ambiguity-fixed rate with two baselines. Experimental results demonstrated that, the ambiguity fixed rate of combination observation (0, −1, 1, 0), (0, −3, 5, −2), (1, −1, 0, 0) and (0, 0, 0, 1) is the highest and the positioning accuracy of VWL combination observation (0, −3, 5, −2) is equivalent to that of the EWL combination observation (0, −1, 1, 0). The positioning accuracies of WL combination observation (1, −1, 0, 0) are preferable to 3 cm and 10 cm in the horizontal and vertical, respectively. The positioning accuracy of NL combination observation E5 in the horizontal direction is about 1 cm, and is better than 4 cm in the vertical direction. Therefore, we can use Galileo observations to realize high-precise navigation services utilizing the proposed GB-QCAR method. Full article
Show Figures

Figure 1

22 pages, 5498 KiB  
Article
RFI Detection and Mitigation for Advanced Correlators in Interferometric Radiometers
by Adrian Perez-Portero, Jorge Querol, Adriano Camps, Manuel Martin-Neira, Martin Suess, Juan Ignacio Ramirez, Alberto Zurita, Josep Closa, Roger Oliva and Raul Onrubia
Remote Sens. 2022, 14(18), 4672; https://doi.org/10.3390/rs14184672 - 19 Sep 2022
Cited by 2 | Viewed by 1859
Abstract
This work presents the first RFI detection and mitigation algorithm for the interferometric radiometers that will be implemented in its correlator unit. The algorithm operates in the time and frequency domains, applying polarimetric and statistical tests in both domains, and exhibiting a tunable [...] Read more.
This work presents the first RFI detection and mitigation algorithm for the interferometric radiometers that will be implemented in its correlator unit. The algorithm operates in the time and frequency domains, applying polarimetric and statistical tests in both domains, and exhibiting a tunable and arbitrary low probability of false alarm. It is scalable to a configurable number of receivers, and it is optimized in terms of quantization bits and the implementation of the cross-correlations in the time or frequency domains for hardware resource saving. New features of this algorithm are the computation of the Stokes parameters per frequency bin in the Short-Time Fourier Transform and a new parameter called Polarimetric Kurtosis. If RFI is detected in one domain or in both, it is removed using the calculated blanking masks. The optimum algorithm parameters are computed, such as length of the FFTs, the threshold selection for a given probability of false alarm, and the selection of the blanking masks. Last, an important result refers to the application of Parseval’s theorem for the computation of the cross-correlations in the frequency domain, instead of in the time domain, which is more efficient and leads to smaller errors even when using moderate quantization levels. The algorithm has been developed in the framework of the ESA’s technology preparation for a potential L-band radiometer mission beyond SMOS. However, it is also applicable to (polarimetric) real aperture radiometers, and its performance would improve if more than one bit is used in the signal quantization. Full article
(This article belongs to the Special Issue New Technologies for Earth Remote Sensing)
Show Figures

Figure 1

24 pages, 4718 KiB  
Article
An Integrated Quantitative Method Based on ArcGIS Evaluating the Contribution of Rural Straw Open Burning to Urban Fine Particulate Pollution
by Xin Wen, Weiwei Chen, Pingyu Zhang, Jie Chen and Guoqing Song
Remote Sens. 2022, 14(18), 4671; https://doi.org/10.3390/rs14184671 - 19 Sep 2022
Cited by 2 | Viewed by 1696
Abstract
This study presents a GIS-based method integrating hourly transport pathways and wind-field grid reconstruction, straw open burning (SOB) source identification, and a two-stage spatiotemporal multi-box modeling approach to quantify the contribution of external sources of SOB to elevated urban PM2.5 concentrations during [...] Read more.
This study presents a GIS-based method integrating hourly transport pathways and wind-field grid reconstruction, straw open burning (SOB) source identification, and a two-stage spatiotemporal multi-box modeling approach to quantify the contribution of external sources of SOB to elevated urban PM2.5 concentrations during a specific pollution episode (PE) at a high temporal resolution of 1 h. Taking Jilin Province as an empirical study, the contribution of SOB in province-wide farmlands to urban haze episodes in Changchun during the SOB season of 2020–2021 was evaluated quantitatively using a combination of multi-source datasets. The results showed that Changchun experienced three severe PEs and one heavy PE during the study period, and the total PM2.5 contributions from SOB sources were 352 μg m−3, 872 μg m−3, and 1224 μg m−3 during the three severe PEs, respectively; these accounted for 7%, 27%, and 23% of the urban cumulative PM2.5 levels, which were more obvious than the contribution during the PE. The total PM2.5 contribution from SOB sources (4.9 μg m−3) was only 0.31% of the urban cumulative PM2.5 level during the heavy PE. According to the analysis of the impact of individual factors, some policy suggestions are put forward for refined SOB management, including control spatial scope, burning time interval, as well as burning area limit under different urban and transport pathways’ meteorological conditions and different transport distances. Full article
(This article belongs to the Special Issue Effect of Biomass-Burning on Atmosphere Using Remote Sensing)
Show Figures

Figure 1

19 pages, 11871 KiB  
Article
A Ship Detection and Imagery Scheme for Airborne Single-Channel SAR in Coastal Regions
by Zhenyu Li, Jianlai Chen, Yi Xiong, Hanwen Yu, Huaigen Zhang and Bing Gao
Remote Sens. 2022, 14(18), 4670; https://doi.org/10.3390/rs14184670 - 19 Sep 2022
Cited by 1 | Viewed by 1447
Abstract
Ship detection and management in coastal regions are challenging tasks due to the complex appearances of ships and the background. For further applications in the context of fisheries monitoring and vessel traffic services, a single-channel synthetic aperture radar (SAR) is mounted on a [...] Read more.
Ship detection and management in coastal regions are challenging tasks due to the complex appearances of ships and the background. For further applications in the context of fisheries monitoring and vessel traffic services, a single-channel synthetic aperture radar (SAR) is mounted on a number of maneuvering and inexpensive rotor platforms, which are utilized according to the consideration of flexible observation, cost savings, weight, and space constraints. In this paper, a hierarchical scheme of ship detection, ship imaging, and classification is proposed. It mainly includes three parts. First, a mixture statistical model of semi-parametric K-lognormal distribution based on adaptive background windows with a constant false alarm rate (CFAR) is proposed for ship prescreening in SAR imagery. Then, the discrimination stage, combined with ship imaging via the difference between the true ship targets and the false ones in the aspects of micro-Doppler motion properties, is performed. Finally, the simulation and field data processing results are presented to validate the proposed scheme. Full article
Show Figures

Figure 1

20 pages, 19379 KiB  
Article
SEAN: A Simple and Efficient Attention Network for Aircraft Detection in SAR Images
by Ping Han, Dayu Liao, Binbin Han and Zheng Cheng
Remote Sens. 2022, 14(18), 4669; https://doi.org/10.3390/rs14184669 - 19 Sep 2022
Cited by 3 | Viewed by 2559
Abstract
Due to the unique imaging mechanism of synthetic aperture radar (SAR), which leads to a discrete state of aircraft targets in images, its detection performance is vulnerable to the influence of complex ground objects. Although existing deep learning detection algorithms show good performance, [...] Read more.
Due to the unique imaging mechanism of synthetic aperture radar (SAR), which leads to a discrete state of aircraft targets in images, its detection performance is vulnerable to the influence of complex ground objects. Although existing deep learning detection algorithms show good performance, they generally use a feature pyramid neck design and large backbone network, which reduces the detection efficiency to some extent. To address these problems, we propose a simple and efficient attention network (SEAN) in this paper, which takes YOLOv5s as the baseline. First, we shallow the depth of the backbone network and introduce a structural re-parameterization technique to increase the feature extraction capability of the backbone. Second, the neck architecture is designed by using a residual dilated module (RDM), a low-level semantic enhancement module (LSEM), and a localization attention module (LAM), substantially reducing the number of parameters and computation of the network. The results on the Gaofen-3 aircraft target dataset show that this method achieves 97.7% AP at a speed of 83.3 FPS on a Tesla M60, exceeding YOLOv5s by 1.3% AP and 8.7 FPS with 40.51% of the parameters and 86.25% of the FLOPs. Full article
(This article belongs to the Special Issue Microwave Remote Sensing for Object Detection)
Show Figures

Figure 1

19 pages, 14591 KiB  
Article
Large-Scale Monitoring of Glacier Surges by Integrating High-Temporal- and -Spatial-Resolution Satellite Observations: A Case Study in the Karakoram
by Linghong Ke, Jinshan Zhang, Chenyu Fan, Jingjing Zhou and Chunqiao Song
Remote Sens. 2022, 14(18), 4668; https://doi.org/10.3390/rs14184668 - 19 Sep 2022
Cited by 2 | Viewed by 1882
Abstract
Glacier surges have been increasingly reported from the mountain and high-latitude cryosphere. They represent active glaciological processes that affect the evolution of natural landscapes, and they possibly lead to catastrophic consequences, such as ice collapse, which threatens the downstream communities. Identifying and monitoring [...] Read more.
Glacier surges have been increasingly reported from the mountain and high-latitude cryosphere. They represent active glaciological processes that affect the evolution of natural landscapes, and they possibly lead to catastrophic consequences, such as ice collapse, which threatens the downstream communities. Identifying and monitoring surge-type glaciers has been challenging due to the irregularity of the behavior and limitations on the spatiotemporal coverage of remote-sensing observations. With a focus on the Karakoram region, with concentrated surge-type glaciers, we present a new method to efficiently detect glacier-surging activities by integrating the high temporal resolution of MODIS imagery and the long-term archived medium spatial resolution of Landsat imagery. This method first detects the location and initial time of glacier surges by trend analysis (trend and breakpoint) from MODIS data, which is implemented by the Breaks for Additive Seasonal and Trend (BFAST) tool. The initial location and time information is then validated with the detailed surging features, such as the terminus-position changes from Landsat, and the thickness-change patterns from surface-elevation-change maps. Our method identified 74 surging events during 2000–2020 in the Karakoram, including three tributary-glacier surges, and seven newly detected surge-type glaciers. The surge-type glaciers tend to have longer lengths and smaller mean slopes compared with nonsurge-type glaciers. A comparison with previous studies demonstrated the method efficiency for detecting the surging of large-scale and mesoscale glaciers, with limitations on small and narrow glaciers due to the spatial-resolution limitation of MODIS images. For the 38 surge-type nondebris-covered glaciers, we provide details of the surging, which depict the high variability (heavy-tailed distribution) in the surging parameters in the region, and the concentration of the surge initiation during 2008–2010 and 2013–2015. The updated glacier-surging information solidifies the basis for a further investigation of the surging processes at polythermal glaciers, and for an improved assessment of the glacier-mass balance and monitoring of glacier hazards. Full article
Show Figures

Figure 1

14 pages, 2993 KiB  
Technical Note
Water Levels in the Major Reservoirs of the Nile River Basin—A Comparison of SENTINEL with Satellite Altimetry Data
by Prakrut Kansara and Venkataraman Lakshmi
Remote Sens. 2022, 14(18), 4667; https://doi.org/10.3390/rs14184667 - 19 Sep 2022
Cited by 5 | Viewed by 2568
Abstract
With the increasing number of reservoirs on the Nile River Basin, it has become important to understand the reservoir operations in the basin for coordinated water management among the various countries. With the lack of a proper framework for data sharing amongst the [...] Read more.
With the increasing number of reservoirs on the Nile River Basin, it has become important to understand the reservoir operations in the basin for coordinated water management among the various countries. With the lack of a proper framework for data sharing amongst the Nile basin countries, satellite remote sensing provides a simple transparent way to continuously monitor the changes taking place in reservoirs in all regions of the Nile River Basin. This paper presents a comparison between Sentinel-1- and Sentinel-2-derived reservoir water levels and the altimetry-based water level from G-REALM (Global Reservoirs and Lakes Monitor) for three major reservoirs downstream of the Millennium Reservoir impounded by the Grand Ethiopian Renaissance Dam (GERD) on the Nile River for the period of 2014–2021. Water surface extents were derived from Sentinel-1 using dynamic thresholds and from Sentinel-2 with the use of the NDWI (Normalized Difference Water Index). The water levels were estimated using a DEM-based contour matching technique. For Roseires Reservoir, the water levels from Sentinel agreed well with those from G-REALM (RMSE = 0.92 m; R2 = 0.82). For Lake Nasser, the water levels also agreed well (RMSE = 0.72 m; R2 = 0.85). For Lake Merowe, there was a significant mismatch in the derived water levels, mostly due to a lack of sufficient data from both sources. Overall, satellite imagery from Sentinel provides a very good alternative to altimetry-based water levels for the Nile River Basin. Full article
Show Figures

Graphical abstract

17 pages, 3970 KiB  
Article
An Automatic Individual Tree 3D Change Detection Method for Allometric Parameters Estimation in Mixed Uneven-Aged Forest Stands from ALS Data
by Claudio Spadavecchia, Elena Belcore, Marco Piras and Milan Kobal
Remote Sens. 2022, 14(18), 4666; https://doi.org/10.3390/rs14184666 - 19 Sep 2022
Cited by 2 | Viewed by 1848
Abstract
Forests play a central role in the management of the Earth’s climate. Airborne laser scanning (ALS) technologies facilitate the monitoring of large and impassable areas and can be used to monitor the 3D structure of forests. While the ALS-based forest measures have been [...] Read more.
Forests play a central role in the management of the Earth’s climate. Airborne laser scanning (ALS) technologies facilitate the monitoring of large and impassable areas and can be used to monitor the 3D structure of forests. While the ALS-based forest measures have been studied in depth, 3D change detection in forests is still a subject of little attention in the literature due to the challenges introduced by comparing point cloud pairs. In this study, we propose an innovative methodology to (i) automatically perform a 3D change detection of forests on an individual tree level; (ii) estimate tree parameters with allometric equations; and (iii) perform an assessment of the aboveground biomass (AGB) variation over time. The area in which the tests were carried out was hit by an ice storm that occurred in the time interval between the two LiDAR acquisitions; furthermore, field measurements were carried out and used to validate the results. The single-tree segmentation of the point clouds was automatically performed with a local maxima algorithm to detect the treetop, and a decision tree method to define the individual crowns around the local maxima. The multitemporal comparison of the point clouds was based on the identification of single trees, which were matched when there was a correlation between the position of the treetops. For each tree, the DBH (diameter at breast height) and the AGB were also estimated using allometric equations. The results are promising and allowed us to identify the uprooted trees and estimate that about 40% of the AGB of the area under examination had been destroyed, with an RMSE over the estimation ranging between 4% and 21% in four scenarios. Full article
(This article belongs to the Special Issue 3D Point Clouds in Forest Remote Sensing II)
Show Figures

Graphical abstract

26 pages, 9669 KiB  
Article
Proportional Similarity-Based Openmax Classifier for Open Set Recognition in SAR Images
by Elisa Giusti, Selenia Ghio, Amir Hosein Oveis and Marco Martorella
Remote Sens. 2022, 14(18), 4665; https://doi.org/10.3390/rs14184665 - 19 Sep 2022
Cited by 11 | Viewed by 2382
Abstract
Most of the existing Non-Cooperative Target Recognition (NCTR) systems follow the “closed world” assumption, i.e., they only work with what was previously observed. Nevertheless, the real world is relatively “open” in the sense that the knowledge of the environment is incomplete. Therefore, unknown [...] Read more.
Most of the existing Non-Cooperative Target Recognition (NCTR) systems follow the “closed world” assumption, i.e., they only work with what was previously observed. Nevertheless, the real world is relatively “open” in the sense that the knowledge of the environment is incomplete. Therefore, unknown targets can feed the recognition system at any time while it is operational. Addressing this issue, the Openmax classifier has been recently proposed in the optical domain to make convolutional neural networks (CNN) able to reject unknown targets. There are some fundamental limitations in the Openmax classifier that can end up with two potential errors: (1) rejecting a known target and (2) classifying an unknown target. In this paper, we propose a new classifier to increase the robustness and accuracy. The proposed classifier, which is inspired by the limitations of the Openmax classifier, is based on proportional similarity between the test image and different training classes. We evaluate our method by radar images of man-made targets from the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset. Moreover, a more in-depth discussion on the Openmax hyper-parameters and a detailed description of the Openmax functioning are given. Full article
(This article belongs to the Special Issue SAR-Based Signal Processing and Target Recognition)
Show Figures

Graphical abstract

20 pages, 9865 KiB  
Article
Mapping Multi-Decadal Mangrove Extent in the Northern Coast of Vietnam Using Landsat Time-Series Data on Google Earth Engine Platform
by Thuy Thi Phuong Vu, Tien Dat Pham, Neil Saintilan, Andrew Skidmore, Hung Viet Luu, Quang Hien Vu, Nga Nhu Le, Huu Quang Nguyen and Bunkei Matsushita
Remote Sens. 2022, 14(18), 4664; https://doi.org/10.3390/rs14184664 - 19 Sep 2022
Cited by 5 | Viewed by 2967
Abstract
A pixel-based algorithm for multi-temporal Landsat (TM/ETM+/OLI/OLI-2) imagery between 1990 and 2022 monitored mangrove dynamics and detected their changes in the three provinces (i.e., Thai Binh, Nam Dinh and Hai Phong), which are located on the Northern coast of Vietnam, through the Google [...] Read more.
A pixel-based algorithm for multi-temporal Landsat (TM/ETM+/OLI/OLI-2) imagery between 1990 and 2022 monitored mangrove dynamics and detected their changes in the three provinces (i.e., Thai Binh, Nam Dinh and Hai Phong), which are located on the Northern coast of Vietnam, through the Google Earth Engine (GEE) cloud computing platform. Results showed that the mangrove area in the study area decreased from 2960 ha in 1990 to 2408 ha in 1995 and then significantly increased to 4435 ha in 2000 but later declined to 3502 ha in 2005. The mangrove areas experienced an increase from 4706 ha in 2010 to 10,125 ha in 2020 and reached a highest peak of 10,630 ha in 2022. In 2022, Hai Phong province had the largest area of mangrove (3934 ha), followed by Nam Dinh (3501 ha) and Thai Binh (3195 ha) provinces. The overall accuracies for 2020 and 2022 were 94.94% and 91.98%, while the Kappa coefficients were 0.90 and 0.84, respectively. The mangrove restoration programs and policies by the Vietnamese government and local governments are the key drivers of this increase in mangroves in the three provinces from 1990 to 2022. The results also demonstrated that the combination of Landsat time series images, a pixel-based algorithm, and the GEE platform has a high potential for monitoring long-term change of mangrove forests during 32 years in the tropics. Moreover, the obtained mangrove forest maps at a 30-m spatial resolution can serve as a useful and up-to-date dataset for sustainable management and conservation of these mangrove forests in the Red River Delta, Vietnam. Full article
(This article belongs to the Special Issue Remote Sensing in Mangroves II)
Show Figures

Figure 1

19 pages, 2738 KiB  
Article
Link Ecological and Social Composite Systems to Construct Sustainable Landscape Patterns: A New Framework Based on Ecosystem Service Flows
by Shixi Cui, Zenglin Han, Xiaolu Yan, Xiuzhen Li, Wenzhen Zhao, Chenghao Liu, Xinyuan Li and Jingqiu Zhong
Remote Sens. 2022, 14(18), 4663; https://doi.org/10.3390/rs14184663 - 19 Sep 2022
Cited by 13 | Viewed by 2353
Abstract
Integrating the flow of supply and demand of ecosystem services (ESs) into the ecological security pattern (ESP) of coastal ecosystems with extremely fragile ecological backgrounds and contradictory human–land relationships is beneficial to the coordinated development of human–land systems. However, existing studies ignore the [...] Read more.
Integrating the flow of supply and demand of ecosystem services (ESs) into the ecological security pattern (ESP) of coastal ecosystems with extremely fragile ecological backgrounds and contradictory human–land relationships is beneficial to the coordinated development of human–land systems. However, existing studies ignore the issue of scales of supply–demand linkages, making the ESP not properly guide sustainable development. Based on ESs delivery chain theory and landscape ecology approaches, we developed a sustainable development framework consisting of coupled microscopic natural–social systems. The method was tested using data from the Liao River Delta. In this study area, the natural supply potential and demand mapping distribution of key ESs were assessed to identify ecological sources in the Liao River Delta, a typical coastal zone in northern China. The resistance surface based on land use type assignment was modified using hydrological connectivity frequency and nighttime light intensity. Ecological corridors were extracted and optimized using a minimum cumulative resistance model and connectivity evaluation. The study found that the high supply area and the high demand reflection area are not consistent in location and supply level. Ecological source areas are evenly distributed, accounting for 12% of the total area. The ecological corridors are mainly concentrated in the west and southeast and do not cross the built-up areas in the east. This ESP framework safeguards the local demand for natural products and the natural potential to maintain services over the longer term and to a larger scale while informing the development of environmental management measures. Full article
Show Figures

Graphical abstract

24 pages, 8714 KiB  
Article
A Data-Driven Model on Google Earth Engine for Landslide Susceptibility Assessment in the Hengduan Mountains, the Qinghai–Tibetan Plateau
by Wenhuan Wu, Qiang Zhang, Vijay P. Singh, Gang Wang, Jiaqi Zhao, Zexi Shen and Shuai Sun
Remote Sens. 2022, 14(18), 4662; https://doi.org/10.3390/rs14184662 - 19 Sep 2022
Cited by 13 | Viewed by 3948
Abstract
Amplifying landslide hazards in the backdrop of warming climate and intensifying human activities calls for an integrated framework for accurately evaluating landslide susceptibility at fine spatiotemporal resolutions, which is critical for the mitigation of increasingly high landslide disaster risks. Yet, dynamic landslide susceptibility [...] Read more.
Amplifying landslide hazards in the backdrop of warming climate and intensifying human activities calls for an integrated framework for accurately evaluating landslide susceptibility at fine spatiotemporal resolutions, which is critical for the mitigation of increasingly high landslide disaster risks. Yet, dynamic landslide susceptibility mapping is still lacking. Using high-quality data, from 14,435 landslides and non-landslides, we developed an efficient holistic framework for evaluating landslide susceptibility, considering landslide-relevant internal and external factors based on cloud computing platform and algorithmic models, which enables dynamic updating of a landslide susceptibility map at the regional scale, particularly in regions with highly complicated topographical features such as the Hengduan Mountains, as considered in this study. We compared Classification and Regression Trees (CART), Support Vector Machines (SVM), and Random Forest (RF) classifiers to screen out the best portfolio model for landslide susceptibility mapping on the Google Earth Engine (GEE) platform. We found that the Random Forest (RF) classifier integrated with synergy mode had the best modeling performance, with 90.48% and 89.24% accuracy and precision, respectively. We also found that forests and grasslands had the controlling effect on the occurrence of landslides, while human activities had a notable inducing effect on the occurrence of landslides within the Hengduan Mountains. This study highlights the performance of the holistic landslide susceptibility evaluation framework proposed in this study and provides a viable technique for landslide susceptibility evaluation in other regions of the globe. Full article
(This article belongs to the Special Issue Geographic Data Analysis and Modeling in Remote Sensing)
Show Figures

Graphical abstract

16 pages, 4568 KiB  
Article
Promising Uses of the iPad Pro Point Clouds: The Case of the Trunk Flare Diameter Estimation in the Urban Forest
by Rogério Bobrowski, Monika Winczek, Lucas Polo Silva, Tarik Cuchi, Marta Szostak and Piotr Wężyk
Remote Sens. 2022, 14(18), 4661; https://doi.org/10.3390/rs14184661 - 19 Sep 2022
Cited by 2 | Viewed by 2162
Abstract
The rule of thumb “the right tree in the right place” is a common idea in different countries to avoid damages caused by trees on sidewalks. Although many new planting techniques can be used, the estimation of the trunk flare diameter (TFD) could [...] Read more.
The rule of thumb “the right tree in the right place” is a common idea in different countries to avoid damages caused by trees on sidewalks. Although many new planting techniques can be used, the estimation of the trunk flare diameter (TFD) could help the planning process to give tree roots more space to grow over the years. As such, we compared the applicability of point clouds based on iPad Pro 2020 image processing and a precise terrestrial laser scanner (TLS FARO) for the modeling of the TFD using different modeling procedures. For both scanning methods, 100 open-grown and mature trees of 10 different species were scanned in an urban park in Cracow, Poland. To generate models, we used the PBH (perimeter at breast height) and TFD variables and simple linear regression procedures. We also tested machine learning algorithms. In general, the TFD value corresponded to two times the size of a given DBH (diameter at breast height) for both methods of point cloud acquisition. Linearized models showed similar statistics to machine learning techniques. The random forest algorithm showed the best fit for the TFD estimation, R2 = 0.8780 (iPad Pro), 0.8961 (TLS FARO), RMSE (m) = 0.0872 (iPad Pro), 0.0702 (TLS FARO). Point clouds generated from iPad Pro imageries (matching approach) promoted similar results as TLS FARO for the TFD estimations. Full article
(This article belongs to the Special Issue 3D Point Clouds in Forest Remote Sensing II)
Show Figures

Figure 1

22 pages, 4401 KiB  
Article
Winter Wheat SPAD Value Inversion Based on Multiple Pretreatment Methods
by Lanzhi Shen, Maofang Gao, Jingwen Yan, Qizhi Wang and Hua Shen
Remote Sens. 2022, 14(18), 4660; https://doi.org/10.3390/rs14184660 - 18 Sep 2022
Cited by 10 | Viewed by 1821
Abstract
SPAD value was measured by a portable chlorophyll instrument, which can reflect the relative chlorophyll content of vegetation well. Chlorophyll is an important organic chemical substance in plants that acquires and transmits energy during photosynthesis. The continuous spectral curve of winter wheat can [...] Read more.
SPAD value was measured by a portable chlorophyll instrument, which can reflect the relative chlorophyll content of vegetation well. Chlorophyll is an important organic chemical substance in plants that acquires and transmits energy during photosynthesis. The continuous spectral curve of winter wheat can be obtained rapidly in a specific band range by using hyperspectral remote sensing technology to estimate the SPAD value of winter wheat, which is of great significance to the growth monitoring and yield estimation research of winter wheat. In this study, with winter wheat as the research object, the spectral data and corresponding SPAD value in different growth stages were used as the data source, 20 kinds of data preprocessing spectra and sensitive spectral indices set the data as model input values, the partial least square regression (PLSR) model was established to estimate the SPAD value, and the model estimation results of different model input values at different growth stages were compared in detail. The results showed that the set of sensitive spectral indices selected in this study as input values can effectively improve the accuracy and stability of the PLSR model. In addition, the effects of 20 spectral data pretreatment methods on the estimation results of the SPAD value were compared and analyzed in different growth stages. It was found that the spectral data pretreated by the combination of wavelet packet denoising, first-order derivative transformation and principal component analysis can improve the accuracy and stability of PLSR model, and it is suitable for all growth stages. The results also showed that the estimation model is highly sensitive to the standard deviation of the SPAD value (STDchl) in sample sets. When the standard deviation is greater than 5.5 SPAD, the larger the STDchl is, the higher the model estimation accuracy is, and the more stable the model is. At this time, the model estimation accuracy is higher (R2V is greater than 0.5, ratio of performance to deviation is greater than 1.4), which can meet the estimation requirements of the SPAD value. Full article
Show Figures

Graphical abstract

17 pages, 12070 KiB  
Article
Monitoring Asian Dust Storms from NOAA-20 CrIS Double CO2 Band Observations
by Chenggege Fang, Yang Han and Fuzhong Weng
Remote Sens. 2022, 14(18), 4659; https://doi.org/10.3390/rs14184659 - 18 Sep 2022
Cited by 2 | Viewed by 1865
Abstract
Sand and dust storms (SDSs) are common environmental hazards in spring in Asian continent and have significant impacts on human health, weather, and climate. While many technologies have been developed to monitor SDSs, this study investigates the spectral characteristics of SDSs in satellite [...] Read more.
Sand and dust storms (SDSs) are common environmental hazards in spring in Asian continent and have significant impacts on human health, weather, and climate. While many technologies have been developed to monitor SDSs, this study investigates the spectral characteristics of SDSs in satellite hyperspectral infrared observations and propose a new methodology to monitor the storms. An SDS emission and scattering index (SESI) is based on the differential responses of infrared CO2 shortwave and longwave IR bands to the scattering and emission of sand and dust particles. For a severe dust storm process during 14–17 March 2021, the SESI calculated by the Cross-track Infrared Sounder (CrIS) observations shows very negative values in the dusty region and is consistent with the spatial distribution of dust identified from the true-color RGB imagery and the dust RGB imagery of the Visible Infrared Imaging Radiometer Suite (VIIRS) on the NOAA-20 Satellite. The use of the SESI index in the near-surface layer allows for monitoring of the dust storm process and enables an effective classification between surface variations and dust weather events. Full article
Show Figures

Figure 1

21 pages, 12549 KiB  
Article
The Effect of Drought on Vegetation Gross Primary Productivity under Different Vegetation Types across China from 2001 to 2020
by Xiaoping Wu, Rongrong Zhang, Virgílio A. Bento, Song Leng, Junyu Qi, Jingyu Zeng and Qianfeng Wang
Remote Sens. 2022, 14(18), 4658; https://doi.org/10.3390/rs14184658 - 18 Sep 2022
Cited by 31 | Viewed by 2995
Abstract
Climate change has exacerbated the frequency and severity of droughts worldwide. Evaluating the response of gross primary productivity (GPP) to drought is thus beneficial to improving our understanding of the impact of drought on the carbon cycle balance. Although many studies have investigated [...] Read more.
Climate change has exacerbated the frequency and severity of droughts worldwide. Evaluating the response of gross primary productivity (GPP) to drought is thus beneficial to improving our understanding of the impact of drought on the carbon cycle balance. Although many studies have investigated the relationship between vegetation productivity and dry/wet conditions, the capability of different drought indices of assessing the influence of water deficit is not well understood. Moreover, few studies consider the effects of drought on vegetation with a focus on periods of drought. Here, we investigated the spatial-temporal patterns of GPP, the standardized precipitation evapotranspiration index (SPEI), and the vapor pressure deficit (VPD) in China from 2001 to 2020 and examined the relationship between GPP and water deficit/drought for different vegetation types. The results revealed that SPEI and GPP were positively correlated over approximately 70.7% of the total area, and VPD was negatively correlated with GPP over about 66.2% of the domain. Furthermore, vegetation productivity was more negatively affected by water deficit in summer and autumn. During periods of drought, the greatest negative impact was on deciduous forests and croplands, and woody savannas were the least impacted. This research provides a scientific reference for developing mitigation and adaptation measures to lessen the impact of drought disasters under a changing climate. Full article
(This article belongs to the Special Issue Remote Sensing of Watershed)
Show Figures

Figure 1

17 pages, 3960 KiB  
Article
3D Sea Surface Electromagnetic Scattering Prediction Model Based on IPSO-SVR
by Chunlei Dong, Xiao Meng, Lixin Guo and Jiamin Hu
Remote Sens. 2022, 14(18), 4657; https://doi.org/10.3390/rs14184657 - 18 Sep 2022
Cited by 5 | Viewed by 1573
Abstract
An Improved Particle Swarm Optimization Algorithm-Support Vector Regression Machine (IPSO-SVR) prediction model is developed in this paper to predict the electromagnetic (EM) scattering coefficients of the three-dimensional (3D) sea surface for large scenes in real-time. At first, the EM scattering model of the [...] Read more.
An Improved Particle Swarm Optimization Algorithm-Support Vector Regression Machine (IPSO-SVR) prediction model is developed in this paper to predict the electromagnetic (EM) scattering coefficients of the three-dimensional (3D) sea surface for large scenes in real-time. At first, the EM scattering model of the 3D sea surface is established based on the Semi-Deterministic Facet Scattering Model (SDFSM), and the validity of SDFSM is verified by comparing with the measured data. Using the SDFSM, the data set of backscattering coefficients from 3D sea surface is generated for different polarizations as the training samples. Secondly, an improved particle swarm optimization algorithm is proposed by combining the Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The combined algorithm is utilized to optimize the parameters and train the SVR to build a regression prediction model. In the end, the extrapolated prediction for backscattering coefficients of the 3D sea surface is performed. The Root Mean Square Error (RMSE) of the IPSO-SVR-based prediction model is less than 1.2 dB, and the correlation coefficients are higher than 91%. And the prediction accuracy of the PSO-SVR-based, GA-SVR-based and IPSO-SVR-based prediction models is compared. The average RMSE of the PSO-SVR-based and GA-SVR-based prediction models is 1.4241 dB and 1.6289 dB, respectively. While the average RMSE of the IPSO-SVR-based prediction model is reduced to 1.1006 dB. Besides, the average correlation coefficient of the PSO-SVR-based and GA-SVR-based prediction models is 94.36% and 93.93%, respectively. While the average correlation coefficient of the IPSO-SVR-based prediction model reached 95.12%. It demonstrated that the IPSO-SVR-based prediction model can effectively improve the prediction accuracy compared with the PSO-SVR-based and GA-SVR-based prediction models. Moreover, the simulation time of IPSO-SVR-based prediction model is significantly decreased compared with the SDFSM, and the speedup ratio is greater than 15.0. Therefore, the prediction model in this paper has practical application in the real-time computation of sea surface scattering coefficients in large scenes. Full article
Show Figures

Figure 1

23 pages, 29793 KiB  
Article
Land Cover Classification for Polarimetric SAR Images Based on Vision Transformer
by Hongmiao Wang, Cheng Xing, Junjun Yin and Jian Yang
Remote Sens. 2022, 14(18), 4656; https://doi.org/10.3390/rs14184656 - 18 Sep 2022
Cited by 15 | Viewed by 2515
Abstract
Deep learning methods have been widely studied for Polarimetric synthetic aperture radar (PolSAR) land cover classification. The scarcity of PolSAR labeled samples and the small receptive field of the model limit the performance of deep learning methods for land cover classification. In this [...] Read more.
Deep learning methods have been widely studied for Polarimetric synthetic aperture radar (PolSAR) land cover classification. The scarcity of PolSAR labeled samples and the small receptive field of the model limit the performance of deep learning methods for land cover classification. In this paper, a vision Transformer (ViT)-based classification method is proposed. The ViT structure can extract features from the global range of images based on a self-attention block. The powerful feature representation capability of the model is equivalent to a flexible receptive field, which is suitable for PolSAR image classification at different resolutions. In addition, because of the lack of labeled data, the Mask Autoencoder method is used to pre-train the proposed model with unlabeled data. Experiments are carried out on the Flevoland dataset acquired by NASA/JPL AIRSAR and the Hainan dataset acquired by the Aerial Remote Sensing System of the Chinese Academy of Sciences. The experimental results on both datasets demonstrate the superiority of the proposed method. Full article
Show Figures

Graphical abstract

22 pages, 7445 KiB  
Article
Determining Ionospheric Drift and Anisotropy of Irregularities from LOFAR Core Measurements: Testing Hypotheses behind Estimation
by Marcin Grzesiak, Mariusz Pożoga, Barbara Matyjasiak, Dorota Przepiórka, Katarzyna Beser, Lukasz Tomasik, Hanna Rothkaehl and Helena Ciechowska
Remote Sens. 2022, 14(18), 4655; https://doi.org/10.3390/rs14184655 - 18 Sep 2022
Viewed by 1547
Abstract
We try to assess the validity of assumptions taken when deriving drift velocity. We give simple formulas for characteristics of the spatiotemporal correlation function of the observed diffraction pattern for the frozen flow and the more general Briggs model. Using Low-Frequency Array (LOFAR) [...] Read more.
We try to assess the validity of assumptions taken when deriving drift velocity. We give simple formulas for characteristics of the spatiotemporal correlation function of the observed diffraction pattern for the frozen flow and the more general Briggs model. Using Low-Frequency Array (LOFAR) Cassiopeia intensity observation, we compare the experimental velocity scaling factor with a theoretical one to show that both models do not follow observations. We also give a qualitative comparison of our drift velocity estimates with SuperDARN convection maps. The article is essentially an extended version of the conference paper: “Determining ionospheric drift and anisotropy of irregularities from LOFAR core measurements”, Signal Processing Symposium 2021 (SPSympo 2021). Full article
(This article belongs to the Special Issue Recent Advances in Signal Processing and Radar for Remote Sensing)
Show Figures

Figure 1

Previous Issue
Back to TopTop