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Remote Sens., Volume 12, Issue 12 (June-2 2020) – 164 articles

Cover Story (view full-size image): This study by Walter et al. is the first comprehensive geophysical, geomorphological, and structural analysis at Ebeko volcano, a site of intense Vulcanian explosions. Repeat unoccupied aircraft system (UAS) overflights enabled the generation of cm-scale digital terrain models and orthomosaics of the optical and thermal infrared expression of the nested volcano craters, fumaroles, and sites of degassing, proposing an edifice-wide volcano-tectonic control. The photo shows a volcano explosion that occurred just at a time when the UAS batteries were being replaced. View this paper
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22 pages, 45052 KiB  
Article
A Multi-Sensor Fusion Framework Based on Coupled Residual Convolutional Neural Networks
by Hao Li, Pedram Ghamisi, Behnood Rasti, Zhaoyan Wu, Aurelie Shapiro, Michael Schultz and Alexander Zipf
Remote Sens. 2020, 12(12), 2067; https://doi.org/10.3390/rs12122067 - 26 Jun 2020
Cited by 21 | Viewed by 5921
Abstract
Multi-sensor remote sensing image classification has been considerably improved by deep learning feature extraction and classification networks. In this paper, we propose a novel multi-sensor fusion framework for the fusion of diverse remote sensing data sources. The novelty of this paper is grounded [...] Read more.
Multi-sensor remote sensing image classification has been considerably improved by deep learning feature extraction and classification networks. In this paper, we propose a novel multi-sensor fusion framework for the fusion of diverse remote sensing data sources. The novelty of this paper is grounded in three important design innovations: 1- a unique adaptation of the coupled residual networks to address multi-sensor data classification; 2- a smart auxiliary training via adjusting the loss function to address classifications with limited samples; and 3- a unique design of the residual blocks to reduce the computational complexity while preserving the discriminative characteristics of multi-sensor features. The proposed classification framework is evaluated using three different remote sensing datasets: the urban Houston university datasets (including Houston 2013 and the training portion of Houston 2018) and the rural Trento dataset. The proposed framework achieves high overall accuracies of 93.57%, 81.20%, and 98.81% on Houston 2013, the training portion of Houston 2018, and Trento datasets, respectively. Additionally, the experimental results demonstrate considerable improvements in classification accuracies compared with the existing state-of-the-art methods. Full article
(This article belongs to the Special Issue Advanced Multisensor Image Analysis Techniques for Land-Cover Mapping)
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18 pages, 4987 KiB  
Article
Centimeter Precision Geoid Model for Jeddah Region (Saudi Arabia)
by Alessandra Borghi, Riccardo Barzaghi, Omar Al-Bayari and Suhail Al Madani
Remote Sens. 2020, 12(12), 2066; https://doi.org/10.3390/rs12122066 - 26 Jun 2020
Cited by 8 | Viewed by 3580
Abstract
In 2014, the Jeddah Municipality made a call for an estimate of a centimetric precision geoid model to be used for engineering and surveying applications, because the regional geoid model available at that time did not reach a sufficient precision. A project was [...] Read more.
In 2014, the Jeddah Municipality made a call for an estimate of a centimetric precision geoid model to be used for engineering and surveying applications, because the regional geoid model available at that time did not reach a sufficient precision. A project was set up to this end and dedicated sets of gravity and Global Positioning System (GPS)/levelling data were acquired in the framework of this project. In this paper, a thorough analysis of these newly acquired data and of the last available Global Gravity Field Models (GGMs) has been done in order to obtain a geoid undulation estimate with the prescribed precision. In the framework of the Remove–Compute–Restore (RCR) approach, the collocation method was used to obtain the height anomaly estimation that was then converted to geoid undulation. The remove and restore steps of the RCR approach were based on GGMs, derived from the Gravity Field and Steady-State Ocean Circulation Explorer (GOCE) and Gravity Recovery and Climate Experiment (GRACE) dedicated gravity satellite missions, which were used to improve the long wavelength components of the Earth’s gravity field. Furthermore, two different quasi-geoid collocation estimates were computed, based on gravity data only and on gravity plus GPS/levelling data (the so-called hybrid estimate). The best solutions were obtained with the hybrid geoid estimate. This was tested by comparison with an independent set of GPS/levelling geoid undulations that were not included in the computed solutions. By these tests, the precision of the hybrid geoid is estimated to be 3.7 cm. This precision proved to be better, by a factor of two, than the corresponding one estimated from the pure gravimetric geoid. This project has been also useful to verify the importance and reliability of GGMs developed from the last satellite gravity missions (GOCE and GRACE) that have significantly improved our knowledge of the long wavelength components of the Earth’s gravity field, especially in areas with poor coverage of terrestrial gravity data. In fact, the geoid models based on satellite-only GGMs proved to have a better performance, despite the lower spatial resolution with respect to high-resolution models (i.e., Earth Gravitational Model 2008 (EGM2008)). Full article
(This article belongs to the Special Issue Geodesy for Gravity and Height Systems)
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20 pages, 9264 KiB  
Article
Mapping Winter Wheat with Combinations of Temporally Aggregated Sentinel-2 and Landsat-8 Data in Shandong Province, China
by Feng Xu, Zhaofu Li, Shuyu Zhang, Naitao Huang, Zongyao Quan, Wenmin Zhang, Xiaojun Liu, Xiaosan Jiang, Jianjun Pan and Alexander V. Prishchepov
Remote Sens. 2020, 12(12), 2065; https://doi.org/10.3390/rs12122065 - 26 Jun 2020
Cited by 52 | Viewed by 4489
Abstract
Winter wheat is one of the major cereal crops in China. The spatial distribution of winter wheat planting areas is closely related to food security; however, mapping winter wheat with time-series finer spatial resolution satellite images across large areas is challenging. This paper [...] Read more.
Winter wheat is one of the major cereal crops in China. The spatial distribution of winter wheat planting areas is closely related to food security; however, mapping winter wheat with time-series finer spatial resolution satellite images across large areas is challenging. This paper explores the potential of combining temporally aggregated Landsat-8 OLI and Sentinel-2 MSI data available via the Google Earth Engine (GEE) platform for mapping winter wheat in Shandong Province, China. First, six phenological median composites of Landsat-8 OLI and Sentinel-2 MSI reflectance measures were generated by a temporal aggregation technique according to the winter wheat phenological calendar, which covered seedling, tillering, over-wintering, reviving, jointing-heading and maturing phases, respectively. Then, Random Forest (RF) classifier was used to classify multi-temporal composites but also mono-temporal winter wheat development phases and mono-sensor data. The results showed that winter wheat could be classified with an overall accuracy of 93.4% and F1 measure (the harmonic mean of producer’s and user’s accuracy) of 0.97 with temporally aggregated Landsat-8 and Sentinel-2 data were combined. As our results also revealed, it was always good to classify multi-temporal images compared to mono-temporal imagery (the overall accuracy dropped from 93.4% to as low as 76.4%). It was also good to classify Landsat-8 OLI and Sentinel-2 MSI imagery combined instead of classifying them individually. The analysis showed among the mono-temporal winter wheat development phases that the maturing phase’s and reviving phase’s data were more important than the data for other mono-temporal winter wheat development phases. In sum, this study confirmed the importance of using temporally aggregated Landsat-8 OLI and Sentinel-2 MSI data combined and identified key winter wheat development phases for accurate winter wheat classification. These results can be useful to benefit on freely available optical satellite data (Landsat-8 OLI and Sentinel-2 MSI) and prioritize key winter wheat development phases for accurate mapping winter wheat planting areas across China and elsewhere. Full article
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20 pages, 12498 KiB  
Article
Single-Pass Soil Moisture Retrievals Using GNSS-R: Lessons Learned
by Adriano Camps, Hyuk Park, Jordi Castellví, Jordi Corbera and Emili Ascaso
Remote Sens. 2020, 12(12), 2064; https://doi.org/10.3390/rs12122064 - 26 Jun 2020
Cited by 29 | Viewed by 3733
Abstract
In this paper, an algorithm to retrieve surface soil moisture from GNSS-R (Global Navigaton Satellite System Reflectometry) observations is presented. Surface roughness and vegetation effects are found to be the most critical ones to be corrected. On one side, the NASA SMAP (Soil [...] Read more.
In this paper, an algorithm to retrieve surface soil moisture from GNSS-R (Global Navigaton Satellite System Reflectometry) observations is presented. Surface roughness and vegetation effects are found to be the most critical ones to be corrected. On one side, the NASA SMAP (Soil Moisture Active and Passive) correction for vegetation opacity (multiplied by two to account for the descending and ascending passes) seems too high. Surface roughness effects cannot be compensated using in situ measurements, as they are not representative. An ad hoc correction for surface roughness, including the dependence with the incidence angle, and the actual reflectivity value is needed. With this correction, reasonable surface soil moisture values are obtained up to approximately a 30° incidence angle, beyond which the GNSS-R retrieved surface soil moisture spreads significantly. Full article
(This article belongs to the Special Issue Applications of GNSS Reflectometry for Earth Observation)
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17 pages, 7066 KiB  
Article
Centimeter-Level Precise Orbit Determination for the Luojia-1A Satellite Using BeiDou Observations
by Lei Wang, Beizhen Xu, Wenju Fu, Ruizhi Chen, Tao Li, Yi Han and Haitao Zhou
Remote Sens. 2020, 12(12), 2063; https://doi.org/10.3390/rs12122063 - 26 Jun 2020
Cited by 24 | Viewed by 3604
Abstract
Luojia-1A is a scientific experimental satellite operated by Wuhan University, which is the first low earth orbiter (LEO) navigation signal augmentation experimental satellite. The precise orbit is the prerequisite of augmenting existing Global Navigation Satellite System (GNSS) performance and improves users’ positioning accuracy. [...] Read more.
Luojia-1A is a scientific experimental satellite operated by Wuhan University, which is the first low earth orbiter (LEO) navigation signal augmentation experimental satellite. The precise orbit is the prerequisite of augmenting existing Global Navigation Satellite System (GNSS) performance and improves users’ positioning accuracy. Meanwhile, LEO precise orbit determination (POD) with BeiDou-2 observations is particularly challenging since it only provides regional service. In this study, we investigated the method of precise orbit determination (POD) for Luojia-1A satellite with the onboard BeiDou observation to establish the high-precision spatial datum for the LEO navigation augmentation (LEO-NA) system. The multipath characteristic of the BeiDou System (BDS) observations from Luojia-1A satellite is analyzed, and the elevation-dependent BeiDou code bias is estimated with the LEO onboard observations. A weight reduction strategy is adopted to mitigate the negative effect of poor BeiDou-2 geostationary earth orbit (GEO) satellites orbit quality, and the Luojia-1A orbit precision can be improved from 6.3 cm to 2.3 cm with the GEO weighting strategy. The precision improvement of the radial direction, along-track, and out-of-plane directions are 53.47%, 47.29%, and 76.2%, respectively. Besides, tuning the pseudo-stochastic parameters is also beneficial for improving orbit precision. The experiment results indicate that about 2 cm overlapping orbit accuracy are achievable with BeiDou observations from Luojia-1A satellite if proper data processing strategies are applied. Full article
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18 pages, 5752 KiB  
Article
Comparison of Sentinel-2 and High-Resolution Imagery for Mapping Land Abandonment in Fragmented Areas
by Sergio Morell-Monzó, Javier Estornell and María-Teresa Sebastiá-Frasquet
Remote Sens. 2020, 12(12), 2062; https://doi.org/10.3390/rs12122062 - 26 Jun 2020
Cited by 31 | Viewed by 6066
Abstract
Agricultural land abandonment is an important environmental issue in Europe. The proper management of agricultural areas has important implications for ecosystem services (food production, biodiversity, climate regulation and the landscape). In the coming years, an increase of abandoned areas is expected due to [...] Read more.
Agricultural land abandonment is an important environmental issue in Europe. The proper management of agricultural areas has important implications for ecosystem services (food production, biodiversity, climate regulation and the landscape). In the coming years, an increase of abandoned areas is expected due to socio-economic changes. The identification and quantification of abandoned agricultural plots is key for monitoring this process and for applying management measures. The Valencian Region (Spain) is an important fruit and vegetable producing area in Europe, and it has the most important citrus industry. However, this agricultural sector is highly threatened by diverse factors, which have accelerated land abandonment. Landsat and MODIS satellite images have been used to map land abandonment. However, these images do not give good results in areas with high spatial fragmentation and small-sized agricultural plots. Sentinel-2 and airborne imagery shows unexplored potential to overcome this thanks to higher spatial resolutions. In this work, three models were compared for mapping abandoned plots using Sentinel-2 with 10 m bands, Sentinel-2 with 10 m and 20 m bands, and airborne imagery with 1 m visible and near-infrared bands. A pixel-based classification approach was used, applying the Random Forests algorithm. The algorithm was trained with 144 plots and 100 decision trees. The results were validated using the hold-out method with 96 independent plots. The most accurate map was obtained using airborne images, the Enhanced Vegetation Index (EVI) and Thiam’s Transformed Vegetation Index (TTVI), with an overall accuracy of 88.5%. The map generated from Sentinel-2 images (10 m bands and the EVI and TTVI spectral indices) had an overall accuracy of 77.1%. Adding 20 m Sentinel-2 bands and the Normalized Difference Moisture Index (NDMI) did not improve the classification accuracy. According to the most accurate map, 4310 abandoned plots were detected in our study area, representing 32.5% of its agricultural surface. The proposed methodology proved to be useful for mapping citrus in highly fragmented areas, and it can be adapted to other crops. Full article
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19 pages, 9901 KiB  
Article
Near Real-Time Automated Early Mapping of the Perimeter of Large Forest Fires from the Aggregation of VIIRS and MODIS Active Fires in Mexico
by Carlos Ivan Briones-Herrera, Daniel José Vega-Nieva, Norma Angélica Monjarás-Vega, Jaime Briseño-Reyes, Pablito Marcelo López-Serrano, José Javier Corral-Rivas, Ernesto Alvarado-Celestino, Stéfano Arellano-Pérez, Juan Gabriel Álvarez-González, Ana Daría Ruiz-González, William Mathew Jolly and Sean A. Parks
Remote Sens. 2020, 12(12), 2061; https://doi.org/10.3390/rs12122061 - 26 Jun 2020
Cited by 26 | Viewed by 6156
Abstract
In contrast with current operational products of burned area, which are generally available one month after the fire, active fires are readily available, with potential application for early evaluation of approximate fire perimeters to support fire management decision making in near real time. [...] Read more.
In contrast with current operational products of burned area, which are generally available one month after the fire, active fires are readily available, with potential application for early evaluation of approximate fire perimeters to support fire management decision making in near real time. While previous coarse-scale studies have focused on relating the number of active fires to a burned area, some local-scale studies have proposed the spatial aggregation of active fires to directly obtain early estimate perimeters from active fires. Nevertheless, further analysis of this latter technique, including the definition of aggregation distance and large-scale testing, is still required. There is a need for studies that evaluate the potential of active fire aggregation for rapid initial fire perimeter delineation, particularly taking advantage of the improved spatial resolution of the Visible Infrared Imaging Radiometer (VIIRS) 375 m, over large areas and long periods of study. The current study tested the use of convex hull algorithms for deriving coarse-scale perimeters from Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) active fire detections, compared against the mapped perimeter of the MODIS collection 6 (MCD64A1) burned area. We analyzed the effect of aggregation distance (750, 1000, 1125 and 1500 m) on the relationships of active fire perimeters with MCD64A1, for both individual fire perimeter prediction and total burned area estimation, for the period 2012–2108 in Mexico. The aggregation of active fire detections from MODIS and VIIRS demonstrated a potential to offer coarse-scale early estimates of the perimeters of large fires, which can be available to support fire monitoring and management in near real time. Total burned area predicted from aggregated active fires followed the same temporal behavior as the standard MCD64A1 burned area, with potential to also account for the role of smaller fires detected by the thermal anomalies. The proposed methodology, based on easily available algorithms of point aggregation, is susceptible to be utilized both for near real-time and historical fire perimeter evaluation elsewhere. Future studies might test active fires aggregation between regions or biomes with contrasting fuel characteristics and human activity patterns against medium resolution (e.g., Landsat and Sentinel) fire perimeters. Furthermore, coarse-scale active fire perimeters might be utilized to locate areas where such higher-resolution imagery can be downloaded to improve the evaluation of fire extent and impact. Full article
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26 pages, 14774 KiB  
Article
Assessing Snow Phenology over the Large Part of Eurasia Using Satellite Observations from 2000 to 2016
by Yanhua Sun, Tingjun Zhang, Yijing Liu, Wenyu Zhao and Xiaodong Huang
Remote Sens. 2020, 12(12), 2060; https://doi.org/10.3390/rs12122060 - 26 Jun 2020
Cited by 15 | Viewed by 3429
Abstract
Snow plays an important role in meteorological, hydrological and ecological processes, and snow phenology variation is critical for improved understanding of climate feedback on snow cover. The main purpose of the study is to explore spatial-temporal changes and variabilities of the extent, timing [...] Read more.
Snow plays an important role in meteorological, hydrological and ecological processes, and snow phenology variation is critical for improved understanding of climate feedback on snow cover. The main purpose of the study is to explore spatial-temporal changes and variabilities of the extent, timing and duration, as well as phenology of seasonal snow cover across the large part of Eurasia from 2000 through 2016 using a Moderate Resolution Imaging Spectroradiometer (MODIS) cloud-free snow product produced in this study. The results indicate that there are no significant positive or negative interannual trends of snow cover extent (SCE) from 2000 to 2016, but there are large seasonal differences. SCE shows a significant negative trend in spring (p = 0.01) and a positive trend in winter. The stable snow cover areas accounting for 78.8% of the large part of Eurasia, are mainly located north of latitude 45° N and in the mountainous areas. In this stable area, the number of snow-covered days is significantly increasing (p < 0.05) in 6.4% of the region and decreasing in 9.1% of the region, with the decreasing areas being mainly located in high altitude mountain areas and the increasing area occurring mainly in the ephemeral snow cover areas of northeastern and southern China. In central Siberia, Pamir and the Tibetan Plateau, the snow onset date tends to be delayed while the end date is becoming earlier from 2000 to 2016. While in the relatively low altitude plain areas, such as the West Siberian Plain and the Eastern European Plain region, the snow onset date is tending to advance, the end date tends to be delayed, but the increase is not significant. Full article
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17 pages, 2891 KiB  
Article
A Novel 2-D Geometry Reconstruction Approach for Space Debris via Interpolation-Free Operation under Low SNR Conditions
by Xi Luo, Lixin Guo, Dong Li, Hongqing Liu and Mengyi Qin
Remote Sens. 2020, 12(12), 2059; https://doi.org/10.3390/rs12122059 - 26 Jun 2020
Viewed by 2126
Abstract
Two unsolved key issues in inverse synthetic aperture radar (ISAR) imaging for non-cooperative rapidly spinning targets including high computational complexity and poor imaging performance in the case of low signal-to-noise ratio (SNR) are addressed in this work. In the strip-map imaging mode of [...] Read more.
Two unsolved key issues in inverse synthetic aperture radar (ISAR) imaging for non-cooperative rapidly spinning targets including high computational complexity and poor imaging performance in the case of low signal-to-noise ratio (SNR) are addressed in this work. In the strip-map imaging mode of SAR, it is well known that azimuth spatial invariant characteristics exist, and inspired by this, we propose a fast ISAR imaging approach for spinning targets. Our approach involves two steps. First, a precise analytic expression in the range-Doppler (RD) domain is produced using the principle of stationary phase (POSP). Second, a novel interpolation kernel function is designed to remove two-dimensional (2-D) spatial-variant phase errors, and the corresponding fast implementation steps that only require Fourier transform and multiplications are also presented. Finally, a well-focused ISAR image is obtained by compensating the azimuth high-order terms. Compared with current imaging methods, our approach avoids multi-dimensional search and interpolation operations and exploits the 2-D coherent integrated gain; the proposed method is of low computational cost and robustness in the low SNR condition. The effectiveness of the proposed approach is confirmed by numerically simulated experiments. Full article
(This article belongs to the Special Issue Remote Sensing in Engineering Geology)
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20 pages, 17200 KiB  
Article
Quantification of Precipitation Using Polarimetric Radar Measurements during Several Typhoon Events in Southern China
by Qiulei Xia, Wenjuan Zhang, Haonan Chen, Wen-Chau Lee, Lei Han, Yu Ma and Xiantong Liu
Remote Sens. 2020, 12(12), 2058; https://doi.org/10.3390/rs12122058 - 26 Jun 2020
Cited by 11 | Viewed by 3116
Abstract
Accurate quantitative precipitation estimation (QPE) during typhoon events is critical for flood warning and emergency management. Dual-polarization radar has proven to have better performance for QPE, compared to traditional single-polarization radar. However, polarimetric radar applications have not been extensively investigated in China, especially [...] Read more.
Accurate quantitative precipitation estimation (QPE) during typhoon events is critical for flood warning and emergency management. Dual-polarization radar has proven to have better performance for QPE, compared to traditional single-polarization radar. However, polarimetric radar applications have not been extensively investigated in China, especially during extreme events such as typhoons, since the operational dual-polarization system upgrade only happened recently. This paper extends a polarimetric radar rainfall system for local applications during typhoons in southern China and conducts comprehensive studies about QPE and precipitation microphysics. Observations from S-band dual-polarization radar in Guangdong Province during three typhoon events in 2017 are examined to demonstrate the enhanced radar rainfall performance. The microphysical properties of hydrometeors during typhoon events are analyzed through raindrop size distribution (DSD) data and polarimetric radar measurements. The stratiform precipitation in typhoons presents lower mean raindrop diameter and lower raindrop concentration than that of the convection precipitation. The rainfall estimates from the adapted radar rainfall algorithm agree well with rainfall measurements from rain gauges. Using the rain gauge data as references, the maximum normalized mean bias ( N M B ) of the adapted radar rainfall algorithm is 20.27%; the normalized standard error ( N S E ) is less than 40%; and the Pearson’s correlation coefficient ( C C ) is higher than 0.92. For the three typhoon events combined, the N S E and N M B are 36.66% and -15.78%, respectively. Compared with several conventional radar rainfall algorithms, the adapted algorithm based on local rainfall microphysics has the best performance in southern China. Full article
(This article belongs to the Section Atmospheric Remote Sensing)
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32 pages, 6472 KiB  
Article
Impact of Stereo Camera Calibration to Object Accuracy in Multimedia Photogrammetry
by Oliver Kahmen, Robin Rofallski and Thomas Luhmann
Remote Sens. 2020, 12(12), 2057; https://doi.org/10.3390/rs12122057 - 26 Jun 2020
Cited by 16 | Viewed by 4549
Abstract
Camera calibration via bundle adjustment is a well-established standard procedure in single-medium photogrammetry. When using standard software and applying the collinearity equations in multimedia photogrammetry, the effects of refractive interfaces are compensated in an implicit form, hence by the usual parameters of interior [...] Read more.
Camera calibration via bundle adjustment is a well-established standard procedure in single-medium photogrammetry. When using standard software and applying the collinearity equations in multimedia photogrammetry, the effects of refractive interfaces are compensated in an implicit form, hence by the usual parameters of interior orientation. This contribution analyses different calibration strategies for planar bundle-invariant interfaces. To evaluate the effects of implicitly modelling the refractive effects within bundle adjustment, synthetic error-free datasets are simulated. The behaviour of interior, exterior, and relative orientation parameters is analysed using synthetic datasets free of underwater imaging effects. A shift of the camera positions of 0.2% of the acquisition distance along the optical axis can be observed. The relative orientation of a stereo camera shows systematic effects when the angle of convergence varies. The stereo baseline increases by 1% at 25° convergence. Furthermore, the interface is set up at different distances to the camera. When the interface is at 50% distance assuming a parallel camera setup, the stereo baseline also increases by 1%. It becomes clear that in most cases the implicit modelling is not suitable for multimedia photogrammetry due to geometrical errors (scaling) and absolute positioning errors. Explicit modelling of the refractive interfaces is implemented into a bundle adjustment and is also used to analyse calibration parameters and deviations in object space. Real experiments show that it is difficult to separate the effects of implicit modelling, since other effects, such as poor image measurements, affect the final result. However, trends can be seen, and deviations are quantified. Full article
(This article belongs to the Special Issue Underwater 3D Recording & Modelling)
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19 pages, 5261 KiB  
Article
Forest Potential Productivity Mapping by Linking Remote-Sensing-Derived Metrics to Site Variables
by Parinaz Rahimzadeh-Bajgiran, Chris Hennigar, Aaron Weiskittel and Sean Lamb
Remote Sens. 2020, 12(12), 2056; https://doi.org/10.3390/rs12122056 - 26 Jun 2020
Cited by 23 | Viewed by 5303
Abstract
A fine-resolution region-wide map of forest site productivity is an essential need for effective large-scale forestry planning and management. In this study, we incorporated Sentinel-2 satellite data into an increment-based measure of forest productivity (biomass growth index (BGI)) derived from climate, lithology, soils, [...] Read more.
A fine-resolution region-wide map of forest site productivity is an essential need for effective large-scale forestry planning and management. In this study, we incorporated Sentinel-2 satellite data into an increment-based measure of forest productivity (biomass growth index (BGI)) derived from climate, lithology, soils, and topographic metrics to map improved BGI (iBGI) in parts of North American Acadian regions. Initially, several Sentinel-2 variables including nine single spectral bands and 12 spectral vegetation indices (SVIs) were used in combination with forest management variables to predict tree volume/ha and height using Random Forest. The results showed a 10–12 % increase in out of bag (OOB) r2 when Sentinel-2 variables were included in the prediction of both volume and height together with BGI. Later, selected Sentinel-2 variables were used for biomass growth prediction in Maine, USA and New Brunswick, Canada using data from 7738 provincial permanent sample plots. The Sentinel-2 red-edge position (S2REP) index was identified as the most important variable over others to have known influence on site productivity. While a slight improvement in the iBGI accuracy occurred compared to the base BGI model (~2%), substantial changes to coefficients of other variables were evident and some site variables became less important when S2REP was included. Full article
(This article belongs to the Special Issue Remote Sensing and Vegetation Mapping)
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16 pages, 16829 KiB  
Article
New Approach Evaluating Peatland Fires in Indonesian Factors
by Hiroshi Hayasaka, Aswin Usup and Daisuke Naito
Remote Sens. 2020, 12(12), 2055; https://doi.org/10.3390/rs12122055 - 26 Jun 2020
Cited by 18 | Viewed by 4284
Abstract
Until 2018, the El Niño–Southern Oscillation (ENSO) was used as an explanation for fires in Indonesia’s peatlands. However, when the 2019 fires occurred independently of El Niño, more suitable indicators and methods were required to (a) analyze, (b) evaluate and (c) forecast peatland [...] Read more.
Until 2018, the El Niño–Southern Oscillation (ENSO) was used as an explanation for fires in Indonesia’s peatlands. However, when the 2019 fires occurred independently of El Niño, more suitable indicators and methods were required to (a) analyze, (b) evaluate and (c) forecast peatland fires. In this study, we introduced the OLR–MC index—one of the rain-related indices derived from OLR (outgoing longwave radiation) in MC (maritime continent) area in Indonesia. This index showed stronger correlation with active peatland fires than the conventional ENSO index, and is likely to be able to respond to heat and dry weather supposed to be under climate-change conditions. We then analyzed peatland fires in the top six fire years from 2002 to 2018 and showed that peatland fires occurred in three stages—surface fire, shallow peatland fire and deep peatland fire. To explain each stage, we proposed a one-dimensional groundwater level (GWL) prediction model (named as MODEL-0). MODEL-0 predicts GWL from daily rainfall. Analysis using MODEL-0 showed the GWL thresholds for the three fire stages were between -300 mm and -500 mm; peatland fire activities during the three fire stages were dependent on these GWL values. The validity of MODEL-0 was shown by comparison with the measured values of GWL in the top three fire years. Full article
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20 pages, 15885 KiB  
Article
Analysis of Landslide Movements Using Interferometric Synthetic Aperture Radar: A Case Study in Hunza-Nagar Valley, Pakistan
by Mohib Ur Rehman, Yi Zhang, Xingmin Meng, Xiaojun Su, Filippo Catani, Gohar Rehman, Dongxia Yue, Zainab Khalid, Sajjad Ahmad and Ijaz Ahmad
Remote Sens. 2020, 12(12), 2054; https://doi.org/10.3390/rs12122054 - 25 Jun 2020
Cited by 36 | Viewed by 8175
Abstract
From a geological standpoint, northern Pakistan is one of the most active and unstable areas in the world. As a consequence, many massive landslides have occurred in the area in historical times that have destroyed infrastructure, blocked the Hunza River, and damaged the [...] Read more.
From a geological standpoint, northern Pakistan is one of the most active and unstable areas in the world. As a consequence, many massive landslides have occurred in the area in historical times that have destroyed infrastructure, blocked the Hunza River, and damaged the Karakoram Highway repeatedly. However, despite the high frequency of large magnitude landslide events, and the consequent damages, the entire area is largely understudied, mainly due to the difficult logistics and the large distances involved. This work is aimed at applying the potential use of Interferometric Synthetic Aperture Radar (InSAR) for landslide identification and investigation for the Hunza-Nagar Region. Sentinel-1 images covering a period of more than two years (February 2017–August 2019) were used and processed by adopting the small baseline subset (SBAS) method. The obtained deformation rate measured along the line of sight (VLOS) varies from −114 to 20 mm/year. The downslope velocity deformation rates (Vslope) range from 0 to −300 mm/year. The Vslope stability threshold for our study area was calculated to be −14 mm/year from the Vslope standard deviation. Four active landslides with Vslope exceeding 14 mm/year were recognizable and have been confirmed by field inspection. The identified landslides listed from the most active to least active are the Humarri, Mayoon, Khai, and Ghulmet landslides, respectively. VLOS exceeding 114 mm/year was observed in the Humarri landslide, which posed a threat of damming a lake on the Hispar River and was also a risk to the Humarri Village located below the landslide. The maximum mean deformation detected in the Ghulmet, and Mayoon landslide was in the order of 30 mm/year and 20 mm/year, respectively. More importantly, it was found that in places, the slope deformation time series showed a patchy correlation with precipitation and seismic events in the area. This may indicate a complex, and possibly uncoupled, relationship between the two controlling agents promoting the deformation. However, the collective impact of the two factors is evident in the form of a continuously descending deformation curve and clearly indicates the ground distortion. The results indicate a potentially critical situation related to the high deformation rates measured at the Humarri landslide. On this specific slope, conditions leading to a possible catastrophic failure cannot be ruled out and should be a priority for the application of mitigation measures. Full article
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28 pages, 20805 KiB  
Article
Application of Unmanned Aerial Vehicle Data and Discrete Fracture Network Models for Improved Rockfall Simulations
by Mirko Francioni, Federico Antonaci, Nicola Sciarra, Carlo Robiati, John Coggan, Doug Stead and Fernando Calamita
Remote Sens. 2020, 12(12), 2053; https://doi.org/10.3390/rs12122053 - 25 Jun 2020
Cited by 35 | Viewed by 4148
Abstract
In this research, we present a new approach to define the distribution of block volumes during rockfall simulations. Unmanned aerial vehicles (UAVs) are utilized to generate high-accuracy 3D models of the inaccessible SW flank of the Mount Rava (Italy), to provide improved definition [...] Read more.
In this research, we present a new approach to define the distribution of block volumes during rockfall simulations. Unmanned aerial vehicles (UAVs) are utilized to generate high-accuracy 3D models of the inaccessible SW flank of the Mount Rava (Italy), to provide improved definition of data gathered from conventional geomechanical surveys and to also denote important changes in the fracture intensity. These changes are likely related to the variation of the bedding thickness and to the presence of fracture corridors in fault damage zones in some areas of the slope. The dataset obtained integrating UAV and conventional surveys is then utilized to create and validate two accurate 3D discrete fracture network models, representative of high and low fracture intensity areas, respectively. From these, the ranges of block volumes characterizing the in situ rock mass are extracted, providing important input for rockfall simulations. Initially, rockfall simulations were performed assuming a uniform block volume variation for each release cell. However, subsequent simulations used a more realistic nonuniform distribution of block volumes, based on the relative block volume frequency extracted from discrete fracture network (DFN) models. The results of the simulations were validated against recent rockfall events and show that it is possible to integrate into rockfall simulations a more realistic relative frequency distribution of block volumes using the results of DFN analyses. Full article
(This article belongs to the Special Issue Remote Sensing for Landslide Monitoring, Mapping and Modeling)
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31 pages, 7310 KiB  
Article
A Methodology for Comparing the Surface Urban Heat Island in Selected Urban Agglomerations Around the World from Sentinel-3 SLSTR Data
by José Antonio Sobrino and Itziar Irakulis
Remote Sens. 2020, 12(12), 2052; https://doi.org/10.3390/rs12122052 - 25 Jun 2020
Cited by 66 | Viewed by 8250
Abstract
Retrieval of land surface temperature (LST) from satellite data allows to estimate the surface urban heat island (SUHI) as the difference between the LST obtained in the urban area and the LST of its surroundings. However, this definition depends on the selection of [...] Read more.
Retrieval of land surface temperature (LST) from satellite data allows to estimate the surface urban heat island (SUHI) as the difference between the LST obtained in the urban area and the LST of its surroundings. However, this definition depends on the selection of the urban and surroundings references, which translates into greater difficulty in comparing SUHI values in different urban agglomerations across the world. In order to avoid this problem, a methodology is proposed that allows reliable quantification of the SUHI. The urban reference is obtained from the European Space Agency Climate Change Initiative Land Cover and three surroundings references are considered; that is, the urban adjacent (Su), the future adjacent (Sf), and the peri-urban (Sp), which are obtained from mathematical expressions that depend exclusively on the urban area. In addition, two formulations of SUHI are considered: SUHIMAX and SUHIMEAN, which evaluate the maximum and average SUHI of the urban area for each of the three surrounding references. As the urban population growth phenomenon is a world-scale problem, this methodology has been applied to 71 urban agglomerations around the world using LST data obtained from the sea and land surface temperature radiometer (SLSTR) on board Sentinel-3A. The results show average values of SUHIMEAN of (1.8 ± 0.9) °C, (2.6 ± 1.3) °C, and (3.1 ± 1.7) °C for Su, Sf, and Sp, respectively, and an average difference between SUHIMAX and SUHIMEAN of (3.1 ± 1.1) °C. To complete the study, two additional indices have been considered: the Urban Thermal Field Variation Index (UFTVI) and the Discomfort Index (DI), which proved to be essential for understanding the SUHI phenomenon and its consequences on the quality of life of the inhabitants. Full article
(This article belongs to the Special Issue Advancement of Urban Heat Island Studies with Remote Sensing)
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19 pages, 21991 KiB  
Article
A Robust and Versatile Pipeline for Automatic Photogrammetric-Based Registration of Multimodal Cultural Heritage Documentation
by Anthony Pamart, François Morlet, Livio De Luca and Philippe Veron
Remote Sens. 2020, 12(12), 2051; https://doi.org/10.3390/rs12122051 - 25 Jun 2020
Cited by 10 | Viewed by 3345
Abstract
Imaging techniques and Image Based-Modeling (IBM) practices in the field of Cultural Heritage (CH) studies are nowadays no longer used as one-shot applications but as various and complex scenarios involving multiple modalities; sensors, scales, spectral bands and temporalities utilized by various experts. Current [...] Read more.
Imaging techniques and Image Based-Modeling (IBM) practices in the field of Cultural Heritage (CH) studies are nowadays no longer used as one-shot applications but as various and complex scenarios involving multiple modalities; sensors, scales, spectral bands and temporalities utilized by various experts. Current use of Structure from Motion and photogrammetric methods necessitates some improvements in iterative registration to ease the growing complexity in the management of the scientific imaging applied on heritage assets. In this context, the co-registration of photo-documentation among other imaging resources is a key step in order to move towards data fusion and collaborative semantic enrichment scenarios. This paper presents the recent development of a Totally Automated Co-registration and Orientation library (TACO) based on the interoperability of open-source solutions to conduct photogrammetric-based registration. The proposed methodology addresses and solves some gaps in term of robustness and versatility in the field of incremental and global orientation of image-sets dedicated to CH practices. Full article
(This article belongs to the Special Issue 2nd Edition Advances in Remote Sensing for Archaeological Heritage)
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18 pages, 6056 KiB  
Article
Real-Time Estimation of Low Earth Orbit (LEO) Satellite Clock Based on Ground Tracking Stations
by Zhixin Yang, Hui Liu, Chuang Qian, Bao Shu, Linjie Zhang, Xintong Xu, Yi Zhang and Yidong Lou
Remote Sens. 2020, 12(12), 2050; https://doi.org/10.3390/rs12122050 - 25 Jun 2020
Cited by 21 | Viewed by 7523
Abstract
The rapid movement of low Earth orbit (LEO) satellite can improve geometric diversity, which contributes to the rapid convergence of Global Navigation Satellite System (GNSS) precise point positioning (PPP). However, the LEO onboard receiver clock cannot be used directly by PPP users as [...] Read more.
The rapid movement of low Earth orbit (LEO) satellite can improve geometric diversity, which contributes to the rapid convergence of Global Navigation Satellite System (GNSS) precise point positioning (PPP). However, the LEO onboard receiver clock cannot be used directly by PPP users as the LEO satellite clock because the LEO onboard receiver clock and LEO satellite clock absorb different code delays when receiving and transmitting signals. In this study, a real-time estimation approach for the LEO satellite clock based on ground tracking stations was proposed for the first time. The feasibility for the rapid convergence of the LEO satellite clock was analyzed using the satellite time dilution of precision (TDOP) that one satellite is relative to multiple ground tracking stations. The LEO constellation of 168 satellites and observations for 15 ground tracking stations were simulated to verify the proposed method. The experiment results showed that the average convergence time was 31.21 min for the Global Positioning System (GPS) satellite clock, whereas the value for the LEO satellite clock was only 2.86 min. The average root mean square (RMS) and standard deviation (STD) values after convergence were 0.71 and 0.39 ns for the LEO satellite clock, whereas the values were 0.31 and 0.13 ns for the GPS satellite clock. The average weekly satellite TDOP for the LEO satellite was much smaller than that for the GPS satellite. The average satellite TDOPs for all LEO and GPS satellites were 19.13 and 1294.70, respectively. However, the average delta TDOPs caused by satellite motion for all LEO and GPS satellites were both 0.10. Therefore, the rapid convergence of the LEO satellite clock resulted from the better geometric distribution of the LEO satellite relative to ground stations. Despite errors and the convergence time of the LEO satellite clock, the convergence time and positioning accuracy for LEO-augmented GPS and BeiDou Navigation Satellite System (BDS) PPP with the real-time estimated LEO satellite clock can still reach 10.63 min, 1.94 cm, 1.44 cm, and 4.18 cm in the east, north, and up components, respectively. The improvements caused by LEO satellite for GPS/BDS PPP were 59%, 30%, 31%, and 33%, respectively. Full article
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22 pages, 4770 KiB  
Article
Machine Learning for Tree Species Classification Using Sentinel-2 Spectral Information, Crown Texture, and Environmental Variables
by Joongbin Lim, Kyoung-Min Kim, Eun-Hee Kim and Ri Jin
Remote Sens. 2020, 12(12), 2049; https://doi.org/10.3390/rs12122049 - 25 Jun 2020
Cited by 16 | Viewed by 4736
Abstract
The most recent forest-type map of the Korean Peninsula was produced in 1910. That of South Korea alone was produced since 1972; however, the forest type information of North Korea, which is an inaccessible region, is not known due to the separation after [...] Read more.
The most recent forest-type map of the Korean Peninsula was produced in 1910. That of South Korea alone was produced since 1972; however, the forest type information of North Korea, which is an inaccessible region, is not known due to the separation after the Korean War. In this study, we developed a model to classify the five dominant tree species in North Korea (Korean red pine, Korean pine, Japanese larch, needle fir, and Oak) using satellite data and machine-learning techniques. The model was applied to the Gwangneung Forest area in South Korea; the Mt. Baekdu area of China, which borders North Korea; and to Goseong-gun, at the border of South Korea and North Korea, to evaluate the model’s applicability to North Korea. Eighty-three percent accuracy was achieved in the classification of the Gwangneung Forest area. In classifying forest types in the Mt. Baekdu area and Goseong-gun, even higher accuracies of 91% and 90% were achieved, respectively. These results confirm the model’s regional applicability. To expand the model for application to North Korea, a new model was developed by integrating training data from the three study areas. The integrated model’s classification of forest types in Goseong-gun (South Korea) was relatively accurate (80%); thus, the model was utilized to produce a map of the predicted dominant tree species in Goseong-gun (North Korea). Full article
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22 pages, 4630 KiB  
Article
Regional Tropical Aboveground Biomass Mapping with L-Band Repeat-Pass Interferometric Radar, Sparse Lidar, and Multiscale Superpixels
by Charlie Marshak, Marc Simard, Laura Duncanson, Carlos Alberto Silva, Michael Denbina, Tien-Hao Liao, Lola Fatoyinbo, Ghislain Moussavou and John Armston
Remote Sens. 2020, 12(12), 2048; https://doi.org/10.3390/rs12122048 - 25 Jun 2020
Cited by 11 | Viewed by 4557
Abstract
We introduce a multiscale superpixel approach that leverages repeat-pass interferometric coherence and sparse AGB estimates from a simulated spaceborne lidar in order to extend the NISAR mission’s applicable range of aboveground biomass (AGB) in tropical forests. Airborne and spaceborne L-band radar and full-waveform [...] Read more.
We introduce a multiscale superpixel approach that leverages repeat-pass interferometric coherence and sparse AGB estimates from a simulated spaceborne lidar in order to extend the NISAR mission’s applicable range of aboveground biomass (AGB) in tropical forests. Airborne and spaceborne L-band radar and full-waveform airborne lidar data are used to simulate the NISAR and GEDI mission, respectively. In addition to UAVSAR data, we use spaceborne ALOS-2/PALSAR-2 imagery with 14-day temporal baseline, which is comparable to NISAR’s 12-day baseline. Our reference AGB maps are derived from the airborne LVIS data during the AfriSAR campaign for three sites (Mondah, Ogooue, and Lope). Each tropical site has mean AGB of at least 125 Mg/ha in addition to areas with AGB exceeding 700 Mg/ha. Spatially sampling from these LVIS-derived AGB reference maps, we approximate GEDI AGB estimates. To evaluate our methodology, we perform several different analyses. First, we partition each study site into low (≤100 Mg/ha) and high (>100 Mg/ha) AGB areas, in conformity with the NISAR mission requirement to provide AGB estimates for forests between 0 and 100 Mg/ha with a RMSE below 20 Mg/ha. In the low AGB areas, this RMSE requirement is satisfied in Lope and Mondah and it fell short of the requirement in Ogooue by less 3 Mg/ha with UAVSAR and 6 Mg/ha with PALSAR-2. We note that our maps have finer spatial resolution (50 m) than NISAR requires (1 hectare). In the high AGB areas, the normalized RMSE increases to 51% (i.e., <90 Mg/ha), but with negligible bias for all three sites. Second, we train a single model to estimate AGB across both high and low AGB regimes simultaneously and obtain a normalized RMSE that is <60% (or <100 Mg/ha). Lastly, we show the use of both (a) multiscale superpixels and (b) interferometric coherence significantly improves the accuracy of the AGB estimates. The InSAR coherence improved the RMSE by approximately 8% at Mondah with both sensors, lowering the RMSE from 59 Mg/ha to 47.4 Mg/h with UAVSAR and from 57.1 Mg/ha to 46 Mg/ha. This work illustrates one of the numerous synergistic relationships between the spaceborne lidars, such as GEDI, with L-band SAR, such as PALSAR-2 and NISAR, in order to produce robust regional AGB in high biomass tropical regions. Full article
(This article belongs to the Special Issue Estimation of Forest Biomass from SAR)
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18 pages, 12587 KiB  
Article
Enabling Image-Based Streamflow Monitoring at the Edge
by Fabio Tosi, Matteo Rocca, Filippo Aleotti, Matteo Poggi, Stefano Mattoccia, Flavia Tauro, Elena Toth and Salvatore Grimaldi
Remote Sens. 2020, 12(12), 2047; https://doi.org/10.3390/rs12122047 - 25 Jun 2020
Cited by 18 | Viewed by 3639
Abstract
Monitoring streamflow velocity is of paramount importance for water resources management and in engineering practice. To this aim, image-based approaches have proved to be reliable systems to non-intrusively monitor water bodies in remote places at variable flow regimes. Nonetheless, to tackle their computational [...] Read more.
Monitoring streamflow velocity is of paramount importance for water resources management and in engineering practice. To this aim, image-based approaches have proved to be reliable systems to non-intrusively monitor water bodies in remote places at variable flow regimes. Nonetheless, to tackle their computational and energy requirements, offload processing and high-speed internet connections in the monitored environments, which are often difficult to access, is mandatory hence limiting the effective deployment of such techniques in several relevant circumstances. In this paper, we advance and simplify streamflow velocity monitoring by directly processing the image stream in situ with a low-power embedded system. By leveraging its standard parallel processing capability and exploiting functional simplifications, we achieve an accuracy comparable to state-of-the-art algorithms that typically require expensive computing devices and infrastructures. The advantage of monitoring streamflow velocity in situ with a lightweight and cost-effective embedded processing device is threefold. First, it circumvents the need for wideband internet connections, which are expensive and impractical in remote environments. Second, it massively reduces the overall energy consumption, bandwidth and deployment cost. Third, when monitoring more than one river section, processing “at the very edge” of the system efficiency improves scalability by a large margin, compared to offload solutions based on remote or cloud processing. Therefore, enabling streamflow velocity monitoring in situ with low-cost embedded devices would foster the widespread diffusion of gauge cameras even in developing countries where appropriate infrastructure might be not available or too expensive. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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16 pages, 2592 KiB  
Article
Temperature and Emissivity Separation ‘Draping’ Algorithm Applied to Hyperspectral Infrared Data
by Valerio Lombardo, Leonie Pick, Claudia Spinetti, Jacopo Tadeucci and Klemen Zakšek
Remote Sens. 2020, 12(12), 2046; https://doi.org/10.3390/rs12122046 - 25 Jun 2020
Cited by 10 | Viewed by 2985
Abstract
In the presented work, the spectral emissivity of basaltic melts at magmatic temperatures was retrieved in a laboratory-controlled experiment by measuring their spectral radiance. Granulated bombs of Etnean basalts were melted and the radiant energy from the melting surface was recorded by a [...] Read more.
In the presented work, the spectral emissivity of basaltic melts at magmatic temperatures was retrieved in a laboratory-controlled experiment by measuring their spectral radiance. Granulated bombs of Etnean basalts were melted and the radiant energy from the melting surface was recorded by a portable spectroradiometer in the short wavelength infrared (SWIR) spectral range between 1500 and 2500 nm. The Draping algorithm, an improved algorithm for temperature and emissivity separation, was applied for the first time to SWIR hyperspectral data in order to take into account the non-uniform temperature distribution of the melt surface and, at the same time, solving the two temperatures and the spectral emissivity. The results have been validated by comparing our results with the emissivity measured at a "lava simulator". Basalt spectral emissivity does not vary significantly at magmatic temperature, but shows an absorption feature in the range 2180–2290 nm, an atmospheric window pivotal for the IR remote sensing of active volcanoes. Full article
(This article belongs to the Special Issue Satellite Remote Sensing of High-Temperature Thermal Anomalies)
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13 pages, 4394 KiB  
Letter
A Combined Use of TSVD and Tikhonov Regularization for Mass Flux Solution in Tibetan Plateau
by Tianyi Chen, Jürgen Kusche, Yunzhong Shen and Qiujie Chen
Remote Sens. 2020, 12(12), 2045; https://doi.org/10.3390/rs12122045 - 25 Jun 2020
Cited by 13 | Viewed by 2959
Abstract
Limited by the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) measurement principle and sensors, the spatial resolution of mass flux solutions is about 2–3° in mid-latitudes at monthly intervals. To retrieve a mass flux solution in the Tibetan Plateau (TP) [...] Read more.
Limited by the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) measurement principle and sensors, the spatial resolution of mass flux solutions is about 2–3° in mid-latitudes at monthly intervals. To retrieve a mass flux solution in the Tibetan Plateau (TP) with better visual spatial resolution, we combined truncated singular value decomposition (TSVD) and Tikhonov regularization to solve for a mascon modeling. The monthly mass flux parameters resolved at 1° are smoothed to about 2° by truncating the eigen-spectrum of the normal equation (i.e., using the TSVD approach), and then Tikhonov regularization is applied to the truncated normal equation. As a result, the terms beyond the native resolution of GRACE/GRACE-FO data are truncated, and the errors in higher degree and order components are dampened by Tikhonov regularization. In terms of root mean squared errors, the improvements are 27.2% and 12.7% for the combined method over TSVD and Tikhonov regularization, respectively. We confirm a decreasing secular trend with −5.6 ± 4.2 Gt/year for the entire TP and provide maps with 1° resolution from April 2002 to April 2019, generated with the combined TSVD and Tikhonov regularization method. Full article
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23 pages, 4961 KiB  
Article
Exploiting Earth Observation Data to Impute Groundwater Level Measurements with an Extreme Learning Machine
by Steven Evans, Gustavious P. Williams, Norman L. Jones, Daniel P. Ames and E. James Nelson
Remote Sens. 2020, 12(12), 2044; https://doi.org/10.3390/rs12122044 - 25 Jun 2020
Cited by 20 | Viewed by 3240
Abstract
Groundwater resources are expensive to develop and use; they are difficult to monitor and data collected from monitoring wells are often sporadic, often only available at irregular, infrequent, or brief intervals. Groundwater managers require an accurate understanding of historic groundwater storage trends to [...] Read more.
Groundwater resources are expensive to develop and use; they are difficult to monitor and data collected from monitoring wells are often sporadic, often only available at irregular, infrequent, or brief intervals. Groundwater managers require an accurate understanding of historic groundwater storage trends to effectively manage groundwater resources, however, most if not all well records contain periods of missing data. To understand long-term trends, these missing data need to be imputed before trend analysis. We present a method to impute missing data at single wells, by exploiting data generated from Earth observations that are available globally. We use two soil moisture models, the Global Land Data Assimilation System (GLDAS) model and National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC) soil moisture model to impute the missing data. Our imputation method uses a machine learning technique called Extreme Learning Machine (ELM). Our implementation uses 11 input data-streams, all based on Earth observation data. We train and apply the model one well at a time. We selected ELM because it is a single hidden layer feedforward model that can be trained quickly on minimal data. We tested the ELM method using data from monitoring wells in the Cedar Valley and Beryl-Enterprise areas in southwest Utah, USA. We compute error estimates for the imputed data and show that ELM-computed estimates were more accurate than Kriging estimates. This ELM-based data imputation method can be used to impute missing data at wells. These complete time series can be used improve the accuracy of aquifer groundwater elevation maps in areas where in-situ well measurements are sparse, resulting in more accurate spatial estimates of the groundwater surface. The data we use are available globally from 1950 to the present, so this method can be used anywhere in the world. Full article
(This article belongs to the Special Issue Remote Sensing of Groundwater Variations and Ground Response)
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20 pages, 5302 KiB  
Review
The Evolution of Wide-Area DInSAR: From Regional and National Services to the European Ground Motion Service
by Michele Crosetto, Lorenzo Solari, Marek Mróz, Joanna Balasis-Levinsen, Nicola Casagli, Michaela Frei, Anneleen Oyen, Dag Anders Moldestad, Luke Bateson, Luca Guerrieri, Valerio Comerci and Henrik Steen Andersen
Remote Sens. 2020, 12(12), 2043; https://doi.org/10.3390/rs12122043 - 25 Jun 2020
Cited by 127 | Viewed by 8913
Abstract
This study is focused on wide-area deformation monitoring initiatives based on the differential interferometric SAR technique (DInSAR). In particular, it addresses the use of advanced DInSAR (A-DInSAR) techniques, which are based on large sets of synthetic aperture radar (SAR) and Copernicus Sentinel-1 images. [...] Read more.
This study is focused on wide-area deformation monitoring initiatives based on the differential interferometric SAR technique (DInSAR). In particular, it addresses the use of advanced DInSAR (A-DInSAR) techniques, which are based on large sets of synthetic aperture radar (SAR) and Copernicus Sentinel-1 images. Such techniques have undergone a dramatic development in the last twenty years: they are now capable to process big sets of SAR images and can be exploited to realize a wide-area A-DInSAR monitoring. The study describes several initiatives to establish wide-area ground motion services (GMS), both at county- and region-level. In the second part of the study, some of the key technical aspects related to wide-area A-DInSAR monitoring are discussed. Finally, the last part of the study is devoted to the European ground motion service (EGMS), which is part of the Copernicus land monitoring service. It represents the most important wide-area A-DInSAR deformation monitoring system ever developed. The study describes its main characteristics and its main products. The end of the production of the first EGMS baseline product is foreseen for the last quarter of 2021. Full article
(This article belongs to the Special Issue Scaling-Up Deformation Monitoring and Analysis)
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19 pages, 3711 KiB  
Article
Abandoned Mine Tailings Affecting Riverbed Sediments in the Cartagena–La Union District, Mediterranean Coastal Area (Spain)
by Tomás Martín-Crespo, David Gómez-Ortiz, Silvia Martín-Velázquez, Pedro Martínez-Pagán, Cristina de Ignacio-San José, Javier Lillo and Ángel Faz
Remote Sens. 2020, 12(12), 2042; https://doi.org/10.3390/rs12122042 - 25 Jun 2020
Cited by 19 | Viewed by 3789
Abstract
This study presents the results of the geoenvironmental characterization of La Matildes riverbed, affected by mine tailings in the Cartagena–La Unión district, Murcia (southeast Spain). Soils and riverbeds in this area are highly polluted. Two Electrical Resistivity Imaging (ERI) profiles were carried out [...] Read more.
This study presents the results of the geoenvironmental characterization of La Matildes riverbed, affected by mine tailings in the Cartagena–La Unión district, Murcia (southeast Spain). Soils and riverbeds in this area are highly polluted. Two Electrical Resistivity Imaging (ERI) profiles were carried out to obtain information about the thickness of the deposits and their internal structure. For the mine tailings deposits of La Murla, a tributary of the El Miedo riverbed, the geophysical method imaged two different units: the upper one characterized by low resistivity values and 5–8 m thickness, correlated with the mine tailings deposits; and the lower more resistive unit corresponding to the Paleozoic metasediments bedrock. The ERI profile transverse to the Las Matildes dry riverbed revealed the existence of three different units. The uppermost one has the lowest resistivity values and corresponds to the tailings deposits discharged to the riverbeds. An intermediate unit, with intermediate resistivity values, corresponds to the riverbed sediments before the mining operations. The lower unit is more resistive and corresponds to the bedrock. Significant amounts of pyrite, sphalerite, and galena were found both in tailings and riverbed sediments. The geochemical composition of borehole samples from the riverbed materials shows significantly high contents of As, Cd, Cu, Fe, Pb, and Zn being released to the environment. Mining works have modified the natural landscape near La Unión town. Surface extraction in three open-pit mines have changed the summits of Sierra de Cartagena–La Unión. Rock and metallurgical wastes have altered the drainage pattern and buried the headwaters of ephemeral channels. The environmental hazards require remediation to minimize the environmental impact on the Mar Menor coastal lagoon, one of the most touristic areas in SE Spain. Full article
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23 pages, 3166 KiB  
Review
Review of Remote Sensing Methods to Map Coffee Production Systems
by David A. Hunt, Karyn Tabor, Jennifer H. Hewson, Margot A. Wood, Louis Reymondin, Kellee Koenig, Mikaela Schmitt-Harsh and Forrest Follett
Remote Sens. 2020, 12(12), 2041; https://doi.org/10.3390/rs12122041 - 25 Jun 2020
Cited by 27 | Viewed by 11989
Abstract
The coffee sector is working towards sector-wide commitments for sustainable production. Yet, knowledge of where coffee is cultivated and its environmental impact remains limited, in part due to the challenges of mapping coffee using satellite remote sensing. We recognize the urgency to capitalize [...] Read more.
The coffee sector is working towards sector-wide commitments for sustainable production. Yet, knowledge of where coffee is cultivated and its environmental impact remains limited, in part due to the challenges of mapping coffee using satellite remote sensing. We recognize the urgency to capitalize on recent technological advances to improve remote sensing methods and generate more accurate, reliable, and scalable approaches to coffee mapping. In this study, we provide a systematic review of satellite-based approaches to mapping coffee extent, which produced 43 articles in the peer-reviewed and gray literature. We outline key considerations for employing effective approaches, focused on the need to balance data affordability and quality, classification complexity and accuracy, and generalizability and site-specificity. We discuss research opportunities for improved approaches by leveraging the recent expansion of diverse satellite sensors and constellations, optical/Synthetic Aperture Radar data fusion approaches, and advances in cloud computing and deep learning algorithms. We highlight the need for differentiating between production systems and the need for research in important coffee-growing geographies. By reviewing the range of techniques successfully used to map coffee extent, we provide technical recommendations and future directions to enable accurate and scalable coffee maps. Full article
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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13 pages, 5130 KiB  
Article
Uncertainties in CERES Top-of-Atmosphere Fluxes Caused by Changes in Accompanying Imager
by Wenying Su, Lusheng Liang, Hailan Wang and Zachary A. Eitzen
Remote Sens. 2020, 12(12), 2040; https://doi.org/10.3390/rs12122040 - 25 Jun 2020
Cited by 5 | Viewed by 4215
Abstract
The Clouds and the Earth’s Radiant Energy System (CERES) project provides observations of Earth’s radiation budget using measurements from CERES instruments on board the Terra, Aqua, Suomi National Polar-orbiting Partnership (S-NPP), and NOAA-20 satellites. The CERES top-of-atmosphere (TOA) fluxes are produced by converting [...] Read more.
The Clouds and the Earth’s Radiant Energy System (CERES) project provides observations of Earth’s radiation budget using measurements from CERES instruments on board the Terra, Aqua, Suomi National Polar-orbiting Partnership (S-NPP), and NOAA-20 satellites. The CERES top-of-atmosphere (TOA) fluxes are produced by converting radiance measurements using empirical angular distribution models, which are functions of cloud properties that are retrieved from imagers flying with the CERES instruments. As the objective is to create a long-term climate data record, not only calibration consistency of the six CERES instruments needs to be maintained for the entire time period, it is also important to maintain the consistency of other input data sets used to produce this climate data record. In this paper, we address aspects that could potentially affect the CERES TOA flux data quality. Discontinuities in imager calibration can affect cloud retrieval which can lead to erroneous flux trends. When imposing an artificial 0.6 per decade decreasing trend to cloud optical depth, which is similar to the trend difference between CERES Edition 2 and Edition 4 cloud retrievals, the decadal SW flux trend changed from 0.3 5 ± 0.18 Wm 2 to 0.61 ± 0.18 Wm 2 . This indicates that a 13% change in cloud optical depth results in about 1% change in the SW flux. Furthermore, different CERES instruments provide valid fluxes at different viewing zenith angle ranges, and including fluxes derived at the most oblique angels unique to S-NPP (>66 ) can lead to differences of 0.8 Wm 2 and 0.3 Wm 2 in global monthly mean instantaneous SW flux and LW flux. To ensure continuity, the viewing zenith angle ranges common to all CERES instruments (<66 ) are used to produce the long-term Earth’s radiation budget climate data record. The consistency of cloud properties retrieved from different imagers also needs to be maintained to ensure the TOA flux consistency. Full article
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19 pages, 6130 KiB  
Article
Integration of GF2 Optical, GF3 SAR, and UAV Data for Estimating Aboveground Biomass of China’s Largest Artificially Planted Mangroves
by Yuanhui Zhu, Kai Liu, Soe W. Myint, Zhenyu Du, Yubin Li, Jingjing Cao, Lin Liu and Zhifeng Wu
Remote Sens. 2020, 12(12), 2039; https://doi.org/10.3390/rs12122039 - 25 Jun 2020
Cited by 50 | Viewed by 5019
Abstract
Accurate methods to estimate the aboveground biomass (AGB) of mangroves are required to monitor the subtle changes over time and assess their carbon sequestration. The AGB of forests is a function of canopy-related information (canopy density, vegetation status), structures, and tree heights. However, [...] Read more.
Accurate methods to estimate the aboveground biomass (AGB) of mangroves are required to monitor the subtle changes over time and assess their carbon sequestration. The AGB of forests is a function of canopy-related information (canopy density, vegetation status), structures, and tree heights. However, few studies have attended to integrating these factors to build models of the AGB of mangrove plantations. The objective of this study was to develop an accurate and robust biomass estimation of mangrove plantations using Chinese satellite optical, SAR, and Unmanned Aerial Vehicle (UAV) data based digital surface models (DSM). This paper chose Qi’ao Island, which forms the largest contiguous area of mangrove plantation in China, as the study area. Several field visits collected 127 AGB samples. The models for AGB estimation were developed using the random forest algorithm and integrating images from multiple sources: optical images from Gaofen-2 (GF-2), synthetic aperture radar (SAR) images from Gaofen-3 (GF-3), and UAV-based digital surface model (DSM) data. The performance of the models was assessed using the root-mean-square error (RMSE) and relative RMSE (RMSEr), based on five-fold cross-validation and stratified random sampling approach. The results showed that images from the GF-2 optical (RMSE = 33.49 t/ha, RMSEr = 21.55%) or GF-3 SAR (RMSE = 35.32 t/ha, RMSEr = 22.72%) can be used appropriately to monitor the AGB of the mangrove plantation. The AGB models derived from a combination of the GF-2 and GF-3 datasets yielded a higher accuracy (RMSE = 29.89 t/ha, RMSEr = 19.23%) than models that used only one of them. The model that used both datasets showed a reduction of 2.32% and 3.49% in RMSEr over the GF-2 and GF-3 models, respectively. On the DSM dataset, the proposed model yielded the highest accuracy of AGB (RMSE = 25.69 t/ha, RMSEr = 16.53%). The DSM data were identified as the most important variable, due to mitigating the saturation effect observed in the optical and SAR images for a dense AGB estimation of the mangroves. The resulting map, derived from the most accurate model, was consistent with the results of field investigations and the mangrove plantation sequences. Our results indicated that the AGB can be accurately measured by integrating images from the optical, SAR, and DSM datasets to adequately represent canopy-related information, forest structures, and tree heights. Full article
(This article belongs to the Special Issue Remote Sensing in Mangroves)
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29 pages, 1571 KiB  
Article
Sensitivity of Modeled CO2 Air–Sea Flux in a Coastal Environment to Surface Temperature Gradients, Surfactants, and Satellite Data Assimilation
by Ricardo Torres, Yuri Artioli, Vassilis Kitidis, Stefano Ciavatta, Manuel Ruiz-Villarreal, Jamie Shutler, Luca Polimene, Victor Martinez, Claire Widdicombe, E. Malcolm S. Woodward, Timothy Smyth, James Fishwick and Gavin H. Tilstone
Remote Sens. 2020, 12(12), 2038; https://doi.org/10.3390/rs12122038 - 25 Jun 2020
Cited by 6 | Viewed by 3330
Abstract
This work evaluates the sensitivity of CO2 air–sea gas exchange in a coastal site to four different model system configurations of the 1D coupled hydrodynamic–ecosystem model GOTM–ERSEM, towards identifying critical dynamics of relevance when specifically addressing quantification of air–sea CO2 exchange. [...] Read more.
This work evaluates the sensitivity of CO2 air–sea gas exchange in a coastal site to four different model system configurations of the 1D coupled hydrodynamic–ecosystem model GOTM–ERSEM, towards identifying critical dynamics of relevance when specifically addressing quantification of air–sea CO2 exchange. The European Sea Regional Ecosystem Model (ERSEM) is a biomass and functional group-based biogeochemical model that includes a comprehensive carbonate system and explicitly simulates the production of dissolved organic carbon, dissolved inorganic carbon and organic matter. The model was implemented at the coastal station L4 (4 nm south of Plymouth, 50°15.00’N, 4°13.02’W, depth of 51 m). The model performance was evaluated using more than 1500 hydrological and biochemical observations routinely collected at L4 through the Western Coastal Observatory activities of 2008–2009. In addition to a reference simulation (A), we ran three distinct experiments to investigate the sensitivity of the carbonate system and modeled air–sea fluxes to (B) the sea-surface temperature (SST) diurnal cycle and thus also the near-surface vertical gradients, (C) biological suppression of gas exchange and (D) data assimilation using satellite Earth observation data. The reference simulation captures well the physical environment (simulated SST has a correlation with observations equal to 0.94 with a p > 0.95). Overall, the model captures the seasonal signal in most biogeochemical variables including the air–sea flux of CO2 and primary production and can capture some of the intra-seasonal variability and short-lived blooms. The model correctly reproduces the seasonality of nutrients (correlation > 0.80 for silicate, nitrate and phosphate), surface chlorophyll-a (correlation > 0.43) and total biomass (correlation > 0.7) in a two year run for 2008–2009. The model simulates well the concentration of DIC, pH and in-water partial pressure of CO2 (pCO2) with correlations between 0.4–0.5. The model result suggest that L4 is a weak net source of CO2 (0.3–1.8 molCm−2 year−1). The results of the three sensitivity experiments indicate that both resolving the temperature profile near the surface and assimilation of surface chlorophyll-a significantly impact the skill of simulating the biogeochemistry at L4 and all of the carbonate chemistry related variables. These results indicate that our forecasting ability of CO2 air–sea flux in shelf seas environments and their impact in climate modeling should consider both model refinements as means of reducing uncertainties and errors in any future climate projections. Full article
(This article belongs to the Special Issue Synergy of Remote Sensing and Modelling Techniques for Ocean Studies)
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