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Remote Sens., Volume 14, Issue 7 (April-1 2022) – 241 articles

Cover Story (view full-size image): This work shows changes in tropospheric and surface nitrogen dioxide (NO2) that were observed during first two years of the COVID-19 pandemic in the Greater Toronto Area, Canada. Meteorological and satellite data were used to facilitate the analysis and reveal detailed local emission changes from different areas of the City of Toronto. Ground-based remote sensing data show a clear reduction in NO2, especially in the more polluted downtown and airport areas (reductions from 35% to 40% in 2020 compared to 5-year mean value). Compared to the sharp decline in NO2 emissions in 2020, the atmospheric NO2 amounts in 2021 started to recover, but were still below the mean values of the pre-pandemic time. For some sites, the pre-pandemic NO2 peak caused by local morning rush hour had still not returned in 2021, indicating a change in local traffic and commuter patterns. View this paper.
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Article
Hierarchical Disentangling Network for Building Extraction from Very High Resolution Optical Remote Sensing Imagery
Remote Sens. 2022, 14(7), 1767; https://doi.org/10.3390/rs14071767 - 06 Apr 2022
Cited by 1 | Viewed by 1139
Abstract
Building extraction using very high resolution (VHR) optical remote sensing imagery is an essential interpretation task that impacts human life. However, buildings in different environments exhibit various scales, complicated spatial distributions, and different imaging conditions. Additionally, with the spatial resolution of images increasing, [...] Read more.
Building extraction using very high resolution (VHR) optical remote sensing imagery is an essential interpretation task that impacts human life. However, buildings in different environments exhibit various scales, complicated spatial distributions, and different imaging conditions. Additionally, with the spatial resolution of images increasing, there are diverse interior details and redundant context information present in building and background areas. Thus, the above-mentioned situations would create large intra-class variances and poor inter-class discrimination, leading to uncertain feature descriptions for building extraction, which would result in over- or under-extraction phenomena. In this article, a novel hierarchical disentangling network with an encoder–decoder architecture called HDNet is proposed to consider both the stable and uncertain feature description in a convolution neural network (CNN). Next, a hierarchical disentangling strategy is set up to individually generate strong and weak semantic zones using a newly designed feature disentangling module (FDM). Here, the strong and weak semantic zones set up the stable and uncertain description individually to determine a more stable semantic main body and uncertain semantic boundary of buildings. Next, a dual-stream semantic feature description is built to gradually integrate strong and weak semantic zones by the designed component feature fusion module (CFFM), which is able to generate a powerful semantic description for more complete and refined building extraction. Finally, extensive experiments are carried out on three published datasets (i.e., WHU satellite, WHU aerial, and INRIA), and the comparison results show that the proposed HDNet outperforms other state-of-the-art (SOTA) methods. Full article
(This article belongs to the Special Issue Deep Learning for Very-High Resolution Land-Cover Mapping)
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Article
PlumeTraP: A New MATLAB-Based Algorithm to Detect and Parametrize Volcanic Plumes from Visible-Wavelength Images
Remote Sens. 2022, 14(7), 1766; https://doi.org/10.3390/rs14071766 - 06 Apr 2022
Viewed by 1299
Abstract
Tephra plumes from explosive volcanic eruptions can be hazardous for the lives and livelihoods of people living in the proximity of volcanoes. Monitoring and forecasting tephra plumes play essential roles in the detection, characterization and hazard assessment of explosive volcanic events. However, advanced [...] Read more.
Tephra plumes from explosive volcanic eruptions can be hazardous for the lives and livelihoods of people living in the proximity of volcanoes. Monitoring and forecasting tephra plumes play essential roles in the detection, characterization and hazard assessment of explosive volcanic events. However, advanced monitoring instruments, e.g., thermal cameras, can be expensive and are not always available in monitoring networks. Conversely, visible-wavelength cameras are significantly cheaper and much more widely available. This paper proposes an innovative approach to the detection and parametrization of tephra plumes, utilizing videos recorded in the visible wavelengths. Specifically, we have developed an algorithm with the objectives of: (i) identifying and isolating plume-containing pixels through image processing techniques; (ii) extracting the main geometrical parameters of the eruptive column, such as the height and width, as functions of time; and (iii) determining quantitative information related to the plume motion (e.g., the rise velocity and acceleration) using the physical quantities obtained through the first-order analysis. The resulting MATLAB-based software, named Plume Tracking and Parametrization (PlumeTraP), semi-automatically tracks the plume and is also capable of automatically calculating the associated geometric parameters. Through application of the algorithm to the case study of Vulcanian explosions from Sabancaya volcano (Peru), we verify that the eruptive column boundaries are well recognized, and that the calculated parameters are reliable. The developed software can be of significant use to the wider volcanological community, enabling research into the dynamics of explosive volcanic eruptions, as well as potentially improving the use of visible-wavelength cameras as part of the monitoring networks of active volcanoes. Furthermore, PlumeTraP could potentially find a broader application for the analysis of any other plume-shaped natural or anthropogenic phenomena in visible wavelengths. Full article
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Article
Remote Sensing Monitoring of the Spatial Pattern of Greening and Browning in Xilin Gol Grassland and Its Response to Climate and Human Activities
Remote Sens. 2022, 14(7), 1765; https://doi.org/10.3390/rs14071765 - 06 Apr 2022
Cited by 2 | Viewed by 1405
Abstract
As a unique ecosystem with multiple ecological functions but high fragility, grassland in arid areas is very vulnerable to changes in the natural environment or human activities, resulting in various ecological and environmental problems. In order to study the degree and spatial extent [...] Read more.
As a unique ecosystem with multiple ecological functions but high fragility, grassland in arid areas is very vulnerable to changes in the natural environment or human activities, resulting in various ecological and environmental problems. In order to study the degree and spatial extent of the influence of climatic conditions and human activities, especially mining activities, on grasslands in arid regions, we used remote sensing data to monitor the vegetation of the Xilin Gol grassland over a long period. The significant greening and browning areas of Xilin Gol grassland vegetation from 2000 to 2020 were extracted by a time series analysis. At the same time, the correlation analysis method was used to obtain the response of the Xilin Gol grassland vegetation to climatic factors and social and economic factors. In addition, we propose a new method based on buffer analysis and correlation analysis to calculate the influence range of vegetation degradation due to mining. We used this method to determine the influence range of vegetation degradation in the main mining area of the Xilin Gol grassland. The results showed that the vegetation condition of the Xilin Gol grassland were slightly improved from 2000 to 2020. Its vegetation was significantly affected by precipitation, and more than 50% of the area’s vegetation changes were highly correlated with precipitation changes. However, the area with the most serious vegetation degradation was mainly affected by human factors, and this part accounted for about 0.13% of the total area. In the form of direct damage and indirect effects (pulling population and economic growth to expand built-up areas), coal mining has become the main driving factor in the most significant areas of vegetation damage in the study area. Vegetation coverage in areas with significant greening and significant browning was highly correlated with economic factors, indicating that the vegetation changes were significantly affected by economic development. This study can reflect the vegetation changes and main driving factors in the overall and key areas of the Xilin Gol League and is a meaningful reference for the local balance of economic development and environmental protection. Full article
(This article belongs to the Special Issue Remote Sensing for Land Degradation and Drought Monitoring)
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Article
Downscaling Satellite-Based Estimates of Ocean Bottom Pressure for Tracking Deep Ocean Mass Transport
Remote Sens. 2022, 14(7), 1764; https://doi.org/10.3390/rs14071764 - 06 Apr 2022
Viewed by 936
Abstract
Gravimetry measurements from the GRACE and GRACE-Follow-On satellites provide observations of ocean bottom pressure (OBP), which can be differenced between basin boundaries to infer mass transport variability at a given level in the deep ocean. However, GRACE data products are limited in spatial [...] Read more.
Gravimetry measurements from the GRACE and GRACE-Follow-On satellites provide observations of ocean bottom pressure (OBP), which can be differenced between basin boundaries to infer mass transport variability at a given level in the deep ocean. However, GRACE data products are limited in spatial resolution, and conflate signals from many depth levels along steep continental slopes. To improve estimates of OBP variability near steep bathymetry, ocean bottom pressure observations from a JPL GRACE mascon product are downscaled using an objective analysis procedure, with OBP covariance information from an ocean model with horizontal grid spacing of ∼18 km. In addition, a depth-based adjustment was applied to enhance correlations at similar depths. Downscaled GRACE OBP shows realistic representations of sharp OBP gradients across bathymetry contours and strong currents, albeit with biases in the shallow ocean. In validations at intraannual (3–12 month) timescales, correlations of downscaled GRACE data (with depth adjustment) and in situ bottom pressure recorder time series were improved in ∼79% of sites, compared to correlations that did not involve downscaled GRACE. Correlations tend to be higher at sites where the amplitude of the OBP signal is larger, while locations where surface eddy kinetic energy is high (e.g., Gulf Stream extension) are more likely to have no improvement from the downscaling procedure. The downscaling procedure also increases the amplitude (standard deviation) of OBP variability compared to the non-downscaled GRACE at most sites, resulting in standard deviations that are closer to in situ values. A comparison of hydrography-based transport from RAPID with estimates based on downscaled GRACE data suggests substantial improvement from the downscaling at intraannual timescales, though this improvement does not extend to longer interannual timescales. Possible efforts to improve the downscaling technique through process studies and analysis of alongtrack GRACE/GRACE-FO observations are discussed. Full article
(This article belongs to the Section Ocean Remote Sensing)
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Article
Drone-Borne Ground-Penetrating Radar for Snow Cover Mapping
Remote Sens. 2022, 14(7), 1763; https://doi.org/10.3390/rs14071763 - 06 Apr 2022
Cited by 6 | Viewed by 1603
Abstract
Ground-penetrating radar (GPR) is one of the most commonly used instruments to map the Snow Water Equivalent (SWE) in mountainous regions. However, some areas may be difficult or dangerous to access; besides, some surveys can be quite time-consuming. We test a new system [...] Read more.
Ground-penetrating radar (GPR) is one of the most commonly used instruments to map the Snow Water Equivalent (SWE) in mountainous regions. However, some areas may be difficult or dangerous to access; besides, some surveys can be quite time-consuming. We test a new system to fulfill the need to speed up the acquisition process for the analysis of the SWE and to access remote or dangerous areas. A GPR antenna (900 MHz) is mounted on a drone prototype designed to carry heavy instruments, fly safely at high altitudes, and avoid interference of the GPR signal. A survey of two test sites of the Alpine region during winter 2020–2021 is presented, to check the prototype performance for mapping the snow thickness at the catchment scale. We process the data according to a standard flow-chart of radar processing and we pick both the travel times of the air–snow interface and the snow–ground interface to compute the travel time difference and to estimate the snow depth. The calibration of the radar snow depth is performed by comparing the radar travel times with snow depth measurements at preselected stations. The main results show fairly good reliability and performance in terms of data quality, accuracy, and spatial resolution in snow depth monitoring. We tested the device in the condition of low snow density (<200 kg/m3) and this limits the detectability of the air–snow interface. This is mainly caused by low values of the electrical permittivity of the dry soft snow, providing a weak reflectivity of the snow surface. To overcome this critical aspect, we use the data of the rangefinder to properly detect the travel time of the snow–air interface. This sensor is already installed in our prototype and in most commercial drones for flight purposes. Based on our experience with the prototype, various improvement strategies and limitations of drone-borne GPR acquisition are discussed. In conclusion, the drone technology is found to be ready to support GPR-based snow depth mapping applications at high altitudes, provided that the operators acquire adequate knowledge of the devices, in order to effectively build, tune, use and maintain a reliable acquisition system. Full article
(This article belongs to the Special Issue Emerging Remote Sensing Techniques for Monitoring Glaciers and Snow)
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Article
The August 2019 Piton de la Fournaise (La Réunion Island) Eruption: Analysis of the Multi-Source Deformation Pattern Detected through Sentinel-1 DInSAR Measurements
Remote Sens. 2022, 14(7), 1762; https://doi.org/10.3390/rs14071762 - 06 Apr 2022
Cited by 1 | Viewed by 868
Abstract
Piton de la Fournaise is one of the most active worldwide volcanoes, located on the southeastern part of La Réunion Island. In this work, we focus on the eruption that occurred on the southeastern flank of this volcano, inside the Enclos Fouqué caldera, [...] Read more.
Piton de la Fournaise is one of the most active worldwide volcanoes, located on the southeastern part of La Réunion Island. In this work, we focus on the eruption that occurred on the southeastern flank of this volcano, inside the Enclos Fouqué caldera, from 11 to 15 August 2019. This distal event was characterized by the opening of two eruptive fissures and accompanied by shallow volcano–tectonic earthquakes. We exploit the ground displacements using Sentinel-1 Differential Interferometric Synthetic Aperture Radar (DInSAR) measurements, which include the ground deformations generated during both the pre- and co-eruptive phases. To investigate the sources responsible for the detected ground displacements, we perform an analytical modeling of the retrieved DInSAR measurements. Our results reveal the presence of five volcanic sources (i.e., one sill-like source and four dikes), whose concomitant action during the pre- and co-eruptive phases generated the complex detected deformation pattern. The retrieved volcanic sources correlate well with the location of the opened fissures, the spatial distribution and the temporal evolution of the recorded seismicity, and other geophysical evidence already known in the literature. Full article
(This article belongs to the Special Issue Geodetic Observations for Earth System)
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Technical Note
Resolution Enhancement of SMAP Passive Soil Moisture Estimates
Remote Sens. 2022, 14(7), 1761; https://doi.org/10.3390/rs14071761 - 06 Apr 2022
Viewed by 952
Abstract
The Soil Moisture Active Passive (SMAP) mission includes a unique combination of instruments intended to provide daily global soil moisture data with high accuracy and resolution. Due to radar instrument failure, the default resolution of the data product decreased from the intended 9 [...] Read more.
The Soil Moisture Active Passive (SMAP) mission includes a unique combination of instruments intended to provide daily global soil moisture data with high accuracy and resolution. Due to radar instrument failure, the default resolution of the data product decreased from the intended 9 km to 36 km shortly after the mission started to return data. To improve this, we employed the Scatterometer Image Reconstruction algorithm in its radiometer form (rSIR) to enhance the resolution of the radiometer brightness temperature measurements from which the soil moisture was derived. This paper compares the soil moisture estimates created from the rSIR-enhanced brightness temperatures with SMAP project radiometer L2_SM_SP and SMAP-Sentinel L2_SM_P products reported on 9 km and 3 km grids, respectively. We find that the difference of the rSIR-enhanced passive soil moisture product is generally within 0.020 cm3 cm3 RMS of the 9 km SMAP radiometer L2_SM_SP and 0.045 cm3 cm3 RMS of the 3 km SMAP-Sentinel L2_SM_P soil moisture products. The accuracy of the rSIR soil moisture can be improved by including better antenna pattern correction methods applied to the input TB measurements. Full article
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Article
Grassland Conservation Effectiveness of National Nature Reserves in Northern China
Remote Sens. 2022, 14(7), 1760; https://doi.org/10.3390/rs14071760 - 06 Apr 2022
Cited by 2 | Viewed by 1049
Abstract
Grasslands are crucial ecosystem biomes for breeding livestock and combatting climate change. By 2018, the national nature reserves (NNRs) in the Inner Mongolia Autonomous Region (IMAR) had constituted 8.55% of the land area. However, there is still a knowledge gap about their effectiveness [...] Read more.
Grasslands are crucial ecosystem biomes for breeding livestock and combatting climate change. By 2018, the national nature reserves (NNRs) in the Inner Mongolia Autonomous Region (IMAR) had constituted 8.55% of the land area. However, there is still a knowledge gap about their effectiveness in grasslands. Based on a multiyear time series of the growing season composite from 2000 to 2020, we proposed an effectiveness score to assess the effectiveness of the NNRs, using the 250 m MOD13Q1 NDVI data with Theil–Sen and Mann–Kendall trend analysis methods. We found the following: 22 of 30 NNRs were deemed effective in protecting the Inner Mongolian grasslands. The NNRs increased pixels with a sustainable trend 19.26% and 20.55% higher than the unprotected areas and the IMAR, respectively. The pixels with a CVNDVI < 0.1 (i.e., NDVI coefficient of variation) in the NNRs increased >35.22% more than those in the unprotected areas and the IMAR. The NDVI changes within the NNRs showed that 63.64% of NNRs had a more significant trend of greening than before the change point, which suggests a general greening in NNRs. We also found that the NNRs achieved heterogeneous effectiveness scores across protection types. Forest ecology protection and wildlife animal protection types are the most efficient, whereas wildlife vegetation protection is the least effective type. This study enriches the understanding of grassland conservation and sheds light on the future direction of the sustainable management of NNRs. Full article
(This article belongs to the Special Issue Remote Sensing in Applied Ecology)
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Article
InSAR Study of Landslides: Early Detection, Three-Dimensional, and Long-Term Surface Displacement Estimation—A Case of Xiaojiang River Basin, China
Remote Sens. 2022, 14(7), 1759; https://doi.org/10.3390/rs14071759 - 06 Apr 2022
Cited by 4 | Viewed by 1395
Abstract
Landslides, a major natural geohazard, obstruct municipal constructions and may destroy villages and towns, at worst causing significant casualties and economic losses. Interferometric Synthetic Aperture Radar (InSAR) technique offers distinct advantages on landslide detection and monitoring. In this paper, a more systematic workflow [...] Read more.
Landslides, a major natural geohazard, obstruct municipal constructions and may destroy villages and towns, at worst causing significant casualties and economic losses. Interferometric Synthetic Aperture Radar (InSAR) technique offers distinct advantages on landslide detection and monitoring. In this paper, a more systematic workflow is designed for InSAR study of landslides, in terms of three levels: (i) early detection on regional scale, (ii) three-dimensional (3D) surface displacement rates estimation on detailed scale, and (iii) time series analysis on long-term temporal scale. The proposed workflow is applied for landslide research on the Xiaojiang River Basin, China, using ascending and descending Sentinel-1 images acquired from March 2017 to May 2019. First, the landslide inventory has been mapped and updated using InSAR stacking method, supporting geohazard prevention on a regional scale. A total of 22 active landslides are identified, ranging from medium to super large scale. Compared with the existing inventory, three unrecorded landslides are newly detected by our approach, and five recorded landslides are detected significant expansion of their boundaries. Then, specific to a detected landslide, Baobao landslide, a Total Least Squares–Kalman Filter-based approach is presented. Two outcomes are provided for further spatial-temporal pattern analysis: 3D displacement rates, providing an intuitive insight on the spatial characteristics and sliding direction of landslide, which are analyzed to deep the understanding of its kinematic mechanism, and long-term time series, which contribute to deduce the dynamic evolution of landslide, presenting benefits in landslide forecasting. Full article
(This article belongs to the Special Issue Recent Progress and Applications on Multi-Dimensional SAR)
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Article
Agents of Forest Disturbance in the Argentine Dry Chaco
Remote Sens. 2022, 14(7), 1758; https://doi.org/10.3390/rs14071758 - 06 Apr 2022
Cited by 3 | Viewed by 1241
Abstract
Forest degradation in the tropics is a widespread, yet poorly understood phenomenon. This is particularly true for tropical and subtropical dry forests, where a variety of disturbances, both natural and anthropogenic, affect forest canopies. Addressing forest degradation thus requires a spatially-explicit understanding of [...] Read more.
Forest degradation in the tropics is a widespread, yet poorly understood phenomenon. This is particularly true for tropical and subtropical dry forests, where a variety of disturbances, both natural and anthropogenic, affect forest canopies. Addressing forest degradation thus requires a spatially-explicit understanding of the causes of disturbances. Here, we apply an approach for attributing agents of forest disturbance across large areas of tropical dry forests, based on the Landsat image time series. Focusing on the 489,000 km2 Argentine Dry Chaco, we derived metrics on the spectral characteristics and shape of disturbance patches. We then used these metrics in a random forests classification framework to estimate the area of logging, fire, partial clearing, riparian changes and drought. Our results highlight that partial clearing was the most widespread type of forest disturbance from 1990–to 2017, extending over 5520 km2 (±407 km2), followed by fire (4562 ± 388 km2) and logging (3891 ± 341 km2). Our analyses also reveal marked trends over time, with partial clearing generally becoming more prevalent, whereas fires declined. Comparing the spatial patterns of different disturbance types against accessibility indicators showed that fire and logging prevalence was higher closer to fields, while smallholder homesteads were associated with less burning. Roads were, surprisingly, not associated with clear trends in disturbance prevalence. To our knowledge, this is the first attribution of disturbance agents in tropical dry forests based on satellite-based indicators. While our study reveals remaining uncertainties in this attribution process, our framework has considerable potential for monitoring tropical dry forest disturbances at scale. Tropical dry forests in South America, Africa and Southeast Asia are some of the fastest disappearing ecosystems on the planet, and more robust monitoring of forest degradation in these regions is urgently needed. Full article
(This article belongs to the Special Issue Land Degradation Assessment with Earth Observation II)
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Article
Combinations of Feature Selection and Machine Learning Algorithms for Object-Oriented Betel Palms and Mango Plantations Classification Based on Gaofen-2 Imagery
Remote Sens. 2022, 14(7), 1757; https://doi.org/10.3390/rs14071757 - 06 Apr 2022
Cited by 4 | Viewed by 1392
Abstract
Betel palms and mango plantations are two crucial commercial crops in tropical agricultural areas. Accurate spatial distributions of these two crops are essential in tropical agricultural regional planning and management. However, the characteristics of small patches, scattering, and perennation make it challenging to [...] Read more.
Betel palms and mango plantations are two crucial commercial crops in tropical agricultural areas. Accurate spatial distributions of these two crops are essential in tropical agricultural regional planning and management. However, the characteristics of small patches, scattering, and perennation make it challenging to map betel palms and mango plantations in complex tropical agricultural regions. Furthermore, the excessive features of very-high-resolution (VHR) imaging might lead to a reduction in classification accuracy and an increase in computation times. To address these challenges, we selected five feature selection (FS) methods (random forest means a decrease in accuracy (RFMDA), ReliefF, random forest-recursive feature elimination (RFE), aggregated boosted tree (ABT), and logistic regression (LR)) and four machine learning algorithms (random forest (RF), support vector machine (SVM), classification and regression tree (CART), and adaptive boosting (AdaBoost)). Then, the optimal combinations of FS and machine learning algorithms suited for object-oriented classification of betel palms and mango plantations were explored using VHR Gaofen-2 imagery. In terms of overall accuracy, all optimal classification schemes exceeded 80%, and the classifiers using selected features increased the overall accuracy between 1% and 4% compared with classification without FS methods. Specifically, LR was appropriate to RF and SVM classifiers, which produced the highest classification accuracy (89.1% and 89.88% for RF and SVM, respectively). In contrast, ABT and ReliefF were found to be suitable FS methods for CART and AdaBoost classifiers, respectively. Overall, all four optimal combinations of FS methods and classifiers could precisely recognize mango plantations, whereas betel palms were best depicted by using the RF-LR method with 26 features. The results indicated that combination of feature selection and machine learning algorithms contributed to the object-oriented classification of complex tropical crops using Gaofen-2 imagery, which provide a useful methodological reference for precisely recognizing small tropical agricultural patterns. Full article
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Article
Lake Expansion under the Groundwater Contribution in Qaidam Basin, China
Remote Sens. 2022, 14(7), 1756; https://doi.org/10.3390/rs14071756 - 06 Apr 2022
Cited by 1 | Viewed by 851
Abstract
The relationship between groundwater and lakes in Qaidam Basin is often overlooked. Therefore, we employed Landsat satellite images and meteorological data to investigate the causes of lake expansion through model calculation and statistical analysis and then determine groundwater sources through isotope analysis ( [...] Read more.
The relationship between groundwater and lakes in Qaidam Basin is often overlooked. Therefore, we employed Landsat satellite images and meteorological data to investigate the causes of lake expansion through model calculation and statistical analysis and then determine groundwater sources through isotope analysis (2H, 3H, and 18O). In the two study periods of 2003–2011 and 2011–present, temperature, precipitation, and runoff increased at a steady rate, whereas the expansion rate of Tuosu Lake increased from 1.22 km2/yearr to 3.38 km2/yearr. This significant increase in the rate of lake expansion reflects the substantial contribution of groundwater to lake expansion. The groundwater contribution to the lake includes not only the glacial meltwater that infiltrates the piedmont plain but also other, more isotopically deleted water sources from other basins. It is speculated that the 2003 Ms 6.4 earthquake in the northwest of the Delingha region was a possible mechanism for lake expansion. Earthquakes can enhance crustal permeability and keep fractures open, which promotes groundwater contribution to lakes and in turn causes rapid lake expansion and an increased groundwater level. This study is important for understanding the sources, circulation, and evolution of groundwater in Qaidam Basin. Full article
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Article
Impacts of Land Use Changes on Net Primary Productivity in Urban Agglomerations under Multi-Scenarios Simulation
Remote Sens. 2022, 14(7), 1755; https://doi.org/10.3390/rs14071755 - 06 Apr 2022
Cited by 9 | Viewed by 1553
Abstract
Land use is closely related to the sustainability of ecological development. This paper employed a patch-generating land use simulation (PLUS) model for the multi-scenario simulation of urban agglomerations. In addition, mathematical analysis methods such as Theil-Sen Median trend analysis, R/S analysis, Getis-Ord Gi* [...] Read more.
Land use is closely related to the sustainability of ecological development. This paper employed a patch-generating land use simulation (PLUS) model for the multi-scenario simulation of urban agglomerations. In addition, mathematical analysis methods such as Theil-Sen Median trend analysis, R/S analysis, Getis-Ord Gi* index and unary linear regression were used to study the temporal and spatial evolution characteristics of net primary productivity (NPP) for the impact of land use changes on NPP in urban agglomerations from 2000 to 2020 and to forecast the future trend of NPP. The results indicate that urban expansion is obvious in the baseline scenario and in the ecological protection scenario. In the scenario of cropland protection, the urban expansion is consistent with the land use plan of the government for 2035. The NPP in Beijing decreased gradually from northwest to southeast. The hot spot areas are concentrated in the densely forested areas in the mountainous areas of northwest. The cold spot areas are mainly concentrated in the periphery of urban areas and water areas. The NPP will continue to increase in forest and other areas under protection and remain stable in impervious surfaces. The NPP of Beijing showed a strong improvement trend and this trend will continue with the right ecological management and urban planning of the government. The study of land use in urban agglomeration and the development trend of vegetation NPP in the future can help policymakers rationally manage future land use dynamics and maintain the sustainable development of urban regional ecosystems. Full article
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Article
Optimal Band Selection for Airborne Hyperspectral Imagery to Retrieve a Wide Range of Cyanobacterial Pigment Concentration Using a Data-Driven Approach
Remote Sens. 2022, 14(7), 1754; https://doi.org/10.3390/rs14071754 - 06 Apr 2022
Viewed by 1082
Abstract
Understanding the concentration and distribution of cyanobacteria blooms is an important aspect of managing water quality problems and protecting aquatic ecosystems. Airborne hyperspectral imagery (HSI)—which has high temporal, spatial, and spectral resolutions—is widely used to remotely sense cyanobacteria bloom, and it provides the [...] Read more.
Understanding the concentration and distribution of cyanobacteria blooms is an important aspect of managing water quality problems and protecting aquatic ecosystems. Airborne hyperspectral imagery (HSI)—which has high temporal, spatial, and spectral resolutions—is widely used to remotely sense cyanobacteria bloom, and it provides the distribution of the bloom over a wide area. In this study, we determined the input spectral bands that were relevant in effectively estimating the main two pigments (PC, Phycocyanin; Chl-a, Chlorophyll-a) of cyanobacteria by applying data-driven algorithms to HSI and then evaluating the change in the spatio-temporal distribution of cyanobacteria. The input variables for the algorithms consisted of reflectance band ratios associated with the optical properties of PC and Chl-a, which were calculated by the selected hyperspectral bands using a feature selection method. The selected input variable was composed of six reflectance bands (465.7–589.6, 603.6–631.8, 641.2–655.35, 664.8–679.0, 698.0–712.3, and 731.4–784.1 nm). The artificial neural network showed the best results for the estimation of the two pigments with average coefficients of determination 0.80 and 0.74. This study proposes relevant input spectral information and an algorithm that can effectively detect the occurrence of cyanobacteria in the weir pool along the Geum river, South Korea. The algorithm is expected to help establish a preemptive response to the formation of cyanobacterial blooms, and to contribute to the preparation of suitable water quality management plans for freshwater environments. Full article
(This article belongs to the Special Issue Optical Remote Sensing for Surface Water Parameters Retrieval)
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Article
A Scheme for Quickly Simulating Extraterrestrial Solar Radiation over Complex Terrain on a Large Spatial-Temporal Span—A Case Study over the Entirety of China
Remote Sens. 2022, 14(7), 1753; https://doi.org/10.3390/rs14071753 - 06 Apr 2022
Cited by 1 | Viewed by 976
Abstract
Extraterrestrial solar radiation (ESR) is the essential basic background for solar radiation, which determines the occurrence of the weather and atmospheric phenomena. Since the influence of ESR variation on actual rugged terrain is a diverse, complex, and dynamic process, simulating ESR over a [...] Read more.
Extraterrestrial solar radiation (ESR) is the essential basic background for solar radiation, which determines the occurrence of the weather and atmospheric phenomena. Since the influence of ESR variation on actual rugged terrain is a diverse, complex, and dynamic process, simulating ESR over a large spatial-temporal span, especially with a high-resolution digital elevation model (DEM), is a significant challenge. In this paper, we developed a new scheme for simulating ESR over the entirety of China using a DEM with a resolution of 30 m. To fully consider regional terrain status, the feature variables used were elevation, slope, and aspects of the located grid and the surrounding four grids to reveal the topography. In addition, latitude was used as a feature variable to consider the geographical location, and the month number was used to consider the duration. On the basis of different geographical locations, the training dataset was established from 20,000 grids. With the feature variable composition and training dataset, a backpropagation artificial neural network (BP ANN) was found to have the best performance compared with the other three machine learning methods in simulating ESR for a DEM. In terms of the proposed scheme and BP ANN, we drew an ESR map of China with a resolution of 30 m. The determination coefficient of the simulation result achieved 0.99 and the root-mean-square error was less than 50 MJ/m2 in all sample areas, confirming its remarkable accuracy. In terms of efficiency, the time consumption of ESR simulated using the proposed scheme shrinks over 150 times in all sample areas compared to that simulated via the theoretical model. Simultaneously, the developed scheme was also used to simulate an ESR for a DEM with a resolution of 90 m to verify the universality and robustness of the developed scheme. In addition, we used the proposed scheme to derive the direct solar radiation and global solar radiation, thereby further proving the reliability and applicability of our study. Overall, our work convincingly proved that the proposed scheme is a potential and effective approach for quickly simulating ESR with high accuracy. This study provides the basis for different solar radiation inversions of long time series and large spatial scales, offering additional insights for simulating ESR on a large spatial-temporal span. Full article
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Review
Water-Body Segmentation for SAR Images: Past, Current, and Future
Remote Sens. 2022, 14(7), 1752; https://doi.org/10.3390/rs14071752 - 06 Apr 2022
Cited by 6 | Viewed by 1834
Abstract
Synthetic Aperture Radar (SAR), as a microwave sensor that can sense a target all day or night under all-weather conditions, is of great significance for detecting water resources, such as coastlines, lakes and rivers. This paper reviews literature published in the past 30 [...] Read more.
Synthetic Aperture Radar (SAR), as a microwave sensor that can sense a target all day or night under all-weather conditions, is of great significance for detecting water resources, such as coastlines, lakes and rivers. This paper reviews literature published in the past 30 years in the field of water body extraction in SAR images, and makes some proposals that the community working with SAR image waterbody extraction should consider. Firstly, this review focuses on the main ideas and characteristics of traditional water body extraction on SAR images, mainly focusing on traditional Machine Learning (ML) methods. Secondly, how Deep Learning (DL) methods are applied and optimized in the task of water-body segmentation for SAR images is summarized from the two levels of pixel and image. We also pay more attention to the most popular networks, such as U-Net and its modified models, and novel networks, such as the Cascaded Fully-Convolutional Network (CFCN) and River-Net. In the end, an in-depth discussion is presented, along with conclusions and future trends, on the limitations and challenges of DL for water-body segmentation. Full article
(This article belongs to the Special Issue Earth Observation Technologies for Monitoring of Water Environments)
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Technical Note
Surround-Net: A Multi-Branch Arbitrary-Oriented Detector for Remote Sensing
Remote Sens. 2022, 14(7), 1751; https://doi.org/10.3390/rs14071751 - 06 Apr 2022
Cited by 1 | Viewed by 856
Abstract
With the development of oriented object detection technology, especially in the area of remote sensing, significant progress has been made, and multiple excellent detection architectures have emerged. Oriented detection architectures can be broadly divided into five-parameter systems and eight-parameter systems that encounter the [...] Read more.
With the development of oriented object detection technology, especially in the area of remote sensing, significant progress has been made, and multiple excellent detection architectures have emerged. Oriented detection architectures can be broadly divided into five-parameter systems and eight-parameter systems that encounter the periodicity problem of angle regression and the discontinuous problem of vertex regression during training, respectively. Therefore, we propose a new multi-branch anchor-free one-stage model that can effectively alleviate the corner case when representing rotating objects, called Surround-Net. The creative contribution submitted in this paper mainly includes three aspects. Firstly, a multi-branch strategy is adopted to make the detector choose the best regression path adaptively for the discontinuity problem. Secondly, to address the inconsistency between classification and quality estimation (location), a modified high-dimensional Focal Loss and a new Surround IoU Loss are proposed to enhance the unity ability of the features. Thirdly, in the refined process after backbone feature extraction, a center vertex attention mechanism is adopted to deal with the environmental noise introduced in the remote sensing images. This type of auxiliary module is able to focus the model’s attention on the boundary of the bounding box. Finally, extensive experiments were carried out on the DOTA dataset, and the results demonstrate that Surround-Net can solve regression boundary problems and can achieve a more competitive performance (e.g., 75.875 mAP) than other anchor-free one-stage detectors with higher speeds. Full article
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Article
Multi-Source Precipitation Data Merging for Heavy Rainfall Events Based on Cokriging and Machine Learning Methods
Remote Sens. 2022, 14(7), 1750; https://doi.org/10.3390/rs14071750 - 06 Apr 2022
Cited by 4 | Viewed by 1228
Abstract
Gridded precipitation data with a high spatiotemporal resolution are of great importance for studies in hydrology, meteorology, and agronomy. Observational data from meteorological stations cannot accurately reflect the spatiotemporal distribution and variations of precipitation over a large area. Meanwhile, radar-derived precipitation data are [...] Read more.
Gridded precipitation data with a high spatiotemporal resolution are of great importance for studies in hydrology, meteorology, and agronomy. Observational data from meteorological stations cannot accurately reflect the spatiotemporal distribution and variations of precipitation over a large area. Meanwhile, radar-derived precipitation data are restricted by low accuracy in areas of complex terrain and satellite-based precipitation data by low spatial resolution. Therefore, hourly precipitation models were employed to merge data from meteorological stations, Radar, and satellites; the models used five machine learning algorithms (XGBoost, gradient boosting decision tree, random forests (RF), LightGBM, and multiple linear regression (MLR)), as well as the CoKriging method. In the north of Guangdong Province, data of four heavy rainfall events in 2018 were processed with geographic data to obtain merged hourly precipitation data. The CoKriging method secured the best prediction of spatial distribution of accumulated precipitation, followed by the tree-based machine learning (ML) algorithms, and significantly, the prediction of MLR deviated from the actual pattern. All machine learning methods showed poor performances for timepoints with little precipitation during the heavy rainfall events. The tree-based ML method showed poor performance at some timepoints when precipitation was over-related to latitude, longitude, and distance from the coast. Full article
(This article belongs to the Topic Advanced Research in Precipitation Measurements)
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Article
Using Satellite-Based Data to Facilitate Consistent Monitoring of the Marine Environment around Ireland
Remote Sens. 2022, 14(7), 1749; https://doi.org/10.3390/rs14071749 - 06 Apr 2022
Cited by 2 | Viewed by 1870
Abstract
As an island nation, Ireland needs to ensure effective management measures to protect marine ecosystems and their services, such as the provision of fishery resources. The characterization of marine waters using satellite data can contribute to a better understanding of variations in the [...] Read more.
As an island nation, Ireland needs to ensure effective management measures to protect marine ecosystems and their services, such as the provision of fishery resources. The characterization of marine waters using satellite data can contribute to a better understanding of variations in the upper ocean and, consequently, the effect of their changes on species populations. In this study, nineteen years (1998–2016) of monthly data of essential climate variables (ECVs), chlorophyll (Chl-a), and the diffuse attenuation coefficient (K490) were used, together with previous analyses of sea surface temperature (SST), to investigate the temporal and spatial variability of surface waters around Ireland. The study area was restricted to specific geographically delineated divisions, as defined by the International Council of the Exploration of the Seas (ICES). The results showed that SST and Chl-a were positively and significantly correlated in ICES divisions corresponding to oceanic waters, while in coastal divisions, SST and Chl-a showed a significant negative correlation. Chl-a and K490 were positively correlated in all cases, suggesting an important role of phytoplankton in light attenuation. Chl-a and K490 had significant trends in most of the divisions, reaching maximum values of 1.45% and 0.08% per year, respectively. The strongest seasonal Chl-a trends were observed in divisions VIId and VIIe (the English Channel), primarily in the summer months, followed by northern divisions VIa (west of Scotland) and VIb (Rockall) in the winter months. Full article
(This article belongs to the Special Issue Remote Sensing for Shallow and Deep Waters Mapping and Monitoring)
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Article
Machine Learning for Defining the Probability of Sentinel-1 Based Deformation Trend Changes Occurrence
Remote Sens. 2022, 14(7), 1748; https://doi.org/10.3390/rs14071748 - 05 Apr 2022
Cited by 4 | Viewed by 1248
Abstract
The continuous monitoring of displacements occurring on the Earth surface by exploiting MTInSAR (Multi Temporal Interferometry SAR) Sentinel-1 data is a solid reality, as testified by the ongoing operational ground motion service in the Tuscany region (Central Italy). In this framework, anomalies of [...] Read more.
The continuous monitoring of displacements occurring on the Earth surface by exploiting MTInSAR (Multi Temporal Interferometry SAR) Sentinel-1 data is a solid reality, as testified by the ongoing operational ground motion service in the Tuscany region (Central Italy). In this framework, anomalies of movement, i.e., accelerations or deceleration as seen by the time series of displacement of radar targets, are identified. In this work, a Machine Learning algorithm such as the Random Forest has been used to assess the probability of occurrence of the anomalies induced by slope instability and subsidence. About 20,000 anomalies (about 7000 and 13,000 for the slope instability and the subsidence, respectively) were collected between 2018 and 2020 and were used as input, while ten different variables were selected, five related to the morphological and geological setting of the study area and five to the radar characteristics of the data. The resulting maps may provide useful indications of where a sudden change of displacement trend may occur, analyzing the contribution of each factor. The cross-validation with the anomalies collected in a following timespan (2020–2021) and with official landslide and subsidence inventories provided by the regional authority has confirmed the reliability of the final maps. The adoption of a map for assessing the probability of the occurrence of MTInSAR anomalies may serve as an enhanced geohazard prevention measurement, to be periodically updated and refined in order to have the most precise knowledge possible of the territory. Full article
(This article belongs to the Special Issue Machine Learning and Remote Sensing for Geohazards)
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Article
Satellite Observations of Fire Activity in Relation to Biophysical Forcing Effect of Land Surface Temperature in Mediterranean Climate
Remote Sens. 2022, 14(7), 1747; https://doi.org/10.3390/rs14071747 - 05 Apr 2022
Cited by 2 | Viewed by 1006
Abstract
The present work is aimed at gaining more knowledge on the nature of the relation between land surface temperature (LST) as a biophysical parameter, which is related to the coupled effect of the energy and water cycles, and fire activity over Bulgaria, in [...] Read more.
The present work is aimed at gaining more knowledge on the nature of the relation between land surface temperature (LST) as a biophysical parameter, which is related to the coupled effect of the energy and water cycles, and fire activity over Bulgaria, in the Eastern Mediterranean. In the ecosystems of this area, prolonged droughts and heat waves create preconditions in the land surface state that increase the frequency and intensity of landscape fires. The relationships between the spatial–temporal variability of LST and fire activity modulated by land cover types and Soil Moisture Availability (SMA) are quantified. Long-term (2007–2018) datasets derived from geostationary MSG satellite observations are used: LST retrieved by the LSASAF LST product; fire activity assessed by the LSASAF FRP-Pixel product. All fires in the period of July–September occur in days associated with positive LST anomalies. Exponential regression models fit the link between LST monthly means, LST positive anomalies, LST-T2 (as a first proxy of sensible heat exchange with atmosphere), and FRP fire characteristics (number of detections; released energy FRP, MW) at high correlations. The values of biophysical drivers, at which the maximum FRP (MW) might be expected at the corresponding probability level, are identified. Results suggest that the biophysical index LST is sensitive to the changes in the dynamics of vegetation fire occurrence and severity. Dependences are found for forest, shrubs, and cultivated LCs, which indicate that satellite IR retrievals of radiative temperature is a reliable source of information for vegetation dryness and fire activity. Full article
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Article
Change Detection of Amazonian Alluvial Gold Mining Using Deep Learning and Sentinel-2 Imagery
Remote Sens. 2022, 14(7), 1746; https://doi.org/10.3390/rs14071746 - 05 Apr 2022
Cited by 9 | Viewed by 2665
Abstract
Monitoring changes within the land surface and open water bodies is critical for natural resource management, conservation, and environmental policy. While the use of satellite imagery for these purposes is common, fine-scale change detection can be a technical challenge. Difficulties arise from variable [...] Read more.
Monitoring changes within the land surface and open water bodies is critical for natural resource management, conservation, and environmental policy. While the use of satellite imagery for these purposes is common, fine-scale change detection can be a technical challenge. Difficulties arise from variable atmospheric conditions and the problem of assigning pixels to individual objects. We examined the degree to which two machine learning approaches can better characterize change detection in the context of a current conservation challenge, artisanal small-scale gold mining (ASGM). We obtained Sentinel-2 imagery and consulted with domain experts to construct an open-source labeled land-cover change dataset. The focus of this dataset is the Madre de Dios (MDD) region in Peru, a hotspot of ASGM activity. We also generated datasets of active ASGM areas in other countries (Venezuela, Indonesia, and Myanmar) for out-of-sample testing. With these labeled data, we utilized a supervised (E-ReCNN) and semi-supervised (SVM-STV) approach to study binary and multi-class change within mining ponds in the MDD region. Additionally, we tested how the inclusion of multiple channels, histogram matching, and La*b* color metrics improved the performance of the models and reduced the influence of atmospheric effects. Empirical results show that the supervised E-ReCNN method on 6-Channel histogram-matched images generated the most accurate detection of change not only in the focal region (Kappa: 0.92 (± 0.04), Jaccard: 0.88 (± 0.07), F1: 0.88 (± 0.05)) but also in the out-of-sample prediction regions (Kappa: 0.90 (± 0.03), Jaccard: 0.84 (± 0.04), and F1: 0.77 (± 0.04)). While semi-supervised methods did not perform as accurately on 6- or 10-channel imagery, histogram matching and the inclusion of La*b* metrics generated accurate results with low memory and resource costs. These results show that E-ReCNN is capable of accurately detecting specific and object-oriented environmental changes related to ASGM. E-ReCNN is scalable to areas outside the focal area and is a method of change detection that can be extended to other forms of land-use modification. Full article
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Article
On Investigating the Dynamical Factors Modulating Surface Chlorophyll-a Variability along the South Java Coast
Remote Sens. 2022, 14(7), 1745; https://doi.org/10.3390/rs14071745 - 05 Apr 2022
Cited by 5 | Viewed by 1426
Abstract
Twelve years of remotely sensed all-sat merged chlorophyll-a concentration unveils strong signatures of chlorophyll-a blooms along the south Java coast. An unprecedented three-times increase in chlorophyll-a concentration is significantly observed along the south Java coast during the southeast monsoon (June–October) than the northwest [...] Read more.
Twelve years of remotely sensed all-sat merged chlorophyll-a concentration unveils strong signatures of chlorophyll-a blooms along the south Java coast. An unprecedented three-times increase in chlorophyll-a concentration is significantly observed along the south Java coast during the southeast monsoon (June–October) than the northwest monsoon (December–April). The multiple regression analysis of dynamic factors evidently indicates that seasonal upwelling is predominantly controlled by the seasonally evolving coastal eddies associated with the seasonally reversing south Java coastal currents (SJCC) and Ekman mass transport (EMT), followed by the relative roles of sea surface temperature (SST) and wind stress curl. The eddy-induced upwelling and EMT-induced coastal upwelling lead to chlorophyll-a blooms during southeast monsoon, well-supported by the entrainment of cold and saline waters (thermocline doming) with low spiciness. On the other hand, the coastal eddies associated with SJCC and SST anomalies play a significant role in modulating the interannual surface chlorophyll-a variability in the domain. Intense chlorophyll-a blooms are observed during the positive IOD years, whereas the least chlorophyll-a concentration is observed during the negative IOD years. The unprecedentedly least chlorophyll-a concentrations during 2010 and 2016 are attributed to the intense and prolonged surface marine heatwaves. Full article
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Article
A Hybrid Triple Collocation-Deep Learning Approach for Improving Soil Moisture Estimation from Satellite and Model-Based Data
Remote Sens. 2022, 14(7), 1744; https://doi.org/10.3390/rs14071744 - 05 Apr 2022
Cited by 7 | Viewed by 1332
Abstract
Satellite retrieval and land surface models have become the mainstream methods for monitoring soil moisture (SM) over large regions; however, the uncertainty and coarse spatial resolution of these products limit their applications at the regional and local scales. We proposed a hybrid approach [...] Read more.
Satellite retrieval and land surface models have become the mainstream methods for monitoring soil moisture (SM) over large regions; however, the uncertainty and coarse spatial resolution of these products limit their applications at the regional and local scales. We proposed a hybrid approach combining the triple collocation (TC) and the long short-term memory (LSTM) network, which was designed to generate a high-quality SM dataset from satellite and modeled data. We applied the proposed approach to merge SM data from Soil Moisture Active Passive (SMAP), Global Land Data Assimilation System-Noah (GLDAS-Noah), and the land component of the fifth generation of European Reanalysis (ERA5-Land), and we then downscaled the merged SM data from 0.36° to 0.01° resolution based on the relationship between the SM data and auxiliary environmental variables (elevation, land surface temperature, vegetation index, surface albedo, and soil texture). The merged and downscaled SM results were validated against in situ observations. The results showed that: (1) the TC-based validation results were consistent with the in situ-based validation, indicating that the TC method was reasonable for the comparison and evaluation of satellite and modeled SM data. (2) TC-based merging was superior to simple arithmetic average merging when the parent products had large differences. (3) Downscaled SM of the TC-based merged product had better performance than that of the parent products in terms of ubRMSE and bias values, implying that the fusion of satellite and model-based SM data would result in better downscaling accuracy. (4) Downscaled SM of TC-based merged data not only improved the representation of the SM spatial variability but also had satisfactory accuracy with a median of R (0.7244), ubRMSE (0.0459 m3/m3), and bias (−0.0126 m3/m3). The proposed approach was effective for generating a SM dataset with fine resolution and reliable accuracy for wide hydrometeorological applications. Full article
(This article belongs to the Special Issue Remote Sensing of Hydrological Processes: Modelling and Applications)
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Article
Eagle-Eye-Inspired Attention for Object Detection in Remote Sensing
Remote Sens. 2022, 14(7), 1743; https://doi.org/10.3390/rs14071743 - 05 Apr 2022
Cited by 1 | Viewed by 1261
Abstract
Object detection possesses extremely significant applications in the field of optical remote sensing images. A great many works have achieved remarkable results in this task. However, some common problems, such as scale, illumination, and image quality, are still unresolved. Inspired by the mechanism [...] Read more.
Object detection possesses extremely significant applications in the field of optical remote sensing images. A great many works have achieved remarkable results in this task. However, some common problems, such as scale, illumination, and image quality, are still unresolved. Inspired by the mechanism of cascade attention eagle-eye fovea, we propose a new attention mechanism network named the eagle-eye fovea network (EFNet) which contains two foveae for remote sensing object detection. The EFNet consists of two eagle-eye fovea modules: front central fovea (FCF) and rear central fovea (RCF). The FCF is mainly used to learn the candidate object knowledge based on the channel attention and the spatial attention, while the RCF mainly aims to predict the refined objects with two subnetworks without anchors. Three remote sensing object-detection datasets, namely DIOR, HRRSD, and AIBD, are utilized in the comparative experiments. The best results of the proposed EFNet are obtained on the HRRSD with a 0.622 AP score and a 0.907 AP50 score. The experimental results demonstrate the effectiveness of the proposed EFNet for both multi-category datasets and single category datasets. Full article
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Article
Estimation of Rooftop Solar Power Potential by Comparing Solar Radiation Data and Remote Sensing Data—A Case Study in Aichi, Japan
Remote Sens. 2022, 14(7), 1742; https://doi.org/10.3390/rs14071742 - 05 Apr 2022
Cited by 2 | Viewed by 1568
Abstract
There have been significant advances in the shift from fossil-based energy systems to renewable energies in recent years. Decentralized solar photovoltaic (PV) is one of the most promising energy sources because of the availability of rooftop areas, ease of installation, and reduced cost [...] Read more.
There have been significant advances in the shift from fossil-based energy systems to renewable energies in recent years. Decentralized solar photovoltaic (PV) is one of the most promising energy sources because of the availability of rooftop areas, ease of installation, and reduced cost of PV panels. The current modeling method using remote sensing data based on a geographic information system (GIS) is objective and accurate, but the analysis processes are complicated and time-consuming. In this study, we developed a method to estimate the rooftop solar power potential over a wide area using globally available solar radiation data from Solargis combined with a building polygon. Our study also utilized light detection and ranging (LiDAR) data and AW3D to estimate rooftop solar power potential in western Aichi, Japan, and the solar radiation was calculated using GIS. The estimation using LiDAR data took into account the slope and azimuth of rooftops. A regression analysis of the estimated solar power potential for each roof between the three methods was conducted, and the conversion factor 0.837 was obtained to improve the accuracy of the results from the Solargis data. The annual rooftop solar power potential of 3,351,960 buildings in Aichi Prefecture under Scenario A, B, and C was 6.92 × 107, 3.58 × 107, and 1.27 × 107 MWh/year, estimated using Solargis data after the adjustment. The estimated solar power potential under Scenario A could satisfy the total residential power demand in Aichi, revealing the crucial role of rooftop solar power in alleviating the energy crisis. This approach of combining Solargis data with building polygons can be easily applied in other parts of the world. These findings can provide useful information for policymakers and contribute to local planning for cleaner energy. Full article
(This article belongs to the Special Issue Remote Sensing for Green Energy Development)
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Article
A Fast Star Identification Algorithm of Star Sensors in the LIS Mode
Remote Sens. 2022, 14(7), 1739; https://doi.org/10.3390/rs14071739 - 05 Apr 2022
Viewed by 858
Abstract
In the South Atlantic Anomaly (SAA) area, due to the influence of transient noise caused by space radiation, a star sensor can easily stay in the lost-in-space (LIS) mode for a long time. To solve this problem, this paper proposes a fast star [...] Read more.
In the South Atlantic Anomaly (SAA) area, due to the influence of transient noise caused by space radiation, a star sensor can easily stay in the lost-in-space (LIS) mode for a long time. To solve this problem, this paper proposes a fast star identification (FSI) algorithm. First, a noise suppression method based on scale assessment and neighborhood comparison is developed. Next, a fast and accurate search technique of multiple main stars based on the k-vector technique is used to realize star identification. The search technique builds a self-defined attribute database of stars, and a fast search method of a repeated star identity is proposed to realize the positioning of the main star. Lastly, the final main stars are obtained through the comparison of field of view and verification of angular distance. The experimental results showed that when the star sensor works at a speed of 0.1°/s and the level of transient noise signals is lower than 900, the successful identification rate is higher than 70%. In addition, compared with the triangle algorithm, match group algorithm, and multi-pole algorithm (MPA), the proposed FSI algorithm has the advantages of a higher successful identification rate and a faster execution speed. Full article
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Article
A SLAM System with Direct Velocity Estimation for Mechanical and Solid-State LiDARs
Remote Sens. 2022, 14(7), 1741; https://doi.org/10.3390/rs14071741 - 04 Apr 2022
Cited by 2 | Viewed by 1443
Abstract
Simultaneous localization and mapping (SLAM) is essential for intelligent robots operating in unknown environments. However, existing algorithms are typically developed for specific types of solid-state LiDARs, leading to weak feature representation abilities for new sensors. Moreover, LiDAR-based SLAM methods are limited by distortions [...] Read more.
Simultaneous localization and mapping (SLAM) is essential for intelligent robots operating in unknown environments. However, existing algorithms are typically developed for specific types of solid-state LiDARs, leading to weak feature representation abilities for new sensors. Moreover, LiDAR-based SLAM methods are limited by distortions caused by LiDAR ego motion. To address the above issues, this paper presents a versatile and velocity-aware LiDAR-based odometry and mapping (VLOM) system. A spherical projection-based feature extraction module is utilized to process the raw point cloud generated by various LiDARs, hence avoiding the time-consuming adaptation of various irregular scan patterns. The extracted features are grouped into higher-level clusters to filter out smaller objects and reduce false matching during feature association. Furthermore, bundle adjustment is adopted to jointly estimate the poses and velocities for multiple scans, effectively improving the velocity estimation accuracy and compensating for point cloud distortions. Experiments on publicly available datasets demonstrate the superiority of VLOM over other state-of-the-art LiDAR-based SLAM systems in terms of accuracy and robustness. Additionally, the satisfactory performance of VLOM on RS-LiDAR-M1, a newly released solid-state LiDAR, shows its applicability to a wide range of LiDARs. Full article
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Article
Simulation of Isoprene Emission with Satellite Microwave Emissivity Difference Vegetation Index as Water Stress Factor in Southeastern China during 2008
Remote Sens. 2022, 14(7), 1740; https://doi.org/10.3390/rs14071740 - 04 Apr 2022
Viewed by 1033
Abstract
Isoprene is one of the most important biogenic volatile organic compounds (BVOCs) emitted by vegetation. The biogenic isoprene emissions are widely estimated by the Model of Emission of Gases and Aerosols from Nature (MEGAN) considering different environmental stresses. The response of isoprene emission [...] Read more.
Isoprene is one of the most important biogenic volatile organic compounds (BVOCs) emitted by vegetation. The biogenic isoprene emissions are widely estimated by the Model of Emission of Gases and Aerosols from Nature (MEGAN) considering different environmental stresses. The response of isoprene emission to the water stress is usually parameterized using soil moisture in previous studies. In this study, we designed a new parameterization scheme of water stress in MEGAN as a function of a novel, satellite, passive microwave-based vegetation index, Emissivity Difference Vegetation Index (EDVI), which indicates the vegetation inner water content. The isoprene emission rates in southeastern China were simulated with different water stress indicators including soil moisture, EDVI, Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). Then the simulated isoprene emission rates were compared to associated satellite top-down estimations. The results showed that in southeastern China, the spatiotemporal correlations between those simulations and top-down retrieval are all high with different biases. The simulated isoprene emission rates with EDVI-based water stress factor are most consistent with top-down estimation with higher temporal correlation, lower bias and lower RMSE, while soil moisture alters the emission rates little, and optical vegetation indices (NDVI and EVI) slightly increase the correlation with top-down. The temporal correlation coefficients are increased after applied with EDVI water stress factor in most areas; especially in the Yunnan-Guizhou Plateau and Yangtze River Delta (>0.12). Overall, higher consistency of simulation and top-down estimation is shown when EDVI is applied, which indicates the possibility of estimating the effect of vegetation water stress on biogenic isoprene emission using microwave observations. Full article
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Article
An Anchor-Free Method Based on Adaptive Feature Encoding and Gaussian-Guided Sampling Optimization for Ship Detection in SAR Imagery
Remote Sens. 2022, 14(7), 1738; https://doi.org/10.3390/rs14071738 - 04 Apr 2022
Cited by 1 | Viewed by 915
Abstract
Recently, deep-learning methods have yielded rapid progress for object detection in synthetic aperture radar (SAR) imagery. It is still a great challenge to detect ships in SAR imagery due to ships’ small size and confusable detail feature. This article proposes a novel anchor-free [...] Read more.
Recently, deep-learning methods have yielded rapid progress for object detection in synthetic aperture radar (SAR) imagery. It is still a great challenge to detect ships in SAR imagery due to ships’ small size and confusable detail feature. This article proposes a novel anchor-free detection method composed of two modules to deal with these problems. First, for the lack of detailed information on small ships, we suggest an adaptive feature-encoding module (AFE), which gradually fuses deep semantic features into shallow layers and realizes the adaptive learning of the spatial fusion weights. Thus, it can effectively enhance the external semantics and improve the representation ability of small targets. Next, for the foreground–background imbalance, the Gaussian-guided detection head (GDH) is introduced according to the idea of soft sampling and exploits Gaussian prior to assigning different weights to the detected bounding boxes at different locations in the training optimization. Moreover, the proposed Gauss-ness can down-weight the predicted scores of bounding boxes far from the object center. Finally, the effect of the detector composed of the two modules is verified on the two SAR ship datasets. The results demonstrate that our method can effectively improve the detection performance of small ships in datasets. Full article
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