Remote Sensing

20 pages, 91121 KiB

Open AccessEditor’s ChoiceArticle

Comparison of Aerial and Ground 3D Point Clouds for Canopy Size Assessment in Precision Viticulture

by Andrea Pagliai, Marco Ammoniaci, Daniele Sarri, Riccardo Lisci, Rita Perria, Marco Vieri, Mauro Eugenio Maria D’Arcangelo, Paolo Storchi and Simon-Paolo Kartsiotis

Remote Sens. 2022, 14(5), 1145; https://doi.org/10.3390/rs14051145 - 25 Feb 2022

Cited by 42 | Viewed by 4543

Abstract

In precision viticulture, the intra-field spatial variability characterization is a crucial step to efficiently use natural resources by lowering the environmental impact. In recent years, technologies such as Unmanned Aerial Vehicles (UAVs), Mobile Laser Scanners (MLS), multispectral sensors, Mobile Apps (MA) and Structure [...] Read more.

In precision viticulture, the intra-field spatial variability characterization is a crucial step to efficiently use natural resources by lowering the environmental impact. In recent years, technologies such as Unmanned Aerial Vehicles (UAVs), Mobile Laser Scanners (MLS), multispectral sensors, Mobile Apps (MA) and Structure from Motion (SfM) techniques enabled the possibility to characterize this variability with low efforts. The study aims to evaluate, compare and cross-validate the potentiality and the limits of several tools (UAV, MA, MLS) to assess the vine canopy size parameters (thickness, height, volume) by processing 3D point clouds. Three trials were carried out to test the different tools in a vineyard located in the Chianti Classico area (Tuscany, Italy). Each test was made of a UAV flight, an MLS scanning over the vineyard and a MA acquisition over 48 geo-referenced vines. The Leaf Area Index (LAI) were also assessed and taken as reference value. The results showed that the analyzed tools were able to correctly discriminate between zones with different canopy size characteristics. In particular, the R² between the canopy volumes acquired with the different tools was higher than 0.7, being the highest value of R² = 0.78 with a RMSE = 0.057 m³ for the UAV vs. MLS comparison. The highest correlations were found between the height data, being the highest value of R² = 0.86 with a RMSE = 0.105 m for the MA vs. MLS comparison. For the thickness data, the correlations were weaker, being the lowest value of R² = 0.48 with a RMSE = 0.052 m for the UAV vs. MLS comparison. The correlation between the LAI and the canopy volumes was moderately strong for all the tools with the highest value of R² = 0.74 for the LAI vs. V_MLS data and the lowest value of R² = 0.69 for the LAI vs. V_UAV data. Full article

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

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25 pages, 3460 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

The Multisource Vegetation Inventory (MVI): A Satellite-Based Forest Inventory for the Northwest Territories Taiga Plains

by Guillermo Castilla, Ronald J. Hall, Rob Skakun, Michelle Filiatrault, André Beaudoin, Michael Gartrell, Lisa Smith, Kathleen Groenewegen, Chris Hopkinson and Jurjen van der Sluijs

Remote Sens. 2022, 14(5), 1108; https://doi.org/10.3390/rs14051108 - 24 Feb 2022

Cited by 13 | Viewed by 5392

Abstract

Sustainable forest management requires information on the spatial distribution, composition, and structure of forests. However, jurisdictions with large tracts of noncommercial forest, such as the Northwest Territories (NWT) of Canada, often lack detailed forest information across their land base. The goal of the [...] Read more.

Sustainable forest management requires information on the spatial distribution, composition, and structure of forests. However, jurisdictions with large tracts of noncommercial forest, such as the Northwest Territories (NWT) of Canada, often lack detailed forest information across their land base. The goal of the Multisource Vegetation Inventory (MVI) project was to create a large area forest inventory (FI) map that could support strategic forest management in the NWT using optical, radar, and light detection and ranging (LiDAR) satellite remote sensing anchored on limited field plots and airborne LiDAR data. A new landcover map based on Landsat imagery was the first step to stratify forestland into broad forest types. A modelling chain linking FI plots to airborne and spaceborne LiDAR was then developed to circumvent the scarcity of field data in the region. The developed models allowed the estimation of forest attributes in thousands of surrogate FI plots corresponding to spaceborne LiDAR footprints distributed across the project area. The surrogate plots were used as a reference dataset for estimating each forest attribute in each 30 m forest cell within the project area. The estimation was based on the k-nearest neighbour (k-NN) algorithm, where the selection of the four most similar surrogate FI plots to each cell was based on satellite, topographic, and climatic data. Wall-to-wall 30 m raster maps of broad forest type, stand height, crown closure, stand volume, total volume, aboveground biomass, and stand age were created for a ~400,000 km² area, validated with independent data, and generalized into a polygon GIS layer resembling a traditional FI map. The MVI project showed that a reasonably accurate FI map for large, remote, predominantly non-inventoried boreal regions can be obtained at a low cost by combining limited field data with remote sensing data from multiple sources. Full article

(This article belongs to the Section Forest Remote Sensing)

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19 pages, 5400 KiB

Open AccessEditor’s ChoiceArticle

Monitoring of Radial Deformations of a Gravity Dam Using Sentinel-1 Persistent Scatterer Interferometry

by Jannik Jänichen, Christiane Schmullius, Jussi Baade, Katja Last, Volker Bettzieche and Clémence Dubois

Remote Sens. 2022, 14(5), 1112; https://doi.org/10.3390/rs14051112 - 24 Feb 2022

Cited by 16 | Viewed by 3473

Abstract

Dams have many important socio-economic functions, fulfilling roles ranging from storing water to power generation, but also serving as leisure areas. Monitoring of their deformation is usually performed using time-consuming traditional terr estrial techniques, leading to a yearly monitoring cycle. To increase the [...] Read more.

Dams have many important socio-economic functions, fulfilling roles ranging from storing water to power generation, but also serving as leisure areas. Monitoring of their deformation is usually performed using time-consuming traditional terr estrial techniques, leading to a yearly monitoring cycle. To increase the monitoring cycle, new methods are needed. Persistent Scatterer Interferometry (PSI) is a well-established technique for monitoring millimeter deformation of the Earth’s surface. The availability of free and open SAR data with a repeat cycle of 6 to 12 days from the Copernicus mission Sentinel-1, allows PSI to be used complementary to traditional surveying techniques. This present study investigates deformation dynamics at the Moehne gravity dam in North Rhine-Westphalia, Germany. The applicability of the PSI technique to the deformation monitoring of dams is evaluated, in relation to the necessary accuracy requirements. For this purpose, Sentinel-1 data from January 2015 to November 2020 are analyzed and the deformation estimates are assessed with in situ information. Using a precise dam model, the radial deformation of the dam could be extracted and compared to trigonometric and plumb measurements. The first results show that the movements of the Moehne dam follow a seasonal pattern, reaching a maximum radial deformation of up to 4 mm in Spring, following a decline to −4 mm in the late summer. RMSE between 1.1 mm and 1.5 mm were observed between the PSI observations and the in situ data, showing that the PSI technique achieves the necessary accuracy requirements for gravity dam monitoring from space. Full article

(This article belongs to the Special Issue Dam Stability Monitoring with Satellite Geodesy)

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20 pages, 4901 KiB

Open AccessEditor’s ChoiceArticle

Decadal Lake Volume Changes (2003–2020) and Driving Forces at a Global Scale

by Yuhao Feng, Heng Zhang, Shengli Tao, Zurui Ao, Chunqiao Song, Jérôme Chave, Thuy Le Toan, Baolin Xue, Jiangling Zhu, Jiamin Pan, Shaopeng Wang, Zhiyao Tang and Jingyun Fang

Remote Sens. 2022, 14(4), 1032; https://doi.org/10.3390/rs14041032 - 21 Feb 2022

Cited by 26 | Viewed by 5456

Abstract

Lakes play a key role in the global water cycle, providing essential water resources and ecosystem services for humans and wildlife. Quantifying long-term changes in lake volume at a global scale is therefore important to the sustainability of humanity and natural ecosystems. Yet, [...] Read more.

Lakes play a key role in the global water cycle, providing essential water resources and ecosystem services for humans and wildlife. Quantifying long-term changes in lake volume at a global scale is therefore important to the sustainability of humanity and natural ecosystems. Yet, such an estimate is still unavailable because, unlike lake area, lake volume is three-dimensional, challenging to be estimated consistently across space and time. Here, taking advantage of recent advances in remote sensing technology, especially NASA’s ICESat-2 satellite laser altimeter launched in 2018, we generated monthly volume series from 2003 to 2020 for 9065 lakes worldwide with an area ≥ 10 km². We found that the total volume of the 9065 lakes increased by 597 km³ (90% confidence interval 239–2618 km³). Validation against in situ measurements showed a correlation coefficient of 0.98, an RMSE (i.e., root mean square error) of 0.57 km³ and a normalized RMSE of 2.6%. In addition, 6753 (74.5%) of the lakes showed an increasing trend in lake volume and were spatially clustered into nine hot spots, most of which are located in sparsely populated high latitudes and the Tibetan Plateau; 2323 (25.5%) of the lakes showed a decreasing trend in lake volume and were clustered into six hot spots—most located in the world’s arid/semi-arid regions where lakes are scarce, but population density is high. Our results uncovered, from a three-dimensional volumetric perspective, spatially uneven lake changes that aggravate the conflict between human demands and lake resources. The situation is likely to intensify given projected higher temperatures in glacier-covered regions and drier climates in arid/semi-arid areas. The 15 hot spots could serve as a blueprint for prioritizing future lake research and conservation efforts. Full article

(This article belongs to the Special Issue Remote Sensing of Eco-Hydrology Processes under Ongoing Climate Change)

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11 pages, 3260 KiB

Open AccessEditor’s ChoiceTechnical Note

Characterization of Tropical Cyclone Intensity Using the HY-2B Scatterometer Wind Data

by Siqi Liu, Wenming Lin, Marcos Portabella and Zhixiong Wang

Remote Sens. 2022, 14(4), 1035; https://doi.org/10.3390/rs14041035 - 21 Feb 2022

Cited by 11 | Viewed by 3111

Abstract

The estimation of tropical cyclone (TC) intensity using Ku-band scatterometer data is challenging due to rain perturbation and signal saturation in the radar backscatter measurements. In this paper, an alternative approach to directly taking the maximum scatterometer-derived wind speed is proposed to assess [...] Read more.

The estimation of tropical cyclone (TC) intensity using Ku-band scatterometer data is challenging due to rain perturbation and signal saturation in the radar backscatter measurements. In this paper, an alternative approach to directly taking the maximum scatterometer-derived wind speed is proposed to assess the TC intensity. First, the TC center location is identified based on the unique characteristics of wind stress divergence/curl near the TC core. Then the radial extent of 17-m/s winds (i.e., R17) is calculated using the wind field data from the Haiyang-2B (HY-2B) scatterometer (HSCAT). The feasibility of HSCAT wind radii in determining TC intensity is evaluated using the maximum sustained wind speed (MSW) in the China Meteorological Administration best-track database. It shows that the HSCAT R17 value generally better correlates with the best-track MSW than the HSCAT maximum wind speed, therefore indicating the potential of using the HSCAT data to improve the TC nowcasting capabilities. Full article

(This article belongs to the Special Issue Remote Sensing of Ocean Surface Winds)

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19 pages, 10356 KiB

Open AccessEditor’s ChoiceArticle

A Novel Workflow for Seasonal Wetland Identification Using Bi-Weekly Multiple Remote Sensing Data

by Liwei Xing, Zhenguo Niu, Cuicui Jiao, Jing Zhang, Shuqing Han, Guodong Cheng and Jianzhai Wu

Remote Sens. 2022, 14(4), 1037; https://doi.org/10.3390/rs14041037 - 21 Feb 2022

Cited by 13 | Viewed by 3680

Abstract

Accurate wetland mapping is essential for their protection and management; however, it is difficult to accurately identify seasonal wetlands because of irregular rainfall and the potential lack of water inundation. In this study, we propose a novel method to generate reliable seasonal wetland [...] Read more.

Accurate wetland mapping is essential for their protection and management; however, it is difficult to accurately identify seasonal wetlands because of irregular rainfall and the potential lack of water inundation. In this study, we propose a novel method to generate reliable seasonal wetland maps with a spatial resolution of 20 m using a seasonal-rule-based method in the Zhalong and Momoge National Nature Reserves. This study used Sentinel-1 and Sentinel-2 data, along with a bi-weekly composition method to generate a 15-day image time series. The random forest algorithm was used to classify the images into vegetation, waterbodies, bare land, and wet bare land during each time period. Several rules were incorporated based on the intra-annual changes in the seasonal wetlands and annual wetland maps of the study regions were generated. Validation processes showed that the overall accuracy and kappa coefficient were above 89.8% and 0.87, respectively. The seasonal-rule-based method was able to identify seasonal marshes, flooded wetlands, and artificial wetlands (e.g., paddy fields). Zonal analysis indicated that seasonal wetland types, including flooded wetlands and seasonal marshes, accounted for over 50% of the total wetland area in both Zhalong and Momoge National Nature Reserves; and permanent wetlands, including permanent water and permanent marsh, only accounted for 11% and 12% in the two reserves, respectively. This study proposes a new method to generate reliable annual wetland maps that include seasonal wetlands, providing an accurate dataset for interannual change analyses and wetland protection decision-making. Full article

(This article belongs to the Special Issue Remote Sensing of Wetlands and Biodiversity)

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24 pages, 52501 KiB

Open AccessEditor’s ChoiceArticle

A Suitable Retrieval Algorithm of Arctic Snow Depths with AMSR-2 and Its Application to Sea Ice Thicknesses of Cryosat-2 Data

by Zhaoqing Dong, Lijian Shi, Mingsen Lin and Tao Zeng

Remote Sens. 2022, 14(4), 1041; https://doi.org/10.3390/rs14041041 - 21 Feb 2022

Cited by 7 | Viewed by 3435

Abstract

Arctic sea ice and snow affect the energy balance of the global climate system through the radiation budget. Accurate determination of the snow cover over Arctic sea ice is significant for the retrieval of the sea ice thickness (SIT). In this study, we [...] Read more.

Arctic sea ice and snow affect the energy balance of the global climate system through the radiation budget. Accurate determination of the snow cover over Arctic sea ice is significant for the retrieval of the sea ice thickness (SIT). In this study, we developed a new snow depth retrieval method over Arctic sea ice with a long short-term memory (LSTM) deep learning algorithm based on Operation IceBridge (OIB) snow depth data and brightness temperature data of AMSR-2 passive microwave radiometers. We compared climatology products (modified W99 and AWI), altimeter products (Kwok) and microwave radiometer products (Bremen, Neural Network and LSTM). The climatology products and altimeter products are completely independent of the OIB data used for training, while microwave radiometer products are not completely independent of the OIB data. We also compared the SITs retrieved from the above different snow depth products based on Cryosat-2 radar altimeter data. First, the snow depth spatial patterns for all products are in broad agreement, but the temporal evolution patterns are distinct. Snow products of microwave radiometers, such as Bremen, Neural Network and LSTM snow depth products, show thicker snow in early winter with respect to the climatology snow depth products and the altimeter snow depth product, especially in the multiyear ice (MYI) region. In addition, the differences in all snow depth products are relatively large in the early winter and relatively small in spring. Compared with the OIB and IceBird observation data (April 2019), the snow depth retrieved by the LSTM algorithm is better than that retrieved by the other algorithms in terms of accuracy, with a correlation of 0.55 (0.90), a root mean square error (RMSE) of 0.06 m (0.05 m) and a mean absolute error (MAE) of 0.05 m (0.04 m). The spatial pattern and seasonal variation of the SITs retrieved from different snow depths are basically consistent. The total sea ice decreases first and then thickens as the seasons change. Compared with the OIB SIT in April 2019, the SIT retrieved by the LSTM snow depth is superior to that retrieved by the other SIT products in terms of accuracy, with the highest correlation of 0.46, the lowest RMSE of 0.59 m and the lowest MAE of 0.44 m. In general, it is promising to retrieve Arctic snow depth using the LSTM algorithm, but the retrieval of snow depth over MYI still needs to be verified with more measured data, especially in early winter. Full article

(This article belongs to the Special Issue Remote Sensing Monitoring of Arctic Environments)

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24 pages, 7883 KiB

Open AccessEditor’s ChoiceArticle

Active Fire Detection from Landsat-8 Imagery Using Deep Multiple Kernel Learning

by Amirhossein Rostami, Reza Shah-Hosseini, Shabnam Asgari, Arastou Zarei, Mohammad Aghdami-Nia and Saeid Homayouni

Remote Sens. 2022, 14(4), 992; https://doi.org/10.3390/rs14040992 - 17 Feb 2022

Cited by 62 | Viewed by 11438

Abstract

Active fires are devastating natural disasters that cause socio-economical damage across the globe. The detection and mapping of these disasters require efficient tools, scientific methods, and reliable observations. Satellite images have been widely used for active fire detection (AFD) during the past years [...] Read more.

Active fires are devastating natural disasters that cause socio-economical damage across the globe. The detection and mapping of these disasters require efficient tools, scientific methods, and reliable observations. Satellite images have been widely used for active fire detection (AFD) during the past years due to their nearly global coverage. However, accurate AFD and mapping in satellite imagery is still a challenging task in the remote sensing community, which mainly uses traditional methods. Deep learning (DL) methods have recently yielded outstanding results in remote sensing applications. Nevertheless, less attention has been given to them for AFD in satellite imagery. This study presented a deep convolutional neural network (CNN) “MultiScale-Net” for AFD in Landsat-8 datasets at the pixel level. The proposed network had two main characteristics: (1) several convolution kernels with multiple sizes, and (2) dilated convolution layers (DCLs) with various dilation rates. Moreover, this paper suggested an innovative Active Fire Index (AFI) for AFD. AFI was added to the network inputs consisting of the SWIR2, SWIR1, and Blue bands to improve the performance of the MultiScale-Net. In an ablation analysis, three different scenarios were designed for multi-size kernels, dilation rates, and input variables individually, resulting in 27 distinct models. The quantitative results indicated that the model with AFI-SWIR2-SWIR1-Blue as the input variables, using multiple kernels of sizes 3 × 3, 5 × 5, and 7 × 7 simultaneously, and a dilation rate of 2, achieved the highest F1-score and IoU of 91.62% and 84.54%, respectively. Stacking AFI with the three Landsat-8 bands led to fewer false negative (FN) pixels. Furthermore, our qualitative assessment revealed that these models could detect single fire pixels detached from the large fire zones by taking advantage of multi-size kernels. Overall, the MultiScale-Net met expectations in detecting fires of varying sizes and shapes over challenging test samples. Full article

(This article belongs to the Special Issue Detecting, Mapping, and Characterizing Wildfires Using Remote Sensing Data)

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19 pages, 4355 KiB

Open AccessEditor’s ChoiceArticle

Snow Coverage Mapping by Learning from Sentinel-2 Satellite Multispectral Images via Machine Learning Algorithms

by Yucheng Wang, Jinya Su, Xiaojun Zhai, Fanlin Meng and Cunjia Liu

Remote Sens. 2022, 14(3), 782; https://doi.org/10.3390/rs14030782 - 8 Feb 2022

Cited by 22 | Viewed by 6393

Abstract

Snow coverage mapping plays a vital role not only in studying hydrology and climatology, but also in investigating crop disease overwintering for smart agriculture management. This work investigates snow coverage mapping by learning from Sentinel-2 satellite multispectral images via machine-learning methods. To this [...] Read more.

Snow coverage mapping plays a vital role not only in studying hydrology and climatology, but also in investigating crop disease overwintering for smart agriculture management. This work investigates snow coverage mapping by learning from Sentinel-2 satellite multispectral images via machine-learning methods. To this end, the largest dataset for snow coverage mapping (to our best knowledge) with three typical classes (snow, cloud and background) is first collected and labeled via the semi-automatic classification plugin in QGIS. Then, both random forest-based conventional machine learning and U-Net-based deep learning are applied to the semantic segmentation challenge in this work. The effects of various input band combinations are also investigated so that the most suitable one can be identified. Experimental results show that (1) both conventional machine-learning and advanced deep-learning methods significantly outperform the existing rule-based Sen2Cor product for snow mapping; (2) U-Net generally outperforms the random forest since both spectral and spatial information is incorporated in U-Net via convolution operations; (3) the best spectral band combination for U-Net is B2, B11, B4 and B9. It is concluded that a U-Net-based deep-learning classifier with four informative spectral bands is suitable for snow coverage mapping. Full article

(This article belongs to the Special Issue Remote Sensing for Smart Agriculture Management)

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24 pages, 66358 KiB

Open AccessEditor’s ChoiceArticle

Integration of DInSAR Time Series and GNSS Data for Continuous Volcanic Deformation Monitoring and Eruption Early Warning Applications

by Brianna Corsa, Magali Barba-Sevilla, Kristy Tiampo and Charles Meertens

Remote Sens. 2022, 14(3), 784; https://doi.org/10.3390/rs14030784 - 8 Feb 2022

Cited by 12 | Viewed by 5124

Abstract

With approximately 800 million people globally living within 100 km of a volcano, it is essential that we build a reliable observation system capable of delivering early warnings to potentially impacted nearby populations. Global Navigation Satellite System (GNSS) and satellite Synthetic Aperture Radar [...] Read more.

With approximately 800 million people globally living within 100 km of a volcano, it is essential that we build a reliable observation system capable of delivering early warnings to potentially impacted nearby populations. Global Navigation Satellite System (GNSS) and satellite Synthetic Aperture Radar (SAR) document comprehensive ground motions or ruptures near, and at, the Earth’s surface and may be used to detect and analyze natural hazard phenomena. These datasets may also be combined to improve the accuracy of deformation results. Here, we prepare a differential interferometric SAR (DInSAR) time series and integrate it with GNSS data to create a fused dataset with enhanced accuracy of 3D ground motions over Hawaii island from November 2015 to April 2021. We present a comparison of the raw datasets against the fused time series and give a detailed account of observed ground deformation leading to the May 2018 and December 2020 volcanic eruptions. Our results provide important new estimates of the spatial and temporal dynamics of the 2018 Kilauea volcanic eruption. The methodology presented here can be easily repeated over any region of interest where an SAR scene overlaps with GNSS data. The results will contribute to diverse geophysical studies, including but not limited to the classification of precursory movements leading to major eruptions and the advancement of early warning systems. Full article

(This article belongs to the Special Issue Volcanic Processes Monitoring and Hazard Assessment Using Integration of Remote Sensing and Ground-Based Techniques)

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25 pages, 4152 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Forest Disturbance Detection with Seasonal and Trend Model Components and Machine Learning Algorithms

by Jonathan V. Solórzano and Yan Gao

Remote Sens. 2022, 14(3), 803; https://doi.org/10.3390/rs14030803 - 8 Feb 2022

Cited by 8 | Viewed by 4422

Abstract

Forest disturbances reduce the extent of natural habitats, biodiversity, and carbon sequestered in forests. With the implementation of the international framework Reduce Emissions from Deforestation and forest Degradation (REDD+), it is important to improve the accuracy in the estimation of the extent of [...] Read more.

Forest disturbances reduce the extent of natural habitats, biodiversity, and carbon sequestered in forests. With the implementation of the international framework Reduce Emissions from Deforestation and forest Degradation (REDD+), it is important to improve the accuracy in the estimation of the extent of forest disturbances. Time series analyses, such as Breaks for Additive Season and Trend (BFAST), have been frequently used to map tropical forest disturbances with promising results. Previous studies suggest that in addition to magnitude of change, disturbance accuracy could be enhanced by using other components of BFAST that describe additional aspects of the model, such as its goodness-of-fit, NDVI seasonal variation, temporal trend, historical length of observations and data quality, as well as by using separate thresholds for distinct forest types. The objective of this study is to determine if the BFAST algorithm can benefit from using these model components in a supervised scheme to improve the accuracy to detect forest disturbance. A random forests and support vector machines algorithms were trained and verified using 238 points in three different datasets: all-forest, tropical dry forest, and temperate forest. The results show that the highest accuracy was achieved by the support vector machines algorithm using the all-forest dataset. Although the increase in accuracy of the latter model vs. a magnitude threshold model is small, i.e., 0.14% for sample-based accuracy and 0.71% for area-weighted accuracy, the standard error of the estimated total disturbed forest area was 4352.59 ha smaller, while the annual disturbance rate was also smaller by 1262.2 ha year⁻¹. The implemented approach can be useful to obtain more precise estimates in forest disturbance, as well as its associated carbon emissions. Full article

(This article belongs to the Special Issue Advances in the Remote Sensing of Forest Cover Change)

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32 pages, 27455 KiB

Open AccessEditor’s ChoiceReview

Assimilation of Satellite-Derived Soil Moisture and Brightness Temperature in Land Surface Models: A Review

by Reza Khandan, Jean-Pierre Wigneron, Stefania Bonafoni, Arastoo Pour Biazar and Mehdi Gholamnia

Remote Sens. 2022, 14(3), 770; https://doi.org/10.3390/rs14030770 - 7 Feb 2022

Cited by 9 | Viewed by 4122

Abstract

The correction of Soil Moisture (SM) estimates in Land Surface Models (LSMs) is considered essential for improving the performance of numerical weather forecasting and hydrologic models used in weather and climate studies. Along with surface screen-level variables, the satellite data, including Brightness Temperature [...] Read more.

The correction of Soil Moisture (SM) estimates in Land Surface Models (LSMs) is considered essential for improving the performance of numerical weather forecasting and hydrologic models used in weather and climate studies. Along with surface screen-level variables, the satellite data, including Brightness Temperature (BT) from passive microwave sensors, and retrieved SM from active, passive, or combined active–passive sensor products have been used as two critical inputs in improvements of the LSM. The present study reviewed the current status in correcting LSM SM estimates, evaluating the results with in situ measurements. Based on findings from previous studies, a detailed analysis of related issues in the assimilation of SM in LSM, including bias correction of satellite data, applied LSMs and in situ observations, input data from various satellite sensors, sources of errors, calibration (both LSM and radiative transfer model), are discussed. Moreover, assimilation approaches are compared, and considerations for assimilation implementation are presented. A quantitative representation of results from the literature review, including ranges and variability of improvements in LSMs due to assimilation, are analyzed for both surface and root zone SM. A direction for future studies is then presented. Full article

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19 pages, 12659 KiB

Open AccessEditor’s ChoiceArticle

Sea Surface Salinity Variability in the Bering Sea in 2015–2020

by Jian Zhao, Yan Wang, Wenjing Liu, Hongsheng Bi, Edward D. Cokelet, Calvin W. Mordy, Noah Lawrence-Slavas and Christian Meinig

Remote Sens. 2022, 14(3), 758; https://doi.org/10.3390/rs14030758 - 6 Feb 2022

Cited by 6 | Viewed by 4634

Abstract

Salinity in the Bering Sea is vital for the physical environment that is tied to the productive ecosystem and the properties of Pacific waters transported to the Arctic Ocean. Its salinity variability reflects many fundamental processes, including sea ice formation/melting and river runoff, [...] Read more.

Salinity in the Bering Sea is vital for the physical environment that is tied to the productive ecosystem and the properties of Pacific waters transported to the Arctic Ocean. Its salinity variability reflects many fundamental processes, including sea ice formation/melting and river runoff, but its spatial and temporal characteristics require better documentation. This study utilizes remote sensing products and in situ observations collected by saildrone missions to investigate Sea Surface Salinity (SSS) variability. All Satellite products resolve the large-scale pattern set up by the relatively salty deep basin and the fresh coastal region, but they can be inaccurate near the ice edge and near land. The SSS annual cycle exhibits seasonal maxima in winter to spring, and minima in summer to fall. The amplitude and timing of the seasonal cycle are variable, especially on the eastern Bering Sea shelf. SSS variability recorded by both saildrone, and satellite instruments provide unprecedented insights into short-term oceanic processes including sea ice melting, wind-driven currents during weather events, and river plumes etc. In particular, the Soil Moisture Active Passive (SMAP) satellite demonstrates encouraging skills in capturing the freshening signals induced by spring sea ice melting. The Yukon River plume is another source of intense SSS variability. Surface wind forcing plays an essential role in controlling the horizontal movement of plume water and thereby shaping the SSS seasonal cycle in local regions. Full article

(This article belongs to the Special Issue Moving Forward on Remote Sensing of Sea Surface Salinity)

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28 pages, 7779 KiB

Open AccessEditor’s ChoiceArticle

Interannual Variability of Water Level in Two Largest Lakes of Europe

by Andrey G. Kostianoy, Sergey A. Lebedev, Evgeniia A. Kostianaia and Yaan A. Prokofiev

Remote Sens. 2022, 14(3), 659; https://doi.org/10.3390/rs14030659 - 29 Jan 2022

Cited by 8 | Viewed by 3804

Abstract

Regional climate change affects the state of inland water bodies and their water balance, which is determined by a number of hydrometeorological and hydrogeological factors. An integral characteristic of changes in the water balance is the behavior of the level of lakes and [...] Read more.

Regional climate change affects the state of inland water bodies and their water balance, which is determined by a number of hydrometeorological and hydrogeological factors. An integral characteristic of changes in the water balance is the behavior of the level of lakes and reservoirs, which not only largely determines the physical and ecological state of water bodies, but also significantly affects the coastal infrastructure and socio-economic development of the region. This paper investigates the interannual variability of the level of the Ladoga and Onega lakes, the largest lakes in Europe located in the northwest of Russia, according to satellite altimetry data for 1993–2020. For this purpose, we used three specialized altimetry databases: DAHITI, G-REALM, and HYDROWEB. Water level data from these altimetry databases were compared with in-situ records at water level gauge stations. Information on air temperature (1945–2019) and precipitation (1966–2019) acquired at three meteostations located at Ladoga and Onega lakes was used to investigate interannual trends in the regional climate change. Finally, we discuss the potential impact of the lake level rise and regional climate warming on the infrastructure and operability of railways in this region. Full article

(This article belongs to the Special Issue Remote Sensing for Water Resources and Environmental Management)

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21 pages, 9042 KiB

Open AccessEditor’s ChoiceArticle

Logging Trail Segmentation via a Novel U-Net Convolutional Neural Network and High-Density Laser Scanning Data

by Omid Abdi, Jori Uusitalo and Veli-Pekka Kivinen

Remote Sens. 2022, 14(2), 349; https://doi.org/10.3390/rs14020349 - 13 Jan 2022

Cited by 9 | Viewed by 3904

Abstract

Logging trails are one of the main components of modern forestry. However, spotting the accurate locations of old logging trails through common approaches is challenging and time consuming. This study was established to develop an approach, using cutting-edge deep-learning convolutional neural networks and [...] Read more.

Logging trails are one of the main components of modern forestry. However, spotting the accurate locations of old logging trails through common approaches is challenging and time consuming. This study was established to develop an approach, using cutting-edge deep-learning convolutional neural networks and high-density laser scanning data, to detect logging trails in different stages of commercial thinning, in Southern Finland. We constructed a U-Net architecture, consisting of encoder and decoder paths with several convolutional layers, pooling and non-linear operations. The canopy height model (CHM), digital surface model (DSM), and digital elevation models (DEMs) were derived from the laser scanning data and were used as image datasets for training the model. The labeled dataset for the logging trails was generated from different references as well. Three forest areas were selected to test the efficiency of the algorithm that was developed for detecting logging trails. We designed 21 routes, including 390 samples of the logging trails and non-logging trails, covering all logging trails inside the stands. The results indicated that the trained U-Net using DSM (k = 0.846 and IoU = 0.867) shows superior performance over the trained model using CHM (k = 0.734 and IoU = 0.782), DEM_avg (k = 0.542 and IoU = 0.667), and DEM_min (k = 0.136 and IoU = 0.155) in distinguishing logging trails from non-logging trails. Although the efficiency of the developed approach in young and mature stands that had undergone the commercial thinning is approximately perfect, it needs to be improved in old stands that have not received the second or third commercial thinning. Full article

(This article belongs to the Special Issue Deep Learning and Computer Vision in Remote Sensing)

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26 pages, 17206 KiB

Open AccessEditor’s ChoiceArticle

Using Remote Sensing to Estimate Scales of Spatial Heterogeneity to Analyze Evapotranspiration Modeling in a Natural Ecosystem

by Ayman Nassar, Alfonso Torres-Rua, Lawrence Hipps, William Kustas, Mac McKee, David Stevens, Héctor Nieto, Daniel Keller, Ian Gowing and Calvin Coopmans

Remote Sens. 2022, 14(2), 372; https://doi.org/10.3390/rs14020372 - 13 Jan 2022

Cited by 16 | Viewed by 4984

Abstract

Understanding the spatial variability in highly heterogeneous natural environments such as savannas and river corridors is an important issue in characterizing and modeling energy fluxes, particularly for evapotranspiration (ET) estimates. Currently, remote-sensing-based surface energy balance (SEB) models are applied [...] Read more.

Understanding the spatial variability in highly heterogeneous natural environments such as savannas and river corridors is an important issue in characterizing and modeling energy fluxes, particularly for evapotranspiration (ET) estimates. Currently, remote-sensing-based surface energy balance (SEB) models are applied widely and routinely in agricultural settings to obtain ET information on an operational basis for use in water resources management. However, the application of these models in natural environments is challenging due to spatial heterogeneity in vegetation cover and complexity in the number of vegetation species existing within a biome. In this research effort, small unmanned aerial systems (sUAS) data were used to study the influence of land surface spatial heterogeneity on the modeling of ET using the Two-Source Energy Balance (TSEB) model. The study area is the San Rafael River corridor in Utah, which is a part of the Upper Colorado River Basin that is characterized by arid conditions and variations in soil moisture status and the type and height of vegetation. First, a spatial variability analysis was performed using a discrete wavelet transform (DWT) to identify a representative spatial resolution/model grid size for adequately solving energy balance components to derive ET. The results indicated a maximum wavelet energy between 6.4 m and 12.8 m for the river corridor area, while the non-river corridor area, which is characterized by different surface types and random vegetation, does not show a peak value. Next, to evaluate the effect of spatial resolution on latent heat flux (LE) estimation using the TSEB model, spatial scales of 6 m and 15 m instead of 6.4 m and 12.8 m, respectively, were used to simplify the derivation of model inputs. The results indicated small differences in the LE values between 6 m and 15 m resolutions, with a slight decrease in detail at 15 m due to losses in spatial variability. Lastly, the instantaneous (hourly) LE was extrapolated/upscaled to daily ET values using the incoming solar radiation (R_s) method. The results indicated that willow and cottonwood have the highest ET rates, followed by grass/shrubs and treated tamarisk. Although most of the treated tamarisk vegetation is in dead/dry condition, the green vegetation growing underneath resulted in a magnitude value of ET. Full article

(This article belongs to the Special Issue Remote Sensing-Based Evapotranspiration Models)

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22 pages, 19242 KiB

Open AccessEditor’s ChoiceArticle

Open-Source Analysis of Submerged Aquatic Vegetation Cover in Complex Waters Using High-Resolution Satellite Remote Sensing: An Adaptable Framework

by Arthur de Grandpré, Christophe Kinnard and Andrea Bertolo

Remote Sens. 2022, 14(2), 267; https://doi.org/10.3390/rs14020267 - 7 Jan 2022

Cited by 13 | Viewed by 4553

Abstract

Despite being recognized as a key component of shallow-water ecosystems, submerged aquatic vegetation (SAV) remains difficult to monitor over large spatial scales. Because of SAV’s structuring capabilities, high-resolution monitoring of submerged landscapes could generate highly valuable ecological data. Until now, high-resolution remote sensing [...] Read more.

Despite being recognized as a key component of shallow-water ecosystems, submerged aquatic vegetation (SAV) remains difficult to monitor over large spatial scales. Because of SAV’s structuring capabilities, high-resolution monitoring of submerged landscapes could generate highly valuable ecological data. Until now, high-resolution remote sensing of SAV has been largely limited to applications within costly image analysis software. In this paper, we propose an example of an adaptable open-sourced object-based image analysis (OBIA) workflow to generate SAV cover maps in complex aquatic environments. Using the R software, QGIS and Orfeo Toolbox, we apply radiometric calibration, atmospheric correction, a de-striping correction, and a hierarchical iterative OBIA random forest classification to generate SAV cover maps based on raw DigitalGlobe multispectral imagery. The workflow is applied to images taken over two spatially complex fluvial lakes in Quebec, Canada, using Quickbird-02 and Worldview-03 satellites. Classification performance based on training sets reveals conservative SAV cover estimates with less than 10% error across all classes except for lower SAV growth forms in the most turbid waters. In light of these results, we conclude that it is possible to monitor SAV distribution using high-resolution remote sensing within an open-sourced environment with a flexible and functional workflow. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of the Inland and Coastal Water Zones)

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24 pages, 11219 KiB

Open AccessEditor’s ChoiceArticle

B-FGC-Net: A Building Extraction Network from High Resolution Remote Sensing Imagery

by Yong Wang, Xiangqiang Zeng, Xiaohan Liao and Dafang Zhuang

Remote Sens. 2022, 14(2), 269; https://doi.org/10.3390/rs14020269 - 7 Jan 2022

Cited by 42 | Viewed by 4944

Abstract

Deep learning (DL) shows remarkable performance in extracting buildings from high resolution remote sensing images. However, how to improve the performance of DL based methods, especially the perception of spatial information, is worth further study. For this purpose, we proposed a building extraction [...] Read more.

Deep learning (DL) shows remarkable performance in extracting buildings from high resolution remote sensing images. However, how to improve the performance of DL based methods, especially the perception of spatial information, is worth further study. For this purpose, we proposed a building extraction network with feature highlighting, global awareness, and cross level information fusion (B-FGC-Net). The residual learning and spatial attention unit are introduced in the encoder of the B-FGC-Net, which simplifies the training of deep convolutional neural networks and highlights the spatial information representation of features. The global feature information awareness module is added to capture multiscale contextual information and integrate the global semantic information. The cross level feature recalibration module is used to bridge the semantic gap between low and high level features to complete the effective fusion of cross level information. The performance of the proposed method was tested on two public building datasets and compared with classical methods, such as UNet, LinkNet, and SegNet. Experimental results demonstrate that B-FGC-Net exhibits improved profitability of accurate extraction and information integration for both small and large scale buildings. The IoU scores of B-FGC-Net on WHU and INRIA Building datasets are 90.04% and 79.31%, respectively. B-FGC-Net is an effective and recommended method for extracting buildings from high resolution remote sensing images. Full article

(This article belongs to the Special Issue Remote Sensing Based Building Extraction II)

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24 pages, 8089 KiB

Open AccessEditor’s ChoiceArticle

Automatic Mapping and Characterisation of Linear Depositional Bedforms: Theory and Application Using Bathymetry from the North West Shelf of Australia

by Ulysse Lebrec, Rosine Riera, Victorien Paumard, Michael J. O'Leary and Simon C. Lang

Remote Sens. 2022, 14(2), 280; https://doi.org/10.3390/rs14020280 - 7 Jan 2022

Cited by 19 | Viewed by 4555

Abstract

Bedforms are key components of Earth surfaces and yet their evaluation typically relies on manual measurements that are challenging to reproduce. Several methods exist to automate their identification and calculate their metrics, but they often exhibit limitations where applied at large scales. This [...] Read more.

Bedforms are key components of Earth surfaces and yet their evaluation typically relies on manual measurements that are challenging to reproduce. Several methods exist to automate their identification and calculate their metrics, but they often exhibit limitations where applied at large scales. This paper presents an innovative workflow for identifying and measuring individual depositional bedforms. The workflow relies on the identification of local minima and maxima that are grouped by neighbourhood analysis and calibrated using curvature. The method was trialed using a synthetic digital elevation model and two bathymetry surveys from Australia’s northwest marine region, resulting in the identification of nearly 2000 bedforms. The comparison of the metrics calculated for each individual feature with manual measurements show differences of less than 10%, indicating the robustness of the workflow. The cross-comparison of the metrics resulted in the definition of several sub-types of bedforms, including sandwaves and palaeoshorelines, that were then correlated with oceanic conditions, further corroborating the validity of the workflow. Results from this study support the idea that the use of automated methods to characterise bedforms should be further developed and that the integration of automated measurements at large scales will support the development of new classification charts that currently rely solely on manual measurements. Full article

(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)

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23 pages, 12116 KiB

Open AccessEditor’s ChoiceArticle

Very High-Resolution Satellite-Derived Bathymetry and Habitat Mapping Using Pleiades-1 and ICESat-2

by Alyson Le Quilleuc, Antoine Collin, Michael F. Jasinski and Rodolphe Devillers

Remote Sens. 2022, 14(1), 133; https://doi.org/10.3390/rs14010133 - 29 Dec 2021

Cited by 50 | Viewed by 7321

Abstract

Accurate and reliable bathymetric data are needed for a wide diversity of marine research and management applications. Satellite-derived bathymetry represents a time saving method to map large shallow waters of remote regions compared to the current costly in situ measurement techniques. This study [...] Read more.

Accurate and reliable bathymetric data are needed for a wide diversity of marine research and management applications. Satellite-derived bathymetry represents a time saving method to map large shallow waters of remote regions compared to the current costly in situ measurement techniques. This study aims to create very high-resolution (VHR) bathymetry and habitat mapping in Mayotte island waters (Indian Ocean) by fusing 0.5 m Pleiades-1 passive multispectral imagery and active ICESat-2 LiDAR bathymetry. ICESat-2 georeferenced photons were filtered to remove noise and corrected for water column refraction. The bathymetric point clouds were validated using the French naval hydrographic and oceanographic service Litto3D^® dataset and then used to calibrate the multispectral image to produce a digital depth model (DDM). The latter enabled the creation of a digital albedo model used to classify benthic habitats. ICESat-2 provided bathymetry down to 15 m depth with a vertical accuracy of bathymetry estimates reaching 0.89 m. The benthic habitats map produced using the maximum likelihood supervised classification provided an overall accuracy of 96.62%. This study successfully produced a VHR DDM solely from satellite data. Digital models of higher accuracy were further discussed in the light of the recent and near-future launch of higher spectral and spatial resolution satellites. Full article

(This article belongs to the Special Issue Remote Sensing for Coastal and Aquatic Ecosystems’ Monitoring and Biodiversity Management)

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25 pages, 70896 KiB

Open AccessEditor’s ChoiceArticle

Monitoring Cropland Phenology on Google Earth Engine Using Gaussian Process Regression

by Matías Salinero-Delgado, José Estévez, Luca Pipia, Santiago Belda, Katja Berger, Vanessa Paredes Gómez and Jochem Verrelst

Remote Sens. 2022, 14(1), 146; https://doi.org/10.3390/rs14010146 - 29 Dec 2021

Cited by 42 | Viewed by 11868

Abstract

Monitoring cropland phenology from optical satellite data remains a challenging task due to the influence of clouds and atmospheric artifacts. Therefore, measures need to be taken to overcome these challenges and gain better knowledge of crop dynamics. The arrival of cloud computing platforms [...] Read more.

Monitoring cropland phenology from optical satellite data remains a challenging task due to the influence of clouds and atmospheric artifacts. Therefore, measures need to be taken to overcome these challenges and gain better knowledge of crop dynamics. The arrival of cloud computing platforms such as Google Earth Engine (GEE) has enabled us to propose a Sentinel-2 (S2) phenology end-to-end processing chain. To achieve this, the following pipeline was implemented: (1) the building of hybrid Gaussian Process Regression (GPR) retrieval models of crop traits optimized with active learning, (2) implementation of these models on GEE (3) generation of spatiotemporally continuous maps and time series of these crop traits with the use of gap-filling through GPR fitting, and finally, (4) calculation of land surface phenology (LSP) metrics such as the start of season (SOS) or end of season (EOS). Overall, from good to high performance was achieved, in particular for the estimation of canopy-level traits such as leaf area index (LAI) and canopy chlorophyll content, with normalized root mean square errors (NRMSE) of 9% and 10%, respectively. By means of the GPR gap-filling time series of S2, entire tiles were reconstructed, and resulting maps were demonstrated over an agricultural area in Castile and Leon, Spain, where crop calendar data were available to assess the validity of LSP metrics derived from crop traits. In addition, phenology derived from the normalized difference vegetation index (NDVI) was used as reference. NDVI not only proved to be a robust indicator for the calculation of LSP metrics, but also served to demonstrate the good phenology quality of the quantitative trait products. Thanks to the GEE framework, the proposed workflow can be realized anywhere in the world and for any time window, thus representing a shift in the satellite data processing paradigm. We anticipate that the produced LSP metrics can provide meaningful insights into crop seasonal patterns in a changing environment that demands adaptive agricultural production. Full article

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26 pages, 9754 KiB

Open AccessEditor’s ChoiceArticle

Quantitative Precipitation Estimation over Antarctica Using Different Ze-SR Relationships Based on Snowfall Classification Combining Ground Observations

by Alessandro Bracci, Luca Baldini, Nicoletta Roberto, Elisa Adirosi, Mario Montopoli, Claudio Scarchilli, Paolo Grigioni, Virginia Ciardini, Vincenzo Levizzani and Federico Porcù

Remote Sens. 2022, 14(1), 82; https://doi.org/10.3390/rs14010082 - 24 Dec 2021

Cited by 15 | Viewed by 6794

Abstract

Snow plays a crucial role in the hydrological cycle and energy budget of the Earth, and remote sensing instruments with the necessary spatial coverage, resolution, and temporal sampling are essential for snowfall monitoring. Among such instruments, ground-radars have scanning capability and a resolution [...] Read more.

Snow plays a crucial role in the hydrological cycle and energy budget of the Earth, and remote sensing instruments with the necessary spatial coverage, resolution, and temporal sampling are essential for snowfall monitoring. Among such instruments, ground-radars have scanning capability and a resolution that make it possible to obtain a 3D structure of precipitating systems or vertical profiles when used in profiling mode. Radars from space have a lower spatial resolution, but they provide a global view. However, radar-based quantitative estimates of solid precipitation are still a challenge due to the variability of the microphysical, geometrical, and electrical features of snow particles. Estimations of snowfall rate are usually accomplished using empirical, long-term relationships between the equivalent radar reflectivity factor (Ze) and the liquid-equivalent snowfall rate (SR). Nevertheless, very few relationships take advantage of the direct estimation of the microphysical characteristics of snowflakes. In this work, we used a K-band vertically pointing radar collocated with a laser disdrometer to develop Ze-SR relationships as a function of snow classification. The two instruments were located at the Italian Antarctic Station Mario Zucchelli. The K-band radar probes the low-level atmospheric layers, recording power spectra at 32 vertical range gates. It was set at a high vertical resolution (35 m), with the first trusted range gate at a height of only 100 m. The disdrometer was able to provide information on the particle size distribution just below the trusted radar gate. Snow particles were classified into six categories (aggregate, dendrite aggregate, plate aggregate, pristine, dendrite pristine, plate pristine). The method was applied to the snowfall events of the Antarctic summer seasons of 2018–2019 and 2019–2020, with a total of 23,566 min of precipitation, 15.3% of which was recognized as showing aggregate features, 33.3% dendrite aggregate, 7.3% plates aggregate, 12.5% pristine, 24% dendrite pristine, and 7.6% plate pristine. Applying the appropriate Ze-SR relationship in each snow category, we calculated a total of 87 mm water equivalent, differing from the total found by applying a unique Ze-SR. Our estimates were also benchmarked against a colocated Alter-shielded weighing gauge, resulting in a difference of 3% in the analyzed periods. Full article

(This article belongs to the Special Issue Retrieval of Cloud and Precipitation by Ground-Based Radar and In Situ Observations: Application to Atmospheric and Volcanic Ash Clouds)

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25 pages, 13787 KiB

Open AccessEditor’s ChoiceArticle

Spatial and Temporal Distributions of Ionospheric Irregularities Derived from Regional and Global ROTI Maps

by Chinh Thai Nguyen, Seun Temitope Oluwadare, Nhung Thi Le, Mahdi Alizadeh, Jens Wickert and Harald Schuh

Remote Sens. 2022, 14(1), 10; https://doi.org/10.3390/rs14010010 - 21 Dec 2021

Cited by 16 | Viewed by 5510

Abstract

Major advancements in the monitoring of both the occurrence and impacts of space weather can be made by evaluating the occurrence and distribution of ionospheric disturbances. Previous studies have shown that the fluctuations in total electron content (TEC) values estimated from Global Navigation [...] Read more.

Major advancements in the monitoring of both the occurrence and impacts of space weather can be made by evaluating the occurrence and distribution of ionospheric disturbances. Previous studies have shown that the fluctuations in total electron content (TEC) values estimated from Global Navigation Satellite System (GNSS) observations clearly exhibit the intensity levels of ionospheric irregularities, which vary continuously in both time and space. The duration and intensity of perturbations depend on the geographic location. They are also dependent on the physical activities of the Sun, the Earth’s magnetic activities, as well as the process of transferring energy from the Sun to the Earth. The aim of this study is to establish ionospheric irregularity maps using ROTI (rate of TEC index) values derived from conventional dual-frequency GNSS measurements (30-s interval). The research areas are located in Southeast Asia (15°S–25°N latitude and 95°E–115°E longitude), which is heavily affected by ionospheric scintillations, as well as in other regions around the globe. The regional ROTI map of Southeast Asia clearly indicates that ionospheric disturbances in this region are dominantly concentrated around the two equatorial ionization anomaly (EIA) crests, occurring mainly during the evening hours. Meanwhile, the global ROTI maps reveal the spatial and temporal distributions of ionospheric scintillations. Within the equatorial region, South America is the most vulnerable area (22.6% of total irregularities), followed by West Africa (8.2%), Southeast Asia (4.7%), East Africa (4.1%), the Pacific (3.8%), and South Asia (2.3%). The generated maps show that the scintillation occurrence is low in the mid-latitude areas during the last solar cycle. In the polar regions, ionospheric irregularities occur at any time of the day. To compare ionospheric disturbances between regions, the Earth is divided into ten sectors and their irregularity coefficients are calculated accordingly. The quantification of the degrees of disturbance reveals that about 58 times more ionospheric irregularities are observed in South America than in the southern mid-latitudes (least affected region). The irregularity coefficients in order from largest to smallest are as follows: South America, 3.49; the Arctic, 1.94; West Africa, 1.77; Southeast Asia, 1.27; South Asia, 1.24; the Antarctic, 1.10; East Africa, 0.89; the Pacific, 0.32; northern mid-latitudes, 0.15; southern mid-latitudes, 0.06. Full article

(This article belongs to the Special Issue Space Geodesy and Ionosphere)

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29 pages, 14889 KiB

Open AccessEditor’s ChoiceArticle

Assessment of CYGNSS Wind Speed Retrievals in Tropical Cyclones

by Lucrezia Ricciardulli, Carl Mears, Andrew Manaster and Thomas Meissner

Remote Sens. 2021, 13(24), 5110; https://doi.org/10.3390/rs13245110 - 16 Dec 2021

Cited by 19 | Viewed by 4674

Abstract

The NASA CYGNSS satellite constellation measures ocean surface winds using the existing network of the Global Navigation Satellite System (GNSS) and was designed for measurements in tropical cyclones (TCs). Here, we focus on using a consistent methodology to validate multiple CYGNSS wind data [...] Read more.

The NASA CYGNSS satellite constellation measures ocean surface winds using the existing network of the Global Navigation Satellite System (GNSS) and was designed for measurements in tropical cyclones (TCs). Here, we focus on using a consistent methodology to validate multiple CYGNSS wind data records currently available to the public, some focusing on low to moderate wind speeds, others for high winds, a storm-centric product for TC analyses, and a wind dataset from NOAA that applies a track-wise bias correction. Our goal is to document their differences and provide guidance to users. The assessment of CYGNSS winds (2017–2020) is performed here at global scales and for all wind regimes, with particular focus on TCs, using measurements from radiometers that are specifically developed for high winds: SMAP, WindSat, and AMSR2 TC-winds. The CYGNSS high-wind products display significant biases in TCs and very large uncertainties. Similar biases and large uncertainties were found with the storm-centric wind product. On the other hand, the NOAA winds show promising skill in TCs, approaching a level suitable for tropical meteorology studies. At the global level, the NOAA winds are overall unbiased at wind regimes from 0–30 m/s and were selected for a test assimilation into a global wind analysis, CCMP, also presented here. Full article

(This article belongs to the Special Issue Applications of GNSS Reflectometry for Earth Observation II)

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28 pages, 15598 KiB

Open AccessEditor’s ChoiceArticle

QDC-2D: A Semi-Automatic Tool for 2D Analysis of Discontinuities for Rock Mass Characterization

by Lidia Loiotine, Charlotte Wolff, Emmanuel Wyser, Gioacchino Francesco Andriani, Marc-Henri Derron, Michel Jaboyedoff and Mario Parise

Remote Sens. 2021, 13(24), 5086; https://doi.org/10.3390/rs13245086 - 14 Dec 2021

Cited by 6 | Viewed by 4423

Abstract

Quantitative characterization of discontinuities is fundamental to define the mechanical behavior of discontinuous rock masses. Several techniques for the semi-automatic and automatic extraction of discontinuities and their properties from raw or processed point clouds have been introduced in the literature to overcome the [...] Read more.

Quantitative characterization of discontinuities is fundamental to define the mechanical behavior of discontinuous rock masses. Several techniques for the semi-automatic and automatic extraction of discontinuities and their properties from raw or processed point clouds have been introduced in the literature to overcome the limits of conventional field surveys and improve data accuracy. However, most of these techniques do not allow characterizing flat or subvertical outcrops because planar surfaces are difficult to detect within point clouds in these circumstances, with the drawback of undersampling the data and providing inappropriate results. In this case, 2D analysis on the fracture traces are more appropriate. Nevertheless, to our knowledge, few methods to perform quantitative analyses on discontinuities from orthorectified photos are publicly available and do not provide a complete characterization. We implemented scanline and window sampling methods in a digital environment to characterize rock masses affected by discontinuities perpendicular to the bedding from trace maps, thus exploiting the potentiality of remote sensing techniques for subvertical and low-relief outcrops. The routine, named QDC-2D (Quantitative Discontinuity Characterization, 2D) was compiled in MATLAB by testing a synthetic dataset and a real case study, from which a high-resolution orthophoto was obtained by means of Structure from Motion technique. Starting from a trace map, the routine semi-automatically classifies the discontinuity sets and calculates their mean spacing, frequency, trace length, and persistence. The fracture network is characterized by means of trace length, intensity, and density estimators. The block volume and shape are also estimated by adding information on the third dimension. The results of the 2D analysis agree with the input used to produce the synthetic dataset and with the data collected in the field by means of conventional geostructural and geomechanical techniques, ensuring the procedure’s reliability. The outcomes of the analysis were implemented in a Discrete Fracture Network model to evaluate their applicability for geomechanical modeling. Full article

(This article belongs to the Special Issue 3D Point Clouds in Rock Mechanics Applications)

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29 pages, 5699 KiB

Open AccessEditor’s ChoiceArticle

Improvement of the Soil Moisture Retrieval Procedure Based on the Integration of UAV Photogrammetry and Satellite Remote Sensing Information

by Amal Chakhar, David Hernández-López, Rocío Ballesteros and Miguel A. Moreno

Remote Sens. 2021, 13(24), 4968; https://doi.org/10.3390/rs13244968 - 7 Dec 2021

Cited by 6 | Viewed by 4525

Abstract

In countries characterized by arid and semi-arid climates, a precise determination of soil moisture conditions on the field scale is critically important, especially in the first crop growth stages, to schedule irrigation and to avoid wasting water. The objective of this study was [...] Read more.

In countries characterized by arid and semi-arid climates, a precise determination of soil moisture conditions on the field scale is critically important, especially in the first crop growth stages, to schedule irrigation and to avoid wasting water. The objective of this study was to apply the operative methodology that allowed surface soil moisture (SSM) content in a semi-arid environment to be estimated. SSM retrieval was carried out by combining two scattering models (IEM and WCM), supplied by backscattering coefficients at the VV polarization obtained from the C-band Synthetic Aperture Radar (SAR), a vegetation descriptor NDVI obtained from the optical sensor, among other essential parameters. The inversion of these models was performed by Neural Networks (NN). The combined models were calibrated by the Sentinel 1 and Sentinel 2 data collected on bare soil, and in cereal, pea and onion crop fields. To retrieve SSM, these scattering models need accurate measurements of the roughness surface parameters, standard deviation of the surface height (hrms) and correlation length (L). This work used a photogrammetric acquisition system carried on Unmanned Aerial Vehicles (UAV) to reconstruct digital surface models (DSM), which allowed these soil roughness parameters to be acquired in a large portion of the studied fields. The obtained results showed that the applied improved methodology effectively estimated SSM on bare and cultivated soils in the principal early growth stages. The bare soil experimentation yielded an R² = 0.74 between the estimated and observed SSMs. For the cereal field, the relation between the estimated and measured SSMs yielded R² = 0.71. In the experimental pea fields, the relation between the estimated and measured SSMs revealed R² = 0.72 and 0.78, respectively, for peas 1 and peas 2. For the onion experimentation, the highest R² equaled 0.5 in the principal growth stage (leaf development), but the crop R² drastically decreased to 0.08 in the completed growth phase. The acquired results showed that the applied improved methodology proves to be an effective tool for estimating the SSM on bare and cultivated soils in the principal early growth stages. Full article

(This article belongs to the Special Issue Satellite Soil Moisture Validation and Applications)

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28 pages, 14811 KiB

Open AccessEditor’s ChoiceArticle

Accuracy of Sentinel-1 PSI and SBAS InSAR Displacement Velocities against GNSS and Geodetic Leveling Monitoring Data

by Francesca Cigna, Rubén Esquivel Ramírez and Deodato Tapete

Remote Sens. 2021, 13(23), 4800; https://doi.org/10.3390/rs13234800 - 26 Nov 2021

Cited by 73 | Viewed by 10913

Abstract

Correct use of multi-temporal Interferometric Synthetic Aperture Radar (InSAR) datasets to complement geodetic surveying for geo-hazard applications requires rigorous assessment of their precision and accuracy. Published inter-comparisons are mostly limited to ground displacement estimates obtained from different algorithms belonging to the same family [...] Read more.

Correct use of multi-temporal Interferometric Synthetic Aperture Radar (InSAR) datasets to complement geodetic surveying for geo-hazard applications requires rigorous assessment of their precision and accuracy. Published inter-comparisons are mostly limited to ground displacement estimates obtained from different algorithms belonging to the same family of InSAR approaches, either Persistent Scatterer Interferometry (PSI) or Small BAseline Subset (SBAS); and accuracy assessments are mainly focused on vertical displacements or based on few Global Navigation Satellite System (GNSS) or geodetic leveling points. To fill this demonstration gap, two years of Sentinel-1 SAR ascending and descending mode data are processed with both PSI and SBAS consolidated algorithms to extract vertical and horizontal displacement velocity datasets, whose accuracy is then assessed against a wealth of contextual geodetic data. These include permanent GNSS records, static GNSS benchmark repositioning, and geodetic leveling monitoring data that the National Institute of Statistics, Geography, and Informatics (INEGI) of Mexico collected in 2014−2016 in the Aguascalientes Valley, where structurally-controlled land subsidence exhibits fast vertical rates (up to −150 mm/year) and a non-negligible east-west component (up to ±30 mm/year). Despite the temporal constraint of the data selected, the PSI-SBAS inter-comparison reveals standard deviation of 6 mm/year and 4 mm/year for the vertical and east-west rate differences, respectively, thus reassuring about the similarity between the two types of InSAR outputs. Accuracy assessment shows that the standard deviations in vertical velocity differences are 9−10 mm/year against GNSS benchmarks, and 8 mm/year against leveling data. Relative errors are below 20% for any locations subsiding faster than −15 mm/year. Differences in east-west velocity estimates against GNSS are on average −0.1 mm/year for PSI and +0.2 mm/year for SBAS, with standard deviations of 8 mm/year. When discrepancies are found between InSAR and geodetic data, these mostly occur at benchmarks located in proximity to the main normal faults, thus falling within the same SBAS ground pixel or closer to the same PSI target, regardless of whether they are in the footwall or hanging wall of the fault. Establishing new benchmarks at higher distances from the fault traces or exploiting higher resolution SAR scenes and/or InSAR datasets may improve the detection of the benchmarks and thus consolidate the statistics of the InSAR accuracy assessments. Full article

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

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21 pages, 10843 KiB

Open AccessEditor’s ChoiceArticle

Comparative Study of Groundwater-Induced Subsidence for London and Delhi Using PSInSAR

by Vivek Agarwal, Amit Kumar, David Gee, Stephen Grebby, Rachel L. Gomes and Stuart Marsh

Remote Sens. 2021, 13(23), 4741; https://doi.org/10.3390/rs13234741 - 23 Nov 2021

Cited by 49 | Viewed by 6487

Abstract

Groundwater variation can cause land-surface movement, which in turn can cause significant and recurrent harm to infrastructure and the water storage capacity of aquifers. The capital cities in the England (London) and India (Delhi) are witnessing an ever-increasing population that has resulted in [...] Read more.

Groundwater variation can cause land-surface movement, which in turn can cause significant and recurrent harm to infrastructure and the water storage capacity of aquifers. The capital cities in the England (London) and India (Delhi) are witnessing an ever-increasing population that has resulted in excess pressure on groundwater resources. Thus, monitoring groundwater-induced land movement in both these cities is very important in terms of understanding the risk posed to assets. Here, Sentinel-1 C-band radar images and the persistent scatterer interferometric synthetic aperture radar (PSInSAR) methodology are used to study land movement for London and National Capital Territory (NCT)-Delhi from October 2016 to December 2020. The land movement velocities were found to vary between −24 and +24 mm/year for London and between −18 and +30 mm/year for NCT-Delhi. This land movement was compared with observed groundwater levels, and spatio-temporal variation of groundwater and land movement was studied in conjunction. It was broadly observed that the extraction of a large quantity of groundwater leads to land subsidence, whereas groundwater recharge leads to uplift. A mathematical model was used to quantify land subsidence/uplift which occurred due to groundwater depletion/rebound. This is the first study that compares C-band PSInSAR-derived land subsidence response to observed groundwater change for London and NCT-Delhi during this time-period. The results of this study could be helpful to examine the potential implications of ground-level movement on the resource management, safety, and economics of both these cities. Full article

(This article belongs to the Special Issue EO for Mapping Natural Resources and Geohazards)

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33 pages, 23991 KiB

Open AccessEditor’s ChoiceArticle

A Self-Adaptive Method for Mapping Coastal Bathymetry On-The-Fly from Wave Field Video

by Matthijs Gawehn, Sierd de Vries and Stefan Aarninkhof

Remote Sens. 2021, 13(23), 4742; https://doi.org/10.3390/rs13234742 - 23 Nov 2021

Cited by 15 | Viewed by 4378

Abstract

Mapping coastal bathymetry from remote sensing becomes increasingly more attractive for the coastal community. It is facilitated by a rising availability of drone and satellite data, advances in data science, and an open-source mindset. Coastal bathymetry, but also wave directions, celerity and near-surface [...] Read more.

Mapping coastal bathymetry from remote sensing becomes increasingly more attractive for the coastal community. It is facilitated by a rising availability of drone and satellite data, advances in data science, and an open-source mindset. Coastal bathymetry, but also wave directions, celerity and near-surface currents can simultaneously be derived from aerial video of a wave field. However, the required video processing is usually extensive, requires skilled supervision, and is tailored to a fieldsite. This study proposes a video-processing algorithm that resolves these issues. It automatically adapts to the video data and continuously returns mapping updates and thereby aims to make wave-based remote sensing more inclusive to the coastal community. The code architecture for the first time includes the dynamic mode decomposition (DMD) to reduce the data complexity of wavefield video. The DMD is paired with loss-functions to handle spectral noise and a novel spectral storage system and Kalman filter to achieve fast converging measurements. The algorithm is showcased for fieldsites in the USA, the UK, the Netherlands, and Australia. The performance with respect to mapping bathymetry was validated using ground truth data. It was demonstrated that merely 32 s of video footage is needed for a first mapping update with average depth errors of 0.9–2.6 m. These further reduced to 0.5–1.4 m as the videos continued and more mapping updates were returned. Simultaneously, coherent maps for wave direction and celerity were achieved as well as maps of local near-surface currents. The algorithm is capable of mapping the coastal parameters on-the-fly and thereby offers analysis of video feeds, such as from drones or operational camera installations. Hence, the innovative application of analysis techniques like the DMD enables both accurate and unprecedentedly fast coastal reconnaissance. The source code and data of this article are openly available. Full article

(This article belongs to the Section Ocean Remote Sensing)

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27 pages, 25782 KiB

Open AccessEditor’s ChoiceArticle

Understanding the Requirements for Surveys to Support Satellite-Based Crop Type Mapping: Evidence from Sub-Saharan Africa

by George Azzari, Shruti Jain, Graham Jeffries, Talip Kilic and Siobhan Murray

Remote Sens. 2021, 13(23), 4749; https://doi.org/10.3390/rs13234749 - 23 Nov 2021

Cited by 10 | Viewed by 6988

Abstract

This paper provides recommendations on how large-scale household surveys should be conducted to generate the data needed to train models for satellite-based crop type mapping in smallholder farming systems. The analysis focuses on maize cultivation in Malawi and Ethiopia, and leverages rich, georeferenced [...] Read more.

This paper provides recommendations on how large-scale household surveys should be conducted to generate the data needed to train models for satellite-based crop type mapping in smallholder farming systems. The analysis focuses on maize cultivation in Malawi and Ethiopia, and leverages rich, georeferenced plot-level data from national household surveys that were conducted in 2018–20 and integrated with Sentinel-2 satellite imagery and complementary geospatial data. To identify the approach to survey data collection that yields optimal data for training remote sensing models, 26,250 in silico experiments are simulated within a machine learning framework. The best model is then applied to map seasonal maize cultivation from 2016 to 2019 at 10-m resolution in both countries. The analysis reveals that smallholder plots with maize cultivation can be identified with up to 75% accuracy. Collecting full plot boundaries or complete plot corner points provides the best quality of information for model training. Classification performance peaks with slightly less than 60% of the training data. Seemingly little erosion in accuracy under less preferable approaches to georeferencing plots results in the total area under maize cultivation being overestimated by 0.16–0.47 million hectares (8–24%) in Malawi. Full article

(This article belongs to the Collection Sentinel-2: Science and Applications)

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28 pages, 17307 KiB

Open AccessEditor’s ChoiceArticle

Compact Thermal Imager (CTI) for Atmospheric Remote Sensing

by Dong L. Wu, Donald E. Jennings, Kwong-Kit Choi, Murzy D. Jhabvala, James A. Limbacher, Thomas Flatley, Kyu-Myong Kim, Anh T. La, Ross J. Salawitch, Luke D. Oman, Jie Gong, Thomas R. Holmes, Douglas C. Morton, Tilak Hewagama and Robert J. Swap

Remote Sens. 2021, 13(22), 4578; https://doi.org/10.3390/rs13224578 - 14 Nov 2021

Cited by 5 | Viewed by 4457

Abstract

The demonstration of a newly developed compact thermal imager (CTI) on the International Space Station (ISS) has provided not only a technology advancement but a rich high-resolution dataset on global clouds, atmospheric and land emissions. This study showed that the free-running CTI instrument [...] Read more.

The demonstration of a newly developed compact thermal imager (CTI) on the International Space Station (ISS) has provided not only a technology advancement but a rich high-resolution dataset on global clouds, atmospheric and land emissions. This study showed that the free-running CTI instrument could be calibrated to produce scientifically useful radiance imagery of the atmosphere, clouds, and surfaces with a vertical resolution of ~460 m at limb and a horizontal resolution of ~80 m at nadir. The new detector demonstrated an excellent sensitivity to detect the weak limb radiance perturbations modulated by small-scale atmospheric gravity waves. The CTI’s high-resolution imaging was used to infer vertical cloud temperature profiles from a side-viewing geometry. For nadir imaging, the combined high-resolution and high-sensitivity capabilities allowed the CTI to better separate cloud and surface emissions, including those in the planetary boundary layer (PBL) that had small contrast against the background surface. Finally, based on the ISS’s orbit, the stable detector performance and robust calibration algorithm produced valuable diurnal observations of cloud and surface emissions with respect to solar local time during May–October 2019, when the CTI had nearly continuous operation. Full article

(This article belongs to the Special Issue Current and Future Earth Observing Sensor Systems aboard the International Space Station (ISS))

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17 pages, 12571 KiB

Open AccessEditor’s ChoiceArticle

Application of a Convolutional Neural Network for the Detection of Sea Ice Leads

by Jay P. Hoffman, Steven A. Ackerman, Yinghui Liu, Jeffrey R. Key and Iain L. McConnell

Remote Sens. 2021, 13(22), 4571; https://doi.org/10.3390/rs13224571 - 13 Nov 2021

Cited by 23 | Viewed by 4146

Abstract

Despite accounting for a small fraction of the surface area in the Arctic, long and narrow sea ice fractures, known as “leads”, play a critical role in the energy flux between the ocean and atmosphere. As the volume of sea ice in the [...] Read more.

Despite accounting for a small fraction of the surface area in the Arctic, long and narrow sea ice fractures, known as “leads”, play a critical role in the energy flux between the ocean and atmosphere. As the volume of sea ice in the Arctic has declined over the past few decades, it is increasingly important to monitor the corresponding changes in sea ice leads. A novel approach has been developed using artificial intelligence (AI) to detect sea ice leads using satellite thermal infrared window data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS). In this new approach, a particular type of convolutional neural network, a U-Net, replaces a series of conventional image processing tests from our legacy algorithm. Results show the new approach has a high detection accuracy with F1 Scores on the order of 0.7. Compared to the legacy algorithm, the new algorithm shows improvement, with more true positives, fewer false positives, fewer false negatives, and better agreement between satellite instruments. Full article

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

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25 pages, 18504 KiB

Open AccessEditor’s ChoiceArticle

A Dual Network for Super-Resolution and Semantic Segmentation of Sentinel-2 Imagery

by Saüc Abadal, Luis Salgueiro, Javier Marcello and Verónica Vilaplana

Remote Sens. 2021, 13(22), 4547; https://doi.org/10.3390/rs13224547 - 12 Nov 2021

Cited by 16 | Viewed by 5314

Abstract

There is a growing interest in the development of automated data processing workflows that provide reliable, high spatial resolution land cover maps. However, high-resolution remote sensing images are not always affordable. Taking into account the free availability of Sentinel-2 satellite data, in this [...] Read more.

There is a growing interest in the development of automated data processing workflows that provide reliable, high spatial resolution land cover maps. However, high-resolution remote sensing images are not always affordable. Taking into account the free availability of Sentinel-2 satellite data, in this work we propose a deep learning model to generate high-resolution segmentation maps from low-resolution inputs in a multi-task approach. Our proposal is a dual-network model with two branches: the Single Image Super-Resolution branch, that reconstructs a high-resolution version of the input image, and the Semantic Segmentation Super-Resolution branch, that predicts a high-resolution segmentation map with a scaling factor of 2. We performed several experiments to find the best architecture, training and testing on a subset of the S2GLC 2017 dataset. We based our model on the DeepLabV3+ architecture, enhancing the model and achieving an improvement of 5% on IoU and almost 10% on the recall score. Furthermore, our qualitative results demonstrate the effectiveness and usefulness of the proposed approach. Full article

(This article belongs to the Special Issue Semantic Interpretation of Remotely Sensed Images)

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17 pages, 9120 KiB

Open AccessEditor’s ChoiceArticle

Opposite Spatiotemporal Patterns for Surface Urban Heat Island of Two “Stove Cities” in China: Wuhan and Nanchang

by Yao Shen, Chao Zeng, Qing Cheng and Huanfeng Shen

Remote Sens. 2021, 13(21), 4447; https://doi.org/10.3390/rs13214447 - 5 Nov 2021

Cited by 15 | Viewed by 2862

Abstract

Under the circumstance of global climate change, the evolution of thermal environments has attracted more attention, for which the surface urban heat island (SUHI) is one of the major concerns. In this research, we focused on the spatiotemporal patterns for two “stove cities” [...] Read more.

Under the circumstance of global climate change, the evolution of thermal environments has attracted more attention, for which the surface urban heat island (SUHI) is one of the major concerns. In this research, we focused on the spatiotemporal patterns for two “stove cities” in China, i.e., Wuhan and Nanchang, based on the long-term (1984–2018) and fine-scale (Landsat-like) series of satellite images. The results showed opposite spatiotemporal patterns for the two cities, even though they were both widely concerned to be the hottest cities. No matter which definition of surface urban heat island intensity (SUHII) was selected, Nanchang presented higher and more fluctuating SUHII than Wuhan, with a relatively higher land surface temperature (LST) of the urban area and lower LST of the rural area in Nanchang, especially in recent years. For the spatial pattern, the highest LST center (i.e., the SUHI) has expanded obviously for the past 35 years in Nanchang. For Wuhan, the LST in SUHI has gone through a trend of a relatively increase at first, followed by a decrease. For the temporal pattern, an increasing trend of LST could be detected in Nanchang. However, the LST in Wuhan presented a slightly decreasing trend. Moreover, the SUHII evolution in Nanchang decreased at first then increased, while Wuhan showed a slight increasing trend at first, followed by a decrease for SUHII. In addition, different SUHII definitions would not affect the spatial pattern and temporal trend of SUHI, but only controlled the exact SUHII value, especially in those years with extreme weather. Full article

(This article belongs to the Special Issue Applications of Remote Sensing Imagery for Urban Areas)

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94 pages, 38060 KiB

Open AccessEditor’s ChoiceArticle

Recognition of Sedimentary Rock Occurrences in Satellite and Aerial Images of Other Worlds—Insights from Mars

by Kenneth S. Edgett and Ranjan Sarkar

Remote Sens. 2021, 13(21), 4296; https://doi.org/10.3390/rs13214296 - 26 Oct 2021

Cited by 16 | Viewed by 12771

Abstract

Sedimentary rocks provide records of past surface and subsurface processes and environments. The first step in the study of the sedimentary rock record of another world is to learn to recognize their occurrences in images from instruments aboard orbiting, flyby, or aerial platforms. [...] Read more.

Sedimentary rocks provide records of past surface and subsurface processes and environments. The first step in the study of the sedimentary rock record of another world is to learn to recognize their occurrences in images from instruments aboard orbiting, flyby, or aerial platforms. For two decades, Mars has been known to have sedimentary rocks; however, planet-wide identification is incomplete. Global coverage at 0.25–6 m/pixel, and observations from the Curiosity rover in Gale crater, expand the ability to recognize Martian sedimentary rocks. No longer limited to cases that are light-toned, lightly cratered, and stratified—or mimic original depositional setting (e.g., lithified deltas)—Martian sedimentary rocks include dark-toned examples, as well as rocks that are erosion-resistant enough to retain small craters as well as do lava flows. Breakdown of conglomerates, breccias, and even some mudstones, can produce a pebbly regolith that imparts a “smooth” appearance in satellite and aerial images. Context is important; sedimentary rocks remain challenging to distinguish from primary igneous rocks in some cases. Detection of ultramafic, mafic, or andesitic compositions do not dictate that a rock is igneous, and clast genesis should be considered separately from the depositional record. Mars likely has much more sedimentary rock than previously recognized. Full article

(This article belongs to the Special Issue Mars Remote Sensing)

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24 pages, 6072 KiB

Open AccessEditor’s ChoiceArticle

Spatiotemporal Variations in Liquid Water Content in a Seasonal Snowpack: Implications for Radar Remote Sensing

by Randall Bonnell, Daniel McGrath, Keith Williams, Ryan Webb, Steven R. Fassnacht and Hans-Peter Marshall

Remote Sens. 2021, 13(21), 4223; https://doi.org/10.3390/rs13214223 - 21 Oct 2021

Cited by 11 | Viewed by 3469

Abstract

Radar instruments have been widely used to measure snow water equivalent (SWE) and Interferometric Synthetic Aperture Radar is a promising approach for doing so from spaceborne platforms. Electromagnetic waves propagate through the snowpack at a velocity determined by its dielectric permittivity. Velocity estimates [...] Read more.

Radar instruments have been widely used to measure snow water equivalent (SWE) and Interferometric Synthetic Aperture Radar is a promising approach for doing so from spaceborne platforms. Electromagnetic waves propagate through the snowpack at a velocity determined by its dielectric permittivity. Velocity estimates are a significant source of uncertainty in radar SWE retrievals, especially in wet snow. In dry snow, velocity can be calculated from relations between permittivity and snow density. However, wet snow velocity is a function of both snow density and liquid water content (LWC); the latter exhibits high spatiotemporal variability, there is no standard observation method, and it is not typically measured by automated stations. In this study, we used ground-penetrating radar (GPR), probed snow depths, and measured in situ vertically-averaged density to estimate SWE and bulk LWC for seven survey dates at Cameron Pass, Colorado (~3120 m) from April to June 2019. During this cooler than average season, median LWC for individual survey dates never exceeded 7 vol. %. However, in June, LWC values greater than 10 vol. % were observed in isolated areas where the ground and the base of the snowpack were saturated and therefore inhibited further meltwater output. LWC development was modulated by canopy cover and meltwater drainage was influenced by ground slope. We generated synthetic SWE retrievals that resemble the planned footprint of the NASA-ISRO L-band InSAR satellite (NISAR) from GPR using a dry snow density model. Synthetic SWE retrievals overestimated observed SWE by as much as 40% during the melt season due to the presence of LWC. Our findings emphasize the importance of considering LWC variability in order to fully realize the potential of future spaceborne radar missions for measuring SWE. Full article

(This article belongs to the Special Issue Remote Sensing of Global Snow Water Equivalent: Monitoring Snow Water Equivalent from Space)

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17 pages, 3712 KiB

Open AccessEditor’s ChoiceArticle

Important Airborne Lidar Metrics of Canopy Structure for Estimating Snow Interception

by Micah Russell, Jan U. H. Eitel, Timothy E. Link and Carlos A. Silva

Remote Sens. 2021, 13(20), 4188; https://doi.org/10.3390/rs13204188 - 19 Oct 2021

Cited by 7 | Viewed by 4023

Abstract

Forest canopies exert significant controls over the spatial distribution of snow cover. Canopy snow interception efficiency is controlled by intrinsic processes (e.g., canopy structure), extrinsic processes (e.g., meteorological conditions), and the interaction of intrinsic-extrinsic factors (i.e., air temperature and branch stiffness). In hydrological [...] Read more.

Forest canopies exert significant controls over the spatial distribution of snow cover. Canopy snow interception efficiency is controlled by intrinsic processes (e.g., canopy structure), extrinsic processes (e.g., meteorological conditions), and the interaction of intrinsic-extrinsic factors (i.e., air temperature and branch stiffness). In hydrological models, intrinsic processes governing snow interception are typically represented by two-dimensional metrics like the leaf area index (LAI). To improve snow interception estimates and their scalability, new approaches are needed for better characterizing the three-dimensional distribution of canopy elements. Airborne laser scanning (ALS) provides a potential means of achieving this, with recent research focused on using ALS-derived metrics that describe forest spacing to predict interception storage. A wide range of canopy structural metrics that describe individual trees can also be extracted from ALS, although relatively little is known about which of them, and in what combination, best describes intrinsic canopy properties known to affect snow interception. The overarching goal of this study was to identify important ALS-derived canopy structural metrics that could help to further improve our ability to characterize intrinsic factors affecting snow interception. Specifically, we sought to determine how much variance in canopy intercepted snow volume can be explained by ALS-derived crown metrics, and what suite of existing and novel crown metrics most strongly affects canopy intercepted snow volume. To achieve this, we first used terrestrial laser scanning (TLS) to quantify snow interception on 14 trees. We then used these snow interception measurements to fit a random forest model with ALS-derived crown metrics as predictors. Next, we bootstrapped 1000 calculations of variable importance (percent increase in mean squared error when a given explanatory variable is removed), keeping nine canopy metrics for the final model that exceeded a variable importance threshold of 0.2. ALS-derived canopy metrics describing intrinsic tree structure explained approximately two-thirds of the snow interception variability (R² ≥ 0.65, RMSE ≤ 0.52 m³, relative RMSE ≤ 48%) in our study when extrinsic factors were kept as constant as possible. For comparison, a generalized linear mixed-effects model predicting snow interception volume from LAI alone had a marginal R² = 0.01. The three most important predictor variables were canopy length, whole-tree volume, and unobstructed returns (a novel metric). These results suggest that a suite of intrinsic variables may be used to map interception potential across larger areas and provide an improvement to interception estimates based on LAI. Full article

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22 pages, 4106 KiB

Open AccessEditor’s ChoiceArticle

Estimation of Plot-Level Burn Severity Using Terrestrial Laser Scanning

by Michael R. Gallagher, Aaron E. Maxwell, Luis Andrés Guillén, Alexis Everland, E. Louise Loudermilk and Nicholas S. Skowronski

Remote Sens. 2021, 13(20), 4168; https://doi.org/10.3390/rs13204168 - 18 Oct 2021

Cited by 12 | Viewed by 3620

Abstract

Monitoring wildland fire burn severity is important for assessing ecological outcomes of fire and their spatial patterning as well as guiding efforts to mitigate or restore areas where ecological outcomes are negative. Burn severity mapping products are typically created using satellite reflectance data [...] Read more.

Monitoring wildland fire burn severity is important for assessing ecological outcomes of fire and their spatial patterning as well as guiding efforts to mitigate or restore areas where ecological outcomes are negative. Burn severity mapping products are typically created using satellite reflectance data but must be calibrated to field data to derive meaning. The composite burn index (CBI) is the most widely used field-based method used to calibrate satellite-based burn severity data but important limitations of this approach have yet to be resolved. The objective of this study was focused on predicting CBI from point cloud and visible-spectrum camera (RGB) metrics derived from single-scan terrestrial laser scanning (TLS) datasets to determine the viability of TLS data as an alternative approach to estimating burn severity in the field. In our approach, we considered the predictive potential of post-scan-only metrics, differenced pre- and post-scan metrics, RGB metrics, and all three together to predict CBI and evaluated these with candidate algorithms (i.e., linear model, random forest (RF), and support vector machines (SVM) and two evaluation criteria (R-squared and root mean square error (RMSE)). In congruence with the strata-based observations used to calculate CBI, we evaluated the potential approaches at the strata level and at the plot level using 70 TLS and 10 RGB independent variables that we generated from the field data. Machine learning algorithms successfully predicted total plot CBI and strata-specific CBI; however, the accuracy of predictions varied among strata by algorithm. RGB variables improved predictions when used in conjunction with TLS variables, but alone proved a poor predictor of burn severity below the canopy. Although our study was to predict CBI, our results highlight that TLS-based methods for quantifying burn severity can be an improvement over CBI in many ways because TLS is repeatable, quantitative, faster, requires less field-expertise, and is more flexible to phenological variation and biomass change in the understory where prescribed fire effects are most pronounced. We also point out that TLS data can also be leveraged to inform other monitoring needs beyond those specific to wildland fire, representing additional efficiency in using this approach. Full article

(This article belongs to the Special Issue Advances in LiDAR Remote Sensing for Forestry and Ecology)

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25 pages, 6962 KiB

Open AccessEditor’s ChoiceArticle

Wood–Leaf Classification of Tree Point Cloud Based on Intensity and Geometric Information

by Jingqian Sun, Pei Wang, Zhiyong Gao, Zichu Liu, Yaxin Li, Xiaozheng Gan and Zhongnan Liu

Remote Sens. 2021, 13(20), 4050; https://doi.org/10.3390/rs13204050 - 11 Oct 2021

Cited by 26 | Viewed by 4367

Abstract

Terrestrial laser scanning (TLS) can obtain tree point clouds with high precision and high density. The efficient classification of wood points and leaf points is essential for the study of tree structural parameters and ecological characteristics. Using both intensity and geometric information, we [...] Read more.

Terrestrial laser scanning (TLS) can obtain tree point clouds with high precision and high density. The efficient classification of wood points and leaf points is essential for the study of tree structural parameters and ecological characteristics. Using both intensity and geometric information, we present an automated wood–leaf classification with a three-step classification and wood point verification. The tree point cloud was classified into wood points and leaf points using intensity threshold, neighborhood density and voxelization successively, and was then verified. Twenty-four willow trees were scanned using the RIEGL VZ-400 scanner. Our results were compared with the manual classification results. To evaluate the classification accuracy, three indicators were introduced into the experiment: overall accuracy (OA), Kappa coefficient (Kappa), and Matthews correlation coefficient (MCC). The ranges of OA, Kappa, and MCC of our results were from 0.9167 to 0.9872, 0.7276 to 0.9191, and 0.7544 to 0.9211, respectively. The average values of OA, Kappa, and MCC were 0.9550, 0.8547, and 0.8627, respectively. The time costs of our method and another were also recorded to evaluate the efficiency. The average processing time was 1.4 s per million points for our method. The results show that our method represents a potential wood–leaf classification technique with the characteristics of automation, high speed, and good accuracy. Full article

(This article belongs to the Special Issue Advances in LiDAR Remote Sensing for Forestry and Ecology)

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21 pages, 27719 KiB

Open AccessEditor’s ChoiceArticle

The Potential of Multispectral Imagery and 3D Point Clouds from Unoccupied Aerial Systems (UAS) for Monitoring Forest Structure and the Impacts of Wildfire in Mediterranean-Climate Forests

by Sean Reilly, Matthew L. Clark, Lisa Patrick Bentley, Corbin Matley, Elise Piazza and Imma Oliveras Menor

Remote Sens. 2021, 13(19), 3810; https://doi.org/10.3390/rs13193810 - 23 Sep 2021

Cited by 21 | Viewed by 4935

Abstract

Wildfire shapes vegetation assemblages in Mediterranean ecosystems, such as those in the state of California, United States. Successful restorative management of forests in-line with ecologically beneficial fire regimes relies on a thorough understanding of wildfire impacts on forest structure and fuel loads. As [...] Read more.

Wildfire shapes vegetation assemblages in Mediterranean ecosystems, such as those in the state of California, United States. Successful restorative management of forests in-line with ecologically beneficial fire regimes relies on a thorough understanding of wildfire impacts on forest structure and fuel loads. As these data are often difficult to comprehensively measure on the ground, remote sensing approaches can be used to estimate forest structure and fuel load parameters over large spatial extents. Here, we analyze the capabilities of one such methodology, unoccupied aerial system structure from motion (UAS-SfM) from digital aerial photogrammetry, for mapping forest structure and wildfire impacts in the Mediterranean forests of northern California. To determine the ability of UAS-SfM to map the structure of mixed oak and conifer woodlands and to detect persistent changes caused by fire, we compared UAS-SfM derived metrics of terrain height and canopy structure to pre-fire airborne laser scanning (ALS) measurements. We found that UAS-SfM was able to accurately capture the forest’s upper-canopy structure, but was unable to resolve mid- and below-canopy structure. The addition of a normalized difference vegetation index (NDVI) ground point filter to the DTM generation process improved DTM root-mean-square error (RMSE) by ~1 m with an overall DTM RMSE of 2.12 m. Upper-canopy metrics (max height, 95th percentile height, and 75th percentile height) were highly correlated between ALS and UAS-SfM (r > +0.9), while lower-canopy metrics and metrics of density and vertical variation had little to no similarity. Two years after the 2017 Sonoma County Tubbs fire, we found significant decreases in UAS-SfM metrics of bulk canopy height and NDVI with increasing burn severity, indicating the lasting impact of the fire on vegetation health and structure. These results point to the utility of UAS-SfM as a monitoring tool in Mediterranean forests, especially for post-fire canopy changes and subsequent recovery. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Post-fire Environmental Damage and Recovery Dynamics)

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31 pages, 11143 KiB

Open AccessEditor’s ChoiceArticle

The Role of Satellite InSAR for Landslide Forecasting: Limitations and Openings

by Serena Moretto, Francesca Bozzano and Paolo Mazzanti

Remote Sens. 2021, 13(18), 3735; https://doi.org/10.3390/rs13183735 - 17 Sep 2021

Cited by 63 | Viewed by 7378

Abstract

The paper explores the potential of the satellite advanced differential synthetic aperture radar interferometry (A-DInSAR) technique for the identification of impending slope failure. The advantages and limitations of satellite InSAR in monitoring pre-failure landslide behaviour are addressed in five different case histories back-analysed [...] Read more.

The paper explores the potential of the satellite advanced differential synthetic aperture radar interferometry (A-DInSAR) technique for the identification of impending slope failure. The advantages and limitations of satellite InSAR in monitoring pre-failure landslide behaviour are addressed in five different case histories back-analysed using data acquired by different satellite missions: Montescaglioso landslide (2013, Italy), Scillato landslide (2015, Italy), Bingham Canyon Mine landslide (2013, UT, USA), Big Sur landslide (2017, CA, USA) and Xinmo landslide (2017, China). This paper aimed at providing a contribution to improve the knowledge within the subject area of landslide forecasting using monitoring data, in particular exploring the suitability of satellite InSAR for spatial and temporal prediction of large landslides. The study confirmed that satellite InSAR can be successful in the early detection of slopes prone to collapse; its limitations due to phase aliasing and low sampling frequency are also underlined. According to the results, we propose a novel landslide predictability classification discerning five different levels of predictability by satellite InSAR. Finally, the big step forward made for landslide forecasting applications since the beginning of the first SAR systems (ERS and Envisat) is shown, highlighting that future perspectives are encouraging thanks to the expected improvement of upcoming satellite missions that could highly increase the capability to monitor landslides’ pre-failure behaviour. Full article

(This article belongs to the Special Issue SAR Imagery for Landslide Detection and Prediction)

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14 pages, 2939 KiB

Open AccessEditor’s ChoiceArticle

High-Resolution Ocean Currents from Sea Surface Temperature Observations: The Catalan Sea (Western Mediterranean)

by Jordi Isern-Fontanet, Emilio García-Ladona, Cristina González-Haro, Antonio Turiel, Miquel Rosell-Fieschi, Joan B. Company and Antonio Padial

Remote Sens. 2021, 13(18), 3635; https://doi.org/10.3390/rs13183635 - 11 Sep 2021

Cited by 9 | Viewed by 4011

Abstract

Current observations of ocean currents are mainly based on altimetric measurements of Sea Surface Heights (SSH), however the characteristics of the present-day constellation of altimeters are only capable to retrieve surface currents at scales larger than 50–70 km. By contrast, infrared and visible [...] Read more.

Current observations of ocean currents are mainly based on altimetric measurements of Sea Surface Heights (SSH), however the characteristics of the present-day constellation of altimeters are only capable to retrieve surface currents at scales larger than 50–70 km. By contrast, infrared and visible radiometers reach spatial resolutions thirty times higher than altimeters under cloud-free conditions. During the last years, it has been shown how the Surface Quasi-Geostrophic (SQG) approximation is able to reconstruct surface currents from measured Sea Surface Temperature (SST), but it has not been yet used to retrieve velocities at scales shorter than those provided by altimeters. In this study, the velocity field of ocean structures with characteristic lengths between 10 and 20 km has been derived from infrared SST using the SQG approach and compared to the velocities derived from the trajectories of Lagrangian drifters. Results show that the SQG approach is able to reconstruct the direction of the velocity field with observed RMS errors between 8 and 15 degrees and linear correlations between 0.85 and 0.99. The reconstruction of the modulus of the velocity is more problematic due to two limitations of the SQG approach: the need to calibrate the level of energy and the ageostrophic contributions. If drifter trajectories are used to calibrate velocities and the analysis is restricted to small Rossby numbers, the RMS error in the range of 10 to 16 cm/s and linear correlations can be as high as 0.97. Full article

(This article belongs to the Special Issue Observing the Flow of Ocean Currents and Circulation Using Remote Sensing)

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29 pages, 6423 KiB

Open AccessEditor’s ChoiceArticle

Assessing the Reliability of Satellite and Reanalysis Estimates of Rainfall in Equatorial Africa

by Sharon E. Nicholson and Douglas A. Klotter

Remote Sens. 2021, 13(18), 3609; https://doi.org/10.3390/rs13183609 - 10 Sep 2021

Cited by 18 | Viewed by 3012

Abstract

This article examines the reliability of satellite and reanalysis estimates of rainfall in the Congo Basin and over Lake Victoria and its catchment. Nine satellite products and five reanalysis products are considered. They are assessed by way of inter-comparison and by comparison with [...] Read more.

This article examines the reliability of satellite and reanalysis estimates of rainfall in the Congo Basin and over Lake Victoria and its catchment. Nine satellite products and five reanalysis products are considered. They are assessed by way of inter-comparison and by comparison with observational data sets. The three locations considered include a region with little observational gauge data (the Congo), a region with extensive gauge data (Lake Victoria catchment), and an inland water body. Several important results emerge: for one, the diversity of estimates is generally very large, except for the Lake Victoria catchment. Reanalysis products show little relationship with observed rainfall or with the satellite estimates, and thus should not be used to assess rainfall in these regions. Most of the products either overestimate or underestimate rainfall over the lake. The diversity of estimates makes it difficult to assess the factors governing the interannual variability of rainfall in these regions. This is shown by way of correlation with sea-surface temperatures, particularly with the Niño 3.4 temperatures and with the Dipole Mode Index over the Indian Ocean. Some guidance is given as to the best products to utilize. Overall, any user must establish that the is product reliable in the region studied. Full article

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16 pages, 9311 KiB

Open AccessEditor’s ChoiceArticle

Hyperspectral Imaging Combined with Machine Learning for the Detection of Fusiform Rust Disease Incidence in Loblolly Pine Seedlings

by Piyush Pandey, Kitt G. Payn, Yuzhen Lu, Austin J. Heine, Trevor D. Walker, Juan J. Acosta and Sierra Young

Remote Sens. 2021, 13(18), 3595; https://doi.org/10.3390/rs13183595 - 9 Sep 2021

Cited by 12 | Viewed by 5471

Abstract

Loblolly pine is an economically important timber species in the United States, with almost 1 billion seedlings produced annually. The most significant disease affecting this species is fusiform rust, caused by Cronartium quercuum f. sp. fusiforme. Testing for disease resistance in the [...] Read more.

Loblolly pine is an economically important timber species in the United States, with almost 1 billion seedlings produced annually. The most significant disease affecting this species is fusiform rust, caused by Cronartium quercuum f. sp. fusiforme. Testing for disease resistance in the greenhouse involves artificial inoculation of seedlings followed by visual inspection for disease incidence. An automated, high-throughput phenotyping method could improve both the efficiency and accuracy of the disease screening process. This study investigates the use of hyperspectral imaging for the detection of diseased seedlings. A nursery trial comprising families with known in-field rust resistance data was conducted, and the seedlings were artificially inoculated with fungal spores. Hyperspectral images in the visible and near-infrared region (400–1000 nm) were collected six months after inoculation. The disease incidence was scored with traditional methods based on the presence or absence of visible stem galls. The seedlings were segmented from the background by thresholding normalized difference vegetation index (NDVI) images, and the delineation of individual seedlings was achieved through object detection using the Faster RCNN model. Plant parts were subsequently segmented using the DeepLabv3+ model. The trained DeepLabv3+ model for semantic segmentation achieved a pixel accuracy of 0.76 and a mean Intersection over Union (mIoU) of 0.62. Crown pixels were segmented using geometric features. Support vector machine discrimination models were built for classifying the plants into diseased and non-diseased classes based on spectral data, and balanced accuracy values were calculated for the comparison of model performance. Averaged spectra from the whole plant (balanced accuracy = 61%), the crown (61%), the top half of the stem (77%), and the bottom half of the stem (62%) were used. A classification model built using the spectral data from the top half of the stem was found to be the most accurate, and resulted in an area under the receiver operating characteristic curve (AUC) of 0.83. Full article

(This article belongs to the Special Issue Plant Phenotyping for Disease Detection)

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19 pages, 17200 KiB

Open AccessEditor’s ChoiceArticle

Evaluation of a Statistical Approach for Extracting Shallow Water Bathymetry Signals from ICESat-2 ATL03 Photon Data

by Heidi Ranndal, Philip Sigaard Christiansen, Pernille Kliving, Ole Baltazar Andersen and Karina Nielsen

Remote Sens. 2021, 13(17), 3548; https://doi.org/10.3390/rs13173548 - 6 Sep 2021

Cited by 57 | Viewed by 5437

Abstract

In this study we present and validate a simple empirical method to obtain bathymetry profiles using the geolocated photon data from the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) mission, which was launched by NASA in September 2018. The satellite carries the Advanced [...] Read more.

In this study we present and validate a simple empirical method to obtain bathymetry profiles using the geolocated photon data from the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) mission, which was launched by NASA in September 2018. The satellite carries the Advanced Topographic Laser Altimeter System (ATLAS), which is a lidar that can detect single photons and calculate their bounce point positions. ATLAS uses a green laser, causing some of the photons to penetrate the air–water interface. Under the right conditions and in shallow waters (<40 m), these photons are reflected back to ATLAS after interaction with the ocean bottom. Using ICESat-2 data from four different overflights above the Heron Reef, Australia, a comparison with SDB data showed a median absolute deviation of approximately 18 cm and Root Mean Square Errors (RMSEs) down to 28 cm. Crossovers between two different overflights above the Heron Reef showed a median absolute difference of 13 cm. For an area north-west of Sisimiut, Greenland, the comparison was done with multibeam echo sounding data, with RMSEs down to between 35 cm, and correspondingly showed median absolute deviations between 33 and 49 cm. The proposed method works well under good conditions with clear waters such as in the Great Barrier Reef; however, for more difficult areas a more advanced machine learning technique should be investigated in order to find an automated method that can distinguish between bathymetry and other signals and noise. Full article

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25 pages, 11179 KiB

Open AccessEditor’s ChoiceArticle

Mapping Crop Types and Cropping Systems in Nigeria with Sentinel-2 Imagery

by Esther Shupel Ibrahim, Philippe Rufin, Leon Nill, Bahareh Kamali, Claas Nendel and Patrick Hostert

Remote Sens. 2021, 13(17), 3523; https://doi.org/10.3390/rs13173523 - 5 Sep 2021

Cited by 52 | Viewed by 10336

Abstract

Reliable crop type maps from satellite data are an essential prerequisite for quantifying crop growth, health, and yields. However, such maps do not exist for most parts of Africa, where smallholder farming is the dominant system. Prevalent cloud cover, small farm sizes, and [...] Read more.

Reliable crop type maps from satellite data are an essential prerequisite for quantifying crop growth, health, and yields. However, such maps do not exist for most parts of Africa, where smallholder farming is the dominant system. Prevalent cloud cover, small farm sizes, and mixed cropping systems pose substantial challenges when creating crop type maps for sub-Saharan Africa. In this study, we provide a mapping scheme based on freely available Sentinel-2A/B (S2) time series and very high-resolution SkySat data to map the main crops—maize and potato—and intercropping systems including these two crops on the Jos Plateau, Nigeria. We analyzed the spectral-temporal behavior of mixed crop classes to improve our understanding of inter-class spectral mixing. Building on the Framework for Operational Radiometric Correction for Environmental monitoring (FORCE), we preprocessed S2 time series and derived spectral-temporal metrics from S2 spectral bands for the main temporal cropping windows. These STMs were used as input features in a hierarchical random forest classification. Our results provide the first wall-to-wall crop type map for this key agricultural region of Nigeria. Our cropland identification had an overall accuracy of 84%, while the crop type map achieved an average accuracy of 72% for the five relevant crop classes. Our crop type map shows distinctive regional variations in the distribution of crop types. Maize is the dominant crop, followed by mixed cropping systems, including maize–cereals and potato–maize cropping; potato was found to be the least prevalent class. Plot analyses based on a sample of 1166 fields revealed largely homogeneous mapping patterns, demonstrating the effectiveness of our classification system also for intercropped classes, which are temporally and spatially highly heterogeneous. Moreover, we found that small field sizes were dominant in all crop types, regardless of whether or not intercropping was used. Maize–legume and maize exhibited the largest plots, with an area of up to 3 ha and slightly more than 10 ha, respectively; potato was mainly cultivated on fields smaller than 0.5 ha and only a few plots were larger than 1 ha. Besides providing the first spatially explicit map of cropping practices in the core production area of the Jos Plateau, Nigeria, the study also offers guidance for the creation of crop type maps for smallholder-dominated systems with intercropping. Critical temporal windows for crop type differentiation will enable the creation of mapping approaches in support of future smart agricultural practices for aspects such as food security, early warning systems, policies, and extension services. Full article

(This article belongs to the Collection Sentinel-2: Science and Applications)

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28 pages, 14436 KiB

Open AccessEditor’s ChoiceArticle

Continuous Monitoring of the Flooding Dynamics in the Albufera Wetland (Spain) by Landsat-8 and Sentinel-2 Datasets

by Carmela Cavallo, Maria Nicolina Papa, Massimiliano Gargiulo, Guillermo Palau-Salvador, Paolo Vezza and Giuseppe Ruello

Remote Sens. 2021, 13(17), 3525; https://doi.org/10.3390/rs13173525 - 5 Sep 2021

Cited by 41 | Viewed by 4807

Abstract

Satellite data are very useful for the continuous monitoring of ever-changing environments, such as wetlands. In this study, we investigated the use of multispectral imagery to monitor the winter evolution of land cover in the Albufera wetland (Spain), using Landsat-8 and Sentinel-2 datasets. [...] Read more.

Satellite data are very useful for the continuous monitoring of ever-changing environments, such as wetlands. In this study, we investigated the use of multispectral imagery to monitor the winter evolution of land cover in the Albufera wetland (Spain), using Landsat-8 and Sentinel-2 datasets. With multispectral data, the frequency of observation is limited by the possible presence of clouds. To overcome this problem, the data acquired by the two missions, Landsat-8 and Sentinel-2, were jointly used, thus roughly halving the revisit time. The varied types of land cover were grouped into four classes: (1) open water, (2) mosaic of water, mud and vegetation, (3) bare soil and (4) vegetated soil. The automatic classification of the four classes was obtained through a rule-based method that combined the NDWI, MNDWI and NDVI indices. Point information, provided by geo-located ground pictures, was spatially extended with the help of a very high-resolution image (GeoEye-1). In this way, surfaces with known land cover were obtained and used for the validation of the classification method. The overall accuracy was found to be 0.96 and 0.98 for Landsat-8 and Sentinel-2, respectively. The consistency evaluation between Landsat-8 and Sentinel-2 was performed in six days, in which acquisitions by both missions were available. The observed dynamics of the land cover were highly variable in space. For example, the presence of the open water condition lasted for around 60–80 days in the areas closest to the Albufera lake and progressively decreased towards the boundaries of the park. The study demonstrates the feasibility of using moderate-resolution multispectral images to monitor land cover changes in wetland environments. Full article

(This article belongs to the Special Issue Earth Observation Technologies for Monitoring of Water Environments)

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39 pages, 3199 KiB

Open AccessFeature PaperEditor’s ChoiceReview

Hyperspectral and Lidar Data Applied to the Urban Land Cover Machine Learning and Neural-Network-Based Classification: A Review

by Agnieszka Kuras, Maximilian Brell, Jonathan Rizzi and Ingunn Burud

Remote Sens. 2021, 13(17), 3393; https://doi.org/10.3390/rs13173393 - 26 Aug 2021

Cited by 102 | Viewed by 15212

Abstract

Rapid technological advances in airborne hyperspectral and lidar systems paved the way for using machine learning algorithms to map urban environments. Both hyperspectral and lidar systems can discriminate among many significant urban structures and materials properties, which are not recognizable by applying conventional [...] Read more.

Rapid technological advances in airborne hyperspectral and lidar systems paved the way for using machine learning algorithms to map urban environments. Both hyperspectral and lidar systems can discriminate among many significant urban structures and materials properties, which are not recognizable by applying conventional RGB cameras. In most recent years, the fusion of hyperspectral and lidar sensors has overcome challenges related to the limits of active and passive remote sensing systems, providing promising results in urban land cover classification. This paper presents principles and key features for airborne hyperspectral imaging, lidar, and the fusion of those, as well as applications of these for urban land cover classification. In addition, machine learning and deep learning classification algorithms suitable for classifying individual urban classes such as buildings, vegetation, and roads have been reviewed, focusing on extracted features critical for classification of urban surfaces, transferability, dimensionality, and computational expense. Full article

(This article belongs to the Special Issue Hyperspectral LiDAR Cross Analysis of Landscape Processes and Patterns)

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15 pages, 2301 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Sentinel-2 and Landsat-8 Multi-Temporal Series to Estimate Topsoil Properties on Croplands

by Fabio Castaldi

Remote Sens. 2021, 13(17), 3345; https://doi.org/10.3390/rs13173345 - 24 Aug 2021

Cited by 41 | Viewed by 6343

Abstract

The spatial and temporal monitoring of soil organic carbon (SOC), and other soil properties related to soil erosion, is extremely important, both from the environmental and economic perspectives. Sentinel-2 (S2) and Landsat-8 (L8) time series increase the probability to observe bare soil fields [...] Read more.

The spatial and temporal monitoring of soil organic carbon (SOC), and other soil properties related to soil erosion, is extremely important, both from the environmental and economic perspectives. Sentinel-2 (S2) and Landsat-8 (L8) time series increase the probability to observe bare soil fields in croplands, and thus, monitor soil properties over large regions. In this regard, this work suggests an automated pixel-based approach to select only pure soil pixels in S2 and L8 time series, and to make a synthetic bare soil image (SBSI). The SBSIs and the soil properties measured in the framework of the European LUCAS survey were used to calibrate SOC, clay, and CaCO₃ prediction models. The results highlight a high correlation between laboratory soil spectra and the SBSIs median spectra, especially for the SBSI obtained by a three-year S2 collection, which provides satisfactory results in terms of SOC prediction accuracy (RPD: 1.74). The comparison between S2 and L8 results demonstrated the higher capability of the S2 sensor in terms of SOC prediction accuracy, mainly due to the greater spatial resolution of the bands in the visible region. Whereas, neither S2 nor L8 could accurately predict the clay and CaCO₃ content. This is because of the low spectral and spatial resolution of their SWIR bands that prevent the exploitation of the narrow spectral features related to these two soil attributes. The results of this study prove that large S2 time series can estimate and monitor SOC in croplands using an automated pixel-based approach that selects pure soil pixels and retrieves reliable synthetic soil spectra. Full article

(This article belongs to the Special Issue Multi-Sensor Data Fusion and Analysis of Multi-Temporal Remotely Sensed Imagery II)

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12 pages, 1874 KiB

Open AccessEditor’s ChoiceArticle

First Estimation of Global Trends in Nocturnal Power Emissions Reveals Acceleration of Light Pollution

by Alejandro Sánchez de Miguel, Jonathan Bennie, Emma Rosenfeld, Simon Dzurjak and Kevin J. Gaston

Remote Sens. 2021, 13(16), 3311; https://doi.org/10.3390/rs13163311 - 21 Aug 2021

Cited by 92 | Viewed by 28985

Abstract

The global spread of artificial light is eroding the natural night-time environment. The estimation of the pattern and rate of growth of light pollution on multi-decadal scales has nonetheless proven challenging. Here we show that the power of global satellite observable light emissions [...] Read more.

The global spread of artificial light is eroding the natural night-time environment. The estimation of the pattern and rate of growth of light pollution on multi-decadal scales has nonetheless proven challenging. Here we show that the power of global satellite observable light emissions increased from 1992 to 2017 by at least 49%. We estimate the hidden impact of the transition to solid-state light-emitting diode (LED) technology, which increases emissions at visible wavelengths undetectable to existing satellite sensors, suggesting that the true increase in radiance in the visible spectrum may be as high as globally 270% and 400% on specific regions. These dynamics vary by region, but there is limited evidence that advances in lighting technology have led to decreased emissions. Full article

(This article belongs to the Special Issue Light Pollution Monitoring Using Remote Sensing Data)

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