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Volume 12, March-1

Remote Sens., Volume 12, Issue 6 (March-2 2020) – 152 articles

Cover Story (view full-size image): Global decametric-resolution leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and fractional vegetation cover (FVC) products derived from Sentinel-2 images have been available and emerged as a promising dataset for fine-scale ecosystem modeling. To better understand the performance of these products, they were carefully evaluated using global ground measurements. LAI and FAPAR estimates are similar in the rate of best retrievals, but both are lower than FVC estimates. The time-series LAI, FAPAR, and FVC estimates can largely capture the seasonal trajectory of vegetation. The good performance of FAPAR and FVC estimates indicates their great potential in various applications, while the accuracy of LAI estimates can be further improved by refining the retrieval algorithm.View this paper.
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Open AccessArticle
BRRNet: A Fully Convolutional Neural Network for Automatic Building Extraction From High-Resolution Remote Sensing Images
Remote Sens. 2020, 12(6), 1050; https://doi.org/10.3390/rs12061050 - 24 Mar 2020
Cited by 14 | Viewed by 1890
Abstract
Building extraction from high-resolution remote sensing images is of great significance in urban planning, population statistics, and economic forecast. However, automatic building extraction from high-resolution remote sensing images remains challenging. On the one hand, the extraction results of buildings are partially missing and [...] Read more.
Building extraction from high-resolution remote sensing images is of great significance in urban planning, population statistics, and economic forecast. However, automatic building extraction from high-resolution remote sensing images remains challenging. On the one hand, the extraction results of buildings are partially missing and incomplete due to the variation of hue and texture within a building, especially when the building size is large. On the other hand, the building footprint extraction of buildings with complex shapes is often inaccurate. To this end, we propose a new deep learning network, termed Building Residual Refine Network (BRRNet), for accurate and complete building extraction. BRRNet consists of such two parts as the prediction module and the residual refinement module. The prediction module based on an encoder–decoder structure introduces atrous convolution of different dilation rates to extract more global features, by gradually increasing the receptive field during feature extraction. When the prediction module outputs the preliminary building extraction results of the input image, the residual refinement module takes the output of the prediction module as an input. It further refines the residual between the result of the prediction module and the real result, thus improving the accuracy of building extraction. In addition, we use Dice loss as the loss function during training, which effectively alleviates the problem of data imbalance and further improves the accuracy of building extraction. The experimental results on Massachusetts Building Dataset show that our method outperforms other five state-of-the-art methods in terms of the integrity of buildings and the accuracy of complex building footprints. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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Open AccessLetter
SPMF-Net: Weakly Supervised Building Segmentation by Combining Superpixel Pooling and Multi-Scale Feature Fusion
Remote Sens. 2020, 12(6), 1049; https://doi.org/10.3390/rs12061049 - 24 Mar 2020
Cited by 4 | Viewed by 1126
Abstract
The lack of pixel-level labeling limits the practicality of deep learning-based building semantic segmentation. Weakly supervised semantic segmentation based on image-level labeling results in incomplete object regions and missing boundary information. This paper proposes a weakly supervised semantic segmentation method for building detection. [...] Read more.
The lack of pixel-level labeling limits the practicality of deep learning-based building semantic segmentation. Weakly supervised semantic segmentation based on image-level labeling results in incomplete object regions and missing boundary information. This paper proposes a weakly supervised semantic segmentation method for building detection. The proposed method takes the image-level label as supervision information in a classification network that combines superpixel pooling and multi-scale feature fusion structures. The main advantage of the proposed strategy is its ability to improve the intactness and boundary accuracy of a detected building. Our method achieves impressive results on two 2D semantic labeling datasets, which outperform some competing weakly supervised methods and are close to the result of the fully supervised method. Full article
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Open AccessArticle
Harmonization of Space-Borne Infra-Red Sensors Measuring Sea Surface Temperature
Remote Sens. 2020, 12(6), 1048; https://doi.org/10.3390/rs12061048 - 24 Mar 2020
Cited by 1 | Viewed by 1040
Abstract
Sea surface temperature (SST) is observed by a constellation of sensors, and SST retrievals are commonly combined into gridded SST analyses and climate data records (CDRs). Differential biases between SSTs from different sensors cause errors in such products, including feature artefacts. We introduce [...] Read more.
Sea surface temperature (SST) is observed by a constellation of sensors, and SST retrievals are commonly combined into gridded SST analyses and climate data records (CDRs). Differential biases between SSTs from different sensors cause errors in such products, including feature artefacts. We introduce a new method for reducing differential biases across the SST constellation, by reconciling the brightness temperature (BT) calibration and SST retrieval parameters between sensors. We use the Advanced Along-Track Scanning Radiometer (AATSR) and the Sea and Land Surface Temperature Radiometer (SLSTR) as reference sensors, and the Advanced Very High Resolution Radiometer (AVHRR) of the MetOp-A mission to bridge the gap between these references. Observations across a range of AVHRR zenith angles are matched with dual-view three-channel skin SST retrievals from the AATSR and SLSTR. These skin SSTs act as the harmonization reference for AVHRR retrievals by optimal estimation (OE). Parameters for the harmonized AVHRR OE are iteratively determined, including BT bias corrections and observation error covariance matrices as functions of water-vapor path. The OE SSTs obtained from AVHRR are shown to be closely consistent with the reference sensor SSTs. Independent validation against drifting buoy SSTs shows that the AVHRR OE retrieval is stable across the reference-sensor gap. We discuss that this method is suitable to improve consistency across the whole constellation of SST sensors. The approach will help stabilize and reduce errors in future SST CDRs, as well as having application to other domains of remote sensing. Full article
(This article belongs to the Section Ocean Remote Sensing)
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Open AccessArticle
An Empirical Model to Estimate Abundance of Nanophase Metallic Iron (npFe0) in Lunar Soils
Remote Sens. 2020, 12(6), 1047; https://doi.org/10.3390/rs12061047 - 24 Mar 2020
Cited by 2 | Viewed by 834
Abstract
Lunar soils gradually become mature when they are exposed to a space environment, and nanophase metallic iron (npFe0) generates within them. npFe0 significantly changes the optical properties of lunar soils and affects the interpretation of the remotely sensed data of [...] Read more.
Lunar soils gradually become mature when they are exposed to a space environment, and nanophase metallic iron (npFe0) generates within them. npFe0 significantly changes the optical properties of lunar soils and affects the interpretation of the remotely sensed data of the lunar surface. In this study, a correlation analysis was conducted between npFe0 abundance and reflectance spectra at short wavelengths for lunar soil samples in four size groups based on their spectral and compositional data, collected by the Lunar Soil Characterization Consortium (LSCC). Results show that 540 nm single scattering albedo (SSA) of lunar soils correlates well with their corresponding npFe0 abundance for each size group of lunar soil samples. However, it is poorly correlated with npFe0 abundance when all size groups were considered because of the strong interference from grain size variation of lunar soils. To minimize the effect of grain size, the correlation of npFe0 abundance with the spectral ratio of 540 nm/810 nm SSA of all size groups for LSCC samples was calculated and results show that a higher correlation existed between them (R2 = 0.91). This ratio can serve as a simple empirical model for estimating npFe0 abundance in lunar soils. However, bias could be introduced to the estimation result when lunar soils possess a high content of agglutinitic glass and ilmenite. Our future work will focus on improving the model’s performance for these lunar soils. Full article
(This article belongs to the Special Issue Lunar Remote Sensing and Applications)
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Open AccessReview
Forestry Remote Sensing from Unmanned Aerial Vehicles: A Review Focusing on the Data, Processing and Potentialities
Remote Sens. 2020, 12(6), 1046; https://doi.org/10.3390/rs12061046 - 24 Mar 2020
Cited by 16 | Viewed by 3220
Abstract
Currently, climate change poses a global threat, which may compromise the sustainability of agriculture, forestry and other land surface systems. In a changing world scenario, the economic importance of Remote Sensing (RS) to monitor forests and agricultural resources is imperative to the development [...] Read more.
Currently, climate change poses a global threat, which may compromise the sustainability of agriculture, forestry and other land surface systems. In a changing world scenario, the economic importance of Remote Sensing (RS) to monitor forests and agricultural resources is imperative to the development of agroforestry systems. Traditional RS technologies encompass satellite and manned aircraft platforms. These platforms are continuously improving in terms of spatial, spectral, and temporal resolutions. The high spatial and temporal resolutions, flexibility and lower operational costs make Unmanned Aerial Vehicles (UAVs) a good alternative to traditional RS platforms. In the management process of forests resources, UAVs are one of the most suitable options to consider, mainly due to: (1) low operational costs and high-intensity data collection; (2) its capacity to host a wide range of sensors that could be adapted to be task-oriented; (3) its ability to plan data acquisition campaigns, avoiding inadequate weather conditions and providing data availability on-demand; and (4) the possibility to be used in real-time operations. This review aims to present the most significant UAV applications in forestry, identifying the appropriate sensors to be used in each situation as well as the data processing techniques commonly implemented. Full article
(This article belongs to the Special Issue Application of Remote Sensing in Agroforestry)
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Open AccessArticle
Rainfall Monitoring Based on Next-Generation Millimeter-Wave Backhaul Technologies in a Dense Urban Environment
Remote Sens. 2020, 12(6), 1045; https://doi.org/10.3390/rs12061045 - 24 Mar 2020
Cited by 8 | Viewed by 856
Abstract
High-resolution and accurate rainfall monitoring is of great importance to many applications, including meteorology, hydrology, and flood monitoring. In recent years, microwave backhaul links from wireless communication networks have been suggested for rainfall monitoring purposes, complementing the existing monitoring systems. With the advances [...] Read more.
High-resolution and accurate rainfall monitoring is of great importance to many applications, including meteorology, hydrology, and flood monitoring. In recent years, microwave backhaul links from wireless communication networks have been suggested for rainfall monitoring purposes, complementing the existing monitoring systems. With the advances in microwave technology, new microwave backhaul solutions have been proposed and applied for 5G networks. Examples of the latest microwave technology include E-band (71–76 and 81–86 GHz) links, multi-band boosters, and line-of-sight multiple-input multiple-output (LOS-MIMO) backhaul links. They all rely on millimeter-wave (mmWave) technology, which is the fastest small-cell backhaul solution. In this paper, we will study the rain attenuation characteristics of these new microwave backhaul techniques at different mmWave frequencies and link lengths. We will also study the potential of using these new microwave solutions for rainfall monitoring. Preliminary results indicate that all the test mmWave links can be very effective for estimating the path-averaged rain rates. The correlation between the mmWave link measurement-derived rain rate and the local rain gauge is in the range of 0.8 to 0.9, showing a great potential to use these links for precipitation and flood monitoring in urban areas. Full article
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Open AccessCommunication
Copernicus Global Land Cover Layers—Collection 2
Remote Sens. 2020, 12(6), 1044; https://doi.org/10.3390/rs12061044 - 24 Mar 2020
Cited by 27 | Viewed by 3620
Abstract
In May 2019, Collection 2 of the Copernicus Global Land Cover layers was released. Next to a global discrete land cover map at 100 m resolution, a set of cover fraction layers is provided depicting the percentual cover of the main land cover [...] Read more.
In May 2019, Collection 2 of the Copernicus Global Land Cover layers was released. Next to a global discrete land cover map at 100 m resolution, a set of cover fraction layers is provided depicting the percentual cover of the main land cover types in a pixel. This additional continuous classification scheme represents areas of heterogeneous land cover better than the standard discrete classification scheme. Overall, 20 layers are provided which allow customization of land cover maps to specific user needs or applications (e.g., forest monitoring, crop monitoring, biodiversity and conservation, climate modeling, etc.). However, Collection 2 was not just a global up-scaling, but also includes major improvements in the map quality, reaching around 80% or more overall accuracy. The processing system went into operational status allowing annual updates on a global scale with an additional implemented training and validation data collection system. In this paper, we provide an overview of the major changes in the production of the land cover maps, that have led to this increased accuracy, including aligning with the Sentinel 2 satellite system in the grid and coordinate system, improving the metric extraction, adding better auxiliary data, improving the biome delineations, as well as enhancing the expert rules. An independent validation exercise confirmed the improved classification results. In addition to the methodological improvements, this paper also provides an overview of where the different resources can be found, including access channels to the product layer as well as the detailed peer-review product documentation. Full article
(This article belongs to the Special Issue Operational Land Cover/Land Use Mapping)
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Open AccessArticle
Trends in the Stability of Antarctic Coastal Polynyas and the Role of Topographic Forcing Factors
Remote Sens. 2020, 12(6), 1043; https://doi.org/10.3390/rs12061043 - 24 Mar 2020
Cited by 1 | Viewed by 787
Abstract
Polynyas are an important factor in the Antarctic and Arctic climate, and their changes are related to the ecosystems in the polar regions. The phenomenon of polynyas is influenced by the combination of inherent persistence and dynamic factors. The dynamics of polynyas are [...] Read more.
Polynyas are an important factor in the Antarctic and Arctic climate, and their changes are related to the ecosystems in the polar regions. The phenomenon of polynyas is influenced by the combination of inherent persistence and dynamic factors. The dynamics of polynyas are greatly affected by temporal dynamical factors, and it is difficult to objectively reflect the internal characteristics of their formation. Separating the two factors effectively is necessary in order to explore their essence. The Special Sensor Microwave/Imager (SSM/I) passive microwave sensor has been making observations of Antarctica for more than 20 years, but it is difficult for existing current sea ice concentration (SIC) products to objectively reflect how the inherent persistence factors affect the formation of polynyas. In this paper, we proposed a long-term multiple spatial smoothing method to remove the influence of dynamic factors and obtain stable annual SIC products. A halo located on the border of areas of low and high ice concentration around the Antarctic coast, which has a strong similarity with the local seabed in outline, was found using the spatially smoothed SIC products and seabed. The relationship of the polynya location to the wind and topography is a long-understood relationship; here, we quantify that where there is an abrupt slope and wind transitions, new polynyas are best generated. A combination of image expansion and threshold segmentation was used to extract the extent of sea ice and coastal polynyas. The adjusted record of changes in the extent of coastal polynyas and sea ice in the Southern Ocean indicate that there is a negative correlation between them. Full article
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Open AccessArticle
Nine-Year Systematic Evaluation of the GPM and TRMM Precipitation Products in the Shuaishui River Basin in East-Central China
Remote Sens. 2020, 12(6), 1042; https://doi.org/10.3390/rs12061042 - 24 Mar 2020
Cited by 10 | Viewed by 866
Abstract
Owing to their advantages of wide coverage and high spatiotemporal resolution, satellite precipitation products (SPPs) have been increasingly used as surrogates for traditional ground observations. In this study, we have evaluated the accuracy of the latest five GPM IMERG V6 and TRMM 3B42 [...] Read more.
Owing to their advantages of wide coverage and high spatiotemporal resolution, satellite precipitation products (SPPs) have been increasingly used as surrogates for traditional ground observations. In this study, we have evaluated the accuracy of the latest five GPM IMERG V6 and TRMM 3B42 V7 precipitation products across the monthly, daily, and hourly scale in the hilly Shuaishui River Basin in East-Central China. For evaluation, a total of four continuous and three categorical metrics have been calculated based on SPP estimates and historical rainfall records at 13 stations over a period of 9 years from 2009 to 2017. One-way analysis of variance (ANOVA) and multiple posterior comparison tests are used to assess the significance of the difference in SPP rainfall estimates. Our evaluation results have revealed a wide-ranging performance among the SPPs in estimating rainfall at different time scales. Firstly, two post-time SPPs (IMERG_F and 3B42) perform considerably better in estimating monthly rainfall. Secondly, with IMERG_F performing the best, the GPM products generally produce better daily rainfall estimates than the TRMM products. Thirdly, with their correlation coefficients all falling below 0.6, neither GPM nor TRMM products could estimate hourly rainfall satisfactorily. In addition, topography tends to impose similar impact on the performance of SPPs across different time scales, with more estimation deviations at high altitude. In general, the post-time IMERG_F product may be considered as a reliable data source of monthly or daily rainfall in the study region. Effective bias-correction algorithms incorporating ground rainfall observations, however, are needed to further improve the hourly rainfall estimates of the SPPs to ensure the validity of their usage in real-world applications. Full article
(This article belongs to the Special Issue Remote Sensing of Precipitation: Part II)
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Open AccessArticle
Long-Term (2005–2017) View of Atmospheric Pollutants in Central China Using Multiple Satellite Observations
Remote Sens. 2020, 12(6), 1041; https://doi.org/10.3390/rs12061041 - 24 Mar 2020
Cited by 6 | Viewed by 957
Abstract
The air quality in China has experienced dramatic changes during the last few decades. To improve understanding of distribution, variations, and main influence factors of air pollution in central China, long-term multiple satellite observations from moderate resolution imaging spectroradiometer (MODIS) and ozone monitoring [...] Read more.
The air quality in China has experienced dramatic changes during the last few decades. To improve understanding of distribution, variations, and main influence factors of air pollution in central China, long-term multiple satellite observations from moderate resolution imaging spectroradiometer (MODIS) and ozone monitoring instrument (OMI) are used to characterize particle pollution and their primary gaseous precursors, sulfur dioxide (SO2), and nitrogen dioxide (NO2) in Hubei province during 2005–2017. Unlike other regions in eastern China, particle and gaseous pollutants exhibit distinct spatial and temporal patterns in central China due to differences in emission sources and control measures. OMI SO2 of the whole Hubei region reached the highest value of ~0.2 Dobson unit (DU) in 2007 and then declined by more than 90% to near background levels. By contrast, OMI NO2 grew from ~3.2 to 5.9 × 1015 molecules cm−2 during 2005–2011 and deceased to ~3.9 × 1015 molecules cm−2 in 2017. Unlike the steadily declining SO2, variations of OMI NO2 flattened out in 2016 and increased ~0.5 × 1015 molecules cm−2 during 2017. As result, MODIS AOD at 550 nm increased from 0.55 to the peak value of 0.7 during 2005–2011 and then decreased continuously to 0.38 by 2017. MODIS AOD and OMI SO2 has a high correlation (R > 0.8), indicating that annual variations of SO2 can explain most changes of AOD. The air pollution in central China has notable seasonal variations, which is heaviest in winter and light in summer. While air quality in eastern Hubei is dominated by gaseous pollution such as O3 and NOx, particle pollutants are mainly concentrated in central Hubei. The high consistency with ground measurements demonstrates that satellite observation can well capture variations of air pollution in regional scales. The increasing ozone (O3) and NO2 since 2016 suggests that more control measures should be made to reduce O3-related emissions. To improve the air quality in regional scale, it is necessary to monitor the dynamic emission sources with satellite observations at a finer resolution. Full article
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Open AccessArticle
Influence of the Sun Position and Platform Orientation on the Quality of Imagery Obtained from Unmanned Aerial Vehicles
Remote Sens. 2020, 12(6), 1040; https://doi.org/10.3390/rs12061040 - 24 Mar 2020
Cited by 2 | Viewed by 873
Abstract
Images acquired at a low altitude can be the source of accurate information about various environmental phenomena. Often, however, this information is distorted by various factors, so a correction of the images needs to be performed to recreate the actual reflective properties of [...] Read more.
Images acquired at a low altitude can be the source of accurate information about various environmental phenomena. Often, however, this information is distorted by various factors, so a correction of the images needs to be performed to recreate the actual reflective properties of the imaged area. Due to the low flight altitude, the correction of images from UAVs (unmanned aerial vehicles) is usually limited to noise reduction and detector errors. The article shows the influence of the Sun position and platform deviation angles on the quality of images obtained by UAVs. Tilting the camera placed on an unmanned platform leads to incorrect exposures of imagery, and the order of this distortion depends on the position of the Sun during imaging. An image can be considered in three-dimensional space, where the x and y coordinates determine the position of the pixel and the third dimension determines its exposure. This assumption is the basis for the proposed method of image exposure compensation. A three-dimensional transformation by rotation is used to determine the adjustment matrix to correct the image quality. The adjustments depend on the angles of the platform and the difference between the direction of flight and the position of the Sun. An additional factor regulates the value of the adjustment depending on the ratio of the pitch and roll angles. The experiments were carried out for two sets of data obtained with different unmanned systems. The correction method used can improve the block exposure by up to 60%. The method gives the best results for simple systems, not equipped with lighting compensation systems. Full article
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Open AccessArticle
UAV and Structure from Motion Approach to Monitor the Maierato Landslide Evolution
Remote Sens. 2020, 12(6), 1039; https://doi.org/10.3390/rs12061039 - 24 Mar 2020
Cited by 5 | Viewed by 1232
Abstract
In February 2010 a large landslide affected the Maierato municipality (Calabria, Italy). The landslide, mainly caused by a period of prolonged and intense rainfalls, produced a mass displacement of about 5 million m³ and several damages to farmlands, houses and infrastructures. In the [...] Read more.
In February 2010 a large landslide affected the Maierato municipality (Calabria, Italy). The landslide, mainly caused by a period of prolonged and intense rainfalls, produced a mass displacement of about 5 million m³ and several damages to farmlands, houses and infrastructures. In the aftermath several conventional monitoring actions were carried out. In the current post emergency phase, the monitoring was resumed by carrying out unmanned aerial vehicles (UAV) flights in order to describe the recent behavior of the landslide and to assess residual risk. Thanks to the potentialities of the structure from motion algorithms and the availability of post emergency reconnaissance photos and a previous 3D dataset, the three-dimensional evolution of the area was computed. Moreover, an experimental multispectral flight was carried out and its results supported the interpretation of local phenomena. The dataset allowed to quantify the elevation losses and raises in several peculiar sectors of the landslide. The obtained results confirm that the UAV monitoring and the structure from motion approach can effectively contribute to manage residual risk in the medium and long term within an integrated geotechnical monitoring network. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicles in Geomatics)
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Open AccessArticle
Using FengYun-3C VSM Data and Multivariate Models to Estimate Land Surface Soil Moisture
Remote Sens. 2020, 12(6), 1038; https://doi.org/10.3390/rs12061038 - 24 Mar 2020
Cited by 1 | Viewed by 805
Abstract
Land surface soil moisture (SM) monitoring is crucial for global water cycle and agricultural dryness research. The FengYun-3C Microwave Radiation Imager (FY-3C/MWRI) collects various Earth geophysical parameters, and the FY-3C/MWRI SM product (FY-3C VSM) has been widely applied to determine regional-scale surface SM [...] Read more.
Land surface soil moisture (SM) monitoring is crucial for global water cycle and agricultural dryness research. The FengYun-3C Microwave Radiation Imager (FY-3C/MWRI) collects various Earth geophysical parameters, and the FY-3C/MWRI SM product (FY-3C VSM) has been widely applied to determine regional-scale surface SM contents. The FY-3C VSM retrieval accuracy in different seasons was evaluated by calculating the root mean square error (RMSE), unbiased RMSE (ubRMSE), mean absolute error (MAE), and correlation coefficient (R) values between the retrieved and measured SM. A lower accuracy in July (RMSE = 0.164 cm3/cm3, ubRMSE = 0.130 cm3/cm3, and MAE = 0.120 cm3/cm3) than in the other months was found due to the impacts of vegetation and climate variations. To show a detailed relationship between SM and multiple factors, including vegetation coverage, location, and elevation, quantile regression (QR) models were used to calculate the correlations at different quantiles. Except for the elevation at the 0.9 quantile, the QR models of the measured SM with the FY-3C VSM, MODIS NDVI, latitude, and longitude at each quantile all passed the significance test at the 0.005 level. Thus, the MODIS NDVI, latitude, and longitude were selected for error correction during the surface SM retrieval process using FY-3C VSM. Multivariate linear regression (MLR) and multivariate back-propagation neural network (MBPNN) models with different numbers of input variables were built to improve the SM monitoring results. The MBPNN model with three inputs (MBPNN-3) achieved the highest R (0.871) and lowest RMSE (0.034 cm3/cm3), MAE (0.026 cm3/cm3), and mean relative error (MRE) (20.7%) values, which were better than those of the MLR models with one, two, or three independent variables (MLR-1, -2, -3) and those of the MBPNN models with one or two inputs (MBPNN-1, -2). Then, the MBPNN-3 model was applied to generate the regional SM in the United States from January 2019 to October 2019. The estimated SM images were more consistent with the measured SM than the FY-3C VSM. This work indicated that combining FY-3C VSM data with the MBPNN-3 model could provide precise and reliable SM monitoring results. Full article
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Open AccessArticle
Radiometric Cross-Calibration of the Wide Field View Camera Onboard GaoFen-6 in Multispectral Bands
Remote Sens. 2020, 12(6), 1037; https://doi.org/10.3390/rs12061037 - 24 Mar 2020
Cited by 3 | Viewed by 1020
Abstract
GaoFen6 (GF-6), successfully launched on June 2, 2018, is the sixth satellite of the High-Definition Earth observation system (HDEOS). Although GF-6 is the first high-resolution satellite in China to achieve precise agricultural observation, it will be widely used in many other domains, such [...] Read more.
GaoFen6 (GF-6), successfully launched on June 2, 2018, is the sixth satellite of the High-Definition Earth observation system (HDEOS). Although GF-6 is the first high-resolution satellite in China to achieve precise agricultural observation, it will be widely used in many other domains, such as land survey, natural resources management, emergency management, ecological environment and so on. The GF-6 was not equipped with an onboard calibration instrument, so on-orbit radiometric calibration is essential. This paper aimed at the on-orbit radiometric calibration of the wide field of view camera (WFV) onboard GF-6 (GF-6/WFV) in multispectral bands. Firstly, the radiometric capability of GF-6/WFV is evaluated compared with the Operational Land Imager (OLI) onboard Landsat-8, Multi Spectral Instrument (MSI) onboard Sentinel-2 and Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra, which shows that GF-6/WFV has an obvious attenuation. Consequently, instead of vicarious calibration once a year, more frequent calibration is required to guarantee its radiometric consistency. The cross-calibration method based on the Badain Jaran Desert site using the bi-directional reflectance distribution function (BRDF) model calculated by Landsat-8/OLI and ZY-3/Three-Line Camera (TLC) data is subsequently applied to GF-6/WFV and much higher frequencies of calibration are achieved. Finally, the cross-calibration results are validated using the synchronized ground measurements at Dunhuang test site and the uncertainty of the proposed method is analyzed. The validation shows that the relative difference of cross-calibration is less than 5% and it is satisfied with the requirements of cross-calibration. Full article
(This article belongs to the Special Issue Quality Improvement of Remote Sensing Images)
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Open AccessArticle
Development of a 3D Real-Time Atmospheric Monitoring System (3DREAMS) Using Doppler LiDARs and Applications for Long-Term Analysis and Hot-and-Polluted Episodes
Remote Sens. 2020, 12(6), 1036; https://doi.org/10.3390/rs12061036 - 24 Mar 2020
Cited by 7 | Viewed by 1185
Abstract
Heatwaves and air pollution are serious environmental problems that adversely affect human health. While related studies have typically employed ground-level data, the long-term and episodic characteristics of meteorology and air quality at higher altitudes have yet to be fully understood. This study developed [...] Read more.
Heatwaves and air pollution are serious environmental problems that adversely affect human health. While related studies have typically employed ground-level data, the long-term and episodic characteristics of meteorology and air quality at higher altitudes have yet to be fully understood. This study developed a 3-Dimensional Real-timE Atmospheric Monitoring System (3DREAMS) to measure and analyze the vertical profiles of horizontal wind speed and direction, vertical wind velocity as well as aerosol backscatter. The system was applied to Hong Kong, a highly dense city with complex topography, during each season and including hot-and-polluted episodes (HPEs) in 2019. The results reveal that the high spatial wind variability and wind characteristics in the lower atmosphere in Hong Kong can extend upwards by up to 0.66 km, thus highlighting the importance of mountains for the wind environment in the city. Both upslope and downslope winds were observed at one site, whereas downward air motions predominated at another site. The high temperature and high concentration of fine particulate matter during HPEs were caused by a significant reduction in both horizontal and vertical wind speeds that established conditions favorable for heat and air pollutant accumulation, and by the prevailing westerly wind promoting transboundary air pollution. The findings of this study are anticipated to provide valuable insight for weather forecasting and air quality studies. The 3DREAMS will be further developed to monitor upper atmosphere wind and air quality over the Greater Bay Area of China. Full article
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Open AccessEditorial
Editorial for the Special Issue “Remote Sensing of the Terrestrial Hydrologic Cycle”
Remote Sens. 2020, 12(6), 1035; https://doi.org/10.3390/rs12061035 - 23 Mar 2020
Viewed by 723
Abstract
To address global water security issues, it is important to understand the evolving global water system and its natural and anthropogenic influencing factors [...] Full article
(This article belongs to the Special Issue Remote Sensing of the Terrestrial Hydrologic Cycle)
Open AccessEditor’s ChoiceReview
Accounting for Training Data Error in Machine Learning Applied to Earth Observations
Remote Sens. 2020, 12(6), 1034; https://doi.org/10.3390/rs12061034 - 23 Mar 2020
Cited by 5 | Viewed by 2814
Abstract
Remote sensing, or Earth Observation (EO), is increasingly used to understand Earth system dynamics and create continuous and categorical maps of biophysical properties and land cover, especially based on recent advances in machine learning (ML). ML models typically require large, spatially explicit training [...] Read more.
Remote sensing, or Earth Observation (EO), is increasingly used to understand Earth system dynamics and create continuous and categorical maps of biophysical properties and land cover, especially based on recent advances in machine learning (ML). ML models typically require large, spatially explicit training datasets to make accurate predictions. Training data (TD) are typically generated by digitizing polygons on high spatial-resolution imagery, by collecting in situ data, or by using pre-existing datasets. TD are often assumed to accurately represent the truth, but in practice almost always have error, stemming from (1) sample design, and (2) sample collection errors. The latter is particularly relevant for image-interpreted TD, an increasingly commonly used method due to its practicality and the increasing training sample size requirements of modern ML algorithms. TD errors can cause substantial errors in the maps created using ML algorithms, which may impact map use and interpretation. Despite these potential errors and their real-world consequences for map-based decisions, TD error is often not accounted for or reported in EO research. Here we review the current practices for collecting and handling TD. We identify the sources of TD error, and illustrate their impacts using several case studies representing different EO applications (infrastructure mapping, global surface flux estimates, and agricultural monitoring), and provide guidelines for minimizing and accounting for TD errors. To harmonize terminology, we distinguish TD from three other classes of data that should be used to create and assess ML models: training reference data, used to assess the quality of TD during data generation; validation data, used to iteratively improve models; and map reference data, used only for final accuracy assessment. We focus primarily on TD, but our advice is generally applicable to all four classes, and we ground our review in established best practices for map accuracy assessment literature. EO researchers should start by determining the tolerable levels of map error and appropriate error metrics. Next, TD error should be minimized during sample design by choosing a representative spatio-temporal collection strategy, by using spatially and temporally relevant imagery and ancillary data sources during TD creation, and by selecting a set of legend definitions supported by the data. Furthermore, TD error can be minimized during the collection of individual samples by using consensus-based collection strategies, by directly comparing interpreted training observations against expert-generated training reference data to derive TD error metrics, and by providing image interpreters with thorough application-specific training. We strongly advise that TD error is incorporated in model outputs, either directly in bias and variance estimates or, at a minimum, by documenting the sources and implications of error. TD should be fully documented and made available via an open TD repository, allowing others to replicate and assess its use. To guide researchers in this process, we propose three tiers of TD error accounting standards. Finally, we advise researchers to clearly communicate the magnitude and impacts of TD error on map outputs, with specific consideration given to the likely map audience. Full article
(This article belongs to the Section Environmental Remote Sensing)
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Open AccessLetter
Estimating Meltwater Drainage Onset Timing and Duration of Landfast Ice in the Canadian Arctic Archipelago Using AMSR-E Passive Microwave Data
Remote Sens. 2020, 12(6), 1033; https://doi.org/10.3390/rs12061033 - 23 Mar 2020
Viewed by 703
Abstract
Meltwater drainage onset (DO) timing and drainage duration (DD) related to snowmelt-water redistribution are both important for understanding not only the Arctic energy and heat budgets but also the salt/heat balance of the mixed layer in the ocean and sea-ice ecosystem. We present [...] Read more.
Meltwater drainage onset (DO) timing and drainage duration (DD) related to snowmelt-water redistribution are both important for understanding not only the Arctic energy and heat budgets but also the salt/heat balance of the mixed layer in the ocean and sea-ice ecosystem. We present DO and DD as determined from the time series of Advanced Microwave Scanning Radiometer-Earth observing system (AMSR-E) melt pond fraction (MPF) estimates in an area with Canadian landfast ice. To address the lack of evaluation on a day-by-day basis for the AMSR-E MPF estimate, we first compared AMSR-E MPF with the daily Medium Resolution Imaging Spectrometer (MERIS) MPF. The AMSR-E MPF estimate correlates significantly with the MERIS MPF (r = 0.73–0.83). The estimate has a product quality similar to the MERIS MPF only when the albedo is around 0.5–0.7 and a positive bias of up to 10% in areas with an albedo of 0.7–0.9, including melting snow. The DO/DD estimates are determined by using a polynomial regression curve fitted on the time series of the AMSR-E MPF. The DOs/DDs from time series of the AMSR-E and MERIS MPFs are compared, revealing consistency in both DD and DO. The DO timing from 2006 to 2011 is correlated with melt onset timing. To the best of our knowledge, our study provides the first large-scale information on both DO timing and DD. Full article
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Open AccessArticle
A New Remote Sensing Images and Point-of-Interest Fused (RPF) Model for Sensing Urban Functional Regions
Remote Sens. 2020, 12(6), 1032; https://doi.org/10.3390/rs12061032 - 23 Mar 2020
Viewed by 929
Abstract
For urban planning and environmental monitoring, it is essential to understand the diversity and complexity of cities to identify urban functional regions accurately and widely. However, the existing methods developed in the literature for identifying urban functional regions have mainly been focused on [...] Read more.
For urban planning and environmental monitoring, it is essential to understand the diversity and complexity of cities to identify urban functional regions accurately and widely. However, the existing methods developed in the literature for identifying urban functional regions have mainly been focused on single remote sensing image data or social sensing data. The multi-dimensional information which was attained from various data source and could reflect the attribute or function about the urban functional regions that could be lost in some extent. To sense urban functional regions comprehensively and accurately, we developed a multi-mode framework through the integration of spatial geographic characteristics of remote sensing images and the functional distribution characteristics of social sensing data of Point-of-Interest (POI). In this proposed framework, a deep multi-scale neural network was developed first for the functional recognition of remote sensing images in urban areas, which explored the geographic feature information implicated in remote sensing. Second, the POI function distribution was analyzed in different functional areas of the city, then the potential relationship between POI data categories and urban region functions was explored based on the distance metric. A new RPF module is further deployed to fuse the two characteristics in different dimensions and improve the identification performance of urban region functions. The experimental results demonstrated that the proposed method can efficiently achieve the accuracy of 82.14% in the recognition of functional regions. It showed the great usability of the proposed framework in the identification of urban functional regions and the potential to be applied in a wide range of areas. Full article
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Open AccessArticle
A Combined IR-GPS Satellite Analysis for Potential Applications in Detecting and Predicting Lightning Activity
Remote Sens. 2020, 12(6), 1031; https://doi.org/10.3390/rs12061031 - 23 Mar 2020
Cited by 3 | Viewed by 908
Abstract
Continuous estimates of the vertical integrated precipitable water vapor content from the tropospheric delay of the signal received by the antennas of the global positioning system (GPS) are used in this paper, in conjunction with the measurements of the Meteosat Second Generation (MSG) [...] Read more.
Continuous estimates of the vertical integrated precipitable water vapor content from the tropospheric delay of the signal received by the antennas of the global positioning system (GPS) are used in this paper, in conjunction with the measurements of the Meteosat Second Generation (MSG) spinning enhanced visible and infrared imager (SEVIRI) radiometer and with the lightning activity, collected here by the ground-based lightning detection network (LINET), in order to identify links and recurrent patterns useful for improving nowcasting applications. The analysis of a couple of events is shown here as an example of more general behavior. Clear signs appear before the peak of lightning activity on a timescale from 2 to 3 h. In particular, the lightning activity is generally preceded by a period in which the difference between SEVIRI brightness temperature (TB) at channel 5 and channel 6 (i.e., ∆TB) presents quite constant values around 0 K. This trend is accompanied by an increase in precipitable water vapor (PWV) values, reaching a maximum in conjunction with the major flash activity. The results shown in this paper evidence good potentials of using radiometer and GPS measurements together for predicting the abrupt intensification of lightning activity in nowcasting systems. Full article
(This article belongs to the Section Atmosphere Remote Sensing)
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Open AccessArticle
PreciPatch: A Dictionary-based Precipitation Downscaling Method
Remote Sens. 2020, 12(6), 1030; https://doi.org/10.3390/rs12061030 - 23 Mar 2020
Cited by 2 | Viewed by 1382
Abstract
Climate and weather data such as precipitation derived from Global Climate Models (GCMs) and satellite observations are essential for the global and local hydrological assessment. However, most climatic popular precipitation products (with spatial resolutions coarser than 10km) are too coarse for local impact [...] Read more.
Climate and weather data such as precipitation derived from Global Climate Models (GCMs) and satellite observations are essential for the global and local hydrological assessment. However, most climatic popular precipitation products (with spatial resolutions coarser than 10km) are too coarse for local impact studies and require “downscaling” to obtain higher resolutions. Traditional precipitation downscaling methods such as statistical and dynamic downscaling require an input of additional meteorological variables, and very few are applicable for downscaling hourly precipitation for higher spatial resolution. Based on dynamic dictionary learning, we propose a new downscaling method, PreciPatch, to address this challenge by producing spatially distributed higher resolution precipitation fields with only precipitation input from GCMs at hourly temporal resolution and a large geographical extent. Using aggregated Integrated Multi-satellitE Retrievals for GPM (IMERG) data, an experiment was conducted to evaluate the performance of PreciPatch, in comparison with bicubic interpolation using RainFARM—a stochastic downscaling method, and DeepSD—a Super-Resolution Convolutional Neural Network (SRCNN) based downscaling method. PreciPatch demonstrates better performance than other methods for downscaling short-duration precipitation events (used historical data from 2014 to 2017 as the training set to estimate high-resolution hourly events in 2018). Full article
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
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Open AccessArticle
An Approach of Identifying and Extracting Urban Commercial Areas Using the Nighttime Lights Satellite Imagery
Remote Sens. 2020, 12(6), 1029; https://doi.org/10.3390/rs12061029 - 23 Mar 2020
Cited by 1 | Viewed by 888
Abstract
Urban commercial areas can reflect the spatial distribution of business activities. However, the scope of urban commercial areas cannot be easily detected by traditional methods because of difficulties in data collection. Considering the positive correlation between business scale and nighttime lighting, this paper [...] Read more.
Urban commercial areas can reflect the spatial distribution of business activities. However, the scope of urban commercial areas cannot be easily detected by traditional methods because of difficulties in data collection. Considering the positive correlation between business scale and nighttime lighting, this paper proposes a method of urban commercial areas detection based on nighttime lights satellite imagery. First, an imagery preprocess model is proposed to correct imageries and improve efficiency of cluster analysis. Then, an exploratory spatial data analysis and hotspots clustering method is employed to detect commercial areas by geographic distribution metric with urban commercial hotspots. Furthermore, four imageries of Wuhan City and Shenyang City are selected as an example for urban commercial areas detection experiments. Finally, a comparison is made to find out the time and space factors that affect the detection results of the commercial areas. By comparing the results with the existing map data, we are convinced that the nighttime lights satellite imagery can effectively detect the urban commercial areas. The time of image acquisition and the vegetation coverage in the area are two important factors affecting the detection effect. Harsh weather conditions and high vegetation coverage are conducive to the effective implementation of this method. This approach can be integrated with traditional methods to form a fast commercial areas detection model, which can then play a role in large-scale socio-economic surveys and dynamic detection of commercial areas evolution. Hence, a conclusion can be reached that this study provides a new method for the perception of urban socio-economic activities. Full article
(This article belongs to the Special Issue Remote Sensing of Nighttime Observations)
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Open AccessArticle
Satellite-Derived PM2.5 Composition and Its Differential Effect on Children’s Lung Function
Remote Sens. 2020, 12(6), 1028; https://doi.org/10.3390/rs12061028 - 23 Mar 2020
Cited by 3 | Viewed by 1212
Abstract
Studies of the association between air pollution and children’s health typically rely on fixed-site monitors to determine exposures, which have spatial and temporal limitations. Satellite observations of aerosols provide the coverage that fixed-site monitors lack, enabling more refined exposure assessments. Using aerosol optical [...] Read more.
Studies of the association between air pollution and children’s health typically rely on fixed-site monitors to determine exposures, which have spatial and temporal limitations. Satellite observations of aerosols provide the coverage that fixed-site monitors lack, enabling more refined exposure assessments. Using aerosol optical depth (AOD) data from the Multiangle Imaging SpectroRadiometer (MISR) instrument, we predicted fine particulate matter, PM 2.5 , and PM 2.5 speciation concentrations and linked them to the residential locations of 1206 children enrolled in the Southern California Children’s Health Study. We fitted mixed-effects models to examine the relationship between the MISR-derived exposure estimates and lung function, measured as forced expiratory volume in 1 second (FEV 1 ) and forced vital capacity (FVC), adjusting for study community and biological factors. Gradient Boosting and Support Vector Machines showed excellent predictive performance for PM 2.5 (test R 2 = 0.68 ) and its chemical components (test R 2 = –0.71). In single-pollutant models, FEV 1 decreased by 131 mL (95% CI: 232 , 35 ) per 10.7-µg/m 3 increase in PM 2.5 , by 158 mL (95% CI: 273 , 43 ) per 1.2-µg/m 3 in sulfates (SO 4 2 ), and by 177 mL (95% CI: 306 , 56 ) per 1.6-µg/m 3 increase in dust; FVC decreased by 175 mL (95% CI: 310 , 29 ) per 1.2-µg/m 3 increase in SO 4 2 and by 212 mL (95% CI: 391 , 28 ) per 2.5-µg/m 3 increase in nitrates (NO 3 ). These results demonstrate that satellite observations can strengthen epidemiological studies investigating air pollution health effects by providing spatially and temporally resolved exposure estimates. Full article
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Open AccessArticle
PolSAR Image Classification Based on Statistical Distribution and MRF
Remote Sens. 2020, 12(6), 1027; https://doi.org/10.3390/rs12061027 - 23 Mar 2020
Cited by 2 | Viewed by 1024
Abstract
Classification is an important topic in synthetic aperture radar (SAR) image processing and interpretation. Because of speckle and imaging geometrical distortions, land cover mapping is always a challenging task especially in complex landscapes. In this study, we aim to find a robust and [...] Read more.
Classification is an important topic in synthetic aperture radar (SAR) image processing and interpretation. Because of speckle and imaging geometrical distortions, land cover mapping is always a challenging task especially in complex landscapes. In this study, we aim to find a robust and efficient method for polarimetric SAR (PolSAR) image classification. The Markov random field (MRF) has been widely used for capturing the spatial-contextual information of the image. In this paper, we firstly introduce two ways to construct the Wishart mixture model and compare their performances using real PolSAR data. Then, the better mixture model and two other classical statistically distributions are combined with MRF to construct the MRF models. In order to improve the robustness of the models, the constant false alarm rate (CFAR)-based edge penalty term and an adaptive neighborhood system are embedded into the MRF energy functional. Classification is implemented in two schemes, i.e., pixel-based and region-based classifications. Finally, agriculture fields are used as the test scenario to evaluate the robustness and applicability of these algorithms. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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Open AccessArticle
Changes in Atmospheric, Meteorological, and Ocean Parameters Associated with the 12 January 2020 Taal Volcanic Eruption
Remote Sens. 2020, 12(6), 1026; https://doi.org/10.3390/rs12061026 - 23 Mar 2020
Cited by 1 | Viewed by 2163
Abstract
The Taal volcano erupted on 12 January 2020, the first time since 1977. About 35 mild earthquakes (magnitude greater than 4.0) were observed on 12 January 2020 induced from the eruption. In the present paper, we analyzed optical properties of volcanic aerosols, volcanic [...] Read more.
The Taal volcano erupted on 12 January 2020, the first time since 1977. About 35 mild earthquakes (magnitude greater than 4.0) were observed on 12 January 2020 induced from the eruption. In the present paper, we analyzed optical properties of volcanic aerosols, volcanic gas emission, ocean parameters using multi-satellite sensors, namely, MODIS (Moderate Resolution Imaging Spectroradiometer), AIRS (Atmospheric Infrared Sounder), OMI (Ozone Monitoring Instrument), TROPOMI (TROPOspheric Monitoring Instrument) and ground observations, namely, Argo, and AERONET (AErosol RObotic NETwork) data. Our detailed analysis shows pronounced changes in all the parameters, which mainly occurred in the western and south-western regions because the airmass of the Taal volcano spreads westward according to the analysis of airmass trajectories and wind directions. The presence of finer particles has been observed by analyzing aerosol properties that can be attributed to the volcanic plume after the eruption. We have also observed an enhancement in SO2, CO, and water vapor, and a decrease in Ozone after a few days of the eruption. The unusual variations in salinity, sea temperature, and surface latent heat flux have been observed as a result of the ash from the Taal volcano in the south-west and south-east over the ocean. Our results demonstrate that the observations combining satellite with ground data could provide important information about the changes in the atmosphere, meteorology, and ocean parameters associated with the Taal volcanic eruption. Full article
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Open AccessArticle
Dynamic Monitoring of a Mid-Rise Building by Real-Aperture Radar Interferometer: Advantages and Limitations
Remote Sens. 2020, 12(6), 1025; https://doi.org/10.3390/rs12061025 - 23 Mar 2020
Cited by 1 | Viewed by 752
Abstract
In this paper, remote and in situ techniques to estimate the dynamic response of a building to ambient vibration are reported: data acquired through a real-aperture radar (RAR) interferometer and conventional accelerometers are analyzed. A five-story reinforced concrete housing building, which was damaged [...] Read more.
In this paper, remote and in situ techniques to estimate the dynamic response of a building to ambient vibration are reported: data acquired through a real-aperture radar (RAR) interferometer and conventional accelerometers are analyzed. A five-story reinforced concrete housing building, which was damaged during the May 11th 2011 Lorca (Spain) earthquake, is used as a case study. The building was monitored using both types of instruments. The dynamic properties of the building are estimated first taking acceleration measurements using a set of 10 high-precision accelerometers installed on the roof of the building. Further, the displacement–time histories, recorded with the RAR device pointing to a corner of the building, are analyzed. Then, the ability and shortcomings of RAR measurements to deal with the fundamental frequencies of vibration of the structure are investigated. The advantages and limitations of from-inside (accelerometric) and from-outside (RAR) measurements are highlighted and discussed. A relevant conclusion is that, after strong earthquakes, RAR may be an interesting and useful tool, as it allows surveying the structural response of mid-rise buildings remotely, without the need to enter the structures, which may be dangerous for inspectors or technicians in cases of severely damaged buildings. Given that the instrumented building suffered significant damage, the ability of these kinds of measurements to detect damage is also discussed. Full article
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Open AccessArticle
Predicting Wheat Yield at the Field Scale by Combining High-Resolution Sentinel-2 Satellite Imagery and Crop Modelling
Remote Sens. 2020, 12(6), 1024; https://doi.org/10.3390/rs12061024 - 23 Mar 2020
Cited by 12 | Viewed by 1867
Abstract
Accurate prediction of crop yield at the field scale is critical to addressing crop production challenges and reducing the impacts of climate variability and change. Recently released Sentinel-2 (S2) satellite data with a return cycle of five days and a high resolution at [...] Read more.
Accurate prediction of crop yield at the field scale is critical to addressing crop production challenges and reducing the impacts of climate variability and change. Recently released Sentinel-2 (S2) satellite data with a return cycle of five days and a high resolution at 13 spectral bands allows close observation of crop phenology and crop physiological attributes at field scale during crop growth. Here, we test the potential for indices derived from S2 data to estimate dryland wheat yields at the field scale and the potential for enhanced predictability by incorporating a modelled crop water stress index (SI). Observations from 103 study fields over the 2016 and 2017 cropping seasons across Northeastern Australia were used. Vegetation indices derived from S2 showed moderately high accuracy in yield prediction and explained over 70% of the yield variability. Specifically, the red edge chlorophyll index (CI; chlorophyll) (R2 = 0.76, RMSE = 0.88 t/ha) and the optimized soil-adjusted vegetation index (OSAVI; structural) (R2 = 0.74, RMSE = 0.91 t/ha) showed the best correlation with field yields. Furthermore, combining the crop model-derived SI with both structural and chlorophyll indices significantly enhanced predictability. The best model with combined OSAVI, CI and SI generated a much higher correlation, with R2 = 0.91 and RMSE = 0.54 t/ha. When validating the models on an independent set of fields, this model also showed high correlation (R2 = 0.93, RMSE = 0.64 t/ha). This study demonstrates the potential of combining S2-derived indices and crop model-derived indices to construct an enhanced yield prediction model suitable for fields in diversified climate conditions. Full article
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Open AccessFeature PaperArticle
Evaluation of Land Surface Temperature Retrieval from Landsat 8/TIRS Images before and after Stray Light Correction Using the SURFRAD Dataset
Remote Sens. 2020, 12(6), 1023; https://doi.org/10.3390/rs12061023 - 22 Mar 2020
Cited by 3 | Viewed by 1249
Abstract
Landsat 8/thermal infrared sensor (TIRS) is suffering from the problem of stray light that makes an inaccurate radiance for two thermal infrared (TIR) bands and the latest correction was conducted in 2017. This paper focused on evaluation of land surface temperature (LST) retrieval [...] Read more.
Landsat 8/thermal infrared sensor (TIRS) is suffering from the problem of stray light that makes an inaccurate radiance for two thermal infrared (TIR) bands and the latest correction was conducted in 2017. This paper focused on evaluation of land surface temperature (LST) retrieval from Landsat 8 before and after the correction using ground-measured LST from five surface radiation budget network (SURFRAD) sites. Results indicated that the correction increased the band radiance at the top of the atmosphere for low temperature but decreased such radiance for high temperature. The root-mean-square error (RMSE) of LST retrieval decreased by 0.27 K for Band 10 and 0.78 K for Band 11 using the single-channel algorithm. For the site with high temperature, the LST retrieval RMSE of the single-channel algorithm for Band 11 even reduced by 1.4 K. However, the accuracy of two of three split-window algorithms adopted in this paper decreased. After correction, the single-channel algorithm for Band 10 and the linear split-window algorithm had the least RMSE (approximately 2.5 K) among five adopted algorithms. Moreover, besides SURFRAD sites, it is necessary to validate using more robust and homogeneous ground-measured datasets to help solely clarify the effect of the correction on LST retrieval. Full article
(This article belongs to the Special Issue Remote Sensing Monitoring of Land Surface Temperature (LST))
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Open AccessArticle
Phenology-Based Rice Paddy Mapping Using Multi-Source Satellite Imagery and a Fusion Algorithm Applied to the Poyang Lake Plain, Southern China
Remote Sens. 2020, 12(6), 1022; https://doi.org/10.3390/rs12061022 - 22 Mar 2020
Cited by 5 | Viewed by 1306
Abstract
Accurate information about the spatiotemporal patterns of rice paddies is essential for the assessment of food security, management of agricultural resources, and sustainability of ecosystems. However, accurate spatial datasets of rice paddy fields and multi-cropping at fine resolution are still lacking. Landsat observation [...] Read more.
Accurate information about the spatiotemporal patterns of rice paddies is essential for the assessment of food security, management of agricultural resources, and sustainability of ecosystems. However, accurate spatial datasets of rice paddy fields and multi-cropping at fine resolution are still lacking. Landsat observation is the primary source of remote sensing data that has continuously mapped regional rice paddy fields at a 30-m spatial resolution since the 1980s. However, Landsat data used for rice paddy studies reveals some challenges, especially data quality issues (e.g., cloud cover). Here, we present an algorithm that integrates time-series Landsat and MODIS (Moderate-resolution Imaging Spectroradiometer) images with a phenology-based approach (ILMP) to map rice paddy planting fields and multi-cropping patterns. First, a fusion of MODIS and Landsat data was used to reduce the cloud contamination, which added more information to the Landsat time series data. Second, the unique biophysical features of rice paddies during the flooding and open-canopy periods (which can be captured by the dynamics of the vegetation indices) were used to identify rice paddy regions as well as those of multi-cropping. This algorithm was tested for 2015 in Nanchang County, which is located on the Poyang Lake plain in southern China. We evaluated the resultant map of the rice paddy and multi-cropping systems using ground-truth data and Google Earth images. The overall accuracy and kappa coefficient of the rice paddy planting areas were 93.66% and 0.85, respectively. The overall accuracy and kappa coefficient of the multi-cropping regions were 92.95% and 0.89, respectively. In addition, our algorithm was more capable of capturing detailed information about areas with fragmented cropland than that of the National Land Cover Dataset (NLCD) from 2015. These results demonstrated the great potential of our algorithm for mapping rice paddy fields and using the multi-cropping index in complex landscapes in southern China. Full article
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Open AccessArticle
Measuring Winds and Currents with Ka-Band Doppler Scatterometry: An Airborne Implementation and Progress towards a Spaceborne Mission
Remote Sens. 2020, 12(6), 1021; https://doi.org/10.3390/rs12061021 - 22 Mar 2020
Cited by 1 | Viewed by 1151
Abstract
Ocean surface winds and currents are tightly coupled, essential climate variables, synoptic measurements of which require a remote sensing approach. Global measurements of ocean vector winds have been provided by scatterometers for decades, but a synoptic approach to measuring total vector surface currents [...] Read more.
Ocean surface winds and currents are tightly coupled, essential climate variables, synoptic measurements of which require a remote sensing approach. Global measurements of ocean vector winds have been provided by scatterometers for decades, but a synoptic approach to measuring total vector surface currents has remained elusive. Doppler scatterometry is a coherent burst-scatterometry technique that builds on the long heritage of spinning pencil beam scatterometers to enable the wide-swath, simultaneous measurement of ocean surface vector winds and currents. To prove the measurement concept, NASA funded the DopplerScatt airborne Doppler scatterometer through the Instrument Incubator Program (IIP) and Airborne Instrument Technology Transition (AITT) program. DopplerScatt has successfully shown that pencil beam Doppler scatterometry can be used to form wide swath measurements of ocean winds and currents, and has increased the technology readiness level of key instrument components, including: Ka-band pulsed radar hardware, optimized scatterometer burst-mode operation, calibration techniques, geophysical model functions, and processing algorithms. With the promise and progress shown by DopplerScatt, and the importance of air-sea interactions in mind, the National Academy’s Decadal Survey has targeted simultaneous measurements of winds and currents from a Doppler scatterometer for an Earth Explorer class spaceborne mission. Besides DopplerScatt’s place as a technology stepping stone towards a satellite mission, DopplerScatt provides scientifically important measurements of ocean currents and winds (400 m resolution) and their derivatives (1 km resolution) over a 25 km swath. These measurements are enabling studies of the submesoscales and air-sea interactions that were previously impossible, and are central to the upcoming NASA Earth Ventures Suborbital-3 Submesoscale Ocean Dynamics Experiment (S-MODE). This paper summarizes the development of DopplerScatt hardware, systems, calibration, and operations, and how advances in each relate to progress towards a spaceborne Doppler scatterometer mission. Full article
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