bluuebunny/crossref_metadata_2025 huggingface dataset:
Water-Vapour Monitoring from Ground-Based GNSS Observations in Northwestern Argentina
Authors: Antonoglou Nikolaos , Balidakis Kyriakos , Wickert Jens , Dick Galina , de la Torre Alejandro , Bookhagen Bodo
Published: October 2022

The Central Andes in northwestern Argentina are characterized by steep topographic and climatic gradients. The humid foreland areas at 1 km asl elevation rapidly rise to over 5 km in the eastern Cordillera, and they form an orographic rainfall barrier on the eastern windward side. This topographic setting combined with seasonal moisture transport through the South American monsoon system leads to intense rainstorms with cascading effects such as landsliding and flooding. In order to better quantify the dynamics of water vapour transport, we use high-temporal-resolution global navigation satellite system (GNSS) remote sensing techniques. We are particularly interested in better understanding the dynamics of high-magnitude storms with high water vapour amounts that have destructive effects on human infrastructure. We used an existing GNSS station network with 12 years of time series data, and we installed two new ground stations along the climatic gradient and collected GNSS time series data for three years. For several stations we calculated the GNSS signal delay gradient to determine water vapour transport direction. Our statistical analysis combines in situ rainfall measurements and ERA5 reanalysis data to reveal the water vapour transport mechanism for the study area. The results show a strong relationship between altitude and the water vapour content, as well as between the transportation pathways and the topography.
Mapping Ice Flow Velocity of Tidewater Glaciers in Hornsund Fiord Area with the Use of Autonomous Repeat Image Feature Tracking (2018–2022)
Authors: Milczarek Wojciech , Kopeć Anna , Głowacki Tadeusz
Published: October 2022

Dynamic climate changes are particularly apparent in polar regions. Glaciers are retreatng towards the land at a very fast pace. This study demonstrates the application of the feature tracking method in the analysis of ice flow velocity in the region of the Hornsund fiord, southern Spitsbergen, in the years 2018–2022. The calculations were based on the Geogrid and autoRIFT environments and on the Sentinel 1 images. The study also employed external data, such as a numerical terrain model and reference velocity values. The input data, e.g., the chip size and the search limit, were prepared accounting for the specific character of the investigated area. The velocities were calculated for nine biggest glaciers which terminated in the fiord. The accuracy of the results was identified by calculating the median absolute deviation (MAD) of the obtained displacement velocity values from the reference value for areas identified as stable. The study also attempted a causal analysis of the influence of weather factors on the dynamics of ice mass displacement. A systematic year-to-year decrease of the velocity was observed for the entire fiord. In the case of several glaciers, changes related to the ablation season (summer) are also clearly visible. The research results are promising and fill a research gap related to the absence of permanent monitoring and analysis of the dynamics of ice flow in polar regions. It is the first complex and precise study of glacier surface velocity changes, performed on the basis of satellite radar images for the entire Hornsund fiord.
Twenty Years of Remote Sensing Applications Targeting Landscape Analysis and Environmental Issues in Olive Growing: A Review
Authors: Messina Gaetano , Modica Giuseppe
Published: October 2022

The olive (Olea europaea L.) is an iconic tree linked to the birth of some of the most ancient civilizations and one of the most important cultivated tree species in the Mediterranean basin. Over the last few decades, given the high socio-economic importance of the olive sector, there has been much research involving remote sensing (RS) applications in olive growing, especially in precision agriculture. This review article is part of a review that aims to cover the past, from the 2000s onwards, and the most recent applications of remote sensing (RS) in olive growing to be able to include research and all topics related to the use of RS on olive trees. As far as the use of RS platforms such as satellites, aircraft, and unmanned aerial vehicles (UAVs) in olive growing is concerned, a review of the literature showed the presence of several works devoted to it. A brief introduction on the history of the olive tree and its distribution and cultivation around the world, together with a summary of the leading RS platforms (a good portion of which are satellites) used in olive research, anticipates the discussion of four topics about olive growing that have as their common thread positive (and non-positive) impacts on the environment: preservation of olive landscape and soil erosion, identification of olive groves, olive oil mill wastewater (OOMW) and relative environmental risks, irrigation water management and the use of RS platforms for water stress monitoring. The preservation of olive groves as an element of Mediterranean identity and strategic economic resource in agriculture depends on sustainable environmental management alongside technological advances brought by precision agriculture.
Susceptibility Assessment of Flash Floods: A Bibliometrics Analysis and Review
Authors: Duan Le , Liu Chao , Xu Hui , Huali Huali , Liu Haizhi , Yan Xufeng , Liu Tiegang , Yang Zhengli , Liu Gang , Dai Xiaoai , Zhang Donghui , Fu Xiao , Liu Xuemei , Lu Heng
Published: October 2022

A flash flood disaster is one of the most destructive natural disasters. With the increase in extreme rainfall events, more and more areas will be threatened by flash floods. The flash flood susceptibility assessment is the basis of flash flood risk assessment and is also an important step in flash flood disaster management. Based on Citespace analysis tools, this study made a bibliometric and visualized analysis of 305 documents collected in the core collection of Web of Science in the past 15 years, including the analysis of the number of publications and citation frequency, influence analysis, keyword analysis, author co-citation analysis, and institutional co-operation analysis. This paper summarizes the current research status and future development trend of flash flood susceptibility assessment from five key research subfields, including assessment scale, assessment unit, assessment index, assessment model, and model assessment method, discusses the analysis of the application of remote sensing and GIS in flash flood susceptibility assessment, discusses the problems encountered in the current research of the five subfields, and provides suggestions for flash flood hazard control.
Detecting the Greatest Changes in Global Satellite-Based Precipitation Observations
Authors: Kazemzadeh Majid , Hashemi Hossein , Jamali Sadegh , Uvo Cintia B. , Berndtsson Ronny , Huffman George J.
Published: October 2022

In recent years, the analysis of abrupt and non-abrupt changes in precipitation has received much attention due to the importance of climate change-related issues (e.g., extreme climate events). In this study, we used a novel segmentation algorithm, DBEST (Detecting Breakpoints and Estimating Segments in Trend), to analyze the greatest changes in precipitation using a monthly pixel-based satellite precipitation dataset (TRMM 3B43) at three different scales: (i) global, (ii) continental, and (iii) climate zone, during the 1998–2019 period. We found significant breakpoints, 14.1%, both in the form of abrupt and non-abrupt changes, in the global scale precipitation at the 0.05 significance level. Most of the abrupt changes were observed near the Equator in the Pacific Ocean and Asian continent, relative to the rest of the globe. Most detected breakpoints occurred during the 1998–1999 and 2009–2011 periods on the global scale. The average precipitation change for the detected breakpoint was ±100 mm, with some regions reaching ±3000 mm. For instance, most portions of northern Africa and Asia experienced major changes of approximately +100 mm. In contrast, most of the South Pacific and South Atlantic Ocean experienced changes of −100 mm during the studied period. Our findings indicated that the larger areas of Africa (23.9%), Asia (22.9%), and Australia (15.4%) experienced significant precipitation breakpoints compared to North America (11.6%), South America (9.3%), Europe (8.3%), and Oceania (9.6%). Furthermore, we found that the majority of detected significant breakpoints occurred in the arid (31.6%) and polar (24.1%) climate zones, while the least significant breakpoints were found for snow-covered (11.5%), equatorial (7.5%), and warm temperate (7.7%) climate zones. Positive breakpoints’ temporal coverage in the arid (54.0%) and equatorial (51.9%) climates were more than those in other climates zones. Here, the findings indicated that large areas of Africa and Asia experienced significant changes in precipitation (−250 to +250 mm). Compared to the average state (trend during a specific period), the greatest changes in precipitation were more abrupt and unpredictable, which might impose a severe threat to the ecology, environment, and natural resources.
Contrasting Mesoscale Convective System Features of Two Successive Warm-Sector Rainfall Episodes in Southeastern China: A Satellite Perspective
Authors: Huang Yipeng , Zhang Murong
Published: October 2022

Based on Himawari-8 satellite observations, the mesoscale convective system (MCS) behaviors of two successive but distinct warm-sector rainfall episodes (EP1 and EP2) on 6–7 May 2018 over southeastern China were compared, with the latter episode being a record-breaking rainfall event. Results showed that MCSs played a dominant role in EP2, but not in EP1, by contributing over 80% of the extreme rainfall total and all the 10-min rainfalls over 20 mm. MCS occurrences were more frequent in EP2 than EP1, especially in the coastal rainfall hotspots, along with more frequent merging processes. Overall, the MCS samples in EP2 were larger in size, more intense, and moved slower and more in parallel to their orientation, which facilitated local rainfall accumulation. Two new indices are proposed—the overlap index (OLI) and merging potential index (MPI)—to evaluate two MCS processes vital for rainfall production: the repeated passage of an individual MCS over given areas and the merging between MCSs, respectively. Both OLI and MPI in EP2 were significantly larger than in EP1, which tended to produce larger maximum rainfall amount and stronger 10-min rain rates in the following hour. These results demonstrate the potential value of satellite-based MCS information for heavy rainfall nowcasting, which is particularly significant for warm-sector rainfall with its limited predictability.
A New Algorithm of Atmospheric Boundary Layer Height Determined from Polarization Lidar
Authors: Han Bisen , Zhou Tian , Zhou Xiaowen , Fang Shuya , Huang Jianping , He Qing , Huang Zhongwei , Wang Minzhong
Published: October 2022

Accurately determining the atmospheric boundary layer height (ABLH) is needed when one is addressing the air quality-related issues in highly urbanized areas, as well as when one is investigating issues that are related to the emission and transport of dust aerosols over the source region. In this study, we propose a new ABLH retrieval method, which is named ADEILP (ABLH that is determined by polarization lidar); it is based on the short-term polarized lidar observation that took place during the intensive field campaign in July 2021 in Tazhong, the hinterland of Taklimakan Desert. Furthermore, we conducted comparisons between the ABLH that was identified using a radiosonde (ABLHsonde), the ABLH that was identified by ERA5 (ABLHERA5) and the ABHL that was identified by ADELIP (ABLHADELIP), and we discussed the implications of the dust events. The ADELIP method boasts remarkable advancements in two parts: (1) the lidar volume linear depolarization ratio (VLDR) that represented the aerosol type was adopted, which is very effective in distinguishing between the different types of boundary layers (e.g., mixing layer and residual layer); (2) the idea of breaking up the entire layer into sub-layers was applied on the basis of the continues wavelet transform (CWT) method, which is favorable when one is considering the effect of fine stratification in an aerosol layer. By combining the appropriate height limitations, these factors ensured that there was good robustness of the ADELIP method, thereby enabling it to deal with complex boundary layer structures. The comparisons revealed that ABLHADELIP shows good consistency with ABLHsonde and ABLHERA5 for non-dust events. Nevertheless, the ADELIP method overestimated the stable boundary layer and underestimated the heights of the mixing layer. The dust events seem to be a possible reason for the great difference between ABLHERA5 and ABLHsonde. Thus, it is worth suggesting that the influence that is caused by the differences of the vertical profile in the ERA5 product should be carefully considered when the issues on dust events are involved. Overall, these findings support the climatological analysis of the atmosphere boundary layer and the vertical distribution characteristics of aerosols over typical climatic zones.
An Adaptive Joint Bilateral Interpolation-Based Color Blending Method for Stitched UAV Images
Authors: Chung Kuo-Liang , Row Dai-Yu
Published: October 2022

Given a source UAV (unmanned aerial vehicle) image Is and a target UAV image It, it is a challenging problem to correct the color of all target pixels so that the subjective and objective quality effects between Is and It can be as consistent as possible. Recently, by referring to all stitching color difference values on the stitching line, a global bilateral joint interpolation-based (GBJI-based) color correction method was proposed. However, because all stitching color difference values may contain aligned and misaligned stitching pixels, the GBJI-based method suffers from a perceptual artifact near the misaligned stitching pixels. To remedy this perceptual artifact, in this paper, we propose an adaptive joint bilateral interpolation-based (AJBI-based) color blending method such that each target pixel only adaptively refers to an adequate interval of stitching color difference values locally. Based on several testing stitched UAV images under different brightness and misalignment situations, comprehensive experimental results demonstrate that in terms of PSNR (peak signal-to-noise ratio), SSIM (structural similarity index), and FSIM (feature similarity index), our method achieves higher objective quality effects and also achieves better perceptual effects, particularly near the misaligned stitching pixels, when compared with the GBJI-based method and the other state-of-the-art methods.
Geostatistical Modelling of Soil Spatial Variability by Fusing Drone-Based Multispectral Data, Ground-Based Hyperspectral and Sample Data with Change of Support
Authors: Belmonte Antonella , Riefolo Carmela , Lovergine Francesco , Castrignanò Annamaria
Published: October 2022

Traditional soil characterization methods are time consuming, laborious and invasive and do not allow for long-term repeatability of measurements. The overall aim of this paper was to assess and model spatial variability of the soil in an olive grove in south Italy by using data from two sensors of different types: a multi-spectral on-board drone radiometer and a hyperspectral visible-near infrared-shortwave infrared (VIS-NIR-SWIR) reflectance radiometer, as well as sample data, to arrive at a delineation of homogeneous areas. The hyperspectral data were processed using Continuum Removal (CR) methodology to obtain information about the content and composition of clay. Differently, the multispectral data were firstly upscaled to the support of soil data using geostatistics and taking into account the change of support. Secondly, the data acquired with the two different sensors were integrated with soil granulometric properties by using two multivariate geostatistical techniques: multi-collocated cokriging to achieve a more exhaustive and finer-scale soil characterization, and multi-collocated factor cokriging to extract synthetic scale-dependent indices (regionalized factors) for the delineation of soil in homogeneous zones. This paper shows the impact of change of support on the uncertainty of soil prediction that can have a significant effect on decision making in Precision Agriculture. Moreover, four regionalized factors at two different scales (two for each scale) were retained and mapped. Each factor provided a different delineation of the field with areas characterized by different granulometries and clay compositions. The applied method is sufficiently flexible and could be applied to any number and type of sensors.
Analysis of Data-Derived SeaWinds Normalized Radar Cross-Section Noise
Authors: Grieco Giuseppe , Stoffelen Ad , Verhoef Anton , Vogelzang Jur , Portabella Marcos
Published: October 2022

The normalized standard deviation (Kp) of the noise that affects scatterometer Normalized Radar Cross-Sections (σ0s) plays a key role in the ocean and more in particular coastal wind retrieval procedures and the a posteriori quality control. This paper presents a method based on SeaWinds measurements to estimate Kps. The method computes the standard deviation of the differences between the full-resolution (slice) σ0s and the footprint (egg) σ0. The results are compared to the median of Kps provided with SeaWinds σ0s, showing some non-negligible differences. Kps estimated on non-homogeneous surfaces are larger than those estimated on sea, whereas no differences are appreciated in the provided Kps, which is likely due to the ability of this methodology to account for the effect of the scene variability in the estimates. The presence of inter-slice biases is demonstrated with a trend with the antenna azimuth angle. A multi-collocation slice cross-calibration procedure is suggested for the retrieval stage. Finally, a theoretical model of the distribution of σ0s is proposed and used to validate Kps. The results prove the efficacy of this model and that the provided Kps seem to be largely underestimated at low-wind regimes.
High Resolution 3D Mapping of Hurricane Flooding from Moderate-Resolution Operational Satellites
Authors: Li Sanmei , Goldberg Mitchell , Kalluri Satya , Lindsey Daniel T. , Sjoberg Bill , Zhou Lihang , Helfrich Sean , Green David , Borges David , Yang Tianshu , Sun Donglian
Published: October 2022

Floods are often associated with hurricanes making landfall. When tropical cyclones/hurricanes make landfall, they are usually accompanied by heavy rainfall and storm surges that inundate coastal areas. The worst natural disaster in the United States, in terms of loss of life and property damage, was caused by hurricane storm surges and their associated coastal flooding. To monitor coastal flooding in the areas affected by hurricanes, we used data from sensors aboard the operational Polar-orbiting and Geostationary Operational Environmental Satellites. This study aims to apply a downscaling model to recent severe coastal flooding events caused by hurricanes. To demonstrate how high-resolution 3D flood mapping can be made from moderate-resolution operational satellite observations, the downscaling model was applied to the catastrophic coastal flooding in Florida due to Hurricane Ian and in New Orleans due to Hurricanes Ida and Laura. The floodwater fraction data derived from the SNPP/NOAA-20 VIIRS (Visible Infrared Imaging Radiometer Suite) observations at the original 375 m resolution were input into the downscaling model to obtain 3D flooding information at 30 m resolution, including flooding extent, water surface level and water depth. Compared to a 2D flood extent map at the VIIRS’ original 375 m resolution, the downscaled 30 m floodwater depth maps, even when shown as 2D images, can provide more details about floodwater distribution, while 3D visualizations can demonstrate floodwater depth more clearly in relative to the terrain and provide a more direct perception of the inundation situations caused by hurricanes. The use of 3D visualization can help users clearly see floodwaters occurring over various types of terrain conditions, thus identifying a hazardous flood from non-hazardous flood types. Furthermore, 3D maps displaying floodwater depth may provide additional information for rescue efforts and damage assessments. The downscaling model can help enhance the capabilities of moderate-to-coarse resolution sensors, such as those used in operational weather satellites, flood detection and monitoring.
Development of the Statistical Errors Raster Toolbox with Six Automated Models for Raster Analysis in GIS Environments
Authors: Dimitriadou Stavroula , Nikolakopoulos Konstantinos G.
Published: October 2022

The Statistical Errors Raster Toolbox includes models of the most popular error metrics in the interdisciplinary literature, namely, root mean square error (RMSE), normalized root mean square error (NRMSE), mean bias error (MBE), normalized mean bias error (NMBE), mean absolute error (MAE) and normalized mean absolute error (NMAE), for computing the areal errors of any raster file in .tiff format as compared with a reference raster file. The models are applicable to any size of raster files, no matter if no-data pixels are included. The only prerequisites are that the two raster files share the same units, cell size, and projection system. The novelty lies in the fact that, to date, there is no such application in ArcGIS Pro 3/ArcMap 10.8. Therefore, users who work with raster files require external software, plus the relevant expertise. An application on the reference evapotranspiration (ETo) of Peloponnese peninsula (Greece) is presented. MODIS ET products and ETo raster files for empirical methods are employed. The results of the models (for 20,440 valid values) are compared to the results of external software (for 1000 random points). Considering that the different sample sizes can lead to different accuracies and the inhomogeneity of the area, it is obvious that the results are almost identical.
Ceiling-View Semi-Direct Monocular Visual Odometry with Planar Constraint
Authors: Wang Yishen , Zhang Shaoming , Wang Jianmei
Published: October 2022

When the SLAM algorithm is used to provide positioning services for a robot in an indoor scene, dynamic obstacles can interfere with the robot’s observation. Observing the ceiling using an upward-looking camera that has a stable field of view can help the robot avoid the disturbance created by dynamic obstacles. Aiming at the indoor environment, we propose a new ceiling-view visual odometry method that introduces plane constraints as additional conditions. By exploiting the coplanar structural constraints of the features, our method achieves better accuracy and stability in a ceiling scene with repeated texture. Given a series of ceiling images, we first use the semi-direct method with the coplanar constraint to preliminarily calculate the relative pose between camera frames and then exploit the ceiling plane as an additional constraint. In this step, the photometric error and the geometric constraint are both optimized in a sliding window to further improve the trajectory accuracy. Due to the lack of datasets for ceiling scenes, we also present a dataset for the ceiling-view visual odometry for which the LiDAR-Inertial SLAM method provides the ground truth. Finally, through an actual scene test, we verify that, in the ceiling environment, our method performs better than the existing visual odometry approach.
Use of GAN to Help Networks to Detect Urban Change Accurately
Authors: He Chenyang , Zhao Yindi , Dong Jihong , Xiang Yang
Published: October 2022

Mastering urban change information is of great importance and significance in practical areas such as urban development planning, land management, and vegetation cover. At present, high-resolution remote sensing images and deep learning techniques have been widely used in the detection of urban information changes. However, most of the existing change detection networks are Siamese networks based on encoder–decoder architectures, which tend to ignore the pixel-to-pixel relationships and affect the change detection results. To solve this problem, we introduced a generative adversarial network (GAN). The change detection network based on the encoder–decoder architecture was used as the generator of the GAN, and the Jensen-Shannon(JS) scatter in the GAN model was replaced by the Wasserstein distance. An urban scene change detection dataset named XI’AN-CDD was produced to verify the effectiveness of the algorithm. Compared with the baseline model of the change detection network, our generator outperformed it significantly and had higher feature integrity. When the GAN was added, the detected feature integrity was better, and the F1-score increased by 4.4%.
Estimation of Potato Above-Ground Biomass Using UAV-Based Hyperspectral images and Machine-Learning Regression
Authors: Liu Yang , Feng Haikuan , Yue Jibo , Fan Yiguang , Jin Xiuliang , Zhao Yu , Song Xiaoyu , Long Huiling , Yang Guijun
Published: October 2022

Above-ground biomass (AGB) is an important indicator for monitoring crop growth and plays a vital role in guiding agricultural management, so it must be determined rapidly and nondestructively. The present study investigated the extraction from UAV hyperspectral images of multiple variables, including canopy original spectra (COS), first-derivative spectra (FDS), vegetation indices (VIs), and crop height (CH) to estimate the potato AGB via the machine-learning methods of support vector machine (SVM), random forest (RF), and Gaussian process regression (GPR). High-density point clouds were combined with three-dimensional spatial information from ground control points by using structures from motion technology to generate a digital surface model (DSM) of the test field, following which CH was extracted based on the DSM. Feature bands in sensitive spectral regions of COS and FDS were automatically identified by using a Gaussian process regression-band analysis tool that analyzed the correlation of the COS and FDS with the AGB in each growth period. In addition, the 16 Vis were separately analyzed for correlation with the AGB of each growth period to identify highly correlated Vis and excluded highly autocorrelated variables. The three machine-learning methods were used to estimate the potato AGB at each growth period and their results were compared separately based on the COS, FDS, VIs, and combinations thereof with CH. The results showed that (i) the correlations of COS, FDS, and VIs with AGB all gradually improved when going from the tuber-formation stage to the tuber-growth stage and thereafter deteriorated. The VIs were most strongly correlated with the AGB, followed by FDS, and then by COS. (ii) The CH extracted from the DSM was consistent with the measured CH. (iii) For each growth stage, the accuracy of the AGB estimates produced by a given machine-learning method depended on the combination of model variables used (VIs, FDS, COS, and CH). (iv) For any given set of model variables, GPR produced the best AGB estimates in each growth period, followed by RF, and finally by SVM. (v) The most accurate AGB estimate was achieved in the tuber-growth stage and was produced by combining spectral information and CH and applying the GPR method. The results of this study thus reveal that UAV hyperspectral images can be used to extract CH and crop-canopy spectral information, which can be used with GPR to accurately estimate potato AGB and thereby accurately monitor crop growth.
Comparison between Different TomoSAR Imaging Models for Airborne Platform Flying at Low Altitude
Authors: Yan Qiancheng , Jiao Zekun , Qiu Xiaolan , Wang Bingnan , Ding Chibiao
Published: October 2022

The classical planar-wavefront-based TomoSAR imaging model suffers from the problem that the effective integration interval is not enough to cover the target distribution region in the low-altitude airborne case. It will lead to a deterioration of the performance of tomogram reconstruction and inaccuracy of estimated scatterers. This paper reviews the exact and approximate forms of the aforementioned inaccurate model based on planar wavefront and points out the problem with the conventional model. To solve this problem, we propose spherical wavefront models with the exact form or an approximate form of the slant range formula. The estimated variable for the scatterer’s location is converted from elevation to off-nadir angle, and the effective integration interval has been extended. In addition, we explore relationships between the exact form of the conventional model and the exact form of the proposed model, and the relationship between the approximate form of the conventional model and the approximate form of the proposed model. This provides a basis for modifying the inversion algorithm that is designed based on the conventional model to adapt to the low-altitude airborne case. Eventually, through experiments based on simulated data and measured data, the imprecise reconstructions obtained with the conventional model are demonstrated, and the correctness of spherical wavefront models and the effectiveness of transformation between models are proved.
An Efficient Imaging Method for Medium-Earth-Orbit Multichannel SAR-GMTI Systems
Authors: Li Yongkang , Huo Tianyu , Cao Cuiqian
Published: October 2022

Medium-Earth-orbit (MEO) synthetic aperture radar (SAR) has the advantages of short revisit time and wide coverage, and thus is a potential tool for implementing ground moving target indication (GMTI) tasks. In the paper, aiming at MEO SAR’s problems of low signal-to-noise ratio and limited computation resource, an efficient imaging method is proposed for MEO multichannel SAR-GMTI systems with relatively low resolution. The proposed imaging method is designed with the consideration of both static scenes and ground moving targets, and it can simultaneously correct the range cell migrations of static scenes and multiple moving targets of no Doppler ambiguity. It needs only four Fourier transforms and twice phase multiplications, and thus is computationally efficient. Moreover, moving targets’ signal characteristics, including the azimuth and range displacements and along-track interferometric phase, in the SAR image obtained by the proposed imaging method are figured out. Experimental results validate the proposed imaging method and the theoretical analyses.
A Strict Validation of MODIS Lake Surface Water Temperature on the Tibetan Plateau
Authors: Lazhu  , Yang Kun , Qin Jun , Hou Juzhi , Lei Yanbin , Wang Junbo , Huang Anning , Chen Yingying , Ding Baohong , Li Xin
Published: October 2022

Lake surface water temperature (LSWT) is a key parameter in understanding the variability of lake thermal conditions and evaporation. The MODIS-derived LSWT is widely used as a reference for lake model validations and process studies in data-scarce regions. In this study, the accuracy of the MODIS LSWT was examined on the Tibetan Plateau (TP). In-situ subsurface temperatures were collected at five large lakes. Although the observation period covers from summer to winter, only the observations during the lake turnover period (from October to freeze-up), when the lakes are well mixed, can be used as ground truth. The MODIS LSWT agrees well with the selected in-situ data for the five large lakes, with root mean square error (RMSE) < 1 °C at nighttime and <2 °C in the daytime, indicating a high accuracy of the MODIS LSWT data. Before the turnover period, the water is thermally stratified and the surface water is warmer than the subsurface water, and thus the in-situ subsurface water temperature data and the MODIS LSWT have different representativeness. In this case, if the observations are used as a validation basis, the MODIS errors could be much magnified. This in turn indicates the importance of period selection for the validation.
A Land Cover Classification Method for High-Resolution Remote Sensing Images Based on NDVI Deep Learning Fusion Network
Authors: Zhao Jingzheng , Wang Liyuan , Yang Hui , Wu Penghai , Wang Biao , Pan Chengrong , Wu Yanlan
Published: October 2022

High-resolution remote sensing (HRRS) images have few spectra, low interclass separability and large intraclass differences, and there are some problems in land cover classification (LCC) of HRRS images that only rely on spectral information, such as misclassification of small objects and unclear boundaries. Here, we propose a deep learning fusion network that effectively utilizes NDVI, called the Dense-Spectral-Location-NDVI network (DSLN). In DSLN, we first extract spatial location information from NDVI data at the same time as remote sensing image data to enhance the boundary information. Then, the spectral features are put into the encoding-decoding structure to abstract the depth features and restore the spatial information. The NDVI fusion module is used to fuse the NDVI information and depth features to improve the separability of land cover information. Experiments on the GF-1 dataset show that the mean OA (mOA) and the mean value of the Kappa coefficient (mKappa) of the DSLN network model reach 0.8069 and 0.7161, respectively, which have good applicability to temporal and spatial distribution. The comparison of the forest area released by Xuancheng Forestry Bureau and the forest area in Xuancheng produced by the DSLN model shows that the former is consistent with the latter. In conclusion, the DSLN network model is effectively applied in practice and can provide more accurate land cover data for regional ESV analysis.
Mapping Forest Aboveground Biomass with MODIS and Fengyun-3C VIRR Imageries in Yunnan Province, Southwest China Using Linear Regression, K-Nearest Neighbor and Random Forest
Authors: Chen Huafang , Qin Zhihao , Zhai De-Li , Ou Guanglong , Li Xiong , Zhao Gaojuan , Fan Jinlong , Zhao Chunliang , Xu Hui
Published: October 2022

The aboveground biomass (AGB) of a forest is an important indicator of the forest’s terrestrial carbon storage and its relation to climate change. Due to the advantage of extensive spatial coverage and low cost, coarse-resolution remote sensing data is the main data source for wall-to-wall mapping of forest AGB at the regional scale. Despite this, improving the accuracy and efficiency of forest AGB estimation is a major challenge. In this study, two optical imageries, Moderate Resolution Imaging Spectroradiometer (MODIS) 500 m imagery and Fengyun-3C Visible and Infrared Radiometer (FY-3C VIRR) 1000 m imagery, were used and compared for forest AGB estimation in Yunnan Province, southwest China. One parametric approach, multiple linear regression (MLR), and two nonparametric approaches, k-nearest neighbor (KNN) and random forest (RF), were applied for the two imagery datasets, respectively. We evaluated the performance of the combination of remote sensing data and modeling approaches by comparing the accuracies and also explored the potential of FY-3C imagery data in forest AGB estimation at the regional scale as it was used for this purpose for the first time. We found that the machine learning models KNN and RF provided better results than MLR. From the three approaches for both MODIS and FY-3C imagery, RF performed best with R2 values of 0.84 and 0.81 and RMSE of 23.18 and 23.43, respectively. Estimation of forest AGB based on MODIS was marginally better than the estimation based on FY-3C. FY-3C imagery could therefore be an additional optical remote sensing data source of coarse spatial resolution, comparable to MODIS data which has been widely used for regional forest AGB estimation. Indices related to forest canopy moisture levels from both types of imagery were sensitive to forest AGB. The RF model and MODIS imagery were then applied to map the spatial variation of forest AGB of Yunnan Province. As a result of our study, we determined that Yunnan Province has a total forest AGB of 2123.22 Mt, with a mean value of 58.05 t/ha for forestland in 2016.
MSSDet: Multi-Scale Ship-Detection Framework in Optical Remote-Sensing Images and New Benchmark
Authors: Chen Weiming , Han Bing , Yang Zheng , Gao Xinbo
Published: October 2022

Ships comprise the only and most important ocean transportation mode. Thus, ship detection is one of the most critical technologies in ship monitoring, which plays an essential role in maintaining marine safety. Optical remote-sensing images contain rich color and texture information, which is beneficial to ship detection. However, few optical remote-sensing datasets are open publicly due to the issue of sensitive data and copyrights, and only the HRSC2016 dataset is built for the ship-detection task. Moreover, almost all general object detectors suffer from the failure of multi-scale ship detection because of the diversity of spatial resolution and ship size. In this paper, we re-annotate the HRSC2016 dataset and supplement 610 optical remote-sensing images to build a new open source ship-detection benchmark dataset with rich multi-scale ship objects named the HRSC2016-MS dataset. In addition, we further explore the potential of a recursive mechanism in the field of object detection and propose a novel multi-scale ship-detection framework (MSSDet) in optical remote-sensing images. The success of detecting multi-scale objects depends on the hierarchical pyramid structure in the object-detection framework. However, the inherent semantic and spatial gaps among hierarchical pyramid levels seriously affect detection performance. To alleviate this problem, we propose a joint recursive feature pyramid (JRFP), which can generate semantically strong and spatially refined multi-scale features. Extensive experiments were conducted on the HRSC2016-MS, HRSC2016, and DIOR datasets. Detailed ablation studies directly demonstrated the effectiveness of the proposed JRFP architecture and also showed that the proposed method has excellent generalizability. Comparisons with state-of-the-art methods showed that the proposed method achieves competitive performance, i.e., 77.3%, 95.8%, and 73.3% mean average precision accuracy on the HRSC2016-MS, HRSC2016, and DIOR datasets, respectively.
Applying Deep Learning in the Prediction of Chlorophyll-a in the East China Sea
Authors: Cen Haobin , Jiang Jiahan , Han Guoqing , Lin Xiayan , Liu Yu , Jia Xiaoyan , Ji Qiyan , Li Bo
Published: October 2022

The ocean chlorophyll-a (Chl-a) concentration is an important variable in the marine environment, the abnormal distribution of which is closely related to the hazards of red tides. Thus, the accurate prediction of its concentration in the East China Sea (ECS) is greatly important for preventing water eutrophication and protecting the coastal ecological environment. Processed by two different pre-processing methods, 10-year (2011–2020) satellite-observed chlorophyll-a data and logarithmic data were used as the long short-term memory (LSTM) neural network training datasets in this study. The 2021 data were used for comparison to prediction results. The past 15 days’ data were used to predict the concentration of chlorophyll-a for the five following days. Results showed that the predictions obtained by both pre-processing methods could simulate the seasonal distribution of the Chl-a concentration in the ECS effectively. Moreover, the prediction performance of the model driven by the original values was better in the medium- and low-concentration regions. However, in the high-concentration region, the prediction of extreme concentrations by the two data-driven LSTM models showed underestimation, considering that the prediction performance of the model driven by the original values was better. Results of sensitivity experiments showed that the prediction accuracy of the model decreased considerably when the backward prediction time step increased. In this study, the neural network was driven only by chlorophyll-a, whose concentration in the ECS was forecasted, and the effect of other relevant marine elements on Chl-a was not considered, which is the current weakness of this study.
NanoMap: A GPU-Accelerated OpenVDB-Based Mapping and Simulation Package for Robotic Agents
Authors: Walker Violet , Vanegas Fernando , Gonzalez Felipe
Published: October 2022

Encoding sensor data into a map is a problem that must be undertaken by any robotic agent operating in unknown or uncertain environments, and real-time updates are crucial to safe planning and control. Most modern robotic sensors produce some form of depth data or point cloud information that is only useful to the agent after being processed into the appropriate data structure, oftentimes an occupancy map. However, as the quality of sensor technology improves, so does the magnitude of the input data, which can creates a problem when trying to construct occupancy maps in real-time. Populating such an occupancy map using these dense point clouds can quickly become an expensive process, and many robotic agents have limited onboard computational bandwidth and memory. This results in delayed map updates and reduced operational performance in dynamic environments where real-time information is crucial for safe operation. However, while many modern robotic agents are still relatively limited by the power of onboard central processing units (CPUs), many platforms are gaining access to onboard graphics processing units (GPUs), and these resources remain underutilised with respect to the problem of occupancy mapping. We propose a novel probabilistic mapping solution that leverages a combination of OpenVDB, NanoVDB, and Nvidia’s Compute Unified Device Architecture (CUDA) to encode dense point clouds into OpenVDB data structures, leveraging the parallel compute strength of GPUs to provide significant speed advantages and further free up resources for tasks that cannot as easily be performed in parallel. An evaluation of our solution is provided, with performance benchmarks provided for both a laptop and a low power single board computer with onboard GPU. Similar performance improvements should be accessible on any system with access to a CUDA-compatible GPU. Additionally, our library provides the means to simulate one or more sensors on an agent operating within a randomly generated 3D-grid environment and create a live map for the purposes of evaluating planning and control techniques and for training agents via deep reinforcement learning. We also provide interface packages for the Robotic Operating System (ROS1) and the Robotic Operating System 2 (ROS2), and a ROS2 visualisation (RVIZ2) plugin for the underlying OpenVDB data structure.
Satellite and Machine Learning Monitoring of Optically Inactive Water Quality Variability in a Tropical River
Authors: Li Ning , Ning Ziyu , Chen Miao , Wu Dongming , Hao Chengzhi , Zhang Donghui , Bai Rui , Liu Huiran , Chen Xin , Li Wei , Zhang Wen , Chen Yicheng , Li Qinfen , Zhang Lifu
Published: October 2022

Large-scale monitoring of water quality parameters (WQPs) is one of the most critical issues for protecting and managing water resources. However, monitoring optically inactive WQPs, such as total nitrogen (TN), ammoniacal nitrogen (AN), and total phosphorus (TP) in inland waters, is still challenging. This study constructed retrieval models to explore the spatiotemporal evolution of TN, AN, and TP by Landsat 8 images, water quality sampling, and five machine learning algorithms (support vector regression, SVR; random forest regression, RFR; artificial neural networks, ANN; regression tree, RT; and gradient boosting machine, GBM) in the Nandu River downstream (NRD), a tropical river in China. The results indicated that these models can effectively monitor TN, AN, and TP concentrations at in situ sites. In particular, TN by RFR as well as AN and TP by ANN had better accuracy, in which the R2 value ranged between 0.44 and 0.67, and the RMSE was 0.03–0.33 mg/L in the testing dataset. The spatial distribution of TN, AN, and TP was seasonal in NRD from 2013–2022. TN and AN should be paid more attention to in normal wet seasons of urban and agricultural zones, respectively. TP, however, should be focus on in the normal season of agricultural zones. Temporally, AN decreased significantly in the normal and wet seasons while the others showed little change. These results could provide a large-scale spatial overview of the water quality, find the sensitive areas and periods of water pollution, and assist in identifying and controlling the non-point source pollution in the NRD. This study demonstrated that multispectral remote sensing and machine learning algorithms have great potential for monitoring optically inactive WQPs in tropical large-scale inland rivers.
Ionospheric Inhomogeneities and Their Influences on the Earth’s Remote Sensing from Space
Authors: Kryukovsky Andrew S. , Kutuza Boris G. , Stasevich Vladimir I. , Rastyagaev Dmitry V.
Published: October 2022

An important problem that arises when planning experiments on remote sensing from space in the P-band is taking into account the influence of the Earth’s ionosphere. We investigated the influence of ionospheric inhomogeneities on the results of remote sensing of the Earth from space, taking into account the curvature of the propagation medium. One- and two-layer models of the ionosphere, both with and without large-scale inhomogeneities of the cold ionospheric plasma, were considered. To obtain numerical results, a bicharacteristic system was used, which makes it possible to adequately take into account the complex structures of ionospheric plasma layers. The dependence of the rate of phase change on the height and the dependence of the total electron concentration on the horizontal distance and group time were investigated. The case was compared when the vector of the strength of the Earth’s magnetic field is perpendicular to the plane of propagation, and the case when this vector lies in the plane of propagation. The dependence of the difference between the refractive indices on the height along the rays was studied. Estimates of the Faraday rotation angle and phase deviation were obtained for various models. The magnitude of the angle of Faraday rotation depends significantly on the orientation of the trajectory relative to the Earth’s magnetic field. Polarization coefficients are investigated. It is shown that the o- and x-waves are separately circularly polarized, and the contribution of the longitudinal component of the electric field in the electromagnetic wave is insignificant.