bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of Wind and Solar Insolation Influence on Ocean Near-Surface Temperature from In Situ Observations and the Geostationary Himawari-8 Satellite
Authors: Hsu Po-Chun
Published: October 2022

The skin sea surface temperature (SST) observed by the geostationary Himawari-8 satellite and bulk SST, including four in situ observations from ships, drifters, Argo, and buoys constitute more than 90,000 SST pairs used to analyze near-surface temperature variations. From July 2015 to May 2022, an average SST bias of 0.10 °C and root mean square error of 0.99 °C were observed in the waters adjacent to Taiwan. This study effectively observed that the skin effect generated by ocean wind and solar shortwave radiation caused the occurrence of a cool skin layer and diurnal warm layer (DWL), and 90% of the SST bias was in a range of −1.55~1.71 °C. In the daytime, the skin layer received solar shortwave radiation, thus increasing temperature and causing a DWL. With the increase in insolation, the SST bias in the DWL became more obvious. During winter, strong wind, or low shortwave radiation, the DWL may disappear and turn into a cool skin layer. At night, the near-surface SST was dominated by the cool skin effect, but the DWL generated in the daytime would remain if the wind speed was weak. However, the different hydrological characteristics of the observation position and its distance from the coast could affect the results of the skin effect. Whether there is a rapid change in ocean stratification in a spatial grid of nearly four square kilometers needs to be explored in the future.
Hydrological Connectivity Improves the Water-Related Environment in a Typical Arid Inland River Basin in Xinjiang, China
Authors: Liu Chuanxiu , Chen Yaning , Fang Gonghuan , Zhou Honghua , Huang Wenjing , Liu Yongchang , Wang Xuanxuan , Li Zhi
Published: October 2022

Hydrological connectivity directly affects aquatic ecological processes, water environment and wetland ecological security, which is essential to the stability of arid ecosystems. However, the mechanism between hydrological connectivity and water-related environment has not been revealed completely. To address these issues, we use a landscape connectivity approach to assess the connectivity of water patches for analyzing the hydrological connectivity of the Bosten Lake Basin (BLB), as well as its response to human activities and climate change, based on the Joint Research Centre (JRC) global surface water dataset. It shows that the integral index of connectivity (IIC) of the BLB is low (ranging from 0 to 0.2) from 1990 to 2019, with an increasing interannual trend. The connectivity is higher in wet periods and in oases compared with dry periods and high-altitude mountain regions. Correlation and regression analyses indicate that hydrological connectivity has a strong correlation (r > 0.5, p ≤ 0.05) with water area and water level. The interannual and seasonal trends of eight hydrochemical indices in the Bosten Lake have been investigated to systematically elaborate the complex relationships between hydrological connectivity and water quality in the BLB. Results indicated that better hydrological connectivity can improve water quality, and the minimum of pollutants were observed in high hydrological connectivity period, covering approximately 75% of the high-water quality period. These findings could provide scientific support for the water management in the BLB.
Comparison of Land Use Land Cover Classifiers Using Different Satellite Imagery and Machine Learning Techniques
Authors: Basheer Sana , Wang Xiuquan , Farooque Aitazaz A. , Nawaz Rana Ali , Liu Kai , Adekanmbi Toyin , Liu Suqi
Published: October 2022

Accurate land use land cover (LULC) classification is vital for the sustainable management of natural resources and to learn how the landscape is changing due to climate. For accurate and efficient LULC classification, high-quality datasets and robust classification methods are required. With the increasing availability of satellite data, geospatial analysis tools, and classification methods, it is essential to systematically assess the performance of different combinations of satellite data and classification methods to help select the best approach for LULC classification. Therefore, this study aims to evaluate the LULC classification performance of two commonly used platforms (i.e., ArcGIS Pro and Google Earth Engine) with different satellite datasets (i.e., Landsat, Sentinel, and Planet) through a case study for the city of Charlottetown in Canada. Specifically, three classifiers in ArcGIS Pro, including support vector machine (SVM), maximum likelihood (ML), and random forest/random tree (RF/RT), are utilized to develop LULC maps over the period of 2017–2021. Whereas four classifiers in Google Earth Engine, including SVM, RF/RT, minimum distance (MD), and classification and regression tree (CART), are used to develop LULC maps for the same period. To identify the most efficient and accurate classifier, the overall accuracy and kappa coefficient for each classifier is calculated throughout the study period for all combinations of satellite data, classification platforms, and methods. Change detection is then conducted using the best classifier to quantify the LULC changes over the study period. Results show that the SVM classifier in both ArcGIS Pro and Google Earth Engine presents the best performance compared to other classifiers. In particular, the SVM in ArcGIS Pro shows an overall accuracy of 89% with Landsat, 91% with Sentinel, and 94% with Planet. Similarly, in Google Earth Engine, the SVM shows an accuracy of 87% with Landsat 8 and 92% with Sentinel 2. Furthermore, change detection results show that 13.80% and 14.10% of forest areas have been turned into bare land and urban class, respectively, and 3.90% of the land has been converted into the urban area from 2017 to 2021, suggesting the intensive urbanization. The results of this study will provide the scientific basis for selecting the remote sensing classifier and satellite imagery to develop accurate LULC maps.
A New Data Processing System for Generating Sea Ice Surface Roughness Products from the Multi-Angle Imaging SpectroRadiometer (MISR) Imagery
Authors: Mosadegh Ehsan , Nolin Anne W.
Published: October 2022

Sea ice roughness can serve as a proxy for other sea ice characteristics such as ice thickness and ice age. Arctic-wide maps that represent spatial patterns of sea ice roughness can be used to better characterize spatial patterns of ice convergence and divergence processes. Sea ice surface roughness can also control and quantify turbulent exchange between sea ice surface and atmosphere and therefore influence surface energy balance at the basin scale. We have developed a data processing system that produces georeferenced sea ice roughness rasters that can be mosaicked to produce Arctic-wide maps of sea ice roughness. This approach starts with Top-of-Atmosphere radiance data from the Multi-angle Imaging SpectroRadiometer (MISR). We used red-band angular data from three MISR cameras (Ca, Cf, An). We created a training data set in which MISR pixels were matched with co-located and concurrent lidar-derived roughness measurements from the Airborne Topographic Mapper (ATM). We used a K-nearest neighbor algorithm with the training data to calibrate the multi-angle data to values of surface roughness and then applied the algorithm to Arctic-wide MISR data for two 16-day periods in April (spring) and July (summer). After georeferencing the roughness rasters, we then mosaicked each 16-day roughness dataset to produce Arctic-wide maps of sea ice roughness for spring and summer. Assessment of the results shows good agreement with independent ATM roughness data, not used in model development. A preliminary exploration of spatial and seasonal changes in sea ice roughness for two locations shows the ability to characterize the roughness of different ice types and the results align with previous studies. This processing system and its data products can help the sea ice research community to gain insights into the seasonal and interannual changes in sea ice roughness over the Arctic.
Characterization of Extremely Fresh Biomass Burning Aerosol by Means of Lidar Observations
Authors: De Rosa Benedetto , Amato Francesco , Amodeo Aldo , D’Amico Giuseppe , Dema Claudio , Falconieri Alfredo , Giunta Aldo , Gumà-Claramunt Pilar , Kampouri Anna , Solomos Stavros , Mytilinaios Michail , Papagiannopoulos Nikolaos , Summa Donato , Veselovskii Igor , Mona Lucia
Published: October 2022

In this paper, characterization of the optical and microphysical properties of extremely fresh biomass burning aerosol is presented. This work aims to characterize, for the first time to our knowledge, freshly formed smoke particles observed only a few minutes after they were emitted from a nearby forest fire. The smoke particles were detected by combining passive (sun-photometer) and active (Raman lidar) techniques. On 14 August 2021, an EARLINET (European Aerosol Research Lidar Network) multi-wavelength Raman lidar and a co-located AERONET sun-photometer in Potenza, South Italy, observed an extremely fresh smoke plume. The lidar measurements, carried out from 22:27 to 02:16 UTC, revealed a thick biomass burning layer below 2.7 km. The particle depolarization ratio at 532 nm was 0.025, and lidar ratios at 355 and 532 nm were, respectively, 40 and 38 sr. The mean value of the Ångström exponent was 1.5. The derived size distribution was bimodal with a peak at 0.13 µm, an effective radius mean value of 0.15 µm, and a single scattering albedo of 0.96 at all wavelengths. The real part of the refractive index was 1.58 and the imaginary was 0.006. The AERONET measurements at 5:34 UTC confirmed the lidar measurements.
Assessing Spatiotemporal Changes of SDG Indicators at the Neighborhood Level in Guilin, China: A Geospatial Big Data Approach
Authors: Han Liying , Lu Linlin , Lu Junyu , Liu Xintong , Zhang Shuangcheng , Luo Ke , He Dan , Wang Penglong , Guo Huadong , Li Qingting
Published: October 2022

Due to the challenges in data acquisition, especially for developing countries and at local levels, spatiotemporal evaluation for SDG11 indicators was still lacking. The availability of big data and earth observation technology can play an important role to facilitate the monitoring of urban sustainable development. Taking Guilin, a sustainable development agenda innovation demonstration area in China as a case study, we developed an assessment framework for SDG indicators 11.2.1, 11.3.1, and 11.7.1 at the neighborhood level using high-resolution (HR) satellite images, gridded population data, and other geospatial big data (e.g., road network and point of interest data). The findings showed that the proportion of the population with convenient access to public transport in the functional urban area gradually improved from 42% in 2013 to 52% in 2020. The increase in built-up land was much faster than the increase in population. The areal proportion of public open space decreased from 56% in 2013 to 24% in 2020, and the proportion of the population within the 400 m service areas of open public space decreased from 73% to 59%. The township-level results indicated that low-density land sprawling should be strictly managed, and open space and transportation facilities should be improved in the three fast-growing towns, Lingui, Lingchuan, and Dingjiang. The evaluation results of this study confirmed the applicability of SDG11 indicators to neighborhood-level assessment and local urban governance and planning practices. The evaluation framework of the SDG11 indicators based on HR satellite images and geospatial big data showed great promise to apply to other cities for targeted planning and assessment.
Multi-Scale Analysis for Coherent Change Detection: A Method for Extracting Typical Changed Area
Authors: Wang Zhiheng , Li Shiqiang , Wang Jili
Published: October 2022

Coherent change detection is a technology that utilizes phase information in complex-valued synthetic aperture radar images. It is mostly used to detect subtle changes that cannot be detected by amplitude images on the ground, and it also has excellent detection performance when it comes to low-coherence typical changed areas. However, due to its high sensitivity to changes, this technology will falsely detect areas of natural change such as vegetation disturbance, river flow, and low signal-to-noise ratio areas (e.g., uninteresting areas) as changes, resulting in false-alarm interference areas. In order to tackle this problem, this paper studies a coherent change detection method based on multi-scale analysis to extract typical changed areas in complicated scenes. The method uses an equal variance coherence estimator to calculate the coherence value, separates the interference areas and the typical changed areas using a multi-scale method, and then extracts a binary image of the typical changed areas through noise filtering and threshold segmentation. The method in this paper is experimentally verified with publicly available Airbus spaceborne SAR data, and ESAR airborne data, which is provided by the ESA. The experimental results are visualized and quantitatively evaluated. Through the results, by calculating the probability of correct classification and false-positive and other performance parameters, as well as drawing the receiver operating characteristic curve and the kappa coefficient curve of different threshold values, we find that the method has the capability to suppress the interference areas and the high detection performance of the typical changed areas. The experimental data are complicated scenes that include various types of ground object changes. The results show that the method is effective and universal and can provide reference value for the application of coherent change detection.
Logging Pattern Detection by Multispectral Remote Sensing Imagery in North Subtropical Plantation Forests
Authors: Hu Yue , Wang Zhuna , Zhang Yahao , Dian Yuanyong
Published: October 2022

Forest logging detection is important for sustainable forest management. The traditional optical satellite images with visible and near-infrared bands showed the ability to identify intensive timber logging. However, less intensive logging is still difficult to detect with coarse spatial resolution such as Landsat or high spatial resolution in fewer spectral bands. Although more high-resolution remote sensing images containing richer spectral bands can be easily obtained nowadays, the questions of whether they facilitate the detection of logging patterns and which spectral bands are more effective in detecting logging patterns, especially in selective logging, remain unresolved. Therefore, this paper aims to evaluate the combinations of visible, near-infrared, red-edge, and short-wave infrared bands in detecting three different logging intensity patterns, including unlogged (control check, CK), selective logging (SL), and clear-cutting (CC), in north subtropical plantation forests with the random forest algorithm using Sentinel-2 multispectral imagery. This study aims to explore the recognition performance of different combinations of spectral bands (visual (VIS) and near-infrared bands (NIR), VIS, NIR combined with red-edge, VIS, NIR combined with short-wave infrared bands (SWIR), and full-spectrum bands combined with VIS, NIR, red edge and SWIR) and to determine the best spectral variables to be used for identifying logging patterns, especially in SL. The study was conducted in Taizishan in Hubei province, China. A total of 213 subcompartments of different logging patterns were collected and the random forest algorithm was used to classify logging patterns. The results showed that full-spectrum bands which contain the red-edge and short-wave infrared bands improve the ability of conventional optical satellites to monitor forest logging patterns and can achieve an overall accuracy of 85%, especially for SL which can achieve 79% and 64% for precision and recall accuracy, respectively. The red-edge band (698–713 nm, B5 in Sentinel-2), short-wave infrared band (2100–2280 nm, B12 in Sentinel-2), and associated vegetation indices (NBR, NDre2, and NDre1) enhance the sensitivity of the spectral information to logging patterns, especially for the SL pattern, and the precision and recall accuracy can improve by 10% and 6%, respectively. Meanwhile, both clear-cutting and unlogged patterns could be well-classified whether adding a red-edge or SWIR band or both in VIS and NIR bands; the best precision and recall accuracies for clear-cutting were enhanced to 97%, 95% and 81%, 91% for unlogged, respectively. Our results demonstrate that the optical images have the potential ability to detect logging patterns especially for the clear-cutting and unlogged patterns, and the selective logging detection accuracy can be improved by adding red-edge and short-wave infrared spectral bands.
Experimental Study of Accuracy of High-Rate GNSS in Context of Structural Health Monitoring
Authors: Qu Xuanyu , Shu Bao , Ding Xiaoli , Lu Yangwei , Li Guopeng , Wang Li
Published: October 2022

Global Navigation Satellite Systems (GNSS)-based technologies have become an indispensable part of current structural health monitoring (SHM) systems because of the unique capability of the GNSS technologies to provide accurate real-time displacement information. GNSS equipment with a data sampling rate of up to about 20 Hz has been widely used for this purpose. High-rate GNSS systems (typically up to about 100 Hz) offer additional advantages in structural health monitoring as some highly dynamic civil structures, such as some bridges, require high-rate monitoring data to capture the dynamic behaviors. However, the performance of high-rate GNSS positioning in the context of structural health monitoring is not entirely known, as studies on structural monitoring with high-rate GNSS positioning are very limited, especially considering that some of the satellite systems just reached their full constellations very recently. We carried out a series of experiments with the help of a shaking table to assess the SHM performance of a set of 100 Hz GNSS equipment and three commonly used GNSS positioning techniques, PPP (precise point positioning), PPP-AR (precise point positioning with ambiguity resolution), and RTK (real-time kinematic). We found that the standard deviations of the 100 Hz GNSS displacement solutions derived from PPP, PPP-AR, and RTK techniques were 5.5 mm, 3.6 mm, and 0.8 mm, respectively, when the antenna was in quasi-static motion, and about 9.2 mm, 6.2 mm, and 3.5 mm, respectively, when the antenna was in vibration (up to about 0.7 Hz), under typical urban observational conditions in Hong Kong. We also found that the higher a sampling rate is, the lower the accuracy of a measured displacement series is. On average, the 10 Hz and 100 Hz results are 5.5% and 10.3%, respectively, noisier than the 1 Hz results.
Improving Clear-Sky Solar Power Prediction over China by Assimilating Himawari-8 Aerosol Optical Depth with WRF-Chem-Solar
Authors: Wang Su , Dai Tie , Li Cuina , Cheng Yueming , Huang Gang , Shi Guangyu
Published: October 2022

Although the Weather Research and Forecasting model with solar extensions (WRF-Solar) is tailed for solar energy applications, its official version lacks the consideration of the online aerosol-radiation process. To overcome this limitation, we have coupled the aerosol module online with the radiation module, then assimilated the high-resolution aerosol optical depth (AOD) from the Himawari-8 next-generation geostationary satellite using a three-dimensional variational (3DVAR) AOD data assimilation system to optimize the irradiance predictions with the better aerosol–radiation interaction. The results show that data assimilation can significantly eliminate the AOD underestimations and reasonably reproduce the AOD temporal distributions, improving 51.63% for biases and 61.29% for correlation coefficients. Compared with the original WRF-Solar version, coupled online with an advanced aerosol module minifies the bias value of global horizontal irradiance (GHI) up to 44.52%, and AOD data assimilation contributes to a further reduction of 17.43%.
Robustness of Deep Learning-Based Specific Emitter Identification under Adversarial Attacks
Authors: Sun Liting , Ke Da , Wang Xiang , Huang Zhitao , Huang Kaizhu
Published: October 2022

Deep learning (DL)-based specific emitter identification (SEI) technique can automatically extract radio frequency (RF) fingerprint features in RF signals to distinguish between legal and illegal devices and enhance the security of wireless network. However, deep neural network (DNN) can easily be fooled by adversarial examples or perturbations of the input data. If a malicious device emits signals containing a specially designed adversarial samples, will the DL-based SEI still work stably to correctly identify the malicious device? To the best of our knowledge, this research is still blank, let alone the corresponding defense methods. Therefore, this paper designs two scenarios of attack and defense and proposes the corresponding implementation methods to specializes in the robustness of DL-based SEI under adversarial attacks. On this basis, detailed experiments are carried out based on the real-world data and simulation data. The attack scenario is that the malicious device adds an adversarial perturbation signal specially designed to the original signal, misleading the original system to make a misjudgment. Experiments based on three different attack generation methods show that DL-based SEI is very vulnerability. Even if the intensity is very low, without affecting the probability density distribution of the original signal, the performance can be reduced to about 50%, and at −22 dB it is completely invalid. In the defense scenario, the adversarial training (AT) of DL-based SEI is added, which can significantly improve the system’s performance under adversarial attacks, with ≥60% improvement in the recognition rate compared to the network without AT. Further, AT has a more robust effect on white noise. This study fills the relevant gaps and provides guidance for future research. In the future research, the impact of adversarial attacks must be considered, and it is necessary to add adversarial training in the training process.
DeepForest: Novel Deep Learning Models for Land Use and Land Cover Classification Using Multi-Temporal and -Modal Sentinel Data of the Amazon Basin
Authors: Cherif Eya , Hell Maximilian , Brandmeier Melanie
Published: October 2022

Land use and land cover (LULC) mapping is a powerful tool for monitoring large areas. For the Amazon rainforest, automated mapping is of critical importance, as land cover is changing rapidly due to forest degradation and deforestation. Several research groups have addressed this challenge by conducting local surveys and producing maps using freely available remote sensing data. However, automating the process of large-scale land cover mapping remains one of the biggest challenges in the remote sensing community. One issue when using supervised learning is the scarcity of labeled training data. One way to address this problem is to make use of already available maps produced with (semi-) automated classifiers. This is also known as weakly supervised learning. The present study aims to develop novel methods for automated LULC classification in the cloud-prone Amazon basin (Brazil) based on the labels from the MapBiomas project, which include twelve classes. We investigate different fusion techniques for multi-spectral Sentinel-2 data and synthetic aperture radar Sentinel-1 time-series from 2018. The newly designed deep learning architectures—DeepForest-1 and DeepForest-2—utilize spatiotemporal characteristics, as well as multi-scale representations of the data. In several data scenarios, the models are compared to state-of-the-art (SotA) models, such as U-Net and DeepLab. The proposed networks reach an overall accuracy of up to 75.0%, similar to the SotA models. However, the novel approaches outperform the SotA models with respect to underrepresented classes. Forest, savanna and crop were mapped best, with F1 scores up to 85.0% when combining multi-modal data, compared to 81.6% reached by DeepLab. Furthermore, in a qualitative analysis, we highlight that the classifiers sometimes outperform the inaccurate labels.
Design and Analysis of a New Deployable Docking Mechanism for Microsatellites
Authors: Zhao Yong , Yang Tao , Yue Honghao , Yang Xiaoze , Bai Dong , Yang Fei
Published: October 2022

In-orbit docking technology of microsatellites to realize combined reconfiguration has a wide application prospect, such as large antennas and space telescopes. In order to reduce collision impact and improve docking accuracy, a new deployable docking mechanism is proposed based on the slider-crank principle, which has the advantages of smaller volume and larger posture tolerance. To achieve large capture tolerance and increase the success rate of docking, the posture error is analyzed by considering the specific boundary of the position and pose. And a step-by-step cooperative capture strategy is proposed to complete the velocity selection and action matching among multiple capture arms. The reliable docking of posture correction in the docking process is realized by designing the action path of the docking mechanism. The effects of tolerance capture under different initial posture conditions are analyzed by dynamic simulation. The effectiveness and superiority of the step-by-step cooperative capture strategy are valid by comparison with the synchronized capture strategy. The comparison results show that the impact force is reduced by 8% than the synchronized strategy. The capture experiments are carried out to verify the docking performance. The results show the proposed configuration with a step-by-step cooperative capture strategy achieves successfully reliable capture, weak impact, and large posture tolerance under eight extreme initial pose conditions.
High-Sensitivity MEMS Shear Probe for Autonomous Profiling Observation of Marine Turbulence
Authors: Wang Renxin , Qiao Qingyu , Yang Shasha , Kong Xiangzheng , Liu Guochang , Chen Xu , Yang Hua , Song Dalei , Jia Licheng , Cui Jiangong , He Changde , Yang Yuhua , Zhang Guojun , Xue Chenyang , Zhang Wendong
Published: October 2022

Autonomous profiling observation of full-depth marine turbulence is very important for ocean research. Anisotropic turbulence near the boundary layer needs to be observed well. However, there is lack of high-spatial-resolution and high-sensitivity methods to fulfill vector turbulence observation. Hence, a highly sensitive bullet-headed MEMS shear probe for observing ocean vector turbulence is developed in this manuscript. The sensing mechanism, design and fabrication are demonstrated in detail. In order to meet the bandwidth requirements for observing ocean turbulence, we perform wet-mode simulations of probe structure to achieve an eigenfrequency of 490 Hz. Through sensitivity calibration experiments, it achieves a sensitivity of 4.84 × 10−2 V·m·s2/kg, which is much higher than those of shear probes reported previously. In addition, the vector test validates that the probe can measure ocean vector turbulence. The results show that the proposed probe is promising in autonomous profiling observation of marine turbulence.
Wind Field Retrieval with Rain Correction from Dual-Polarized Sentinel-1 SAR Imagery Collected during Tropical Cyclones
Authors: Shao Weizeng , Lai Zhengzhong , Nunziata Ferdinando , Buono Andrea , Jiang Xingwei , Zuo Juncheng
Published: October 2022

The purpose of this study is to include rain effects in wind field retrieval from C-band synthetic aperture radar (SAR) imagery collected under tropical cyclone conditions. An effective and operationally attractive approach to detect rain cells in SAR imagery is proposed and verified using four Sentinel-1 (S-1) SAR images collected in dual-polarized (vertical-vertical (VV) and vertical-horizontal (VH)) interferometric-wide swath imaging mode during the Satellite Hurricane Observation Campaign. SAR images were collocated with ancillary observations that include sea surface wind and rain rate from the Stepped-Frequency Microwave Radiometer (SFMR) on board of the National Oceanic and Atmospheric Administration aircraft. The winds are inverted from VV- and VH-polarized S-1 image using the CMOD5.N and S1IW.NR geophysical model functions (GMFs), respectively. Location and radius of cyclone’s eye, together with the TC central pressure, are calculated from the VV-polarized SAR-derived wind and a parametric model. A cost function is proposed that consists of the difference between the measured VV-polarized SAR normalized radar cross section (NRCS) and the NRCS predicted using CMOD5.N forced with the wind speed retrieved by the VH-polarized SAR images using S1IW.NR GMF and the wind direction retrieved from the patterns visible in the SAR image. This cost function is related to the SFMR rain rate. Experimental results show that the difference between measured and predicted NRCS values range from 0.5 dB to 5 dB within a distance of 100 km from the cyclone’s eye, while the difference increases spanning from 3 dB to 6 dB for distances larger than 100 km. Following this rationale, first the rain bands are extracted from SAR imagery and, then, the composite wind fields are reconstructed by replacing: (1) dual-polarized SAR-derived winds over the rain-free regions; (2) winds simulated using the radial-vortex model over the rain-affected regions. The validation of the composite wind speed against SFMR winds yields a <2 m s−1 and >0.7 correlation (COR) at all flow directions up to retrieval speeds of 70 m s−1. This result outperforms the winds estimated using the VH-polarized S1IW.NR GMF, which call for high error accuracy, such as about 4 m s−1 with a 0.45 COR ranged from 330° to 360°.
Quantifying Plant Species α-Diversity Using Normalized Difference Vegetation Index and Climate Data in Alpine Grasslands
Authors: Tian Yuan , Fu Gang
Published: October 2022

Quantitative plant species α-diversity of grasslands at multiple spatial and temporal scales is important for investigating the responses of biodiversity to global change and protecting biodiversity under global change. Potential plant species α-diversity (i.e., SRp, Shannonp, Simpsonp and Pieloup: potential species richness, Shannon index, Simpson index and Pielou index, respectively) were quantified by climate data (i.e., annual temperature, precipitation and radiation) and actual plant species α-diversity (i.e., SRa, Shannona, Simpsona and Pieloua: actual species richness, Shannon index, Simpson index and Pielou index, respectively) were quantified by normalized difference vegetation index and climate data. Six methods (i.e., random forest, generalized boosted regression, artificial neural network, multiple linear regression, support vector machine and recursive regression trees) were used in this study. Overall, the constructed random forest models performed the best among the six algorithms. The simulated plant species α-diversity based on the constructed random forest models can explain no less than 96% variation of the observed plant species α-diversity. The RMSE and relative biases between simulated α-diversity based on the constructed random forest models and observed α-diversity were ≤1.58 and within ±4.49%, respectively. Accordingly, plant species α-diversity can be quantified from the normalized difference vegetation index and climate data using random forest models. The random forest models of plant α-diversity build by this study had enough predicting accuracies, at least for alpine grassland ecosystems, Tibet. The proposed random forest models of plant α-diversity by this current study can help researchers to save time by abandoning plant community field surveys, and facilitate researchers to conduct studies on plant α-diversity over a long-term temporal scale and larger spatial scale under global change.
Interannual and Monthly Variability of Typical Inland Lakes on the Tibetan Plateau Located in Three Different Climatic Zones
Authors: Ma Weiyao , Bai Ling , Ma Weiqiang , Hu Wei , Xie Zhipeng , Su Rongmingzhu , Wang Binbin , Ma Yaoming
Published: October 2022

Changes in lake water volume can reflect variations in regional hydrometeorology and are a sensitive indicator of regional environmental change. The Tibetan Plateau, referred to as the “Asian Water Tower”, has a large number of lakes. These lakes are in a natural state and are relatively unaffected by human activities. Understanding the changes to lake water volume is a key issue for the study of lake-atmosphere interactions and the effects of lake expansion and contraction on regional climate. By using multisource remote sensing and water level observations, this study systematically analyzed inter-annual changes from 1970 to 2021 of three typical inland lakes basin (Bamu Co-Peng Co basin, Langa Co-Mapum Yumco basin andLongmu Co-Songmuxi Co basin), which are located in different climatic regions of the Tibetan Plateau and monthly changes from 2019 to 2021 of Bamu Co, Langa Co and Longmu Co in the lake area, water level, and water volume. In addition, the study analyzed the response of lakes in different climate regions to climate change from 1979 to 2018. The main conclusions are as follows. (1) From 1970 to 2021, there were similar trends in lake changes between the primary and twin lakes. (2) The changes to lakes in different climatic regions are different: lakes in the monsoon-dominated region showed a significant trend of expansion from 2000 to 2014, but the trend slowed down and stabilized after 2014; lakes in the westerlies-dominated region showed a small expansion trend; lakes in the region affected by both westerlies and the monsoon showed an overall shrinking trend. (3) The monthly variation of lake water volume showed a trend of first increasing and then decreasing, with the largest relative change of lake water volume in August and September. (4) Precipitation is a dominant factor controlling lake variation during the year. (5) Temperature and precipitation are dominant meteorological elements affecting the decadal variation of the lake, and with the warming of the TP, temperature plays an increasingly important role.
Multiple Characteristics of Precipitation Inferred from Wind Profiler Radar Doppler Spectra
Authors: Garcia-Benadi Albert , Bech Joan , Udina Mireia , Campistron Bernard , Paci Alexandre
Published: October 2022

A methodology to process radar wind profiler Doppler spectra is presented and implemented for an UHF Degreane PCL1300 system. First, double peak signal detection is conducted at each height level and, then, vertical continuity checks for each radar beam ensure physically consistent measurements. Second, horizontal and vertical wind, kinetic energy flux components, Doppler moments, and different precipitation-related variables are computed. The latter include a new precipitation type estimate, which considers rain, snow, and mixed types, and, finally, specific variables for liquid precipitation, including drop size distribution parameters, liquid water content and rainfall rate. The methodology is illustrated with a 48 h precipitation event, recorded during the Cerdanya-2017 field campaign, carried out in the Eastern Pyrenees. Verification is performed with a previously existing process for wind profiler data regarding wind components, plus precipitation estimates derived from Micro Rain Radar and disdrometer observations. The results indicated that the new methodology produced comparable estimates of wind components to the previous methodology (Bias < 0.1 m/s, RMSE ≈ 1.1 m/s), and was skilled in determining precipitation type when comparing the lowest estimate of disdrometer data for snow and rain, but did not correctly identify mixed precipitation cases. The proposed methodology, called UBWPP, is available at the GitHub repository.
Stand Structural Characteristics Derived from Combined TLS and Landsat Data Support Predictions of Mushroom Yields in Mediterranean Forest
Authors: Martínez-Rodrigo Raquel , Gómez Cristina , Toraño-Caicoya Astor , Bohnhorst Luke , Uhl Enno , Águeda Beatriz
Published: October 2022

Forest fungi provide recreational and economic services, as well as ecosystem biodiversity. Wild mushroom yields are difficult to estimate; climatic conditions are known to trigger temporally localised yields, and forest structure also affects productivity. In this work, we analyse the capacity of remotely sensed variables to estimate wild mushroom biomass production in Mediterranean Pinus pinaster forests in Soria (Spain) using generalised additive mixed models (GAMMs). In addition to climate variables, multitemporal NDVI derived from Landsat data, as well as structural variables measured with mobile Terrestrial Laser Scanner (TLS), are considered. Models are built for all mushroom species as a single pool and for Lactarius deliciosus individually. Our results show that, in addition to autumn precipitation, the interaction of multitemporal NDVI and vegetation biomass are most explanatory of mushroom productivity in the models. When analysing the productivity models of Lactarius deliciosus, in addition to the interaction between canopy cover and autumn minimum temperature, basal area (BA) becomes relevant, indicating an optimal BA range for the development of this species. These findings contribute to the improvement of knowledge about wild mushroom productivity, helping to meet Goal 15 of the 2030 UN Agenda.
Water Quality Retrieval from ZY1-02D Hyperspectral Imagery in Urban Water Bodies and Comparison with Sentinel-2
Authors: Yang Zhe , Gong Cailan , Ji Tiemei , Hu Yong , Li Lan
Published: October 2022

Non-optically active water quality parameters in water bodies are important evaluation indicators in monitoring urban water quality. Over the past years, satellite remote sensing techniques have increasingly been used to assess different types of substances in urban water bodies. However, it is challenging to retrieve accurate data for some of the non-optically active water quality parameters from satellite images due to weak spectral characteristics. This study aims to examine the potential of ZY1-02D hyperspectral images in retrieving non-optical active water quality parameters, including dissolved oxygen (DO), permanganate index (CODMn), and total phosphorus (TP) in urban rivers and lakes. We first simulated the in situ measured reflectance to the satellite equivalent reflectance using the ZY1-02D and Sentinel-2 spectral response function. Further, we used four machine learning models to compare the retrieval performance of these two sensors with different bandwidths. The mean absolute percentage errors (MAPE) are 24.28%, 18.44%, and 37.04% for DO, CODMn, and TP, respectively, and the root mean square errors (RMSE) are 1.67, 0.96, and 0.07 mg/L, respectively. Finally, we validated the accuracy and consistency of aquatic products retrieved from ZY1-02D and Sentinel-2 images. The remote sensing reflectance (Rrs) products of ZY1-02D are slightly overestimated compared to Sentinel-2 Rrs. ZY1-02D has high accuracy and consistency in mapping CODMn products in urban water. The results show the potential of ZY1-02D hyperspectral images in mapping non-optically active water quality parameters.
Integration of Sentinel-1A, ALOS-2 and GF-1 Datasets for Identifying Landslides in the Three Parallel Rivers Region, China
Authors: Zhao Cong , Liang Jingtao , Zhang Su , Dong Jihong , Yan Shengwu , Yang Lei , Liu Bin , Ma Xiaobo , Li Weile
Published: October 2022

In the process of using InSAR technology to identify active landslides, phenomena such as steep terrain, dense vegetation, and complex clouds may lead to the missed identification of some landslides. In this paper, an active landslide identification method combining InSAR technology and optical satellite remote sensing technology is proposed, and the method is successfully applied to the Three Parallel Rivers Region (TPRR) in the northwest of Yunnan Province, China. The results show that there are 442 landslides identified in the TPRR, and the fault zone is one of the important factors affecting the distribution of landslides in this region. In addition, 70% of the active landslides are distributed within 1 km on both sides of the fault zone. The larger the scale of the landslide, the closer the relationship between landslides and the fault zone. In this identification method, the overall landslide identification rate based on InSAR technology is 51.36%. The combination of Sentinel-1 and ALOS-2 data is beneficial to improve the active landslide identification rate of InSAR. In the northern region with large undulating terrain, shadows and overlaps occur easily. The southern area with gentle terrain is prone to the phenomenon where the scale of landslides is too small. Both phenomena are not conducive to the application of InSAR. Thus, in the central region, with moderate terrain and slope, the identification rate of active landslides based on InSAR is highest. The active landslide identification method proposed in this paper, which combines InSAR and optical satellite remote sensing technology, can integrate the respective advantages of the two technical methods, complement each other’s limitations and deficiencies, reduce the missed identification of landslides, and improve the accuracy of active landslide inventory maps.
Understanding Intensity–Duration–Frequency (IDF) Curves Using IMERG Sub-Hourly Precipitation against Dense Gauge Networks
Authors: Lau Alcely , Behrangi Ali
Published: October 2022

The design storm derived from intensity–duration–frequency (IDF) curves is the main input for hydrologic analysis or hydraulic design for flood control. The regions with higher flood risks due to extreme precipitation are often deficient in precipitation gauges. This study presents a detailed evaluation of IDF curves derived using IMERG Final half-hourly precipitation (V06), fitted with the widely used CDFs: Gumbel and MLE, Gumbel and MM, Pearson 3, and GEV. As benchmarks and following the same method, we also derived IDF curves using areal average gridded precipitation constructed from two dense gauges networks over (1) the WegenerNET Feldbach region in the Alpine forelands of Austria and (2) the gauge network of the Walnut Gulch Experimental Watershed, in a semiarid region of the United States. In both regions, the frequency analysis for return periods between 2 and 100 years was based on half-hourly rainfall and compared at a grid-scale with a spatial resolution of IMERG, 0.1° × 0.1° lat/lon. The impact of order in which the gridded gauge-based precipitation average is performed within an IMERG grid was evaluated by computing two different Annual Maximum Series (AMS). In one, the average was computed before obtaining the AMS (AB-AMS), and in the other, the average was computed after obtaining the AMS for each gauge grid (AA-AMS) within the IMERG grid. The evaluation revealed that IMERG AMS agrees better with AB-AMS than AA-AMS for the two study regions. Lastly, it was found that the use of Gumbel distribution in calculating IMERG IDF curves results in better agreement with the ground truth than the use of the other three distributions studied here. The outcomes should provide valuable knowledge for the application of IMERG precipitation over regions with sparse gauges.
Gradient Boosting and Linear Regression for Estimating Coastal Bathymetry Based on Sentinel-2 Images
Authors: Abdul Gafoor Fahim , Al-Shehhi Maryam R. , Cho Chung-Suk , Ghedira Hosni
Published: October 2022

Thousands of vessels travel around the world every day, making the safety, efficiency, and optimization of marine transportation essential. Therefore, the knowledge of bathymetry is crucial for a variety of maritime applications, such as shipping and navigation. Maritime applications have benefited from recent advancements in satellite navigation technology, which can utilize multi-spectral bands for retrieving information on water depth. As part of these efforts, this study combined deep learning techniques with satellite observations in order to improve the estimation of satellite-based bathymetry. The objective of this study is to develop a new method for estimating coastal bathymetry using Sentinel-2 images. Sentinel-2 was used here due to its high spatial resolution, which is desirable for bathymetry maps, as well as its visible bands, which are useful for estimating bathymetry. The conventional linear model approach using the satellite-derived bathymetry (SDB) ratio (green to blue) was applied, and a new four-band ratio using the four visible bands of Sentienl-2 was proposed. In addition, three atmospheric correction models, Sen2Cor, ALOCITE, and C2RCC, were evaluated, and Sen2Cor was found to be the most effective model. Gradient boosting was also applied in this study to both the conventional band ratio and the proposed FVBR ratio. Compared to the green to blue ratio, the proposed ratio FVBR performed better, with R2 exceeding 0.8 when applied to 12 snapshots between January and December. The gradient boosting method was also found to provide better estimates of bathymetry than linear regression. According to findings of this study, the chlorophyll-a (Chl-a) concentration, sediments, and atmospheric dust do not affect the estimated bathymetry. However, tidal oscillations were found to be a significant factor affecting satellite estimates of bathymetry.
Analysis of Diurnal Evolution of Cloud Properties and Convection Tracking over the South China Coastal Area
Authors: Wang Xinyue , Iwabuchi Hironobu , Courbot Jean-Baptiste
Published: October 2022

Different diurnal rainfall cycles occur over the offshore and inland regions of the South China coastal area (SCCA). Inspired by these findings, in this study, we investigated the diurnal evolution features of cloud systems and cloud properties inside such systems for both the SCCA offshore and inland regions, using cloud data retrieved from a recently developed deep neural network model. Rainy day data for June 2017 revealed that the ice cloud optical thickness and top height reached their peak intensities at noon (~12 local standard time (LST)) over the offshore region, approximately 2 h later than the rainfall peak. Over the inland region, cloud and rainfall peaks simultaneously appeared from ~18 to 20 LST. When further examining the cloud-amount variation of different ice-cloud types, we found a clear diurnal oscillation in the medium-thick cloud amount over the offshore region, while for the inland region, this cloud type had no obvious diurnal peak, showing a low cloud amount throughout the day. This phenomenon suggests different inner structures and intensities between offshore and inland convections. To better elucidate the convection features over different regions, a tracking algorithm was applied to obtain various parameters, such as size, number, and duration of mesoscale convective systems. The strongest convections, which lasted over 12 h, tended to be abundant over the offshore region from ~03 to 12 LST, and an inland to offshore migration at ~03 LST was facilitated by the beneficial meteorological conditions observed at 113–116˚E, 20.5–22.5˚N.