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Guofeng Wu
MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area & Guangdong-Hong Kong-Macau Joint Laboratory for Smart Cities & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China

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Journal article
Published: 27 July 2021 in International Journal of Applied Earth Observation and Geoinformation
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Urban agglomeration is the most obvious regions in the Chinese rapid urban land expansion. The developed urban agglomerations in China (i.e., Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD) and Guangdong–Hong Kong–Macau Greater Bay Area (GBA)) have entered a suburban urbanization period; however, it is not clear whether the urbanization on low-slope hilly regions (hillside urbanization) exist in these urban agglomerations. In this study, we proposed a quantification framework to detect hillside urbanization with multiple earth observation data and socio-economic data and further compared their spatiotemporal patterns from 2007 to 2017 in these three urban agglomerations. The results showed: (1) the urban area of BTH, YRD and GBA has expanded by 1.82, 2.37 and 1.53 times, respectively; (2) widespread hillside urbanization regions were found in BTH (475.82 km2), YRD (440.41 km2) and GBA (298.14 km2); (3) GBA had the largest hillside urbanization rate (10.55%), followed by BTH (6.33%) and YRD (3.18%); (4) the hillside urbanization of BTH, YRD and GBA provided accommodation and workplaces for about 1.05, 0.97 and 1.37 million people, respectively; and (5) the minimum and maximum high environmental cost (HEC) hillside urbanization rates were found in BTH (0.53%) and GBA (2.92%), respectively. Our findings may provide some new insights into urban sustainability.

ACS Style

Chao Yang; Rongling Xia; Qingquan Li; Huizeng Liu; Tiezhu Shi; Guofeng Wu. Comparing hillside urbanizations of Beijing-Tianjin-Hebei, Yangtze River Delta and Guangdong–Hong Kong–Macau greater Bay area urban agglomerations in China. International Journal of Applied Earth Observation and Geoinformation 2021, 102, 102460 .

AMA Style

Chao Yang, Rongling Xia, Qingquan Li, Huizeng Liu, Tiezhu Shi, Guofeng Wu. Comparing hillside urbanizations of Beijing-Tianjin-Hebei, Yangtze River Delta and Guangdong–Hong Kong–Macau greater Bay area urban agglomerations in China. International Journal of Applied Earth Observation and Geoinformation. 2021; 102 ():102460.

Chicago/Turabian Style

Chao Yang; Rongling Xia; Qingquan Li; Huizeng Liu; Tiezhu Shi; Guofeng Wu. 2021. "Comparing hillside urbanizations of Beijing-Tianjin-Hebei, Yangtze River Delta and Guangdong–Hong Kong–Macau greater Bay area urban agglomerations in China." International Journal of Applied Earth Observation and Geoinformation 102, no. : 102460.

Journal article
Published: 17 June 2021 in Ecological Indicators
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Hyperspectral imaging data have been rarely focused on studies of mangrove pests and diseases. With leaf hyperspectral imaging data, this study aims to extract the sensitive spectral and textural features related to information of mangrove pest and disease using successive projection algorithm (SPA), and to model and visualize leaf traits in response to different pest and disease severity using random forest (RF). The results showed that multiple repetitions of SPA and RF modeling operations could provide a robust set of sensitive features and reliable accuracies of vegetation parameter estimation. Among the five types of features (450 bands of original and first derivative reflectance, 52 vegetation indices, 112 texture features, and all coupling features), the RF models with 33 sensitive features chosen from the coupling of all the 1064 features, 13 sensitive wavelengths with first derivative reflectance, and 30 sensitive wavelengths with first derivative reflectance reported the optimal validation performance (mean R2Val = 0.752, 0.671, and 0.658) in estimating pest and disease severity, leaf SPAD-502, and leaf NBI values, respectively. Moreover, the two leaf trait values increased with decreasing severity of pest and disease based on the leaf trait visualization map using the optimal SPA-RF model. We conclude that the combination of SPA-RF model and hyperspectral imaging had great potential in detecting the spatial distribution of leaf traits under different pest and disease severity. The leaf-level study could lay foundation for early warning and monitoring of mangrove pests and diseases at the landscape or region level.

ACS Style

Xiapeng Jiang; Jianing Zhen; Jing Miao; Demei Zhao; Junjie Wang; Sen Jia. Assessing mangrove leaf traits under different pest and disease severity with hyperspectral imaging spectroscopy. Ecological Indicators 2021, 129, 107901 .

AMA Style

Xiapeng Jiang, Jianing Zhen, Jing Miao, Demei Zhao, Junjie Wang, Sen Jia. Assessing mangrove leaf traits under different pest and disease severity with hyperspectral imaging spectroscopy. Ecological Indicators. 2021; 129 ():107901.

Chicago/Turabian Style

Xiapeng Jiang; Jianing Zhen; Jing Miao; Demei Zhao; Junjie Wang; Sen Jia. 2021. "Assessing mangrove leaf traits under different pest and disease severity with hyperspectral imaging spectroscopy." Ecological Indicators 129, no. : 107901.

Journal article
Published: 14 June 2021 in International Journal of Applied Earth Observation and Geoinformation
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Chlorophyll is a good indicator of health status and nutritional condition as plant grows. Many studies have investigated the feasibility of retrieving leaf chlorophyll content (LCC) using vegetation indices (VIs) of multiple plant species, yet very few studies have examined the multi-temporal Sentinel-2 images for mapping LCC of mangrove forests. With field collected leaf SPAD values (relative chlorophyll content), this study explored the relationship of leaf SPAD values against five types of newly-developed VIs derived from leaf hyperspectral data and Sentinel-2 data of four periods (May 2018, January 2019, August 2019, and December 2019). Linear regression with best-performing VIs and Kernel Ridge Regression (KRR) were developed to construct the SPAD retrieval model in each period. The leave-one-out cross-validation technique was employed to compare the estimation results of VIs and KRR method, and the four periods of SPAD maps were produced by the best-performing model. The results showed that the newly-developed index (ratio of single-band reflectance to the sum of two bands reflectance, RSSI) achieved the high correlation coefficient with leaf SPAD value at both leaf and canopy level. At canopy level, the linear model using RSSI (B8/(B2 + B5), B8a/(B2 + B4), B8/(B2 + B5), and B8/(B2 + B3)) outperformed than that using traditional broadband indices and KRR model with R2adjust = 0.496, 0.742, 0.681, and 0.801; RMSE = 5.75, 4.29, 4.00, and 3.46; and RE = 7.67%, 5.68%, 4.97%, and 4.63% in each period. We concluded that there are great potentials of newly-developed index of RSSI using Sentinel-2 data for regional retrieving and mapping LCC of mangrove forests across different time periods, which is essential for mangrove ecological conservation and restoration.

ACS Style

Jianing Zhen; Xiapeng Jiang; Yi Xu; Jing Miao; Demei Zhao; Junjie Wang; Jingzhe Wang; Guofeng Wu. Mapping leaf chlorophyll content of mangrove forests with Sentinel-2 images of four periods. International Journal of Applied Earth Observation and Geoinformation 2021, 102, 102387 .

AMA Style

Jianing Zhen, Xiapeng Jiang, Yi Xu, Jing Miao, Demei Zhao, Junjie Wang, Jingzhe Wang, Guofeng Wu. Mapping leaf chlorophyll content of mangrove forests with Sentinel-2 images of four periods. International Journal of Applied Earth Observation and Geoinformation. 2021; 102 ():102387.

Chicago/Turabian Style

Jianing Zhen; Xiapeng Jiang; Yi Xu; Jing Miao; Demei Zhao; Junjie Wang; Jingzhe Wang; Guofeng Wu. 2021. "Mapping leaf chlorophyll content of mangrove forests with Sentinel-2 images of four periods." International Journal of Applied Earth Observation and Geoinformation 102, no. : 102387.

Journal article
Published: 23 March 2021 in Remote Sensing of Environment
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In recent years, ultraviolet (UV) bands have received increasing attention from the ocean colour remote sensing community, as they may contribute to improving atmospheric correction and inherent optical properties (IOPs) retrieval. However, most ocean colour satellite sensors do not have UV bands, and the accurate retrieval of UV remote sensing reflectance (Rrs) from UV satellite data is still a challenge. In order to address this problem, this study proposes a hybrid approach for estimating UV Rrs from the visible bands. The approach was implemented with two popular ocean colour satellite sensors, i.e. GCOM-C SGLI and Sentinel-3 OLCI. In situ Rrs collected globally and simulated Rrs spectra were used to develop UV Rrs retrieval models, and UV Rrs values at 360, 380 and 400 nm were estimated from visible Rrs spectra. The performances of the established models were evaluated using in situ Rrs and satellite data, and applied to a semi-analytical algorithm for IOPs retrieval. The results showed that: (i) UV Rrs retrieval models had low uncertainties with mean absolute percentage differences (MAPD) less than 5%; (ii) the model assessment with in situ Rrs showed high accuracy (r = 0.92–1.00 and MAPD = 1.11%–10.95%) in both clear open ocean and optically complex waters; (iii) the model assessment with satellite data indicated that model-estimated UV Rrs were more consistent with in situ values than satellite-derived UV Rrs; and (iv) model-estimated UV Rrs may improve the decomposition accuracy of absorption coefficients in semi-analytical IOPs algorithm. Thus, the proposed method has great potentials for reconstructing UV Rrs data and improving IOPs retrieval for historical satellite sensors, and might also be useful for UV-based atmospheric correction algorithms.

ACS Style

Huizeng Liu; Xianqiang He; Qingquan Li; Susanne Kratzer; Junjie Wang; Tiezhu Shi; Zhongwen Hu; Chao Yang; Shuibo Hu; Qiming Zhou; Guofeng Wu. Estimating ultraviolet reflectance from visible bands in ocean colour remote sensing. Remote Sensing of Environment 2021, 258, 112404 .

AMA Style

Huizeng Liu, Xianqiang He, Qingquan Li, Susanne Kratzer, Junjie Wang, Tiezhu Shi, Zhongwen Hu, Chao Yang, Shuibo Hu, Qiming Zhou, Guofeng Wu. Estimating ultraviolet reflectance from visible bands in ocean colour remote sensing. Remote Sensing of Environment. 2021; 258 ():112404.

Chicago/Turabian Style

Huizeng Liu; Xianqiang He; Qingquan Li; Susanne Kratzer; Junjie Wang; Tiezhu Shi; Zhongwen Hu; Chao Yang; Shuibo Hu; Qiming Zhou; Guofeng Wu. 2021. "Estimating ultraviolet reflectance from visible bands in ocean colour remote sensing." Remote Sensing of Environment 258, no. : 112404.

Journal article
Published: 02 February 2021 in Remote Sensing of Environment
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Particulate organic carbon (POC) plays vital roles in marine carbon cycle, serving as a part of “biological pump” moving carbon to the deep ocean. The blue-to-green band ratio algorithm is applied operationally to derive POC concentrations in global oceans; it, however, tends to underestimate high values in optically complex waters. With an attempt to develop accurate and robust oceanic POC models, this study aimed to explore machine learning methods in satellite retrieval of POC concentrations. Three machine learning methods, i.e. extreme gradient boosting (XGBoost), support vector machine (SVM) and artificial neural network (ANN), were tested, and the recursive feature elimination (RFE) method was employed to identify sensitive features. Matchups of global in situ POC measurements and Ocean Colour Climate Change Initiative (OC-CCI) products were used to train and evaluate POC models. Results showed that machine learning methods produced obvious better performance than the blue-to-green band ratio algorithm, and XGBoost was the most robust among the tested three machine learning methods. However, the blue-to-green band ratio algorithm still worked well for clear open ocean waters with low POC, and ANN was more effective for optically complex waters with extremely high POC. This study provided globally applicable methods for satellite retrieval of POC concentrations, which should be helpful for studying POC dynamics in global oceans as well as in productive marginal seas.

ACS Style

Huizeng Liu; Qingquan Li; Yan Bai; Chao Yang; Junjie Wang; Qiming Zhou; Shuibo Hu; Tiezhu Shi; Xiaomei Liao; Guofeng Wu. Improving satellite retrieval of oceanic particulate organic carbon concentrations using machine learning methods. Remote Sensing of Environment 2021, 256, 112316 .

AMA Style

Huizeng Liu, Qingquan Li, Yan Bai, Chao Yang, Junjie Wang, Qiming Zhou, Shuibo Hu, Tiezhu Shi, Xiaomei Liao, Guofeng Wu. Improving satellite retrieval of oceanic particulate organic carbon concentrations using machine learning methods. Remote Sensing of Environment. 2021; 256 ():112316.

Chicago/Turabian Style

Huizeng Liu; Qingquan Li; Yan Bai; Chao Yang; Junjie Wang; Qiming Zhou; Shuibo Hu; Tiezhu Shi; Xiaomei Liao; Guofeng Wu. 2021. "Improving satellite retrieval of oceanic particulate organic carbon concentrations using machine learning methods." Remote Sensing of Environment 256, no. : 112316.

Journal article
Published: 14 January 2021 in Forests
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Forest stand volume is one of the key forest structural attributes in estimating and forecasting ecosystem productivity and carbon stock. However, studies on growth modeling and environmental influences on stand volume are still rare to date, especially in subtropical forests in karst areas, which are characterized by a complex species composition and are important in the global carbon budget. In this paper, we developed growth models of stand volume for all the dominant tree species (groups) (DTSG) in a subtropical karst area, the Guizhou Plateau based on an investigation of the effects of various environmental factors on stand volume. The Richards growth function, space-for-time substitution and zonal-hierarchical modeling method were applied in the model fitting, and multiple indices were used in the model evaluation. The results showed that the climatic factors of annual temperature and precipitation, as well as the site factors of stand origin, elevation, slope gradient, topsoil thickness, site quality degree, rocky desertification type and rocky desertification degree, have significant influences on stand volume, and the topsoil thickness and site quality degree have the strongest positive effect. A total of 959 growth equations of stand volume were fitted with a five-level stand classifier (DTSG–climatic zone–site quality degree–stand origin–rocky desertification type). All the growth equations were qualified, because all passed the TRE test (≤30%), and the majority of the R2 ≥ 0.50, above 70% of the RMSE were between 5.0 and 20.0, and above 80% of the P ≥ 75%. These findings provide updated knowledge about the environmental effect on the stand volume growth of subtropical forests in karst areas, and the developed stand volume growth models are convenient for forest management and planning, further contributing to the study of forest carbon storage assessments and global carbon cycling.

ACS Style

Yuzhi Tang; Quanqin Shao; Tiezhu Shi; Guofeng Wu. Developing Growth Models of Stand Volume for Subtropical Forests in Karst Areas: A Case Study in the Guizhou Plateau. Forests 2021, 12, 83 .

AMA Style

Yuzhi Tang, Quanqin Shao, Tiezhu Shi, Guofeng Wu. Developing Growth Models of Stand Volume for Subtropical Forests in Karst Areas: A Case Study in the Guizhou Plateau. Forests. 2021; 12 (1):83.

Chicago/Turabian Style

Yuzhi Tang; Quanqin Shao; Tiezhu Shi; Guofeng Wu. 2021. "Developing Growth Models of Stand Volume for Subtropical Forests in Karst Areas: A Case Study in the Guizhou Plateau." Forests 12, no. 1: 83.

Article
Published: 13 January 2021 in Chinese Geographical Science
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China has experienced rapid urbanizations with dramatic land cover changes since 1978. Forest loss is one of land cover changes, and it induces various eco-environmental degradation issues. As one of China’s hotspot regions, the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) has undergone a dramatic urban expansion. To better understand forest dynamics and protect forest ecosystem, revealing the processes, patterns and underlying drivers of forest loss is essential. This study focused on the spatiotemporal evolution and potential driving factors of forest loss in the GBA at regional and city level. The Landsat time-series images from 1987 to 2017 were used to derive forest, and landscape metrics and geographic information system (GIS) were applied to implement further spatial analysis. The results showed that: 1) 14.86% of the total urban growth area of the GBA was obtained from the forest loss in 1987–2017; meanwhile, the forest loss area of the GBA reached 4040.6 km2, of which 25.60% (1034.42 km2) was converted to urban land; 2) the percentages of forest loss to urban land in Dongguan (19.14%), Guangzhou (18.35%) and Shenzhen (15.81%) were higher than those in other cities; 3) the forest became increasingly fragmented from 1987–2007, and then the fragmentation decreased from 2007 to 2017); 4) the landscape responses to forest changes varied with the scale; and 5) some forest loss to urban regions moved from low-elevation and gentle-slope terrains to higher-elevation and steep-slope terrains over time, especially in Shenzhen and Hong Kong. Urbanization and industrialization greatly drove forest loss and fragmentation, and, notably, hillside urban land expansion may have contributed to hillside forest loss. The findings will help policy makers in maintaining the stability of forest ecosystems, and provide some new insights into forest management and conservation.

ACS Style

Chao Yang; Huizeng Liu; Qingquan Li; Aihong Cui; Rongling Xia; Tiezhu Shi; Jie Zhang; Wenxiu Gao; Xiang Zhou; Guofeng Wu. Rapid Urbanization Induced Extensive Forest Loss to Urban Land in the Guangdong-Hong Kong-Macao Greater Bay Area, China. Chinese Geographical Science 2021, 31, 93 -108.

AMA Style

Chao Yang, Huizeng Liu, Qingquan Li, Aihong Cui, Rongling Xia, Tiezhu Shi, Jie Zhang, Wenxiu Gao, Xiang Zhou, Guofeng Wu. Rapid Urbanization Induced Extensive Forest Loss to Urban Land in the Guangdong-Hong Kong-Macao Greater Bay Area, China. Chinese Geographical Science. 2021; 31 (1):93-108.

Chicago/Turabian Style

Chao Yang; Huizeng Liu; Qingquan Li; Aihong Cui; Rongling Xia; Tiezhu Shi; Jie Zhang; Wenxiu Gao; Xiang Zhou; Guofeng Wu. 2021. "Rapid Urbanization Induced Extensive Forest Loss to Urban Land in the Guangdong-Hong Kong-Macao Greater Bay Area, China." Chinese Geographical Science 31, no. 1: 93-108.

Journal article
Published: 24 November 2020 in Environmental Pollution
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Due to rapid urbanization in China, lead (Pb) continues to accumulate in urban topsoil, resulting in soil degradation and increased public exposure. Mapping Pb concentrations in urban topsoil is therefore vital for the evaluation and control of this exposure risk. This study developed spatial models to map Pb concentrations in urban topsoil using proximal and remote sensing data. Proximal sensing reflectance spectra (350–2500 nm) of soils were pre-processed and used to calculate the principal components as landscape factors to represent the soil properties. Other landscape factors, including vegetation and land-use factors, were extracted from time-sequential Landsat images. Two hybrid statistical approaches, regression kriging (RK) and geographically weighted regression (GWR), were adopted to establish prediction models using the landscape factors. The results indicated that the use of landscape factors derived from combined remote and proximal sensing data improved the prediction of Pb concentrations compared with useing these data individually. GWR obtained better results than RK for predicting soil Pb concentration. Thus, joint proximal and remote sensing provides timely, easily accessible, and suitable data for extracting landscape factors.

ACS Style

Tiezhu Shi; Chao Yang; Huizeng Liu; Chao Wu; Zhihua Wang; He Li; Huifang Zhang; Long Guo; Guofeng Wu; Fenzhen Su. Mapping lead concentrations in urban topsoil using proximal and remote sensing data and hybrid statistical approaches. Environmental Pollution 2020, 272, 116041 .

AMA Style

Tiezhu Shi, Chao Yang, Huizeng Liu, Chao Wu, Zhihua Wang, He Li, Huifang Zhang, Long Guo, Guofeng Wu, Fenzhen Su. Mapping lead concentrations in urban topsoil using proximal and remote sensing data and hybrid statistical approaches. Environmental Pollution. 2020; 272 ():116041.

Chicago/Turabian Style

Tiezhu Shi; Chao Yang; Huizeng Liu; Chao Wu; Zhihua Wang; He Li; Huifang Zhang; Long Guo; Guofeng Wu; Fenzhen Su. 2020. "Mapping lead concentrations in urban topsoil using proximal and remote sensing data and hybrid statistical approaches." Environmental Pollution 272, no. : 116041.

Journal article
Published: 02 November 2020 in Remote Sensing
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The ability to precisely map urban land use types can significantly aid urban planning and urban system understanding. In recent years, remote sensing images and social sensing data have been frequently used for urban land use mapping. However, there still remains a problem: what is the best basic unit for fusing remote sensing images with social sensing data? The aim of this study is to explore the impact of spatial units on urban land use mapping, with remote sensing images and social sensing data of Shenzhen City, China. Three different basic units were first applied to delineate urban land use types, and for each unit, a word dictionary was built by fusing natural–physical features from high spatial resolution (HSR) remote sensing images and the socioeconomic semantic features from point of interest (POI) data. The latent Dirichlet allocation (LDA) algorithm and random forest methods were then applied to map the land use of the Futian district—the core region of Shenzhen. The experiment demonstrates that: (1) No matter what kind of spatial unit, it is beneficial to fuse multisource data to improve the performance. However, when using different spatial units, the importances of features are different. (2) Using block-based spatial units results in the final map looking the best. However, a great challenge of this approach is that the scale is too coarse to handle mixed functional areas. (3) Using grid- and object-based units, the problem of mixed functional areas can be better solved. Additionally, the object-based land use map looks better from our visual interpretation. Accordingly, the results of this study could give other researchers references and advice for future studies.

ACS Style

Xuanyan Dong; Yue Xu; Leping Huang; Zhigang Liu; Yi Xu; Kangyong Zhang; Zhongwen Hu; Guofeng Wu. Exploring Impact of Spatial Unit on Urban Land Use Mapping with Multisource Data. Remote Sensing 2020, 12, 3597 .

AMA Style

Xuanyan Dong, Yue Xu, Leping Huang, Zhigang Liu, Yi Xu, Kangyong Zhang, Zhongwen Hu, Guofeng Wu. Exploring Impact of Spatial Unit on Urban Land Use Mapping with Multisource Data. Remote Sensing. 2020; 12 (21):3597.

Chicago/Turabian Style

Xuanyan Dong; Yue Xu; Leping Huang; Zhigang Liu; Yi Xu; Kangyong Zhang; Zhongwen Hu; Guofeng Wu. 2020. "Exploring Impact of Spatial Unit on Urban Land Use Mapping with Multisource Data." Remote Sensing 12, no. 21: 3597.

Research article
Published: 15 August 2020 in International Journal of Remote Sensing
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Small water bodies have always been an important part of water ecology systems. In the past, due to the limitations of satellite spatial resolution and recognition method precision, there have been few satisfactory remote sensing small water bodies extraction methods. In this article, a method based on index composition and HSI (hue, saturation, and intensity) colour space transformation is proposed to precisely extract small water bodies. An easy-to-deploy, fast, universal, and effective algorithm is used to accurately identify paddy fields and exclude shadows. This method is tested and verified with Sentinel-2 MSI (MultiSpectral Imager) images in seven cities in the Guangdong-Hong Kong-Macao Greater Bay Area. Compared with the traditional modified normalized difference water index (MNDWI) and enhanced water index (EWI) water extraction methods, the proposed HSI method has shown a better performance in small water bodies mapping with a kappa coefficient of 0.94, overall accuracy of 97%, producer’s accuracy of 96%, and user’s accuracy of 98% in test regions, which is significantly higher than the benchmarking water extraction methods. It provides a powerful supplement for the remote sensing monitoring of water resources in surface water bodies. The method proposed in this study exhibits extendibility, it also has the potential to extract other small features with minor modifications of the method.

ACS Style

Wanjuan Bie; Teng Fei; Xinyu Liu; Huizeng Liu; Guofeng Wu. Small water bodies mapped from Sentinel-2 MSI (MultiSpectral Imager) imagery with higher accuracy. International Journal of Remote Sensing 2020, 41, 7912 -7930.

AMA Style

Wanjuan Bie, Teng Fei, Xinyu Liu, Huizeng Liu, Guofeng Wu. Small water bodies mapped from Sentinel-2 MSI (MultiSpectral Imager) imagery with higher accuracy. International Journal of Remote Sensing. 2020; 41 (20):7912-7930.

Chicago/Turabian Style

Wanjuan Bie; Teng Fei; Xinyu Liu; Huizeng Liu; Guofeng Wu. 2020. "Small water bodies mapped from Sentinel-2 MSI (MultiSpectral Imager) imagery with higher accuracy." International Journal of Remote Sensing 41, no. 20: 7912-7930.

Journal article
Published: 13 August 2020 in Remote Sensing
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The Guangdong–Hong Kong–Macau Greater Bay Area (GBA) of China is one of the largest bay areas in the world. However, the spatiotemporal characteristics and driving mechanisms of urban expansions in this region are poorly understood. Here we used the annual remote sensing images, Geographic Information System (GIS) techniques, and geographical detector method to characterize the spatiotemporal patterns of urban expansion in the GBA and investigate their driving factors during 1986–2017 on regional and city scales. The results showed that: the GBA experienced an unprecedented urban expansion over the past 32 years. The total urban area expanded from 652.74 km2 to 8137.09 km2 from 1986 to 2017 (approximately 13 times). The annual growth rate during 1986–2017 was 8.20% and the annual growth rate from 1986 to 1990 was the highest (16.89%). Guangzhou, Foshan, Dongguan, and Shenzhen experienced the highest urban expansion rate, with the annual increase of urban areas in 51.51, 45.54, 36.76, and 23.26 km2 y−1, respectively, during 1986–2017. Gross Domestic Product (GDP), income, road length, and population were the most important driving factors of the urban expansions in the GBA. We also found the driving factors of the urban expansions varied with spatial and temporal scales, suggesting the general understanding from the regional level may not reveal detailed urban dynamics. Detailed urban management and planning policies should be made considering the spatial and internal heterogeneity. These findings can enhance the comprehensive understanding of this bay area and help policymakers to promote sustainable development in the future.

ACS Style

Jie Zhang; Le Yu; Xuecao Li; Chenchen Zhang; Tiezhu Shi; Xiangyin Wu; Chao Yang; Wenxiu Gao; Qingquan Li; Guofeng Wu. Exploring Annual Urban Expansions in the Guangdong-Hong Kong-Macau Greater Bay Area: Spatiotemporal Features and Driving Factors in 1986–2017. Remote Sensing 2020, 12, 2615 .

AMA Style

Jie Zhang, Le Yu, Xuecao Li, Chenchen Zhang, Tiezhu Shi, Xiangyin Wu, Chao Yang, Wenxiu Gao, Qingquan Li, Guofeng Wu. Exploring Annual Urban Expansions in the Guangdong-Hong Kong-Macau Greater Bay Area: Spatiotemporal Features and Driving Factors in 1986–2017. Remote Sensing. 2020; 12 (16):2615.

Chicago/Turabian Style

Jie Zhang; Le Yu; Xuecao Li; Chenchen Zhang; Tiezhu Shi; Xiangyin Wu; Chao Yang; Wenxiu Gao; Qingquan Li; Guofeng Wu. 2020. "Exploring Annual Urban Expansions in the Guangdong-Hong Kong-Macau Greater Bay Area: Spatiotemporal Features and Driving Factors in 1986–2017." Remote Sensing 12, no. 16: 2615.

Journal article
Published: 16 July 2020 in Remote Sensing
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Moderate spatial resolution (MSR) satellite images, which hold a trade-off among radiometric, spectral, spatial and temporal characteristics, are extremely popular data for acquiring land cover information. However, the low accuracy of existing classification methods for MSR images is still a fundamental issue restricting their capability in urban land cover mapping. In this study, we proposed a hybrid convolutional neural network (H-ConvNet) for improving urban land cover mapping with MSR Sentinel-2 images. The H-ConvNet was structured with two streams: one lightweight 1D ConvNet for deep spectral feature extraction and one lightweight 2D ConvNet for deep context feature extraction. To obtain a well-trained 2D ConvNet, a training sample expansion strategy was introduced to assist context feature learning. The H-ConvNet was tested in six highly heterogeneous urban regions around the world, and it was compared with support vector machine (SVM), object-based image analysis (OBIA), Markov random field model (MRF) and a newly proposed patch-based ConvNet system. The results showed that the H-ConvNet performed best. We hope that the proposed H-ConvNet would benefit for the land cover mapping with MSR images in highly heterogeneous urban regions.

ACS Style

Xin Luo; Xiaohua Tong; Zhongwen Hu; Guofeng Wu. Improving Urban Land Cover/use Mapping by Integrating A Hybrid Convolutional Neural Network and An Automatic Training Sample Expanding Strategy. Remote Sensing 2020, 12, 2292 .

AMA Style

Xin Luo, Xiaohua Tong, Zhongwen Hu, Guofeng Wu. Improving Urban Land Cover/use Mapping by Integrating A Hybrid Convolutional Neural Network and An Automatic Training Sample Expanding Strategy. Remote Sensing. 2020; 12 (14):2292.

Chicago/Turabian Style

Xin Luo; Xiaohua Tong; Zhongwen Hu; Guofeng Wu. 2020. "Improving Urban Land Cover/use Mapping by Integrating A Hybrid Convolutional Neural Network and An Automatic Training Sample Expanding Strategy." Remote Sensing 12, no. 14: 2292.

Journal article
Published: 15 June 2020 in Proceedings of the National Academy of Sciences
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The total amount of rainfall associated with tropical cyclones (TCs) over a given region is proportional to rainfall intensity and the inverse of TC translation speed. Although the contributions of increase in rainfall intensity to larger total rainfall amounts have been extensively examined, observational evidence on impacts of the recently reported but still debated long-term slowdown of TCs on local total rainfall amounts is limited. Here, we find that both observations and the multimodel ensemble of Global Climate Model simulations show a significant slowdown of TCs (11% in observations and 10% in simulations, respectively) from 1961 to 2017 over the coast of China. Our analyses of long-term observations find a significant increase in the 90th percentile of TC-induced local rainfall totals and significant inverse relationships between TC translation speeds and local rainfall totals over the study period. The study also shows that TCs with lower translation speed and higher rainfall totals occurred more frequently after 1990 in the Pearl River Delta in southern China. Our probability analysis indicates that slow-moving TCs are more likely to generate heavy rainfall of higher total amounts than fast-moving TCs. Our findings suggest that slowdown of TCs tends to elevate local rainfall totals and thus impose greater flood risks at the regional scale.

ACS Style

Yangchen Lai; Jianfeng Li; Xihui Gu; Yongqin David Chen; Dongdong Kong; Thian Yew Gan; Maofeng Liu; Qingquan Li; Guofeng Wu. Greater flood risks in response to slowdown of tropical cyclones over the coast of China. Proceedings of the National Academy of Sciences 2020, 117, 14751 -14755.

AMA Style

Yangchen Lai, Jianfeng Li, Xihui Gu, Yongqin David Chen, Dongdong Kong, Thian Yew Gan, Maofeng Liu, Qingquan Li, Guofeng Wu. Greater flood risks in response to slowdown of tropical cyclones over the coast of China. Proceedings of the National Academy of Sciences. 2020; 117 (26):14751-14755.

Chicago/Turabian Style

Yangchen Lai; Jianfeng Li; Xihui Gu; Yongqin David Chen; Dongdong Kong; Thian Yew Gan; Maofeng Liu; Qingquan Li; Guofeng Wu. 2020. "Greater flood risks in response to slowdown of tropical cyclones over the coast of China." Proceedings of the National Academy of Sciences 117, no. 26: 14751-14755.

Journal article
Published: 29 April 2020 in Ecological Indicators
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To understand the drivers of the mangrove degradation, more attention should be paid to the spatio-temporal dynamics of leaf biochemical parameters in response to changes of climatic or eco-environmental conditions in mangrove forests. With leaf reflectance of 135 mangrove samples collected from five sites, this study aimed to employ continuous wavelet transform (CWT, 128 scales) to estimate leaf biochemical parameters (SPAD-502 value, water content, nitrogen concentration, and phosphorus concentration) using random forest regression (RFR) model, and further to explore the effect of species and soil properties on the estimation of leaf biochemical parameters. Three dataset splitting methods (random splitting, RS; Kennard-Stone, KS; sample set partitioning based on joint x-y distance, SPXY) were employed to divide the original dataset into training (60% samples) and test set (40% samples). Compared with the 128 RFR models using original and first derivative reflectance (OR and FDR) spectra, the results showed that the models using wavelet power spectra (OR_CWT and FDR_CWT) at specific scales (≤32) achieved better accuracy in estimating leaf biochemical parameters. The RFR models with SPXY method outperformed those with RS and KS methods, and leaf SPAD-502 and nitrogen estimation accuracies were higher than leaf water and phosphorus estimation. Compared with the RFR models using wavelet power spectra alone in leaf biochemical parameter estimation, the models using wavelet power spectra with the integration of species information and soil properties increased mean R2CV (determination coefficient of cross-validation) values by 0.34%–23.73%, mean R2Val (determination coefficient of independent validation) values by 0.24%–8.16%, and mean RPD (residual prediction deviation) values by 0.90%–11.95%. Moreover, the factors of species and soil total carbon showed major contribution to the RFR models in leaf SPAD-502 and N estimation. We concluded that the integration of species information and soil properties with SPXY and CWT method had great potentials in the accurate estimation of leaf biochemical parameters in mangrove forests, which could further help to understand the interaction between soil and mangrove plants, and to provide theoretical support for the ecological conservation and management of mangroves.

ACS Style

Junjie Wang; Yi Xu; Guofeng Wu. The integration of species information and soil properties for hyperspectral estimation of leaf biochemical parameters in mangrove forest. Ecological Indicators 2020, 115, 106467 .

AMA Style

Junjie Wang, Yi Xu, Guofeng Wu. The integration of species information and soil properties for hyperspectral estimation of leaf biochemical parameters in mangrove forest. Ecological Indicators. 2020; 115 ():106467.

Chicago/Turabian Style

Junjie Wang; Yi Xu; Guofeng Wu. 2020. "The integration of species information and soil properties for hyperspectral estimation of leaf biochemical parameters in mangrove forest." Ecological Indicators 115, no. : 106467.

Research article
Published: 24 April 2020 in International Journal of Geographical Information Science
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Human emotion is an intrinsic psychological state that is influenced by human thoughts and behaviours. Human emotion distribution has been regarded as an important part of emotional geography research. However, it is difficult to form a global scaled map reflecting human emotions at the same sampling density because various emotional sampling data are usually positive occurrences without absence data. In this study, a methodological framework for mapping the global geographic distribution of human emotion is proposed and applied, combining a species distribution model with physical environment factors. State-of-the-art affective computing technology is used to extract human emotions from facial expressions in Flickr photos. Various human emotions are considered as different species to form their ‘habitats’ and predict the suitability, termed as ‘Emotional Habitat’. To our knowledge, this framework is the first method to predict emotional distribution from an ecological perspective. Different geographic distributions of seven dimensional emotions are explored and depicted, and emotional diversity and abnormality are detected at the global scale. These results confirm the effectiveness of our framework and offer new insights to understand the relationship between human emotions and the physical environment. Moreover, our method facilitates further rigorous exploration in emotional geography and enriches its content.

ACS Style

Yizhuo Li; Teng Fei; YingJing Huang; Jun Li; Xiang Li; Fan Zhang; Yuhao Kang; Guofeng Wu. Emotional habitat: mapping the global geographic distribution of human emotion with physical environmental factors using a species distribution model. International Journal of Geographical Information Science 2020, 35, 227 -249.

AMA Style

Yizhuo Li, Teng Fei, YingJing Huang, Jun Li, Xiang Li, Fan Zhang, Yuhao Kang, Guofeng Wu. Emotional habitat: mapping the global geographic distribution of human emotion with physical environmental factors using a species distribution model. International Journal of Geographical Information Science. 2020; 35 (2):227-249.

Chicago/Turabian Style

Yizhuo Li; Teng Fei; YingJing Huang; Jun Li; Xiang Li; Fan Zhang; Yuhao Kang; Guofeng Wu. 2020. "Emotional habitat: mapping the global geographic distribution of human emotion with physical environmental factors using a species distribution model." International Journal of Geographical Information Science 35, no. 2: 227-249.

Journal article
Published: 15 April 2020 in Cities
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Various fields have widely used place emotion extracted from social networking sites (SNS) information in recent years. However, the emotional information may contain biases as users are a particular subset of the whole population. This research studies whether there are significant differences between place emotion extracted from SNS and the place in-situ (a campus of Wuhan University). Two datasets from different sources, Weibo (a platform similar to twitter) and in-situ cameras, are collected over the same time periods in the same geographical range. By utilizing online cognitive services on the photos collected, the diversity of people with a recognizable face in terms of age, gender, and emotions are determined. The results suggest that there are significant differences in place emotion extracted from Weibo and in-situ. Furthermore, the pattern of differences varies among diverse demographic groups. This paper quantitatively contrasts place emotion extracted from SNS and the place in-situ, which can help researchers achieve a more profound understanding of human behavior differences between online and offline place emotion. This research also provides a theoretical basis to calibrate the emotion metrics obtained from SNS facial expressions on future place emotion studies.

ACS Style

YingJing Huang; Jun Li; Guofeng Wu; Teng Fei. Quantifying the bias in place emotion extracted from photos on social networking sites: A case study on a university campus. Cities 2020, 102, 102719 .

AMA Style

YingJing Huang, Jun Li, Guofeng Wu, Teng Fei. Quantifying the bias in place emotion extracted from photos on social networking sites: A case study on a university campus. Cities. 2020; 102 ():102719.

Chicago/Turabian Style

YingJing Huang; Jun Li; Guofeng Wu; Teng Fei. 2020. "Quantifying the bias in place emotion extracted from photos on social networking sites: A case study on a university campus." Cities 102, no. : 102719.

Research article
Published: 11 April 2020 in Land Degradation & Development
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Fuxian Lake is one of the most important freshwater supply lakes of China; however, its water quality is affected by the land degradation in its basin inevitably. This study aimed to monitor and simulate the spatiotemporal characteristics of land degradation in Fuxian Lake Basin. A comprehensive land degradation index (LDI), which integrates the indices for land use/land cover (LULC), vegetation coverage (VC), water loss and soil erosion (WLSE), wind erosion(WE) and soil moisture content (SMC), was proposed to describe the spatiotemporal characteristics of land degradation in 1990–2015; a CA‐Markov model was used to simulate and forecast the land degradation in 2020 and 2025; and the mechanisms behind land degradation were analysed. The results showed that: (1) the degree of land degradation from 1990 to 2015 generally decreased, the severe years of land degradation occurred in 1990, 2000 and 2005, and the total area of degraded land remained steady at around 250 km2 from 2005 to 2015; (2) the land degradation in different years was primarily moderate, with few severe or extreme degradation; (3) the simulated land degradation would remain relatively stable in 2020 and 2025, and the degraded area was slight fluctuation compared with that in 2015; and (4) the land degradation in this region was driven by both human factors and natural drivers. Therefore, some land degradation control measures should be set in advance for protecting this important supply area for freshwater. This article is protected by copyright. All rights reserved.

ACS Style

Chao Yang; Qingquan Li; Junyi Chen; Junjie Wang; Tiezhu Shi; Zhongwen Hu; Kai Ding; Guihua Wang; Guofeng Wu. Spatiotemporal characteristics of land degradation in the Fuxian Lake Basin, China: Past and future. Land Degradation & Development 2020, 31, 1 .

AMA Style

Chao Yang, Qingquan Li, Junyi Chen, Junjie Wang, Tiezhu Shi, Zhongwen Hu, Kai Ding, Guihua Wang, Guofeng Wu. Spatiotemporal characteristics of land degradation in the Fuxian Lake Basin, China: Past and future. Land Degradation & Development. 2020; 31 (16):1.

Chicago/Turabian Style

Chao Yang; Qingquan Li; Junyi Chen; Junjie Wang; Tiezhu Shi; Zhongwen Hu; Kai Ding; Guihua Wang; Guofeng Wu. 2020. "Spatiotemporal characteristics of land degradation in the Fuxian Lake Basin, China: Past and future." Land Degradation & Development 31, no. 16: 1.

Journal article
Published: 08 April 2020 in Ecological Indicators
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As one of the major bay areas in the world, the Guangdong-Hong Kong-Macau Greater Bay Area (GBA) has been experiencing a remarkable urbanization process, and its ecological quality also has been suffering from intense human activities and urbanization inevitably. This study aimed to reveal the spatiotemporal characteristics of ecological quality evolution under the pressure of urbanization in the GBA from 1987 to 2017. A comprehensive ecological evaluation index (CEEI) was proposed to describe the spatiotemporal characteristics change of ecological quality by integrating the remote sensing-based parameters of vegetative cover (VC), vegetative health index (VHI), normalized differential build-up and bare soil index (NDBSI), land surface moisture (LSM) and land surface temperature (LST). The results revealed that: (1) the evolution of ecological quality showed a trend of first improvement then degradation from 1987 to 2017 and the regions with poor and fair ecological quality gradually shifted from suburbs to urban areas; (2) the ecological quality was not optimistic in 1987, with the areas with poor and fair ecological quality reaching 3.45% and 30.36% of total area, respectively; (3) the ecological quality greatly improved in 1997 and 2007, and the areas with poor ecological quality only accounted for 0.41% and 0.70% of total area, respectively; and (4) the ecological quality degraded again in 2017, and the degraded area reached 52% and the areas with poor and fair ecological quality reached 4.3% and 17.35%, respectively. The changes of ecological quality were mainly driven by urbanization process and policy variation, and these results may provide helpful information for the ecological conservation and sustainable development of GBA.

ACS Style

Chao Yang; Chenchen Zhang; Qingquan Li; Huizeng Liu; Wenxiu Gao; Tiezhu Shi; Xu Liu; Guofeng Wu. Rapid urbanization and policy variation greatly drive ecological quality evolution in Guangdong-Hong Kong-Macau Greater Bay Area of China: A remote sensing perspective. Ecological Indicators 2020, 115, 106373 .

AMA Style

Chao Yang, Chenchen Zhang, Qingquan Li, Huizeng Liu, Wenxiu Gao, Tiezhu Shi, Xu Liu, Guofeng Wu. Rapid urbanization and policy variation greatly drive ecological quality evolution in Guangdong-Hong Kong-Macau Greater Bay Area of China: A remote sensing perspective. Ecological Indicators. 2020; 115 ():106373.

Chicago/Turabian Style

Chao Yang; Chenchen Zhang; Qingquan Li; Huizeng Liu; Wenxiu Gao; Tiezhu Shi; Xu Liu; Guofeng Wu. 2020. "Rapid urbanization and policy variation greatly drive ecological quality evolution in Guangdong-Hong Kong-Macau Greater Bay Area of China: A remote sensing perspective." Ecological Indicators 115, no. : 106373.

Journal article
Published: 26 March 2020 in Journal of Geophysical Research: Oceans
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This study investigates extreme phytoplankton blooms in the southern tropical Indian Ocean (TIO) in January–April 2011 using the extended ocean color products and in situ data. The amplitude of the blooms was approximately 4 times higher than the climatological value and 2 times higher than that in 1998–1999. The anomalous enhancement of surface chlorophyll‐a concentration (Chla) was associated with the strong upwelling Rossby waves that forced by the extraordinary strong wind stress curl in the southeastern TIO during 2010–2011 La Niña, which was much stronger than that of 1998–1999 La Niña. The Rossby waves uplifted the thermocline, which was shoaled by more than 70 m relative to the climatology. The results of the nonlinear 1.5‐layer reduced‐gravity model further suggest that the thermocline variations are mainly due to the wind stress curl over the interior TIO. In the vertical direction, the Argo data show a distinct upward and westward propagation of subsurface cooling, indicating that the upwelling of cold nutrient‐rich waters leads to the abnormally high Chla. The strong upwelling processes are also well captured by the Moored Array for African‐Asian‐Australian Monsoon Analysis and Prediction observations at 8°S, 80.5°E. Based on the climatological in situ nitrate data, the Rossby wave‐induced nitrate supply and potential new production at the mooring site are estimated. The potential f ratio ranges from 50% to 87% when the water brought to the surface is assumed to have originated from 60 to 200 m, thus indicating the dominant role of nutrient supply by the upwelling processes.

ACS Style

Xiaomei Liao; Yan Du; Tianyu Wang; QingYou He; Haigang Zhan; Shuibo Hu; Guofeng Wu. Extreme Phytoplankton Blooms in the Southern Tropical Indian Ocean in 2011. Journal of Geophysical Research: Oceans 2020, 125, 1 .

AMA Style

Xiaomei Liao, Yan Du, Tianyu Wang, QingYou He, Haigang Zhan, Shuibo Hu, Guofeng Wu. Extreme Phytoplankton Blooms in the Southern Tropical Indian Ocean in 2011. Journal of Geophysical Research: Oceans. 2020; 125 (4):1.

Chicago/Turabian Style

Xiaomei Liao; Yan Du; Tianyu Wang; QingYou He; Haigang Zhan; Shuibo Hu; Guofeng Wu. 2020. "Extreme Phytoplankton Blooms in the Southern Tropical Indian Ocean in 2011." Journal of Geophysical Research: Oceans 125, no. 4: 1.

Journal article
Published: 08 February 2020 in Remote Sensing
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River plumes play an important role in the cross-margin transport of phytoplankton and nutrients, which have profound impacts on coastal ecosystems. Using recently available Soil Moisture Active Passive (SMAP) sea surface salinity (SSS) data and high-resolution ocean color products, this study investigated summertime high-frequency variations in the Pearl River plume of China and its biological response. The SMAP SSS captures the intraseasonal oscillations in the offshore transport of the Pearl River plume well, which has distinct 30–60 day variations from mid-May to late September. The offshore transport of freshwater varies concurrently with southwesterly wind anomalies and is roughly in phase with the Madden–Julian Oscillation (MJO) index in phases 1–5, thus implying that the MJO exerts a significant influence. During MJO phases 1–2, the southwest wind anomalies in the northeastern South China Sea (SCS) enhanced cross-shore Ekman transport, while the northeast wind anomalies during MJO phases 3–5 favored the subsequent southwestward transport of the plume. The high chlorophyll-a concentration coincided well with the low-salinity water variations, emphasizing the important role of the offshore transport of the Pearl River plume in sustaining biological production over the oligotrophic northern SCS. The strong offshore transport of the plume in June 2015 clearly revealed that the proximity of a cyclonic eddy plays a role in the plume’s dispersal pathway. In addition, heavy rainfall related to the landfall of tropical cyclones in the Pearl River Estuary region contributed to the episodic offshore transport of the plume.

ACS Style

Xiaomei Liao; Yan Du; Tianyu Wang; Shuibo Hu; Haigang Zhan; Huizeng Liu; Guofeng Wu. High-Frequency Variations in Pearl River Plume Observed by Soil Moisture Active Passive Sea Surface Salinity. Remote Sensing 2020, 12, 563 .

AMA Style

Xiaomei Liao, Yan Du, Tianyu Wang, Shuibo Hu, Haigang Zhan, Huizeng Liu, Guofeng Wu. High-Frequency Variations in Pearl River Plume Observed by Soil Moisture Active Passive Sea Surface Salinity. Remote Sensing. 2020; 12 (3):563.

Chicago/Turabian Style

Xiaomei Liao; Yan Du; Tianyu Wang; Shuibo Hu; Haigang Zhan; Huizeng Liu; Guofeng Wu. 2020. "High-Frequency Variations in Pearl River Plume Observed by Soil Moisture Active Passive Sea Surface Salinity." Remote Sensing 12, no. 3: 563.