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Wei Shangguan
Researcher at Sun Yat-sen University
Publications - 72
Citations - 4582
Wei Shangguan is an academic researcher from Sun Yat-sen University. The author has contributed to research in topics: Computer science & Environmental science. The author has an hindex of 14, co-authored 33 publications receiving 2690 citations. Previous affiliations of Wei Shangguan include Beijing Normal University.
Papers
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Journal ArticleDOI
SoilGrids250m: Global gridded soil information based on machine learning
Tomislav Hengl,Jorge Mendes de Jesus,Gerard B. M. Heuvelink,Maria Ruiperez Gonzalez,Milan Kilibarda,Aleksandar Blagotić,Wei Shangguan,Marvin N. Wright,Xiaoyuan Geng,Bernhard Bauer-Marschallinger,Mario Guevara,Rodrigo Vargas,R. A. MacMillan,Niels H. Batjes,Johan G. B. Leenaars,Eloi Ribeiro,Ichsani Wheeler,Stephan Mantel,Bas Kempen +18 more
TL;DR: Improvements in the relative accuracy considering the amount of variation explained, in comparison to the previous version of SoilGrids at 1 km spatial resolution, range from 60 to 230%.
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A global soil data set for earth system modeling
TL;DR: The Global Soil Dataset (GSDE) as discussed by the authors provides soil information such as soil particle-size distribution, organic carbon, and nutrients, and quality control information in terms of confidence level at 30″ × 30″ horizontal resolution and for eight vertical layers to a depth of 2.3 m.
Journal ArticleDOI
A China data set of soil properties for land surface modeling
Wei Shangguan,Yongjiu Dai,Baoyuan Liu,A-Xing Zhu,Qingyun Duan,Lizong Wu,Duoying Ji,Aizhong Ye,Hua Yuan,Qian Zhang,Dongdong Chen,Ming Chen,Jianting Chu,Youjun Dou,Jianxia Guo,Haiqin Li,Junjia Li,Lu Liang,Xiao Liang,Heping Liu,Shuyan Liu,Chiyuan Miao,Yizhou Zhang +22 more
TL;DR: Li et al. as mentioned in this paper used the polygon linkage method to derive the spatial distribution of soil properties and linked the profile attribute database and soil map under the framework of the Genetic Soil Classification of China which avoids uncertainty in taxon referencing.
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Reprocessing the MODIS Leaf Area Index products for land surface and climate modelling
TL;DR: In this paper, an integrated two-step method was developed to derive the improved MODIS LAI products effectively and efficiently on a global scale, taking advantage of background values and quality control information at each pixel to do simple data assimilation for relatively low quality data.
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Development of a China Dataset of Soil Hydraulic Parameters Using Pedotransfer Functions for Land Surface Modeling
TL;DR: In this paper, the authors developed a dataset of the soil hydraulic parameters associated with two empirical soil functions (i.e., a water retention curve and hydraulic conductivity) using multiple pedotransfer functions (PTFs).