About

A-Xing Zhu is a Professor at the University of Wisconsin-Madison in the Department of Geography. He oversees the SoLIM project, which focuses on soil inference and knowledge extraction related to geographic and environmental studies. His work involves applying geographic information systems (GIS) and related technologies to analyze soil data, with specific research interests including soil inference for the Dane County, Wisconsin study area. As a key member of the SoLIM group, Professor Zhu contributes to advancing methods for soil classification and geographic knowledge extraction, supporting environmental and geographic research efforts.

Research topics

• Soil science
• Geology
• Physical geography
• Geography
• Environmental science

Selected publications

• Mapping high resolution National Soil Information Grids of China

  Science Bulletin · 2021 · 561 citations

  • Environmental science
  • Soil science
  • Physical geography

  Soil spatial information has traditionally been presented as polygon maps at coarse scales. Solving global and local issues, including food security, water regulation, land degradation, and climate change requires higher quality, more consistent and detailed soil information. Accurate prediction of soil variation over large and complex areas with limited samples remains a challenge, which is especially significant for China due to its vast land area which contains the most diverse soil landscapes in the world. Here, we integrated predictive soil mapping paradigm with adaptive depth function fitting, state-of-the-art ensemble machine learning and high-resolution soil-forming environment characterization in a high-performance parallel computing environment to generate 90-m resolution national gridded maps of nine soil properties (pH, organic carbon, nitrogen, phosphorus, potassium, cation exchange capacity, bulk density, coarse fragments, and thickness) at multiple depths across China. This was based on approximately 5000 representative soil profiles collected in a recent national soil survey and a suite of detailed covariates to characterize soil-forming environments. The predictive accuracy ranged from very good to moderate (Model Efficiency Coefficients from 0.71 to 0.36) at 0-5 cm. The predictive accuracy for most soil properties declined with depth. Compared with previous soil maps, we achieved significantly more detailed and accurate predictions which could well represent soil variations across the territory and are a significant contribution to the GlobalSoilMap.net project. The relative importance of soil-forming factors in the predictions varied by specific soil property and depth, suggesting the complexity and non-stationarity of comprehensive multi-factor interactions in the process of soil development.

  DOI

Frequent coauthors

• Cheng‐Zhi Qin

  Chinese Academy of Sciences

  781 shared

• Lin Yang

  Nanjing University

  286 shared

• Junzhi Liu

  273 shared

• Liang‐Jun Zhu

  Institute of Geographic Sciences and Natural Resources Research

  143 shared

• Yunqiang Zhu

  Chinese Academy of Sciences

  117 shared

• Fang-He Zhao

  University of Chinese Academy of Sciences

  114 shared

• Tao Pei

  Guangdong Province Women and Children Hospital

  107 shared

• Changchun Huang

  Nanjing Normal University

  103 shared

Labs

• The SoLIM GroupPI

Awards & honors

• 2017 Recipient of CPGIS Education Excellence Award, Chinese…
• 2012 Manasse Chair Professor, University of Wisconsin-Madiso…
• 2009 The Hamel Faculty Fellow Award, University of Wisconsin…
• 2008 The Vilas Associate Award, University of Wisconsin-Madi…
• 1997 The ASPRS Intergraph Award for best scientific paper in…

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