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Open AccessArticle

Digital Soil Mapping over Large Areas with Invalid Environmental Covariate Data

by Nai-Qing Fan 1,2, A-Xing Zhu 1,2,3,4,5, Cheng-Zhi Qin 1,2,3,* and Peng Liang 1,2
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, Jiangsu 210023, China
Key Laboratory of Virtual Geographic Environment (Ministry of Education), Nanjing Normal University, Nanjing, Jiangsu 210023, China
Department of Geography, University of Wisconsin-Madison, Madison, WI 53706, USA
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(2), 102;
Received: 10 January 2020 / Revised: 1 February 2020 / Accepted: 5 February 2020 / Published: 6 February 2020
Environmental covariates are fundamental inputs of digital soil mapping (DSM) based on the soil–environment relationship. It is normal to have invalid values (or recorded as NoData value) in individual environmental covariates in some regions over an area, especially over a large area. Among the two main existing ways to deal with locations with invalid environmental covariate data in DSM, the location-skipping scheme does not predict these locations and, thus, completely ignores the potentially useful information provided by valid covariate values. The void-filling scheme may introduce errors when applying an interpolation algorithm to removing NoData environmental covariate values. In this study, we propose a new scheme called FilterNA that conducts DSM for each individual location with NoData value of a covariate by using the valid values of other covariates at the location. We design a new method (SoLIM-FilterNA) combining the FilterNA scheme with a DSM method, Soil Land Inference Model (SoLIM). Experiments to predict soil organic matter content in the topsoil layer in Anhui Province, China, under different test scenarios of NoData for environmental covariates were conducted to compare SoLIM-FilterNA with the SoLIM combined with the void-filling scheme, the original SoLIM with the location-skipping scheme, and random forest. The experimental results based on the independent evaluation samples show that, in general, SoLIM-FilterNA can produce the lowest errors with a more complete spatial coverage of the DSM result. Meanwhile, SoLIM-FilterNA can reasonably predict uncertainty by considering the uncertainty introduced by applying the FilterNA scheme.
Keywords: digital soil mapping; invalid data; environmental covariate; SoLIM; uncertainty; large areas; China digital soil mapping; invalid data; environmental covariate; SoLIM; uncertainty; large areas; China
MDPI and ACS Style

Fan, N.-Q.; Zhu, A.-X.; Qin, C.-Z.; Liang, P. Digital Soil Mapping over Large Areas with Invalid Environmental Covariate Data. ISPRS Int. J. Geo-Inf. 2020, 9, 102.

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