A high-resolution map of soil pH in China made by hybrid modelling of sparse soil data and environmental covariates and its implications for pollution

Autor: Zongzheng Liang, Bifeng Hu, Richard Webster, Gan-Lin Zhang, Dominique Arrouays, Zhou Shi, Songchao Chen, Yin Zhou, Hongfen Teng
Přispěvatelé: InfoSol (InfoSol), Institut National de la Recherche Agronomique (INRA), Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Rothamsted Research, State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences [Changchun Branch] (CAS), Unité de recherche Science du Sol (USS), Sciences de la Terre et de l'Univers, Université d'Orléans (UO), Unité INFOSOL (ORLEANS INFOSOL), Sol Agro et hydrosystème Spatialisation (SAS), Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, AGROCAMPUS OUEST-Institut National de la Recherche Agronomique (INRA)
Rok vydání: 2019
Předmět:
Zdroj: Science of the Total Environment
Science of the Total Environment, Elsevier, 2019, 655, pp.273-283. ⟨10.1016/j.scitotenv.2018.11.230⟩
ISSN: 0048-9697
1879-1026
Popis: The soil's pH is the single most important indicator of the soil's quality, whether for agriculture, pollution control or environmental health and ecosystem functioning. Well documented data on soil pH are sparse for the whole of China — data for only 4700 soil profiles were available from China's Second National Soil Inventory. By combining those data, standardized for the topsoil (0–20 cm), with 17 environmental covariates at a fine resolution (3 arc-second or 90 m) we have predicted the soil's pH at that resolution, that is at more than 109 points. We did so by parallel computing over tiles, each 100 km × 100 km, with two machine learning techniques, namely Random Forest and XGBoost. The predictions for the tiles were then merged into a single map of soil pH for the whole of China. The quality of the predictions were assessed by cross-validation. The root mean squared error (RMSE) was an acceptable 0.71 pH units per point, and Lin's Concordance Correlation Coefficient was 0.84. The hybrid model revealed that climate (mean annual precipitation and mean annual temperature) and soil type were the main factors determining the soil's pH. The pH map showed acid soil mainly in southern and north-eastern China, and alkaline soil dominant in northern and western China. This map can provide a benchmark against which to evaluate the impacts of changes in land use and climate on the soil's pH, and it can guide advisors and agencies who make decisions on remediation and prevention of soil acidification, salinization and pollution by heavy metals, for which we provide examples for cadmium and mercury.
Databáze: OpenAIRE
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