期刊
GEODERMA
卷 339, 期 -, 页码 40-58出版社
ELSEVIER
DOI: 10.1016/j.geoderma.2018.12.037
关键词
Digital soil mapping; Soil organic carbon fractions; Feature selection; Machine learning; Regression kriging; Residual spatial autocorrelation
类别
资金
- project Rapid Assessment and Trajectory Modeling of Changes in Soil Carbon across a Southeastern Landscape (USDA-CSREES-NRI
- Agricultural and Food Research Initiative -National Institute of Food and Agriculture) [2007-35107-18368]
- Republic of Turkey Ministry of Agriculture and Forestry - General Directorate of Combating Desertification and Erosion
Our understanding of the spatial distribution of soil carbon (C) pools across diverse land uses, soils, and climatic gradients at regional scale is still limited. Research in digital soil mapping and modeling that investigates the interplay between (i) soil C pools and environmental factors (deterministic trend model) and (ii) stochastic, spatially dependent variations in soil C fractions (stochastic model) is just emerging. This evoked our motivation to investigate soil C pools in the State of Florida covering about 150,000 km(2). Our specific objectives were to (i) compare different soil C pool models that quantify stochastic and/or deterministic components, (ii) assess the prediction performance of soil C models, and (iii) identify environmental factors that impart most control on labile and recalcitrant pools and soil total C (TC). We used soil data (0-20 cm) from a research collected at 1014 georeferenced sites including measured bulk density, recalcitrant carbon (RC), labile (hot-water extractable) carbon (HC) and TC. A comprehensive set of 327 geospatial soil-environmental variables was acquired. The Boruta method was employed to identify all-relevant soil-environmental predictors. We employed eight methods - Classification and Regression Tree (CaRT), Bagged Regression Tree (BaRT), Boosted Regression Tree (BoRT), Random Forest (RF), Support Vector Machine (SVM), Partial Least Square Regression (PLSR), Regression Kriging (RK), and Ordinary Kriging (OK) - to predict soil C fractions and TC. Overall, 36, 20 and 25 predictors stood out as all-relevant to estimate TC, RC and HC, respectively. We predicted a mean of 5.29 +/- 3.58 kg TC m(-2) in the top 20 cm with the best model. The prediction performance assessed by the Ratio of Prediction Error to Inter-quartile Range for TC stocks was as follows: RF > SVM > BoRT > BaRT > PLSR > RK > CART > OK. The best models explained 71.6%, 71.7% and 30.5% of the total variation for TC, RC and HC, respectively. Biotic and hydro-pedological factors explained most of the variation in soil C pools and TC; lithologic and climatic factors showed some relationships to soil C pools and TC, whereas topographic factors faded from soil C models.
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