Can meteorological data and normalized difference vegetation index be used to quantify soil pH in grasslands?

作者：Erfu Dai, Guangyu Zhang, Gang Fu, Xinjie Zha · 发表于：Frontiers in Ecology and Evolution · 年份：2023 · DOI：10.3389/fevo.2023.1206581 · 被引用次数：13 · 研究领域：Soil Geostatistics and Mapping、Soil Carbon and Nitrogen Dynamics、Soil and Unsaturated Flow

Quantifying soil pH at manifold spatio-temporal scales is critical for examining the impacts of global change on soil quality. It is still unclear whether meteorological data and normalized difference vegetation index (NDVI) can be used to quantify soil pH in grasslands. Here, nine methods (i.e., RF: random-forest, GLR: generalized-linear-regression, GBR: generalized-boosted-regression, MLR: multiple-linear-regression, ANN: artificial-neural-network, CIT: conditional-inference-tree, SVM: support-vector-machine, eXGB: eXtreme-gradient-boosting, RRT: recursive-regression-tree) were applied to quantify soil pH. Three independent variables (i.e., AP: annual precipitation, AT: annual temperature, ARad: annual radiation) were used to quantify potential soil pH (pH p ), and four independent variables (i.e., AP, AT, ARad and NDVI max : maximum NDVI during growing season) were applied to quantify actual soil pH (pH a ). Overall, the developed eXGB models performed the worst (linear regression slope < 0.60; R 2 = 0.99; relative deviation ≤ –43.54%; RMSE ≥ 3.14), but developed RF models performed the best (linear regression slope: 0.99–1.01; R 2 = 1.00; relative deviation: from –1.26% to 0.65%; RMSE ≤ 0.28). The linear regression slope, R 2 , absolute value of relative deviation and RMSE between modelled and measured soil pH were 0.96–1.03, 0.99–1.00, ≤ 3.87% and ≤ 0.88 for the other seven methods, respectively. Accordingly, except the developed eXGB approach, the developed other...