AUTHOR=Dai Erfu , Zhang Guangyu , Fu Gang , Zha Xinjie 

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

JOURNAL=Frontiers in Ecology and Evolution

VOLUME=11

YEAR=2023

URL=https://www.frontiersin.org/journals/ecology-and-evolution/articles/10.3389/fevo.2023.1206581

DOI=10.3389/fevo.2023.1206581

ISSN=2296-701X

ABSTRACT=<p>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<sub>p</sub>), and four independent variables (i.e., AP, AT, ARad and NDVI<sub>max</sub>: maximum NDVI during growing season) were applied to quantify actual soil pH (pH<sub>a</sub>). Overall, the developed eXGB models performed the worst (linear regression slope &lt; 0.60; <italic>R</italic><sup>2</sup> = 0.99; relative deviation ≤ –43.54%; RMSE ≥ 3.14), but developed RF models performed the best (linear regression slope: 0.99–1.01; <italic>R</italic><sup>2</sup> = 1.00; relative deviation: from –1.26% to 0.65%; RMSE ≤ 0.28). The linear regression slope, <italic>R</italic><sup>2</sup>, 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 eight methods can have relative greater accuracies in quantifying soil pH. However, the developed RF had the uppermost quantification accuracy for soil pH. Whether or not meteorological data and normalized difference vegetation index can be used to quantify soil pH was dependent on the chosen models. The RF developed by this study can be used to quantify soil pH from measured meteorological data and NDVI<sub>max</sub>, and may be conducive to scientific studies related to soil quality and degradation (e.g., soil acidification and salinization) at manifold spatial-temporal under future globe change.</p>