Can meteorological data and normalized difference vegetation index be used to quantify soil pH in grasslands?
نویسندگان
چکیده
Quantifying soil pH at manifold spatio-temporal scales is critical for examining the impacts of global change on quality. It still unclear whether meteorological data and normalized difference vegetation index (NDVI) can be used to quantify 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 pH. Three independent variables AP: annual precipitation, AT: temperature, ARad: radiation) potential (pH p ), four AP, AT, ARad NDVI max : maximum during growing season) actual a ). Overall, developed eXGB models performed worst (linear regression slope < 0.60; R 2 = 0.99; relative deviation ≤ –43.54%; RMSE ≥ 3.14), but RF best slope: 0.99–1.01; 1.00; deviation: from –1.26% 0.65%; 0.28). The linear slope, , absolute value between modelled measured 0.96–1.03, 0.99–1.00, 3.87% 0.88 other seven methods, respectively. Accordingly, except approach, eight have greater accuracies quantifying However, had uppermost quantification accuracy Whether or not was dependent chosen models. by this study may conducive scientific studies related quality degradation (e.g., acidification salinization) spatial-temporal under future globe change.
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ژورنال
عنوان ژورنال: Frontiers in Ecology and Evolution
سال: 2023
ISSN: ['2296-701X']
DOI: https://doi.org/10.3389/fevo.2023.1206581