Can normalized difference vegetation index and climate data be used to estimate soil carbon, nitrogen, and phosphorus and their ratios in the Xizang grasslands?

Abstract

Accurately quantifying the relative effects of climate change and human activities on soil carbon, nitrogen, and phosphorus in alpine grasslands and their feedback is an important aspect of global change, and high-precision models are the key to solving this scientific problem with high quality. Therefore, nine models, the random forest model (RFM), generalized boosted regression model (GBRM), multiple linear regression model (MLRM), support vector machine model (SVMM), recursive regression tree model (RRTM), artificial neural network model (ANNM), generalized linear regression model (GLMR), conditional inference tree model (CITM), and eXtreme gradient boosting model (eXGBM), were used for modeling soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), the ratio of SOC to TN (C:N), the ratio of SOC to TP (C:P), and the ratio of TN to TP (N:P) at depths of 0–10, 10–20, and 20–30 cm under non-grazing and free-grazing scenarios in the Xizang grasslands. Annual radiation (ARad), annual precipitation (AP), and annual temperature (AT) were used as independent variables under non-grazing scenarios, whereas ARad, AP, AT, and growing season maximum normalized difference vegetation index (NDVImax) were used as independent variables under free-grazing scenarios. Overall, the RFM and GBRM were more accurate than the other seven models. However, the tree numbers of the GBRM were much larger than those of the RFM, indicating that the GBRM may have a greater model complexity and lower running speed. Therefore, the RFM had the best performance among the nine models in modeling SOC, TN, TP, C:N, C:P, and N:P in the Xizang grasslands. The RFM established in this study can not only help scientists save time and money on massive sampling and analysis, but can also be used to construct a database of SOC, TN, and TP, and their ratios, and further scientific research related to ecological and environmental issues (e.g., examining whether soil systems intensified global warming over the past few decades by exploring whether climate change and human activities altered soil organic carbon) in the grasslands of Xizang Plateau.

Modelling Soil Ammonium Nitrogen, Nitrate Nitrogen and Available Phosphorus Using Normalized Difference Vegetation Index and Climate Data in Xizang’s Grasslands

“…Previous studies have suggested that there are some uncertainties when using climate variables and NDVI to model plant and soil variables in terrestrial ecosystems. It included the accuracy of NDVI itself (such as easy saturation), the accuracy of climate variables (such as the error caused by spatial interpolation), the spatial-temporal matching error between dependent variables and independent variables, the acquisition time of dependent variables and topographic factors (such as slope) [25,26]. In this study, the random forest models of soil NH 4 + -N, NO 3 − -N and AP at only 0-10 cm and 10-20 cm were constructed, and soil depth can alter the model accuracies of these soil variables.…”

“…However, the extent to which the climate trivariate (temperature, precipitation and radiation) and NDVI can col-lectively explain variations in soil available nitrogen and phosphorus in alpine grasslands of the Qinghai-Xizang Plateau, compared to the climate trivariate alone, remains unknown due to a lack of relevant studies. Third, while previous research confirms that random forest algorithm is more accurate than other algorithms in quantifying pasture nutrient quality and nutrient pool, plant diversity, soil moisture, soil pH, soil carbon, nitrogen and phosphorus in alpine grasslands of the Qinghai-Xizang Plateau [22][23][24][25][26], it is uncertain whether the random forest algorithm also exhibits superior performance compared to other models in quantifying variations in soil-available nitrogen and phosphorus in alpine grasslands of the Qinghai-Xizang Plateau.…”

“…The nine algorithms employed in this study were the RFA, generalized boosted regression algorithm (GBRA), support vector machine algorithm (SVA), multiple linear regression algorithm (MLRA), recursive regression tree algorithm (RRTA), artificial neural network algorithm (ANNA), generalized linear regression algorithm (GLRA), conditional inference tree algorithm (CITA) and eXtreme gradient boosting algorithm (eXGBA). According to some previous studies [22][23][24][25][26], we hypothesized that the performances of RFA were stronger than the other eight algorithms in estimating soil NH 4 + -N, NO 3 − -N and AP.…”

“…These studies have explored the feedback effects on grassland ecosystem productivity and forage nutrition quality [19][20][21]. Radiation, as a significant climate variable, has been demonstrated to markedly influence the nutritional quality of herbage, herbage stock, plant diversity, soil moisture, soil pH, soil carbon, nitrogen and phosphorus in the Qinghai-Xizang grassland ecosystems [22][23][24][25][26]. The combined influence of temperature, precipitation and radiation appears to explain the productivity and diversity of alpine grassland more than the individual factors of temperature and precipitation [27,28].…”