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

作者：Wei Sun, Huxiao Qi, Tianyu Li, Yong Qin, Gang Fu, Fusong Han, Shaohua Wang, Xu Pan · 发表于：Sustainability · 年份：2024 · DOI：10.3390/su16114695 · 被引用次数：7 · 研究领域：Soil Carbon and Nitrogen Dynamics、Soil Geostatistics and Mapping、Soil and Land Suitability Analysis

There is still a lack of high-precision and large-scale soil ammonium nitrogen (NH4+-N), nitrate nitrogen (NO3−-N) and available phosphorus (AP) in alpine grasslands at least on the Qinghai–Xizang Plateau, which may limit our understanding of the sustainability of alpine grassland ecosystems (e.g., changes in soil NH4+-N, NO3−-N and AP can affect the sustainability of grassland productivity, which in turn may alter the sustainability of livestock development), given that nitrogen and phosphorus are important limiting factors in alpine regions. The construction of big data mining models is the key to solving the problem mentioned above. Therefore, observed soil NH4+-N, NO3−-N and AP at 0–10 cm and 10–20 cm, climate data (air temperature, precipitation and radiation) and/or normalized vegetation index (NDVI) data were used to model NH4+-N, NO3−-N and AP in alpine grasslands of Xizang under fencing and grazing conditions. Nine algorithms, including random forest algorithm (RFA), generalized boosted regression algorithm (GBRA), multiple linear regression algorithm (MLRA), support vector machine algorithm (SVMA), recursive regression tree algorithm (RRTA), artificial neural network algorithm (ANNA), generalized linear regression algorithm (GLMA), conditional inference tree algorithm (CITA), and eXtreme gradient boosting algorithm (eXGBA), were used. The RFA had the best performance among the nine algorithms. Climate data based on the RFA can explain 78–92% variation of NH4+-N, NO3...