Data set of soil total nitrogen content and soil total phosphorus content of the Kherlen River Basin in 2022

Understanding the soil total nitrogen and soil total phosphorus content in the basin is crucial for exploring the sources and quantities of non-point source pollution. This dataset utilizes a total of 18 auxiliary variables, including terrain, climate, soil properties, vegetation, and spatial location, and uses a two-point machine learning method to produce spatial distribution data of soil total nitrogen and soil total phosphorus content at 250 m spatial resolution in the Krulun River Basin.

And based on the ten-fold cross-validation method, it is proved that the two-point machine learning algorithm can improve the prediction accuracy by more than 10 % compared with the ordinary kriging algorithm. This dataset can be used for estimating the distribution of non-point source pollution load in the Krulun River Basin, as well as for soil and vegetation research.