2022年克鲁伦河流域土壤全氮含量与土壤全磷含量数据集

doi:10.19788/j.issn.2096-6369.230314

Abstract

Abstract:

The ecological and environmental security of the Kherlen River Basin has attracted more and more attention in China and Mongolia. It is of great significance to investigate the contents of soil total nitrogen (STN) and soil total phosphorus (STP) in the basin for accurately estimating the load of non-point sources (NPS) and studying the state of resources and environment and sustainable development. It is time-consuming and labor-intensive to obtain a wide range of STN and STP contents using traditional sampling methods, while STN and STP not only have spatial heterogeneity, but also have heterogeneity in their relationships with auxiliary variables. Moreover, a single global model cannot fit complex heterogeneous relationships, and it is difficult for the local modeling method to overcome dimensional disaster problems. Therefore, the two-point machine learning (TPML) method is introduced in this paper. The TPML method first establishes a global model based on the difference of paired points, and then constructs a local model based on the prediction difference of the global model. It can expand the sample size from n to n², achieving the prediction of high-precision and large-scale STN and STP contents using limited sampling points. Based on 18 auxiliary variables of topography, climate, soil properties, vegetation and spatial location, etc, the study produced the distribution dataset of STN and STP contents in the basin using the TPML method. Futhermore, using the ten-fold cross-validation method, the study confirmed that the prediction accuracy of TPML model is more than 10% higher than that of Ordinary Kriging (OK) model. The mean absolute deviation (MAE) and mean root mean squared error (RMSE) of STN content predicted by the TPML method are 0.309% and 0.456% respectively. The mean MAE of STN content predicted by random forest (RF), inverse distance weighted (IDW) and OK methods is 0.329%, 0.247% and 1.864%, and the mean RMSE is 0.468%, 0.387% and 1.976%, respectively. The mean MAE and mean RMSE of STP content predicted by TPML method are 0.640% and 0.861%. The mean MAE of STP content predicted by RF, IDW and OK methods is 0.643%, 0.396% and 1.357%, and the mean RMSE is 0.862%, 0.523% and 1.651%, respectively.

Data summary:

Item	Description
Dataset name	Dataset of Soil Total Nitrogen Content and Soil Total Phosphorus Content of the Kherlen River Basin in 2022
Specific subject area	Land resources and information technology
Research topic	Prediction of soil total nitrogen content and soil total phosphorus content
Time range	2022
Geographical scope	Kherlen River Basin
Spatial resolution	250 m
Data types and technical formats	250 m high resolution distribution map of soil total nitrogen content 250 m high resolution distribution map of soil total phosphorus content
Dataset structure	The dataset is soil total nitrogen (STN) and soil total phosphorus (STP) content at 250 m resolution in the Kherlen River Basin in 2022
Volume of data	32.84 MB
Key index in dataset	Soil total nitrogen content, Soil total phosphorus content
Data accessibility	CSTR：17058.11.sciencedb.agriculture.00018 DOI：10.57760/sciencedb.agriculture.00018
Financial support	Research and development on remote sensing monitoring and assessment technology of non-point source pollution in Kherlen River Basin under the National Key Research and Development Program（2021YFE0102300）

Key words: Kherlen River Basin, two-point machine learning, soil total nitrogen, soil total phosphorus

WANG ChenYi, GAO BingBo, Sukhbaatar Chinzorig, FENG QuanLong, FENG AiPing, JIANG ChuanLiang, ZHANG ZhongHao, JI ShuRui. Dataset of Soil Total Nitrogen Content and Soil Total Phosphorus Content of the Kherlen River Basin in 2022[J].Journal of Agricultural Big Data, 2023, 5(3): 104-111.

Figures/Tables 6

Table 1

Fig. 1

Fig. 2

Table 2

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Fig. 4

References 16

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数据名称	数据类型	数据来源
土地利用数据	类别型	https://data.casearth.cn/sdo/detail/5fbc7904819aec1ea2dd7061
数字高程	连续型	https://www.gscloud.cn
粗碎屑（0-5 cm、5-15 cm）	连续型	https://soilgrids.org/
氮（0-5 cm、5-15 cm）	连续型	https://soilgrids.org/
堆积密度（0-5 cm、5-15 cm）	连续型	https://soilgrids.org/
粉砂（0-5 cm、5-15 cm）	连续型	https://soilgrids.org/
黏土（0-5 cm、5-15 cm）	连续型	https://soilgrids.org/
砂土（0-5 cm、5-15 cm）	连续型	https://soilgrids.org/
水的pH值（0-5 cm、5-15 cm）	连续型	https://soilgrids.org/
土壤有机碳储量(0-30 cm)	连续型	https://soilgrids.org/
有机碳（0-5 cm、5-15 cm）	连续型	https://soilgrids.org/
阳离子交换容量（0-5 cm、5-15 cm）	连续型	https://soilgrids.org/
有机碳密度（0-5 cm、5-15 cm）	连续型	https://soilgrids.org/
植被覆盖度	连续型	https://earthengine.google.com/
年平均降水	连续型	https://www.worldclim.org/
年平均气温	连续型	https://www.worldclim.org/
经度	连续型	由土壤采样点得到
纬度	连续型	由土壤采样点得到

Dataset of Soil Total Nitrogen Content and Soil Total Phosphorus Content of the Kherlen River Basin in 2022

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References 16

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土壤属性	机器学习方法	MAE (%)	RMSE (%)
STN	TPML	0.309	0.456
	RF	0.329	0.468
	IDW	0.247	0.387
	OK	1.864	1.976
STP	TPML	0.640	0.861
	RF	0.643	0.862
	IDW	0.396	0.523
	OK	1.357	1.651