流域绿洲土壤盐分多模型反演效果评估

doi:10.13866/j.azr.2024.07.04

Abstract

Abstract:

A case study was conducted on the plain oasis in the Sangong River Basin of Xinjiang, China, to monitor and control soil salinity to improve the sustainable development of oases. Based on the climate, topography, vegetation, groundwater, and salinity of the soil survey data, many model methods, such as the Random Forest model, Support Vector Machine, Decision Tree, and Ordinary Kriging, were applied to estimate the inversion accuracy and the spatial distribution of soil salinity in the topsoil. The results revealed that the range of soil salinity values was 0.29-30.18 g·kg^-1 and an average of 4.06 g·kg^-1 for the sample sites. The value of the coefficients of variation was 149.73%, indicating a robust spatial variability. Among the four models, the Random Forest model showed a higher simulation precision compared to the others, with a coefficient of determination value of 0.73, a root-mean-square error value of 1.89 g·kg^-1, and an absolute mean error value of 1.49 g·kg^-1. The results of the Random Forest model inversion revealed that areas of higher soil salinity were concentrated in the northwest and the midbasin. Among the nine environmental covariates, elevation and groundwater salinity had a significant impact on the accuracy of identifying spatial distribution characteristics of soil salinity. In general, the Random Forest model as a machine learning method can not only avoid the smoothing effect and abrupt changes on both sides of the map boundary but also identify the local spatial distribution of soil salinity in the basin. The results of this study can provide technical and methodological applications for the long-term monitoring of soil salinization in arid areas.

Key words: machine learning, soil salinity, spatial distribution, Sangong River Basin, arid area

LONG Weiyi, SHI Jianfei, LI Shuangyuan, SUN Jinjin, WANG Yugang. Evaluation of multimodel inversion effects on soil salinity in oasis basin[J].Arid Zone Research, 2024, 41(7): 1120-1130.

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References 42

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数据名称	数据精度	数据格式	简介及来源
气象数据	1 km	TIFF	气象数据包括气温和降水数据，采用的1901—2022年中国1 km逐月平均气温和降水量数据集，数据来源于国家科技基础条件平台—国家地球系统科学数据中心（http://www.geodata.cn）
高程数据	30 m	TIFF	高程数据包括高程、坡向、坡度，数据来源于美国NASA和NGA的航天飞机雷达地形测绘项目（Shuttle Radar Topography Mission，SRTM）的成果产品，下载地址为 https://srtm.csi.cgiar.org，坡向和坡度是基于高程在ArcGIS中计算得出
土壤湿度数据	1 km	TIFF	土壤湿度数据采用2000—2020年中国1 km土壤湿度日尺度数据集，数据来源于“国家青藏高原科学数据中心”（http://data.tpdc.ac.cn）
NDVI数据	30 m	TIFF	NDVI数据基于Google Earth Engine（GEE）平台，选择可用的Landsat 8数据的波段4（红光波段，R）和波段5（近红外波段，NIR）计算NDVI，其中NDVI=（NIR-R）/（NIR+R）
矿化度数据	30 m	TIFF	地下水矿化度数据采用研究区历史数据
pH数据	30 m	TIFF	pH数据采用研究区实测数据

盐渍化等级	含盐量/（g·kg^-1）	数量占比/%	均值/（g·kg^-1）	最小值/（g·kg^-1）	最大值/（g·kg^-1）	变异系数/%
非盐渍化	<3	68	1.02	0.29	2.86	66.81
轻度盐渍化	3~6	10	4.36	3.01	5.95	28.67
中度盐渍化	6~9	7	7.67	6.27	8.74	13.82
重度盐渍化	9~12	7	10.57	9.25	11.61	8.50
盐土化	>12	8	20.79	13.40	30.18	27.86
总体样本		100	4.06	0.29	30.18	149.73

模型	指标	最小值 /（g·kg^-1）	最大值 /（g·kg^-1）	均值 /（g·kg^-1）	标准差 /（g·kg^-1）
RF	R²	0.66	0.87	0.73	0.05
	RMSE	1.02	4.20	1.89	0.44
	MAE	0.86	3.11	1.49	0.33
SVM	R²	0.61	0.79	0.66	0.04
	RMSE	1.35	1.92	1.73	0.15
	MAE	1.31	1.76	1.57	0.12
DT	R²	0.67	0.74	0.70	0.02
	RMSE	0.56	2.33	1.66	0.42
	MAE	0.73	2.16	1.53	0.32
OK	R²	0.23	0.32	0.28	0.03
	RMSE	3.74	5.23	4.46	0.41
	MAE	2.81	3.69	3.27	0.25

模型	最小值 /（g·kg^-1）	均值 /（g·kg^-1）	最大值 /（g·kg^-1）	标准差 /（g·kg^-1）
RF	0.50	4.80	22.41	2.37
SVM	0.50	4.03	21.00	2.22
DT	0.29	3.91	30.18	5.26
OK	0.46	4.71	36.22	3.96

Evaluation of multimodel inversion effects on soil salinity in oasis basin

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