集成土壤-环境关系与机器学习的干旱区土壤属性数字制图

doi:10.3864/j.issn.0578-1752.2020.03.009

Abstract

Abstract:

【Objective】The spatial distribution of soil properties is an important factor affecting agricultural productivity, land management and ecological security. Utilizing the coupling relationship between soil and environment within framework of machine learning algorithm, the spatial distribution of soil pH, soil salt content (SSC) and soil organic matter (SOM) was quantitatively predicted to provide a scientific basis on ecological security and agricultural production in the arid region. 【Method】A total of 82 topsoil (0-20 cm) samples were collected from the Ugan-Kuqa River basin oasis in Xinjiang Uyghur Autonomous Region in July 2017. Furthermore, Digital elevation model (DEM) data and Landsat 8 data were used to extract 32 environmental covariates according to the soil-environment relationship. The 32 extracted variables were resampled to 90 m spatial resolution via raster resampling and were converted to grid format for participate in modeling. According to the importance of environmental covariates, they were ranked respectively using Gradient Boosting Decision Tree (GBDT) algorithm on the three soil attributes. We considered three strategies to estimate soil properties, including random forest, bagging and Cubist algorithm. Compared with non-linear models, we introduced classic linear model (MLR) to conduct optimization. On this foundation, we mapped the soil properties (pH, SSC and SOM) with a resolution of 90 m in the Ugan-Kuqa River basin oasis, respectively.【Result】The results showed that GBDT could screen out important covariates effectively. Elevation and Profile Curvature, Difference Vegetation Index, Extended Normalized Difference Vegetation Index, Modified Soil Adjusted Vegetation Index and Salinity Index S1 and Salinity Index S6 were important factors and involved in modeling of three kinds of soil properties, among which SSC selects 15 covariates to participate in modeling, pH and SOM were both 17. Remote sensing index played a significant role in predicting soil property maps. Non-linear models showed more accuracy than MLR as linear model. Random forest performed best in all three soil properties. Among the three soil properties predicted by random forest, the validation dataset of soil pH, SSC and SOM were R ²=0.6779, RMSE=0.2182, ρ_c=0.6084, R ²=0.7945, RMSE=3.1803, ρ_c=0.8377 and R ²=0.7472, RMSE=3.5456, ρ_c=0.7009, respectively. 【Conclusion】 The importance factors selected by GBDT and machine learning algorithm could be used to mapping soil properties in arid areas. The random forest strategy showed the best predictive ability for soil properties. The spatial distribution of mapping three properties could be determined by combining with soil classification map.

Key words: soil property, environment covariates, digital soil mapping, machine learning, Gradient Boosting Decision Tree, GBDT, Random Forest, RF, Bagging Model, Cubist Model

ZHANG ZhenHua,DING JianLi,WANG JingZhe,GE XiangYu,WANG JinJie,TIAN MeiLing,ZHAO QiDong. Digital Soil Properties Mapping by Ensembling Soil-Environment Relationship and Machine Learning in Arid Regions[J].Scientia Agricultura Sinica, 2020, 53(3): 563-573.

Figures/Tables 11

Fig. 1

Table 1

Environmental covariates of digital soil mapping"

编号 Number	数据来源 Data source	协变量定义 Covariable definition	简称 Abbreviation	计算公式 Formula	参考文献 Reference
1	DEM	高程 Elevation	Ele		[23]
2		坡度 Slope	Slo		[23]
3		坡向 Aspect	Asp		[23]
4		曲率 Curvature	Cur		[23]
5		剖面曲率 Profile curvature	PrCu		[23]
6		平面曲率 Plan curvature	PlCu		[23]
7		地形湿度指数 Topographic wetness index	TWI	ln（α/tanβ)	[23]
8	Landsat 8 OLI / TIRS	海岸波段 band 1 coastal	b1
9		蓝波段 Band 2 blue	b2
10		绿波段 Band 3 green	b3
11		红波段 Band 4 Red	b4
12		近红外波段 Band 5 near infrared	b5
13		短波红外1 Band 6 shortwave infrared 1	b6
14		短波红外2 Band 7 shortwave infrared 2	b7
15		增强型植被指数 Enhanced vegetation index	EVI	2.5[(NIR-R)/(NIR+6×R-7.5×R+1)]	[24]
16		差值植被指数 Difference vegetation index	DVI	NIR-R	[9]
17		归一化植被指数 Normalized difference vegetation index	NDVI	(NIR-R)/(NIR+R)	[9]
18		扩展增强型植被指数 Extended normalized difference vegetation index	ENDVI	(NIR+SWIR₂ -R)/(NIR+SWIR₂ +R)	[25]
19		调整土壤亮度植被指数 Modified soil adjusted vegetation index	MSAVI	[2NIR+1-((2NIR+1)²-8(NIR-R))^0.5]/2	[9]
20		强度指数1 Intensity index 1	Int1	(G+R)/2	[26]
21		强度指数2 Intensity index 2	Int2	(G+R+NIR)/2	[26]
22		盐分指数S1 Salinity index S1	S1	B/R	[27]
23		盐分指数S3 Salinity index S3	S3	(G×R)/B	[27]
24		盐分指数S5 Salinity index S5	S5	(B×R)/G	[27]
25		盐分指数S6 Salinity index S6	S6	(R×NIR)/G	[27]
26		盐分指数 Salinity index	SI	(B×R)^0.5	[27]
27		盐分指数1 Salinity index 1	SI1	(G×R)^0.5	[27]
28		盐分指数2 Salinity index 2	SI2	(G² + R² + NIR²)^0.5	[27]
29		盐分指数3 Salinity index 3	SI3	(R² + G²)^0.5	[27]
30		归一化盐分指数 Normalized difference salinity index	NDSI	(R-NIR)/(R+NIR)	[27]
31		综合光谱响应指数 Combined spectral response index	CoSRI	(B+G)/(R+NIR)×NDVI	[28]
32		地表温度 Land surface temperature	LST		[16]

Table 1

Fig. 2

Fig. 3

Fig. 4

Table 2

Table 3

Fig. 5

Fig. 6

Fig. 7

Fig. 8

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土壤属性 Soil Attribute	模型 Model	建模集 Calibration			验证集 Validation
土壤属性 Soil Attribute	模型 Model	R²	ρ_c	RMSE	R²	ρ_c	RMSE
pH	RF	0.7929	0.7786	0.1681	0.6779	0.6084	0.2182
	Cubist	0.5182	0.6356	0.2308	0.4383	0.5805	0.2252
	Bagging	0.6389	0.6409	0.2092	0.5829	0.6159	0.2214
	MLR	0.4154	0.4559	0.2971	0.3004	0.4683	0.2445
SSC	RF	0.9067	0.9219	2.6680	0.7945	0.8377	3.1803
	Cubist	0.8820	0.9237	2.9190	0.7135	0.6194	7.5771
	Bagging	0.7417	0.8331	4.2156	0.6974	0.7791	3.9743
	MLR	0.6478	0.7687	4.0600	0.5050	0.6670	5.7829
SOM	RF	0.8565	0.7872	3.3215	0.7472	0.7009	3.5456
	Cubist	0.5204	0.5774	4.4070	0.4795	0.4305	5.4758
	Bagging	0.7653	0.7551	3.6085	0.6402	0.5494	4.2667
	MLR	0.3835	0.5559	5.0483	0.3250	0.5151	4.8424

Digital Soil Properties Mapping by Ensembling Soil-Environment Relationship and Machine Learning in Arid Regions

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