基于MGWRK的土壤有机质制图及驱动因素研究

doi:10.3864/j.issn.0578-1752.2020.09.011

Abstract

Abstract:

【Objective】Spatial prediction is an important approach to obtain location-specific values of soil organic matter (SOM), which is an important figure of soil fertility and farmland management properly. This study was performed to compare different digital soil spatial mapping methods of SOM to get better prediction accuracy and to reveal the spatial non-stationarity characteristics of environmental covariates and the spatial scale of different environmental covariates at the same time. 【Method】In this study, the digital soil spatial mapping method was used, which was a combination of Multiscale Geographically Weighted Regression Model with simple Kriging of the residuals (MGWGK) for mapping SOM in seven towns from Jinzhong Basin. The performance of MGWRK with those of Ordinary Kriging (OK), Regression Kriging (RK), and Geographically Weighted Regression Kriging (GWRK) were compared to explore the relationship between influence factors and SOM on the influence degree and space scale. 【Result】Based on the stepwise regression method, 13 indexes were selected as environmental covariables in the modeling, including aspect, slope, height, annual average precipitation, annual average temperature, gross primary productivity (GPP), evapotranspiration (ET), topographic wetness index (TWI), plan curvature, stream power index (SPI), terrain position index (TPI), terrain ruggedness index (TRI), and the annual NDVI. In the multiple linear regression (MLR), the formula had statistical significance. In the Radius index, the performance of each model was in order from good to bad: RK, OK, GWRK, MGWRK. In the mapping performance, MGWRK was close to GWRK, and both of which were better than OK method and RK method. The SOM in the study area, showed a spatial pattern of higher in middle than east and west side, among which the SOM was high in the east of the Fenhe river and the Changyuan river. The influence of aspect, annual average precipitation, annual average temperature, height, TPI and the annual NDVI on SOM in the eastern of the study area was stronger than that in the western. Whiles slope, GPP, ET, plan curvature, SPI and TRI showed opposite influence in spatial. The influence of TWI on SOM was stronger in the northern than the southern. 【Conclusion】The spatial prediction accuracy of MGWRK was 69% of RK, 71.74% of OK, and 71.17% of GWRK. MGWRK performed well in the spatial non-stationary features and the spatial visualization, which provided a reference for prediction of SOM and description spatial non-stationarity characteristics.

Key words: soil organic matter (SOM), soil mapping, multiscale geographically weighted regression kriging (MGWRK), geographically weighted regression kriging (GWRK), regression kriging (RK), non-stationarity

Lei QIAO,WuPing ZHANG,MingJing HUANG,GuoFang WANG,Jian REN. Mapping of Soil Organic Matter and Its Driving Factors Study Based on MGWRK[J].Scientia Agricultura Sinica, 2020, 53(9): 1830-1844.

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数据类型 Data type	指标 Index
地形因子数据 Terrain data	坡向Aspect
	坡度 Slope
	海拔 Height
	地形湿度指数 Topographic wetness index, TWI
	平面曲率 Plan curvature, PC
	汇流动力指数 Stream power index, SPI
	地形指数 Terrain position index, TPI
	地形粗糙指数 Terrain ruggedness index, TRI
气象数据 Meteorological data	年平均降水Annual mean precipitation, PRE
气象数据 Meteorological data	年平均温度 Annual mean temperature, TEM
遥感数据 Remote sensing data	年植被总生产量GPP (From MOD17A2)
	年蒸散量 ET (From MOD16A2)
	年平均NDVI (来自MOD13A1,使用最大合成法求得) Annual mean NDVI (Data from MOD13A1, obtained by Maximum Value Composites method)

指标Index	容差 Tolerance	VIF
坡向Aspects (°)	0.993	1.007
坡度Slope (°)	0.548	1.824
年均降水量Annual mean precipitation (mm)	0.514	1.944
年平均温度Annual mean temperature (℃)	0.417	2.400
海拔Height (m)	0.670	1.493
植被年总初级生产力Gross primary productivity (kgC·m^-2)	0.216	4.636
年蒸散量Evapotranspiration (mm)	0.194	5.168
地形湿度指数 Topographic wetness index	0.652	1.534
平面曲率Plan curvature	0.928	1.078
汇流动力指数 Stream power index	0.980	1.021
地形指数 Terrain position index	0.730	1.369
地形粗糙指数 Terrain ruggedness index	0.548	1.824
年平均归一化植被指数 The annual NDVI	0.893	1.120

指标 Index	最小值 Minimum	最大值 Maximum	平均值 Mean	标准差 Standard deviation	方差 Variance	变异系数 Coefficient of variation (%)
有机质Soil organic matter (g·kg^-1)	2.10	33.00	13.91	5.01	25.09	36.00
坡向Aspects (°)	-1.00	359.12	180.32	106.64	11373.13	59.14
坡度Slope (°)	0.00	33.37	6.84	4.60	21.20	67.36
年均降水量 Annual mean precipitation (mm)	499.01	511.01	503.72	2.00	3.98	0.40
年平均温度 Annual mean temperature (℃)	12.25	15.20	13.37	0.73	0.53	5.45
海拔Height (m)	690.00	927.00	744.04	22.77	518.46	3.06
植被年总初级生产力 Gross primary productivity (kgC·m^-2)	270.28	817.96	557.30	68.57	4702.51	12.30
年蒸散量Evapotranspiration (mm)	248.43	461.20	358.95	37.47	1403.98	10.44
地形湿度指数 Topographic wetness index	0.00	19.39	9.61	3.14	9.86	32.67
平面曲率 Plan curvature	-0.01	0.01	0.00	0.00	0.00	1281.06
汇流动力指数 Stream power index	-167350.00	11730.90	-3405.69	18999.56	360983218.24	-557.88
地形指数 Terrain position index	-21.49	21.78	-0.47	4.88	23.78	-1040.39
地形粗糙指数 Terrain ruggedness index	0.00	18.72	3.81	2.25	5.05	58.96
年平均归一化植被指数 The annual NDVI	0.00	2.80	0.71	0.19	0.04	26.44

	拟合模型 Fitting model	块金值 Nugget	基台值 Sill	变程 Range (m)	块金系数 Nugget/Sill (%)
MLR残差 MLR residuals	E	10.59	21.20	2265	49.95
GWR残差 GWR residuals	E	0.38	3.72	636	10.22
MGWR残差MGWR residuals	E	0.81	2.29	657	35.37

模型 Model	RMSE	MAE	ME	R²	Radius
OK	2.8553	2.1009	0.0009	0.6984	107.4383
RK	2.7613	2.0396	-0.0001	0.7207	98.1195
MGWRK	3.5430	2.6292	0.0036	0.5010	172.8181
GWRK	3.0622	2.2402	0.0978	0.6492	133.6925

Mapping of Soil Organic Matter and Its Driving Factors Study Based on MGWRK

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模型 Model	RMSE	MAE	ME	R²	Radius
MLR	4.5402	3.4510	0.0000	0.1782	727.4052
GWR	3.9863	2.9921	0.0375	0.3731	352.6252
MGWR	3.5959	2.6806	-0.0005	0.4916	210.8228

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