基于RF和SGT算法的子区优先建模对绿洲尺度 土壤盐度预测精度的影响

doi:10.3864/j.issn.0578-1752.2018.24.007

Abstract

Abstract:

【Objective】 This study attempts to improve the prediction accuracy of soil salinity in arid oasis by building models preferentially in the sub-area of oasis. At the same time, the difference and uncertainty of accuracy between global model and sub-region model are quantified. 【Method】 Therefore, to investigate the above differences, this study used two machine learning methods (Random Forest, RF and Stochastic Gradient Treeboost, SGT) to quantify the above effects and to prove the necessity of the building model in the sub-region compared with the full-sample model with respect to the simulation precision under the complex background of an arid region. Twenty-seven environmental scenarios (twelve original and fifteen derivatives) were designed based on the driving factors (land use and landform) and response factors (Normalized Difference Vegetation Index, NDVI and electrical conductivity, EC), which reflected variety of variabilities in soil salinity. After analyzing the results, the following preliminary conclusions were drawn. 【Result】 The simulation results from 70.37% (19/27) of the scenarios showed that the predicted value of soil salinity from SGT was closer to the observed value from RF. Ten original sub-regions were modeled individually and compared with the full-sample model under the oasis scale (according to the 10 partition rules to reclassify the simulated values), and the result showed that the prediction accuracy of the former 70% scenario was higher than that of the latter. In particular, the regions of EC≤4 dS·m ^-1 and 2 ddS·m ^-1＜EC＜16 dS·m ^-1 should be modeled separately to predict the spatial variability of regional salinity. By combining the predictions of sub-regions and comparing them with the predicted values of the full-sample model, the former (all four different combination modes) showed a higher prediction accuracy than the latter. In addition, this result also indicated that the preferred medium for partitioning the sub-regions was soil electrical conductivity, followed by landform and land use. 【Conclusion】 The study proposes to establish a soil salinity model based on SGT preferentially on different landscape scales within the oasis, and then combine the predicted values of each landscape scale to improve the prediction accuracy of oasis soil salinity.

Key words: soil salinity, machine learning, arid regions, Landsat OLI, spatial heterogeneity, Random Forest, Stochastic Gradient Treeboost

WANG Fei,YANG ShengTian,WEI Yang,YANG XiaoDong,DING JianLi. Influence of Sub-Region Priority Modeling Constructed by Random Forest and Stochastic Gradient Treeboost on the Accuracy of Soil Salinity Prediction in Oasis Scale[J].Scientia Agricultura Sinica, 2018, 51(24): 4659-4676.

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Figures/Tables 16

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Table 1

Environmental covariates derived from a 30 m spatial resolution DEM and 30 m Landsat imagery"

变量组 Environment variable group	指数 Index
波段及其衍生变量 Band & derivatives	全波段;缨帽变换（亮度（TC1）,绿度（TC2）,湿度（TC3）,主成分分析（前三个波段） Bands;Tasseled Cap (brightness,TC1;Greenness,TC2; Wetness,TC3 ), Principal Component Analysis(PC1,PC2,PC3)
植被指数 Vegetation indices	归一化植被指数;土壤调节植被指数;增强植被指数;广义植被指数;冠层响应盐度指数^[3];比值植被指数;两波段增强植被指数^[30];扩展的归一化植被指数^[31];扩展的增强植被指数^[31] Normalized Difference Vegetation Index, NDVI; Soil Adjusted Vegetation Index, SAVI; Enhanced Vegetation Index, EVI; Generalized Difference Vegetation Index, GDVI^[5]; Canopy Response Salinity Index, CRSI^[3]; Simple Ratio Vegetation Index, SR; Two-Band Enhanced Vegetation Index, EVI2^[30]; Extended NDVI, ENDVI^[31]; Extended EVI, EEVI^[31]
土壤相关指数 Soil-related indices	盐度指数（Salinity Index, SIT）^[1];盐度指数（Salinity Index, SI）^[1];盐度指数（Salinity Index（SI1）^[1];盐度指数（Salinity Index, SI2）^[1];盐度指数（Salinity Index, SI3）^[1];盐度指数（Salinity Index, SIA）^[1];盐度指数（Salinity Index, SIB）;盐度比值指数（Salinity Ratio, SAIO）^[32];黏土指数（Clay Index, CLEX）^[4] ;石膏指数（Gypsum Index, GYEX）^[4];亮度指数（Brightness Index, BREX）^[4];碳酸盐指数（Carbonate Index, CAEX）^[4];FSEN= (B5-B7）/(B5+B7）^[33];颜色指数（Color Indices -色相Hue, 饱和度 Saturation, 色调Value）;归一化热感指数（Normalized Difference Infrared Index, NDII）^[4];全球植被湿度指数（Global Vegetation Moisture Index, GVMI）^[34];地表温度（Temperature）
DEM衍生因子 Dem derivatives	河道相关：谷深（Valley Depth ,VD）;与河网的垂直距离（Vertical Distance To Channel Network ,VDCN）;水文学相关：坡长因子（LS-Factor, LSF）;地形指数指数（Topographic Wetness Index, TWI）;坡长（Slope Length, SL）;通视度（Sky View Factor, SVF）;地形位置指数（Topographic Position Index, TPI）;多尺度谷底平整度（Multiresolution Index Of Valley Bottom Flatness（MRVBF And MRRTF））,坡高（Slope Height, SH）,归一化高度（Normalized Height, NH）,标准化高度（Standardized Height, STH）,坡度中值位置Mid-Slope Position（MSP）,地表纹理（Terrain Surface Texture, TEX）,汇流累积量（Flow Accumulation, FA）;形态：截面曲率（Cross-Section Curvature, CSC）,纵向曲率（Longitudinal Curvature, LC）,相对坡度位置（Relative Slope Position, RSP）,流域坡度（Catchment Slope, CS）（SAGA Development Team, 2011）

Table 1

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Table 2

Table 3

Table 4

Table 5

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情景 Scenario	最小值 Min	最大值 Max	平均值 Average	变异系数 CV	Q25	Q50	Q75
S1	0.14	184.5	31.32	1.29	1.42	13.11	48.00
S2	0.14	128.50	11.25	2.02	0.42	1.50	9.90
S3	0.15	184.50	47.14	0.95	11.84	26.00	79.95
S4	0.15	103.9	5.57	2.97	0.38	0.90	3.36
S5	0.14	184.50	47.33	0.95	11.59	26.00	88.00
S6	0.23	150.50	34.38	1.10	6.76	17.33	60.90
S7	0.27	184.50	50.40	0.92	11.77	35.40	78.82
S8	0.14	147.40	28.20	1.37	1.34	10.69	33.89
S9	0.14	3.99	0.99	0.95	0.33	0.55	1.41
S10	2.01	15.92	8.47	0.50	4.54	7.97	12.04
S11	4.06	184.50	46.43	0.90	13.11	25.70	75.60
S12	16.28	184.5	64.10	0.63	24.85	61.10	94.15

情景 Scenario	随机森林 Random Forest			随机梯度增进算法 Stochastic Gradient Treeboost
情景 Scenario	R²	RMSE	RPD	R²	RMSE	RPD
S2	0.33**	18.52	1.23	0.35**	18.11	1.26
S3	0.34**	36.487	1.23	0.34**	36.485	1.23
S4	0.39**	35.36	1.28	0.41**	34.84	1.30
S5	0.47**	27.36	1.39	0.58**	24.15	1.58
S6	0.50**	33.15	1.17	0.48**	27.80	1.39
S7	0.33**	37.88	1.23	0.50**	33.15	1.40
S8	0.29**	0.79	1.20	0.23**	0.83	1.14
S9	0.51**	2.94	1.44	0.38**	3.31	1.28
S10	0.32**	34.57	1.22	0.38**	33.26	1.27
S11	0.30**	34.08	1.20	0.35**	32.99	1.24

情景 Scenario	随机森林 Random Forest			随机梯度增进算法 Stochastic Gradient Treeboost
情景 Scenario	R²	RMSE	RPD	R²	RMSE	RPD
S1	0.46**	29.65	1.37	0.48**	29.17	1.39
S2	0.30**	19.20	1.18	0.44**	16.97	1.34
S3	0.38**	35.50	1.27	0.37**	35.71	1.26
S4	0.35**	36.46	1.24	0.34**	36.67	1.24
S5	0.51**	27.94	1.36	0.54**	26.06	1.46
S6	0.46**	28.42	1.36	0.50**	27.31	1.42
S7	0.39**	36.78	1.26	0.36**	37.09	1.25
S8	ns	15.19	0.06	ns	18.52	0.05
S9	0.20**	21.68	0.20	0.18**	21.68	0.20
S10	0.37**	33.73	1.25	0.39**	27.17	1.27
S11	0.28**	37.88	1.08	0.31**	37.12	1.10

情景合并 Scenario combination	随机森林 Random Forest			随机梯度增进算法 Stochastic Gradient Treeboost
情景合并 Scenario combination	R²	RMSE	RPD	R²	RMSE	RPD
S2 & S 3	0.49**	29.73	1.37	0.47**	29.54	1.38
S9 & S11	0.51**	28.25	1.43	0.55**	27.23	1.48
S5 & S6	0.43**	32.86	1.32	0.47**	31.63	1.37
S5 & S6 (RSOSM)	0.40**	33.77	1.29	0.40**	33.47	1.30
S7 & S8	0.43**	32.25	1.32	0.51**	30.01	1.43
S7 & S8 (RSOSM)	0.46**	31.55	1.36	0.47**	31.03	1.38

Influence of Sub-Region Priority Modeling Constructed by Random Forest and Stochastic Gradient Treeboost on the Accuracy of Soil Salinity Prediction in Oasis Scale

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