Scientia Agricultura Sinica ›› 2018, Vol. 51 ›› Issue (24): 4659-4676.doi: 10.3864/j.issn.0578-1752.2018.24.007

• SOIL & FERTILIZER·WATER-SAVING IRRIGATION·AGROECOLOGY & ENVIRONMENT • Previous Articles     Next Articles

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

WANG Fei1,2,3(),YANG ShengTian2,WEI Yang2,YANG XiaoDong2,3,DING JianLi1,2,3()   

  1. 1 Xinjiang Common University Key Laboratory of Smart City and Environmental Stimulation, Xinjiang University, Urumqi 830046
    2 College of Resource and Environmental Sciences, Xinjiang University, Urumqi 830046
    3 Laboratory for Oasis Ecosystem, Ministry of Education, Urumqi 830046
  • Received:2018-05-14 Accepted:2018-07-20 Online:2018-12-16 Published:2018-12-16

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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

Fig. 1

Distribution of the field sampling plots at Weigan-kuqa river oasis"

Fig. 2

Schematic diagram of the interactions of hydrothermal and energy exchange between oasis and desert areas as well as the relative coverage of each scenario (modified from LI et al[10])"

Fig. 3

Statistical histogram of the land use types of the samples in the 12 scenarios"

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）

Fig. 4

Comparison of combined and regrouped scenarios on predictions of soil salinity in method one at oasis scale"

Fig. 5

Comparison of combined and regrouped scenarios on the prediction of soil salinity in method two at the oasis scale"

Fig. 6

The effects of the mtry set in RF on RMSE in different scenarios"

Fig. 7

The effects of the maximum nodes per tree set in SGT on RMSE in different scenarios"

Table 2

Summary statistics of salinity (dS·m-1) for the 12 scenarios used in this study"

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

Table 3

Validation statistics for RF and SGT to predict surface soil salinity for different independent scenario in Fig. 4a"

情景
Scenario		 随机森林 Random Forest 随机梯度增进算法 Stochastic Gradient Treeboost
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

Table 4

Validation statistics of RF and SGT to predict surface soil salinity for different scenarios originating at the oasis scale according to Method 1 in Section 1.2.7 in Fig. 4-b"

情景
Scenario		 随机森林 Random Forest 随机梯度增进算法 Stochastic Gradient Treeboost
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

Table 5

Comparison of combined and regrouped scenarios for soil salinity prediction accuracy according to Method 2 in Section 1.2.7 (Fig.5)"

情景合并
Scenario combination		 随机森林 Random Forest 随机梯度增进算法 Stochastic Gradient Treeboost
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

Fig. 8

Scatterplot of the residuals predicted by RF versus the measured soil salinity in each scenario"

Fig. 9

Scatterplot of the residuals predicted by SGT versus the measured soil salinity in each scenario"

Fig. 10

Important variables iteratively obtained based on the methods described in Section 1.2.6 in different scenarios"

Fig. 11

Spatial distribution of soil salinity (dS·m-1) predicted by SGT and RF for three combined modes and the full sample model (Table 5) in the study area"

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