Mingjie LIU1,2, Zhuokui XU1,3, Yunbing GAO2,4,*, Jing YANG2,4..., Yuchun PAN2,4, Bingbo GAO5, Yanbing ZHOU2,4, Wanpeng ZHOU2,6 and Ling WANG7|Show fewer author(s)
Author Affiliations
1School of Traffic and Transportation Engineering, Changsha University of Science and Technology, Changsha 410114, China2Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China3Engineering Laboratory of Spatial Information Technology of Highway Geological Disaster Early Warning in Hunan Province (Changsha University of Science & Technology),Changsha 410114, China4National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China5China Agricultural University, Beijing 100083, China6Henan Polytechnic University, Jiaozuo 454003, China7Institute of Agricultural Resources and Environment, Hebei Academy of Agriculture and Forestry Sciences, Shijiazhuang 050051, Chinashow less
DOI: 10.12082/dqxxkx.2020.190441
Cite this Article
Mingjie LIU, Zhuokui XU, Yunbing GAO, Jing YANG, Yuchun PAN, Bingbo GAO, Yanbing ZHOU, Wanpeng ZHOU, Ling WANG. Estimating Soil Organic Matter based on Machine Learning Under Sparse Sample[J]. Journal of Geo-information Science, 2020, 22(9): 1799
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Fig. 1. Research Roadmap
Fig. 2. The structure chart of General Regression Neural Network
Fig. 3. The structurechart of Random Forest
Fig. 4. The overview of Daxing district of Bejing
Fig. 5. Variograms founction and related parameters of all experimental groups
Fig. 6. Comparison of the results predicted by the three methods in D2703, D676, D169 and D43
Fig. 7. The change of RMSE of GRNN, RF and Ordinary Kriging when the number of sampling points decreases
| 方差来源 | 偏差平方和 | 自由度Df | 均方 | F | P |
|---|
| 用地类型 | 组间 | 351.750 | 4 | 87.938 | 6.871 | 0.000 | | 组内 | 1023.868 | 80 | 12.796 | | | | 总体 | 1375.618 | 84 | | | | | 土壤质地 | 组间 | 356.405 | 3 | 118.802 | 9.442 | 0.000 | | 组内 | 1019.213 | 81 | 12.583 | | | | 总体 | 1375.618 | 84 | | | | | 畜禽粪便利用强度 | 组间 | 241.351 | 5 | 48.270 | 3.362 | 0.008 | | 组内 | 1134.267 | 79 | 14.358 | | | | 总体 | 1375.618 | 84 | | | | | 土壤类型 | 组间 | 0.945 | 2 | 0.472 | 0.028 | 0.972 | | 组内 | 1374.674 | 82 | 16.764 | | | | 总体 | 1375.618 | 84 | | | | | 植被指数 | 组间 | 17.592 | 2 | 8.796 | 0.531 | 0.590 | | 组内 | 1358.027 | 82 | 16.561 | | | | 总体 | 1375.618 | 84 | | | |
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Table 1. Variance analysis of soil organic matter
| 实验组 | 极大值/（g/kg） | 极小值/（g/kg） | 平均值/（g/kg） | 标准差/（g/kg） | 变异系数 | 偏度 | 峰度 | K-S双侧显著性 |
|---|
| D2703 | 24.73 | 1.20 | 10.49 | 3.91 | 37.28 | 0.147 | -0.098 | 0.302 | | D1352 | 24.73 | 1.65 | 10.53 | 3.92 | 37.23 | 0.159 | -0.003 | 0.632 | | D676 | 24.73 | 1.65 | 10.58 | 4.11 | 38.87 | 0.318 | 0.036 | 0.640 | | D339 | 24.73 | 1.81 | 10.55 | 3.94 | 37.32 | 0.223 | 0.292 | 0.946 | | D169 | 22.98 | 1.98 | 10.20 | 3.99 | 39.08 | 0.290 | 0.112 | 0.964 | | D85 | 18.05 | 2.03 | 10.93 | 4.05 | 37.01 | -0.181 | -0.804 | 0.934 | | D43_1 | 17.97 | 4.35 | 11.07 | 3.46 | 31.24 | -0.187 | -0.616 | 0.972 | | D43_2 | 17.73 | 2.98 | 10.07 | 4.17 | 41.39 | 0.122 | -0.893 | 0.951 | | D43_3 | 17.84 | 3.53 | 11.10 | 3.53 | 31.80 | -0.091 | -0.900 | 0.855 | | D43_4 | 19.67 | 2.02 | 10.33 | 4.04 | 39.13 | 0.128 | -0.064 | 0.906 | | D43_5 | 17.68 | 2.19 | 10.39 | 4.46 | 42.90 | -0.329 | -0.998 | 0.445 | | D22_1 | 18.28 | 3.53 | 10.70 | 4.30 | 40.13 | 0.293 | -0.988 | 0.611 | | D22_2 | 17.61 | 3.91 | 11.28 | 3.84 | 34.03 | -0.037 | -0.634 | 0.940 | | D22_3 | 16.40 | 3.72 | 10.49 | 3.60 | 34.29 | -0.380 | -0.692 | 0.783 | | D22_4 | 15.34 | 4.29 | 10.56 | 3.24 | 30.72 | -0.320 | -0.825 | 0.912 | | D22_5 | 17.86 | 2.36 | 9.35 | 4.36 | 46.60 | 0.260 | -0.540 | 0.999 |
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Table 2. Descriptive statistics of soil organic matter in all experimental groups in the study area
| 土壤属性 | 丰富 | 较丰富 | 中等 | 较缺乏 | 缺乏 | 极缺乏 |
|---|
| 有机质/（g/kg） | >40 | 30~40 | 20~30 | 10~20 | 6~10 | < 6 |
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Table 3. Soil organic matter content grading standard
| 实验组 | RMSE | | MRE/% | | MAE |
|---|
| Krige | GRNN | RF | Krige | GRNN | RF | Krige | GRNN | RF |
|---|
| D2703 | 2.82 | 3.11 | 2.99 | | 26.89 | 29.59 | 28.92 | | 2.21 | 2.43 | 2.35 | | D1352 | 3.02 | 3.17 | 3.01 | 29.54 | 30.28 | 29.50 | 3.02 | 2.46 | 2.36 | | D676 | 3.45 | 3.34 | 3.23 | 33.00 | 31.94 | 30.50 | 2.74 | 2.65 | 2.57 | | D339 | 3.38 | 3.17 | 3.17 | 35.60 | 30.64 | 31.19 | 2.82 | 2.47 | 2.53 | | D169 | 3.55 | 3.41 | 3.31 | 35.92 | 36.64 | 34.39 | 2.83 | 2.75 | 2.61 | | D85 | 4.10 | 3.17 | 2.96 | 43.76 | 33.37 | 30.45 | 3.47 | 2.72 | 2.46 | | D43_1 | 3.36 | 2.84 | 2.72 | 30.40 | 25.63 | 23.73 | 2.74 | 2.31 | 2.18 | | D43_2 | 4.13 | 3.19 | 3.11 | 44.58 | 34.52 | 33.70 | 3.34 | 2.70 | 2.65 | | D43_3 | 3.68 | 3.14 | 3.31 | 35.53 | 30.26 | 31.93 | 3.17 | 2.67 | 2.91 | | D43_4 | 3.95 | 2.76 | 3.18 | 44.86 | 33.59 | 37.36 | 3.21 | 2.36 | 2.69 | | D43_5 | 4.69 | 3.42 | 3.61 | 67.44 | 38.46 | 47.36 | 4.06 | 2.70 | 3.01 | | D22_1 | 4.34 | 3.22 | 3.42 | 43.60 | 35.06 | 33.76 | 3.77 | 2.86 | 2.91 | | D22_2 | 4.31 | 2.43 | 2.62 | 65.64 | 24.60 | 26.55 | 3.83 | 2.13 | 2.28 | | D22_3 | 3.90 | 2.89 | 3.07 | 77.11 | 27.63 | 33.04 | 3.54 | 2.27 | 2.64 | | D22_4 | 3.53 | 2.56 | 2.86 | 58.38 | 23.23 | 27.42 | 3.03 | 2.18 | 2.45 | | D22_5 | 4.31 | 2.98 | 2.98 | 146.35 | 35.83 | 41.13 | 3.79 | 2.44 | 2.55 |
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Table 4. Prediction accuracy of GRNN, RF and Ordinary Krigingin all experimental groups
| 实验组 | 平均最短距离/m | Moran's I值 | Z得分 | P值 | 实验组 | 平均最短距离/m | Moran's I值 | Z得分 | P值 |
|---|
| D2703 | 371.45 | 0.35 | 56.78 | 0.00 | D43_3* | 2615.74 | 0.15 | 1.15 | 0.25 | | D1352 | 425.63 | 0.33 | 30.83 | 0.00 | D43_4* | 3109.01 | 0.15 | 1.30 | 0.19 | | D676 | 567.68 | 0.33 | 14.99 | 0.00 | D43_5* | 2909.11 | 0.08 | 0.83 | 0.41 | | D339 | 810.79 | 0.20 | 8.24 | 0.00 | D22_1* | 4237.90 | -0.32 | -1.39 | 0.17 | | D169 | 1285.91 | 0.17 | 5.68 | 0.00 | D22_2* | 4384.49 | -0.15 | -0.53 | 0.60 | | D85 | 1914.45 | 0.19 | 3.06 | 0.00 | D22_3* | 3662.95 | -0.10 | -0.19 | 0.85 | | D43_1 | 2792.95 | 0.22 | 2.41 | 0.02 | D22_4* | 4342.87 | 0.10 | 0.74 | 0.46 | | D43_2 | 3110.80 | 0.14 | 1.52 | 0.13 | D22_5* | 4723.02 | -0.11 | -0.48 | 0.63 |
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Table 5. Spatial correlation analysis of all experimental groupsamples
| 实验组 | 土地利用类型 | | 土壤质地 | | 畜禽粪便影响强度 |
|---|
| q值 | p值 | q值 | p值 | q值 | p值 |
|---|
| D2703 | 0.182 | 0.000 | | 0.199 | 0.000 | | 0.148 | 0.000 | | D1352 | 0.209 | 0.000 | 0.190 | 0.000 | 0.139 | 0.000 | | D676 | 0.198 | 0.000 | 0.233 | 0.000 | 0.171 | 0.000 | | D339 | 0.212 | 0.000 | 0.189 | 0.000 | 0.138 | 0.000 | | D169 | 0.210 | 0.000 | 0.229 | 0.000 | 0.202 | 0.000 | | D85 | 0.256 | 0.067 | 0.259 | 0.016 | 0.175 | 0.035 | | D43 | 0.500 | 0.004 | 0.177 | 0.071 | 0.345 | 0.050 | | D22 | 0.496 | 0.016 | 0.477 | 0.049 | 0.742 | 0.034 |
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Table 6. Detected result of three influence factor of soil organic matter
| 实验组 | Krige | GRNN | RF | 实验组 | Krige | GRNN | RF |
|---|
| D2703 | 0.686** | 0.608** | 0.642** | D43_3 | 0.155 | 0.388* | 0.392** | | D1352 | 0.635** | 0.590** | 0.637** | D43_4 | 0.153 | 0.669** | 0.576** | | D676 | 0.543** | 0.581** | 0.616** | D43_5 | -0.067 | 0.591** | 0.577** | | D339 | 0.450** | 0.580** | 0.578** | D22_1 | -0.137 | 0.572** | 0.590** | | D169 | 0.433** | 0.500** | 0.545** | D22_2 | -0.343 | 0.709** | 0.630** | | D85 | 0.175 | 0.599** | 0.660** | D22_3 | -0.094 | 0.584** | 0.440* | | D43_1 | 0.210 | 0.532** | 0.594** | D22_4 | -0.102 | 0.547** | 0.398* | | D43_2 | 0.129 | 0.559** | 0.627** | D22_5 | 0.054 | 0.635** | 0.762** |
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Table 7. The correlation analysis between the observed values and predicted values of GRNN, RF and Ordinary Kriging
