光学学报, 2018, 38 (10): 1030001, 网络出版: 2019-05-09 复制并引用该论文  本文已被引 9 次  被引通知

图 & 表

图 1. 全样本MCCV土壤预测残差的均值-标准差分布

Fig. 1. Mean-standard deviation distribution of soil-residual prediction for full-sample MCCV

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图 2. 实验及模型计算过程流程图

Fig. 2. Flow chart of calculation process for experience and model

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图 3. 不同SMC土壤的光谱反射率

Fig. 3. Spectral reflectance of soils with different SMCs

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图 4. CARS算法筛选变量的过程。(a)波长变量个数的变化;(b) RMSECV的变化;(c) RMSECV最小时变量回归系数的趋势

Fig. 4. Variable filtering process using CARS. (a) Variation in wavelength variable number; (b) variation in RMSECV; (c) trend of variable regression coefficient when RMSECV is minimum

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图 5. 土壤样本反射率均值及最优光谱波段

Fig. 5. Mean reflectance of soil samples and optimal spectral bands

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图 6. ELM模型SMC的预测值与实测值

Fig. 6. Predicted and measured SMCs using ELM model

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表 1土壤样品的SMC统计特征

Table1. Statistical characteristics of SMC of soil samples

Sample type Number Maximum Minimum Mean Standard deviation Coefficient of variation Whole set 77 0.252 0.021 0.1421 0.049 0.3458 Calibration set 62 0.252 0.021 0.1406 0.051 0.3659 Validation set 15 0.216 0.067 0.1483 0.039 0.2637

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表 2SMC预测结果

Table2. Estimated SMC

Model Variable number Calibration set Prediction set RMSE R2 RMSE R2 RPD RPIQ PLSR 20 0.484 0.478 0.622 0.617 0.522 0.18401 BPNN 20 0.027 0.706 0.024 0.799 2.016 1.90200 RFR 20 0.024 0.872 0.021 0.898 1.647 2.18900 ELM 20 0.016 0.879 0.015 0.918 3.123 3.32500

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表 3不同建模预测比的SMC预测结果

Table3. Predicted SMC based on different ratios of calibration to prediction

Model Ratio of calculation to prediction Variable number Calibration set Prediction set RMSE R2 RMSE R2 RPD RPIQ BPNN 62∶15 20 0.027 0.706 0.024 0.799 2.016 1.902 57∶20 20 0.020 0.842 0.023 0.800 1.826 1.499 52∶25 20 0.023 0.765 0.024 0.800 1.947 2.010 RFR 62∶15 20 0.024 0.872 0.021 0.898 1.647 2.189 57∶20 20 0.024 0.863 0.013 0.897 2.217 3.073 52∶25 20 0.025 0.856 0.014 0.889 2.202 3.041 ELM 62∶15 20 0.016 0.879 0.015 0.918 3.123 3.325 57∶20 20 0.019 0.869 0.014 0.919 3.102 3.241 52∶25 20 0.015 0.877 0.016 0.918 2.569 2.958

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葛翔宇, 丁建丽, 王敬哲, 王飞, 蔡亮红, 孙慧兰. 基于竞争适应重加权采样算法耦合机器学习的土壤含水量估算[J]. 光学学报, 2018, 38(10): 1030001. Xiangyu Ge, Jianli Ding, Jingzhe Wang, Fei Wang, Lianghong Cai, Huilan Sun. Estimation of Soil Moisture Content Based on Competitive Adaptive Reweighted Sampling Algorithm Coupled with Machine Learning[J]. Acta Optica Sinica, 2018, 38(10): 1030001.