摘要

土壤含水量是干旱区地表水-热-溶质耦合运移的关键指标；以干旱区典型样点实测土壤含水量及其室内可见光-近红外光谱数据作为数据集，通过蒙特卡罗交叉验证确定77个有效样本；基于竞争适应重加权采样算法筛选出最优光谱变量子集，利用3种机器学习方法——BP神经网络、随机森林回归和极限学习机建立土壤含水量预测模型，进而实现土壤含水量估算模型的优选。结果表明：竞争适应重加权采样算法能有效剔除无关变量，从2151个光谱波段中优选出20个特征波段，其中R1848与土壤含水量的最大相关系数为0.531；引入偏最小二乘模型和机器学习方法进行对比，分析发现机器学习方法的预测结果比偏最小二乘模型更高；分析比较BP神经网络、随机森林回归和极限学习机的建模结果可知：极限学习机模型建模在机器学习方法中的效果最佳，决定系数R2=0.918，均方根误差RMSE=0.015，相对分析误差RPD=3.123，四分位数间隔RPIQ=3.325；机器学习能显著提升光谱建模反演土壤含水量的精度和稳定性，显示出其在非线性问题中具有很强的透析力和较好的模型稳健性，针对干旱区土壤水分的精准预测和定量估算具有可行性，可为干旱区土壤墒情、精准农业等研究提供科学参考。

Abstract

Soil moisture content is an important indicator that reflects the coupled surface water-heat-solute transport in arid regions. The visible and near-infrared spectroscopy has been widely used for soil moisture content prediction owing to its rapid response. The soil moisture content and corresponding spectral data are obtained in the laboratory; then, the calibration datasets (n=77) are selected using Monte Carlo cross-validation algorithm. The competitive adaptive reweighted sampling algorithm is used to optimize spectral variables. Three machine learning algorithms, namely back propagation neural network, random forest regression, and extreme learning machine are used to construct predicting models. The results reveal that competitive adaptive reweighted sampling algorithm can effectively filter and eliminate massive irrelevant variables. Herein, a total of 20 feature bands are divided from all spectral bands, where the band of R1848 is the most prominent (the maximum correlation coefficient is 0.531). The performance of models based on machine learning algorithms is superior to those based on partial least squares regression, with the optimal prediction of the coefficient of determination (R2), root mean square error of prediction (RMSE), residual predictive deviation (RPD), and ratio of performance to interquartile range (RPIQ). Compared with the predictive effects of all the models, the extreme learning machine-based predicting model is the most effective (R2=0.918, RMSE=0.015, RPD=3.123, and RPIQ=3.325). Compared with common linear models, the machine learning algorithms can effectively improve the precision and stability of the quantitative estimation of soil moisture content. The results provide scientific guidance and baseline data for the accurate monitoring of soil moisture content and precision agriculture in arid regions.

补充资料

中图分类号：O436

DOI：10.3788/aos201838.1030001

所属栏目：光谱学

基金项目：自治区重点实验室专项基金(2016D03001)、国家自然科学基金(41771470,U1303381,41661046)

收稿日期：2018-04-28

修改稿日期：2018-05-07

网络出版日期：2018-05-12

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联系人作者：丁建丽(watarid@xju.edu.cn); 葛翔宇(xiangyu_gexj@163.com);

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引用该论文

Ge Xiangyu,Ding Jianli,Wang Jingzhe,Wang Fei,Cai Lianghong,Sun Huilan. Estimation of Soil Moisture Content Based on Competitive Adaptive Reweighted Sampling Algorithm Coupled with Machine Learning[J]. Acta Optica Sinica, 2018, 38(10): 1030001

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

被引情况

【1】张子鹏,丁建丽,王敬哲. 基于谐波分析算法的干旱区绿洲土壤光谱特性研究. 光学学报, 2019, 39(2): 228003--1