摘要
高光谱技术已成为预测土壤含水量（soil moisture content,SMC）的重要方法,但因土壤高光谱中包含了大量冗余信息和无效信息,不仅导致SMC的高光谱估算模型复杂度高,而且影响了模型的预测精度。因此,该研究在室内设计SMC梯度试验,测定土壤高光谱反射率,经Savitzky-Golay平滑（Savitzky-Golay smoothing,SG）和连续统去除（continuum removal,CR）预处理后,基于竞争适应重加权采样（competitive adaptive reweighted sampling,CARS）方法分别优选出土壤在全部SMC的水分敏感波长变量,确定适用于土壤在全部SMC的共性波长变量,以其为优选变量集,采用偏最小二乘（partial least squares regression,PLSR）回归方法建立模型并进行验证。结果表明,SG和CR预处理后的光谱曲线在450、1 400、1 900、2 200 nm附近吸收峰的形状特征凸显;基于CARS方法对土壤在不同SMC的光谱曲线进行变量优选后,得出优选变量集为443～449、1 408～1 456、1 916～1 943、2 209～2 225 nm;CARS-PLSR模型性能优于全波段PLSR模型,模型预测R2、均方根误差、相对分析误差分别为0.983、0.0144、8.36,不仅提升了预测精度和预测能力,而且降低了变量维度和模型复杂度。该文通过优选土壤水分的敏感波段,有效提高了SMC预测模型的鲁棒性,为快速准确评估农田墒情提供了新途径,为开发田间SMC测定传感器提供了理论依据。
Hyperspectral technology is a popular method of predicting soil moisture content nowadays, however, soil spectra include quantities of invalid redundant information, which is a serious bottleneck problem that could lead higher complexity and lower accuracy of prediction model. In this study, 96 fluvo-aquic soil samples were collected at 0-20 cm depth in fields in Qianjiang city, Hubei province, China, and then the samples were pretreated by air-drying, grinding and sieving in a laboratory. Samples with different soil moisture content （SMC, mass fraction of 0-40%） were prepared. For each sample, hyperspectral reflectance was measured by an ASD Field Spec3 instrument. Outliers with abnormal data were removed by Monte Carlo cross validation method. After that, the raw spectral reflectance was processed by Savitzky-Golay smoothing method and continuum removal method. Then, spectra of samples were divided into 2 subsets by Kennard-Stone algorithm. One subset was a calibration set with 47 samples and the other subset was a prediction set with 30 samples. The wavelength variables sensitive （SWV） to SMC were selected from the full-spectrum by competitive adaptive reweighted sampling （CARS） method, and they were considered as an optimal variable set. The multivariate calibrations were performed with partial least squares regression by using the full-spectrum （F-PLSR） andthe optimal variables （CARS-PLSR）, respectively. The prediction accuracy was assessed by comparing determination coefficients （R2）, root mean squared error （RMSE） and relative percent deviation （RPD）. Results showed that the SMC greatly affected soil spectral reflectance. The soil spectral reflectance reduced with the SMC increase, and 4 soil moisture absorption peaks were obvious around 450, 1 400, 1 900 and 2 200 nm. The peaks at 1 400 and 1 900 nm had the obvious redshift phenomenon. The SWV of samples with different moisture were obtained by CARS, and then anoptimal variables set was generated including wavelengths of443-449, 1 408-1 456, 1 916-1 943 and 2 209-2 225 nm. Using the CARS-PLSR model calibrated by the optimal variables, the predicting accuracy was improved compared to the F-PLSR model calibrated by the full-spectrum. The predicting accuracy of CARS-PLSR （R2, RMSE and RPD were 0.983, 0.0144 and 8.36, respectively） was higher than F-PLSR model （R2, RMSE and RPD were 0.976, 0.0166 and 7.21, respectively）. Meanwhile, compared to F-PLSR model, the CARS-PLSR model reduced the variable numbers from 2 001 to 101. In brief, the CARS-PLSR model not only enhanced the forecasting capability but also reduced the model complexity. Thus, this approach could facilitate the development of soil moisture sensor in field in the near future.
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2016年第22期138-145,共8页
Transactions of the Chinese Society of Agricultural Engineering
基金
国家自然科学基金项目(41401232
41271534)
中央高校基本科研业务费专项资金项目(CCNU15A05006
CCNU15A05004)
关键词
土壤水分
算法
模型
高光谱
竞争适应重加权采样算法
变量优选
潮土
soil moisture
algorithms
models
hyperspectra
competitive adaptive reweighted sampling （CARS）
variableselection
fluvo-aquic soil
