摘要
高光谱遥感可以实现水稻土排水期有机碳含量的快速预测,但土壤反射率受多种噪声的影响,有机碳光谱信号探测受阻,预测模型性能低下,如何在去除噪声的同时最大限度地保持有机碳光谱信号十分重要。以原状新鲜水稻土为研究对象,采用Bior1.3小波系对反射光谱进行1~7层小波包变换,通过相关分析确定最大分解层;将原始反射率至最大分解层以内的各层光谱相关系数组成相关系数集,采用局部最相关算法(local correlation maximization,LCM)构造土壤有机碳最优光谱;最后基于最优光谱建立有机碳含量偏最小二乘预测模型并进行分析。结果显示:1)随着小波包分解层数的增加,土壤反射率与有机碳含量的相关性不断增强,到第6层达到最高,确定为小波包最大分解层;2)基于LCM构造的最优光谱比未去噪光谱平滑,比小波包去噪光谱保留了更多光谱细节;3)未去噪光谱、小波包去噪光谱和LCM最优光谱有机碳预测模型的验证决定系数分别为0.693、0.727和0.781,均方根误差为1.952、1.840和1.679 g/kg,残留预测偏差为1.85、1.97和2.17。小波包-局部最相关算法在去噪同时有效保持了土壤有机碳光谱信号,可提高水稻土有机碳含量高光谱预测精度。
Soil organic carbon(SOC) is an essential soil property for assessing the fertility of paddy soils. It can be measured with visible and near infrared spectroscopy effectively in the field. Meanwhile, there are a lot of factors, such as soil water, surface conditions and so on, which might affect the spectra, increasing the difficulty in extracting the effective information, and reducing the prediction accuracy of SOC content. Noise reduction must be considered in developing hyperspectral estimation models, but how to reduce noise while retaining as much useful information as possible needs for investigation. As advanced spectral mining methods, local correlation maximization(LCM) arithmetic was used to solve this problem in this study. In the present study, a total of 70 soil samples of paddy soil were collected from rice fields in Zhulin town, Jintian city, Jiangsu Province. The sample holders were clear aluminum boxes in 7 cm diameter and 3 cm deep, which were filled and leveled at the rim with a spatula. Reflectance of soil samples measured using ASD Fieldspec 3 Spectrometer in a dark room when brought these samples indoor immediately to keep them in the field conditions. We used the following steps to process soil reflectance: First, discrete wavelet packet transformation(DWPT) was used to decompose the original spectral(result from 0.6-order differential) in 7 levels using Bior1.3 wavelet basis by MATLAB programming language. In order to select the maximum level of DWPT, correlation coefficients between SOC and the spectra of each level was computed. Secondly, LCM method was used to develop the local optimal correlation coefficient(LOCC) and optimal band which was determined from the optimal correlative curve and the optimal spectra(OS), respectively. Thirdly, a PLSR model was built to predict SOC contents. And then, determination coefficient of validation(R2 v), root mean square error of validation(RMSEV), and residual prediction deviation(RPD) were used for accuracy assessment. We also used variable in the projection(VIP) analysis to identify the reason why LCM could improve the accuracy of predict model at the same time. The results showed: 1) significant correlated bands followed increasing-decreasing trend with the increase of wavelet decomposed level and the maximum level identified as level 6. This implied that the wavelet packet transformation amplified some useful SOC information that was previously obscured by noise. 2) optimal spectra that established from LCM could effectively remove noise while preserving the detail information of SOC simultaneously. 3) compared with raw spectral(R2 v=0.693, RMSEV=1.952 g/kg, RPD=1.85), the wavelet packet transformation provided good results(R2 v=0.727, RMSEV=1.840 g/kg, RPD=1.97) of SOC prediction, combined with LCM arithmetic, the model had the best performance(R2 v=0.781, RMSEV=1.679 g/kg, RPD=2.17) to predict SOC content. According to VIP score, important bands for SOC prediction hadthree pink values, two of them located in the characteristic bands of soil water, this illustrated LCM can't remove the effects of soil water thorough. Results indicated that the discrete wavelet packet transformation and local correlation maximization(DWPT-LCM) method had great potential to monitor SOC contents in paddy soils when reduced white noise while retaining as much soil organic carbon information as possible.
作者
张锐
李兆富
潘剑君
Zhang Rui Li Zhaofu Pan Jianjun(College of Resources and Environment Science, Nanjing Agriculture University, Nanjing 210095, China)
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2017年第1期175-181,共7页
Transactions of the Chinese Society of Agricultural Engineering
基金
中央高校基本科研业务费专项资金(KYZ201522)
江苏高校优势学科建设工程项目
国家自然科学基金项目(41571171)
关键词
光谱分析
土壤
有机质
小波包
局部最相关
spectrum analysis
soils
organic matter
discrete wavelet packet
local correlation maximization