摘要
为实现基于光谱分析土壤有机质含量的快速测定,该文以江汉平原公安县的土壤为研究对象,进行室内理化分析、光谱测量与处理等一系列工作,在土壤原始光谱反射率(raw spectral reflectance,R)的基础上,提取了其倒数之对数(inverse-log reflectance,LR)、一阶微分(first order differential reflectance,FDR)和连续统去除(continuum removal, CR) 3种光谱指标,分析4种不同形式的光谱指标与有机质含量的相关性,对相关系数进行P=0.01水平上的显著性检验来确定显著性波段的范围,并基于全波段(400~2 400 nm)和显著性波段运用偏最小二乘回归(partial least squares regression, PLSR)建立了该区域土壤有机质高光谱的预测模型,通过模型精度的比较确定最优模型.结果表明,进行CR变换后,光谱曲线的特征吸收带更加明显,相关系数在可见光波段范围内有所提高;基于全波段的 PLSR 建模效果要优于显著性波段,其中以CR的预测精度最为突出,其模型的决定系数R2和相对分析误差RPD分别为0.84、2.58;显著性波段的PLSR模型与全波段对比在模型精度方面虽有一定差距,但从模型的复杂程度来比较,具有模型简单、运算量小、变量更少的特点;最后,综合比较了全波段和显著性波段 4 种光谱指标的反演精度,发现 CR-PLSR 模型的建模和预测的效果比R-PLSR、LR-PLSR、FDR-PLSR 模型都要显著.该研究可为将 CR-PLSR 高光谱反演模型用于该区域土肥信息的遥感监测提供参考.
Soil organic matter (SOM) plays an important role in soil fertility and carbon (C) cycle. Soil spectral reflectance provides an alternative method to soil's classical physical and chemical analysis in laboratory for the estimation of a large range of key soil properties. In order to achieve rapid measurement of soil organic matter content (SOMC) based on hyperspectral analysis, in this paper, 46 soil samples at 0-20 cm depth were collected as research objects from Gong'an County in Jianghan Plain, and these samples were highly representative for the SOM. The raw hyperspectral reflectance of soil samples was measured by the standard procedure with an ASD FieldSpec3 instrument equipped with a high intensity contact probe under the laboratory conditions. Meanwhile, physical and chemical properties of these soil samples were analyzed. Twenty-eight of 46 samples were used for building hyperspectral estimation models of SOMC and the other 18 samples were used for model prediction. In the next, the raw spectral reflectance (R) was transformed to 3 spectral indices, i.e. logarithm of reciprocal reflectance (LR), first-order differential reflectance (FDR) and continuum removal reflectance (CR) to analyze the correlation coefficients between the 4 spectral indices and their SOMC. Then, the correlation coefficients of the 4 spectral indices by F significant test were got (P<0.01), which could be used to extract significant bands. At last, we used partial least squares regression (PLSR) method to build quantitative inversion model of SOMC based on full bands (400-2 400 nm) and significant bands for this study area, respectively. The prediction accuracies of these optimal models were assessed by comparing determination coefficients (R2), root mean squared error (RMSE) and relative percent deviation (RPD) between the estimated and measured SOMC. The results showed that, after conducting the CR transformation on raw soil spectral data, there were prominent differences among the absorption peaks of spectral curves in different soil samples, and the heterogeneity of different spectral curves was decreased to a certain extent, at the same time, their correlations were also improved by about 0.2 in the range of visible bands. Compared to the significant bands, the full bands using PLSR method could obtain more robust prediction accuracies. Among all of the 4 spectral indices based on processing inversion models in full bands, the prediction accuracy of CR was the best, and its values ofR2, RMSE and RPD between the estimated and measured SOMC for the predicted model were 0.84, 3.86 and 2.58, respectively, which were better than those in significant bands. For the PLSR models based on significant bands, although there was a slight gap in the prediction accuracy with that based on full bands, they also had their own unique advantages: these models were much simpler and thus the model computation was reduced significantly, and they could play an important role under the circumstances in which increasing modeling speed and reducing model computation were more important than improving prediction accuracy. At last, it could be concluded that the CR-PLSR model for SOMC was better than R-PLSR, LR-PLSR, FDR-PLSR models not only in full bands but also in significant bands. In the future, the CR-PLSR hyperspectral inversion model can be used as a reference for aerospace hyperspectral remote sensing of soil fertility information in this region, and can realize the timely monitoring of SOMC.
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2015年第14期103-109,共7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)