Wang Chao, Feng Mei-chen, Yang Wu-de, Xiao Lu-jie, Li Guang-xin, Zhao Jia-jia, Ren Peng
Guang Pu Xue Yu Guang Pu Fen Xi. 2015 Dec;35(12):3495-9.
Soil organic matter (SOM) is one of the most important indexes to reflect the soil fertility, and soil moisture is a main factor to limit the application of hyperspectral technology in monitoring soil attributes. To study the effect of soil moisture on the accuracy for monitoring SOM with hyperspectral remote sensing and monitor the SOM quickly and accurately, SOM, soil water content (SWC) and soil spectrum for 151 natural soil samples in winter wheat field were measured and the soil samples were classified with the method of traditional classification of SWC and Normalized Difference Soil Moisture Index (NSMI) based on the hyperspectral technology. Moreover, the relationship among SWC, SOM and NSMI were analyzed. The results showed that the accuracy of spectral monitor for SOM among the classifications were significantly different, its accuracy was higher than the soils (5%-25%) which was not classified. It indicated that the soil moisture affected the accuracy for monitoring the SOM with hyperspectral technology and the study proved that the most beneficent soil water content for monitoring the SOM was less 10% and higher 20%. On the other hand, the four models for monitoring the SOM by the hyperspectral were constructed by the classification of NSMI, and its accuracy was higher than the classification of SWC. The models for monitoring the SOM by the classification of NSMI were calibrated with the validation parameters of R², RMSE and RPD, and it showed that the four models were available and reliable to quickly and conveniently monitor the SOM by heperspectral. However, the different classifiable ways for soil samples mentioned in the study were naturally similar as all soil samples were classified again with another way. Namely, there may be another optimal classifiable way or method to overcome and eliminate the SWC effect on the accuracy for monitoring SOM. The study will provide some theoretical technology to monitor the SWC and SOM by remote sensing.
土壤有机质（SOM）是反映土壤肥力的最重要指标之一，而土壤水分是限制高光谱技术在土壤属性监测中应用的主要因素。为了研究土壤水分对高光谱遥感监测SOM准确性的影响，并快速准确地监测SOM，对冬小麦田151个自然土壤样本的SOM、土壤含水量（SWC）和土壤光谱进行了测定，并基于高光谱技术采用传统的SWC分类方法和归一化差异土壤湿度指数（NSMI）对土壤样本进行了分类。此外，分析了SWC、SOM和NSMI之间的关系。结果表明，不同分类中SOM光谱监测的准确性存在显著差异，其准确性高于未分类的土壤（5%-25%）。这表明土壤水分影响了高光谱技术监测SOM的准确性，研究证明监测SOM最适宜的土壤含水量小于10%且大于20%。另一方面，通过NSMI分类构建了4种高光谱监测SOM的模型，其准确性高于SWC分类。用R²、RMSE和RPD验证参数对NSMI分类监测SOM的模型进行了校准，结果表明这4种模型可快速方便地通过高光谱监测SOM，是可行且可靠的。然而，本研究中提到的土壤样本不同分类方式自然相似，因为所有土壤样本都用另一种方式重新进行了分类。也就是说，可能存在另一种最佳分类方式或方法来克服和消除SWC对SOM监测准确性的影响。该研究将为遥感监测SWC和SOM提供一些理论技术。
