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一种降低土壤水分对土壤有机质光谱监测精度的新方法

A New Method to Decline the SWC Effect on the Accuracy for Monitoring SOM with Hyperspectral Technology

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摘要

土壤有机质是土壤肥力的重要体现, 土壤水分是限制利用光谱技术进行土壤属性光谱监测的重要因子之一。 为了研究土壤水分对土壤有机质光谱监测精度的影响和实现土壤有机质(soil organic matter, SOM)的准确、 实时监测, 对151份麦田土壤样品的土壤水分、 土壤有机质和土壤光谱进行了测定。 基于土壤含水量(soil water content, SWC)分类法和归一化土壤水分指数(normalized difference soil moisture index, NSMI)光谱参数分类法对麦田土壤样品进行分类, 并对土壤含水量、 土壤有机质和土壤光谱参数之间的关系进行研究。 结果表明: 以土壤含水量对土壤样品进行分类后, 各分组之间的土壤有机质光谱监测精度各异, 且都高于不分组条件下(5%~20%)土壤有机质光谱监测精度, 表明土壤水分确实影响土壤有机质的光谱监测。 土壤含水量低于10%和高于20%时, 土壤水分对土壤有机质光谱监测精度的影响较小, 表明此时的土壤水分状态易于土壤有机质的光谱监测。 另一方面, 以NSMI光谱参数对土壤样品进行分类后, 各分组条件下的土壤有机质光谱监测的拟合精度优于基于土壤含水量的分类方法, 通过R2, RMSE和RPD模型验证参数的验证, 各模型可靠, 表明利用NSMI光谱参数的分类方法, 在一定程度上可以实现对土壤自然条件下土壤有机质的实时、 准确监测。 但是, 所提到的两种土壤分类方法在本质上一样, 说明仍然可能存在最优的土壤分类方法, 来克服和消除土壤水分对土壤有机质光谱监测精度的影响。 为土壤水分和土壤有机质的大面积遥感提供一定的理论基础。

Abstract

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.

Newport宣传-MKS新实验室计划
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中图分类号:S127

DOI:10.3964/j.issn.1000-0593(2015)12-3495-05

基金项目:国家自然科学基金项目(31371572, 31201168), 山西省科技攻关项目(20110311038), 山西省青年基金项目(2012021023-5)和山西省研究生优秀创新项目(2015BY31)资助

收稿日期:2014-08-21

修改稿日期:2014-11-25

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作者单位    点击查看

王超:山西农业大学旱作农业工程研究所, 山西 太谷 030801
冯美臣:山西农业大学旱作农业工程研究所, 山西 太谷 030801
杨武德:山西农业大学旱作农业工程研究所, 山西 太谷 030801
肖璐洁:山西农业大学旱作农业工程研究所, 山西 太谷 030801
李广信:山西农业大学旱作农业工程研究所, 山西 太谷 030801山西省农业科学院作物科学研究所, 山西 太原 030031
赵佳佳:山西农业大学旱作农业工程研究所, 山西 太谷 030801
任鹏:山西农业大学旱作农业工程研究所, 山西 太谷 030801

联系人作者:王超(wcqxx2005@126.com)

备注:王超, 1988年生, 山西农业大学旱作农业工程研究所博士研究生

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引用该论文

WANG Chao,FENG Mei-chen,YANG Wu-de,XIAO Lu-jie,LI Guang-xin,ZHAO Jia-jia,REN Peng. A New Method to Decline the SWC Effect on the Accuracy for Monitoring SOM with Hyperspectral Technology[J]. Spectroscopy and Spectral Analysis, 2015, 35(12): 3495-3499

王超,冯美臣,杨武德,肖璐洁,李广信,赵佳佳,任鹏. 一种降低土壤水分对土壤有机质光谱监测精度的新方法[J]. 光谱学与光谱分析, 2015, 35(12): 3495-3499

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