土壤全钾含量高光谱估测模型  

Hyperspectral Estimation Models of Soil Total Potassium Content

作　　者：尤承增 杨新源[2] 束安 柳树福[1] 王树东[1] YOU Chengzeng YANG Xinyuan SHU An LIU Shufu WANG Shudong(Institute of Remote Sensing and Digital Earth, CAS, Beijing 100094, China Shandong Agriculture University, Tai＇an, Shandong 271018, China Shanghai Institute of Technical Physics of the Chinese Academy of Sciences, Shanghai 200083, China)

机构地区：[1]中国科学院遥感与数字地球研究所,北京100094 [2]山东农业大学,山东泰安271018 [3]中国科学院上海技术物理研究所,上海200083

出　　处：《遥感信息》2017年第4期92-97,共6页Remote Sensing Information

基　　金：国家自然科学基金(41401208;41371359);国家科技支撑计划(2015BAJ02B01;2015BAJ02B03)

摘　　要：鉴于进行高光谱遥感全钾含量估测时,全钾含量及其高光谱反射率数据受到诸多因素的影响(复杂非线性问题),具有较大的模糊性和随机性,采用模糊识别理论建立数学模型对全钾含量进行估测。对横山县采集的84个土样350~2 500nm波段的光谱曲线,利用对数的一阶微分方法计算土壤全钾含量与光谱反射率的相关系数,然后根据极大相关性原则选择最佳波段作为光谱反演指标。经剔除异常样本后,考虑所有反演指标的组合方式,通过各个预测模型精度的对比,找出最佳全钾含量估测模型。When estimating soil total potassium content by hyperspectral remote sensing,total potassium content and data of hyperspectral reflectance are affected by many factors, which is a complex nonlinear question. Given the fuzziness and randomness of the research questions, we used the fuzzy recognition theory to establish mathematical model to estimate the total potassium content. Collecting 350 ; 2 500 nm band spectrum curve of 84 soil samples of Hengshan county, we used the logarithm of the first-order differentiator to calculate the correlation coefficient of soil total potassium content and spectral reflectance. Then according to the maximum relevance principle,the best band as an index of spectrum inversion was chosen. After excluding abnormal samples, all combinations of inversion indicators were taken into account. And with comparing the accuracy of all combinations, we found out the best model for estimating the total potassium content.

关 键 词：土壤全钾含量 高光谱遥感 模糊识别 光谱反演 特征因子 

分 类 号：P237[天文地球—摄影测量与遥感]