| 基于灰度关联-岭回归的荒漠土壤有机质含量高光谱估算 |
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| 引用本文： | 王海峰,张智韬,Arnon Karnieli,陈俊英,韩文霆.基于灰度关联-岭回归的荒漠土壤有机质含量高光谱估算[J].农业工程学报,2018,34(14):124-131. |
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| 作者姓名： | 王海峰 张智韬 Arnon Karnieli 陈俊英 韩文霆 |
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| 作者单位： | 西北农林科技大学水利与建筑工程学院旱区农业水土工程教育部重点实验室;西北农林科技大学中国旱区节水农业研究院;本古里安大学Blaustein沙漠研究所 |
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| 基金项目： | 国家重点研发计划项目（2017YFC0403302、2016YFD0200700）;杨凌示范区科技计划项目（2016NY-26） |
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| 摘 要： | 为改善高光谱技术对荒漠土壤有机质的估测效果,该文采集了以色列Seder Boker地区的荒漠土壤,经预处理、理化分析后将土样分为砂质土和黏壤土2类,再通过光谱采集、处理得到6种光谱指标:反射率(reflectivity,REF)、倒数之对数变换(inverse-log reflectance,LR)、去包络线处理(continuum removal,CR)、标准正态变量变换(standard normal variable reflectance,SNV)、一阶微分变换(first order differential reflectance,FDR)和二阶微分变换(second order differential reflectance,SDR)。通过灰度关联(gray correlation,GC)法确定SNV、FDR、SDR为敏感光谱指标,采用偏最小二乘回归(partial least squares regression,PLSR)法和岭回归(ridge regression,RR)法,构建基于敏感光谱指标的土壤有机质高光谱反演模型,并对模型精度进行比较。结果表明:砂质土有机质含量的反演效果要优于黏壤土;基于SNV指标建立的模型决定系数R~2和相对分析误差RPD均为最高、均方根误差RMSE最低,所以SNV是土壤有机质的最佳光谱反演指标;对SNV-PLSR模型和SNV-RR模型综合比较得出,SNV-RR模型仅用全谱4%左右的波段建模,实现了更为理想的反演效果:其中,对砂质土有机质的预测能力极强(R_p~2为0.866,RMSE为0.610 g/kg、RPD为2.72),对黏壤土有机质的预测能力很好(Rp2为0.863,RMSE为0.898 g/kg、RPD为2.37)。荒漠土壤有机质GC-SNV-RR反演模型的建立为高光谱模型的优化、土壤有机质的快速测定提供了一种新的途径。
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| 关 键 词： | 遥感 模型 有机质 荒漠土壤 高光谱 灰度关联 岭回归 |
| 收稿时间： | 2018/3/4 0:00:00 |
| 修稿时间： | 2018/6/25 0:00:00 |
Hyperspectral estimation of desert soil organic matter content based on gray correlation-ridge regression model |
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| Wang Haifeng,Zhang Zhitao,Arnon Karnieli,Chen Junying and Han Wenting.Hyperspectral estimation of desert soil organic matter content based on gray correlation-ridge regression model[J].Transactions of the Chinese Society of Agricultural Engineering,2018,34(14):124-131. |
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| Authors: | Wang Haifeng Zhang Zhitao Arnon Karnieli Chen Junying and Han Wenting |
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| Institution: | 1. College of Water Resources and Architectural Engineering, Key Laboratory of Agricultural Soil and Water Engineering in the Ministry of Education, Northwest A&F University, Yangling 712100, China; 2. Institute of Water Saving Agriculture in Arid Areas of China, Northwest A&F University, Yangling 712100, China;,1. College of Water Resources and Architectural Engineering, Key Laboratory of Agricultural Soil and Water Engineering in the Ministry of Education, Northwest A&F University, Yangling 712100, China; 2. Institute of Water Saving Agriculture in Arid Areas of China, Northwest A&F University, Yangling 712100, China;,3. Jocob Blaustein Institute for Desert Research, Ben-Gurion University of the Negev, Sede Boker 84990, Israel,1. College of Water Resources and Architectural Engineering, Key Laboratory of Agricultural Soil and Water Engineering in the Ministry of Education, Northwest A&F University, Yangling 712100, China; 2. Institute of Water Saving Agriculture in Arid Areas of China, Northwest A&F University, Yangling 712100, China; and 2. Institute of Water Saving Agriculture in Arid Areas of China, Northwest A&F University, Yangling 712100, China; |
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| Abstract: | Abstract: Organic matter content in soil is one of the most significant indicators evaluating the soil fertility, and its dynamic monitoring is good for further development of accurate agriculture. In recent years, obtaining Vis-NIR (visible - near infrared) continuous spectrum data of soil through hyperspectral technique and realizing accurate inversion prediction according to organic matter spectrum reflection characteristics have become a hot topic in current remote sensing field. However, in the hyperspectral inversion process of desert soil organic matter, there exists the problem of "low organic matter content, weak spectrum response and low model precision". The research collected different soil samples in Seder Boker region, south of Israel, divided the experimental soil samples into sandy soil and clay loam after particle size analysis in the lab, and applied potassium dichromate external heating method to measure the organic matter content in the soil. The raw hyperspectral reflectance of soil samples was measured by the ASD FieldSpec 3 instrument. After data preprocessing and different mathematical manipulation, 6 spectral indicators were obtained, i.e. reflectivity (REF), inverse-log reflectance (LR), continuum removal reflectance (CR), standard normal variable reflectance (SNV), first-order differential reflectance (FDR) and second-order differential reflectance (SDR). Then, gray correlation degree (GCD) between different spectral indicators and organic matter content was calculated, and SNV, FDR and SDR through gray correlation (GC) test (GCD>0.90) were chosen as the sensitive spectral indicators. Moreover, hyperspectral inversion model of soil organic matter was built based on sensitive spectral indicator using partial least squares regression (PLSR) method and ridge regression (RR) method, and the precision of inversion result was verified and compared. And then, the performances of these models were evaluated by the determination coefficient for calibration set (Rc2), determination coefficient for prediction set (Rp2), root mean squared error (RMSE) and relative percent deviation (RPD). The results indicated that: Soil particle size has a certain impact on the spectral response of organic matter, and the inversion effect of hyperspectral model on the organic matter content in sandy soil is superior to clay loam; after comparing and analyzing the models built according to different spectral indicators, Rc2, Rp2 and RPD of SNV-PLSR soil model and SNV-RR soil model built according to SNV are the highest and RMSE is the lowest, so SNV is the optimal spectral inversion indicator of soil organic matter; SNV-RR model has the most ideal inversion effect on organic matter content of these 2 kinds of soil: For sandy soil, Rc2 is 0.887, Rp2 is 0.866, RMSE is 0.610 g/kg and RPD is 2.72; for clay loam, Rc2 is 0.889, Rp2 is 0.863, RMSE is 0.898g/kg and RPD is 2.37. After analysis, it is known that SNV-RR model has extremely strong forecast ability for organic matter of sandy soil, and very good quantitative forecast ability for organic matter of clay loam. In addition, compared with PLSR model, Rc2 and Rp2 of RR model decline slightly. However, on the premise of ensuring precision, the number of band section used in modeling only accounts for about 4% of total spectrum. Not only does it simplify the model greatly, but also realizes "dimensionality reduction" and "optimization" of hyperspectral data. Through band selection function effect of RR method, the significant band section of soil organic matter is analyzed: The sensitive band of organic matter of sandy soil is mainly concentrated at 820-860 and 940-970 nm, but the sensitive band of organic matter of clay loam is concentrated at 730-790 and 800-820 nm. The united application of gray correlation analysis and RR method in the modeling analysis of soil organic matter provides a new approach to optimize the hyperspectral model and quickly measure the organic matter content in soil. GC-SNV-RR organic matter inversion model of 2 kinds of soil is simple and has good prediction. It provides support for remote sensing analysis on desert soil organic matter, which realizes the speedability and accuracy in monitoring the desert soil organic matter. |
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| Keywords: | remote sensing models organic matter desert soil hyperspectral gray correlation ridge regression |
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