基于相关分析和偏最小二乘回归的黄绵土土壤全氮和碱解氮含量的高光谱预测

应用生态学报

基于相关分析和偏最小二乘回归的黄绵土土壤全氮和碱解氮含量的高光谱预测

刘秀英^1,2,王力¹,常庆瑞^1**,王晓星¹,尚艳¹

（¹西北农林科技大学资源环境学院，陕西杨凌 712100； ^2河南科技大学农学院，河南洛阳 471003）

出版日期:2015-07-18 发布日期:2015-07-18

Prediction of total nitrogen and alkali hydrolysable nitrogen content in loess using hyperspectral data based on correlation analysis and partial least squares regression.

LIU Xiu-ying^1,2, WANG Li¹, CHANG Qing-rui¹, WANG Xiao-xing¹, SHANG Yan¹

(¹College of Resources and Environment, Northwest A&F University, Yangling 712100, Shaanxi, China； ^2College of Agronomy, Henan University of Science and Technology, Luoyang 471003, Henan, China)

Online:2015-07-18 Published:2015-07-18

摘要/Abstract

摘要： 以采取植被恢复措施的陕西省吴起县为研究区，实地采集24个土壤剖面不同层次的黄绵土土样100个，在进行土壤样本全氮（TN）和碱解氮（AHN）含量及实验室反射光谱数据测量和分析的基础上，用相关分析（CA）结合偏最小二乘回归（PLS）方法建立黄绵土土壤TN和AHN含量的校正模型，并用独立样本对校正模型进行验证.结果表明：利用6种光谱变换方式建立的校正模型中，微分光谱建立的校正模型是预测研究区土壤TN含量的最佳模型，校正和验证R²分别为0.929和0.935，均方根误差（RMSE）分别为0.045和0.047 g·kg^-1，相对预测偏差（RPD）为3.12；而归一化变换建立的校正模型是预测土壤AHN含量的最佳模型，校正和验证R²分别为0.873和0.773，RMSE分别为9.946和16.204 mg·kg^-1，RPD为1.538.所建立的全氮预测模型可以对0～40 cm土层的TN进行有效预测，而碱解氮的预测模型对同一深度只能进行粗略预测.本研究为采取植被恢复措施的退化生态系统区黄绵土土壤全氮的快速预测提供了一种较好的方法，但是对于碱解氮的准确、快速预测，需要进一步研究.

Abstract: Wuqi County of Shaanxi Province, where the vegetation recovering measures have been carried out for years, was taken as the study area. A total of 100 loess samples from 24 different profiles were collected. Total nitrogen (TN) and alkali hydrolysable nitrogen (AHN) contents of the soil samples were analyzed, and the soil samples were scanned in the visible/nearinfrared (VNIR) region of 350-2500 nm in the laboratory. The calibration models were developed between TN and AHN contents and VNIR values based on correlation analysis (CA) and partial least squares regression (PLS). Independent samples validated the calibration models. The results indicated that the optimum model for predicting TN of loess was established by using first derivative of reflectance. The best model for predicting AHN of loess was established by using normal derivative spectra. The optimum TN model could effectively predict TN in loess from 0 to 40 cm, but the optimum AHN model could only roughly predict AHN at the same depth. This study provided a good method for rapidly predicting TN of loess where vegetation recovering measures have been adopted, but prediction of AHN needs to be further studied.

刘秀英1,2,王力1,常庆瑞1**,王晓星1,尚艳1. 基于相关分析和偏最小二乘回归的黄绵土土壤全氮和碱解氮含量的高光谱预测[J]. 应用生态学报.

LIU Xiu-ying1,2, WANG Li1, CHANG Qing-rui1, WANG Xiao-xing1, SHANG Yan1. Prediction of total nitrogen and alkali hydrolysable nitrogen content in loess using hyperspectral data based on correlation analysis and partial least squares regression.[J]. Chinese Journal of Applied Ecology.