Mapping of Total Carbon and Clay Contents in Glacial Till Soil Using On-the-Go Near-Infrared Reflectance Spectroscopy and Partial Least Squares Regression  

作　　者：SHEN Zhang-Quan SHAN Ying-Jie PENG Li JIANG Yu-Gen 

机构地区：[1]Institute of Agricultural Remote Sensing and Information Technology Application, Zhejiang University [2]Zhejiang Soil and Fertilizer Station [3]Fuyang Soil and Fertilizer Station

出　　处：《Pedosphere》2013年第3期305-311,共7页土壤圈（英文版）

基　　金：Supported by the Agricultural S&T Cooperation Program of Zhejiang Province, China (No. N20100015)

摘　　要：Characterizing spatial variability of soil attributes, using traditional soil sampling and laboratory analysis, is cost prohibitive. The potential benefit of managing soils on a site-specific basis is well established. High variations in glacial till soil render detailed soil mapping difficult with limited number of soil samples. To overcome this problem, this paper demonstrates the feasibility of soil carbon and clay mapping using the newly developed on-the-go near-infrared reflectance spectroscopy (NIRS). Compared with the geostatistics method, the partial least squares regression (PLSR), with NIRS measurements, could yield a more detailed map for both soil carbon and clay. Further, by using independent validation dataset, the accuracy of predicting could be improved significantly for soil clay content and only slightly for soil carbon content. Owing to the complexity of field conditions, more work on data processing and calibration modeling might be necessary for using on-the-go NIRS measurements.Characterizing spatial variability of soil attributes, using traditional soil sampling and laboratory analysis, is cost prohibitive. The potential benefit of managing soils on a site-specific basis is well established. High variations in glacial till soil render detailed soil mapping difficult with limited number of soil samples. To overcome this problem, this paper demonstrates the feasibility of soil carbon and clay mapping using the newly developed on-the-go near-infrared reflectance spectroscopy （NIRS）. Compared with the geostatistics method, the partial least squares regression （PLSR）, with NIRS measurements, could yield a more detailed map for both soil carbon and clay. Further, by using independent validation dataset, the accuracy of predicting could be improved significantly for soil clay content and only slightly for soil carbon content. Owing to the complexity of field conditions, more work on data processing and calibration modeling might be necessary for using on-the-go NIRS measurements.

关 键 词：independent validation on-the-go measurement soil carbon mapping soil clay mapping soil sensors 

分 类 号：S152[农业科学—土壤学] TP79[农业科学—农业基础科学]