期刊
REMOTE SENSING
卷 7, 期 9, 页码 11434-11448出版社
MDPI AG
DOI: 10.3390/rs70911434
关键词
hyperspectral snapshot camera; hyperspectral imaging; proximal soil sensing; multivariate calibration; spectral variable selection; partial least squares regression
类别
资金
- Deutsche Forschungsgemeinschaft DFG [VO 1509/3-1, TH 678/12-1]
In soil proximal sensing with visible and near-infrared spectroscopy, the currently available hyperspectral snapshot camera technique allows a rapid image data acquisition in a portable mode. This study describes how readings of a hyperspectral camera in the 450-950 nm region could be utilised for estimating soil parameters, which were soil organic carbon (OC), hot-water extractable-C, total nitrogen and clay content; readings were performed in the lab for raw samples without any crushing. As multivariate methods, we used PLSR with full spectra (FS) and also combined with two conceptually different methods of spectral variable selection (CARS, competitive adaptive reweighted sampling and IRIV, iteratively retaining informative variables). For the accuracy of obtained estimates, it was beneficial to use segmented images instead of image mean spectra, for which we applied a regular decomposing in sub-images all of the same size and k-means clustering. Based on FS-PLSR with image mean spectra, obtained estimates were not useful with RPD values less than 1.50 and R-2 values being 0.51 in the best case. With segmented images, improvements were marked for all soil properties; RPD reached values 1.68 and R-2 0.66. For all image data and variables, IRIV-PLSR slightly outperformed CARS-PLSR.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据