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Open AccessArticle

Use of A Portable Camera for Proximal Soil Sensing with Hyperspectral Image Data

1
Geoinformatics and Remote Sensing, Institute for Geography, Leipzig University, Johannisallee 19a, Leipzig D-04103, Germany
2
Soil Science, University of Trier, Trier D-54286, Germany
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Author to whom correspondence should be addressed.
Academic Editors: Lenio Soares Galvao, Nicolas Baghdadi and Prasad S. Thenkabail
Remote Sens. 2015, 7(9), 11434-11448; https://doi.org/10.3390/rs70911434
Received: 31 July 2015 / Revised: 25 August 2015 / Accepted: 1 September 2015 / Published: 9 September 2015
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 R2 values being 0.51 in the best case. With segmented images, improvements were marked for all soil properties; RPD reached values ≥ 1.68 and R2 ≥ 0.66. For all image data and variables, IRIV-PLSR slightly outperformed CARS-PLSR. View Full-Text
Keywords: hyperspectral snapshot camera; hyperspectral imaging; proximal soil sensing; multivariate calibration; spectral variable selection; partial least squares regression hyperspectral snapshot camera; hyperspectral imaging; proximal soil sensing; multivariate calibration; spectral variable selection; partial least squares regression
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MDPI and ACS Style

Jung, A.; Vohland, M.; Thiele-Bruhn, S. Use of A Portable Camera for Proximal Soil Sensing with Hyperspectral Image Data. Remote Sens. 2015, 7, 11434-11448.

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