{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:11:29Z","timestamp":1769904689625,"version":"3.49.0"},"reference-count":42,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2015,9,9]],"date-time":"2015-09-09T00:00:00Z","timestamp":1441756800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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\u2013950 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, \u201ccompetitive adaptive reweighted sampling\u201d and IRIV, \u201citeratively retaining informative variables\u201d). 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 \u2265 1.68 and R2 \u2265 0.66. For all image data and variables, IRIV-PLSR slightly outperformed CARS-PLSR.<\/jats:p>","DOI":"10.3390\/rs70911434","type":"journal-article","created":{"date-parts":[[2015,9,9]],"date-time":"2015-09-09T13:16:18Z","timestamp":1441804578000},"page":"11434-11448","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Use of A Portable Camera for Proximal Soil Sensing with Hyperspectral Image Data"],"prefix":"10.3390","volume":"7","author":[{"given":"Andr\u00e1s","family":"Jung","sequence":"first","affiliation":[{"name":"Geoinformatics and Remote Sensing, Institute for Geography, Leipzig University, Johannisallee 19a, Leipzig D-04103, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6048-1163","authenticated-orcid":false,"given":"Michael","family":"Vohland","sequence":"additional","affiliation":[{"name":"Geoinformatics and Remote Sensing, Institute for Geography, Leipzig University, Johannisallee 19a, Leipzig D-04103, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2721-7333","authenticated-orcid":false,"given":"S\u00f6ren","family":"Thiele-Bruhn","sequence":"additional","affiliation":[{"name":"Soil Science, University of Trier, Trier D-54286, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2015,9,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1147","DOI":"10.1126\/science.228.4704.1147","article-title":"Imaging spectrometry for earth remote sensing","volume":"228","author":"Goetz","year":"1985","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1807","DOI":"10.1080\/01431168708954818","article-title":"Principles of field spectroscopy","volume":"8","author":"Milton","year":"1987","journal-title":"Int. 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