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Pellikka P, Luotamo M, Sädekoski N, Hietanen J, Vuorinne I, Räsänen M, Heiskanen J, Siljander M, Karhu K, Klami A. Tropical altitudinal gradient soil organic carbon and nitrogen estimation using Specim IQ portable imaging spectrometer. Sci Total Environ 2023; 883:163677. [PMID: 37105488 DOI: 10.1016/j.scitotenv.2023.163677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 03/25/2023] [Accepted: 04/19/2023] [Indexed: 05/03/2023]
Abstract
The largest actively cycling terrestrial carbon pool, soil, has been disturbed during latest centuries by human actions through reduction of woody land cover. Soil organic carbon (SOC) content can reliably be estimated in laboratory conditions, but more cost-efficient and mobile techniques are needed for large-scale monitoring of SOC e.g. in remote areas. We demonstrate the capability of a mobile hyperspectral camera operating in the visible-near infrared wavelength range for practical estimation of soil organic carbon (SOC) and nitrogen content, to support efficient monitoring of soil properties. The 191 soil samples were collected in Taita Taveta County, Kenya representing an altitudinal gradient comprising five typical land use types: agroforestry, cropland, forest, shrubland and sisal estate. The soil samples were imaged using a Specim IQ hyperspectral camera under controlled laboratory conditions, and their carbon and nitrogen content was determined with a combustion analyzer. We use machine learning for estimating SOC and N content based on the spectral images, studying also automatic selection of informative wavelengths and quantification of prediction uncertainty. Five alternative methods were all found to perform well with a cross-validated R2 of approximately 0.8 and an RMSE of one percentage point, demonstrating feasibility of the proposed imaging setup and computational pipeline.
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Affiliation(s)
- Petri Pellikka
- University of Helsinki, Department of Geosciences and Geography, Helsinki, Finland; State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, PR China
| | - Markku Luotamo
- University of Helsinki, Department of Computer Science, Helsinki, Finland.
| | - Niklas Sädekoski
- University of Helsinki, Department of Geosciences and Geography, Helsinki, Finland
| | - Jesse Hietanen
- University of Helsinki, Department of Geosciences and Geography, Helsinki, Finland
| | - Ilja Vuorinne
- University of Helsinki, Department of Geosciences and Geography, Helsinki, Finland
| | - Matti Räsänen
- University of Helsinki, Department of Geosciences and Geography, Helsinki, Finland
| | - Janne Heiskanen
- University of Helsinki, Department of Geosciences and Geography, Helsinki, Finland
| | - Mika Siljander
- University of Helsinki, Department of Geosciences and Geography, Helsinki, Finland
| | - Kristiina Karhu
- University of Helsinki, Department of Forest Sciences, Helsinki, Finland; Helsinki Institute of Life Science (HiLIFE), Helsinki, Finland
| | - Arto Klami
- University of Helsinki, Department of Computer Science, Helsinki, Finland
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