Assessment of Soil Characteristics Using a Three-Band Agricultural Digital Camera

Glinko Agnieszka (), Kaźmierowski Cezary, Piekarczyk Jan and Królewicz Sławomir
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Glinko Agnieszka: Department of Nature, Institute of Technology and Life Sciences, National Research Institute, Falenty, Poland
Kaźmierowski Cezary: Department of Environmental Remote Sensing and Soil Science, Institute of Physical Geography and Environmental Planning, Adam Mickiewicz University, Poznań, Poland
Piekarczyk Jan: Department of Environmental Remote Sensing and Soil Science, Institute of Physical Geography and Environmental Planning, Adam Mickiewicz University, Poznań, Poland
Królewicz Sławomir: Department of Environmental Remote Sensing and Soil Science, Institute of Physical Geography and Environmental Planning, Adam Mickiewicz University, Poznań, Poland

Quaestiones Geographicae, 2022, vol. 41, issue 3, 127-140

Abstract: Remote sensing techniques based on soil spectral characteristics are the key to future land management; however, they still require field measurement and an agrochemical laboratory for the calibration of the soil property model. Visible and near-infrared diffuse reflectance spectroscopy has proven to be a rapid and effective method. This study aimed to assess the suitability of multispectral data acquired with the agricultural digital camera in determining soil properties. This 3.2-Mpx camera captures images in three spectral bands – green, red and near-infrared. First, the reference data were collected, which consist of 151 samples that were later examined in the laboratory to specify the granulometric composition and to quantify some chemical elements. Second, additional soil properties such as cation exchange capacity, organic carbon and soil pH were measured. Finally, the agricultural digital camera photograph was taken for every soil sample. Reflectance values in three available spectra bands were used to calculate the spectra indices. The relationships between the collected data were calculated using the independent validation regression model such as Cubist and cross-validation model like partial least square in R Studio. Additionally, different types of data normalisation multiplicative scatter correction, standard normal variate, min–max normalisation, conversion into absorbance] were used. The results proved that the agricultural digital camera is suitable for soil property assessment of sand and silt, pH, K, Cu, Pb, Mn, F, cation exchange capacity and organic carbon content. Coefficient of determination varied from 0.563 (for K) to 0.986 (for soil organic carbon). Higher values were obtained with the Cubist regression model than with partial least squares.

Keywords: remote sensing; soil; spectroscopy; cubist; R Studio (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:quageo:v:41:y:2022:i:3:p:127-140:n:10

DOI: 10.2478/quageo-2022-0029

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