Method for 3D soil horizonation using digital images

Abstract

We created a three-dimensional (3D) model of the spatial arrangement of soil horizons with broken boundaries using digital images. This technique was tested on a Retisol — an Alfisol with a glossic horizon. Photographs were taken for 11 vertical sections (2.5 cm distance between sections) of the soil profile for an area of 30 × 45 cm. Colorimetric accuracy of the images was tested against measurements of moist soil samples made with a portable spectrophotometer. The selected best images were calibrated using an internal color calibration method for color correction. The images were combined into a common array to build a 3D optical soil horizon map using the CIELAB color space. A protocol for processing the 3D soil images was created that showed 3D soil structure. It was found that the CIELAB color coordinates can be used to distinguish and delineate AE, E, and EB horizons. We then tested the method to assess soil carbon stocks and found that the stocks using the 3D model were 28% higher than when calculated using the 2D model. We conclude that the optical 3D mapping method can accurately represent the 3D structure and can be used to quantify soil horizon variations.

References

1.    Абрукова В.В., Акульшина Е.А., Афанасьева Т.В. и др. Почвенно-агрономическая характеристика АБС «Чашниково». Ч. 1. М., 1986.
2.    Егоров В.В., Фридланд В.М., Иванова Е.Н. и др. Классификация и диагностика почв СССР. М., 1977.
3.    Качинский Н.А. Механический и микроагрегатный состав почвы, методы его изучения / Академия наук СССР. Почв. ин-т им В.В. Докучаева. М., 1958.
4.    Кириллова Н.П., Силева Т.М. Анализ цвета почв с использованием цифровой фотокамеры // Вестн. Моск. ун-та. Сер. 17. Почвоведение. 2017. Т. 17, № 1. https://doi.org/10.3103/S0147687417010045
5.    Кириллова Н.П., Силева Т.М., Ульянова Т.Ю. и др. Цифровая почвенная карта УО ПЭЦ «Чашниково» МГУ им. М.В. Ломоносова // Вестн. Моск. ун-та. Сер. 17. Почвоведение. 2015. № 2. https://doi.org/10.3103/S0147687415020040
6.    Корнблюм Э.А., Любимова И.Н., Турсина Т.В. Мозаичные почвенные профили и способ их описания // Почвоведение. 1972. № 8.
7.    Романенко К.А., Рогов В.В., Юдина А.В. и др. Исследования микростроения мерзлых почв и дисперсных пород с помощью рентгеновской компьютерной томографии: методы, подходы, перспективы // Бюл. почв. ин-та им. В.В. Докучаева. 2016. № 83.
8.    Теории и методы физики почв: Коллективная монография / Под общ. ред. Шеина Е.В. и Карпачевского Л.О. М., 2007.
9.    Тонконогов В.Д. Глинисто-дифференцированные почвы Европейской России. М., 1999.
10.    Aeby P., Schultze U., Braichotte D. et al. Fluorescence imaging tracer distributions in soil profiles // Environmental Science and Technology. 2001. Vol. 35. https://doi.org/10.1021/es000096x
11.    Cathey B., Obaid S., Zolotarev A.M. et al. Open-Source Multiparametric Optocardiography. Scientific Reports, 2019. Vol. 9:721. https://dx.doi.org/10.1038/s41598-018-36809-y
12.    Colorimetry, CIE Techn. Rep., Vienna: CIE Central Bureau. 2004. № 15: 2004.
13.    Conrad O., Bechtel B., Bock M. et al. System for Automated Geoscientific Analyses (SAGA) Geosci. Model Dev., 8, 1991-2007. https://dx.doi.org/10.5194/gmd-8-1991-2015
14.    Conrad O., Wichmann V. Description of saga_cmd command line interface. 2019. https://doi.org/sourceforge.net/p/saga-gis/wiki/Changelog%207.2.0 [saga_cmd_interface_v7_2_0.txt 681590 bytes] (accessed 02 August 2020).
15.    Grunwald S. What do we really know about the space-time continuum of soil-landscapes // Grunwald S. (ed.) Environmental Soil-Landscape Modeling: Geographic Information Technologies and Pedometrics. New York, USA, 2006.
16.    Grunwald S., Lowery B., Rooney D.J. et al. Profile cone penetrometer data used to distinguish between soil materials // Soil Tillage Res. 2001. Vol. 62. https://dx.doi.org/10.1016/S0167-1987(01)00201-X
17.    Hardeberg J.Y. Colorimetric scanner characterisation // Acta Gr. 2015. Vol. 15.
18.    Hartemink A.E., Minasny B. Towards digital soil morphometrics // Geoderma. 2014. Vol. 230–231. https://doi.org/10.1016/j.geoderma.2014.03.008
19.    IUSS Working Group WRB. World Reference Base for Soil Resources 2014, update 2015 International soil classification system for naming soils and creating legends for soil maps. World Soil Resources Reports № 106. FAO, Rome, 2015.
20.    Kirillova N.P., Zhang Y., Hartemink A.E. et al. Calibration methods for measuring the color of moist soils with digital cameras // Catena. 2021. № 202. https://doi.org/10.1016/j.catena.2021.105274
21.    Kremer A.M. Heterogeneity of soil cover as the self-regulating system // Glazovskaya, M.A., Dmitriev, E.A. (eds.) Patterns of spatial variation of soil properties and information and statistical methods of their study. M., 1970.
22.    Lindbloom B. Useful Color Equations. 2010. https://www.brucelindbloom.com/Eqn_XYZ_to_Lab.html (accessed on 17 July 2021).
23.    Montagne D., Cousin I., Le Forestier L. et al. Quantification of soil volumes in the Eg&Bt-horizon of an Albeluvisol using image analysis // Can. J. Soil Sci. 2007. Vol. 87.
24.    Monteiro Santos F.A., Triantafilis J., Bruzgulis K. A spatially constrained 1D inversion algorithm for quasi-3D conductivity imaging: application to DUALEM-421 data collected in a riverine plain // Geophysics. 2011. Vol. 76. https://doi.org/10.1190/1.3537834
25.    Pereira V., FitzPatrick E.A. Three-dimensional representation of tubular horizons in sandy soils // Geoderma. 1998. Vol. 81.
26.    Persson M. Image Analysis in Agrophysics // Glinski J., Horabik J., and Lipiec J. (eds.) Encyclopedia of Agrophysics, 2011.
27.    Poggio L., Gimona A. National scale 3D modelling of soil organic carbon stocks with uncertainty propagation — An example from Scotland // Geoderma. 2014. Vol. 232–234. https://doi.org/10.1016/j.geoderma.2014.05.004
28.    SAGA     CMD. Saga_cmd_interface_v2_2_0a.doc https://doi.org/sourceforge.net/projects/saga-gis/files/SAGA%20-%20Documentation/Tutorials/Command_Line_Scripting/ (accessed 02 August 2020).
29.    Schoeneberger P.J., Wysocki D.A., Benham E.C. Field Book for Describing and Sampling Soils, Version 3.0 // USDA Natural Resources Conservation Service, National Soil Survey Center, Lincoln, NE, 2012.
30.    Séger M., Guérin R., Frison A. et al. A 3D electrical resistivity tomography survey to characterise the structure of an albeluvic tonguing horizon composed of distinct elementary pedological volumes // Geoderma, 2014. Vol. 219–220. https://doi.org/10.1016/j.geoderma.2013.12.018
31.    Torre I.G., Losada J.C., Tarquis A.M. Multiscaling properties of soil images. Biosystems Engineering, 2016. Vol. 168. https://dx.doi.org/10.1016/j.biosystemseng.2016.11.006
32.    Voxler     4. https://doi.org/support.goldensoftware.com/hc/en-us/categories/115000653867-Voxler (accessed 02 August 2020).
33.    Yarilova E.A., Rubilina N.E. Comparative micromorphology of soddypodzolic soils derived from loamy moraine and non-calcareous loesslike loam. Geoderma, 1976. Vol. 15.
35.    Zhang Y., Hartemink A.E. A method for automated soil horizon delineation using digital images // Geoderma. 2019. Vol. 343. https://dx.doi.org/10.1016/J.GEODERMA.2019.02.002