Development of Models to Estimate Total Soil Carbon across Different Croplands at a Regional Scale Using RGB Photography

Abstract

A quick, accurate and cost-effective method for estimating total soil carbon is necessary for monitoring its levels due to its environmentally and agronomically irreplaceable importance. There are several impediments to both laboratory analysis and spectroscopic sensor technology because the former is both expensive and time-consuming whereas the initial cost of the latter is too high for farmers to afford. RGB photography obtained from digital cameras could be used to quickly and cheaply estimate the total carbon (TC) content of the soil. In this study, we developed models to predict soil TC contents across different cropland types including paddy, upland and orchard fields as well as the TC content of the soil combined from all the aforementioned cropland types on a regional scale. Soil colour measurements were made on samples from the Chungcheongnam-do province of South Korea. The soil TC content ranged from 0.045% to 6.297%. Modelling was performed using multiple linear regression considering the soil moisture levels and illuminance. The best soil TC prediction model came from the upland soil and gave training and validation r2 values of 0.536 and 0.591 with RMSE values of 0.712% and 0.441%, respectively. However, the most accurate equation is the one that produces the lowest RMSE value. Hence, although the model for the upland soil was the most stable of all, the paddy soil model which gave training and validation r2 values of 0.531 and 0.554 with RMSE values of 0.240% and 0.199%, respectively, was selected as the best soil TC prediction equation of all due to its comparatively high r2 value and the lowest RMSE of all equations.

Prediction of Soil Carbon Contents Using Smartphone Images and Multiple Regression Analysis

“…최근 이를 보완하기 위한 분광학적 분석 방식이 개발되었으나 이 또한 전문적인 장비 가 필요하며, 비용이 많이 들고, 사용 방법이 복잡하다는 한계점을 가지고 있다 (Kim and Hong, 2002;Angelopoulou et al, 2020;Yang et al, 2021). 이러한 상황에서 최근 스마트폰의 보급 및 사용량 증가와 함께 스마트폰 사진의 품질 이 향상되면서 스마트폰의 사진과 토양 색의 관계를 기반으로 토양 내 탄소 함량을 예측하는 방식과 관련된 연구가 활 발하게 진행되고 있다 (Moritsuka et al, 2014;Vodyanitskii and Savichev, 2017;Yang et al, 2021;Yoo et al, 2021;Park and Choi, 2022;Sonn et al, 2022).…”

Quantifying soil organic matter for sustainable agricultural land management with soil color and machine learning technique

“…Yang et al, 2021). Additionally, previous studies have provided supportive evidence for the relationship between soil color and SOM (Fu et al, 2020;Heil et al, 2022;Kang et al, 2022;Sonn et al, 2022;Yang et al, 2021).…”

“…This study considered the wavelength range of 620-750 nm as the basis for the R value, while the G and B values corresponded to wavelength ranges of 495-570 nm and 450-475 nm, respectively, as proposed in Fu et al (2020). The averaged values of the R, G, and B parameters were transformed into color indices, such as B I , H I , and S I , as described in Table 1 (Fu et al, 2020;Heil et al, 2022;Kang et al, 2022;Sonn et al, 2022;Wu et al, 2017). Table 1 represents a brief description of the color indices and conversion formulas.…”

“…Soil organic matter (SOM), which comprises organic C, serves as a pivotal environmental factor crucial in assessing terrestrial ecosystem services (e.g., soil health, soil microorganism community) and striving for sustainable land management (Heil et al, 2022;Lal, 2016Lal, , 2020. Soil C has been recognized as a key factor by South Korea and the Sustainable Development Goals for 2023 in addressing climate change and combating major threats to soil degradation and food security (Castaldi et al, 2019;Kang et al, 2022;Sonn et al, 2022). Furthermore, the IPCC (2023) report described soil as a huge C storage, indicating that soil C sequestration is an important agenda for climate change mitigation.…”

“…content by incorporating both soil color and texture. Moreover, Sonn et al (2022) required a crucial step of measuring illuminance at the sampling site to calibrate the extracted soil color, adding complexity. The process of measuring soil moisture content during analysis takes over 24 h, making it time-consuming.…”