=Paper=
{{Paper
|id=Vol-3930/paper1
|storemode=property
|title=Estimation of soil organic carbon content using remote sensing and GIS techniques
|pdfUrl=https://ceur-ws.org/Vol-3930/paper1.pdf
|volume=Vol-3930
|authors=Grigorios Karampetian,Konstantinos Zoukidis,Athanasios Gertsis,Athanasios Falaras,Christos Vasilikiotis,Antonios Apostolidis,Evangelos Vergos,Panagiotis Tziachris
|dblpUrl=https://dblp.org/rec/conf/haicta/KarampetianZGFV24
}}
==Estimation of soil organic carbon content using remote sensing and GIS techniques==
https://ceur-ws.org/Vol-3930/paper1.pdf
Estimation of soil organic carbon content using remote
sensing and GIS techniques⋆
Grigorios Karampetian1,∗,†, Konstantinos Zoukidis1,†, Athanasios Gertsis1,†, Athanasios
Falaras1,†, Christos Vasilikiotis1,†, Antonios Apostolidis1,†, Evangelos Vergos1,† and
Panagiotis Tziachris2,†
1
Perrotis College/ American Farm School - Thessaloniki, Greece
2
Institute of Soils and Water ELGO-Demetra - Thessaloniki, Greece
Abstract
The depletion of Soil Organic Carbon (SOC) due to intensive agricultural practices poses a significant threat
to soil health, impacting agricultural productivity, soil structure, and carbon sequestration. Remote methods
to evaluate the surface SOC content will enhance efficient mapping and therefore, apply appropriate
methods for remediation. A research study was developed to provide a cost-effective, non-invasive method
for SOC estimation and mapping, contributing to sustainable agriculture and environmental conservation.
The focus of the study included using remote sensing (RS), satellite imagery and Geographic Information
Systems (GIS) software to estimate soil organic carbon (SOC) content through various vegetation indices
(VIs). Statistical analysis included both descriptive statistics and multivariate analyses. The SOC data did
not follow a normal distribution, necessitating the use of non-parametric tests. The study employed
multivariate correlation, Spearman's rho as non-parametric tests, and ordinal logistic regression to create
SOC estimation models. The transformation of SOC data into ordinal classes allowed for more robust
regression analysis, improving the predictive power of the models. The results showed significant
correlations between SOC and the VIs, particularly with NDVI, GNDVI, and SAVI, with correlation
coefficients above 0.9, indicating strong predictive capabilities. BSI exhibited an inverse relationship with
SOC, as expected. The distribution analyses of the indices highlighted varying vegetation health and density
across the study area, confirming the suitability of these indices for SOC estimation. The study underscored
the potential of RS and GIS technologies in providing reliable SOC estimates, promoting Precision
Agriculture (PA) and sustainable land management. It also suggests further refinement and validation of
the models using Unmanned Aerial Systems (UASs) equipped with multispectral cameras of high resolution,
to enhance spatial resolution and accuracy. Additionally, future research should explore the integration of
more environmental variables and advanced statistical techniques to improve SOC prediction models. In
conclusion, the utilization of RS and GIS for estimating SOC through VIs a promising avenue for enhancing
soil management and conservation efforts. By leveraging advanced technologies and statistical methods,
this study provides valuable insights into the complex interactions between vegetation and soil carbon
dynamics, paving the way for more effective and sustainable agricultural practices. The study is under
further validation in the same and other areas.
Keywords
Soil Organic Carbon, Remote Sensing, Vegetation Indices, GIS, Precision Agriculture, Sentinel-2, NDVI,
GNDVI, SAVI, BSI 1
⋆ Short Paper Proceedings, Volume I of the 11th International Conference on Information and Communication Technologies in
Agriculture, Food & Environment (HAICTA 2024), Karlovasi, Samos, Greece, 17-20 October 2024.
∗
Corresponding author.
†
These authors contributed equally.
gkaram@perrotis.edu.gr (G. Karampetian); kzouki@afs.edu.gr (K. Zoukidis); agerts@afs.edu.gr (A. Gertsis);
afalar@afs.edu.gr (A. Falaras); cvasil@afs.edu.gr (C. Vasilikiotis); 1960roughsea@gmail.com (A. Apostolidis);
vvergos@afs.edu.gr (E. Vergos); ptziac@afs.edu.gr (P. Tziachris)
0009-0005-4147-3079 (G. Karampetian); 0009-0005-9881-0013 (K. Zoukidis); 0009-0007-5726-7733 (A. Gertsis); 0000-
0002-6802-3669 (A. Falaras); 0000-0001-7537-9228 (C. Vasilikiotis); 0009-0007-1645-9405 (A. Apostolidis); 0000-0002-3454-
4132 (P. Tziachris)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
ceur-ws.org 1
ISSN 1613-0073
Proceedings
1. Introduction
Soil organic carbon (SOC) are essential components of soil health and agricultural productivity,
playing a pivotal role in maintaining the biological, chemical, and physical functions of soil
ecosystems [1]. In agricultural terms, Soil Organic Matter (SOM) primarily consists of decomposed
plant and animal residues that enhance soil structure, permeability, aeration, and nutrient retention,
making it one of the three foundational elements of soil along with minerals and living organisms
[2]. SOC, the measurable part of SOM, reflects the concentration of organic carbon in soil and
provides a clear indicator of soil fertility and health, especially given the challenges in analyzing
broader organic matter content [3]. High SOC levels benefit agriculture by improving soil structure,
reducing erosion risk, and fostering microbial activity, which increases the availability of essential
nutrients like nitrogen and phosphorus. Moreover, SOC plays a critical role in climate change
mitigation by trapping carbon in the soil, preventing its release as greenhouse gases like carbon
dioxide and methane [4]. However, unsustainable agricultural practices, including deep tilling and
excessive chemical use, have led to a decline in SOC levels globally, threatening soil productivity
and, in some areas, rendering soils nearly infertile [5]. The Industrial Revolution and recent decades
of intensified land use have accelerated this degradation, highlighting an urgent need for SOC
restoration to ensure sustainable agriculture [6]. Recognizing SOC loss as a global environmental
crisis, various governmental bodies, including the European Parliament, the U.S. Congress, and COP
conferences, have taken steps to address the issue. International efforts, such as the UN’s initiatives
and the EU’s Green Deal and Common Agricultural Policy (CAP), aim to monitor and mitigate SOC
depletion, promoting soil health and sustainability [7]. Remote sensing (RS) techniques, especially
within the Visible-Near Infrared–Shortwave Infrared (VNIR–SWIR, 400–2500 nm) spectrum, present
a promising solution for efficient SOC monitoring. These cost-effective, chemical-free methods
enable precise SOC estimation, aiding scientists and farmers in sustainable soil management [8].
Automated RS processes, integrated with computer software, align with Precision Agriculture (PA)
by providing rapid, data-driven insights into soil variability, enhancing resource efficiency,
productivity, and sustainability. Through this synergy with PA, RS methods offer a valuable pathway
to soil restoration and a more resilient agricultural future [9].
This study aimed to develop a custom model for estimating SOC levels using remote sensing (RS)
techniques, building on insights from existing models and research. By creating a tailored system,
the study intended to support Precision Agriculture (PA), sustainable monitoring, and soil carbon
management in a controlled, smaller-scale context. The study employed QGIS, an open-source
geographic information system software and JMP (Student Edition) for statistical analysis, among
other tools, to complete project tasks and analyze data. By choosing accessible, cost-effective
software, the study aimed to highlight a secondary objective, demonstrating that effective PA results
can be achieved through careful planning, practical knowledge, and innovative approaches rather
than reliance on high-cost technology.
2. Materials & Methods
The study, experiment, and design were conducted at the American Farm School of Thessaloniki
and its affiliated areas, including Perrotis College, located in Thessaloniki, Greece. Research activities
took place in an agricultural field on the campus's northeast side (Figure 1), and various laboratory
facilities and university equipment were used to support different phases of the study. The study
area’s coordinates are 40.5710034° latitude and 22.9964484° longitude, at an altitude of 76 meters.
2
Figure 1: The Map of the Study Area is based on Sentinel-2 Spectral Bands and GIS. Source: Personal
Collection & Creation (Karampetian Grigorios).
The study’s experimental design was developed and refined over nearly a year, originating in fall
2022 and finalizing in summer 2023, with the goal of creating a SOC estimation model linking soil
data with VIs from open-source satellite images. Once the design was established, the first step
involved collecting soil samples from the field’s top 15 cm. A small team used standardized
equipment to gather and tag 73 samples, which were dried and processed for SOC analysis in the
laboratory using a Dumatherm machine. This machine, known for its precision, enabled the analysis
of soil carbon and nitrogen content through careful milling and measurement.
Following sample collection and processing, satellite data from the Copernicus Sentinel-2
database was accessed in January 2024. The imagery was carefully selected for minimal cloud cover
and vegetative phases ideal for accurately assessing SOC-related VIs. Using QGIS software, the
satellite data allowed the calculation of indices such as NDVI, BSI, GNDVI, and SAVI, each important
for understanding soil characteristics. Soil sample coordinates were then added to QGIS, where each
sample’s VI values were recorded. Additionally, six maps were created within QGIS to visually
represent the SOC study area, each VI, and the SOC ground truth data.
For statistical analysis, data was imported into JMP v18 (jmp.com) to assess relationships between
SOC and each VI using multivariate methods and Spearman’s correlation coefficients. To further
explore these relationships, an ordinal logistic regression model was developed, providing insight
into the predictive power of each VI. Results from each analysis were systematically documented in
Word and Excel files, ensuring data integrity and supporting the study’s overall goal of promoting
accessible precision agriculture techniques.
3. Results & Discussion
In this study, the distribution analysis of Soil Organic Carbon (SOC) revealed a median of 0.85
and a mean of 0.8, indicating low SOC levels without significant outliers. Notably, the distribution
3
exhibited four distinct peaks, suggesting a complex, multi-modal structure and the potential presence
of four distinct groups within the dataset. Traditional measures of central tendency may not
adequately represent this data due to the clusters identified. The Shapiro-Wilk Test confirmed this
non-normality, with a p-value below 0.05 leading to the rejection of the null hypothesis. As a result,
the analysis was adapted to employ non-parametric tests, including multivariate correlation and
Spearman’s rho. The SOC data was also transformed into ordinal data, allowing for the use of ordinal
logistic regression, detailed later in the study.
The distribution analysis of the Normalized Difference Vegetation Index (NDVI) indicated a
median of 0.47 and a mean of 0.46, suggesting medium vegetative performance. The p-value from
the Shapiro-Wilk Test for NDVI was again below 0.05, indicating non-normality and revealing a
slight negative skewness. Similarly, the Bare Soil Index (BSI) had a median of 0.15 and a mean of
0.16, indicating low values and suggesting that much of the study area is covered by healthy
vegetation. The Shapiro-Wilk Test p-value was also below 0.05, showing a slight positive skewness.
The Soil Adjusted Vegetation Index (SAVI) exhibited a median of 0.59 and a mean of 0.57, reflecting
moderate vegetation density. The analysis revealed a slight negative skewness, confirming non-
normality. The Green Normalized Difference Vegetation Index (GNDVI) presented a median of 0.41
and a mean of 0.40, also showing medium-level vegetative health and a slightly skewed distribution.
Further analysis revealed strong associations between SOC and various vegetation indices (VIs).
Correlation coefficients indicated a strong positive relationship between SOC and NDVI (0.9095), as
well as between SOC and GNDVI (0.9143) and SAVI (0.9095). In contrast, the correlation with BSI
was -0.9091, highlighting a strong negative relationship, consistent with existing literature. To
further validate these findings, the non-parametric Spearman’s rho test was employed, indicating
strong correlations (coefficients around 0.9) between SOC and the VIs, while the coefficient for BSI
was -0.9, suggesting a negative relationship (Figure 2).
Figure 2: Nonparametric: Spearman’s ρ
Lastly, ordinal logistic regression results underscored the VIs' roles in SOC estimation. The “Effect
Likelihood Ratio Tests” indicated that NDVI, BSI, GNDVI, and SAVI significantly affect SOC
estimation, with p-values below 0.05. Specifically, the “Parameter Estimates” for NDVI, SAVI, and
GNDVI were 78, 62, and 99, respectively, indicating strong positive relationships. In contrast, BSI
had a “Parameter Estimate” of -77, signifying a strong negative relationship. The log odds imply an
extraordinarily large odds ratio when exponentiated. That happens because the carbon classes are
separated only by 0.1 units and simultaneously since Vis ranges from -1 to +1. Hence even small
variations might be exaggerated in the model. While calculating “Odds Ratios” is typically
recommended to assess the effect magnitude in ordinal logistic regression, the high “Parameter
Estimate” values suggest a strong relationship between the VIs and SOC. These findings align with
existing literature emphasizing the critical role of vegetation health in estimating SOC content.
4. Conclusions & Recommendations
In conclusion, SOC estimation can be effectively achieved through models based on vegetation
indices (VIs). This study demonstrates that VIs have a significant relationship with SOC and can
predict its fluctuations. Scientists, farmers, and stakeholders can leverage these findings to reduce
costs associated with traditional laboratory SOC analysis, which often yields imprecise results. The
4
models proposed can provide a more comprehensive tool for SOC estimation, utilizing multiple VNIR
and SWIR wavelengths to gather varied information about agricultural fields, their limitations, and
SOC levels simultaneously.
Moreover, SOC estimation through remote sensing (RS) can enable instant global assessments
without the constraints of time, chemicals, or labor. With just one ground-truth soil analysis to
correlate SOC with VIs, farmers can access vital information remotely, facilitating informed decisions
about their fields. This approach also reduces transportation costs and fosters a well-organized data
environment for agricultural businesses, assisting policymakers in evaluating fields for subsidies or
investments. Additionally, it can help identify fields at risk of degradation, allowing for appropriate
interventions to promote sustainable practices.
SOC estimation via RS and VIs could create job opportunities for young scientists and farmers,
fostering a sustainable working environment. Notably, the study highlights those essential tools, like
GIS statistical programs, do not require substantial investment, rather, accurate data can be obtained
through the application of knowledge and technology, saving time and resources.
However, the study has limitations, and further research is encouraged. Additional soil samples
from similarly sized fields could lead to more precise maps and models. Although the results were
satisfactory, a larger data pool might yield improved models with reduced errors. Future studies
should develop SOC estimation models that incorporate multiple VIs simultaneously. While this adds
complexity, it could result in more accurate models that effectively address the intricate factors
influencing agricultural parameters, especially under climate change scenarios.
Lastly, the study suggests employing UASs equipped with VNIR–SWIR cameras, as satellite
imagery offers limited pixel resolution, with each pixel from the Sentinel 2 database covering a 10m
x 10m area. In contrast, UASs can capture spectral imagery with 4K resolution, yielding better data
and minimizing errors from factors such as weeds, water, cloud cover, and wildlife, ultimately
enhancing the accuracy of VIs and RS data analyses.
Declaration on Generative AI
The author(s) have not employed any Generative AI tools.
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