Estimating carbon sequestration potential of dryland enclosures: A comparative assessment assisted by Sentinel 2 time-series and machine learning framework

Tumuzghi Tesfay; Tesfalem W. Ghebretnsae; Elsayed S. Mohamed

doi:10.18393/ejss.1881636

Estimating carbon sequestration potential of dryland enclosures: A comparative assessment assisted by Sentinel 2 time-series and machine learning framework

Abstract

In Eritrea, in response to widespread land degradation, rehabilitation works are underway. However, the impacts of such works on soil carbon remain unknown. This study assessed the impacts of enclosures by comparing four land uses: permanent enclosure, oxen enclosure, communal grazing, and rainfed farming. Seasonal Sentinel-2 time-series data were extracted using Google Earth Engine and soil organic carbon (SOC) concentration and stock (SOCs) predictive models were developed using soil, topographic, and Sentinel 2 data using machine learning. We compared Random Forest versus Ridge Regression models across four seasons with correlation-based versus Boruta algorithm-based selected features. Kruskal-Wallis H and Dunn post-hoc tests showed that conservation enclosures significantly enhanced soil carbon compared to grazing and rainfed cropping lands. The highest median SOC (1.28%) and SOCs (37.66 Mg ha-1, up to 30 cm soil layer) were found in the permanent enclosure followed by the oxen enclosure, while rainfed farming had the lowest values (0.45% and 14.85 Mg ha-1). These improvements correspond to potential carbon sequestration gains of up to 153.6% when converting degraded land to permanent enclosure. Modelling revealed that SOC was predicted with excellent accuracy (R2 = 0.85, RPD = 2.57) using Ridge Regression and with very good accuracy (R2 = 0.80, RPD = 2.22) with Random Forest with Boruta selected winter season features. For SOCs, the best models of both models achieved similar fair accuracy (R2 = 0.60, RPD = 1.58). Key predictors for SOC were bulk density, Sentinel 2 bands B5 and B8A, while for SOCs B5, Soil Organic Carbon Index (SOCI), B11, and soil pH. Winter (Jan-Mar) was the optimal window season and Boruta-selected features gave the best results for SOC and SOCs prediction. The study demonstrates that enclosure systems are effective for soil carbon restoration in dryland landscapes. This framework highlights the potential for cost effective satellite based monitoring of restoration outcomes. These findings provide evidence based support for scaling up enclosure based restoration in Eritrea and similar dryland regions, contributing to both land degradation neutrality and climate change mitigation goals.

Keywords

Thanks

The authors acknowledge the RUDN University for its unreserved supports, the Ministry of Agriculture, Eritrea; National Agricultural Research Institute; Hamelmalo Agricultural College; and Adi Teklezan Subzone Agricultural Office and its staff for their supports from soil sampling to analysis.

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Details

Primary Language

English

Subjects

Soil Sciences and Plant Nutrition (Other)

Journal Section

Research Article

Authors

Tumuzghi Tesfay ^* This is me
0000-0002-0771-5522
Russian Federation

Tesfalem W. Ghebretnsae This is me
0009-0000-6076-9098
Russian Federation

Elsayed S. Mohamed This is me
0000-0001-5703-4621
Russian Federation

Publication Date

April 1, 2026

Submission Date

October 31, 2025

Acceptance Date

January 23, 2026

Published in Issue

Year 2026 Volume: 15 Number: 2

DOI

https://doi.org/10.18393/ejss.1881636

IZ

https://izlik.org/JA28ED49MG

Cite

RIS / Bibtex

APA

Tesfay, T., Ghebretnsae, T. W., & Mohamed, E. S. (2026). Estimating carbon sequestration potential of dryland enclosures: A comparative assessment assisted by Sentinel 2 time-series and machine learning framework. Eurasian Journal of Soil Science, 15(2), 209-227. https://doi.org/10.18393/ejss.1881636

AMA

1.Tesfay T, Ghebretnsae TW, Mohamed ES. Estimating carbon sequestration potential of dryland enclosures: A comparative assessment assisted by Sentinel 2 time-series and machine learning framework. EJSS. 2026;15(2):209-227. doi:10.18393/ejss.1881636

Chicago

Tesfay, Tumuzghi, Tesfalem W. Ghebretnsae, and Elsayed S. Mohamed. 2026. “Estimating Carbon Sequestration Potential of Dryland Enclosures: A Comparative Assessment Assisted by Sentinel 2 Time-Series and Machine Learning Framework”. Eurasian Journal of Soil Science 15 (2): 209-27. https://doi.org/10.18393/ejss.1881636.

EndNote

Tesfay T, Ghebretnsae TW, Mohamed ES (April 1, 2026) Estimating carbon sequestration potential of dryland enclosures: A comparative assessment assisted by Sentinel 2 time-series and machine learning framework. Eurasian Journal of Soil Science 15 2 209–227.

IEEE

[1]T. Tesfay, T. W. Ghebretnsae, and E. S. Mohamed, “Estimating carbon sequestration potential of dryland enclosures: A comparative assessment assisted by Sentinel 2 time-series and machine learning framework”, EJSS, vol. 15, no. 2, pp. 209–227, Apr. 2026, doi: 10.18393/ejss.1881636.

ISNAD

Tesfay, Tumuzghi - Ghebretnsae, Tesfalem W. - Mohamed, Elsayed S. “Estimating Carbon Sequestration Potential of Dryland Enclosures: A Comparative Assessment Assisted by Sentinel 2 Time-Series and Machine Learning Framework”. Eurasian Journal of Soil Science 15/2 (April 1, 2026): 209-227. https://doi.org/10.18393/ejss.1881636.

JAMA

1.Tesfay T, Ghebretnsae TW, Mohamed ES. Estimating carbon sequestration potential of dryland enclosures: A comparative assessment assisted by Sentinel 2 time-series and machine learning framework. EJSS. 2026;15:209–227.

MLA

Tesfay, Tumuzghi, et al. “Estimating Carbon Sequestration Potential of Dryland Enclosures: A Comparative Assessment Assisted by Sentinel 2 Time-Series and Machine Learning Framework”. Eurasian Journal of Soil Science, vol. 15, no. 2, Apr. 2026, pp. 209-27, doi:10.18393/ejss.1881636.

Vancouver

1.Tumuzghi Tesfay, Tesfalem W. Ghebretnsae, Elsayed S. Mohamed. Estimating carbon sequestration potential of dryland enclosures: A comparative assessment assisted by Sentinel 2 time-series and machine learning framework. EJSS. 2026 Apr. 1;15(2):209-27. doi:10.18393/ejss.1881636