Eurasian Journal of Soil Science

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

SOC stock, Random Forest, Ridge Regression, Carbon sequestration, enclosure, Sentinel-2 time-series data.

References

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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: SOC stock, Random Forest, Ridge Regression, Carbon sequestration, enclosure, Sentinel-2 time-series data.

References

Abdellatif, M.A., Hassan, F.O., Rashed, H.S.A., El Baroudy, A.A., Mohamed, E.S., Kucher, D.E., Abd-Elmabod, S.K., Shokr, M.S., Abuzaid, A.S., 2023. Assessing soil organic carbon pool for potential climate-change mitigation in agricultural soils: A case study in Fayoum Depression, Egypt. Land 12(9): 1755. 

Alsaleh, A.R.S., Alcibahy, M., Gafoor, F.A., Hashemi, H.A., Athamneh, B., Al Hammadi, A.A., Seneviratne, L., Al Shehhi, M.R., 2025. Estimation of soil organic carbon in arid agricultural fields based on hyperspectral satellite images. Geoderma 453: 117151. 

Behera, M.D., Barnwal, S., Paramanik, S., Das, P., Bhattyacharya, B.K., Jagadish, B., Roy, P.S., Ghosh, S.M., Behera, S.K., 2021. Species-level classification and mapping of a mangrove forest using random forest—utilisation of aviris-ng and sentinel data. Remote Sensing 13(11): 2027. 

Berhe, S.M., 2018. Final country report of the Land Degradation Neutrality Target Setting Programme in Eritrea. The State of Eritrea; UNCCD National Focal Point. Bonn, Germany; LDN National Working Group, Ministry of Agriculture: Asmara, Eritrea.

Castaldi, F., Chabrillat, S., Don, A., van Wesemael, B., 2019. Soil organic carbon mapping using LUCAS topsoil database and Sentinel-2 data: An approach to reduce soil moisture and crop residue effects. Remote Sensing 11(18): 2121. 

Castaldi, F., Koparan, M.H., Wetterlind, J., Žydelis, R., Vinci, I., Savaş, A.Ö., Kıvrak, C., Tunçay, T., Volungevičius, J., Obber, S., Ragazzi, F., Malo, D., Vaudour, E., 2023. Assessing the capability of Sentinel-2 time-series to estimate soil organic carbon and clay content at local scale in croplands. ISPRS Journal of Photogrammetry and Remote Sensing 199: 40-60. 

Chen, Q., Wang, Y., Zhu, X., 2024. Soil organic carbon estimation using remote sensing data-driven machine learning. PeerJ 12(8): e17836. 

Chen, W., Chang, H., Liu, R., 2020. Fractal features of soil particle size distributions and their implications for indicating enclosure management in a semiarid grassland ecosystem. Polish Journal of Ecology 68(2): 132-144. 

Cohen, J., 1988. Statistical Power Analysis for the Behavioral Sciences. 2nd Edition. Routledge, New York, USA. 567p.

Cui, Z., Chen, S., Hu, B., Wang, N., Feng, C., Peng, J., 2025. Mapping soil organic carbon by ıntegrating time-series sentinel-2 data, environmental covariates and multiple ensemble models. Sensors 25(7): 2184. 

Denboba, M. A., 2022. Grazing management and carbon sequestration in the dry lowland rangelands of Southern Ethiopia. Sustainable Environment 8(1): 2046959. 

Derakhshan, F., Abdi, N., Toranjzar, H., Ahmadi, A., 2021. Simulating soil organic carbon dynamics under climate change scenarios in an arid ecosystem. Polish Journal of Environmental Studies 30(3): 2063–2072. 

Deshpande, M.V., Pillai, D., Krishna, V.V., Jain, M., 2024. Detecting and quantifying zero tillage technology adoption in Indian smallholder systems using Sentinel-2 multi-spectral imagery. International Journal of Applied Earth Observation and Geoinformation 128: 103779. 

Don, A., Schumacher, J., Freibauer, A., 2011. Impact of tropical land-use change on soil organic carbon stocks–a meta-analysis. Global Change Biology 17(4): 1658–1670. 

Dunn, O.J., 1961. Multiple comparisons among means. Journal of the American Statistical Association 56(293): 52-64.

Dvorakova, K., Heiden, U., Pepers, K., Staats, G., van Os, G., van Wesemael, B., 2023. Improving soil organic carbon predictions from a Sentinel–2 soil composite by assessing surface conditions and uncertainties. Geoderma 429: 116128. 

FAO, 2017. Soil organic carbon: The hidden potential. Food and Agriculture Organization of the United Nations. Available at [Access date: 03.09.2025]: https://openknowledge.fao.org/server/api/core/bitstreams/b382a255-5bd5-4656-a8cd-e30fff1a8bfe/content

FAO, 2019. Standard operating procedure for soil organic carbon Walkley-Black method (titration and colorimetric method). Global Soil Laboratory Network GLOSOLAN. Available at [Access date: 03.09.2025]: https://www.fao.org/3/ca7471en/ca7471en.pdf 

FAO, 2021. National Agricultural Innovation System Assessment in Eritrea– Consolidated report. Rome, Italy. Available at [Access date: 03.09.2025]: https://doi.org/10.4060/cb7296en

Farzamian, M., Castanheira, N., Gonçalves, M.C., Freitas, P., Saberioon, M., Ramos, T.B., Antunes, J., Paz, A.M., 2025. Predicting soil organic carbon from Sentinel-2 imagery and regional calibration approach in salt-affected agricultural lands: Feasibility and influence of soil properties. Remote Sensing 17(16): 2877.

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