A Review Of Soil Organic Carbon (SOC) Prediction Techniques In Agricultural Lands Using Remote Sensing
Journal
IEEE EXPLORE
Date Issued
September 5, 2024
Author(s)
Neofytou, Eleni
Neophytides, Stelios
Eliades, Marinos
Papoutsa, Christiana
Hadjimitsis, Diofantos G.
Tzouvaras, Marios
DOI
10.1109/IGARSS53475.2024.10641028
Abstract
The geological, ecological, and biological ecosystems of the planet have changed because of the global climate crisis, and this poses a serious threat to humanity as well as the conservation of agricultural productivity and food security. The European Commission outlined the continent's objective to become climate neutral by 2050 with zero net Greenhouse Gas (GHG) emissions. Soil organic carbon (SOC) is closely related to soil quality and has a significant impact on how soil and plants interact. SOC monitoring gives a unique role in agricultural sustainability thus precise prediction and monitoring of SOC is essential. Remote Sensing (RS) evolution, big data accessibility and Deep Learning (DL) architectures present enormous potential for extensive SOC
monitoring. Several RS applications (e.g., Sentinels, MODIS, Landsat etc.) along with machine learning and DL
methodologies (e.g., RF, ANN, CNN etc.) used in literature for SOC prediction. The current review paper emphasizes on the latest RS approaches used for SOC monitoring.
monitoring. Several RS applications (e.g., Sentinels, MODIS, Landsat etc.) along with machine learning and DL
methodologies (e.g., RF, ANN, CNN etc.) used in literature for SOC prediction. The current review paper emphasizes on the latest RS approaches used for SOC monitoring.
Funding(s)
The authors acknowledge the “EXCELSIOR”: ERATOSTHENES: Excellence Research Centre for Earth Surveillance and Space-Based Monitoring of the Environment H2020 Widespread Teaming project (www.excelsior2020.eu). The “EXCELSIOR” project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement No 857510, from the Government of the Republic of Cyprus through the Directorate General for the European Programmes, Coordination and Development and the Cyprus University of Technology. The authors acknowledge the “AI OBSERVER” project funded by the European Union’s Horizon Europe Framework Programme HORIZON WIDERA-2021-ACCESS-03 (Twinning) under the Grant Agreement No 101079468.
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