@misc{Chandran2023, type = {Master Thesis}, author = {Chandran, Akhil}, title = {Soil organic carbon estimation using proximal and remote sensing at field-scale level}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:eb1-opus-4407}, school = {Hochschule f{\"u}r nachhaltige Entwicklung Eberswalde}, pages = {52}, year = {2023}, abstract = {Soil organic carbon (SOC) is an important environmental factor that impacts soil quality and function, global food security, and efforts to mitigate climate change. It is essential to accurately estimate and predict SOC levels on a large scale. While spectroscopy through proximal sensing is effective in accurately predicting SOC levels, its limitation in estimating SOC on a large spatial scale is a concern. Hence, there is a need for faster and more cost-effective techniques for quantifying SOC content. Recent research has proposed the use of remote sensing (RS) methods as a potential solution. The main objective of this research was to assess and compare the proximal soil sensing (field spectroscopy measurements) in monitoring and estimating SOC levels with data obtained from spaceborne Unmanned Aerial Vehicle (UAV) and Sentinel-2A on an agricultural field. To improve the accuracy of the RS methods (UAV and Sentinel-2A) in predicting SOC levels, nine spectral indices were created. The modelling process involved the use of different bands and wavelengths, specifically four bands for UAV and eight for Sentinel-2A. In addition, the computed spectral indices were used as independent variables to create prediction models for soil content, Random Forest (RF) model is trained with 90 soil samplings collected from the field, and validated by ten-fold cross-validation. Prior to conducting the SOC predictions, the study investigated the covariate importance. The models created from proximal sensing data had better accuracy in making predictions with the help of RF compared to the other two methods. The study demonstrated that proximal and remote sensing technologies can be exploited efficiently for SOC prediction.}, language = {en} }