This study introduces a remote sensing approach to estimate soil organic carbon in arid agricultural fields, emphasizing sustainable land management. The United Arab Emirates (UAE) serves as the case study, representing a region where soil organic carbon dynamics have not been previously assessed. A total of 186 topsoil samples were collected and analyzed for soil organic carbon. Spectral data from field measurements, the DLR Earth Sensing Imaging Spectrometer (DESIS), and Sentinel-2 were integrated, marking the first application of this combination for soil organic carbon prediction. To address the challenges of arid environments, the study introduced specialized preprocessing techniques, including a novel vegetation index (UAEVI) for masking vegetation, principal component analysis for filling missing attributes, area normalization, and Savitzky-Golay smoothing to reduce noise and enhance spectral data. Soil organic carbon exhibited significant spectral correlations, with negative relationships observed in the wavelength ranges 401-416, 670-698, and 926-957 nm, and strong positive relationships in the ranges 519-560, 744-785, 937, and 1610 nm. A ridge regression model was developed and validated, achieving an Coefficient of Determination (R2) of 0.671, Root Mean Squared Error (RMSE) of 0.120 %, and Ratio of Performance to InterQuartile distance (RPIQ) of 2.271. The model demonstrated reliable performance in mapping soil organic carbon, achieving results comparable to studies in non-arid climates. Seasonal analysis highlighted the influence of meteorological parameters on soil organic carbon trends, and the model was successfully applied to monitor temporal changes in soil organic carbon within a sub-region from June 2022 to December 2023, revealing a slight increase in soil organic carbon over this period. This research emphasizes the effectiveness of integrating hyperspectral (DESIS) and multispectral (Sentinel-2) data with advanced preprocessing techniques for soil organic carbon estimation in arid environments. This study offers a scalable framework for more accurate and timely soil assessments, promising significant improvements in the management of arid soil ecosystems.