Comparing sentinel-2 and Landsat 8 spectral reflectance indices for predicting soil organic carbon

Abstract

Soil organic carbon (SOC) significantly improves soil properties, but traditional measurement methods are time-consuming and costly, emphasizing the need for faster, cost-effective alternatives for sustainable soil management. This study aimed to assess the potential of using the standardized spectral reflectance index (ZPC), derived from satellite images, to estimate SOC content. A total of 410 soil samples were collected from agricultural lands in Xuchang County, and the SOC content was measured. To reduce the volume and complexity of the calculations, Principal Component Analysis (PCA) was applied to the band data from both Landsat 8 and Sentinel-2 satellite images. The principal component (PC) with the highest correlation to SOC content was then standardized and considered as the ZPC1 index. Subsequently, a regression relationship between ZPC1 and SOC content was established. The findings revealed a strong correlation between the different bands of the Landsat 8 and Sentinel-2 satellite imagery. Additionally, the PC1 of both satellites, Landsat 8 (r = 0.65) and Sentinel-2 (r = 0.82), demonstrated a high correlation with SOC content and was therefore standardized. A robust and significant regression relationship was established between ZPC1 and SOC content. When comparing the accuracy of SOC content estimation using ZPC1 from the two satellites, Sentinel-2 outperformed Landsat 8, showing higher accuracy (R² = 0.65, RMSE = 0.28, and MBE = 0.08) compared to Landsat 8 (R² = 0.52, RMSE = 0.32, and MBE = 0.10). Overall, the results indicate that the ZPC1 index provides a rapid and accurate method for SOC content monitoring, significantly reducing the complexity of traditional methods. Therefore, it is recommended that future study further validate this method to ensure its accuracy and efficiency for rapid SOC content assessment.