Comparison between Sentinel-2 MSI and Landsat 8 OLI data in the application of remote sensing estimation for crop residue cover

The remote sensing estimation of crop residue cover (CRC) can quickly obtain the information of straw cover returning to the field in a large range of cultivated land, which has important and practical significance for the government departments to carry out conservation tillage planning and promote straw returning to the field. Based on sentinel-2 MSI and Landsat 8 OLI data, this study calculated a variety of spectral indexes, analyzed the correlation with the field measured straw coverage data, and selected the spectral indexes with most significant correlation. Then constructed the correlation model between index and straw coverage, and compared the accuracy of the model characterized by the coefficient of determination (R²) and root mean square error (RMSE) to compare the influence of CRC inversion model caused by the difference of spectral and spatial scales between sentinel-2 MSI and Landsat 8 OLI data. The results showed that the correlation coefficients between the six spectral indices and CRC were greater than 0.4. The higher correlation was NDTI and STI obtained from sentinel-2 MSI 20 m resolution data, and the correlation coefficients were 0.878 and 0.894, respectively. The lowest correlation was NDSVI obtained from sentinel-2 MSI 10 m resolution data, and the correlation coefficient was 0.476. When using univariate linear regression method to construct the model, the spectral indices STI and NDTI constructed from sentinel-2 MSI 20 m resolution data had the highest model accuracy with R² of 0.810 and 0.800, RMSE of 6.84% and 7.01%; while R² of 0.770 and 0.771, RMSE of 7.52% and 7.50% in 30 m resampled data, both showing a downward trend with the reduction of spatial resolution. The precision of all models constructed by the spectral index obtained from sentinel-2 MSI 30 m resampled data was slightly higher than that constructed by Landsat 8 OLI data. Therefore, the spectral indexes NDTI and STI obtained from sentinel-2 MSI data would be more suitable for the estimation of straw coverage in the study area.