Improving maize residue cover estimation with the combined use of optical and SAR remote sensing images

Abstract

Conservation tillage is an important conservation measure for arable land in modern agricultural production, which plays an essential role in protecting black soil and improving the quality of arable land. The estimation of maize residue cover (MRC) can be used to obtain the spatial distribution characteristics of conservation tillage, which is essential for government departments to promote conservation tillage technology and understand the implementation of it. In this paper the southern part of the Songnen Plain was used as the study area, and Sentinel-2 MSI images and Sentinel-1 SAR images were used as data sources to correlate the spectral indices and radar backscatter coefficients with the field sampling data in the study area. The MRC estimation model of the study area was constructed using the Random Forest (RF) model, the Multiple Linear Stepwise Regression (MLSR) model, and Back Propagation Neural Network (BPNN) model, respectively. The results of the study showed that the correlation coefficients of normalized difference tillage index (NDTI), simple tillage index (STI), normalized difference index (NDI5), NDI7, shortwave infrared normalized difference residue index (SINDRI), normalized difference senescent vegetation index (NDSVI), normalized difference residue index 2 (NDRI2), NDRI3, NDRI4, NDRI5, NDRI6, NDRI7, NDRI8, NDRI9, and MRC in the study area were greater than 0.4, and the correlation coefficients were higher for NDTI and STI, which reached 0.861 and 0.860, respectively. The correlation coefficient between VV and MRC was 0.56 and between VH and MRC was 0.594. We used MLSR, RF, and BPNN methods in combination with Sentinel-2 MSI images and Sentinel-1 SAR images for MRC estimation. The synergistic use of Sentinel-2 MSI images and Sentinel-1 SAR images helped to improve the accuracy of the MRC estimation models and the correlation coefficient R2 of all three models to greater than 0.8. Based on the statistical analysis of remote sensing estimation results, we found that the average value of the MRC of the maize growing areas in Changchun, Siping, and eastern Songyuan in November 2020 was 66%, and 2% of farmland in the study area had a MRC of less than 30%.