Black soil is an extremely precious soil resource on Earth. Unfortunately, the black soil layer gradually becomes thinner, leaner, and harder due to long-term high-intensity utilization and soil erosion. Crop residue covering (CRC) is an important way to protect the black soil. Therefore, monitoring the crop residue coverage is one of the key indicators for assessing the implementation of conservation tillage measures. The Chinese Gaofen-6 (GF-6) satellite is the first high-resolution satellite dedicated to precision agricultural observation. Compared with previous Chinese high-resolution satellites, there are four new bands in GF-6 including ultraviolet, yellow, and red-edge bands sensitive to vegetation changes. The main goal of this study is to determine whether these new spectral bands have potential applications in estimating crop residue coverage in black soil regions. The study was conducted in Lishu County where the “Lishu Model” of conservation tillage was set up. The GF-6 WFV multispectral image acquired on November 5, 2020, was used to explore the potential of GF-6 WFV multispectral image for corn residue coverage estimation , including developing spectral indices and applying the Dimidiate Pixel Model. The research results indicate that (1) these 5 spectral indices including NDI87, NDI37, NDI47, NDI32 and NDI38, combined from green, red, near-infrared, ultraviolet, and yellow bands, were found to be more correlated with the measured residue coverage measured in the field, with the determination coefficient R2 greater than 0.5, explaining more than 50% of the corn residue coverage information. (2) There were good correlations between the estimated CRC using GF-6 WFV multi-spectral image and the results using Sentinel-2 MSI and Landsat8 OLI multi-spectral image, with R2 of 0.833 and 0.732, respectively. This demonstrates the reliability and effectiveness of Chinese GF-6 WFV multispectral imagery for crop residue coverage estimation. (3) The estimation accuracy of corn residue coverage was improved by considering the black soil background and using the Dimidiate Pixel Model. Compared with the linear regression model, the correlation coefficient R2 of the Dimidiate Pixel Modelwas improved from 0.740 to 0.769. After considering the soil texture zoning, the R2 was improved furtherly to 0.822. A new way is provided to improve the accuracy of regional crop residue cover estimation in the black soil region.