Precisely extracting the essential parameters of karst rocky desertification (KRD) grades is crucial for enhancing the monitoring of degradation status and recognition accuracy. The representation of vegetation cover is a critical parameter, and employing the vegetation index to quantify it is a challenging topic in current research. The Gaofen-6 satellite (GF-6) serves as a novel data source with red edge bands; however, research on the ability of GF-6 to construct crucial red edge parameters to optimize mapping KRD grades is inadequate. We utilized the GF-6 WFV red edge bands to rebuild the vegetation index, which was computed to estimate the crucial parameter, FVC, and subsequently integrated with other parameters to produce the KRD grade map. The results and accuracy of various schemes were evaluated to investigate the capability of GF-6 in enhancing the discrimination accuracy of KRD grades by optimizing key parameters utilizing its red edge band, as well as the comparison between GF-6 WFV and Landsat-8 in classifying KRD grades. The results are as follows: (1) The method of the mean of GF-6 WFV red edge bands 1 and 2 replacing the red band optimized the FVC and KRD grade inversion accuracy has the best effect. Among them, NDVI used this method to obtain the highest accuracy, which FVC and KRD grades overall accuracy (OA) was increased by 5.65 % and 1.61 %, respectively. And it is mainly reflected in the improvement of the accuracy of the potential and light KRD. (2) Compared with the results of Landsat-8, the OA of the FVC and KRD utilizing GF-6 WFV red edge bands improved by 0.40-50.00 % and 6.85-35.08 %, respectively. The rationale was that the synergistic impact of the GF-6 WFV red edge bands' sensitivity to vegetation and the higher resolution of GF-6 WFV. In conclusion, GF-6 WFV serves as an important data source for calculating KRD grade and can be integrated with Landsat-8 to optimize mapping KRD grades. This study provides a reference for the improvement of multispectral remote sensing monitoring methods for KRD grades determination and the combined application of Landsat and GF series satellites. (c) 2025 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.