## 4 Conclusions  
In this study, a method is proposed to improve the accuracy of land cover classification products by coupling Geo-Eco zoning and Markov chain geostatistical simulation. This method can be used to evaluate the spatial accuracy variation of land cover classification products and improve the drawbacks of the overall accuracy evaluation of the general confusion matrix method. In this study, the verification points collected from the network are reused. The Geo-Eco zoning attribute data and pre-classified images are set as auxiliary data for co-simulation. The simulation results are more reliable if the simulation area is limited in the Geo-eco zone. The local accuracy of the pre-classified image can be quantified and improved. Therefore, the coupling of Geo-Eco zoning and Markov chain geostatistical simulation can enhance the accuracy of Globeland30 data by more than 10%.  
However, some deficiencies should be improved in the future.  
1. In this study, the image size that can be processed is limited because of the high operation cost of the algorithm. The experimental area only contains one Geo-eco zone. In future studies, the algorithm should be optimized, and a GPU-parallel acceleration method should be used to increase the amount of data that can be processed and make the algorithm more practical.
2. Existing data are limited, thus only the attribute data related to elevation are tested, and the impact of other types of auxiliary data are yet to be described. The role of Geo-Eco zoning in geostatistical simulation should be further explored. For example, geoscience knowledge on Geo-Eco zoning can be applied and combined with verification points to generate reasonable transiograms and further reduce the number of sample pixels required by the algorithm.
3. The case study only tested in one site with a high density of the sample points. The method needs to be verified with a wider scope and more example sites in the future.  
Conceptualization, L.Z.; Data curation, J.L. and Y.L.; Funding acquisition, L.Z.; Investigation, J.L.; Methodology, L.Z. and J.L.; Resources, T.J.; Software, Y.L.; Validation, T.J. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Simulation Method** & **Number of Matching** & **Proportion** \\ \hline Pre-classified image (GlobeLand30-2015) & 5962 & 75.34\% \\ Simulation results of MCRF & 6451 & 81.52\% \\ Simulation results (with auxiliary data) & 6843 & 86.48\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Accuracy assessment.  
**Funding:** This work was supported by The National Key Research and Development Program of China under Grant (NO.2016 YFB0501404) and state key laboratory of remote sensing science open fund projects (OFSLRSS201927).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Acknowledgments:** The authors gratefully acknowledge the support of NGCC (National Geomatics Center of China).  
**Conflicts of Interest:** The authors declare no conflict of interest.