**5. Conclusions**

Fine cropping intensity mapping is essential for agricultural production and the sustainable development of agriculture. This study reports our work on developing a new method to estimate cropping intensity from time series remote sensing data for a specific region of China. A novel feature space was constructed, and three unique endmembers (double-cropping, natural vegetation and water bodies) were found. A new TMA method was developed to map cropping intensity at the abundance level. The estimated results were compared with sample data and cropping intensity product data at the pixel and county levels, respectively. The experiments demonstrated that the method is a highly accurate, semi-automatic, and easily implemented approach suitable for large-scale and long time-series cropping intensity mapping.

The study provided a novel method for cropping intensity estimation from historical archived time-series coarse-resolution remote sensing data. Firstly, a new TMA method was developed to conduct spatio-temporal continuous fine-resolution cropping intensity mapping from coarse-resolution remote sensing data. The phenology information was fully mined considering the seasonal variation in vegetation, including the phenological difference between crops and other land-cover types. Secondly, a unique feature space was constructed, along with three endmembers: double-cropping, natural vegetation and water bodies. The estimated results expressed crop extent and cropping intensity at the abundance level, improving the precision of cropping intensity estimation and avoiding dividing crop patterns rigidly into double-cropping or single-cropping. Thirdly, the MDE method has the advantage of high efficiency and ease of implementation, facilitating the endmember selection and unmixing process. The research provided insights into TMA-based cropping intensity mapping.

Future work will involve extending the method to a wider area and discussing the impact of regional differentiation on the unmixing accuracy.

**Author Contributions:** Conceptualization, J.T.; Methodology, J.T.; Validation, X.Z. and Q.J.; Data curation, X.Z. and Y.L.; Writing—original draft, Y.L.; Writing—review & editing, Y.Z. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by the National Natural Science Foundation of China (Grant No. 41971371 and 32001368), the National Key Technologies Research and Development Program (Grant No. 2022YFB3903504) and the Fundamental Research Funds for the Central Universities (CCNU22JC022).

**Data Availability Statement:** No new data were created or analyzed in this study. Data sharing is not applicable to this article.

**Acknowledgments:** The authors appreciate the comments and suggestions from anonymous reviewers.

**Conflicts of Interest:** The authors declare no conflict of interest.
