## 5 Conclusions  
The study area was located in Huize County, Qujing City, Yunnan Province, China. We utilized the GF and Landsat satellite images as data and proposed the F-Pix2 Pix semantic segmentation method employing the concept of deep learning image translation. The identification of forest/non-forest and needleleaf/broadleaf forest provided a foundation for the accurate estimation of regional carbon sinks. The main conclusions were as follows:  
1. The F-Pix2 Pix proposed in this paper had the best results, with higher accuracy than the existing products, 21.48% in non-forest/forest and 29.44% in needleleaf/broadleaf forest, for MIoU improvement. The distinction between forests/non-forests is higher than that between needleleaf and broadleaf forests. In future applications, the former can provide support for the latter to improve identification accuracy.
2. Applying transfer learning domain adaptation to semantic segmentation showed significant benefits, and this approach yielded better results for needleleaf/broadleaf forests in comparison to forest/non-forest. Furthermore, this method utilized satellite images of different resolutions to solve the class imbalance problem.
3. We implemented semantic segmentation by image-to-image translation, and the F-Pix2 Pix not only effectively solved the class imbalance but also demonstrated good performance in different satellite images. It can be used for multi-source images with strong generalization, which provides the possibility of long-term monitoring. The identification of needleleaf and broadleaf forests combined with the actual geographical characteristics of the forest was the basic work for the accurate estimation of regional carbon sources/sinks.  
[PERSON] supervised and organized the project. [PERSON] developed the code and wrote the manuscript. [PERSON] provided the data. [PERSON], [PERSON], [PERSON] and [PERSON] revised the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China [2021 YFE0117300].  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **Product** & **Kappa (\%)** & **OA (\%)** & **PA (\%)** & **UA (\%)** \\ \hline \multirow{4}{*}{Forest/Non-Forest} & Ours & **77.43** & **87.84** & **84.80** & **80.95** \\  & FROM-GLC-seg & 52.51 & 84.56 & 77.61 & 75.12 \\  & Forest 2010 & 28.35 & 82.16 & 82.93 & 60.39 \\  & ESA GlobCover & 35.80 & 80.67 & 71.33 & 65.85 \\ \hline \multirow{4}{*}{Needle/Broadleaf Forest} & Ours & **70.55** & **83.27** & **70.03** & **69.63** \\  & FROM-GLC-seg & 49.10 & 83.02 & 55.61 & 54.29 \\ \cline{1-1}  & Forest 2010 & 28.03 & 81.99 & 57.79 & 40.76 \\ \cline{1-1}  & ESA GlobCover & 32.68 & 79.44 & 47.01 & 43.66 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of product accuracy. Notes: ours refers to the F-Pix2 Pix model. The comparative area is the study area, namely Huize County, Qujing City, Yunnan Province, China. The remote sensing image is Landsat TM data, and the imaging time is 2010. Bold is the best, and underline is the second. Our data are generated from the trained model of Landsat OLI images in 2020.