Oil palm is an important oilseed that has received widespread attention because of its involvement in issues such as tropical deforestation. In this paper, based on their 2021 global oil palm mapping, the algorithm is optimized to achieve a higher accuracy of the global 10m oil palm mapping. Meanwhile, they further track the planting year of oil palms from 1990 to 2021 and demonstrated to achieve the fulfilling performance. In summary, this manuscript is well-written and presents a high-quality global oil palm dataset, which meets the ESSD standard for high data quality. Below are some comments to this manuscript:

The methodology should be strengthen appropriately, some parts are difficult to follow. For example:

• In section 2.2.1, how to correct the effect of local incident angle in Sentinel-1 imagery? The annual composites come from the average of the ascending and descending scenes, why not just ignore ascending and descending orbits in the median compositing?
• In section 2.2.2, I suggest add some descriptions about the “closed-canopy industrial oil palm” and “closed-canopy smallholder oil palm”, what are the significant differences between these two oil palms?
• In section 2.2.5, authors proposed to use the time-series NDWI to identify the planting years, and gave an example. However, I have two concerns as: 1) the reasonableness of your choice of NDWI, it must be explained. Does it also apply in case of conversion of cropland to oil palm? 2) You've labeled vegetation, clouds, and bare land with random forest classification over each Landsat observations, but how do you account for the effects of classification error? If an area has gone through "deforestation - bare land - grassland/cropland - oil palm", how can the year of planting be determined?

In section 2.2.4, the rigor of the validation sample is very clearly stated, but the number of validation samples for oil palm is too sparse (only 973 points). Thus, I suggest increasing the number of oil palm sample points.

I suggest that the authors give a map of the global spatial distribution of oil palm in 2021 in Section 3.1.

The new developed oil palm has been validated to achieve better performance, thus, I suggest that the authors can give some regional, visual comparison results.

If possible, I would also suggest a global map of the age distribution of oil palms is presented in the manuscript, it would be very interesting!

I agree with the comment in the Community Comment (CC1) as: the global oil palm area discrepancy between your dataset of 23.98 Mha and the FAO estimate about 30 Mha should be explained in the manuscript

Oil palm has received substantial mapping efforts, but up-to-date and accurate maps detailing both the extent and age of oil palm plantations are essential for monitoring impacts and informing concurrent debates. This study achieved global 10-meter resolution mapping of oil palm and confirmed plantation years from 1990 to 2021. Overall, the manuscript is well-written, but there are still areas that could be improved:

1. In line 22, "We found oil palm plantations covering a total mapped area of 23.98 Mha," while in line 38, "30 Mha in 2022" is mentioned. It is recommended to explain the differences between these two data points in the subsequent discussions.
2. It is recommended to add descriptions for "closed-canopy industrial oil palm" and "closed-canopy smallholder oil palm". Additionally, using comparative charts to illustrate the differences between these land cover categories both qualitatively and quantitatively is advised, enabling readers to better understand the distinctions between these categories."
3. In line 92, it is suggested to briefly explain the data organization format, whether temporal information was used, whether monthly synthesis was employed, and if data stacking akin to multi-channel image data was implemented when inputting into DeepLabv3+.
4. Due to regional distribution differences, it is recommended to increase the number of validation samples to better demonstrate the effectiveness of the method.
5. In line 119, "manually digitized false positives, and subsequently reclassified these commission errors as class ‘other’", was it first masking and then checking all categories, followed by manually correcting misclassified information?
6. In the figure on line 179, the author uses NDWI to determine the planting year and provides an example. It is recommended to explain the rationale for using NDWI.
7. In line 228, "only represent the regions where oil palm was found worldwide in this study", did the study detect globally and then filter out these current regions, or was it based on previous research?
8. In Section 3.1, it is suggested to first present the global oil palm distribution map obtained in this study.
9. In Section 3.3, consider increasing the number of sample points to assess validity further.

It is recommended to add comparative results with previous studies in Section 3.3 to visually demonstrate the effectiveness of the current research.