This paper presents a LUT-based method to generate Surface downward shortwave radiation (DSR) and photosynthetically active radiation (PAR) products with ABI and AHI measurements. The LUT-based method used in this paper, was initially developed by Liang (2006), and then extended by Wang et al. (2020) for MODIS DSR/PAR product (MCD18). Actually, this LUT-based method was widely used many times, such as Zhang et al., (2014, 2019), and many other authors. Thus, highlights of this paper are not significant. Too much repeatability work. In addition, direct and diffuse components of DSR or PAR are not calculated by this study, only having global DSR or PAR. I suggest to refine your highlights and avoid to do repeat work.

Liang, S., Cheng, J., Jia, K., Jiang, B., Liu, Q., Xiao, Z., Yao, Y., Yuan, W., Zhang, X., Zhao, X., Zhou, J., 2021. The Global Land Surface Satellite (GLASS) Product Suite. Bulletin of the American Meteorological Society 102, E323–E337.

Wang, D., Liang, S., Zhang, Y., Gao, X., Brown, M.G.L., Jia, A., 2020. A New Set of MODIS Land Products (MCD18): Downward Shortwave Radiation and Photosynthetically Active Radiation. Remote Sensing 12.

Zhang, X., Liang, S., Zhou, G., Wu, H., Zhao, X., 2014. Generating Global LAnd Surface Satellite incident shortwave radiation and photosynthetically active radiation products from multiple satellite data. Remote Sensing of Environment 152, 318–332.

Zhang, X., Zhao, X., Li, W., Liang, S., Wang, D., Liu, Q., Yao, Y., Jia, K., He, T., Jiang, B., Wei, Y., Ma, H., 2019. An Operational Approach for Generating the Global Land Surface Downward Shortwave Radiation Product From MODIS Data. IEEE Trans. Geosci. Remote Sensing 57, 4636–4650.

New-generation geostationary satellites have provided us with more chances to investigate diurnal to seasonal vegetation dynamics. Recently, more and more studies have widely used high temporal resolution geostationary satellite datasets and solar radiation data is one of the most required products in many topics. Although this study has no strong novelty for generating DSR and PAR Although this study has no strong novelty for generating DSR and PAR, providing a high temporal resolution product itself has a strong advantage.

• Major comments

The author highlight "GeoNEX ...". Considering the unique geostationary satellite network, the strong advantage should be the larger spatial scale integrating the full disk of each geostationary satellite. For example, even if there is no significant improvement in the performance, providing global hourly DSR/PAR itself would have great importance. In this context, this study needs to include Meteosat which covers Europe and Africa. As the authors know, several studies already reported continental-scale hourly solar radiation products using a single geostationary satellite, not GeoNEX data. If the focus of this study is limited to generating continent-scale geostationary satellite-based radiation products, further novelty of the study is required.  

• Minor comments

Line 78-80: Too vague expression.

Line 82-83: To highlight this point, the author should consider global coverage.

Line 82-85: Just using one more geostationary satellite cannot be the novelty of this topic.

Line 85-86: Out of context.

Line 87-89: How LUT method address the research gap which the author mentioned in the above paragraph?

Table1: The author highlights the higher spatial resolution (1km) of this study, but input TPW from MERRA2 has over 50km spatial resolution. Is it acceptable?

Section 3.1.3 is well examined the uncertainty of large VZA, which is critical in geostationary satellites. Section 4.1 also well highlighted the advantage of the geostationary satellite-based product.

This study presented a new DSR and PAR dataset derived from GOES-R and Himawari at high spatial (1 km) and temporal (hourly) resolutions. The dataset achieved <20% and <10% relative errors for hourly and daily DSR, respectively, which were claimed to be higher than existing datasets. The manuscripts demonstrated the benefits of high spatial and temporal resolutions, and therefore partly justified the importance of developping this new dataset. In particular, Figure 9 is interesting, revealing that high resolution is critical for hourly radiation. However, I'm not convinced by this study for the following reasons:

1. The innovation is questionable. There are already many radiation datasets derived from geostationary satellite data, either from GOES-R or Himawari. Some of them are also high resolution. The manuscrit needs to clearly address the questions: why do we need a new one? What's the advantage of this study, e.g., distinct data sources or distinct algorithm? 

2. As a data paper, the Method part is too short. A flow chart is needed, including graphyical links between Eq. (1), Eq. (2), inputs and outputs. 

3. Terrain effect was not considered. Considering many mountain areas are involved, this could be a big limitation.

4. As a data product, no detailed QC and quantitative uncertainty was provided. This is also a big limitation.

5. The sensitivity to inputs/parameters could provide deeper insights for potential users.  

6. There was no map of the DSR and PAR products in the manuscript.

7. Temporal coverage of the dataset was not mentioned. Is it operational and real time?

8. Why does this dataset has higher accuracy than other geostationary-based dataset? If high resolution only matters for hourly data, why does this dataset has much lower errors than others at daily scale?