[Overview]

This study attempts to create a gap-filled (seamless) surface reflectance datasets from the corresponding MODIS products. The work is important, the methodology is largely OK (but with caveats indicated below), and the writing of the paper is clear. Overall I think the paper and the data products have the potential to be published. My main concern with the current paper is that it lacks a quantitative description of the uncertainties associated with the gap-filling method and thus the final results. At least these uncertainties should be discussed in the paper. 

[Major]

1. Line 167-170 (Section 3.1, Generation of the a priori database). Predicting missing values at a location from sample values at other locations is a typical problem of spatial analysis or geostatistics. There are rich methodologies to address the problem (e.g., Kriging, Sequential Gaussian Simulation, etc.) but you appear to use a very simple home-made approach. My concern with your approach is that it lacks a way to quantify the uncertainties associated with the predicted values or, more rigorously, the random spatial field. Take the example of the grid cell in a tropical desert described here, there are simply no adjacent pixels to provide information of snow, crop, or forest. Therefore the information you fill in is purely random with large uncertainties (compared to other cases). And based on my understanding, this random information (time series) might be used in the next step (Landcover-based BRDF correction), which can be problematic! Even though this could be an extreme example, the point is that your methodology is too simple and lacks necessary mathematical/statistical rigor. There are numerous curves that can make the data fitting “looks” good (e.g., artists can do it) but, for science, we must make our choice based on solid quantitative reasoning.
2. For the same Section 3.1, Land cover type is not the only local factor that regulates BRDF. Topographic characteristics of the terrain (e.g., slope structure) can have significant influence on BRDF as well. It should be at least discussed.
3. Line 175-179. I don’t fully understand why you’d use all the 17 curves to fit the observed time series of surface reflectance. Why not just pick the curve of the same land cover type? Or broad categories (e.g., forests or vegetation)?

[Minor]

1. Line 55-57. “Urgent” may not be the best word to use here. After all, the MODIS instruments are > 20 years old already.
2. Line 146. “BRDR” should be “BRDF”.
3. Line 157-158. The last sentence “They are roughly independent … ” is confusing and can be removed.
4. Line 185. The Greek letter “phi” on the left-hand-side of the equation should be “phi 0”.
5. Line 305 and Fig. 5. The gray thin lines (spline-filled) show spikes at some of the raw observations. How is it possible, for the spline base functions are smooth low-frequency functions?
6. Figs. 5-16. What is the relationship between the SDC500 curves and the original MODIS observations? It doesn’t seem to be the time-smoothed mean at locations. Again, is there a way to quantify the uncertainties associated with the smoothed curves versus the raw observations?

Seamless data of surface reflectance is required by various applications. This paper presented a study to generate 500m daily surface reflectance from MODIS data. The algorithm and assessment results were clearly documented. For further enhancement and clarity, I suggest considering the following recommendations:

1. The defined reference geometry, characterized by a nadir view and a solar angle at 10:30 am, may not universally meet all application demands. Offering BRDF kernel parameters would be advantageous, facilitating users to adapt the data to their desired geometry.
2. Many efforts were devoted to filling gaps in satellite data. The authors proposed a new approach combining math and ecosystem curve fitting. It would be beneficial if the rationale behind selecting this method were expanded upon. How does this newly introduced approach measure up against its pre-existing counterparts?
3. An established seamless dataset exists: the MODIS nadir view BRDF-adjusted reflectance (NBAR) (Ju et al. 2010). Could the authors elucidate on the distinct advantages their new dataset presents over the gap-filled NBAR data?
4. The construction of BRDF prior data employs a 100km x 100km grid. How does the values change across the grid boundary? Spatial filtering is suggested to smooth the boundary.
5. The mixed land cover types might inadvertently introduce errors during LC-based BRDF normalization. How have such potential inconsistencies been addressed in this study?
6. A salient feature of the study is the distinct processing of snow-laden and snow-devoid seasons. This raises questions about other potential abrupt shifts, such as those induced by land cover alterations. What magnitude of uncertainty is attributed to such scenarios?
7. The data sets, in their current form, seem to lack integral quality control metrics, which would immensely aid end users. . For example, the abovemetioned cases of abrupt changes (snow, land cover change) can be labelled for user convenience.

Reference

Junchang Ju, David P. Roy, Yanmin Shuai, Crystal Schaaf, Development of an approach for generation of temporally complete daily nadir MODIS reflectance time series, Remote Sensing of Environment, Volume 114, Issue 1, 2010, Pages 1-20

The paper proposes a BRDF-corrected gap-filled MODIS dataset. Overall the paper reads well and looks scientifically sound. I was able to download a few tiles of the dataset over Asia, but not over the test sites. I was especially interested into downloading those over the Amazon forests, where I am used to the reflectance values and its spatial patterns, and I would've been able to confirm if the data are making sense. Therefore, at this momment I am unable to confirm if data are good by my own inspection. Since this is a dataset paper, I cannot recommend its acceptance without looking at data for locations I can give an expert opinion. I apologize in advance if the data were available and I was not able to find them, please let me know and I'll be willing to look at it.

Specific comment:

Figure 22, non-fired -> "unburned" would look better