The study proposes to generate a sensor-independent LAI and FPAR climate data record (CDR) based on existing global MODIS and VIIR data sets. A spatial-temporal tensor completion model was used to fill the gaps from the selected highest quality data. The paper is generally organized. The new data set would be useful for the Earth system science studies.

GENERAL COMMENTS

I have two major concerns.

1. I feel a bit lost with the huge difference (2~3) between the original and filtered LAIs in Fig. 3. I understand the filtered LAI is from the highest quality original data. It’s difficult to understand the huge increase after filtering.

Is this because of the small fraction of main algorithm used? Authors may show the percentage of main algorithms or the retrieval index in the time series.

2. I don’t think Fig. 6 is useful to explain Step 3. Authors used 5% of the pixels to test the ST-tensor; this is an independent exercise that is not very related to the context of this study. As a matter of fact, the effect of the TS-tensor has been demonstrated in many other studies and was already adopted in this study.

Readers would be more interested to see: 1) the real % of missing pixels that were gap-filled by the TS-tensor; 2) whether the recovered pixels are spatial-temporally reasonable. Therefore, Fig. 6 can be exchanged with spatial-temporal maps before and after gap filling.

SPECIFIC COMMENTS

L64-65. This sentence is confusing. Rephrase.

L136. Sentence incomplete.

Eq. (4) The function sgn() was not explained.

L227. This filtered timeseries that are used as reference are crucial intermediate results. This reference data should be presented either in time series or with maps. This would greatly help readers understand Figs. 7 & 8 in section 4.2.

L306-314. This texts are difficult to read because of the missing link between text and Table 3 (difficult to read). Suggest to rephrase the text and add a column showing the percentage of all high quality pixels that were presented in the texts.

Figs. 7 & 8 As noted above (L227), the reference maps may be displayed somewhere earlier.

L494. (Pu et al., 2023a) is not in references.

I have reviewed the paper, and I must acknowledge that it is well-written and comprehensive in addressing the development of a Sensor-Independent (SI) Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) Climate Data Record (CDR). The study addresses the limitations of existing LAI/FPAR products and takes a significant step forward in improving their accuracy and reliability. Below are my key observations:

Strengths:

1. Clarity of Presentation: The paper is written in a clear and organized manner. It effectively communicates the significance of LAI and FPAR, the challenges faced, and the proposed solution.
2. Research Significance: The paper makes a strong case for the importance of LAI and FPAR in ecological and climate studies, providing a comprehensive background on their relevance.
3. Awareness of Weaknesses: The authors appropriately identify the weaknesses in existing LAI/FPAR products, emphasizing the need for a new approach.
4. Comprehensive Approach: The study outlines a detailed methodology, including sensor-specific quality assessment, ST-Tensor completion model, and various spatial and temporal resolutions, providing a holistic approach to CDR development.
5. Use of Citations: Proper citations of prior research strengthen the credibility of the paper and demonstrate a thorough literature review.

Areas that may be considered for further improvement:

1. Sentence Structure: A few complex sentences could be simplified to enhance readability, particularly for a broader audience.

Conclusion:

In summary, this paper is a commendable contribution to the field of LAI and FPAR data records. Its clear presentation, strong research significance, and comprehensive approach make it valuable. Addressing the minor points of improvement would enhance its accessibility and overall readability. I recommend it for publication with these suggestions in mind.