Drone-Sensed and Sap Flux-Derived Leaf Phenology in a Cool Temperate Deciduous Forest: A Tree-Level Comparison of 17 Species

Round 1

Reviewer 1 Report

In this work sap flux--derived leaf phenology was explored by using some UAV-derived metrics.

The issue is interesting and worth publishing. Nevertheless, some critical concerns are present.

UAV surveys characteristic and UAV-data processing was not proper described, making the reader (and also this reviewer) unable to fully understand (and critically discuss) the method adopted. Since the work is manly based on UAV derive measures, these are very important gaps of this work.  Therefore, I suggest a major revision of current manuscript. Below some specific comments:

1. Please use the proper way to cite species. Therefore, add also the initial name letter who find the species according to international taxonomy rules.
2. Table 1. Table is not formatted according to journal style rules. Please fix this issue.
3. L182-183. Please, provide a graph (timeline) or a table with each data reporting day and hour of UAV survey.
4. L204-205. Since UAV survey is the main issue in this work, you must clearly report all information about the UAV survey in order to allow the reader (and reviewer) conscious about your experimental design. In particular, you have to provide: (a) forward and side overlap between images; (b) baseline or frame rate; (c) survey flight above ground level; (d) camera physical pixel size.
5. L206-207. Did you collimate control points within photogrammetric bundle adjustment software? How did you model the camera internal orientation? Which radiometric blending method did you adopt for the image block restitution?
6. L207-208. Is this reviewer opinion that automated manner and automatic processes are not always a good choice, especially in research context where you need to know and control each parameter involved. Note that whole paper is focused on this kind of surveys/processing. Therefore, add all required information to make clear the processing adopted to process UAV imagery.
7. L208-209. Please provide a map of control points per plot.
8. Please better report points positional accuracy. This could inform the reader about the accuracy of your image block bundle adjustment and the related delineated crown positional accuracy.
9. L210-2011. Since you photo interpreted tree crowns and you joint the information about SFD to those trees, please report the accuracy of orthophoto restitution.
10. L249-252. This is a critical issue. If your data about spectral proprieties were not calibrated (e.g., in surface reflectance) the derived indices are not comparable in times. In fact, sun irradiance changes during the years and therefore also the reflected energy is different. How did you take in to account this issue?
11. L331-334. Note that your native UAV derived indices have not the same scale (e.g., GRVI is [-1;1] while GCC always > 0), how did you normalize indices? This is not reported yet.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

This paper presents a study of tree-level comparisons between leaf phenology derived from a UAV camera and sap flow measurements in a deciduous forest in Japan. The paper is very well written, with a clear description of the data, methods, and results, but also an in-depth discussion. This is the best manuscript I’ve ever reviewed for RS. I recommend accepting it for publication, with minor comments as follows:

Line 20: individual-level -> individual tree-level ?

Line 146: annual precipitation of 4099 is a lot, even higher than a tropical rainforest. I can see from figure A5 that there was an extreme event in July 2019. So, maybe better to indicate the normal annual precipitation here in the study site description.

Lines 341-343: with intra-species variations, I guess you mean the inter-annual variation (2019-2020) for the same species. (?). If so, visually from Figure 2, I think the mean intra-species is comparable with mean inter-species variations, not smaller as stated in the text. You may consider adding a quantitive analysis for the statement.

Figure 4: Can you align the x-axis consistently to make the figures easier to interoperate. Or considering merging A and C into one, and B and D into another one. Then put the merged figures in one aligned column. With this figure, I understand your purpose is to show the phenology of the sap flow, but it would be nice if you could add a scatterplot between the SFDday and Rs/VPD.

Finally, I would like to draw your attention to our paper comparing different RS vegetation indices with GPP and GCC, based on which the European Copernicus vegetation phenology dataset was produced. The PPI index showed a better performance than NDVI and EVI2.

Tian, F., Cai, Z., Jin, H., Hufkens, K., Scheifinger, H., Tagesson, T., Smets, B., Van Hoolst, R., Bonte, K., Ivits, E., Tong, X., Ardö, J., Eklundh, L., 2021. Calibrating vegetation phenology from Sentinel-2 using eddy covariance, PhenoCam, and PEP725 networks across Europe. Remote Sensing of Environment 260, 112456. https://doi.org/10.1016/j.rse.2021.112456

Jin, H., Eklundh, L., 2014. A physically based vegetation index for improved monitoring of plant phenology. Remote Sensing of Environment 152, 512–525. https://doi.org/10.1016/j.rse.2014.07.010 

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Authors have improved their manuscript according to my suggestions. Therefore, i think that now the paper quality is proper for publication.