Improving the monitoring of deciduous broadleaf phenology using the Geostationary Operational Environmental Satellite (GOES) 16 and 17

Wheeler, Kathryn I.; Dietze, Michael C.

doi:https://doi.org/10.5194/bg-18-1971-2021

Articles | Volume 18, issue 6

https://doi.org/10.5194/bg-18-1971-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/bg-18-1971-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 18, issue 6

Research article

|

19 Mar 2021

Research article |

| 19 Mar 2021

Improving the monitoring of deciduous broadleaf phenology using the Geostationary Operational Environmental Satellite (GOES) 16 and 17

Kathryn I. Wheeler and Michael C. Dietze

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Final revised paper (published on 19 Mar 2021)
Supplement to the final revised paper
Preprint (discussion started on 21 Aug 2020)
Supplement to the preprint

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'review comments', Anonymous Referee #1, 04 Sep 2020
- AC1: 'Response to RC1', Kathryn Wheeler, 23 Oct 2020
RC2: 'Review of GOES phenology manuscript', Anonymous Referee #2, 07 Sep 2020
- AC2: 'Response to RC2', Kathryn Wheeler, 23 Oct 2020

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

ED: Reconsider after major revisions (25 Oct 2020) by Paul Stoy

AR by Kathryn Wheeler on behalf of the Authors (25 Nov 2020) Author's response Manuscript

ED: Referee Nomination & Report Request started (26 Nov 2020) by Paul Stoy

RR by Anonymous Referee #2 (13 Dec 2020)

RR by Anonymous Referee #3 (01 Jan 2021)

Suggestions for revision or reasons for rejection

Wheeler and Dietze investigate phenological transition dates across different deciduous broadleaf forests in the eastern U.S. using geostationary, polar-orbiting, and mounted camera (‘phenocam’) platforms. The manuscript makes a number of interesting points and certainly improves our ability to understand phenological transitions using frequent GOES imagery. With regards to this I was expecting more of a conversation about atmospheric attenuation and bidirectional reflectance factors in order to generate the best possible NDVI observations from complex and frequent GOES data. If I’m not mistaken ABI L1b is top of atmosphere, but NDVI of course is a surface observable. How was this taken into account? Some discussion is at least warranted. The manuscript was also rather light on comparisons among sites. Were there forest types that had better or worse comparisons and might this be related to factors like forest diversity (different trees have different transition dates) or amount of evergreen needleleaf trees in the pixel(s)? Despite these rather major concerns the primary point of the manuscript is on transition dates which should be minimally effected by these issues.

Minor comments:
Make passages more direct and efficient when possible. For example, “To more broadly remotely sense phenology, satellite-based data that has lower temporal resolution has primarily been used.” Can be re-written “Satellite-based data with lower temporal resolution has primarily been used to measure phenology” or similar. This removes four words and ‘remotely sense’ is implied. See also the next line to avoid the phrasal verb. ‘allow the monitoring of NDVI’ can be ‘can monitor NDVI’. Such changes may seem trivial but they make the text more powerful and easier to read. Many passages are great but others (e.g. 257, 326, etc.) would best be untangled a bit.

Page 2: 750 site-years is the value at time of writing.

‘continental U.S.’ -> ‘conterminous (or contiguous) U.S.’ (Alaska is on the North American continent, Hawaii arguably isn’t.)

Section 2.2: I’m not sure how else to account for this but averaging red pixels first before calculating NDVI will result in a Jensen’s inequality…which shouldn’t be much of an issue most of the time. If NDVI was calculated individually the four red pixels (and single NIR pixel), would there be much of a difference? I assume in this case probably not because of the pixel selection process.

(note: the blue band can also be used to calculate ‘blue NDVI’. For what it’s worth.)

Line 135: this isn’t ‘noise’ per se but erroneous data.

Line 142: this gets at my major question. How was atmospheric attenuation treated? It won’t impact (much) the daily transitions but certainly will impact the NDVI value calculated from the surface using what I am assuming to be top-of-atmosphere reflectance. 6S is commonly used for this purpose (http://www.py6s.rtwilson.com/6s.html). I feel that at least some discussion of the topic of atmospheric attenuation is warranted because I see the main challenge being one of physics rather than statistics for interpreting NDVI from geostationary platforms. GOES has a constant viewing angle of course as you note but solar zenith angle changes dramatically, especially toward the poles, and considering the bidirectional reflectance function will help alleviate this. I’m assuming that this is a major reason for the differences in NDVI magnitudes observed (Fig. 3). I understand that the focus here is on transition dates, and the comparison amongst platforms of course is primarily a challenge for statistics, but atmospheric correction and BRDF need to enter the discussion section in some way in order to make (pseudo) apples-to-apples comparisons.

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ED: Publish subject to minor revisions (review by editor) (04 Jan 2021) by Paul Stoy

AR by Kathryn Wheeler on behalf of the Authors (18 Jan 2021) Author's response Author's tracked changes Manuscript

ED: Publish as is (18 Jan 2021) by Paul Stoy

AR by Kathryn Wheeler on behalf of the Authors (26 Jan 2021)

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Short summary

Monitoring leaf phenology (i.e., seasonality) allows for tracking the progression of climate change and seasonal variations in a variety of organismal and ecosystem processes. Recent versions of the Geostationary Operational Environmental Satellites allow for the monitoring of a phenological-sensitive index at a high temporal frequency (5–10 min) throughout most of the western hemisphere. Here we show the high potential of these new data to measure the phenology of deciduous forests.