Assessment of vegetation water dynamics by comparing microwave remote sensing signals from satellites and field-based GNSS reflectometry

Feldman, Andrew F.; Smith, William K.; Konings, Alexandra G.; Baur, Martin J.; Browne, Marvin; Chaparro, David; Devine, Charles; Diehl, Jennifer L.; Endsley, Arthur; Jagdhuber, Thomas; Larson, Kristine M.; del Mar Valle-Rodriguez, Coral; Schellenberg, Konstantin; Zotta, Ruxandra-Maria; Serbin, Shawn P.

doi:10.5194/egusphere-2026-1759

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https://doi.org/10.5194/egusphere-2026-1759

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09 Apr 2026

| 09 Apr 2026

Assessment of vegetation water dynamics by comparing microwave remote sensing signals from satellites and field-based GNSS reflectometry

Andrew F. Feldman, William K. Smith, Alexandra G. Konings, Martin J. Baur, Marvin Browne, David Chaparro, Charles Devine, Jennifer L. Diehl, Arthur Endsley, Thomas Jagdhuber, Kristine M. Larson, Coral del Mar Valle-Rodriguez, Konstantin Schellenberg, Ruxandra-Maria Zotta, and Shawn P. Serbin

Abstract. Monitoring plant water stress and biomass is limited by labor intensive measurements techniques. Observing plant water conditions more broadly is now enabled by microwave remote sensing. Specifically, satellite-based vegetation optical depth (VOD) provides daily observations of vegetation water volume at tens of kilometers. While satellite VOD has been used for many applications, VOD validations have rarely been carried out. A new method has enabled direct measurements of in-situ VOD, from Global Navigation Satellite Systems (GNSS). However, GNSS measurements have yet to be applied to more globally dominant grasslands and shrublands. Here, we explore how satellite-based VOD from SMAP and AMSR-2 compares with field-based microwave observations from 272 GNSS-based interferometric reflectometry (GNSS-IR) sites across the Western U.S as a part of the Plate Boundary Observatory (PBO) H20 network. These sensors measure a proxy for VOD at a scale of tens of meters, the normalized microwave reflectance index (NMRI). We find that satellite VOD generally positively correlates with GNSS NMRI with correlations between 0.2 to 0.6 across sites. These correlations increase to 0.3 to 0.7 when evaluating sites in regions with low spatial vegetation type heterogeneity, low tree cover, and large seasonal vegetation dynamics. The correlations are higher for X-band VOD, likely related to our finding that both X-band VOD and NMRI are both more sensitive to seasonal vegetation variations than C-band and L-band VOD products. These findings suggest that satellite VOD is capturing field-based GNSS signals in dryland ecosystems, and therefore that these sensors are a critical resource for validating satellite VOD at scale.

Received: 28 Mar 2026 – Discussion started: 09 Apr 2026

Competing interests: Andrew F. Feldman is currently an associate editor of Biogeosciences. The other authors declare that they have no conflict of interest.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.

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RC1: 'Comment on egusphere-2026-1759', Anonymous Referee #1, 11 May 2026

Title: Assessment of vegetation water dynamics by comparing microwave

remote sensing signals from satellites and field-based GNSS

reflectometry
Authors: Feldman, A. F., et al.
Summary:

This study compares satellite-based vegetation optical depth (VOD)

with field-based measurements of VOD using GNSS-IR techniques. There

is a lot of information within this paper; different wavelengths and

algorithms are used to calculate VOD. Overall, I think the manuscript

is well-written, the presentation is clear, and the figures

appropriate. This manuscript seems appropriate for Biogeosciences.

This seems like a study where a lot of data are collected and analyzed

and then one sees what comes out of it (this is not necessarily a bad

starting point, and it is done well, but correlations of 0.2 or 0.3

are not great and some deeper understanding of why those particular

sites have such low/poor correlations is needed). Not being an expert

in satellite measurements, there are a lot of acronyms and terms which

I was not readily familiar with. With that said, my comments (listed

below) should probably be considered as comments from a "non-expert"

so please take them (or leave them) as you like.

Major Comments/Questions:

1. There is a lot of emphasis on correlations. As a non-expert, I do

not have a good feel for what these correlations actually look like

(ie, I assume one can generate a x-y scatter plot between two

different measurements). Using Fig. 1 as a specific example, it seems

surprising how some sites have relationships with a strongly positive

correlation and others have a negative one (ie, Fig. 1E). What does a

scatter plot from strongly positive and negative correlations actually

look like? Maybe I missed it, but is there something distinctive that

leads to the negative correlations?
2. There is a lot of pre-processing that is described, but not shown.

One that seems like it would be useful for the reader to see is the

different seasonal time series for NMRI and VOD. As described in

Sect. 2.3, this analysis is looking at differences from the seasonal

cycle...this is fine, but it would be great to also see the actual

seasonal cycle so one can see things like: how large the peak is

relative to the rest of the year, how the timing change year-to-year,

etc.
3. Is the linear fit in Figs. 3B and C really significant? It seems

like there is a LOT of scatter in these plots.
4. For capturing the wetting and drying associated with individual

rainfall events---if the objective is to capture anything like

"interception on the leaf surfaces", the a 1-day temporal resolution

is going to be an issue. Precipitation intercepted by the vegetation

will be evaporated within a day. So, it seems like the daily

resolution of this analysis will miss any shorter-term (ie, hourly

scale) precipitation/evaporation effects. I realize this is likely

known by the authors and part of the discussion in Sect. 3.5.2 about

the lack of a clear "pulse" signal in Fig. 7.
Minor Comments:

* l.50, provide the specific name of the indices being referred to?
* l.96, remove "Nevertheless"
* l.167, Eq.1 does the "max" refer to a max over a certain time

period? Or, something else?
* l.204-205, how are "short statured" and "dense short statured"

vegetation distinguished from each other? What is the height

cut-off to make the vegetation "short"? Is a grassland considered a

"cropland"? More clear definitions would be helpful.
* l.253, why are you referring back to Section 2.2?
* l.255, is a 16-day value useful?
* l.371, can examples of the situation where the GNSS site is in a

non-forested area, but the satellite pixel has a forest be

explicitly explored/shown?
* l.523, is the peak in soil moisture expected to be before the peak

of VOD?
* l.531, what do you mean by "physical representation differences"?
* how dramatic are the seasonal peaks? Example time series that show

the actual annual cycle?

Citation: https://doi.org/10.5194/egusphere-2026-1759-RC1
RC2: 'Comment on egusphere-2026-1759', Anonymous Referee #2, 23 Jun 2026

This manuscript by Feldman et al. systematically compares the Normalized Microwave Reflection Index (NMRI), derived from pseudorange multipath at geodetic GNSS stations of the PBO H2O network using an interferometric reflectometry approach (GNSS-IR), against multiple satellite Vegetation Optical Depth (VOD) products across a range of land cover types, retrieval algorithms, and microwave frequencies. NMRI has been proposed as a cost-effective and scalable means to validate satellite VOD. Particularly significant is the application of GNSS-IR to short vegetation, biomes for which VOD validation is difficult with other approaches such as the related method of GNSS-Transmissometry. Given that satellite VOD validation remains an open challenge, this study represents an important step toward addressing it and developing a cross-biome VOD validation framework.
The manuscript is well-written and clearly structured. The main findings, for example that X-band VOD shows stronger agreement with NMRI than L-band, and that correlations are substantially higher at sites with low spatial heterogeneity, are clearly presented. I consider this a valuable contribution to the remote sensing community and hope the comments below are helpful pointers for the authors, focusing mostly on the definition of NMRI, its relation to vegetation metrics, and its consistency with the literature.

Major Comments:
1. P4 LL114.: A small note that might help unfamiliar readers: Chen et al. (2016) extracts the SNR multipath amplitude, the oscillation amplitude from the SNR interference pattern. This retrieval is distinct from the MP1rms (pseudorange multipath) approach underlying NMRI in the sense that, while in both cases GNSS-IR setups were employed, different GNSS observables and their subsequent multipath were used for downstream retrievals (SNR vs. pseudorange). The term 'reflectometry GNSS' as used in this line is therefore ambiguous, as it encompasses at least these two methodologically distinct GNSS-IR retrieval approaches, as well as GNSS-Reflectometry (commonly abbreviated as GNSS-R), which is introduced in line 120. With GNSS-T, GNSS-IR (and GNSS-R), a brief disambiguation of these configurations early on could be very helpful for readers not already familiar with the literature. Also, Chen et al. (2016) uses a horizontally polarized antenna, which is non-standard in typical geodetic GNSS applications, where RHCP antennas are standard.

2. P4 LL116: The claim that NMRI 'isolates the vegetation signal from the soil signal' might be worth revisiting. To my understanding, Small et al. (2010), Larson & Small (2014) and Small et al. (2014) describe NMRI to appear to exploit an empirical sensitivity difference, where soil moisture affects MP1rms (and subsequently NMRI, which in a first approximation contains a topography correction) far less than vegetation does, rather than achieving a formal isolation of the two contributions. The same applies to P4 LL132.

3. P4 L135: Small et al. (2014) [https://doi.org/10.1109/JSTARS.2014.2320597] explicitly found no clear relationship between NMRI and vegetation height in natural grasslands in Montana, but Small et al. (2010) [https://doi.org/10.1029/2010GL042951] does for the agricultural ecosystems studied there. The citation may therefore be worth revisiting for the height claim specifically.
Related, P20 L506 & P28 L659 the authors further propose a potential sensitivity of NMRI to biomass, yet according to Small et al. (2014) only two of the studied grassland sites showed weak correlations between NMRI and biomass. Maybe I am unaware of a more recent study showing such relationships, but otherwise a clarification in the manuscript would be beneficial.

4. P17 LL458: Small et al. (2014) discussed the impact of ‘hot spots’ within the NMRI footprint on the retrieved signal, even though NMRI does try to correct for topography. Without detailed knowledge on the local topography, which is likely not available for all sites a./o. at sufficiently high resolution, the NMRI signal may not be assumed to be representative of the entire area surrounding the antenna. I could imagine such effects further impacting the comparability of NMRI and VOD.

5. P29 L684: The manuscript characterizes NMRI as ‘a measure of roughness of the surface due to vegetation cover.’ This conflicts with the other NMRI literature: Small et al. (2014) explicitly define it as 'a measure of vegetation water content', and Jones et al. (2014) describe it as 'sensitive to daily vegetation water content changes'.

Minor Comments:
6. P1 LL27: Maybe mention the full name of the method, GNSS-T?
7. P1 L31: H20 instead of H2O (same here: P5 L162, P15 L407, P28 L666) and as H₂0 here: P30 L721 and P31 L744
8. P4 LL113: The authors argue that GNSS-T cannot be used to evaluate non-forested biomes with shorter vegetation. Yet, Zribi et al. (2017) [https://doi.org/10.1155/2017/6941739] successfully deployed a GNSS-T setup in a sunflower field over one vegetation period, with the sunflowers growing up to 140cm in height. The transmissivity was calculated from the GPS L1 signal. It seems there might exist a range of vegetation that may be probed with both GNSS-T and GNSS-IR, delivering potentially invaluable new information for validating VOD products. The authors should adapt the statement accordingly, that both methods are complementary to a certain degree, rather than mutually exclusive. A similar statement is found in P29 L695.
9. P4 L137: Do the authors maybe have a hardware recommendation for the newer options? This could be interesting for potential new users, even though this is not the scope of this manuscript per se.
10. P5 L169: L1 is at 1.575GHz, it would be more appropriate to round to 1.6GHz than 1.5GHz therefore.
11. P6 LL184: How were the 9km products upscaled? The chosen strategy may impact the final product and thus could be interesting to the reader.
12. P6 L204: Forest LC class: To my knowledge, no in situ study has validated the MP1rms/NMRI retrieval at forested sites (Larson 2016 used SNR-based retrievals for snow depth analysis in a forest-adjacent meadow clearing, but this is a methodologically distinct case). Further, the literature advises against GNSS-IR deployment in forests due to direct-signal obstruction. I do believe it is genuinely interesting to retain the Forest LC analysis in the manuscript, but a brief note acknowledging that this is the first time NMRI has been tested at forest-classified sites would be an important addition.
13. P10 LL332: The manuscript describes NMRI as 'capturing vegetation attenuation of incoming signals from satellites' and cites Humphrey and Frankenberg (2023), a GNSS-T study. This definition of NMRI reads practically identical to the one of GNSS-T-retrieved VOD, even though both approaches are fundamentally different in geometry and retrieval. A small side note clarifying this could help readers unfamiliar with the different in situ GNSS methods to not conflate them. A similar situation exists in l. 544, where sensitivity of GNSS-IR NMRI to rainfall interception on vegetation is discussed, but a GNSS-T study (Schellenberg et al. (2024)) is cited.
14. P14 L387: The 30m LC product pixel size is potentially smaller than the GNSS-IR footprint [P14 L393 states ~100m scale]. Thus, GNSS-IR footprints may extend beyond the chosen LC pixel, even if a perfectly centered co-location is assumed. Did the authors verify for edge cases, where between neighboring LC pixels within the same GNSS-IR footprint a drastic change in LC type occurred, that may have impacted the NMRI?
15. P22 L541: Parentheses around the two citations
16. P28 L668 & P29 LL681: And GNSS-IR captures signal that passed the vegetation layer twice, if reflected back from the ground; unlike GNSS-T or satellite radiometers.

Citation: https://doi.org/10.5194/egusphere-2026-1759-RC2

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Short summary

Vegetation water observations from 272 field-based GNSS sensors and satellite-based vegetation optical depth are compared across the western U.S. We find significant positive correlations across the sites and identify common conditions that increase the correlations including less forest cover, more homogeneous vegetation across landscapes, and large seasonal amplitudes. These field-based sensors are thus useful tools to evaluate grass and shrub water stress and validate satellite signals.


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