the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 21 Aug 2025
Very-High-Resolution, Multi-Season Monitoring of Crop Evapotranspiration and Water Stress with UAV Data and TSEB Integration
Abstract. Field-scale estimation of evapotranspiration (ET) using high-resolution data supports water conservation and yield optimization by enabling localized water use monitoring and early detection of crop stress. This study applies the Priestley–Taylor Two-Source Energy Balance (TSEB-PT) model at 15 cm resolution using unmanned aerial vehicle (UAV) data over a 10-hectare field across three seasons: sugar beet (2021), potato (2022), and winter wheat (2023). Key inputs included thermal infrared (TIR) for land surface temperature (LST), multispectral (MS) and LiDAR data for canopy characterization, and a fusion of MS derived green area index (GAI) and LiDAR derived plant area index (PAI) to derive the fraction of green LAI (fg). Model outputs were validated against eddy covariance (EC) flux data using footprint modeling. Results showed high sensitivity to LST, emphasizing the importance of accurate thermal calibration. While both GAI and PAI provided comparable LAI inputs during peak growth, GAI better captured functional canopy decline during stress and senescence, especially in winter wheat, where dense structure led to cooling effects unrelated to transpiration. Dynamic fg improved ET accuracy across all crops, particularly under declining canopy function. Overall, TSEB-PT showed strong agreement with EC measurements (RMSE = 0.14 mm/h, R² = 0.49; R² = 0.81 excluding senescence). UAV TIR based ET maps also revealed early stress signals prior to changes in MS or LiDAR based metrics. This study demonstrates the value of integrating very-high-resolution UAV data with the TSEB-PT model for multi-crop and season-long ET monitoring and early stress detection.
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Status: final response (author comments only)
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RC1: 'Comment on egusphere-2025-3919', Anonymous Referee #1, 28 Oct 2025
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RC2: 'Comment on egusphere-2025-3919', William Kustas, 12 Dec 2025
The paper by Bates et al entitled “Very-High-Resolution, Multi-Season Monitoring of Crop Evapotranspiration and Water Stress with UAV Data and TSEB Integration” evaluates the utility of the TSEB-PT model applied to 3 cropping systems using UAV imagery with advances in correction of thermal imagery and the use of LiDAR to capture PAI and use this information to reliable estimate the green fraction of vegetation during crop senescent stage. Overall, the paper is fairly well written, and the authors do a good job describing the benefits of the innovations made in producing more reliable LST data and estimates of green fraction which is critical for estimating ET during crop senescence.
However it is unclear what resolution is used in the the application of the TSEB model. The 15 cm resolution is very high and it is not clear how it can be used to define the inputs to TSEB. This needs to be clearly discussed. They also only compare ET and not the other energy balance components nor do they mention how they dealt with energy balance closure. Comparison with closed or unclosed H and LE should be discussed. Furthermore, an X-Y scatter plot comparing all 4 components for each crop type would be very helpful to the reader. There are also several fractional cover terms discussed but is confusing to the reader which ones are applied under the different crop conditions (e.g., pre and post senescence).
With such high resolution thermal and multispectral imagery, could the authors have also used the TSEB-2T version? If not, they should provide rationale in going with TSEB-PT.
Some of the figures should be modified for better clarity. For example, in Figure 5 the authors should consider adding a 2nd y-axis to show the difference in LST_OG and LST_TC as the temperature range from 10 to 40 C significantly suppresses differences in the plots. The figures 8 ands 10 would be more useful to the reader if they were shown as scatter plots (2 x 3 separating the two LST inputs versus EC observations and for LAI a 3x3 for the three LAI/fg estimates) and distinguishing with symbols pre and post senescence. Also for both current figures 8 and 10 the symbols for LST=LST_OG and LAI=GAI, fg=1 are very faint and hard to see. Plus, the vertical dashed lines I believe indicate pre and post crop senescence but are not mentioned in the figure captions and should be a darker color (black?) so is easily visible to the reader. Finally, there are unexplained dashed light blue lines for the potato plots which are not described. However, I believe scatter plots would be much more useful to the reader and easier to evaluate differences with the EC data as scatter plots. On the other hand, I like the way the authors show the difference statistics for the different model output in figures 9 and 11. In Figure 13, the figures have an insert with a blue circle…seems out of place and not explained.
Under section 4.5 Practical impacts and considerations for farming practices, the authors should go the extra mile and compute daily ET using the simple approach evaluated by Cammalleri et al (2014). This approach is used and tested in many applications even with UAV data (e.g., Nassar et al. 2021). I recommend the authors make a final calculation using this approach and compare with daily ET from the EC data using their best inputs. The daily ET is more relevant to farmers and for stress perhaps they can show these daily ET maps relative to reference crop or potential ET from FAO56 to highlight the stress area.
Cammalleri, C., Anderson, M.C., Kustas, W.P. (2014). Upscaling of evapotranspiration fluxes from instantaneous to daytime scales for thermal remote sensing applications. Hydrol. Earth Syst. Sci. 18, 1885–1894
Nassar, A., Torres-Rua, A., Kustas, W., Alfieri, J., Hipps, L., Prueger, J., Nieto, H., Alsina, M.M., White, W., McKee, L. et al. (2021) Assessing Daily Evapotranspiration Methodologies from One-Time-of- Day sUAS and EC Information in the GRAPEX Project. Remote Sens. 13, 2887. https://doi.org/10.3390/rs13152887
Citation: https://doi.org/10.5194/egusphere-2025-3919-RC2 - AC2: 'Reply on RC2', Jordan Bates, 02 Feb 2026
- AC1: 'Reply on RC1', Jordan Bates, 02 Feb 2026
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RC2: 'Comment on egusphere-2025-3919', William Kustas, 12 Dec 2025
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EC1: 'Comment on egusphere-2025-3919', Mallory Barnes, 04 Aug 2026
As promised, I have reviewed the revised manuscript and response letter myself. The revision is thorough and addresses the referees' concerns well. My comments are aimed at tightening the paper, which has grown substantially during the major-revision round and now has 17 main figures. Most of these are recommendations left to your judgment, but the data availability point is a journal requirement.
When assessing which figures could move to supplemental I considered whether a figure was a diagnostic or sensitivity check only, and whether the figure was required to support the paper's central claims about the importance of thermal calibration, divergence between GAI & PAI under senescence, dynamic fg, and daily ET upscaling.
- Figure 13 is the clearest candidate (the ET difference maps). The correction lowers ET across the whole field so the maps look predominantly uniform red. The spatial structure described in the caption is difficult to see in the figure, and the stress-related spatial contrast that matters to the paper's argument is better shown in Figures 14 & 15. Whether or not you move it, I suggest revising the caption to match what the figure shows, since the variation with evaporative source and stress condition is hard to make out here.
- Figure 12 is also a good candidate for supplemental since it is a sensitivity diagnostic (input vs. output CV). Fg influences spatial ET variability more than LAI type is a secondary point and the sentence at 663 that references it in the discussion could simply redirect to the same figure in the supplement. I would suggest moving this one.
- FIgures 8 & 10 are the four-component flux scatterplots for the LST & LAI/fg experiments, and Figures 9 & 11 distill each to the ET performance the argument actually uses. The four-component detail is useful but not needed to follow the paper's main claims so it is a candidate for moving to supplement (and keeping 9 & 11 in main text). These were explicitly requested by Referee 2, so I leave this to your preference. Keeping them in the supplemental or in the main text are both defensible.
- In the abstract, please report the R2 range, not just the highest value across crops. So "R2 values up to 0.80 across crops" should be "R2 values from 0.27 to 0.80 across crops" to represent the crop-dependent performance more accurately.
- Data availability currently says "Data will be available upon request". This does not meet the journal's data policy: https://www.biogeosciences.net/policies/data_policy.html. The underlying data should be deposited in a FAIR-aligned repository with a persistent identifier (preferably a DOI), or a specific explanation of any legal or ethical reason the data cannot be shared. Please add a compliant Data availability section. The ICOS eddy covariance datasets already carry handles that can be cited directly; code and pretrained weights can be archived via Zenodo to obtain a DOI; and any restricted dataset should be accompanied by a stated reason consistent with the policy.Citation: https://doi.org/10.5194/egusphere-2025-3919-EC1
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Cited
2 citations as recorded by crossref.
- Leaf Angle Distribution Effects on Modelling Accuracy of Sensible and Latent Heat Fluxes in Sunflower and Wheat Crops K. Pintér & Z. Nagy https://doi.org/10.3390/rs18111732
- Estimation of Crop Coefficients of a High-Density Hazelnut Orchard Using Traditional Methods vs. UAV-Derived Thermal and Spectral Indices A. Vinci et al. https://doi.org/10.3390/agriculture16060677
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