the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 26 May 2026
A pan-tropical 5-km monthly L-band vegetation optical depth dataset from pan-sharpening-based downscaling
Abstract. L-band Vegetation optical depth (L-VOD), as a microwave-derived vegetation indicator, has been widely applied in the monitoring of vegetation dynamics. However, the spatial resolution of 25-km or coarser in existing L-band VOD products limits their applications in ecological monitoring requiring a higher level of spatial details. To mitigate this limitation, we introduce a pan-sharpening-based downscaling method to improve the spatial resolution of L-VOD. By fusing the spatial structural features of the aggregated 5-km resolution European Space Agency Climate Change Initiative (ESA CCI) aboveground biomass (AGB) product, the SMOS L-VOD product over tropical regions was downscaled to generate a monthly 5-km resolution L-VOD dataset spanning 2015 to 2021. The downscaling model demonstrated high accuracy, with a correlation coefficient (R2) of 0.95 and a root mean square error (RMSE) of 0.11 when comparing the simulated 25-km L-VOD (L-VOD25kmsim) with the original L-VOD (L-VOD25km) product. Spatially, the 5-km resolution L-VOD (L-VOD5km) yielded a strong correlation with above-ground biomass (R=0.91, R2=0.86), and temporally dynamics, it accurately characterized the LAI variations of short vegetation and forest area loss at the pixel level over the study period. The results demonstrate that our downscaling method can effectively enhance the spatial resolution of L-VOD while preserving its original spatiotemporal dynamics, and is capable of capturing forest disturbance. This dataset can be downloaded at https://doi.org/10.11888/RemoteSen.tpdc.303391 (Shi and Fan, 2026).
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Status: open (until 26 Jul 2026)
- RC1: 'Comment on essd-2026-193', Anonymous Referee #1, 26 Jun 2026 reply
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- 1
General Comments
This manuscript proposes a new 5-km L-band Vegetation Optical Depth (L-VOD) dataset by downscaling the 25-km L-VOD product using a 5-km Aboveground Biomass (AGB) dataset through a pan-sharpening-based framework. The resulting product is comprehensively evaluated against higher-resolution auxiliary datasets, including AGB and vegetation indices, and further demonstrated through a spatiotemporal analysis of forest loss. Overall, I appreciate the authors' effort in developing this dataset and conducting extensive experiments and comparisons. The topic is relevant and the generated product has potential value for the remote sensing community. However, I believe the manuscript would benefit from improvements in the presentation, particularly in the description of the methodology and the interpretation of the results. Therefore, I recommend major revision.
Major Comments
Study area description (Section 2).
The study area section would benefit from additional background information on the ecosystems of the two selected regions to better motivate their selection and provide readers with more context. In addition, please explicitly explain what P1 and P2 represent in Figure 1.
Potential numerical instability (Section 3.2, Eq. (2)).
Since the injection gain is computed as (g = L\text{-}VOD_{5km,NNR}/AGB_{5km,LowPass}), what happens when (AGB_{5km,LowPass}) approaches zero? Would this lead to numerical instability or extremely large gain values? Please explain how such cases are handled.
Description of the iterative optimization of the injection gain (around Lines 190–205).
The description of the iterative optimization of the injection gain (g) (Eqs. (2)–(4)) is difficult to follow. Since this section describes an algorithm rather than a mathematical derivation, I strongly recommend presenting it as pseudocode or an algorithm box.
At present, it is unclear what variable is actually being optimized and how the optimization is performed. Specifically, Eq. (3) states that g is optimized by maximizing the Pearson correlation between the downscaled L-VOD and AGB, whereas the subsequent text only describes progressively reducing the upper bound (g_{limit}) and clipping g. It is therefore unclear whether g is recomputed following some math optimization rule for equation (3), or whether only the clipped version of the initial gain map is updated.
A pseudocode description would clearly specify the initialization, the variables updated in each iteration, how Eq. (1) is repeatedly evaluated, how the correlation is used during optimization, and what constitutes the final optimized gain map. This would greatly improve both the clarity and reproducibility of the proposed method.
Figure 2 (algorithm flowchart).
The flowchart does not sufficiently clarify the iterative optimization process. In particular, the step "g = adjusted g" is ambiguous, as it is unclear how the adjusted gain is obtained. Similarly, the role of (\Delta R) in the optimization is not well illustrated. Revising the flowchart to explicitly show the update rule for g (or (g_{limit})) would substantially improve readability.
Comparison with existing downscaling methods.
The manuscript evaluates the proposed product against auxiliary datasets but does not compare the proposed downscaling approach with existing methods. Since the Introduction discusses alternative approaches (e.g., SAR-based methods, statistical downscaling, MF-MRA), it would strengthen the manuscript to compare the proposed method with one or more representative baselines and demonstrate its advantages.
Forest loss analysis.
The forest-loss analysis could be further strengthened. Since the Hansen Global Forest Change product has well-known limitations (e.g., it represents tree-cover loss rather than a broader definition of forest loss, with trees defined as taller than 5 m), these limitations should be briefly discussed in either the Results or Discussion section.
In addition, I suggest performing the analysis at the regional level rather than emphasizing pixel-level dynamics (Figure 10c,d), which may be sensitive to uncertainties in the Hansen product. Furthermore, Figure 10 only shows a single blue bar for forest loss in 2015. It would be more informative to visualize annual forest loss throughout the entire study period (2015–2021).
Minor Comments
Section 3.3 (Accuracy assessment).
Consider formatting the subsection headings such as "(i) Evaluation of the downscaling method" in bold to improve readability.
Figure 4 (d, f).
Why is the standard deviation (error bars) of the downscaled (L)-VOD({5km}) consistently smaller than that of the original (L)-VOD({25km})? Since the downscaled product is expected to capture more spatial details, one might expect increased variability. Please provide an explanation.
Figure 5.
Consider including the corresponding AGB image and an optical Sentinel-2 image alongside the L-VOD visualization to facilitate interpretation.
Figure 6 (j–l).
Since all three panels show relationships involving (L)-VOD(_{5km}), I suggest using a consistent x-axis label across all three plots.
Section 4.2 (Accuracy assessment).
The manuscript states that the performance was evaluated using data from 2019. Why was only a single year selected instead of evaluating the entire study period (2015–2021)?
Figure 8.
Consider adding labels such as "DRC" and "Brazil" directly to the figure to improve readability.
Consistency between Figures 5 and 8.
Figures 5 and 8 appear to use the same study regions. To improve consistency, I recommend using the same naming convention throughout the manuscript. For example, instead of using Region 1/Region 2 in Figure 5 and Rondônia/DRC in Figure 8, consistently use Brazil and DRC.
Terminology consistency (around Line 365).
The manuscript inconsistently uses AGB(_{100}), AGB(_{100m}). besides, based on the methodology, it seems more appropriate to refer to AGB(_{5km}), since the downscaling uses the aggregated 5-km AGB product rather than the original 100-m spatial details.
Section title (Section 5.2).
The current title "Uncertainties and Perspectives" would be more appropriately renamed "Limitations and Perspectives", as the section primarily discusses methodological limitations and future research directions.