| 20 Jan 2026
Decadal surge of water-surface solar in China's Yangtze Delta: A high-fidelity SAR-optical fusion inventory (2015–2024)
Abstract. China hosts approximately 97 % of the world's water-surface photovoltaics (WPV), with nearly two-thirds of its national capacity concentrated in the Yangtze River Delta (YRD), a densely populated economic powerhouse facing intense land-energy trade-offs. Despite this dominance, no high-resolution, decade-long inventory has existed to track this rapid expansion. WPV detection using optical RS imagery is severely limited by persistent cloud cover, water surface reflections, and spectral confusion, compromising long-term consistency over aquatic environments. Here, we developed a multi-sensor fusion framework integrating all-weather Sentinel-1 Synthetic Aperture Radar (SAR) and annual composite Sentinel-2 optical imagery. Key features include six Sentinel-2 bands, spectral indices (NDVI, MNDWI, NDBI, NDPI, and SAVI), texture metrics, and dual-polarization SAR backscatter. We trained a Random Forest classifier on 55,849 verified samples to generate annual WPV maps for 2015–2024. Afterwards, we applied post-processing procedures, including noise removal, patch merging, and area thresholding, and further validated installation years and eliminated errors through manual inspection of Google Earth time-series imagery. The well-constructed dataset of the first 10 m-resolution WPV atlas for the YRD maps 401 validated projects with a cumulative area of 145.4 km2 by 2024. It outperforms existing global PV inventories with an overall accuracy of 97.3 % and a Kappa coefficient of 0.94. The results reveal rapid expansion from 17.4 km2 in 2015 to 145.4 km2 in 2024, with 87 % deployed on natural lakes, with a marked shift in leadership from Jiangsu to Anhui, and clear spatial clustering near grid infrastructure and stable water bodies. This high-fidelity inventory provides a robust foundation for monitoring WPV evolution, assessing environmental impacts, and informing sustainable energy planning in the world's leading floating solar region.
Reviewer Report
General Evaluation
The manuscript presents an important and timely contribution by producing the first decade‑long, high‑resolution (10 m) water‑surface PV (WPV) inventory for the Yangtze River Delta using a SAR–optical fusion approach. The topic is highly relevant and the dataset could be valuable for future studies.
However, several methodological aspects require clarification, and certain results need deeper interpretation before the manuscript can be considered for publication.
Major Comments
The methodology states that several Sentinel‑2–derived spectral indices (e.g., NDVI, MNDWI, NDBI, NDPI, SAVI) were used in the Random Forest classifier.
However, the manuscript does not provide any quantitative values, ranges, or thresholds that explain how these indices contribute to distinguishing:
water surfaces
non‑vegetated land
rocky or bare surfaces
Given the importance of spectral indices in the fusion approach, the authors should provide, at minimum:
typical value ranges for water vs. land features,
variable importance scores from the Random Forest model, and
examples of how specific indices helped resolve misclassification challenges.
This transparency is essential for reproducibility.
The surface area of lakes and reservoirs in the YRD can fluctuate significantly due to seasonal or multi‑year droughts.
The manuscript does not explain how these hydrological variations were handled.
Please clarify:
Were annual water masks independently derived for each year?
Did the classifiers incorporate hydrological seasonality?
How were changes in water extent prevented from being misinterpreted as WPV presence or absence?
This point is critical, especially when estimating decadal trends.
The authors mention the use of texture metrics and SAR backscatter features.
However, floating PV (FPV) systems—unlike fixed structures—can move due to wind, currents, or water‑level fluctuations.
Please discuss:
whether FPV motion affects texture features,
whether SAR temporal variability could introduce classification noise,
and whether the method is equally robust for fixed installations and mobile floating platforms.
This clarification is important since China hosts many FPV plants.
The developed dataset could potentially support research on the environmental effects of FPV installations.
Please comment on the feasibility of using this method to investigate:
water‑surface temperature variations due to partial shading;
changes in water colour or turbidity, especially related to algae bloom development or suppression;
whether SAR–optical fusion offers the sensitivity needed for such environmental applications.
These points would strengthen the broader applicability of the work.
Figure 11 shows that several basins have extremely high WPV coverage (85–95%).
The manuscript should clarify:
Which area was used as the denominator when computing the WPV percentage (e.g., maximum historical water extent, annual water extent, permanent water core).
Whether such high coverage is physically accurate, or if classification steps may have overestimated WPV area in small or seasonally shrinking basins.
The implications of these very high coverage levels for hydrological, ecological, or energy‑planning impacts.
A deeper interpretation is needed.
Please provide a clear definition of lake versus reservoir, since the distinction is relevant for WPV siting policies, water‑level stability, and ownership/management regimes.
A short paragraph is needed in the Methods or Study Area section.
Minor Comments
Figure 2 labeling error
There is an inconsistency between the letters shown in the images and those referenced in the caption.
Please correct the figure annotations to ensure correspondence