China leads the world in WPV system development, accounting for approximately 96.82 % of the global installed WPV surface area in 2019, as shown in Fig. 1a. Within China, the YRD region represents a significant WPV hub, accounting for nearly two-thirds of the national total. Specifically, Jiangsu (30.21 %), Anhui (19.75 %), and Zhejiang (9.97 %) provinces contain the largest installed WPV areas (Xia et al., 2022, 2023).

Located in eastern China along the lower reaches of the Yangtze River (Fig. 1b), the YRD is characterized by abundant water resources, including a dense network of rivers, lakes, and reservoirs (Fig. 1c). These natural conditions are highly favorable for large-scale WPV deployment. Moreover, as one of China's most economically developed and industrially concentrated areas, the YRD experiences substantial electricity demand (Xu et al., 2023). The rapid growth of WPV in this region not only helps alleviate regional energy pressures but also plays a vital role in energy restructuring and the transition toward low-carbon development. Considering its current deployment scale, water resource conditions, and future development potential, this study focuses on the YRD as its primary research area.

To evaluate the performance of our WPV extraction results, we conducted a multi-level validation and comparison process. This section presents a systematic assessment of classification performance from three complementary perspectives. First, the initial classification outputs were visually inspected and refined to examine the influence of the post-processing procedure. Then, single-year and multi-year merged datasets were compared to evaluate the temporal stability of WPV detection results. Finally, our dataset was compared with existing global PV products, including a global inventory of photovoltaic solar energy generating units as of the end of 2018 (Kruitwagen et al., 2021) and Global Renewables Watch in 2024 (Robinson et al., 2025), to assess spatial coverage and consistency. Both qualitative and quantitative analyses were performed, including confusion-matrix-based accuracy assessment and detailed visual interpretation across representative regions. Together, these evaluations provide a thorough verification of the accuracy, continuity, and advantages of our WPV dataset.

WPV projects in the study area show clear spatiotemporal patterns and growth trends. Spatially, current WPV projects are primarily concentrated in Anhui and Jiangsu provinces, where hydrological and land-use conditions are favorable for large-scale deployment (Fig. 9a). By 2024, a total of 401 WPV projects have been identified in the YRD, covering a cumulative area of 145.4 km². Among these, Anhui Province hosts the largest share (68.7 km²), accounting for 47 % of the total WPV area, followed by Jiangsu (64.8 km²) and Zhejiang (12 km²). The inset bar chart further illustrates the proportion of WPV area relative to the total water area in each province in 2024, showing that Anhui had the highest deployment intensity (1.01 %), followed by Jiangsu (0.67 %) and Zhejiang (0.48 %). Based on the five largest WPV projects in each province (Table 2), Jiangsu generally exhibits a larger overall project scale, with most large WPV installations located on lakes.

From a temporal perspective, WPV installations have expanded markedly between 2015 and 2024, with the total area increasing by 128 km² (Fig. 9b). This expansion was concentrated primarily during the early phase (2015–2019), during which approximately 59.2 % of the total increase occurred, followed by a relative slowdown during 2019–2024. At the provincial level, Anhui experienced the greatest increase in WPV area over the decade, adding 66.9 km², followed by Jiangsu (49.1 km²), while Zhejiang's growth remained modest, with a total addition of only 12 km². Notably, the spatial evolution trajectories of WPV deployment show different patterns among the three provinces. In Jiangsu, WPV development began in the northern region and gradually expanded southward and toward the coastal areas during the early years. In contrast, early WPV projects in Anhui were also concentrated in the north, but from 2022 onward, installations rapidly expanded along the Yangtze River corridor, forming a more continuous, belt-shaped distribution. Zhejiang Province, by comparison, saw only limited WPV deployment, characterized by a short burst of growth between 2017 and 2020, with most projects clustered in its northern and western regions.

In addition to the overall spatial and temporal patterns in the YRD, WPV projects of different sizes exhibit distinct trends in quantity and growth, providing additional insights into the structural characteristics of PV development in the region (Fig. 9b). Large-scale projects (>1.0 km²) are relatively few, with most expansion occurring between 2015 and 2018 before stabilizing. Conversely, medium-scale (0.1–1.0 km²) and small-scale (<0.1 km²) projects far outnumber large ones and have grown rapidly overall. Overall, the spatial pattern shifted from localized concentrations toward broader regional deployment, and from predominantly large-scale projects to a more diversified mix of small-scale and medium-scale systems.

Different types of water bodies show distinct patterns of WPV deployment, with implications for assessing spatial distribution and potential capacity (Bai et al., 2024; Château et al., 2019). Within the study area, WPV installations are predominantly located on lakes, with only a minor proportion situated on reservoirs (Fig. 10a). Lakes contained the vast majority of total WPV area, reaching 126.8 km² by 2024, or 87.2 % of the cumulative WPV area. In contrast, reservoirs account for 12.8 % (18.6 km²) of total WPV deployment. However, the distribution across water body types differs considerably among provinces. Jiangsu shows the largest difference, with approximately 98.1 % of its WPV systems deployed on lakes and only 1.9 % on reservoirs. Anhui and Zhejiang demonstrate higher shares of reservoir-based WPV, at 28.9 % and 20.2 %, respectively. The temporal evolution of WPV deployment also reveals distinct trajectories for lakes and reservoirs (Fig. 10b). Lake-based WPV installations expanded steadily between 2015 and 2024, with a total increase of 109.4 km². In contrast, the development of reservoir-based WPV proceeded more slowly. Its expansion was limited between 2015 and 2019 (14.9 km²), and coinciding with the introduction of policy restrictions in 2019, reservoir-based WPV expansion slowed, with the total area remaining below 20 km². These trends indicate the increasing dominance of lakes in WPV deployment, while reservoir-based deployment showed slower growth during the study period, potentially associated with operational constraints and regulatory changes (General Office of the Ministry of Water Resources of the People's Republic of China, 2020).

WPV deployment intensity varies among water bodies of different sizes and characteristics, with surface coverage providing an important metric for assessing the spatial extent of WPV occupation (Exley et al., 2021). By integrating WPV distribution data with surface water datasets, WPV coverage was calculated for 385 water bodies across the study area (Fig. 11a, b). Coverage rates varied substantially among different size classes. In smaller water bodies (0–4 km²), WPV coverage exhibited high variability, ranging from 0 % to 100 %. However, as water body size increased, WPV coverage declined sharply, falling below 50 % in medium-sized water bodies and below 10 % in large ones (>100 km²). Most reservoirs covered by WPV installations are relatively small (<4 km²), and their overall WPV coverage is slightly lower than that of lakes of comparable size.

At the regional scale (Fig. 12a), WPV projects exhibit a clear pattern of localized clustering across the study area. These projects are primarily distributed along major rivers and lakes, with project sizes varying considerably, from less than 0.1 km² to over 3 km². Notably, several large, high-density clusters are observed in parts of Jiangsu and Anhui provinces (Fig. 12b–d).

This spatial aggregation is associated with a combination of natural and socio-economic factors. Natural conditions may influence WPV suitability, with large water bodies characterized by relatively stable water levels and suitable water conditions often preferred for deployment (Woolway et al., 2024). For example, Sanlihe Reservoir (Fig. 12e), Gaoyou Lake (Fig. 12f), and Xizi Lake (Fig. 12g) possess extensive and relatively stable water surfaces, coinciding with large WPV installations. Grid accessibility is another factor potentially associated with the spatial distribution of WPV systems (Essak and Ghosh, 2022). Around Gaoyou Lake and Xizi Lake, WPV installations are located close to settlements and existing grid infrastructure, which may facilitate grid connection and electricity transmission. The co-occurrence of suitable water surfaces and accessible infrastructure may therefore help explain the concentration of large WPV projects in these areas.

This study constructs a high-resolution, decade-long (2015–2024) WPV dataset for the YRD region, providing an improved basis for remote sensing-based WPV mapping. We address several limitations in existing time-series mapping approaches, including the vulnerability of optical imagery to cloud contamination and water-surface reflections, as well as the accumulation of temporal uncertainty in long-term mapping (e.g., annual accuracy of ∼0.96 may translate into only ∼0.67 temporal consistency over ten years). By integrating Sentinel-1 SAR observations with multi-temporal Sentinel-2 imagery, our framework combines complementary optical and radar information to enhance WPV discrimination and temporal consistency. In addition, post-processing and manual verification using high-resolution Google Earth imagery enabled the generation of a quality-controlled long-term WPV inventory.

Using this dataset, we performed a systematic decadal assessment of WPV spatial patterns and growth trajectories in the YRD. We found that total WPV area increased from 17.4 km² in 2015 to 145.4 km² in 2024, accompanied by a shift in development dominance from Jiangsu to Anhui. The analysis further revealed contrasting growth trajectories among large-, medium-, and small-scale WPV projects, indicating a transition from localized deployment toward broader regional expansion.

The high-resolution, decade-long WPV dataset developed in this study has both practical and scientific relevance. By quantifying WPV coverage, spatial distribution, and waterbody-specific deployment characteristics, the dataset provides a spatial basis for WPV planning, site selection, and comparative analyses across water body types and coverage levels. High-coverage configurations, including those associated with “fishing-solar complementarity” systems, may represent intensive deployment modes with greater potential for local hydrological or ecological interactions (Pringle et al., 2017). Such settings may be associated with greater modification of light availability, air–water exchange, evaporation, and aquatic habitat conditions. From an energy-planning perspective, they also represent an intensive deployment mode, although future expansion should account for environmental carrying capacity and site-specific management constraints (Bai et al., 2024; Château et al., 2019).

Beyond inventory mapping, the dataset is also relevant to broader Earth system science applications. As a spatially explicit record of water-surface solar deployment, it can support analyses of energy–water–environment interactions by linking renewable-energy expansion with water body functions, hydrological conditions, and ecological constraints. In this sense, the dataset may serve as a useful spatial input for regional sustainability assessments and integrated analyses of low-carbon transitions, infrastructure planning, and trade-offs among energy generation, water use, and ecosystem management.

The dataset may also support investigation of the potential environmental implications of WPV deployment. Its high-resolution delineation of WPV-covered and uncovered areas within the same water body facilitates spatially explicit comparisons between shaded and unshaded zones, as well as before-and-after analyses based on installation timing. When combined with other observations, these maps may support assessment of possible water-surface temperature differences associated with partial shading using thermal infrared products, as well as potential changes in water optical properties and algal dynamics using water-color or water-quality indicators such as chlorophyll-a, turbidity, or algal bloom proxies (Chen et al., 2025; Chu and He, 2023). These applications may be particularly relevant for identifying ecological concerns associated with high-coverage installations in small water bodies and for informing more environmentally informed deployment strategies (Sahu et al., 2016; Armstrong et al., 2020; Nobre et al., 2023; Ma and Liu, 2022). In addition, the dataset can facilitate field validation by enabling targeted selection of representative WPV sites for in situ investigation. It should be noted, however, that the SAR–optical fusion framework is primarily designed to detect WPV extent, boundaries, and deployment timing. Although these attributes are relevant for environmental exposure assessment, the framework does not directly quantify thermal or biogeochemical responses, which require dedicated thermal infrared, water-quality remote sensing, or field observations.

Despite constructing a high-resolution WPV inventory, this study has several limitations. First, uncertainties in the underlying water body datasets remain a challenge; small water bodies (e.g., ponds) may be omitted, while imprecise boundaries and spectral confusion with nearby buildings or bare soil may lead to omissions and misclassifications at water edges (Valerio et al., 2024; Wang et al., 2022b). Second, the diversity in WPV installation methods and structural designs may introduce variations in spectral and textural characteristics, posing challenges for consistent extraction (Shi et al., 2023). The current framework is most suitable for stationary installations and anchored, medium- to large-scale floating PV systems, which represent the dominant commercial deployment type in the study region. However, uncertainty may increase for small, loosely arranged, or highly mobile floating platforms, especially where local displacement alters patch boundaries or weakens texture regularity. Finally, the area threshold applied during post-processing to eliminate noise likely leads to an underestimation of the total WPV area, particularly by excluding small-scale systems on rural ponds or aquaculture facilities (Iqra et al., 2024).

In light of these limitations, future research can focus on three key directions. Methodologically, integrating deep learning methods (e.g., U-Net, DeepLabV3+) holds promise for enhancing accuracy, particularly in delineating complex boundaries and detecting morphologically diverse or small-scale targets (Chen et al., 2018; Ronneberger et al., 2015). Spatially, expanding the study to national or global scales would reveal large-scale deployment trends driven by policy and market dynamics, offering valuable guidance for macro-level energy planning. Thematically, future work should place greater emphasis on evaluating the ecological impacts of WPV systems by integrating remote-sensing retrievals with in situ monitoring data, thereby promoting a coordinated approach to renewable energy development and ecosystem conservation.