Evaluation of Gap-Filling Methods for Inland Water Color Remote Sensing Data: A Case Study in Lake Taihu

Abstract

Highlights: What are the main findings? The study shows that while traditional gap-filling methods like Kriging and SG Filtering perform well with small data gaps, their accuracy diminishes when the missing data is extensive or the environment is dynamic. However, such conditions are quite common in inland lakes. DINEOF and DINCAE outperform in capturing spatiotemporal variability, maintaining high accuracy even with over 60% missing data, making them suitable for eutrophic lake across cloudy-rainy regions. What are the implications of the main findings? The data reconstruction method can be used to generate spatiotemporal seamless datasets, enhancing the accuracy and completeness of lake water quality monitoring data and enabling a more precise capture of dynamic changes in lakes. The spatiotemporal seamless reconstructed data can provide crucial data support for practical applications, such as short-term forecasting of lake water color parameters, addressing the issue of data scarcity in lake management. Satellite remote sensing is an important approach for monitoring lake water environments. However, in regions with frequent cloud and rainfall, optical remote sensing imagery often suffers from extensive data gaps caused by cloud cover, rainfall, and sun glint, which severely limit its continuity and reliability for long-term monitoring. To address this issue, this study uses Lake Taihu—a typical eutrophic lake located in a cloudy and rainy region—as a case study and systematically compares four representative gap-filling methods: Kriging Interpolation, Savitzky–Golay (SG) Filtering, Data Interpolating Empirical Orthogonal Functions (DINEOF), and the Data Interpolating Convolutional Auto Encoder (DINCAE). The results show that traditional methods retain some accuracy under low missing-data conditions (for Kriging: R = 0.84, RMSE = 7.85 μg/L; for SG Filtering: R = 0.88, RMSE = 6.67 μg/L), but tend to produce over-smoothing or distorted estimations in cases of extensive gaps or highly dynamic environments. In contrast, both DINEOF and DINCAE capture the spatiotemporal variability of chlorophyll-a more effectively, maintaining relatively high accuracy and robustness even when the missing rate exceeds 60% (for DINEOF: R = 0.84, RMSE = 6.91 μg/L; for DINCAE: R = 0.79, RMSE = 8 μg/L). Based on the optimal algorithm, a seamless long-term dataset of chlorophyll-a concentration covering Lake Taihu can be constructed, providing a solid data foundation for eutrophication trend analysis and algal bloom early warning. This study demonstrates the effectiveness of integrating statistical and deep learning approaches for lake water color remote sensing data reconstruction, offering important implications for enhancing continuous monitoring of lake water environments and supporting ecological management decisions.