Comprehensive assessment of Spatiotemporal fusion methods in inland water monitoring

Abstract

ABSTRACT Monitoring rapidly changing inland water bodies using remote sensing requires a temporal resolution of at least 3 days and a spatial resolution of at least 30 meters. However, the current satellite data falls short of meeting these monitoring requirements. Spatiotemporal fusion (STF) presents an effective method for obtaining continuous high-resolution data. This study explores five established STF algorithms, namely Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), Robust Adaptive Spatial and Temporal Fusion Model (RASTFM), Flexible Spatiotemporal Data Fusion (FSDAF) and Unmixing-Based Data Fusion (UBDF), through experimentation across 45 lakes worldwide. Results are comprehensively compared and analyzed using two indicators: Root Mean Square Error (RMSE) and Robert’s edge (Edge) based on a dataset that contains 360 image pairs collected in 2021. The five algorithms underwent 950 random experiments. The results indicate that: (1) ESTARFM outperforms the other four algorithms in terms of visual effect and accuracy assessment. Its average RMSE of and EDGE are 0.0023 sr−1 and −0.0419, respectively. Followed by: FSDAF (RMSE: 0.0026 sr−1, EDGE: −0.0584), RASTFM (RMSE: 0.0029 sr−1, EDGE: −0.4128), STARFM (RMSE: 0.0027 sr−1, EDGE: −0.5207), and UBDF (RMSE: 0.0030 sr−1, EDGE: −0.6505) algorithms. (2) the algorithms have varied performance in different water monitoring missions: for water body extraction, the ESTARFM and FSDAF algorithms are recommended; for algae bloom detection, the FSDAF algorithm is recommended; for seasonal water body monitoring, the ESTARFM is recommended; for quantitative monitoring, the ESTARFM, FSDAF, and UBDF algorithms are recommended. (3) STARFM-like algorithms are sensitive to radiometric uncertainties and UBDF algorithm is sensitive to spatial uncertainties. The findings of this study can help generate high spatiotemporal resolution remote sensing datasets for water quality monitoring, waterbody detection, algae bloom tracking, and other water environmental monitoring missions.