High-quality remotely sensed satellite data series are important for many ecological and environmental applications. Unfortunately, irregular spatiotemporal samples, frequent image gaps and inevitable observational biases can greatly hinder their application. As one of the most effective gap filling and noise reduction approaches, the harmonic analysis of time series (HANTS) method has been widely used to reconstruct geographical variables; however, when applied on multi-year time series over large spatial areas, the optimal harmonic formulas are generally varied in different locations or change across different years. The question of how to choose the optimal harmonic formula is still unanswered due to the deficiency of appropriate criteria. In this study, an adaptive piecewise harmonic analysis method (AP-HA) is proposed to reconstruct multi-year seasonal data series. The method introduces a cross-validation scheme to adaptively determine the optimal harmonic model and employs an iterative piecewise scheme to better track the local traits. Whenapplied to the satellite-derived sea surface chlorophyll-a time series over the Bohai and Yellow Seas of China, the AP-HA obtains reliable reconstruction results and outperforms the conventional HANTS methods, achieving improved accuracy. Due to its generic approach to filling missing observations and tracking detailed traits, the AP-HA method has a wide range of applications for other seasonal geographical variables.
This is an open access article distributed under the Creative Commons Attribution License
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited