Abstract
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.
Highlights
Satellite remote sensing is a useful tool for obtaining global-scale datasets synoptically [1,2]
We examine the use of the adaptive piecewise harmonic analysis method (AP-HA) method for the reconstruction of a satellite-derived sea surface chlorophyll-a dataset and achieve significant improvements in performance when compared with conventional harmonic analysis of time series (HANTS) methods
The iterative piecewise fitting procedure was applied on the new training data series to produce multiple fitted data series, which gradually converged to the final reconstructed data series after 8 iterations, giving the minimum root mean squarewere errorused (RMSE) for the testing data series (Figure 4c)
Summary
Satellite remote sensing is a useful tool for obtaining global-scale datasets synoptically [1,2]. Remote sensing has been widely applied in many fields, providing various geographical and biological datasets [6,7,8,9]; it is challenging to obtain temporally and spatially continuous data series of high quality, mainly due to cloud coverage and other disturbances, such as atmospheric contamination, instrumentation malfunctions and the complexity of sea surface conditions [10,11,12]. Even though many noise reduction and image fusion studies have been performed to promote quality during the generation of these products, there gaps and noise in the produced time series probably remain that hinder their further application [13,14,15]. For specific applications such as phenological studies, the missing
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