A Parameter and Flag Adaptive Reconstruction Method for Satellite Vegetation Index Time Series

The data quality issue induced by atmospheric and other disturbances can significantly impede the application of remote sensing vegetation indices (VIs). Despite the development of numerous VI reconstruction techniques, two major challenges remain, i.e., the reliance on quality flag inputs and parameter settings. Quality flags are usually necessary as inputs for the different methods to improve the reconstruction accuracy, but mislabeling can be common in the quality flag data, which can directly introduce uncertainties. Furthermore, constant parameter schemes are usually assigned during the reconstruction applications, but the optimal parameters for all the models can show great spatial heterogeneity. Accordingly, in this paper, we propose a parameter-free and flag-free adaptive time-series method based on a variational reconstruction framework (PF-Free) to address the afore-mentioned issues, which can be applied without any parameter or flag inputs. PF-Free makes full use of the time-series temporal smoothness and inter-annual similarity to label the data after time-series rearrangement, and the parameters are adaptively selected using an improved generalized cross-validation (GCV) technique. Simulation and real-data experiments all demonstrate that PF-Free can achieve better and more stable reconstruction results, compared to other comparative methods. The adaptive quality flags can denote the data quality robustly and accurately, while guaranteeing better reconstruction performance, as long as there is any mislabeling in the original flags. Moreover, the adaptive parameter selection strategy considers the great spatial heterogeneity in the optimal parameters on a pixel-by-pixel basis, leading to more stable reconstruction outcomes under complex conditions. Further experiments also prove the effectiveness of PF-Free in processing data without quality flags or severely contaminated data, using Advanced Very High-Resolution Radiometer (AVHRR) data and Moderate Resolution Imaging Spectroradiometer (MODIS) daily normalized difference vegetation index (NDVI) data. This work provides a practical reconstruction method for VI time series, which is both flexible and convenient, without requiring any parameter or flag input, which we believe will advance VI reconstruction applications.