A novel algorithm for the generation of gap-free time series by fusing harmonized Landsat 8 and Sentinel-2 observations with PhenoCam time series for detecting land surface phenology

Vegetation phenology is one of the most sensitive indicators to environmental and climate changes. In order to characterize the seasonal variation in relatively pure or homogenous vegetation types, fine spatial resolution satellite data (≤ 30 m), such as Landsat, Sentinel-2, PlanetScope, or Harmonized Landsat and Sentinel-2 (HLS), have been increasingly applied to detect land surface phenology (LSP). However, the most critical challenge in LSP detections is the gaps in temporal satellite observations caused by noise and persistent cloud/snow cover. Therefore, this study presented a novel algorithm for generating synthetic gap-free time series at the field scale (30 m) for LSP detections. Specifically, we first developed a framework to establish a large collection of temporal shapes of vegetation growth with as many as 100 grid-based Green Chromatic Coordinate (GCC) time series in a single PhenoCam site. For a given HLS pixel, the two-band Enhanced Vegetation Index (EVI2) time series was matched and fused with the most comparable temporal GCC shape selected from the collection of PhenoCam GCC time series to generate a synthetic gap-free HLS-PhenoCam EVI2 time series, which was used to detect the 30 m phenometrics. The detected phenometrics were evaluated using manually selected and spatially matched GCC observations as well as phenology detections from HLS alone. The result indicates that the HLS-PhenoCam phenometrics are very close to the observations from PhenoCam network with a correlation coefficient (R) of 0.82–0.97, a mean absolute difference (MAD) of 2.8–3.5 days, a root mean squared error (RMSE) of 3.5–4.0 days, and a mean systematic bias (MSB) of 0.1–2.2 days. The HLS-PhenoCam detections are significantly improved relative to the HLS phenometrics that have a statistic accuracy of R = 0.57–0.78, MAD = 6.4–9.3 days, RMSE = 8.8–13.9 days, MSB = -5.2–5.9 days. The difference between HLS-PhenoCam and HLS alone LSP detections over a HLS tile could be on average larger than two weeks if high-quality observation (HQO) proportion in the annual HLS time series is <10%, which exponentially reduces with the increase of HQO in HLS observations. The analyses in this study suggest that the gap-free HLS-PhenoCam time series is able to be generated for producing high-quality phenology datasets across a local and regional scale, to bridge near-surface PhenoCam observations with satellite observations data at various scales, and to be used as a scalable phenology dataset for the validation of global MODIS and VIIRS LSP products.