Continental-scale land surface phenology from harmonized Landsat 8 and Sentinel-2 imagery

TLDR

Dense time series from Landsat 8 and Sentinel‑2 provide unprecedented spatial detail for monitoring land surface dynamics, but the large data volume poses challenges; land surface phenology algorithms offer a way to reduce redundancy and support ecosystem monitoring. The study presents a continental‑scale land surface phenology algorithm and data product derived from harmonized Landsat 8 and Sentinel‑2 imagery. The algorithm generates high‑quality vegetation‑index time series from harmonized Landsat 8 and Sentinel‑2 imagery and uses them to estimate 30 m‑resolution phenophase transition timings. Assessment shows the algorithm accurately captures phenology in strongly seasonal ecosystems such as deciduous forests, but its performance is less reliable in evergreen systems.

Abstract

Dense time series of Landsat 8 and Sentinel-2 imagery are creating exciting new opportunities to monitor, map, and characterize temporal dynamics in land surface properties with unprecedented spatial detail and quality. By combining imagery from the Landsat 8 Operational Land Imager and the MultiSpectral Instrument on-board Sentinel-2A and -2B, the remote sensing community now has access to moderate (10–30 m) spatial resolution imagery with repeat periods of ~3 days in the mid-latitudes. At the same time, the large combined data volume from Landsat 8 and Sentinel-2 introduce substantial new challenges for users. Land surface phenology (LSP) algorithms, which estimate the timing of phenophase transitions and quantify the nature and magnitude of seasonality in remotely sensed land surface conditions, provide an intuitive way to reduce data volumes and redundancy, while also furnishing data sets that are useful for a wide range of applications including monitoring ecosystem response to climate variability and extreme events, ecosystem modelling, crop-type discrimination, and land cover, land use, and land cover change mapping, among others. To support the need for operational LSP data sets, here we describe a continental-scale land surface phenology algorithm and data product based on harmonized Landsat 8 and Sentinel-2 (HLS) imagery. The algorithm creates high quality times series of vegetation indices from HLS imagery, which are then used to estimate the timing of vegetation phenophase transitions at 30 m spatial resolution. We present results from assessment efforts evaluating LSP retrievals, and provide examples illustrating the character and quality of information related to land cover and terrestrial ecosystem properties provided by the continental LSP dataset that we have developed. The algorithm is highly successful in ecosystems with strong seasonal variation in leaf area (e.g., deciduous forests). Conversely, results in evergreen systems are less interpretable and conclusive.