Abstract
The high spatial resolution and revisit time of Sentinel-2A/B tandem satellites allow a potentially improved retrieval of land surface phenology (LSP). The biome and regional characteristics, however, greatly constrain the design of the LSP algorithms. In the Arctic, such biome-specific characteristics include prolonged periods of snow cover, persistent cloud cover, and shortness of the growing season. Here, we evaluate the feasibility of Sentinel-2 for deriving high-resolution LSP maps of the Arctic. We extracted the timing of the start and end of season (SoS and EoS, respectively) for the years 2019 and 2020 with a simple implementation of the threshold method in Google Earth Engine (GEE). We found a high level of similarity between Sentinel-2 and PhenoCam metrics; the best results were observed with Sentinel-2 enhanced vegetation index (EVI) (root mean squared error (RMSE) and mean error (ME) of 3.0 d and −0.3 d for the SoS, and 6.5 d and −3.8 d for the EoS, respectively), although other vegetation indices presented similar performances. The phenological maps of Sentinel-2 EVI compared well with the same maps extracted from the Moderate Resolution Imaging Spectroradiometer (MODIS) in homogeneous landscapes (RMSE and ME of 9.2 d and 2.9 d for the SoS, and 6.4 and −0.9 d for the EoS, respectively). Unreliable LSP estimates were filtered and a quality flag indicator was activated when the Sentinel-2 time series presented a long period (>40 d) of missing data; discontinuities were lower in spring and early summer (9.2%) than in late summer and autumn (39.4%). The Sentinel-2 high-resolution LSP maps and the GEE phenological extraction method will support vegetation monitoring and contribute to improving the representation of Artic vegetation phenology in land surface models.
Highlights
Studies of the Arctic are becoming increasingly important, in the context of the onset of an expected tipping point in the function of its ecosystems as a result of ongoing climate warming [1] that is lengthening the growing season and increasing vegetation productivity [2,3]
Reliable Land surface phenology (LSP) maps of the Arctic could be used to support models that show positive feedbacks between trends in climate warming and a lengthening growing season [3], while studies of Arctic LSP may reveal a link between the advancement of spring onset, drought severity, and an increase in the incidence of artic fires over recent decades [9], and support reports of links between carbon uptake and phenology metrics related to vegetation greenness, such as the amplitude of the vegetation index [10]
The Sentinel-2 vegetation index that showed the best results in the comparison with PhenoCam was the enhanced vegetation index (EVI) (SoS and end of season (EoS) mean error (ME): −0.3 and −3.8 d, and start of season (SoS) and EoS root mean squared error (RMSE): 3.0 and 6.5 d) for the threshold method without time series smoothing (Figure 3)
Summary
Studies of the Arctic are becoming increasingly important, in the context of the onset of an expected tipping point in the function of its ecosystems as a result of ongoing climate warming [1] that is lengthening the growing season and increasing vegetation productivity [2,3]. LSP methods tend to erroneously detect the end of a snow period as the start of season (SoS), in methods that extract phenology metrics from greenness indices, such as the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI), where distinct changes in reflectance during the snowmelt period are incorrectly detected as the onset of vegetation growth. To account for this problem, vegetation indices that are insensitive to snow have been developed to improve phenology estimation [11,12]. A lack of valid satellite observations due to persistent cloud cover hampers LSP estimation, so biome-specific algorithms based on the combination of multi-satellite data and spatio-temporal gap filling methods have been developed [3]
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