Vegetation Observation in the Big Data Era : Sentinel-2 data for mapping the seasonality of land vegetation

Abstract: Using satellite remote sensing data for observing vegetation seasonality is an important approach to estimate phenology and carbon uptake of land vegetation. The successful launch of Sentinel-2B in 2017 initiated full operation of the Sentinel-2 twin satellites, and they now provide 10 - 60 m spatial resolution satellite data at 5 days temporal resolution worldwide, releasing approximately 3.2 TB of image data per day. With Sentinel-2's huge amount of high spatial resolution and high temporal resolution data, Earth observation is facing new opportunities and challenges. To adapt to the characteristics of Sentinel-2 MSI data, the existing time-series analysis methods used for vegetation seasonality studies with regular time step data (e.g., from the MODIS sensor) require modification and improvements. In this thesis, a new time-series analysis method, based on the currently available methods, was developed for estimating vegetation seasonality from high spatial resolution Sentinel-2 data. The new method is applied to Sentinel-2 data to estimate vegetation phenology and photosynthetic carbon uptake, and the outputs are evaluated based on ground reference data and compared to MODIS products. By comparing with ground reference data (in-situ NDVI time-series, flux tower GPP time-series, and elevation), function fitting methods (e.g., double logistic function fitting) provide the most robust description of the seasonal dynamics for MODIS NDVI time-series among five tested smoothing methods. Based on this finding, we developed box constrained separable least squares fits to double logistic functions with seasonal shape priors, and tested the robustness of the method on six years of simulated Sentinel-2 data by use of MODIS data. The results show that the new method is flexible enough to simulate interannual variations and robust enough when data are sparse. The box constrained function fitting method applied to Sentinel-2 MSI 2-band Enhanced Vegetation Index (EVI2) data was further used to estimate vegetation phenology and gross primary productivity (GPP) across diverse Nordic vegetation types. The results indicate that daily EVI2 time-series derived from Sentinel-2 is more accurate than from MODIS, with an RMSE of 0.08 for Sentinel-2 and 0.13 for MODIS versus the ground spectral data. With reference to the dates of greenness rising estimated from digital cameras, the dates estimated from Sentinel-2 (RMSE: 8.1 days) are closer than those from MODIS (RMSE: 14.4 days). Sentinel-2 data also generate more phenological details along elevation gradients and land cover variations than MODIS. However, Sentinel-2 does not show any advantage in estimating GPP, when comparing with data from flux towers. The average error between the modelled GPP from Sentinel-2 EVI2 and the GPP derived from flux tower data was similar to that from MODIS. This result partly reflects inabilities in the flux tower data to resolve variation at the same high resolution as Sentinel-2, and further studies will be required to fully evaluate the capability of the sensor in this respect.In conclusion, the new method, box constrained separable least squares fits to double logistic functions with seasonal shape priors, is useful and computationally efficient for robustly reconstructing daily vegetation index time-series and estimating vegetation phenology from Sentinel-2 data. In addition, by applying the new method to Sentinel-2 data is useful for describing the spatial variation of GPP in the footprint area, although Sentinel-2 did not show improvements in estimating GPP compared with MODIS data. The developed time-series methods will be implemented in a subsequent version of the TIMESAT software package for processing of irregular time step data.