Abstract
Quantifying the accuracy of remote sensing products is a timely endeavor given the rapid increase in Earth observation missions. A validation site for Sentinel-2 products was hence established in central Germany. Automatic multispectral and hyperspectral sensor systems were installed in parallel with an existing eddy covariance flux tower, providing spectral information of the vegetation present at high temporal resolution. Normalized Difference Vegetation Index (NDVI) values from ground-based hyperspectral and multispectral sensors were compared with NDVI products derived from Sentinel-2A and Moderate-resolution Imaging Spectroradiometer (MODIS). The influence of different spatial and temporal resolutions was assessed. High correlations and similar phenological patterns between in situ and satellite-based NDVI time series demonstrated the reliability of satellite-based phenological metrics. Sentinel-2-derived metrics showed better agreement with in situ measurements than MODIS-derived metrics. Dynamic filtering with the best index slope extraction algorithm was nevertheless beneficial for Sentinel-2 NDVI time series despite the availability of quality information from the atmospheric correction procedure.
Highlights
Quantifying the accuracy of satellite-based remote sensing products is an important task that is increasingly timely given the rising number of Earth observation missions
Different measurement strategies are pursued at established validation sites, including continuous in situ measurements, airborne remote sensing campaigns at the landscape level, as well as the collection of independent satellite sensor data [1,2]
We evaluated whether dynamic filtering procedures, commonly used with NOAA Advanced Very High Resolution Radiometer (AVHRR) and Moderate-resolution Imaging Spectroradiometer (MODIS), are obsolete with new generation satellite data
Summary
Quantifying the accuracy of satellite-based remote sensing products is an important task that is increasingly timely given the rising number of Earth observation missions. Linking small-scale observations with large-scale satellite products is crucial to understand the relationships between optical information and ecological, as well as physiological processes [3]. This facilitates prediction and mapping of processes at regional and global scales, such as terrestrial carbon assimilation [4]. Automatic continuous spectral measurements are bridging the gap between continuous micro-meteorological measurements and remote-sensing products by providing optical information about vegetation processes with high temporal resolution [5,6]. They are helpful to improve the understanding of satellite measurements
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