Validating MODIS and Sentinel-2 NDVI Products at a Temperate Deciduous Forest Site Using Two Independent Ground-Based Sensors

Maximilian Lange, Benjamin Dechant, Corinna Rebmann, Michael Vohland, Matthias Cuntz, Daniel Doktor
2017 Sensors  
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
more » ... yperspectral 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. Phenology plays a central role in the estimation of vegetation-dependent processes [7] . Research on vegetation phenology uses phenological and spectral ground observation networks, phenological modeling, eddy covariance towers and satellite-derived imagery to assess and monitor vegetation status and dynamics [8, 9] . Several studies are dealing with the question of which vegetation index to choose for satellite phenology depiction [10] . Although the Normalized Difference Vegetation Index (NDVI) shows saturation effects in dense forest canopies [11] , it is one of the most widely-used indices for this purpose, mainly due to data availability and its robustness against noise and varying illumination geometries [10, 12] . A large number of studies [13-16] mapped land surface phenology from satellite NDVI data. These studies outlined two main problems in the derivation of phenology from satellite data: firstly, the low spatial resolution of satellite data, e.g., from the NOAA Advanced Very High Resolution Radiometer (AVHRR) and the NASA Moderate-resolution Imaging Spectroradiometer (MODIS). This results in mixed pixels, integrating phenological signals of different vegetation and land cover types, e.g., deciduous forests and nearby grasslands. New satellite generations, such as the Copernicus Sentinel-2, may overcome this issue with higher spatial resolution. Secondly, non-vegetational effects may alter the received phenological signal. These effects include atmospheric conditions, snow cover [11, 17] , soil-wetness [8], viewing geometry and illumination conditions [18, 19] , as well as the distorted signal under overcast conditions. Noise introduced by these effects can be eliminated by using methods like Maximum Value Composite (MVC) [20] or using dynamic filtering, such as the Best Index Slope Extraction (BISE) [21] . While MVC is effective at reducing cloud and viewing condition effects, it can include outliers with higher NDVI. Short-term vegetation-state changes might be masked using a long composition period, while a short period retains noise [21] . BISE excludes outliers, and its parametrization options allow for the removal of sudden decreases in NDVI, mainly due to non-vegetational effects. This, however, also excludes sudden decreases in NDVI due to vegetational processes, followed by rapid regrowth [21] . Several approaches are used to fill gaps and smooth the phenological time series after the removal of noisy measurements: interpolation methods, signal filter or model fitting algorithms [12] . Subsequently, phenological metrics are extracted using different approaches: global or local thresholds, conceptual-mathematical models [8] or local extrema of transition rates [22] . Here, we used BISE to filter NDVI data, followed by a simple linear interpolation to remove gaps in the time series, and finally, we extracted phenological metrics with a local threshold method. The aim of this study is to validate different satellite phenology products by comparing them to multi-sensor ground measurements. This also requires a detailed description of the site's instrumentation, as well as information on calibration, but the main focus of this study is not on the latter. Details on calibration will be described in a separate publication [23] . In the following, we briefly introduce measurement approaches commonly applied at spectral ground observation sites. Compact multispectral sensors such as the SKYE SKR1860 series (Skye Instruments Ltd., Llandrindod Wells, Powys, U.K.) are comparably low-cost and fairly straightforward to set up and maintain [6] . Typically, no optical fibers are required, and data can be logged with data loggers commonly used at eddy covariance sites. Therefore, this type of instrument is widely used [24] . Hyperspectral systems, in contrast, are usually more expensive and require computers to control the timing of measurements and instrument settings [6] . Furthermore, it is common that fiber optics are used to connect the spectrometer to the point of measurement [25] [26] [27] , and this introduces additional potential for calibration issues on top of spectrometer calibration itself [6] . However, they provide more detailed spectral information. Both sensor types are potentially affected by issues of long-term continuous field measurements [5, 6] , such as degradation and sensitivity to temperature and humidity. While other studies focused mainly on either hyperspectral [24] [25] [26] 28] or multispectral sensors [4, 19] , we used both types of sensors in order to assess measurement quality issues introduced by environmental conditions and instrument settings [24] , which might be relevant for the validation of satellite-derived phenological profiles.
doi:10.3390/s17081855 pmid:28800065 pmcid:PMC5579479 fatcat:3ak3wvgtnjhr7o3o5ginq7v7jm