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delivered directly to the users. While Sentinel satellites are calibrated, users are provided with top-of-atmosphere reflectance and have to perform the atmospheric correction (using plug-ins such as sen2cor (http://step.esa.int/main/third-party-plugins-2/
sen2cor/)). That said, Vuolo et al. (2016) have demonstrated very good agreement
between calibrated Sentinel-2 and Landsat-8 data for six test sites. A Harmonized
Landsat and Sentinel-2 land surface reflectance data set is now readily available
(Claverie et al. 2018).
The requirement for high-temporal-resolution RS data has, since 1982, been
provided by the NOAA Advanced Very High Resolution Radiometer (AVHRR)
sensor and its successors, with much regional scale analyses undertaken using
NDVI (Reed et al. 2009). Capturing the seasonal pattern of photosynthetically
active radiation absorbed by the land surface, using repeated measures of vegetation indices such as NDVI throughout the year for an area of interest, allows depicting the cycle of events that drive the seasonal progression of vegetation through
stages of dormancy, active growth, and senescence. Several phenological metrics
can be extracted from the temporal sequence, relating to leaf-on and leaf-off,
length of growing season, peak of growing season, trough, and measures of seasonal amplitude (integral, trough), and their patterns over time and space. Specially
written and customizable open-source software, such as TIMESAT (Eklundh and
Jonsson 2015) and QPhenoMetrics (Duarte et al. 2018), afford some robustness to
the study of phenology.
With this emphasis on using satellite RS of land surface phenology to assess
changes in vegetation, validation of extracted metrics is imperative. Three principal
approaches are suitable, all of which use observations taken at ground level. The
first relies on collaboration between experts to ensure suitable spatial and temporal
coverage. The PEP725 ground phenology database generated as a result of the Pan
European Phenology (PEP) project (a European infrastructure to promote and facilitate phenological research, education, and environmental monitoring) is one example (Templ et al. 2018). The second approach is an extension of the first and uses
citizen science projects where the public exploits Web 2.0 technologies and contributes ground-based observations (Kosmala et al. 2016). The idea of citizens as sensors is not new, but full use of their input still requires effort. This is the focus of a
current European Union (EU) Horizon 2020 project, LandSense (https://landsense.
eu/). The third approach uses proximal sensing (often automated systems) to provide detailed information at particular locations (e.g., traffic cameras, Morris et al.
2013; archived TV video footage, De Frenne et al. 2018; and webcams, e.g., the
PhenoCam project, Richardson et al. 2018). However, as Brown et al. (2016) pointed
out, these technological advances present challenges with respect to data standards.
These authors suggest that continental-scale ecological research networks, such as
the US National Ecological Observatory Network (NEON) and the EU’s Integrated
Carbon Observation System (ICOS), can serve as templates for developing rigorous
data standards and extending the utility of PhenoCam data through standardized
protocols for ground-truthing.
F. Schrodt et al.
delivered directly to the users. While Sentinel satellites are calibrated, users are provided with top-of-atmosphere reflectance and have to perform the atmospheric correction (using plug-ins such as sen2cor (http://step.esa.int/main/third-party-plugins-2/
sen2cor/)). That said, Vuolo et al. (2016) have demonstrated very good agreement
between calibrated Sentinel-2 and Landsat-8 data for six test sites. A Harmonized
Landsat and Sentinel-2 land surface reflectance data set is now readily available
(Claverie et al. 2018).
The requirement for high-temporal-resolution RS data has, since 1982, been
provided by the NOAA Advanced Very High Resolution Radiometer (AVHRR)
sensor and its successors, with much regional scale analyses undertaken using
NDVI (Reed et al. 2009). Capturing the seasonal pattern of photosynthetically
active radiation absorbed by the land surface, using repeated measures of vegetation indices such as NDVI throughout the year for an area of interest, allows depicting the cycle of events that drive the seasonal progression of vegetation through
stages of dormancy, active growth, and senescence. Several phenological metrics
can be extracted from the temporal sequence, relating to leaf-on and leaf-off,
length of growing season, peak of growing season, trough, and measures of seasonal amplitude (integral, trough), and their patterns over time and space. Specially
written and customizable open-source software, such as TIMESAT (Eklundh and
Jonsson 2015) and QPhenoMetrics (Duarte et al. 2018), afford some robustness to
the study of phenology.
With this emphasis on using satellite RS of land surface phenology to assess
changes in vegetation, validation of extracted metrics is imperative. Three principal
approaches are suitable, all of which use observations taken at ground level. The
first relies on collaboration between experts to ensure suitable spatial and temporal
coverage. The PEP725 ground phenology database generated as a result of the Pan
European Phenology (PEP) project (a European infrastructure to promote and facilitate phenological research, education, and environmental monitoring) is one example (Templ et al. 2018). The second approach is an extension of the first and uses
citizen science projects where the public exploits Web 2.0 technologies and contributes ground-based observations (Kosmala et al. 2016). The idea of citizens as sensors is not new, but full use of their input still requires effort. This is the focus of a
current European Union (EU) Horizon 2020 project, LandSense (https://landsense.
eu/). The third approach uses proximal sensing (often automated systems) to provide detailed information at particular locations (e.g., traffic cameras, Morris et al.
2013; archived TV video footage, De Frenne et al. 2018; and webcams, e.g., the
PhenoCam project, Richardson et al. 2018). However, as Brown et al. (2016) pointed
out, these technological advances present challenges with respect to data standards.
These authors suggest that continental-scale ecological research networks, such as
the US National Ecological Observatory Network (NEON) and the EU’s Integrated
Carbon Observation System (ICOS), can serve as templates for developing rigorous
data standards and extending the utility of PhenoCam data through standardized
protocols for ground-truthing.
F. Schrodt et al.