84
based, with spatial characteristics depending on sensor resolution and coverage,
similar to the concept of grain and extent in ecology (Turner 1989).
For large-scale assessments (i.e., regional, continental, global), cost and effort of
fieldwork is a limiting factor with respect to in-situ observations. Data from the
newest generation of optical satellites (e.g., Landsat 8 and Sentinel-2) have high
potential for a global biodiversity assessment due to their high spatial resolution
(10–30 m), multispectral information, and temporal coverage, with repeat passes
within 5–6 days, depending on the area of interest. Nevertheless, due to their recent
launch, these sensors do not provide a long time series, and the complementarity of
lower resolution satellite data or airborne or terrestrial RS data in combination with
in-situ observation is beneficial to map changes at decadal or longer timescales.
All optical RS approaches use reflected light of the vegetation canopy to infer
information about its state (Schaepman et al. 2009; Homolov a et al. 2013). Leaflevel biochemistry (e.g., traits such as chlorophyll and water content) has strong
links with leaf reflectance and transmittance (Jacquemoud and Baret 1990).
However, when light interacts with the canopy, a multitude of scattering and absorption processes have to be considered (North 1996), taking place at different levels
(e.g., leaf, tree, canopy; Niinemets et al. 1998) of the canopy. Thus, passive optical
observational approaches of forested ecosystems are susceptible to the effects of
forest structure because directional effects associated with illumination and observation geometry may interact with signals related to leaf-level biochemistry (Hilker
et al. 2008; Knyazikhin et al. 2013). Consequently, the reflectance signal at the
canopy level is influenced by both vegetation structure and leaf-level physiology,
and disentangling those based on passive optical data alone remains a difficult problem (Kotz et al. 2004). The effect of vegetation structure on RS indices and products
(e.g., RS-EBVs) is difficult to assess, and its impact on current observations and
predictions may be large. The validation of advanced wall-to-wall RS products
becomes increasingly difficult because of spatiotemporal mismatches of in-situ
observations with RS data. Hence, we need a framework to be able to upscale and
validate leaf-level physiological traits to the level of RS data to test potential observables for RS-informed EBVs.
Radiative transfer (RT) modeling has been used for several decades to simulate
and understand the signals in passive optical data (Myneni et al. 1995, 1997; Meroni
et al. 2004; Lewis and Disney 2007; Gastellu-Etchegorry et al. 1996). In addition,
RT models (RTMs) have been used with existing medium- to low-resolution spaceborne missions for the retrieval of products such as leaf area index (LAI) or fraction
of absorbed photosynthetic radiation (fAPAR) through inversion (Myneni et al.
1997; Running et al. 2004). One particular issue with RTMs of vegetation is their
parameterization. While modeling approaches simulating low-resolution data [such
as Moderate Resolution Imaging Spectroradiometer (MODIS) or MEdium
Resolution Imaging Spectrometer (MERIS)] mainly used one-dimensional parameterizations of the vegetation (Jacquemoud 1993; Huemmrich 2001; Verhoef and
Bach 2007), higher-resolution sensors will need 3-D parameterization to account
for effects like shadowing and multiple scattering (Asner and Warner 2003; Disney
et al. 2006; Widlowski et al. 2015). The first RTMs incorporating 3-D forest structure
were called geometric-optical radiative transfer (GORT)-type models (Ni et al. 1999).
F. Morsdorf et al.
based, with spatial characteristics depending on sensor resolution and coverage,
similar to the concept of grain and extent in ecology (Turner 1989).
For large-scale assessments (i.e., regional, continental, global), cost and effort of
fieldwork is a limiting factor with respect to in-situ observations. Data from the
newest generation of optical satellites (e.g., Landsat 8 and Sentinel-2) have high
potential for a global biodiversity assessment due to their high spatial resolution
(10–30 m), multispectral information, and temporal coverage, with repeat passes
within 5–6 days, depending on the area of interest. Nevertheless, due to their recent
launch, these sensors do not provide a long time series, and the complementarity of
lower resolution satellite data or airborne or terrestrial RS data in combination with
in-situ observation is beneficial to map changes at decadal or longer timescales.
All optical RS approaches use reflected light of the vegetation canopy to infer
information about its state (Schaepman et al. 2009; Homolov a et al. 2013). Leaflevel biochemistry (e.g., traits such as chlorophyll and water content) has strong
links with leaf reflectance and transmittance (Jacquemoud and Baret 1990).
However, when light interacts with the canopy, a multitude of scattering and absorption processes have to be considered (North 1996), taking place at different levels
(e.g., leaf, tree, canopy; Niinemets et al. 1998) of the canopy. Thus, passive optical
observational approaches of forested ecosystems are susceptible to the effects of
forest structure because directional effects associated with illumination and observation geometry may interact with signals related to leaf-level biochemistry (Hilker
et al. 2008; Knyazikhin et al. 2013). Consequently, the reflectance signal at the
canopy level is influenced by both vegetation structure and leaf-level physiology,
and disentangling those based on passive optical data alone remains a difficult problem (Kotz et al. 2004). The effect of vegetation structure on RS indices and products
(e.g., RS-EBVs) is difficult to assess, and its impact on current observations and
predictions may be large. The validation of advanced wall-to-wall RS products
becomes increasingly difficult because of spatiotemporal mismatches of in-situ
observations with RS data. Hence, we need a framework to be able to upscale and
validate leaf-level physiological traits to the level of RS data to test potential observables for RS-informed EBVs.
Radiative transfer (RT) modeling has been used for several decades to simulate
and understand the signals in passive optical data (Myneni et al. 1995, 1997; Meroni
et al. 2004; Lewis and Disney 2007; Gastellu-Etchegorry et al. 1996). In addition,
RT models (RTMs) have been used with existing medium- to low-resolution spaceborne missions for the retrieval of products such as leaf area index (LAI) or fraction
of absorbed photosynthetic radiation (fAPAR) through inversion (Myneni et al.
1997; Running et al. 2004). One particular issue with RTMs of vegetation is their
parameterization. While modeling approaches simulating low-resolution data [such
as Moderate Resolution Imaging Spectroradiometer (MODIS) or MEdium
Resolution Imaging Spectrometer (MERIS)] mainly used one-dimensional parameterizations of the vegetation (Jacquemoud 1993; Huemmrich 2001; Verhoef and
Bach 2007), higher-resolution sensors will need 3-D parameterization to account
for effects like shadowing and multiple scattering (Asner and Warner 2003; Disney
et al. 2006; Widlowski et al. 2015). The first RTMs incorporating 3-D forest structure
were called geometric-optical radiative transfer (GORT)-type models (Ni et al. 1999).
F. Morsdorf et al.