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Chapter 11
Research data are needed both for process studies and for the
improvement and validation of parameterizations in regional and global
forecast and climate models. The typical scales in both cases are local to
regional, the latter usually with imbedded intensive field sites.
Operational data are needed in the context of global observing systems,
for the purpose of serving as operational input for forecast and climate
models on one hand, and as long-term data bases for change analysis on the
other.
The information that should be derived from remote sensing data
includes
a quantitative description of the general characteristics of the landsurface (topography, soil type and properties, land-use and vegetation
type) their spatial extent and changes therein;
biophysical characteristics of the land-cover (e.g., biomass, leaf area
index, photosynthetically active radiation, spectral radiances, roughness
length);
biogeophysical parameters, such as surface temperature, components of
the surface radiation and energy budgets, soil moisture, runoff,
precipitation; and
meteorological parameters, such as temperature, wind, humidity,
atmospheric composition (near-surface values, vertical profiles, total
column values for the latter two), cloud cover.
Current and future Earth Observation data can satisfy many of these
requirements, provided adequate algorithms are (further) developed and
validated. There will be a continuing need, however, for complementary
ancillary data to be supplied by conventional observing systems and
numerical models. Increasing emphasis is also on the development of
adequate assimilation schemes which are required to blend information from
many data sources of very different characteristics into (globally
homogeneous for years to decades) gridded datasets.
It is important to note here that many of the methods developed in the
research mode described above can, at a certain stage of maturity, be
transferred to be used in operational environments for the management of
natural resources (land, forest, water). Examples are refined land-use and
land-cover classifications or evapotranspiration maps. This kind of spin-off
needs to be taken into account when assessing the market potential of EO
data. Recent land-surface experiments like LBA have this goal clearly in
mind. The outcome of research into the land-surface processes under BAHC,
ISLSCP and related projects will, in the long run, increase the usefulness of
EO data for customers in sustainable land and water management.
Chapter 11
Research data are needed both for process studies and for the
improvement and validation of parameterizations in regional and global
forecast and climate models. The typical scales in both cases are local to
regional, the latter usually with imbedded intensive field sites.
Operational data are needed in the context of global observing systems,
for the purpose of serving as operational input for forecast and climate
models on one hand, and as long-term data bases for change analysis on the
other.
The information that should be derived from remote sensing data
includes
a quantitative description of the general characteristics of the landsurface (topography, soil type and properties, land-use and vegetation
type) their spatial extent and changes therein;
biophysical characteristics of the land-cover (e.g., biomass, leaf area
index, photosynthetically active radiation, spectral radiances, roughness
length);
biogeophysical parameters, such as surface temperature, components of
the surface radiation and energy budgets, soil moisture, runoff,
precipitation; and
meteorological parameters, such as temperature, wind, humidity,
atmospheric composition (near-surface values, vertical profiles, total
column values for the latter two), cloud cover.
Current and future Earth Observation data can satisfy many of these
requirements, provided adequate algorithms are (further) developed and
validated. There will be a continuing need, however, for complementary
ancillary data to be supplied by conventional observing systems and
numerical models. Increasing emphasis is also on the development of
adequate assimilation schemes which are required to blend information from
many data sources of very different characteristics into (globally
homogeneous for years to decades) gridded datasets.
It is important to note here that many of the methods developed in the
research mode described above can, at a certain stage of maturity, be
transferred to be used in operational environments for the management of
natural resources (land, forest, water). Examples are refined land-use and
land-cover classifications or evapotranspiration maps. This kind of spin-off
needs to be taken into account when assessing the market potential of EO
data. Recent land-surface experiments like LBA have this goal clearly in
mind. The outcome of research into the land-surface processes under BAHC,
ISLSCP and related projects will, in the long run, increase the usefulness of
EO data for customers in sustainable land and water management.