11. THE ROLE OF REMOTE SENSING IN LAND SURFACE
EXPERIMENTS WITHIN BAHC AND ISLSCP
97
already extended their time frame as also more ecological aspects grew in
importance. Currently in its implementation phase, the Large Scale
Biosphere Experiment in the Amazon basin (LBA) is the example of the
culmination of this trend. It will study the integrated functioning of
Amazonia as a regional entity. It specifically addresses the effects of
changes in land-use and climate on the biological, chemical and physical
functions of Amazonia, including the sustainability of development in the
region and the influence of Amazonia on global climate. This involves
studies of the multi-annual dynamics of the tropical forest biome and its
links with the global energy, water, carbon and other biogeochemical cycles
(Nobre et al., 1996, see Section 7).
In 1994, the “Tucson Aggregation Workshop” was held to assess the
state of the art in land-surface aggregation research (Michaud and
Shuttleworth, 1997). Among the conclusions relevant for remote sensing we
find that
field-based modeling shows for both full and sparse canopies, the
measured area-average value of remotely sensed variables is a
reasonable estimate of the sub-pixel, linear average value, but their ecohydrological relevance is only partly explored;
experimental studies suggest, remotely sensed vegetation information
contains valuable information on
assimilation and stomatal
resistance, but the dependence of algorithms on biome and nutrients is
not known.
In the area of aggregation of remotely sensed variables, “substantial
progress has been made, and it is now recognized that surface temperature
and spectral vegetation indices can be aggregated easily at scales ranging
from meters to kilometers. However, it is also recognized that there can be
significant differences with respect to aggregation strategies for sparse and
dense canopies for variables derived from remotely sensed data. We do,
however, have some knowledge of the factors that may introduce
troublesome non-linearities into the remotely sensed measurements. In order
of decreasing importance, these are soil and soil moisture, vegetation, and
topography” (Michaud and Shuttleworth, 1997).
In general the LSEs have greatly improved our understanding of the role
of the land surface and of changes therein on weather and climate (Feddes et
al., 1998). The importance of this role is now well recognized by major
operational weather forecasting services and climate prediction groups.
Several climate and weather modeling centers are or will soon be ready to
assimilate different kind of land surface data for model-initialization and
operational purposes. These data could include remotely sensed ‘real time’
data, e.g., on vegetation and or soil moisture dynamics. This is a new,
EXPERIMENTS WITHIN BAHC AND ISLSCP
97
already extended their time frame as also more ecological aspects grew in
importance. Currently in its implementation phase, the Large Scale
Biosphere Experiment in the Amazon basin (LBA) is the example of the
culmination of this trend. It will study the integrated functioning of
Amazonia as a regional entity. It specifically addresses the effects of
changes in land-use and climate on the biological, chemical and physical
functions of Amazonia, including the sustainability of development in the
region and the influence of Amazonia on global climate. This involves
studies of the multi-annual dynamics of the tropical forest biome and its
links with the global energy, water, carbon and other biogeochemical cycles
(Nobre et al., 1996, see Section 7).
In 1994, the “Tucson Aggregation Workshop” was held to assess the
state of the art in land-surface aggregation research (Michaud and
Shuttleworth, 1997). Among the conclusions relevant for remote sensing we
find that
field-based modeling shows for both full and sparse canopies, the
measured area-average value of remotely sensed variables is a
reasonable estimate of the sub-pixel, linear average value, but their ecohydrological relevance is only partly explored;
experimental studies suggest, remotely sensed vegetation information
contains valuable information on
assimilation and stomatal
resistance, but the dependence of algorithms on biome and nutrients is
not known.
In the area of aggregation of remotely sensed variables, “substantial
progress has been made, and it is now recognized that surface temperature
and spectral vegetation indices can be aggregated easily at scales ranging
from meters to kilometers. However, it is also recognized that there can be
significant differences with respect to aggregation strategies for sparse and
dense canopies for variables derived from remotely sensed data. We do,
however, have some knowledge of the factors that may introduce
troublesome non-linearities into the remotely sensed measurements. In order
of decreasing importance, these are soil and soil moisture, vegetation, and
topography” (Michaud and Shuttleworth, 1997).
In general the LSEs have greatly improved our understanding of the role
of the land surface and of changes therein on weather and climate (Feddes et
al., 1998). The importance of this role is now well recognized by major
operational weather forecasting services and climate prediction groups.
Several climate and weather modeling centers are or will soon be ready to
assimilate different kind of land surface data for model-initialization and
operational purposes. These data could include remotely sensed ‘real time’
data, e.g., on vegetation and or soil moisture dynamics. This is a new,