Review of Understanding of Earth’s Hydrological Cycle: Observations, Theory and Modelling - The Earth's Hydrological Cycle - page 13

water body extent. In addition, the potential of monitoring inland water levels by using
radar altimeters mainly from ERS and Envisat became apparent to the extent that river and
lake heights have been produced on a global scale (ESA 2013 ).
Given the high revisit period, the Sentinel-1 sensor, which is planned to be launched
towards the end of 2014, holds a great potential for high-resolution water extent mapping
( http://www.esa.int/Our_Activities/Observing_the_Earth/GMES/Sentinel-1 ).
As for the oceans, areas of interest including the use of optical wavelengths to assess
ocean colour, i.e., phyto-plankton and other water borne materials (e.g., MERIS, MODIS,
SeaWIFS), and the exploration of radar altimetry to measure water levels in lakes and
rivers are aimed to be continued into the future. Regarding the latter, our knowledge of the
global dynamics of terrestrial surface waters and their interactions with coastal oceans in
estuaries is expected to significantly advance with the planned launch of the joint NASA-
CNES-CSA Surface Water Topography Mission (SWOT) in 2020 ( http://swot.jpl.nasa.
gov/ ) . By measuring water storage changes in all wetlands, lakes and reservoirs and
making it possible to estimate discharge in rivers more accurately, SWOT will contribute
to a fundamental understanding of the terrestrial branch of the global water cycle. SWOT
will also map wetlands and non-channelized flow.
7 Vegetation Stage
Optical vegetation indices and land-cover classifications, as well as passive and active
microwave derived estimates of vegetation water content, biomass and vegetation structure
can be used to initialize hydrological models. There seems to be a good understanding and
variety of independent algorithms that estimate vegetation stage by using data acquired in
optical, near-infrared and thermal-infrared spectrum or derived products such as fPAR or
LAI. Also, a variety of land-cover classification approaches have been employed in land
surface models that implement Normalized Difference Vegetation Index (NDVI) data from
AVHRR or SPOT/Vegetation. (DeFries 2008 ) gives an excellent review of the current
status and role of remote sensing on observing the terrestrial vegetation.
Synthetic data experiments undertaken with simulated Sentinel-2 data showed a
reduction in the uncertainty in Leaf Area Index (LAI) (Richter et al. 2012 ; Bach et al.
2012 ). Severe improvements are expected also in the land-cover classification in the future.
A variety of products is also derived from passive and active microwave observations that
include estimates of vegetation water content, biomass, or vegetation height and structure.
The latter can be used to estimate variables such as emissivity, canopy conductance and
vegetation roughness, which affect the partitioning of radiation into ET and other terms (Van
Dijk and Renzullo 2011 ). Further potentials for greater use of satellite microwave obser-
vations include parameterization of biomass, height or aerodynamic roughness. The possi-
bility to observe forest biomass has been proposed by the new Earth Explorer Mission
Biomass that uses P-band synthetic aperture polarimetric radar (ESA 2012 ).
Lastly, to gain a detailed knowledge about the observed medium and to improve
understanding of upcoming high-resolution Sentinel and potential Biomass mission, a
combination of airborne and terrestrial LiDAR observations is investigated (ESA 2012 ).
8 Water Vapour
A large variety of space-borne sensors are used to retrieve atmospheric profiles of humidity
or the water vapour column amount (microwave, infrared, optical, UV). SSM/I total