Remote Sensing and Climate Modeling: Synergies and Limitations

Optimized vegetation indices provide a convenient approach to estimate crucial plant properties on the basis of satellite data. This paper describes the steps followed to implement an index optimized to estimate the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) on the basis of data generated by the SeaWiFS instrument, and the preliminary results obtained. Index values are computed on the basis of top of atmosphere bidirectional reflectance factor values in the blue, red and near-infrared domains, as well as information on the geometry of illumination and observation. Results obtained with SeaWiFS data are used to evaluate the performance of the index. This case study documents the ability of the index to discriminate between various surface types, and its insensitivity to changes in the geometrical conditions of observation and to atmospheric effects. The operational environment set up at SAI to process SeaWiFS data is outlined and selected standard retrievals resulting from a monthly composite analysis are shown as examples of the products generated.

Heat fluxes at heterogeneous land surfaces are often modeled using single-source resistance-type transport equations, i.e. assuming horizontal homogeneity of the land surface and of the boundary layer. Large deviations from these conditions occur at partial canopies which are geometrically and thermally heterogeneous. Improved models of heat transfer have been proposed in literature to deal with these conditions. Such models require a measure of thermal heterogeneity of the land surface. Directional measurements of the radiance emitted by the land surface have the potential of providing a measure of thermal heterogeneity and improved parameterizations of sensible heat transfer. The paper proposes a methodology, together with two case studies on the use of directional measurements of spectral radiance to estimate the component temperatures of soil and vegetation and their subsequent use to model sensible heat fluxes at length scales of 10–1m and 10³m.

The first case study relied on multi-temporal field surface temperature measurements at view angles of 0°, 23° and 52° collected at sparse grass covered surface during the Inner-Mongolia Grassland-Atmosphere Surface Study (IMGRASS) experiment in China. This provided useful insights on the applicability of a simple linear mixture model to the analysis of observed directional radiances. Sensible heat fluxes were estimated both at field and regional scales by using The Along-Track Scanning Radiometer (ATSR)-2 observations. The second was done with directional ATSR-1 observations only and was a contribution to the Hei He International Field Experiment (HEIFE) in China. The HEIFE case study was focused on the large oasis of Zhang-Ye and led to useful estimate of soil and vegetation temperatures. Sensible heat flux is modeled separately for each component heat source, i.e. soil and vegetation. Heat flux densities were compared with field measurements made with an eddy correlation device and values obtained with vertical profiles of air temperature and horizontal wind speed. Agreement was good for the IMGRASS case study based on field measurements. ATSR-based estimates were also in good agreement with values obtained with observed and modeled through vertical profiles, although few data points were available because of the large spatial scale of the ATSR estimates.

Land surface albedo constitutes a critical climatic variable, since it largely controls the actual amount of solar energy available to the Earth system. From a mathematical point of view, the determination of the surface albedo corresponds to the estimation of a boundary condition for the radiation transfer problem in the coupled surface-atmosphere system. A relatively large database of 10 years or more of Meteosat data has been accumulated by EUMETSAT. These data, collected at half-hourly intervals over the entire Earth disk visible from longitude 0 degree, constitute a unique resource to describe the anisotropy of the coupled surface-atmosphere system, and provide the opportunity to document changes in surface albedo which may have occurred in these regions over that period. An advanced algorithm to retrieve the radiative properties of terrestrial surfaces sampled by the Meteosat visible instrument has been derived and a preliminary analysis of a one-year (1996) set of Meteosat data was performed. The accumulation of results in 10-day periods permits evaluating the seasonal albedo changes occurring at a continental scale. These first results, supported by additional radiation transfer simulations, suggest that anthropogenic fire activities induce significant perturbations of the surface albedo values in the inter-tropical zones at that scale.

This paper describes the motivation and an approach for deriving a time series of albedo maps of Australia from historical Advanced Very High Resolution Radiometer (AVHRR) data. Polarization and Directionality of the Earth’s Reflectances (POLDER) measurements will be used to test the angular correction algorithm. Some initial results from a survey of POLDER directional reflectance signatures of Australian land cover are presented. Those results show that, while there is much correspondence between the spatial patterns of directional signatures and land cover types, there is a large spread of signatures within each land cover type. However, the similarity of two of the kernels of the bidirectional reflectance distribution function (BRDF) model used to parameterise the directional signatures can produce spurious variations in the model parameters. Finally, some field measurements of grassland albedo are used to make the point that for the greatest accuracy in the estimation of land surface albedo from satellites, it is necessary to account for the detailed shape of the diurnal variation and the effect of the cloudiness on albedo.

Satellite measurements show that the exchange of solar energy between the global climate system and outer space is well simulated by the current generation of General Circulation Models (GCM). However, this alone does not ensure that these models also reproduce the distribution of solar energy within the simulated climate system correctly. Thus, the present study uses in addition to the satellite data a collocated set of surface observations for a more vigorous assessment of the solar energy in the climate system than could ever be achieved using satellite data alone. It is shown that GCMs typically underestimate the absorption of solar energy in the atmosphere, by 10–20 Wm^-2. In other words, the present study suggests that the global mean shortwave atmospheric absorption, a highly debated quantity, should rather be between 80–90 Wm^-2, than around 70 Wm^-2 as found in many current GCMs. This leads to excessive insolation at the GCM surface compared to more than 700 globally distributed observation sites. In a case study based on data from observation sites in Germany, the relative portion of solar energy absorbed in the cloud-free atmosphere and its cloudy counterpart is investigated. No indications are found that the absorption of solar radiation in the GCM atmospheres should be significantly enhanced when clouds are present, which has been postulated in other studies. Rather, the underestimation in the atmospheric absorption in many GCMs seems to be caused by a lack of absorption in the cloud-free atmosphere, related to an underestimated water-vapor and aerosol absorption.

Statistics from sky/sunphotometers at AERONET sites throughout the world provide the background for a comparison of monthly or seasonally averaged aerosol optical depths to retrievals by operational satellites and to representations in global models. Available data-sets, however, rarely relate to the same year(s). With strong year-to-year variations even for monthly averaged aerosol optical depths and open issues on sampling biases and regional representation by local measurements only larger discrepancies are investigated.

Aerosol optical depths retrievals of five different satellites and five different global models are compared. Quantitative accurate satellite retrievals over land remain a challenge and even their relative difference cannot provide clear answers on regional representation. Model predicted aerosol optical depth averages are usually smaller than AERONET. The behavior of models is further explored on a component basis. For sulfate, dust, carbon and sea-salt optical depths, mass and assumed aerosol sizes are compared. For the conversion of the column (dry) component mass in optical depth in models, assumptions for component aerosol size and aerosol humidification are critical.

Statistical comparisons to ground-based monitoring will be more useful, if temporal differences are removed. This requires data from the same time-period and the use of sampling screens, to accommodate less frequent measurements. For the understanding of regional representation by local measurements, satellite data play a key role. Necessities to validate critical aerosol assumptions in models or satellite retrievals require field- experiments that focus on individual aerosol components plus continued and additional monitoring (e.g. AERONET) at sites, where a particular aerosol component dominates.

Accurate estimates of the spatial distribution and albedo of snow cover are needed for climate models, that use surface albedo as a lower boundary condition. We perform a sensitivity study that shows how model parameterizations of snow albedo affect computed snow-atmosphere fluxes. When albedo is calculated as a function of snow surface grain size, the variable albedo is significantly more realistic and representative than constant albedo values. We then describe new and planned satellite-derived products that will monitor seasonal changes in snow extent and albedo and have particular relevance to the climate modeling community.

The current network of in situ stations is inadequate for monitoring regional temperature and moisture anomalies across the land surface, leaving the climate monitoring community insufficient information to identify spatial structure and variations over many areas of the world. Therefore, we need to blend satellite observations with in situ data to obtain global coverage. In order to accomplish this task, we have calibrated and independently validated an algorithm that derives land surface temperatures from the Special Sensor Microwave Imager (SSMI). The goal of this exercise is to blend both the in situ and satellite data sets into one superior product, then merge this product with an sea surface temperature anomaly field form the same base period. The value of the global product has extremely valuable applications to climate modeling community, since it can serve as a validation tool and/or direct input to the surface parameterization, allowing the radiation feed back to be realistically grounded on surface temperature and humidity observations.

Snow cover fraction (SCF) has a significant influence on the surface albedo and thus on the radiation balance and surface climate. Long-term three dimensional simulations with General Circulation Models (GCMs) showed that the SCF greatly affects the climate in the Northern Hemisphere.

By means of both ground observations and remotely sensed data, several deficiencies in the SCF parameterization used in the current ECHAM4 GCM were identified: over mountainous areas a substantial overestimation in the SCF was found whereas flat areas showed a distinctly underestimated SCF. This paper proposes a new parameterization of the SCF for use in GCMs. Evaluations illustrate that it is beneficial to include the effects of (i) flat, non-forested areas, (ii) mountainous regions and (iii) forests.

A new SCF parameterization for flat, non-forested areas was derived by using global datasets of ground-based snow depth and remote sensing observations of snow cover data. A 3-dimensional Echam4 simulation showed that this modification raises the SCF by up to approximately 20%, mainly in areas with a relatively thin snow cover.

The comparison between remotely sensed and simulated mean monthly surface albedo revealed a significant overestimation of the surface albedo in snow covered mountainous areas. The extension of the current SCF parameterization in Echam4, according to the French climate model Arpège, yielded a close agreement with satellite-derived surface albedo.

Using remotely-sensed SCF data in Echam4 over forested areas produced unrealistic results due to the masking of snow cover on the ground underlying the canopy. Therefore, we adopted the submodel for snow albedo as used in the Canadian Land Surface Scheme (Class) to simulate the SCF of snow-covered canopies. This model combined with a newly-developed simple snow interception model demonstrated the ability to capture the main physical processes of snow covered canopies, including the albedo. This modification has a beneficial impact on the delayed snow melt in spring, a well-known problem in many current GCMs: The simulated surface albedo over the boreal forests decreases by approximately 0.1 during winter and spring, which is in better agreement with ground-based observations. This induces a significant rise in the surface temperature over extended parts of Eurasia and North America in late spring, which subsequently yields a faster snowmelt and an accelerated retreat of the snow line.

Stretching in two broad transcontinental bands across Eurasia and North America, the global boreal zone covers approximately 12 million square kilometres, two-thirds in Russia and Scandinavia and the remainder in Canada and Alaska. Situated generally between 45 and 70 degrees north latitude, with northern and southern boundaries determined by the July 13°C and July 18°C isotherms respectively, the boreal zone contains extensive tracts of coniferous forest which provide a vital natural and economic resource for northern circumpolar countries. The export value of forest products from global boreal forests is ca. 47% of the world total (Kusela 1990, 1992).

The boreal forest is composed of hardy species of pine (Pinus), spruce (Picea), larch (Larix), and fir (Abies), mixed, usually after disturbance, with deciduous hardwoods such as birch (Betula), poplar (Populus), willow (Salix), and alder (Alnus), and interspersed with extensive lakes and organic terrain. This closed-crown forest, with its moist and deeply shaded forest floor where mosses predominate, is bounded immediately to the north by a lichen-floored open forest or woodland which in turn becomes progressively more open and tundra-dominated with increasing latitude. To the south the boreal forest zone is succeeded by temperate forests or grasslands.

Forest fire is the dominant disturbance regime in boreal forests, and is the primary process which organizes the physical and biological attributes of the boreal biome over most of its range, shaping landscape diversity and influencing energy flows and biogeochemical cycles, particularly the global carbon cycle since the last Ice Age. The physiognomy of the boreal forest is therefore largely dependent, at any given time, on the frequency, size and severity of forest fires. The overwhelming impact of wildfires on ecosystem development and forest composition in the boreal forest is readily apparent and understandable. Large contiguous expanses of even-aged stands of spruce and pine dominate the landscape in an irregular patchwork mosaic, the result of periodic severe wildfire years and a testimony to the adaptation of boreal forest species to natural fire over millennia. The result is a classic example of a fire dependent ecosystem, capable, during periods of extreme fire weather, of sustaining the very large, high intensity wildfires which are responsible for its existence. This chapter presents data on recent and current trends in circumpolar boreal fire activity, with particular emphasis on Canada, Russia and Alaska, and describes the physical characteristics of boreal fires in terms of fuel consumption, spread rates, and energy release rates. The potential impact of a changing climate on boreal fire occurrence and severity, with resultant impacts on atmospheric chemistry and the global carbon budget, is discussed in detail, with reference to current research activities in this area.

Certain aspects of the specification of the land cover characteristics for use in high-resolution regional climate models (RCMs) are considered in this paper. We demonstrate the importance of specifying the appropriate surface characteristics at high horizontal resolution and discuss their impacts on the simulated surface prognostic variables, on the surface energy flux as well as on the surface winds in the alpine domain of Switzerland, using the Canadian regional climate model (CRCM). Fixing lower boundary conditions consists in prescribing primary ground characteristics such as land-use (vegetation and soil types and their relative spatial coverage), and the surface height with respect to mean sea level. In the current version of the CRCM land-surface scheme, the land-use serves to fix the surface albedo and the large-scale roughness height, the vegetation type affects the soil water holding capacity, the evapotranspiration efficiency, the snow masking depth, while the soil type determines the soil thermal conductivity and specific heat, thus determining the behaviour of the momentum and sensible heat fluxes, as well as the evapotranspiration at the surface. This in turn may have significant effects on mesoscale circulations. The sensitivity of certain simulated surface fields in the CRCM is assessed through an appropriate specification of glaciers in the Swiss Alps. Until recently, the reference file containing primary ground characteristics was only available at a grid spacing of 1 ° resolution, so its use in high resolution RCMs is inadequate. Modern techniques used in the exploitation of high-resolution geographical data bases combined with existing satellite imagery now enable the resolution of surface characteristics with much improved definition, hence leading to greater confidence on the spatial distribution of the simulated fields computed by the land-surface scheme in RCMs.

The importance of land surface and vegetation characteristics for climate has long been hypothesisized and is reflected by increasingly sophisticated land surface schemes used in climate models. However, accurate parameterisation of land surface processes is still hampered by the complexity of the processes, and by data availability at the global scale required for general circulation models. It is, therefore, desirable to utilise additional data sources for land surface models, of which satellite data appear to be the most promising in terms of availability and spatial and temporal coverage. Here, monthly satellite-derived fields of the fraction of Absorbed Photosynthetically Active Radiation (fAPAR) are assimilated into a land surface and vegetation model, the Biosphere Energy-Transfer Hydrology scheme (Bethy). Assimilation offers the advantage that uncertainties of both the satellite-derived fAPAR and model parameters can be accounted for. Since fAPAR can also be predicted by the model, this information is not discarded as in other approaches where satellite data are used as forcing. During assimilation, a number of model parameters are adjusted until a cost function reaches its minimum. This cost function is defined by the squared deviation between monthly model-simulated and satellite-derived fAPAR as well as between initial and adjusted model parameters, both normalised by their assumed error variances. One of the adjusted parameters, the maximum plant-available soil moisture, is used in a subsequent sensitivity study with the Echam-4 climate model. The results show that changes in this parameter as a result of satellite data assimilation can lead to significant changes in simulated soil moisture and 2m air temperature over large parts of the tropics, where soil water storage is usually underestimated in climate and vegetation models. A comparison of Bethy simulations with soil water measurements from Amazonia supports this finding, and also shows that using fAPAR as forcing would have lead to inconsistencies between the carbon balance, predicting a strong decrease in fAPAR at negative carbon gains, and the value of fAPAR prescribed from the satellite data. The study aims at demonstrating the potential of assimilating satellite data into land surface models, as well as the significance of vegetation for the land surface climate. It is further intended to indicate a methodology for the assimilation of satellite data into general circulation models that include an interactive, i.e. climate-responsive, vegetation component.

The knowledge of the spatial distribution of biophysical parameters related to the surface energy balance, such as surface albedo and surface temperature, is of great interest for various applications, such as the modelling of atmospheric behaviour and the monitoring of water resources.

Satellite-based remotely-sensed data may provide an important contribution in the estimation of energy fluxes, at the surface-atmosphere interface, through the determination of biophysical parameters in a distributed way.

In this study the determination of actual evapotranspiration is estimated as a key input to the hydrological balance at catchment scale. The experiment was conducted using high resolution satellite data of Landsat Thematic Mapper in an high mountainous catchment (Valmasino) of the Italian Alps. The watershed surface covers an area of 188 km² and elevation ranges from 250 m to 3650 m, including different land cover types from prairie to forest.

Remotely-sensed images were integrated with ground based meteorological measurements and with a Digital Elevation Model in a GIS environment to solve latent heat flux as residual term of the one-dimensional surface energy balance equation.

Daily values of evapotranspiration, estimated from spatially distributed instantaneous latent heat fluxes, are compared with daily rate of actual evapotranspiration computed according to the Priestley-Taylor and Penmann-Monteith methods.

The surface energy balance has been modelled over the region of Sicily, Italy, in order to monitor the moisture status of natural vegetation and agricultural land by following the evolution of the evaporative fraction. In order to ensure the transferability of the approach throughout Europe, emphasis was placed on applying data from operationally available sources only. Daily meteorological parameters have been taken from the synoptic network, remote sensing data stem from the AVHHR sensor aboard the NOAA satellites, and land cover data have been taken from the European CORINE database.

In the one-source model EVA, the sensible heat flux has been estimated from the difference between the surface skin temperature and the surface-measured air temperature, and the formulation of a bulk aerodynamic resistance. The latent heat flux has been determined as the residual of the difference between the estimated available energy and the sensible heat flux. Additionally, daily rates of evapotranspiration have been estimated by assuming a constant evaporative fraction over the entire day. This simplistic approach is thought to make best use of the limited data available.

Validation by direct measurements of the energy balance components has been impossible, so that EVA model results had to be compared to few pan evaporation data, evapotranspiration estimates from the standard method of Priestley-Taylor and to results of the GCM of ECMWF. This comparison highlights the limited value of point measurements on the one hand and results from global circulation models on the other hand for validation purposes on the intermediate regional scale.

It is expected that near-future sensors will provide physical parameters more accurately so that more sophisticated models can be confidently applied in regions with a restricted number of ground measurements. In this sense part of the validation problem will be overcome in the future.