Session: Land II

Here we propose a novel concept to develop global evapotranspiration (E) and drought product using the thermal infrared (TIR) and optical bands of the future SLSTR (Sea and Land Surface Temperature Radiometer) and OLCI (Ocean and Land Colour Instrument) payload on-board Sentinel-3 mission scheduled to be launched in mid-2015. This multi-instrument mission will measure sea- and land-surface temperature, ocean colour and land colour with high-end accuracy and reliability in terms of better spatial, temporal and radiometric resolution as compared to those achieved by the Envisat Advanced Along Track Scanning Radiometer (AATSR) and Medium Resolution Imaging Spectrometer (MERIS). This makes Sentinel-3 mission as an ideal to develop global terrestrial E and surface moisture condition products by exploiting the multiple TIR and optical bands. Radiometric surface temperature (T_R) through TIR remote sensing serves as a direct metric for the land surface moisture status and vegetation ecophysiological conditions, which in turn influences the surface energy fluxes and their partitioning. Similarly, vegetation and leaf area indices retrieved from the shortwave optical bands offer information on the biophysical controls of E. Allied to these, the multiple radiance channels in the shortwave and longwave spectrum of Sentinel-3 can be used to retrieve the shortwave and longwave radiative fluxes required to drive the E models. Having retrieved the information of all the core radiative, biophysical and ancillary variables necessary, a comprehensive approach will be further adopted to determine E by employing four different process-based algorithms and creating an ensemble product and drought indices derived from the evaporation ratios. The four algorithms are Surface Temperature Initiated Closure (STIC) (Mallick et al., 2013, 2014), Two Source Energy Balance (TSEB) (Norman et al., 1995), Maximum Entropy Production (MEP) (Kleidon et al., 2014), and Surface Energy Balance System (SEBS) (Su, 2002). STIC is an analytical model which physically integrates the radiometric surface temperature into the Penman-Monteith equation. The method combines T_R data with the standard surface energy balance equations and a modified advection–aridity hypothesis in order to derive a hybrid equation closure that does not require specifications of the surface to atmosphere conductance terms; instead the conductances are analytically retrieved. STIC is formed by the simultaneous solution of four state equations. On the contrary, SEBS (Su, 2002) and TSEB (Norman et al., 1995) require explicit estimates of the surface to atmospheric conductance terms. Both are land surface parameterization-based approaches proposed for the estimation of atmospheric turbulent fluxes using satellite observations of biophysical variables (T_R, fractional vegetation cover, albedo), in combination with ancillary meteorological information (air temperature, relative humidity and wind speed). These models rely on the measurements of T_R to directly estimate the sensible heat flux and indirectly obtain E as a residual of surface energy balance. To estimate aerodynamic conductances they determine the roughness length of heat and momentum transfer; but while SEBS is a single-source scheme using the energy balance at extreme dry and wet cases to solve the evaporative fraction, TSEB is a two source soil-vegetation model which requires partition of T_R into soil and vegetation. This partition can be better achieved via angular estimates of T_R or with single observations via an iterative process. Thus, TSEB will greatly benefit from the multi-angular capabilities from Sentinel-3 –SLTSR and will reduce the uncertainty from approaches based on single-angle T_R observations. MEP is also another novel physical approach which is built on constraining the land-atmosphere exchanges based on the thermodynamic limit of maximum power to derive analytical expressions for the partitioning of the terrestrial surface energy and water balances based on the information of T_R, radiation and rainfall. Once retrievals of E from these four process-based models are obtained, we shall adopt a Simple Averaging (SA) as well as Bayesian model averaging (BMA) method to improve satellite-based global terrestrial E estimation by merging the algorithms. Surface moisture availability will be simultaneously estimated using multiple approaches, such as, employing day-night T_R and radiative flux information (Mallick et al., 2015), using the ratio of evaporation to potential evaporation, psychrometric principles and Budyko aridity principle. Here also a SA and BMA method will be followed by merging the multiple surface moisture estimates from different algorithms.

Using the combination of geophysical land surface data from MERIS and AATSR in conjunction with atmospheric data from other satellites, we will develop a proof of concept for Sentinel-3 (OLCI/SLSTR) data and a synthetic framework to simultaneously retrieve global E and surface moisture status at 1 km spatial resolution. Upon the availability of the land and atmosphere data from Sentinel-3 the framework to estimate E and surface moisture would likely be adapted to demonstrate the potential of SLSTR and OLCI channels for near real-time mapping of terrestrial E and drought. This will not only greatly benefit the climate studies but a consistent thermal remote sensing satellite based E, surface moisture and drought data record will also be established for the post NOAA and MODIS era.

Land surface temperature (LST) has a long heritage of satellite observations which have facilitated our understanding of land surface and climate change processes, such as desertification, urbanization, deforestation and land/atmosphere coupling. These observations have been acquired from a variety of satellite instruments on platforms in both low-earth orbit and in geostationary orbit. Retrieval accuracy can be a challenge though; surface emissivities can be highly variable owing to the heterogeneity of the land, and atmospheric effects caused by the presence of aerosols and by water vapour absorption can give a bias to the underlying LST. As such, a rigorous validation is critical in order to assess the quality of the data and the associated uncertainties.

A validation protocol (Schneider et al., 2012) for satellite-based LST has been developed to provide a standardized framework for structuring LST validation activities, which had not previously followed any uniform approach. This set of guidelines, which has incorporated feedback from the LST community, is swiftly gaining international recognition with several LST projects having adopted this protocol. It attempts to render LST consistent with many other biophysical parameters within the field of Earth Observation for structuring and standardizing calibration and validation approaches. The protocol introduces a four-pronged approach which can be summarised thus: i) in situ validation where ground-based observations are available; ii) radiance-based validation over sites that are homogeneous in emissivity; iii) intercomparison with retrievals from other satellite sensors; iv) time-series analysis to identify artefacts on an interannual time-scale.

The approach recommended in the protocol has been written into the Sentinel-3 Cal-Val Plan, and will thus be adopted within the framework of the Sentinel-3 Mission Performance Centre (S3MPC) for evaluating the level-2 SL_2_LST product. This multi-dimensional approach is a necessary requirement for assessing the performance of the LST algorithm for SLSTR which is designed around biome-based coefficients, thus emphasizing the importance of non-traditional forms of validation such as radiance-based techniques. Here we present examples of the application of the protocol to data produced within the ESA DUE GlobTemperature Project. The lessons learnt here will help fine-tune the methodology in preparation for the launch of Sentinel-3.

References

Schneider, P., Ghent, D., Corlett, G., Prata, F., and Remedios, J. Land Surface Temperature Validation Protocol, Report to ESA: Report No. UL-NILU-ESA-LST-LVP, 2012

We present new products for land surface reflectance and global atmospheric aerosol and for the Copernicus Sentinel-3 mission. The land surface product will provide high accuracy surface reflectance intended to facilitate global services such as mapping of biophysical parameters such as albedo, leaf area index and absorbed radiation, and land cover classification. The retrieval has been developed for application to Sentinel-3 to make synergistic use of the two sensors, OLCI and SLSTR, and will provide co-registered surface reflectance spectrum from both, corrected for the effects of atmospheric gases and aerosol scattering. The algorithm has been implemented on the ESA BEAM system and tested on MERIS and AATSR data and results show improved estimation of aerosol properties compared to single-instrument retrievals. The method for global aerosol retrieval using SLSTR is based on the dual angle algorithm developed for the (A)ATSR instrument series, to allow retrieval of aerosol properties and their uncertainties over both land and ocean. This has been developed under the ESA Aerosol Climate Change Initiative for generation of the first 17 year dataset from global aerosol retrieval from ERS-2 and ENVISAT (1995-2012), and evaluated using the global AERONET sun photometer network.

The open burning of biomass in landscape-scale fires affects, on average, an area of Earth'sland surface equivalent to the size of India every year, releasing gases and particulates in amounts that for many of the emitted species are extremely significant. For example, biomass burning is amongst the largest source of atmospheric carbon monoxide and black carbon, and is also a very significant source of CO₂. Such emissions therefore greatly affect atmospheric composition, air quality and climate, and whilst the (A)ATSR series of instruments were quite widely used for studies of global biomass burning, the relatively narrow swath and limited dynamic range of the instruments thermal channels (which were primarily focused on sea-surface temperature retrieval) limited their applicability to quantiative characterisation of biomass burning emissions. The SLSTR instrument onboard Sentinel-3 has a number of features that will significantly improve this situation, including a wider swath with daily revisit capaility (once two satellites are operating), more spectral bands useful for active fire detection and false alarm rejection, and two extended dynamic range thermal bands that are specifically aimed at supporing active fire detection and the derivation of fire radiative power (FRP) estimates.  FRP is related to the rate of fuel consumpion and trace gas and aerosol emission, and thus provides a direct approach to estimating these quantiaties from the remote sensing observations, including in near-real time whilst the fires are still burning. This information is expected to be of significant use in, for example, the Copernicus Atmosphere Service, where FRP data from the long-running MODIS instruments is currently used for this purpose.  Here we will describe the active fire product that is planned to be generated from SLSTR observations, outline the algorithm and product details, and desribe the early algorithm validation that has been undertaken using MODIS and ASTER datasets to, respectively, simulate SLSTR observations and act as an independent confirmation of the algorithm outputs. We will describe how the SLSTR Active Fire Detection & FRP prodict is to be generated, and explain its potential use within the Copernicus Atmospere Service for prescribing global, real-time fire emissions.