Kurt P. Günther

AVHRR compatible vegetation index derived from MERIS data

The Advanced Very High Resolution Radiometer (AVHRR) compatible vegetation index derived from data of the MEdium Resolution Imaging Spectrometer (MERIS) is regarded as a continuity index for the well known Normalized Difference Vegetation Index (NDVI) derived from AVHRR with 300 meter resolution. For deriving the AVHRR compatible vegetation index MERIS full resolution (FR) top of atmosphere radiances (ESA L1b data) are used as input. The design of the “AVHRR compatible NDVI” is based on radiative transfer models for simulating the reflectance of the broad spectral channels of the NOAA – AVHRR and the narrow spectral bands of MERIS. Linear combinations of the MERIS reflectances in the red and near‐infrared describe the AVHRR reflectance in channel 1 and 2. The weighting factors for adjusting the reflectances of both sensors are determined from the results of the modelling approach taking into account atmospheric effects by using the Simplified Method for Atmospheric Correction (SMAC), by regarding canopy and soil reflectance and transmission using the Scattering of Arbitrarily Inclined Leaf (SAIL) model and by calculating leaf reflectance and transmittance using the Stochastic model for Leaf Optical Properties Extended for fluorescence (SLOPE). The first validation results show good agreement between the AVHRR NDVI and the “AVHRR compatible NDVI”. The mean difference between the two vegetation indices is of the order of 0.025 units. In 2005 the “AVHRR compatible NDVI” will be available for Europe as maximum value composite (daily, 10‐days and monthly) on a regular basis from the German Aerospace Centre (DLR).

How sensitive are estimates of carbon fixation in agricultural models to input data

BackgroundProcess based vegetation models are central to understand the hydrological and carbon cycle. To achieve useful results at regional to global scales, such models require various input data from a wide range of earth observations. Since the geographical extent of these datasets varies from local to global scale, data quality and validity is of major interest when they are chosen for use. It is important to assess the effect of different input datasets in terms of quality to model outputs. In this article, we reflect on both: the uncertainty in input data and the reliability of model results. For our case study analysis we selected the Marchfeld region in Austria. We used independent meteorological datasets from the Central Institute for Meteorology and Geodynamics and the European Centre for Medium-Range Weather Forecasts (ECMWF). Land cover / land use information was taken from the GLC2000 and the CORINE 2000 products.ResultsFor our case study analysis we selected two different process based models: the Environmental Policy Integrated Climate (EPIC) and the Biosphere Energy Transfer Hydrology (BETHY/DLR) model. Both process models show a congruent pattern to changes in input data. The annual variability of NPP reaches 36% for BETHY/DLR and 39% for EPIC when changing major input datasets. However, EPIC is less sensitive to meteorological input data than BETHY/DLR. The ECMWF maximum temperatures show a systematic pattern. Temperatures above 20°C are overestimated, whereas temperatures below 20°C are underestimated, resulting in an overall underestimation of NPP in both models. Besides, BETHY/DLR is sensitive to the choice and accuracy of the land cover product.DiscussionThis study shows that the impact of input data uncertainty on modelling results need to be assessed: whenever the models are applied under new conditions, local data should be used for both input and result comparison.

Global Gap-Free MERIS LAI Time Series (2002–2012)

This article describes the principles used to generate global gap-free Leaf Area Index (LAI) time series from 2002–2012, based on MERIS (MEdium Resolution Imaging Spectrometer) full-resolution Level1B data. It is produced as a series of 10-day composites in geographic projection at 300-m spatial resolution. The processing chain comprises geometric correction, radiometric correction, pixel identification, LAI calculation with the BEAM (Basic ERS & Envisat (A)ATSR and MERIS Toolbox) MERIS vegetation processor, re-projection to a global grid and temporal aggregation selecting the measurement closest to the mean value. After the LAI pre-processing, we applied time series analysis to fill data gaps and to filter outliers using the technique of harmonic analysis (HA) in combination with mean annual and multiannual phenological data. Data gaps are caused by clouds, sensor limitations due to the solar zenith angle (<10°), topography and intermittent data reception. We applied our technique for the whole period of observation (July 2002–March 2012). Validation, carried out with VALERI (Validation of Land European Remote Sensing Instruments) and BigFoot data, revealed a high degree (R2 : 0.88) of agreement on a global scale.

A Process-Based Vegetation Model for Estimating Agricultural Bioenergy Potentials

We present an approach to estimate sustainable straw energy potentials by means of a modelled net primary productivity (NPP) product validated against empirical data on the managed area and mean yields of the main crops in Germany. We used the Biosphere Energy Transfer Hydrology Model (BETHY/DLR) as a theoretical framework for estimating the NPP of agricultural areas in Germany. The BETHY/DLR was driven by remote sensing data from SPOT-VEGETATION, meteorological data from the European Centre for Medium-Range Weather Forecast (ECMWF) and additional static datasets such as land cover information (GLC2000), a soil map (ISRIC-WISE) and an elevation model (ETOP05). The output of the BETHY/DLR, i.e. the yearly accumulated NPP, was first converted into straw potentials through simple allocation rules (root-to-shoot and yield-to-straw ratios). Thereafter it was converted into energy potentials through species-specific lower heating values. The 2006 and 2007 results were compared with data from the literature. Using this method for estimating sustainable bioenergy potentials, we found good compatibility between the established approaches with only little overestimations (up to 12 %) and high correlations with the R2 of up to 0.78. Our analysis shows that the presented approach fills an important gap in estimating energy potentials from the modelled NPP. The estimated straw biomass energy potentials play an important role in the sustainable energy debate.

New Approaches for Biomass Estimation and Monitoring

The future contribution of bioenergy to the energy supply strongly depends on its availability, in other words on the biomass potential that can be assessed from regional to global scale. Since certain biomass fractions have a low energy density, their spatial distribution is a crucial economical and ecological factor. For other biomass fractions a super-regional or global market is envisaged. Thus spatial and temporal information is vital for the future expansion of bioenergy use. This paper discuss the limits of the traditional biomass potential estimation approaches - material flow balance, site examinations and statistical surveys – in their accuracy and spatial localisation. Moreover, it shows new approaches in the potential estimation via GIS analysis (Geographic Information System) and remote sensing (RS). In addition, first results of German biomass potential estimated with help of these new approaches are shown and discussed.