Adrian Hauser

Validation of operational AVHRR subpixel snow retrievals over the European Alps based on ASTER data

Snow is of great economic and social importance for the European Alps. Accurate monitoring of the alpine snow cover is a key component in studying regional climate change as well as in daily weather forecasting and snowmelt run‐off modelling. These applications require snow cover information on a high temporal resolution in near‐real time. For the European Alps, operational snow cover fraction maps are generated on a daily basis using data from the Advanced Very High Resolution Radiometer (AVHRR) on board the National Oceanic and Atmospheric Administration (NOAA) platforms. Snow cover distribution is inherently discontinuous and heterogeneous in this mountainous region. We have therefore implemented a straightforward multiple endmember unmixing approach to estimate fractional snow cover. Subpixel proportions are difficult to validate because similar products are not available and appropriate ground‐based observations do not exist. In this study, we validate AVHRR subpixel snow retrievals using binary classified data sets from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) to establish absolute errors of our operational approach at three test sites. Our analysis indicates that the AVHRR subpixel maps compare well with the aggregated ASTER data, showing an overall correlation of 0.78 and providing subpixel estimates with a mean absolute error of 10.4% fractional snow cover. Discrepancies between AVHRR and ASTER snow fraction maps can be attributed to varying snow conditions, terrain effects and density in forest cover.

Retrieval of aerosol optical depth (AOD) using NOAA AVHRR data in an alpine environment

The aim of this study is the retrieval of aerosol optical depth from the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) sensor over land. The region of interest covers central Europe ranging from 50°N to 40.5°N and from 0°E to 17°E including the European Alps. On the temporal scale, we limit the data set to afternoon NOAA-16 passes of the entire year 2002. In this region, there are sixteen stations from the Aerosol Robotic Network (AERONET) at which we can compare the ground based versus the space borne measurements. The most crucial parameter in the retrieval procedure is the estimate of a correct surface reflectance since inaccuracies of 0.01 can result in AOD variations of ±0.1. Surface reflectance has been estimated by extracting the minimum reflectance within 10° intervals of the satellite zenith angle within two-month intervals. This method eliminates the varying reflectance with varying satellite zenith angle but the extracted surface reflectance still contains an aerosol signal. Most stations show a clear relationship between the AVHRR and the AERONET data. In case of a weak or non-existing relationship, we were able to identify reasons for this behavior. The standard error of estimate is about 0.18. The largest potential for increasing the accuracy of this product posses an improvement of the cloud mask. We can conclude that aerosol retrieval over land using AVHRR is a challenging task but it is possible to extract some valuable results.

Operational snow cover estimation at subpixel scale using NOAA-AVHRR data

Snow and ice play an important role in the earth`s radiation balance because of the high albedo in comparison to other natural surfaces. Furthermore ice and snow is the largest contributor to rivers and ground water over major parts of the middle and high altitudes. These are reasons why hydrological and climatological studies require estimates of snow covered areas. Most of such snow cover maps generated from satellite data include information of snow or not snow for each image pixel. In this study a linear spectral unmixing algorithm is used to calculate snow cover portions within each data cell. We examine the ability of this algorithm for operational and near-real time snow cover estimation at subpixel scale using medium spatial resolution satellite data from NOAA-AVHRR. The automated methodology is presented which produces snow cover fraction maps showing plausible distribution of snow in comparison to TERRA-ASTER data. The qualitative analysis of the results present how suitable the approach implemented in the preliminary processing chain is. Simplifying assumptions are made to the procedure which explains some variation between derived snow cover fraction map and reference data. Further work should include an accurate quantification of areal snow coverage comparison to traditional approaches.

Operational estimation of vegetation index (NDVI), vegetation cover, and leaf area index using NOAA-AVHRR data in an alpine environment

The operational processing of NOAA-AVHRR data and the derivation of vegetation index (NDVI), leaf area index (LAI) and vegetation cover fraction for the European Alps is presented. The analysis was done for three elevation zones (<500m, 1000-1500m and >2500m) to show the dynamic characteristic of vegetation in the years 1995 to 1998. The vegetation cover fraction shows a high variability in lower elevations during winter caused by the not persistent snow cover. In elevations above 2500m the high variability could be detected during summer. The exponential approach to derive LAI using NDVI data is only valid for elevations above 2000m or for NDVI less than 0.5. Otherwise the LAI values are saturated because small changes in NDVI result in an increased range of LAI up to 1.5. This prevents an exact derivation of leaf area index based on the normalized difference vegetation index.

Operational application of NOAA-AVHRR data for mapping lake surface temperatures in an alpine environment: feasibilty and validation

Lake surface water temperature (LSWT) are operationally derived from the National Oceanic and Atmospheric Administration operated Advanced Very High Resolution Radiometer (NOAA - AVHRR) data using a nonlinear sea surface temperature (NLSST) algorithm. The adapted method has been widely examined with the bias of the algorithm around 0.5°C or better. Preliminary analysis shows good agreement between satellite derived LSWT and in - situ measurements at two different lakes. A comparison of LSWT at noon (mean local time) for three lakes is presented. Surface water temperature variations are dominating the annual cycle, however, the varying geospatial attributes of each lake result in specific surface temperature characteristics. Lakes located close to each other can display considerable differences in average surface temperatures by as much as 3°C. Knowledge of this fact gives new insights and possibilities for modeling local scale meteorological phenomena like heat flux, energy budget and evapotranspiration. Using operational satellite-derived lake surface temperature can also improve numerical weather prediction models on local scales.