Corinne Frey

Urban radiation balance of two coastal cities in a hot and dry environment

Four Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER) satellite scenes of Dubai and Abu Dhabi with channels from the visible–near infrared (VNIR) to the thermal infrared (TIR) were analysed to show variations in surface temperature, albedo, emissivity and net radiation in different urban and rural classes. For a better understanding of the spatial coherences of surface properties a land use classification was derived. The different classes were then spatially and temporally compared. The investigations show in the daytime a distinct surface cool island for Dubai and surface cool areas at Abu Dhabi city and its surrounding mangrove areas. Net radiation is mainly controlled by the albedo. The albedo in urban areas is lower than in their surrounding desert environments, therefore the net radiation is higher in the urban areas. The surface temperatures behave contrary to the net radiation and are higher in land use classes, where water is available.

Flux Measurements in Cairo. Part 2: On the Determination of the Spatial Radiation and Energy Balance Using ASTER Satellite Data

This study highlights the possibilities and constraints of determining instantaneous spatial surface radiation and land heat fluxes from satellite images in a heterogeneous urban area and its agricultural and natural surroundings. Net radiation was determined using ASTER satellite data and MODTRAN radiative transfer calculations. The soil heat flux was estimated with two empirical methods using radiative terms and vegetation indices. The turbulent heat fluxes finally were determined with the LUMPS (Local-Scale Urban Meteorological Parameterization Scheme) and the ARM (Aerodynamic Resistance Method) method. Results were compared to in situ measured ground data. The performance of the atmospheric correction was found to be crucial for the estimation of the radiation balance and thereafter the heat fluxes. The soil heat flux could be modeled satisfactorily by both of the applied approaches. The LUMPS method, for the turbulent fluxes, appeals by its simplicity. However, a correct spatial estimation of associated parameters could not always be achieved. The ARM method showed the better spatial results for the turbulent heat fluxes. In comparison with the in situ measurements however, the LUMPS approach rendered the better results than the ARM method.

Automated Improvement of Geolocation Accuracy in AVHRR Data Using a Two-Step Chip Matching Approach—A Part of the TIMELINE Preprocessor

The geolocation of Advanced Very High Resolution Radiometer (AVHRR) data is known to be imprecise due to minor satellite position and orbit uncertainties. These uncertainties lead to distortions once the data are projected based on the provided orbit parameters. This can cause geolocation errors of up to 10 km per pixel which is an obstacle for applications such as time series analysis, compositing/mosaicking of images, or the combination with other satellite data. Therefore, a fusion of two techniques to match the data in orbit projection has been developed to overcome this limitation, even without the precise knowledge of the orbit parameters. Both techniques attempt to find the best match between small image chips taken from a reference water mask in the first, and from a median Normalized Difference Vegetation Index (NDVI) mask in the second round. This match is determined shifting around the small image chips until the best correlation between reference and satellite data source is found for each respective image part. Only if both attempts result in the same shift in any direction, the position in the orbit is included in a third order polynomial warping process that will ultimately improve the geolocation accuracy of the AVHRR data. The warping updates the latitude and longitude layers and the contents of the data layers itself remain untouched. As such, original sensor measurements are preserved. An included automated quality assessment generates a quality layer that informs about the reliability of the matching.

Detection of Water Bodies from AVHRR Data—A TIMELINE Thematic Processor

The assessment of water body dynamics is not only in itself a topic of strong demand, but the presence of water bodies is important information when it comes to the derivation of products such as land surface temperature, leaf area index, or snow/ice cover mapping from satellite data. For the TIMELINE project, which aims to derive such products for a long time series of Advanced Very High Resolution Radiometer (AVHRR) data for Europe, precise water masks are therefore not only an important stand-alone product themselves, they are also an essential interstage information layer, which has to be produced automatically after preprocessing of the raw satellite data. The respective orbit segments from AVHRR are usually more than 2000 km wide and several thousand km long, thus leading to fundamentally different observation geometries, including varying sea surface temperatures, wave patterns, and sediment and algae loads. The water detection algorithm has to be able to manage these conditions based on a limited amount of spectral channels and bandwidths. After reviewing and testing already available methods for water body detection, we concluded that they cannot fully overcome the existing challenges and limitations. Therefore an extended approach was implemented, which takes into account the variations of the reflectance properties of water surfaces on a local to regional scale; the dynamic local threshold determination will train itself automatically by extracting a coarse-scale classification threshold, which is refined successively while analyzing subsets of the orbit segment. The threshold is then interpolated by fitting a minimum curvature surface before additional steps also relying on the brightness temperature are included to reduce possible misclassifications. The classification results have been validated using Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) data and proven an overall accuracy of 93.4%, with the majority of errors being connected to flawed geolocation accuracy of the AVHRR data. The presented approach enables the derivation of long-term water body time series from AVHRR data and is the basis for applied geoscientific studies on large-scale water body dynamics.

A Fully Automatic Instantaneous Fire Hotspot Detection Processor Based on AVHRR Imagery—A TIMELINE Thematic Processor

The German Aerospace Center’s (DLR) TIMELINE project aims to develop an operational processing and data management environment to process 30 years of National Oceanic and Atmospheric Administration (NOAA)—Advanced Very High Resolution Radiometer (AVHRR) raw data into L1b, L2 and L3 products. This article presents the current status of the fully automated L2 active fire hotspot detection processor, which is based on single-temporal datasets in orbit geometry. Three different probability levels of fire detection are provided. The results of the hotspot processor were tested with simulated fire data. Moreover, the processing results of real AVHRR imagery were validated with five different datasets: MODIS hotspots, visually confirmed MODIS hotspots, fire-news data from the European Forest Fire Information System (EFFIS), burnt area mapping of the Copernicus Emergency Management Service (EMS) and data of the Piedmont fire database.

Assessment of Mono- and Split-Window Approaches for Time Series Processing of LST from AVHRR—A TIMELINE Round Robin

Processing of land surface temperature from long time series of AVHRR (Advanced Very High Resolution Radiometer) requires stable algorithms, which are well characterized in terms of accuracy, precision and sensitivity. This assessment presents a comparison of four mono-window (Price 1983, Qin et al., 2001, Jimenez-Munoz and Sobrino 2003, linear approach) and six split-window algorithms (Price 1984, Becker and Li 1990, Ulivieri et al., 1994, Wan and Dozier 1996, Yu 2008, Jimenez-Munoz and Sobrino 2008) to estimate LST from top of atmosphere brightness temperatures, emissivity and columnar water vapour. Where possible, new coefficients were estimated matching the spectral response curves of the different AVHRR sensors of the past and present. The consideration of unique spectral response curves is necessary to avoid artificial anomalies and wrong trends when processing time series data. Using simulated data on the base of a large atmospheric profile database covering many different states of the atmosphere, biomes and geographical regions, it was assessed (a) to what accuracy and precision LST can be estimated using before mentioned algorithms and (b) how sensitive the algorithms are to errors in their input variables. It was found, that the split-window algorithms performed almost equally well, differences were found mainly in their sensitivity to input bands, resulting in the Becker and Li 1990 and Price 1984 split-window algorithm to perform best. Amongst the mono-window algorithms, larger deviations occurred in terms of accuracy, precision and sensitivity. The Qin et al., 2001 algorithm was found to be the best performing mono-window algorithm. A short comparison of the application of the Becker and Li 1990 coefficients to AVHRR with the MODIS LST product confirmed the approach to be physically sound.