Land surface temperature (LST) products of two different sensors – the Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) – were cross-compared. The analysis was conducted on a daily basis for four different years. Only pixels that stuck to certain homogeneity criteria were chosen. Furthermore, a time criterion defining the maximal time difference between two acquisitions was introduced. The differences between the two products showed both a diurnal and an annual pattern, with LST of AVHRR being higher than that of MODIS at high surface temperatures and AVHRR LST being lower than MODIS LST at lower temperatures. Additionally, some irregular patterns were identified and attributed to the different algorithm approaches. Mean annual absolute differences were relatively low: only 2.2 K for the daytime and 1.4 K for the night-time scenes, speaking for a general good agreement between the two products. The coefficient of determination (R ²) between the LST of AVHRR and MODIS of both day and night scenes was 0.99.

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.

Cairo Air Pollution and Climate (CAPAC) is dedicated to the understanding of the urban energy balance in Cairo, Egypt, through measurements from space and at ground stations. The in situ measurements will provide a focussed insight into three carefully chosen microclimates (urban, suburban‐agriculture, and suburban‐desert) and provide at the same time ground‐truth data for satellite image analysis, which will expand the acquired knowledge into the spatial domain. In situ measurements were made during a field campaign in Greater Cairo from November 2007 to February 2008. In this study, the dataset of the CAPAC measurement campaign will be presented and analysed in terms of use for a remote sensing study. Measured variables complied with our expectations. The urban area featured a distinct nocturnal heat island. During the day the choice of reference station was responsible for the magnitude of the heat island. The diurnal cycle of radiative temperature at the suburban‐desert station clearly exceeded the one at the urban station, thus the urban setting seemed to have a better heat storage than the suburban‐desert. The stations also determined the partitioning of the turbulent heat fluxes. While in Cairo and at the suburban‐desert station most of the available energy was partitioned into the sensible heat flux, the suburban‐agricultural station maintained a high latent heat flux. The radiation and soil heat flux measurements proved to be applicable for comparison with remotely sensed data. However, the analysis of the turbulent heat fluxes showed that several constraints exist: measured fluxes tend to underestimate the actual flux and directional effects complicate the interpretation. An energy balance closure and footprint modelling is necessary to compare measured fluxes with satellite image retrieved products. Finally, turbulent fluxes are time averages, which is contrary to the remote sensing principle. Consequently, a direct use is problematic. Copyright © 2010 Royal Meteorological Society

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, Jiménez-Muñoz 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, Jiménez-Muñoz 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.

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.

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.