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In this paper, we will evaluate the Vermote et al. method, hereafter referred to as VJB, in comparison to the MCD43 MODerate Resolution Imaging Spectroradiometer (MODIS) product, focusing on the white sky albedo parameter. We also present and study three different methods based on the VJB assumption, the 4param, 5param Rsqr, and 5param Vsqr. We use daily MODIS Climate Modeling Grid data both from Terra and Aqua platforms from 2002 to 2011 for all the pixels over Europe. We obtain an overall root-mean-square error of 5% when using the VJB method and 6.1%, 5.1%, and 5.3% for the 4param, 5param Rsqr, and 5param Vsqr methods, respectively. The main differences between the methods are located in areas where only few cloud-free snow-free samples were available that correspond mainly to mountainous areas during the winter. We finally conclude that the VJB method has an equivalent performance in deriving the white sky albedo results to the MODIS product with the advantage of daily temporal resolution. Additionally, we propose the 5param Rsqr method as an alternative to the VJB method due to its decreased data processing time. [ABSTRACT FROM PUBLISHER]

The surface urban heat island (SUHI) effect is defined as the increased surface temperatures in urban areas in contrast to cooler surrounding rural areas. In this article, the evaluation of the SUHI effect in the city of Madrid (Spain) from thermal infrared (TIR) remote-sensing data is presented. The data were obtained from the framework of the Dual-use European Security IR Experiment (DESIREX) campaign that was carried out during June and July 2008 in Madrid. The campaign combined the collection of airborne hyperspectral andin situmeasurements. Thirty spectral and spatial high-resolution images were acquired with the Airborne Hyperspectral Scanner (AHS) sensor in a 11, 21, and 4 h UTC scheme. The imagery was used to retrieve the SUHI effect by applying the temperature and emissivity separation (TES) algorithm. The results show a nocturnal SUHI effect with a highest value of 5 K. This maximum value agrees within 1 K with the highest value of the urban heat island (UHI) observed using air temperature data (AT). During the daytime, this situation is reversed and the city becomes a negative heat island. [ABSTRACT FROM PUBLISHER]

Abstract: Land cover change can be assessed from ground measurements or remotely sensed data. As regards remotely sensed data, such as NDVI (Normalized Difference Vegetation Index) parameter, the presence of atmospherically contaminated data in the time series introduces some noise that may blur the change analysis. Several methods have already been developed to reconstruct NDVI time series, although most methods have been dedicated to reconstruction of acquired time series, while publicly available databases are usually composited over time. This paper presents the IDR (iterative Interpolation for Data Reconstruction) method, a new method designed to approximate the upper envelope of the NDVI time series while conserving as much as possible of the original data. This method is compared quantitatively to two previously applied methods to NDVI time series over different land cover classes. The IDR method provides the best profile reconstruction in most cases. Nevertheless, the IDR method tends to overestimate low NDVI values when high rates of change are present, although this effect can be lowered with shorter compositing periods. This method could also be applied to data before compositing, as well as to reconstruct time series for other biophysical parameters such as land surface temperature, as long as atmospheric contamination affects these parameters negatively. [Copyright &y& Elsevier]

This paper presents a new method for NOAA's (National Ocean and Atmospheric Administration) orbital drift correction. This method is pixel-based, and in opposition with most methods previously developed, does not need explicit knowledge of land cover. This method is applied to AVHRR (Advanced Very High Resolution Radiometer) channel information, and relies only on the additional knowledge of solar zenithal angle (SZA) and acquisition date information. In a first step, anomalies in SZA and channel time series are retrieved, and screened out for anomalous values. Then, the part of the parameter anomaly which is explained by SZA anomaly is removed from the data, to estimate new parameter anomalies, and this iteratively until the influence of SZA anomalies is totally removed from the parameter data. This correction has been applied to bimonthly AVHRR data provided by the GIMMS group (Global Inventory Modeling and Mapping Studies), covering Africa from November 2000 to December 2006. NDVI and LST (Land Surface Temperature) have been estimated from raw and corrected data, and averaged over homogeneous vegetation classes. Differences between raw and corrected averaged parameters show an improvement in the quality of the data. In order to validate this method, a whole week (10 to 17 July 2004) of METEOSAT SEVIRI (Spinning Enhanced Visible and InfraRed Imager) data have been used, from which LST have been estimated using a similar method to the one used to retrieve LST from AVHRR data. The comparison between both platforms at the same time of acquisition shows good concordance. [ABSTRACT FROM AUTHOR]