A large number of remote sensing data sets have been collected in recent years by Earth observation instruments such as the moderate resolution imaging spectroradiometer (MODIS) aboard the Terra/Aqua satellite and the spinning enhanced visible and infrared imager (SEVIRI) aboard the geostationary platform Meteosat Second Generation. The advanced remote sensing products resulting from the analysis of these data are useful in a wide variety of applications but require significant resources in terms of storage, retrieval, and analysis. Despite the wide availability of these MODIS/SEVIRI products, the data coming from these instruments are spread among different locations and retrieved from different sources, and there is no common data repository from which the data or the associated products can be retrieved. We take a first step toward the development of a geo-portal for storing and efficiently retrieving MODIS/SEVIRI remote sensing products. The products are obtained using an automatic system that processes the data as soon as they are provided by the collecting antennas, and then the final products are uploaded with a one day delay in the geo-portal. Our focus in this work is on describing the design and efficient implementation of the geo-portal, which allows for a user-friendly and effective access to a full repository of MODIS/SEVIRI advanced products (comprising tens of terabytes of data) using geolocation retrieval capabilities. The geo-portal has been implemented as a web application composed of different layers. Its modular design provides quality of service and scalability (capacity for growth without any quality losing), allowing for the addition of components without the need to modify the entire system. On the client layer, an intuitive web browser interface provides users with remote access to the system. On the server layer, the system provides advanced data management and storage capabilities. On the storage layer, the system provides a secure massive storage service. An experimental evaluation of the geo-portal in terms of efficiency and product retrieval accuracy is also presented and discussed.
We have applied a Land Surface Temperature algorithm to the whole Pathfinder AVHRR Land (PAL) database, aiming at studying the evolution of the vegetation at a global scale. The Land Surface Temperature parameter, along with NDVI, will allow retrieving vegetation changes between July 1981 and September 2001. We have also built a classification which takes into account both vegetation variations and thermal patterns, from NDVI and Air Temperature at 2 meters height data. This classification allows differentiating areas which present close vegetation changes throughout the year, but totally different climates, as for example in mountainous and semiarid regions. The main quality of this classification is that it does not need any a priori information on the encountered vegetation, and thus can evolve from year to year. Through the 20 years of data, the evolution of Land Surface Temperature shows to be strongly affected by orbital drift and satellite changes. This will require an adequate correction to allow deeper study. On the other hand, NDVI does not show this trend, but aerosol absorption from Mount Pinatubo's eruption in June 1991 seems to corrupt temporarily the data in the northern hemisphere.