ARIN software para la normalización automática de imágenes remotas multi-temporales en base a usos de suelo vegetales pseudo-invariantes

Abstract

A research group of the Institute for Sustainable Agriculture (CSIC, Cordoba, Spain) has developed an original radiometric normalization procedure for multitemporal remote images, named ARIN, to be used in agricultural and forestry scenes. ARIN is governed by registered ARIN software, which executes it semi-automatically, at short-time, in an economically feasible manner. ARIN procedure was patented and it is described in a recent publication. The original problem to overcome is that remote sensing observations are usually instantaneous and are affected by many factors, such as atmospheric conditions, sun angle, and viewing angle, dynamic changes in the soil and plant–atmosphere system, and changes in the sensor calibration over time. So, the goal of radiometric corrections is to remove or compensate for all of the above effects. Absolute radiometric corrections (ARC) make it possible to relate the digital counts in satellite image data to radiance at the surface of the Earth. Relative radiometric normalization (RRN) based on the radiometric information intrinsic to the images themselves is an alternative whenever absolute surface radiances are not required. In remote sensing multitemporal images are required for most agricultural, forestry and environmental parameters assessment such as cover change detection, mosaicking and tracking vegetation indices over time, supervised and unsupervised land cover classification, crop nutrient status level, weed or disease patches, water stress, among many others. Furthermore, the key point is that parameters assessment from multitemporal images require first the image calibration or normalization to get contrastable/ comparable results. ARIN is much easier to be implemented than the absolute calibration methods and normalization procedures currently available, which uses physical parameters derived from the solar position and/ or weather conditions at the time of image taking. For example, ARIN was slightly more efficient than the absolute calibration QUAC method and as efficient as the highly tunable FLAASH method. ARIN procedure can greatly contribute to make easier the normalization of multitemporal scenes, and so to widen the uses of remote sensing in agricultural and forestry.