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BOMBER: A tool for estimating water quality and bottom properties from remote sensing images

Academic Article
Publication Date:
2012
abstract:
BOMBER (Bio-Optical Model Based tool for Estimating water quality and bottom properties from Remote sensing images) is a software package for simultaneous retrieval of the optical properties of water column and bottom 1:from remotely sensed imagery, which makes use of bio-optical models for optically deep and optically shallow waters. Several menus allow the user to choose the model type, to specify the input and output files, and to set all of the variables involved in the model parameterization and inversion. The optimization technique allows the user to retrieve the maps of chlorophyll concentration, suspended particulate matter concentration, coloured dissolved organic matter absorption and, in case of shallow waters, bottom depth and distributions of up to three different types of substrate, defined by the user according to their albedo. The software requires input image data that must be atmospherically corrected to remote sensing reflectance values. For both deep and shallow water models, a map of the relative error involved in the inversion procedure is also given. The tool was originally intended to estimate water quality in lakes: however thanks to its general design, it can be applied to any other aquatic environments (e.g., coastal zones, estuaries, lagoons) for which remote sensing reflectance values are known. BOMBER is fully programmed in IDL (Interactive Data Language) and uses IDL widgets as graphical user interface. It runs as an add-on tool for the ENVI+IDL image processing software and is available on request. (C) 2011 Elsevier Ltd. All rights reserved.
Iris type:
01.01 Articolo in rivista
Keywords:
Remote sensing images; Water; Bio-optical modelling; Inversion
List of contributors:
Giardino, Claudia; Pepe, MONICA PIERA LIVIA; Bresciani, Mariano; Candiani, Gabriele
Authors of the University:
BRESCIANI MARIANO
CANDIANI GABRIELE
GIARDINO CLAUDIA
PEPE MONICA PIERA LIVIA
Handle:
https://iris.cnr.it/handle/20.500.14243/308823
Published in:
COMPUTERS & GEOSCIENCES
Journal
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