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Proceedings Paper

Application of Landsat TM data to evaluate soil hydrological status in the Arno basin, Italy: preliminary results
Author(s): Francesca Caparrini; Enrica Caporali; Giuliana Profeti
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Paper Abstract

Remote sensing can be a very interesting source of distributed data for large or medium scale hydrological modeling, where soil status and land conditions can be extremely different from one zone to another and a large amount of in-situ measurement would be necessary. In this study two Landsat TM images of the lower part of the Arno basin (Tuscany, Italy) taken in 1991 have been processed using several techniques. Cluster analysis gave interesting results in monitoring the state of soil and vegetation in the two different periods of the year. Clusters obtained have been compared with the distribution of different pedological classes and soil use and with geomorphological information derived from the DTM. Landsat data have been used also to obtain several soil water content indexes, and produce maps of soil moisture. A principal component analysis has been used to obtain data that are directly dependent on soil and as less influenced as possible by other factors like vegetation. Finally, an algorithm to retrieve soil hydraulic properties (permeability, gravitational storage, capillary storage) from geomorphologic data (slope, aspect) and pedological class has been studied, using Monte Carlo simulation and optimization techniques. The spatially distributed hydraulic properties of soil have been applied in a physically based hydrological model. The results have been compared with soil water content indexes obtained from Landsat data analysis on two sub-basins of the Arno river.

Paper Details

Date Published: 11 December 1998
PDF: 6 pages
Proc. SPIE 3499, Remote Sensing for Agriculture, Ecosystems, and Hydrology, (11 December 1998); doi: 10.1117/12.332781
Show Author Affiliations
Francesca Caparrini, Univ. degli Studi di Firenze (Italy)
Enrica Caporali, Univ. degli Studi di Firenze (Italy)
Giuliana Profeti, Univ. degli Studi di Firenze (Italy)

Published in SPIE Proceedings Vol. 3499:
Remote Sensing for Agriculture, Ecosystems, and Hydrology
Edwin T. Engman, Editor(s)

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