Paper
19 October 2005 Crop classification and crop water need estimation of Piave river basin by using MIVIS, Landsat-TM/ETM+ and ground-climatological data
Francesco Baruffi, Massimo Cappelletto, Matteo Bisaglia, Angelo Zandonella
Author Affiliations +
Abstract
In this work a classification of the main irrigated crops of the Piave river basin and an estimation of crop water requirements during the growing season are presented. The work is divided into two parts. The first includes recognition, mapping and quantification of the main irrigated crops for thematic map production and a database creation. MIVIS hyperspectral airborne data, Landsat-TM/ETM+ multispectral satellite data and ground truth data were used for crop classification. A specific method of knowledge-based image classification was designed and used. The proposed method was compared with other per point conventional classification methods. In the second part the crop water need estimation is discussed. Ground-climatological data of the study area ground-climatological stations were used. The water balance equation parameters were estimated on a ten-days basis. A spatial interpolation method was used to propagate these parameters at pixel spatial resolution to study area. Soil water deficit map for irrigation was produced and a flow rate estimation was performed.
© (2005) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Francesco Baruffi, Massimo Cappelletto, Matteo Bisaglia, and Angelo Zandonella "Crop classification and crop water need estimation of Piave river basin by using MIVIS, Landsat-TM/ETM+ and ground-climatological data", Proc. SPIE 5976, Remote Sensing for Agriculture, Ecosystems, and Hydrology VII, 59761Q (19 October 2005); https://doi.org/10.1117/12.627550
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Cited by 4 scholarly publications.
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KEYWORDS
Earth observing sensors

Landsat

Image classification

Spatial resolution

Data analysis

Error analysis

Data acquisition

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