Dr. Giacinto Manfron Profile

Dr. Giacinto Manfron

at CNR-IREA

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Proceedings Article | 19 October 2012 Paper

Testing automatic procedures to map rice area and detect phenological crop information exploiting time series analysis of remote sensed MODIS data

Giacinto Manfron, Alberto Crema, Mirco Boschetti, Roberto Confalonieri

Proceedings Volume 8531, 85311E (2012) https://doi.org/10.1117/12.974662

KEYWORDS: MODIS, Vegetation, Clouds, Floods, Composites, Detection and tracking algorithms, Agriculture, Error analysis, Reflectivity, Remote sensing

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Rice farming, one of the most important agricultural activities in the world producing staple food for nearly one-fifth of the global population, covers 153 MHa every year corresponding to a production of more than 670 Mton. Retrieve updated information on actual rice cultivated areas and on key phenological stages occurrence is fundamental to support policy makers, rice farmers and consumers providing the necessary information to increase food security and control market prices. In particular, remote sensing is very important to retrieve spatial distributed information on large scale fundamental to set up operational agro-ecosystem monitoring tool. The present work wants to assess the reliability of automatic image processing algorithm for the identification of rice cultivated areas. A method, originally tested for Asian tropical rice areas, was applied on temperate European Mediterranean environment. Modifications of the method have been evaluated to adapt the original algorithm to the different experimental conditions. Finally, a novel approach based on phenological detection analysis has been tested on Northern Italy rice district. Rice detection was conducted using times series of Vegetation Indices derived by MODIS MOD09A1 products for the year 2006 and the accuracy of the maps was assessed using available thematic cartography. Error matrix analysis shows that the new proposed method, applied in a fully automatic way, is comparable to the results of the original approach when it is customized and adapted for the specific study area. The new algorithm minimizes the use of external data and provides also spatial distributed information on crop phenological stages.

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