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Assessing canopy PRI from airborne imagery to map water stress in maize

Rossini, Micol and Fava, Francesco and Cogliati, Sergio and Meroni, Michele and Marchesi, A. and Panigada, Cinzia and Giardino, Claudia and Busetto, Lorenzo and Migliavacca, Mirco and Amaducci, Stefano and Colombo, R. (2013) Assessing canopy PRI from airborne imagery to map water stress in maize. ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 86 , p. 168-177. ISSN 0924-2716. eISSN 1872-8235. Article.

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DOI: 10.1016/j.isprsjprs.2013.10.002

Abstract

This paper presents a method for mapping water stress in a maize field using hyperspectral remote sensing imagery. An airborne survey using AISA (Specim, Finland) was performed in July 2008 over an experimental farm in Italy. Hyperspectral data were acquired over a maize field with three different irrigation regimes. An intensive field campaign was also conducted concurrently with imagery acquisition to measure relative leaf water content (RWC), active chlorophyll fluorescence (ΔF /Fm), leaf temperature (T l) and Leaf Area Index (LAI). The analysis of the field data showed that at the time of the airborne overpass the maize plots with irrigation deficits were experiencing a moderate water stress, affecting the plant physiological status (ΔF/Fm, difference between T l and air temperature (T air), and RWC) but not the canopy structure (LAI). Among the different Vegetation Indices (VIs) computed from the airborne imagery the Photochemical Reflectance Index computed using the reflectance at 570 nm as the reference band (PRI570) showed the strongest relationships with ΔF/Fm(r2 = 0.76), TlTair (r2 = 0.82) and RWC (r2 = 0.64) and the red-edge Chlorophyll Index (CIred-edge) with LAI (r2 = 0.64). Thus PRI has been proven to be related to water stress at early stages, before structural changes occurred.
A method based on an ordinal logit regression model was proposed to map water stress classes based on airborne hyperspectral imagery. PRI570 showed the highest performances when fitted against water stress classes, identified by the irrigation amounts applied in the field, and was therefore used to map water stress in the maize field. This study proves the feasibility of mapping stress classes using hyperspectral indices and demonstrates the potential applicability of remote sensing data in precision agriculture for optimizing irrigation management.

Item Type:Article
ID Code:9866
Status:Published
Refereed:Yes
Uncontrolled Keywords:Hyperspectral, vegetation, monitoring, aerial, crop
Subjects:Area 07 - Scienze agrarie e veterinarie > AGR/14 Pedologia
Divisions:001 Università di Sassari > 02 Centri > Centro interdipartimentale Nucleo di ricerca sulla desertificazione
Publisher:Elsevier
ISSN:0924-2716
eISSN:1872-8235
Copyright Holders:© 2013 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier
Deposited On:11 Jun 2014 09:29

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