Discriminating Irrigated and Rainfed Maize with Diurnal Fluorescence and Canopy Temperature Airborne Maps

Abstract

This study evaluates the potential of airborne remote sensing images to detect water stress in maize. Visible and near infrared CASI (Itres Research Ltd., Calgary, AL, Canada) and thermal AHS-160 (Sensytech Inc., Beverly, MA, USA) data were acquired at three different times during the day on a maize field (Zea mays L.) grown with three different irrigation treatments. An intensive field campaign was also conducted concurrently with image acquisition to measure leaf ecophysiological parameters and the leaf area index. The analysis of the field data showed that maize plants were experiencing moderate to severe water stress in rainfed plots and a weaker stress condition in the plots with a water deficit imposed between stem elongation and flowering. Vegetation indices including the normalized difference vegetation index (NDVI) and the photochemical reflectance index (PRI) computed from the CASI images, sun-induced chlorophyll fluorescence (F760) and canopy temperature (Tc) showed different performances in describing the water stress during the day. During the OPEN ACCESS ISPRS Int. J. Geo-Inf. 2015, 4 627 morning overpass, NDVI was the index with the highest discriminant power due to the sensitivity of NDVI to maize canopy structure, affected by the water irrigation treatment. As the day progressed, processes related to heat dissipation through plant transpiration became more and more important and at midday Tc showed the best performances. Furthermore, Tc retrieved from the midday image was the only index able to distinguish all the three classes of water status. Finally, during the afternoon, PRI and F760 showed the best performances. These results demonstrate the feasibility to detect water stress using thermal and optical airborne data, pointing out the importance of careful planning of the airborne surveys as a function of the specific aims of the study.

Connection of the Photochemical Reflectance Index (PRI) with the Photosystem II Quantum Yield and Nonphotochemical Quenching Can Be Dependent on Variations of Photosynthetic Parameters among Investigated Plants: A Meta-Analysis

“…There are numerous experimental studies [49,54,66,86,90,93,96] that were devoted to the analysis of the connection between PRI and photosynthetic parameters. The results require theoretical investigations that analyze the current experimental data.…”

Diurnal Response of Sun-Induced Fluorescence and PRI to Water Stress in Maize Using a Near-Surface Remote Sensing Platform

“…Some work has been conducted using SIF to track physiological data at the leaf scale, but the efficiency of using this tracking to study this relationship is unclear when the scale is expanded to the canopy level. Therefore, studying the behavior of SIF in red (F R ) and far red (F FR ) wavelengths can be used to track the stress of plants that are physiologically stressed by water deficit [4,[13][14][15][16]. Nevertheless, the SIF intensity at the canopy scale depends on the fluorescence yield, which can represent the physiological status of the plant and the canopy structure, which affects the absorbed solar light and the fluorescence that escapes from the canopy [17,18].…”

“…Analogous to NDVI, PRI is calculated using Equation (1), which uses reflectance at 531 nm and a reference reflectance at 570 nm that is insensitive to the xanthophyll cycle [11]. It has been demonstrated that PRI can track changes in the photosynthesis and water stress of plants at the canopy level using ground or aerial platforms [4,12,16,20].…”

Surface Reflectance and Sun-Induced Fluorescence Spectroscopy Measurements Using a Small Hyperspectral UAS

“…These indicators, related to vegetation energy dissipation pathways, are characterized by strong diurnal and seasonal dynamics [48][49][50]. UASs, equipped with calibrated spectrometers, could provide accurate retrieval of PRI and SIF with the necessary temporal and spatial resolution towards vegetation stress detection and modeling of gross primary productivity [51][52][53][54].…”