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      High-Throughput Prediction of Whole Season Green Area Index in Winter Wheat With an Airborne Multispectral Sensor

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          Abstract

          Introduction

          In recent decades, the interest has grown to quantify the green area index as one of the key characteristics of crop canopies (e.g. for modelling transpiration, light interception, growth). The approach of estimating green area index based on multispectral reflection data from unmanned airborne vehicles with lightweight sensors might have the potential to deliver data with sufficient accuracy and high throughput during the whole season.

          Materials and Methods

          We therefore examined the applicability of a recently launched drone-based multispectral system (Sequoia, Parrot) for the prediction of whole season green area index in winter wheat, with data from field trials in Northern Germany (2017, 2018 and 2019). The explanatory power of different modeling approaches to predict green area index based on multispectral data was tested: linear and non-linear regression models, multivariate techniques, and machine learning algorithms. Further, different predictors were implemented in these models: multispectral data as raw bands and as ratios. Additionally, a new approach for the evaluation of green area index predictions during senescence is introduced. It is shown that a robust calibration during growth phase is applicable during senescence as well.

          Results and Discussion

          A linear model which includes all four wavebands provided by the sensor in three ratios (VIQUO) and a Support Vector Machine (SVM) algorithm allow a reliable and sufficiently accurate whole season prediction. The VIQUO-model is recommended as the best model, as it is precise but still relatively simple, thus easier to communicate and to apply than the SVM. The integrated values of predicted green area indices in an independent trial are highly correlated with their final biomass (R 2: VIQUO = 0.84, SVM = 0.85) which represents the process of radiation interception, one of the determining factors of growths. This is an indicator for both, a robust model calibration and a high potential of the tested multispectral system for agricultural research and crop management.

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          A uniform decimal code for growth stages of crops and weeds

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            Estimation of global leaf area index and absorbed par using radiative transfer models

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              Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring From an Unmanned Aerial Vehicle

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                Author and article information

                Contributors
                Journal
                Front Plant Sci
                Front Plant Sci
                Front. Plant Sci.
                Frontiers in Plant Science
                Frontiers Media S.A.
                1664-462X
                14 February 2020
                2019
                : 10
                : 1798
                Affiliations
                [1] Institute of Crop Science and Plant Breeding, Christian-Albrechts-University , Kiel, Germany
                Author notes

                Edited by: Urs Schmidhalter, Technical University of Munich, Germany

                Reviewed by: Pablo J. Zarco-Tejada, The University of Melbourne, Australia; Martin Gnyp, Yara International (Germany), Germany

                *Correspondence: Josephine Bukowiecki, bukowiecki@ 123456pflanzenbau.uni-kiel.de

                This article was submitted to Technical Advances in Plant Science, a section of the journal Frontiers in Plant Science

                Article
                10.3389/fpls.2019.01798
                7033565
                ed2a0c85-366d-48bc-aa6c-6c52a84dccdb
                Copyright © 2020 Bukowiecki, Rose, Ehlers and Kage

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 29 March 2019
                : 23 December 2019
                Page count
                Figures: 7, Tables: 5, Equations: 0, References: 59, Pages: 14, Words: 7754
                Categories
                Plant Science
                Original Research

                Plant science & Botany
                green area index,unmanned aerial vehicle,multispectral,winter wheat,whole season,vegetation index,sequoia camera

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