Sensitivity of Vegetation Indices for Estimating Vegetative N Status in Winter Wheat

Precise sensor-based non-destructive estimation of crop nitrogen (N) status is essential for low-cost, objective optimization of N fertilization, as well as for early estimation of yield potential and N use efficiency. Several studies assessed the performance of spectral vegetation indices (SVI) for...

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Main Authors: Lukas Prey, Urs Schmidhalter
Format: Article
Language:English
Published: MDPI AG 2019-08-01
Series:Sensors
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Online Access:https://www.mdpi.com/1424-8220/19/17/3712
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spelling doaj-36f166c79880453bb12b5232c559fd752020-11-25T01:11:35ZengMDPI AGSensors1424-82202019-08-011917371210.3390/s19173712s19173712Sensitivity of Vegetation Indices for Estimating Vegetative N Status in Winter WheatLukas Prey0Urs Schmidhalter1Chair of Plant Nutrition, Department of Plant Sciences, Technical University of Munich, 85354 Freising, GermanyChair of Plant Nutrition, Department of Plant Sciences, Technical University of Munich, 85354 Freising, GermanyPrecise sensor-based non-destructive estimation of crop nitrogen (N) status is essential for low-cost, objective optimization of N fertilization, as well as for early estimation of yield potential and N use efficiency. Several studies assessed the performance of spectral vegetation indices (SVI) for winter wheat (<i>Triticum aestivum</i> L.), often either for conditions of low N status or across a wide range of the target traits N uptake (Nup), N concentration (NC), dry matter biomass (DM), and N nutrition index (NNI). This study aimed at a critical assessment of the estimation ability depending on the level of the target traits. It included seven years&#8217; data with nine measurement dates from early stem elongation until flowering in eight N regimes (0&#8722;420 kg N ha<sup>&#8722;1</sup>) for selected SVIs. Tested across years, a pronounced date-specific clustering was found particularly for DM and NC. While for DM, only the R900_970 gave moderate but saturated relationships (R<sup>2</sup> = 0.47, <i>p</i> &lt; 0.001) and no index was useful for NC across dates, NNI and Nup could be better estimated (REIP: R<sup>2</sup> = 0.59, <i>p</i> &lt; 0.001 for both traits). Tested within growth stages across N levels, the order of the estimation of the traits was mostly Nup &#8776; NNI &gt; NC &#8776; DM. Depending on the number (n = 1&#8722;3) and characteristic of cultivars included, the relationships improved when testing within instead of across cultivars, with the relatively lowest cultivar effect on the estimation of DM and the strongest on NC. For assessing the trait estimation under conditions of high&#8722;excessive N fertilization, the range of the target traits was divided into two intervals with NNI values &lt; 0.8 (interval 1: low N status) and with NNI values &gt; 0.8 (interval 2: high N status). Although better estimations were found in interval 1, useful relationships were also obtained in interval 2 from the best indices (DM: R780_740: average R<sup>2</sup> = 0.35, RMSE = 567 kg ha<sup>&#8722;1</sup>; NC: REIP: average R<sup>2</sup> = 0.40, RMSE = 0.25%; NNI: REIP: average R<sup>2</sup> = 0.46, RMSE = 0.10; Nup: REIP: average R<sup>2</sup> = 0.48, RMSE = 21 kg N ha<sup>&#8722;1</sup>). While in interval 1, all indices performed rather similarly, the three red edge-based indices were clearly better suited for the three N-related traits. The results are promising for applying SVIs also under conditions of high N status, aiming at detecting and avoiding excessive N use. While in canopies of lower N status, the use of simple NIR/VIS indices may be sufficient without losing much precision, the red edge information appears crucial for conditions of higher N status. These findings can be transferred to the configuration and use of simpler multispectral sensors under conditions of contrasting N status in precision farming.https://www.mdpi.com/1424-8220/19/17/3712precision farmingphenomicsnitrogen fertilizationred edgehyperspectral proximal sensingnitrogen use efficiencynitrogen uptakenitrogen concentrationnitrogen nutrition indexnoise equivalentsmart farming
collection DOAJ
language English
format Article
sources DOAJ
author Lukas Prey
Urs Schmidhalter
spellingShingle Lukas Prey
Urs Schmidhalter
Sensitivity of Vegetation Indices for Estimating Vegetative N Status in Winter Wheat
Sensors
precision farming
phenomics
nitrogen fertilization
red edge
hyperspectral proximal sensing
nitrogen use efficiency
nitrogen uptake
nitrogen concentration
nitrogen nutrition index
noise equivalent
smart farming
author_facet Lukas Prey
Urs Schmidhalter
author_sort Lukas Prey
title Sensitivity of Vegetation Indices for Estimating Vegetative N Status in Winter Wheat
title_short Sensitivity of Vegetation Indices for Estimating Vegetative N Status in Winter Wheat
title_full Sensitivity of Vegetation Indices for Estimating Vegetative N Status in Winter Wheat
title_fullStr Sensitivity of Vegetation Indices for Estimating Vegetative N Status in Winter Wheat
title_full_unstemmed Sensitivity of Vegetation Indices for Estimating Vegetative N Status in Winter Wheat
title_sort sensitivity of vegetation indices for estimating vegetative n status in winter wheat
publisher MDPI AG
series Sensors
issn 1424-8220
publishDate 2019-08-01
description Precise sensor-based non-destructive estimation of crop nitrogen (N) status is essential for low-cost, objective optimization of N fertilization, as well as for early estimation of yield potential and N use efficiency. Several studies assessed the performance of spectral vegetation indices (SVI) for winter wheat (<i>Triticum aestivum</i> L.), often either for conditions of low N status or across a wide range of the target traits N uptake (Nup), N concentration (NC), dry matter biomass (DM), and N nutrition index (NNI). This study aimed at a critical assessment of the estimation ability depending on the level of the target traits. It included seven years&#8217; data with nine measurement dates from early stem elongation until flowering in eight N regimes (0&#8722;420 kg N ha<sup>&#8722;1</sup>) for selected SVIs. Tested across years, a pronounced date-specific clustering was found particularly for DM and NC. While for DM, only the R900_970 gave moderate but saturated relationships (R<sup>2</sup> = 0.47, <i>p</i> &lt; 0.001) and no index was useful for NC across dates, NNI and Nup could be better estimated (REIP: R<sup>2</sup> = 0.59, <i>p</i> &lt; 0.001 for both traits). Tested within growth stages across N levels, the order of the estimation of the traits was mostly Nup &#8776; NNI &gt; NC &#8776; DM. Depending on the number (n = 1&#8722;3) and characteristic of cultivars included, the relationships improved when testing within instead of across cultivars, with the relatively lowest cultivar effect on the estimation of DM and the strongest on NC. For assessing the trait estimation under conditions of high&#8722;excessive N fertilization, the range of the target traits was divided into two intervals with NNI values &lt; 0.8 (interval 1: low N status) and with NNI values &gt; 0.8 (interval 2: high N status). Although better estimations were found in interval 1, useful relationships were also obtained in interval 2 from the best indices (DM: R780_740: average R<sup>2</sup> = 0.35, RMSE = 567 kg ha<sup>&#8722;1</sup>; NC: REIP: average R<sup>2</sup> = 0.40, RMSE = 0.25%; NNI: REIP: average R<sup>2</sup> = 0.46, RMSE = 0.10; Nup: REIP: average R<sup>2</sup> = 0.48, RMSE = 21 kg N ha<sup>&#8722;1</sup>). While in interval 1, all indices performed rather similarly, the three red edge-based indices were clearly better suited for the three N-related traits. The results are promising for applying SVIs also under conditions of high N status, aiming at detecting and avoiding excessive N use. While in canopies of lower N status, the use of simple NIR/VIS indices may be sufficient without losing much precision, the red edge information appears crucial for conditions of higher N status. These findings can be transferred to the configuration and use of simpler multispectral sensors under conditions of contrasting N status in precision farming.
topic precision farming
phenomics
nitrogen fertilization
red edge
hyperspectral proximal sensing
nitrogen use efficiency
nitrogen uptake
nitrogen concentration
nitrogen nutrition index
noise equivalent
smart farming
url https://www.mdpi.com/1424-8220/19/17/3712
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