Prediction of Wheat Yield and Protein Using Remote Sensors on Plots—Part I: Assessing near Infrared Model Robustness for Year and Site Variations

Øvergaard, Stein Ivar; Isaksson, Tomas; Korsæth, Audun

doi:10.1255/jnirs.1042

Cited by 18 publications

(18 citation statements)

References 41 publications

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“…Importantly, the p-value was highly significant between number of identifications per category for all categories (p < 0.05), indicating that the slopes are highly associated with CP, NDF and MEC contents and their association is not due to random variation. Overgaard et al [57] showed that one or two years of spectral measurement are insufficient to build fully operational models for cereal property predictions. In this regard, our study employed 10 years (2002-2011) of observations, and should thus adequately represent the expected range of conditions in our study area.…”

Section: Spectral Slope Analysesmentioning

confidence: 99%

Spectral Slope as an Indicator of Pasture Quality

et al. 2014

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Abstract:In this study, we develop a spectral method for assessment of pasture quality based only on the spectral information obtained with a small number of wavelengths. First, differences in spectral behavior were identified across the near infrared-shortwave infrared spectral range that were indicative of changes in chemical properties. Then, slopes across different spectral ranges were calculated and correlated with the changes in crude protein (CP), neutral detergent fiber (NDF) and metabolic energy concentration (MEC). Finally, partial least squares (PLS) regression analysis was applied to identify the optimal spectral ranges for accurate assessment of CP, NDF and MEC. Six spectral domains and a set of slope criteria for real-time evaluation of pasture quality were suggested. The evaluation of three level categories (low, medium, high) for these three parameters showed a success rate of: 73%-96% for CP, 72%-87% for NDF and 60%-85% for MEC. Moreover, only one spectral range, 1748-1764 nm, was needed to provide a good estimation of CP, NDF and MEC. Importantly, five of the six selected spectral regions were not affected by water absorbance. With some OPEN ACCESSRemote Sens. 2015, 7 257 modifications, this rationale can be applied to further analyses of pasture quality from airborne sensors.

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Section: Spectral Slope Analysesmentioning

confidence: 99%

Spectral Slope as an Indicator of Pasture Quality

et al. 2014

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“…There are promising methods for in-season prediction of wheat (Triticum aestivum L.) yield, based on proximal crop canopy (multi-or hyperspectral) reflectance sensors (e.g. Solie et al 2012, Øvergaard et al 2013, Engström and Piikki 2016, Montesinos-López et al 2017, proximal crop canopy fluorescence sensors (Zecha et al 2017) and satellite-borne hyperspectral imaging (Wang et al 2014). Previous work has indicated that predictions made at later growth stages are generally more accurate than earlier predictions and that hyperspectral measurements are generally more accurate than predictions based on vegetation indices calculated from a few wavelength bands.…”

Section: Yield Predictions For In-season Eonr Predictionsmentioning

confidence: 99%

A modified delta yield approach for estimation of economic optimal nitrogen rate (EONR) for wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.)

Piikki

Stenberg

2017

AFSci

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A modified delta yield approach for estimation of economic optimal nitrogen rate (EONR) for wheat (Triticum aestivum L.) A Swedish field trial database was mined for information on economic optimal nitrogen rate (EONR). A total of 100 trials with wheat (Triticum aestivum L.) and 47 trials with barley (Hordeum vulgare L.) were used to parameterise prediction models for EONR based on yields in plots with no nitrogen (N) fertilisation, intended to reflect N mineralisation, and yields in plots with a high N rate, aimed as a proxy variable for yield potential, i.e. a modification of the delta yield (∆Y) approach. When the prediction models were applied to new sites and years, predictions had mean absolute error (MAE) = 10 kg N ha -1 for wheat and = 9 kg N ha -1 for barley. Performing modified ∆Y experiments can complement current N fertilisation trials with many rates, in order to improve the spatial representation of EONR estimations. Moreover, ∆Y experiments can potentially be used for in-season EONR estimation, in which case the accuracy of the EONR predictions depends also on the uncertainty in yield predictions made at the time of supplementary fertilisation.

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“…The information content of hyperspectral data for vegetation has much potential for mapping the biophysical properties of both agricultural and natural vegetation. 6,8,[21][22][23][24]…”

Section: Introductionmentioning

confidence: 99%

Leaf reflectance and transmission properties (350–2500 nm): Implications for vegetation indices

Berard

Cloutis

Mann

2017

Journal of Near Infrared Spectroscopy

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At moderate to high leaf area index (values 3-5), many ratio-based vegetation indices, such as the Normalized Difference Vegetation Index (NDVI), reach an asymptote where the linear relationship between leaf area index and vegetation index value breaks down. The red and near infrared channels are used to calculate most ratio vegetation indices when using sensors such as Landsat; however, these channels sense very different depths in vegetation canopies due to differences in transmittance, which may explain this breakdown of vegetation indices. In laboratory-simulated canopies composed of four deciduous species, visible wavelengths (400-700 nm) were mostly attenuated by the first or second leaf layer, while near infrared wavelengths were substantially transmitted beyond the sixth or seventh leaf layer. Absolute changes in reflectance >1% were seen in some canopies up to four leaf layers thick in the near infrared wavelengths. Therefore, in natural canopies, near infrared wavelengths have a greater probability of penetrating to the soil/litter background than visible wavelengths, which may impact vegetation indices that use both visible and near infrared wavelengths for canopies between two and seven layers thick. While this was a preliminary study that isolated the canopy depth variable, polynomial regression analysis showed that differences in canopy thickness explained most of the observed variability in canopy reflectance. These results will facilitate the development and assessment of spectral vegetation indices that would probe canopies to consistent depths.

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Prediction of Wheat Yield and Protein Using Remote Sensors on Plots—Part I: Assessing near Infrared Model Robustness for Year and Site Variations

Cited by 18 publications

References 41 publications

Spectral Slope as an Indicator of Pasture Quality

Spectral Slope as an Indicator of Pasture Quality

A modified delta yield approach for estimation of economic optimal nitrogen rate (EONR) for wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.)

Leaf reflectance and transmission properties (350–2500 nm): Implications for vegetation indices

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