Ermatinger, Lochlin S.Powell, Scott L.Peterson, Robert K.D.Weaver, David K.2024-11-122024-09Ermatinger LS, Powell SL, Peterson RKD, Weaver DK. Multitemporal Hyperspectral Characterization of Wheat Infested by Wheat Stem Sawfly, Cephus cinctus Norton. Remote Sensing. 2024; 16(18):3505. https://doi.org/10.3390/rs161835052072-4292https://scholarworks.montana.edu/handle/1/18947Wheat (Triticum aestivum L.) production in the Northern Great Plains of North America has been challenged by wheat stem sawfly (WSS), Cephus cinctus Norton, for a century. Damaging WSS populations have increased, highlighting the need for reliable surveys. Remote sensing (RS) can be used to correlate reflectance measurements with nuanced phenomena like cryptic insect infestations within plants, yet little has been done with WSS. To evaluate interactions between WSS-infested wheat and spectral reflectance, we grew wheat plants in a controlled environment, experimentally infested them with WSS and recorded weekly hyperspectral measurements (350–2500 nm) of the canopies from prior to the introduction of WSS to full senescence. To assess the relationships between WSS infestation and wheat reflectance, we employed sparse multiway partial least squares regression (N-PLS), which models multidimensional covariance structures inherent in multitemporal hyperspectral datasets. Multitemporal hyperspectral measurements of wheat canopies modeled with sparse N-PLS accurately estimated the proportion of WSS-infested stems (R2 = 0.683, RMSE = 13.5%). The shortwave-infrared (1289–1380 nm) and near-infrared (942–979 nm) spectral regions were the most important in estimating infestation, likely due to internal feeding that decreases plant-water content. Measurements from all time points were important, suggesting aerial RS of WSS in the field should incorporate the visible through shortwave spectra collected from the beginning of WSS emergence at least weekly until the crop reaches senescence.en-UScc-byhttps://creativecommons.org/licenses/by/4.0/wheat stem sawflyFORESTRY, AGRICULTURAL SCIENCES and LANDSCAPE PLANNING::Area technology::Remote sensinghyperspectralmultiway datarepeated measuresArticle10.3390/rs16183505