Monitoring Maize (Zea mays L.) Phenology with Remote Sensing

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Remote Estimation of Crop Chlorophyll Content Using Spectral Indices Derived From Hyperspectral Data (2008, background): “management since yield maximization requires that crop plants optimize nutrient supply and to remain under favorable environmental conditions [1].”
Active Sensor Reflectance Measurements of Corn Nitrogen Status and Yield Potential (2008, background): “Th e low relationships were more likely due to presence of tassels existing on the plants during sensor readings, as Vina et al. (2004) have shown that tassels modify the spectral refl ectance characteristics of the entire corn canopy, reducing the absorption of light especially in the visible…”
In-Season Prediction of Corn Grain Yield Potential Using Normalized Difference Vegetation Index (2006, result): “As noted by Teal et al. (unpublished data, 2006), due to canopy closure influence on the sensor field of view, the later NDVI readings were unable to distinguish variation, similar to research findings for other remote sensing techniques measuring NDVI (Viña et al., 2004).”
LAI assessment of wheat and potato crops by VENμS and Sentinel-2 bands (2011, background): “Applying non-linear models for plant properties assessment and prediction can lead to uncertain predicted values (Buschmann & Nagel, 1993; Coyne et al., 2009; Ding et al., 2009; Gonzalez-Sanpedro et al., 2008; Lee et al., 2004; Li et al., 2010; Tucker, 1979; Ustin et al., 2009; Vina et al., 2004).”