Assessing newly developed and published vegetation indices for estimating rice leaf nitrogen concentration with ground- and space-based hyperspectral reflectance

By Yun-lu Tian, Xia Yao, Jiashuo Yang, W. Cao, D. Hannaway, Yan Zhu. 2011

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Monitoring leaf potassium content using hyperspectral vegetation indices in rice leaves (2019, background): “Moreover, combining terrestrial hyperspectral and satellite remote sensing (RS) image data has yielded good results for predicting crop N status (Pellissier et al. 2015; Tian et al. 2011).”
Using crop reflectance to determine sidedress N rate in potato saves N and maintains yield (2012, background): “, 1996), and ice (Harrell et al., 2011; Tian et al., 2011) but has been mentioned nly briefly for potato (Booij and Uenk, 2004; Bowen et al.”
Review of optical-based remote sensing for plant trait mapping (2013, background): “Among many empirical approaches, several VIs were 535 proposed specifically to estimate leaf N and they were mainly established for crops (Chen et 536 al., 2010; Tian et al., 2011).”
Reflectance estimation of canopy nitrogen content in winter wheat using optimised hyperspectral spectral indices and partial least squares regression (2014, background): “Similarly, there were inconsistencies observed in the selection of the sensitive bands as reported by Zhu et al. (2007), Stroppiana et al. (2009) and Tian et al. (2011) for rice.”