Detecting Biophysical Characteristics and Nitrogen Status of Finger Millet at Hyperspectral and Multispectral Resolutions - Baath, Gurjinder S.; Flynn, K. Colton; Gowda, Prasanna H.; Kakani, Vijaya Gopal; Northup, Brian K. (Frontiers in Agronomy, 2021)
Finger millet (Eleusine coracana Gaertn L.) is an important grain crop for small farmers in many countries. Reliable estimates of crop parameters, such as crop growth and nitrogen (N) content, through remote sensing techniques can improve in-season management of finger millet. This study investigated the relationships of hyperspectral reflectance with canopy height, green canopy cover, leaf area index (LAI), and N concentrations of finger millet using an optimal waveband selection procedure with partial least square regression (PLSR). Predictive performance of 13 vegetation indices (VIs) computed from the original hyperspectral data as well as synthesized Landsat-8 and Sentinel-2 data were evaluated and compared for estimating various crop parameters with simple linear regression (SLR) and multilinear regression (MLR) models. The optimal wavebands determined by PLSR were mostly concentrated within 1000-1100 nm for both LAI and dry biomass but were scattered for other canopy parameters. The SLR statistics resulted in the simple ratio pigment index (SRPI) and red/green index (RGI) performing best when predicting LAI (R2v = 0.53-0.59) and canopy cover (R2v = 0.72-0.76). The blue/green index (BGI1) was strongly related to canopy height (R2v = 0.65-0.78), dry biomass (R2v = 0.42-0.49), and N concentration (R2v = 0.70-0.83) of finger millet, regardless of spectral resolutions. The MLR approach, using four maximum VIs as input variables, improved the prediction accuracy of N concentration by 14% compared to both SLR and waveband selection methods. VIs computed from synthesized Landsat-8 and Sentinel-2 satellite data resulted in similar or greater prediction accuracy than hyperspectral data for various canopy parameters of finger millet, indicating publicly accessible multispectral data could serve as alternative to hyperspectral data for improved crop management decisions via precision agriculture.