Abstract
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 1,000–1,100 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.
Highlights
Finger millet is an annual grass that serves as an essential cereal crop in several drought-prone areas globally
The predictive performance was significantly improved using multilinear regression (MLR) models based on vegetation indices (VIs) calculated from Hy, L8, and S2 data, even when compared to the waveband selection procedure (Figure 4), which suggests a strong potential for height estimations of finger millet with this approach
BGI1 performed best for canopy height, dry biomass, and N concentration, while red/green index (RGI) and simple ratio pigment index (SRPI) were found strongly related to canopy cover and leaf area index (LAI) of finger millet, respectively
Summary
Finger millet is an annual grass that serves as an essential cereal crop in several drought-prone areas globally. It is extensively cultivated in Asia (India, Nepal, Myanmar, China, Sri Lanka, and Japan), and Africa (Kenya, Uganda, Ethiopia, Zaire, Tanzania, Somalia, and Rwanda) (Upadhyaya et al, 2010). Research conducted in the Southern High Plains reported that the nutrient concentrations of its forage were higher than forage of corn and sorghum. It can be mixed with corn and sorghum to improve the overall quality of silage for dairy cattle (Gowda et al, 2015). Extensive research has focused on developing strategies for agronomic management, including optimum rates of nitrogen (N) application to sustain forage production and quality of finger millet in the southern US
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