Simulation of Soybean Canopy Nutrient Contents by Hyperspectral Remote Sensing

Abstract

Abstract. Precision fertilizer management could help reduce farming costs and maintain production sustainability in current cropping systems. Soybean is a major oil crop and to improve temporal and spatial fertilizer application to demand variations, soybean canopy nutrient status was diagnosed by the hyperspectral remote sensing techonology. First, field canopy spectral reflectance was characterized during key developmental stages with three levels of fertilizer treatments in northeastern China. Then, foliar nitrogen (N), phosphorus (P) and potassium (K) contents were quantified and analyzed for correlation with transformed spectral data formats including reciprocal, logarithm and derivatives, red edge parameters and vegetation index. Last, simulation models for soybean canopy nutrient status (total N, P and K) were constructed. The simulation model (y= -19.153x+3.1114) using second derivatives of spectral data at 432 nm was proved to significantly correlate the predicted value with measured total N content (r=-0.7829, p<0.01; RE=0.1713). The first derivative-derived models y=-0.2939x+0.5889(r=-0.6172, p<0.01; RE=0.2428) at 909 nm and y=-0.4157x+1.874(r=-0.5631, p<0.01; RE=0.1345) at 908 nm produced most accurate prediction for total P and K respectively. Models reported in this work were top selections for the simplicity and practicality in predicting soybean nutrient and growth status.

Evaluating Plant Potassium Status

“…The NDVI correlation was somewhat better than that of the simple ratio. Many other vegetative indices have been tested using tissue K concentration: green-to-red and near-infrared-to-green ratios (Gómez- (Mahajan et al 2014;Pimstein et al 2011); P_1080_1460, S_660_1260, and S_660_1080 (Mahajan et al 2014); vegetation vigor index (VVI) (Noori and Panda 2016); the GM Index, the Vogelmann Index (VOG), the Green Leaf Index (GLI), the normalized difference index (NDI), and a simple ratio RI (Anderson et al 2016); and the re-normalized difference vegetation index (RDVI) (Guo et al 2017). Other normalization procedures are continuum removal and band depth (BD) normalization (Kokaly and Clark 1999); continuum-removed derivative reflectance (CRDR), the normalized band depth ratio (BDR), and the normalized band depth index (NBDI) (Mutanga et al 2004).…”