Investigating the Performance of Hyperspectral and Simulated Sentinel-2 Data for Soybean Canopy Nitrogen Estimation

Nitrogen (N) is one of the key nutrient element needed for optimum crop growth and production. Deficiency of N leads to a decrease in crop production and excess results in poor root growth and leaching into groundwater thereby causing environmental issues. Hence the optimum application of N is needed which is possible by exactly estimating the available quantities of N in the plant. In this study, an attempt has been made to estimate N in Soybean leaves using the hyperspectral and simulated Sentinel-2 observations. Spectral observations of fifteen soybean leaf samples were collected using the EKO MS-720 Spectroradiometer. The instrument operates in the spectral range of 350–1050 nm. and collects data in contiguous 213 bands. Support Vector Regression-based models were evaluated using three feature selection methods, 1) individual hyperspectral bands, 2) Normalized band ratio's and 3) simulated Sentinel-2 bands and indices. Model performance was evaluated using R². Analysis carried out using the individual hyperspectral bands showed that bands from the red and red-edge region are performing best with R² between 0.872 and 0.876. However, NBR's estimated from band combinations in the red-edge region are performing best with R² between 0.938 - 0.956. Further, we identified a subset of wavelengths to simulate Sentinel-2 spectral bands, results showed that red-edge and narrow NIR bands provide the highest R² between 0.878 and 0.893. We observed that indices such as Canopy Chlorophyll Content Index (CCCI) and Chlorophyll Index Red Edge (CIRE) are performing better for N estimation with R² of 0.946, 0.923, respectively. Based on the observations we can conclude that red, red-edge and narrow NIR region is useful for Soybean N estimation.