Remote Sensing Using Canopy and Leaf Reflectance for Estimating Nitrogen Status in Red-blush Pears

Abstract

Reflectance measurements at leaf and canopy scales were made in a red-blush pear (Pyrus communis) orchard for two growing seasons. Canopy reflectance measurements were obtained using a multispectral camera flown on board an unmanned aerial vehicle (UAV), and leaf reflectance measurements were undertaken in a laboratory using a portable spectrometer. These measurements were used to compute reflectance indices as surrogates for direct leaf nitrogen (N) concentration measurements. The indices were evaluated against laboratory analysis of leaf N concentration. Regression results for leaf %N on canopy-level measurements with the multispectral camera resulted in the highest R2 value [R2 = 0.67; root mean square error (RMSE) = 0.24%N] with a new index, Modified Canopy Chlorophyll Content Index (M3CI)_710 nm. Regression results for leaf %N on leaf-level measurements in-laboratory resulted in the highest R2 value (R2 = 0.65) with two other indices, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red-edge Index (NDRE)_720 nm. The corresponding RMSE values were 0.26%N. The results indicate that reflectance indices measured at the leaf level, with a controlled light source and calibration, could be used to estimate leaf %N. An analysis of uncertainty indicated that if leaf %N is estimated from leaf-level reflectance values, 10 or more leaves (from the same tree) should be averaged. The results support the use of a UAV-based assessment for canopy %N using the M3CI_710 nm, which could provide spatial information of leaf N concentration across an orchard.