HYPERSPECTRAL ESTIMATION MODEL FOR NITROGEN CONTENTS OF SUMMER CORN LEAVES UNDER RAINFED CONDITIONS

Abstract

Accuracy and precision of nitrogen estimation can be improved by hyperspectral remote sensing that leads effective management of nitrogen application in precision agriculture. The objectives of this study were to identify N sensitive spectral wavelengths, their combinations and spectral vegetation indices (SVIs) that are indicative of nitrogen nutritional condition and to analyze the accuracy of different spectral parameters for remote estimation of nitrogen status temporally. A study was conducted during 2010 at Northwest A & F University, China, to determine the relationship between leaf hyperspectral reflectance (350-1075 nm) and leaf N contents in the field-grown corn (Zea mays L.) under five nitrogen rates (0, 60, 120, 180 and 240 kg/ha pure nitrogen) were measured at key developmental stages. The accuracy of nitrogen nutrition diagnosis among the single (R) and dual (R1+R2) wavelengths spectral reflectance, spectral ratio (SR) in the green, red and near infrared, NDVI, GNDVI and SAVI were compared. Chose the highest determination of coefficient (R2 ) model and lowest RMSE and RRMSE at each growth stage, fitted the smaller as the best model. The results showed that Y = 4.450+0.00X-17.99X2 +10.496X3 was the best prediction model for remote estimation of leaf N contents with GNDVI at 10-12 leaf stage followed by Y = 3.986X0.161 at silking stage, then Y = 3.092+1.684X+1.995X2 at tasseling stage with R630 nm and Y = -3.860-12.692X+0.00X2 +7.632X3 at early dent stage with R720. The study results indicated that leaf spectral reflectance can be effectively used as nondestructive, quick, and reliable for real time monitoring of corn nitrogen status and important tool for N fertilizer management in precision agriculture.