Monitoring Wheat Leaf Nitrogen Content Using HJ-CCD Images and Ridge Regression

Abstract

Remote sensing has long been used in agricultural applications, especially crop growth monitoring. Leaf nitrogen content (LNC) of field crop It is an important indicator of crop quality final grain yield. Many studies have used remote sensing technology to estimate the LNC of various crops. However, the performances of these estimations vary. To further improve the estimation accuracy, this research investigated the quantifiable relationships between satellite remote sensing variable images acquired from the Chinese four-band HJ-CCD sensor and wheat LNC. The ridge regression algorithms were used to build and verify multivariate remote sensing modelling of wheat LNC estimation. Results revealed that collinearities existed between wheat LNC and most of the chosen remote sensing variables. The ridge regression model for monitoring of wheat LNC adopted NDVI, GNDVI, NRI, SIPI, PSRI, DVI, RVI and EVI as independent variables and obtained optimal regularization coefficient (lambda, λ) 0.024 and RMSE 0.128 using cross validation method. Through validation from data sets of different years and regions, the coefficients of determination (R2) of wheat LNC monitoring model were 0.701 and 0.641, respectively, while its RMSE were 0.114 and 0.121, respectively. The results demonstrated that this model could be used for monitoring wheat LNC with high accuracy and confirmed that model was not limited by years and regions of wheat planting.