Estimating Regional Winter Wheat Leaf N Concentration with MERIS by Integrating a Field Observation-Based Model and Histogram Matching

Abstract

<abstract><title><italic>Abstract.</italic></title> Leaf nitrogen status is a key indicator for evaluating crop growth, yield potential, and grain quality. Non-destructive and rapid assessment of leaf nitrogen is required for improving nitrogen management in wheat production. The Medium-Resolution Imaging Spectrometer (MERIS), one of the payloads on ENVISAT, has an average 20 nm spectral resolution in the red edge region, a 300 m spatial resolution, and a 3-day repeat cycle, making MERIS a potentially valuable sensor for fast measurement and monitoring of crop N status at regional to global scales. The red edge can provide information related directly to the N concentration of vegetation. To develop a model for estimating crop N from field observations, winter wheat (Triticum aestivum L.) leaf nitrogen concentration (LNC) and spectral reflectance data in the North China Plain were collected. A comparison between spectral reflectance, its first derivative, and red edge parameters for estimating wheat LNC found that the red well position (RWP) is the best indicator of LNC (R² = 0.60). A model was developed for LNC estimation with RWP, and the model was calibrated to the satellite imagery using a histogram matching method. A geo-rectified LNC map of Shandong and Henan provinces in the North China Plain was then generated from MERIS data. Validation with independent field observation data showed that the accuracy was higher than 95%, with root mean square error (RMSE) of 0.11 g N g^-1 DW and a coefficient of determination (R²) of 0.90. This research will contribute to improving N fertilizer usage by mapping large-area winter wheat N status.