Nitrogen content estimation of apple trees based on simulated satellite remote sensing data.

INTRODUCTION

Using satellite remote sensing technology to diagnose apple tree nitrogen content is critical for guiding regional precision fertilization of apple trees. However, due to differences in spatial resolution and spectral response, there is a lack of systematic evaluation of satellite data's applicability and accuracy in apple tree nitrogen inversion.

METHODS

This study used apple orchards in Qixia City, Shandong Province as the research area, collecting canopy hyperspectral data through an ASD spectrometer during three key phenological periods: the new-shoot-growing stage (NGS), the new-shoot-stop-growing stage (NSS), and the autumn shoot-growing stage (ASS). The data was resampled based on satellite sensor spectral response functions to match the band resolutions of multiple satellite sources. Correlation coefficient method and partial least squares regression were used to screen sensitive bands for apple tree nitrogen content. Support Vector Machine (SVM) and Backpropagation Neural Network (BPNN) algorithms were used to construct and screen the optimal models for apple tree nitrogen content estimation.

RESULTS

Results showed that visible light, red edge, near-infrared, and yellow edge bands were sensitive bands for estimating apple tree nitrogen content. The support vector machine model constructed based on Sentinel-2 satellite simulated data was the optimal nitrogen content inversion model, with an average R value of 0.81 and an average RMSE value of 0.15 for training sets across different phenological periods, and an average R² value of 0.61 and an average RMSE value of 0.23 for validation sets.

DISCUSSION

This study systematically evaluated the applicability and accuracy differences of multi-source satellite data for estimating nitrogen content in apple trees, and clarified the variation patterns of nitrogen-sensitive spectral bands and optimal modeling strategies across key phenological stages. This research provides a scientific basis for data selection and a technical paradigm for remote sensing-based nutrient diagnosis of apple trees at the regional scale, and holds significant theoretical and practical value for developing region-wide precision fertilization systems based on remote sensing.