ORIGINAL RESEARCH article

Front. Plant Sci.

Sec. Sustainable and Intelligent Phytoprotection

Volume 16 - 2025 | doi: 10.3389/fpls.2025.1613487

This article is part of the Research TopicAdvances in Remote Sensing Techniques for Forest Monitoring and AnalysisView all 10 articles

Nitrogen Content Estimation of Apple Trees Based on Simulated Satellite Remote Sensing Data

Provisionally accepted
Meixuan  LiMeixuan LiXicun  ZhuXicun Zhu*Xinyang  YuXinyang YuCheng  LiCheng LiDongyun  XuDongyun XuLing  WangLing WangDong  LvDong LvYuyang  MaYuyang Ma
  • College of Resources and Environment, Shandong Agricultural University, Tai'an, Shandong Province, China

The final, formatted version of the article will be published soon.

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. 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 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 R² 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. 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.

Keywords: Landsat-81, Sentinel-22, GF-63, Nitrogen estimation4, Phenological period5, Apple tree6

Received: 24 Apr 2025; Accepted: 30 Jun 2025.

Copyright: © 2025 Li, Zhu, Yu, Li, Xu, Wang, Lv and Ma. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Xicun Zhu, College of Resources and Environment, Shandong Agricultural University, Tai'an, 271018, Shandong Province, China

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