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<p>Chlorophyll is a key indicator of crop photosynthetic activity and nitrogen nutritional status, and rapid, accurate monitoring of its temporal dynamics is crucial for implementing precision agricultural management. In this study, we propose an integrated “air–space–ground” remote sensing framework that combines in situ SPAD measurements, unmanned aerial vehicle (UAV) multispectral imagery, and Sentinel-2&#xa0;A satellite data. A ratio–mean–based radiometric normalization is employed to mitigate spectral discrepancies among platforms and thereby enable accurate retrieval of maize leaf chlorophyll content at the regional scale. Ground-based SPAD measurements and UAV multispectral imagery are first used to develop a suite of chlorophyll retrieval models and to identify the optimal model, which is subsequently transferred to the radiometrically corrected Sentinel-2&#xa0;A imagery to derive spatially explicit chlorophyll maps over the maize-growing areas of Minle County. The results indicate that (1) partial least squares regression (PLSR) achieved the best performance in predicting SPAD values (R² = 0.677–0.756), outperforming random forest regression (RFR) and backpropagation neural networks (BPNN); (2) UAV imagery and Sentinel-2&#xa0;A imagery exhibited strong correlations, and the chlorophyll retrieval accuracy based on Sentinel-2&#xa0;A data was substantially improved after radiometric normalization for the same dates (R² increased from 0.469 to 0.756); and (3) the red-edge bands (720–750&#xa0;nm) provided unique sensitivity to changes in crop chlorophyll concentration. This study provides scientific support and a robust data foundation for implementing variable-rate fertigation and precision irrigation strategies.</p>

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Synergistic inversion of maize leaf chlorophyll using multi-source remote sensing data

  • 期刊论文

  • 发表日期：

    2026年6月1日

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  • Shaoping Sun,
  • Jixuan Yan,
  • Guang Li,
  • Zichen Guo,
  • Wenning Wang,
  • Qiang Li,
  • Kejing Cheng,
  • Xuchun Li,
  • Jiaqin Yuan,
  • Changbo Wu

摘要

Chlorophyll is a key indicator of crop photosynthetic activity and nitrogen nutritional status, and rapid, accurate monitoring of its temporal dynamics is crucial for implementing precision agricultural management. In this study, we propose an integrated “air–space–ground” remote sensing framework that combines in situ SPAD measurements, unmanned aerial vehicle (UAV) multispectral imagery, and Sentinel-2 A satellite data. A ratio–mean–based radiometric normalization is employed to mitigate spectral discrepancies among platforms and thereby enable accurate retrieval of maize leaf chlorophyll content at the regional scale. Ground-based SPAD measurements and UAV multispectral imagery are first used to develop a suite of chlorophyll retrieval models and to identify the optimal model, which is subsequently transferred to the radiometrically corrected Sentinel-2 A imagery to derive spatially explicit chlorophyll maps over the maize-growing areas of Minle County. The results indicate that (1) partial least squares regression (PLSR) achieved the best performance in predicting SPAD values (R² = 0.677–0.756), outperforming random forest regression (RFR) and backpropagation neural networks (BPNN); (2) UAV imagery and Sentinel-2 A imagery exhibited strong correlations, and the chlorophyll retrieval accuracy based on Sentinel-2 A data was substantially improved after radiometric normalization for the same dates (R² increased from 0.469 to 0.756); and (3) the red-edge bands (720–750 nm) provided unique sensitivity to changes in crop chlorophyll concentration. This study provides scientific support and a robust data foundation for implementing variable-rate fertigation and precision irrigation strategies.