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Peer-reviewedCC BYGeo-spatial Information Science

Crop biophysical parameter estimation via AI-enhanced hyperspectral remote sensing

  • Bing Zhang1
  • Dailiang Peng1
  • Jinkang Hu1
  • Zihang Lou2
  • Ke Zheng3
  • Quanjun Jiao1
  • Shanshan Li4
  • Yizhou Zhang1
4 affiliations
  1. 1Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
  2. 2Institute of Applied Remote Sensing and Information Technology, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, China
  3. 3College of Geography and Environment, Liaocheng University, Liaocheng, China
  4. 4China Remote Sensing Satellite Ground Station, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
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Abstract

Hyperspectral remote sensing has become a key approach for retrieving crop parameters related to canopy structure, physiological status, biochemical composition and productivity. However, many current methods remain centered on task-specific empirical fitting between spectral features and target parameters. Their transferability is limited when crop growth varies across phenological stages, observation scales, sensor conditions and planting systems. Hyperspectral crop-parameter retrieval is moving toward an intelligent inversion paradigm, which is jointly supported by spatio-temporal-spectral representation, mechanistic constraints and trustworthy decision-making. Accordingly, we synthesize recent progress in crop spectral-response mechanisms, satellite-airborne-ground coordinated observation, spatio-temporal consistency processing, artificial intelligence (AI)-driven retrieval models and mechanism-data fusion. Particular attention is given to the integration of hyperspectral features with crop-growth processes and canopy radiative transfer. We also examine transferable spatio-temporal-spectral representations, applicability-domain assessment and uncertainty quantification. Future advances should move beyond improving the accuracy of multi-spatio-temporal observations and instead progress toward mechanism-informed intelligent retrieval that integrates crop-growth knowledge with deep learning. This approach should explicitly represent crop growth processes, spatial heterogeneity and applicability constraints, thereby supporting more reliable crop monitoring, growth diagnosis, yield prediction and decision-making in complex agricultural systems.

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Cite

BibTeXRIS
BibTeX
@article{zhang2026crop,
  title = {{Crop biophysical parameter estimation via AI-enhanced hyperspectral remote sensing}},
  author = {Bing Zhang and Dailiang Peng and Jinkang Hu and Zihang Lou and Ke Zheng and Quanjun Jiao and Shanshan Li and Yizhou Zhang},
  journal = {Geo-spatial Information Science},
  year = {2026},
  month = nov,
  doi = {10.1080/10095020.2026.2754604},
  url = {https://doi.org/10.1080/10095020.2026.2754604},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Crop biophysical parameter estimation via AI-enhanced hyperspectral remote sensing
AU  - Bing Zhang
AU  - Dailiang Peng
AU  - Jinkang Hu
AU  - Zihang Lou
AU  - Ke Zheng
AU  - Quanjun Jiao
AU  - Shanshan Li
AU  - Yizhou Zhang
PY  - 2026
DA  - 2026/11/04
JO  - Geo-spatial Information Science
DO  - 10.1080/10095020.2026.2754604
UR  - https://doi.org/10.1080/10095020.2026.2754604
AB  - Hyperspectral remote sensing has become a key approach for retrieving crop parameters related to canopy structure, physiological status, biochemical composition and productivity. However, many current methods remain centered on task-specific empirical fitting between spectral features and target parameters. Their transferability is limited when crop growth varies across phenological stages, observation scales, sensor conditions and planting systems. Hyperspectral crop-parameter retrieval is moving toward an intelligent inversion paradigm, which is jointly supported by spatio-temporal-spectral representation, mechanistic constraints and trustworthy decision-making. Accordingly, we synthesize recent progress in crop spectral-response mechanisms, satellite-airborne-ground coordinated observation, spatio-temporal consistency processing, artificial intelligence (AI)-driven retrieval models and mechanism-data fusion. Particular attention is given to the integration of hyperspectral features with crop-growth processes and canopy radiative transfer. We also examine transferable spatio-temporal-spectral representations, applicability-domain assessment and uncertainty quantification. Future advances should move beyond improving the accuracy of multi-spatio-temporal observations and instead progress toward mechanism-informed intelligent retrieval that integrates crop-growth knowledge with deep learning. This approach should explicitly represent crop growth processes, spatial heterogeneity and applicability constraints, thereby supporting more reliable crop monitoring, growth diagnosis, yield prediction and decision-making in complex agricultural systems.
SN  - 1993-5153
SN  - 1009-5020
ER  -