智慧农业（中英文）2025，Vol.7Issue(3)：89-107,19.DOI:10.12133/j.smartag.SA202412019
水稻生育期遥感监测的研究进展、瓶颈问题与技术优化路径
Remote Sensing for Rice Growth Stages Monitoring:Research Progress,Bottleneck Problems and Technical Optimization Paths
摘要
Abstract
[Significance]The efficient and precise identification of rice growth stages through remote sensing technology holds critical significance for varietal breeding optimization and production management enhancement.Remote sensing,characterized by high spa-tial-temporal resolution and automated monitoring capabilities,provides transformative solutions for large-scale dynamic phenology monitoring,offering essential technical support to address climate change impacts and food security challenges in complex agroeco-systems where precise monitoring of growth stage transitions enables yield prediction and stress-resilient cultivation management.[Progress]In recent years,the technical system for monitoring rice growth stages has achieved systematic breakthroughs in the percep-tion layer,decision-making layer,and execution layer,forming a technological ecosystem covering the entire chain of"data acquisi-tion-feature analysis-intelligent decision-making-precise operation".At the perception layer,a"space-air-ground"three-dimensional monitoring network has been constructed:High-altitude satellites(Sentinel-2,Landsat)realize regional-scale phenological dynamic tracking through wide-spectrum multi-temporal observations;low-altitude unmanned aerial vehicle(UAV)equipped with hyperspec-tral and light detection and ranging(LiDAR)sensors analyze the heterogeneity of canopy three-dimensional structure;near-ground sensor networks real-timely capture leaf-scale photosynthetic efficiency and nitrogen metabolism parameters.Radiometric calibration and temporal interpolation algorithms eliminate the spatio-temporal heterogeneity of multi-source data,forming continuous and stable monitoring capabilities.Innovations in technical methods show three integration trends:Firstly,multimodal data collaboration mecha-nisms break through the physical characteristic barriers between optical and radar data;secondly,deep integration of mechanistic mod-els and data-driven approaches embeds the scattering by arbitrarily inclined leaves by arbitrary inclined leaves(PROSPECT+SAIL,PROSAIL)radiative transfer model into the long short-term memory(LSTM)network architecture;thirdly,cross-scale feature analy-sis technology breaks through by constructing organ-population association models based on dynamic attention mechanisms,realizing multi-granularity mapping between panicle texture features and canopy leaf area index(LAI)fluctuations.The current technical sys-tem has completed three-dimensional leaps:From discrete manual observations to full-cycle continuous perception,with monitoring frequency upgraded from weekly to hourly;from empirical threshold-based judgment to mechanism-data hybrid-driven,the cross-re-gional generalization ability of the model can be significantly improved;from independent link operations to full-chain collaboration of"perception-decision-execution",constructing a digital management closed-loop covering rice sowing to harvest,providing core technical support for smart farm construction.[Conclusions and Prospects]Current technologies face three-tiered challenges in data het-erogeneity,feature limitations and algorithmic constraints.Future research should focus on three aspects:1)Multi-source data assimi-lation systems to reconcile spatiotemporal heterogeneity through UAV-assisted satellite calibration and GAN-based cloud-contaminat-ed data reconstruction;2)Cross-scale physiological-spectral models integrating 3D canopy architecture with adaptive soil-adjusted in-dices to overcome spectral saturation;3)Mechanism-data hybrid paradigms embedding thermal-time models into LSTM networks for environmental adaptation,developing lightweight CNNs with multi-scale attention for occlusion-resistant panicle detection,and im-plementing transfer learning for cross-regional model generalization.The convergence of multi-source remote sensing,intelligent algo-rithms,and physiological mechanisms will establish a full-cycle dynamic monitoring system based on agricultural big data.关键词
水稻/生育期识别/遥感/模型/深度学习/多源融合Key words
rice/growth period/remote sensing/models/deep learning/multi-source fusion分类
农业科技引用本文复制引用
李瑞杰,褚光,张运波,陈松,王爱冬,吴华星,李子秋,冯向前,洪卫源,汤学军,覃金华,王丹英..水稻生育期遥感监测的研究进展、瓶颈问题与技术优化路径[J].智慧农业（中英文）,2025,7(3):89-107,19.基金项目
国家重点研发计划项目(2023YFD2300503-01,2022YFD2300702-2) （2023YFD2300503-01,2022YFD2300702-2）
水稻产业体系(CARS-01) （CARS-01）
中国农业科学院科技创新工程重大科研任务(CAAS-ZDRW202001) National Key Research and Development Program of China(2023YFD2300503-01,2022YFD2300702-2) （CAAS-ZDRW202001）
Rice In-dustry System(CARS-01) （CARS-01）
Major Scientific Research Task of the Agricultural Science and Technology Innovation Project of the Chi-nese Academy of Agricultural Sciences(CAAS-ZDRW202001) （CAAS-ZDRW202001）
