农作物遥感监测关键技术研究与应用进展

Research and Applications Progress of Key Technologies for Crop Remote Sensing Monitoring

摘要:

近些年,农作物遥感监测技术在粮食作物、经济作物等领域得到了充分应用与快速发展,这对支撑农业资源高效利用、农业生产指导和宏观政策制定、保障国家粮食安全和重要农产品供给、提升农业综合生产能力具有重要意义。本文对国家粮食安全和重要农产品供给具有重要影响的主要粮油棉糖等农作物遥感监测共性关键技术研究与应用进行总结,系统梳理了农作物遥感监测的信息源和主要获取方法,然后从农作物冠层参数遥感获取、农作物面积遥感监测、农作物长势遥感监测和单产遥感估测等方面对共性关键技术进行归纳,包括主要数据和指标、主要模型算法与技术方法、关键技术大范围应用现状、存在问题和发展趋势。最后,对农作物遥感监测研究与应用进行展望。综述分析结果表明,高时空分辨率多光谱卫星、高光谱遥感、热红外遥感、太阳诱导叶绿素荧光、合成孔径雷达、无人机遥感等多源遥感信息以及机器学习、辐射传输模型、作物生长模型、遥感数据同化等模型方法的发展,显著提升了农作物监测的空间精细化、时间连续性、参数定量化和过程解释能力。未来,农作物遥感监测应进一步强化天空地协同观测、多模态数据融合、机理知识约束与智能算法集成,推动农作物遥感监测向智能化监测、标准化数据产品和“监测—预警—决策”全链条信息服务等方向发展,为保障我国国家粮食安全和重要农产品供给、促进全球粮食安全和可持续发展提供有效服务。

Abstract:

In recent years, crop remote sensing monitoring technologies have been widely applied and rapidly developed in the monitoring of food crops, cash crops, and other major crop types. These developments provide important technical support for efficient utilization of agricultural resources, agricultural production management, agricultural policy formulation and macro-level decision-making, safeguarding national food security and the supply of major agricultural products, as well as for enhancing comprehensive agricultural production capacity. Focusing on major grain, oil, cotton, and sugar crops that are closely related to national food security and the supply of important agricultural products, the research progress and applications of common key technologies for crop remote sensing monitoring were summarized. It systematically reviewed the data sources and observation approaches for crop remote sensing monitoring, followed by a synthesis of common key technologies in the retrieval of crop canopy parameters, crop area mapping and monitoring, crop growth condition monitoring, and remote sensing-based crop yield estimation. Specifically, the review covered major data sources and indicators, models, algorithms and technical methods, large-scale applications of key technologies, existing challenges, and future development trends. Finally, prospects for research and application of crop remote sensing monitoring were discussed. It showed that the development of multi-source remote sensing information (such as high-spatial and high-temporal resolution multispectral satellites, hyperspectral remote sensing, thermal infrared remote sensing, solar-induced chlorophyll fluorescence, synthetic aperture radar, unmanned aerial vehicle remote sensing), as well as machine learning, radiative transfer models, crop growth models coupled with remote sensing data assimilation methods, had significantly improved the spatial refinement, temporal continuity, quantitative parameter retrieval, and process interpretability of crop monitoring. In the future, crop remote sensing monitoring should further strengthen integrated space-air-ground collaborative observation, multimodal data fusion, mechanistic knowledge constraints, and the integration of intelligent algorithms, thereby promoting the transformation of crop remote sensing monitoring toward intelligent monitoring, standardized data products, and full-chain information services covering “monitoring-early warning-decision-making”, providing effective support for China’s national food security, the stable supply of important agricultural products, global food security, and sustainable development.