农业干旱监测研究进展与展望

本文全面分析了农业干旱的概念内涵及其与其他干旱类型之间的关系,进而从基于站点监测和基于遥感监测两个方面,系统梳理了国内外农业干旱监测的近今进展,对比了不同干旱监测指标的适用范围和局限性;同时,通过文献统计和重要文献引用揭示了国内外农业干旱监测研究的发展历程和最新进展,即农业干旱监测指标从传统的单一气象监测指标逐渐向气象与遥感相结合的综合监测指标转变。最后,在分析农业干旱监测现有挑战和困境的基础上,将农业干旱监测未来发展趋向归纳为5点展望,即进一步明晰农业干旱发生机理和受旱过程、识别农业干旱影响因素及其相互作用关系、构建多时空尺度农业干旱监测模型、耦合农业干旱定性表征与定量评估模型以及提高农业干旱监测模型中遥感数据的应用水平。

Agricultural drought monitor: Progress,challenges and prospect

In this paper, we compared the concept of agricultural drought and its relationship with other types of drought, and discussed research progress in agricultural drought monitoring from the site-based and remote sensing aspects, respectively. The applicability and limitations of different drought monitoring indexes were also compared. Results showed that the site-based drought index has experienced a long development history and become the main way of monitoring drought. Meanwhile, the remote sensing based drought index was mainly established from two aspects, soil water and crop water, and has achieved good results in agricultural drought monitoring. In addition, through mathematical statistics and document comparison, the development and latest progress of agricultural drought monitoring has been revealed, suggesting a transformation of agricultural drought monitoring from traditional single meteorological monitoring indicators to integrated meteorology and remote sensing monitoring indicators, mainly reflected in the introduction of multisource data and the innovation of research methods. The applicability and limitations of comprehensive drought monitoring indexes established in recent years were also discussed. Finally, through the analysis of current challenges in agricultural drought monitoring, future research prospects in agricultural drought monitoring are proposed, including further investigating the mechanism of agricultural drought, identifying the influences of agricultural drought, developing a multi spatiotemporal scale agricultural drought monitoring model, coupling the qualitative and quantitative agricultural drought evaluation models, and improving the application level of remote sensing data in agricultural drought monitoring.