农业干旱卫星遥感监测与预测研究进展

干旱是影响农业生产的主要气候因素。传统的农业干旱监测主要是基于气象和水文数据，虽然能提供监测点上较为精确的干旱监测结果，但是在监测面上的农业干旱时，仍存在一定的局限。遥感技术的快速发展，尤其是目前在轨的卫星传感器感测的电磁波段涵盖了可见光、近红外、热红外和微波等波段，为区域尺度农业干旱监测提供了新的手段。充分利用卫星遥感数据获得的丰富地表信息进行农业干旱监测和预测具有重要的研究意义。本文从遥感指数方法、土壤含水量方法和作物需水量方法三个方面阐述了基于卫星遥感的农业干旱监测研究进展。农业干旱预测是在干旱监测的基础上进行时间轴的预测，本文在总结干旱监测进展的基础上，进一步简述了以干旱指数方法和作物生长模型方法为主的农业干旱预测研究进展。

Progress of Agricultural Drought Monitoring and Forecasting Using Satellite Remote Sensing

Agricultural drought is a major factor that affects agricultural production. Traditional agricultural drought monitoring is mainly based on meteorological and hydrological data, and although it can provide more accurate drought monitoring results at the point level, there are still limitations in monitoring agricultural drought at the regional scale. The rapid development of remote sensing technology has provided a new mean of monitoring agricultural droughts at the regional scale, especially since the electromagnetic wavelengths sensed by satellite sensors in orbit now cover visible, near-infrared, thermal infrared and microwave wavelengths. It is important to make full use of the rich surface information obtained from satellite remote sensing data for agricultural drought monitoring and forecasting. This paper described the research progress of agricultural drought monitoring based on satellite remote sensing from three aspects: remote sensing index-based method, soil water content method and crop water demand method. The research progress of agricultural drought monitoring based on remote sensing index-based method was elaborated from five aspects: vegetation drought index, temperature drought index, integrated vegetation and temperature drought index, water drought index and microwave drought index; the research progress of agricultural drought monitoring based on soil water content method was elaborated from two aspects: soil water content retrieval based on visible to thermal infrared data and soil water content retrieval based on microwave data; the research progress of agricultural drought monitoring based on crop water demand method was elaborated from two aspects: agricultural drought monitoring based on crop canopy water content retrieval method and crop growth model method. Agricultural drought forecasting is a timeline prediction based on drought monitoring. Based on the summary of the progress of drought monitoring, the research progress of agricultural drought forecasting by the drought index method and the crop growth model method was further briefly described. The existing agricultural drought monitoring methods based on satellite remote sensing were summarized, and its shortcomings were sorted out, and some prospects were put forward. In the future, different remote sensing data sources can be used to combine deep learning methods with crop growth models and based on data assimilation methods to further explore the potential of satellite remote sensing data in the monitoring of agricultural drought dynamics, which can further promote the development of smart agriculture.