星-地联合的冬小麦区域干旱监测方法研究

鉴于现行的农业干旱监测法多是各自根据田间试验数据、气象数据或卫星遥感数据并行开发的,疏于应用互补的和潜在的信息,本研究以倍受干旱威胁的华北冬小麦为研究对象,在对田间试验数据、气象数据和遥感数据进行尺度转换、同化融合、信息集成的基础上,利用基于能量平衡原理的双源模型TSTIM模拟土壤-植被系统的实际蒸散ETa，通过Penman-Monteith公式和作物系数查找表计算作物的标准蒸散ETc,用ETa和ETc来构建具有物理基础并与作物需水特性相联系的作物干旱监测指数模型CDMI;用遥感数据时空适应性融合模型STARFM对高时频、宽覆盖的MODIS数据和高空间分辨率的Landsat数据进行融合处理,通过星-地联合,将CDMI模型由点扩展到面,最大限度地发挥星-地信息集成的优势和应用潜力,为实现大范围、高时频(逐日)、高空间分辨率(30/60/120m)的作物实时-准实时干旱监测奠定科学与技术基础.

Recently the methods of drought monitoring are mainly based on field measurements, meteorological observations, or remote sensing images, individually. This project will attempt to monitor the characteristics of drought with integrated multi-source data in spatially and temporally for the winter wheat in North China Plain, which suffers from the threat of drought frequently. Two experiments of standard conditions and water stress conditions will be performed in Gucheng Ecological-Meteorological Integrated Observation Experiment Station of CAMS, China. And then all needed data, including experimental observations, meteorological data and remote sensing images, are scaled and assimilated. With focusing on the droughty nature of winter wheat, a Crop Drought Monitor Index (ab. CDMI) model will be developed at different growth periods based on crop evapotranspiration under standard conditions (ETc) and actual evapotranspiration (ETa). Actual evapotranpiration will be simulated with Two-Souce Time Integrated Model (TSTIM) based on land surface energy balance, and crop evapotranspiration under standard conditions will be computed based on Penman-Monteith equation and crop efficient lookup table. In order to monitor drought of winter wheat with a high level of spatial details frequently, a Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) will be introduced and improved to generate fused remotely sensed imagerie at high spatial resolution and high revisiting cycle. High revisiting cycle satellite data, Moderate Resolution Imaging Spectroradiometers (MODIS), and high spatial resolution remote sensing images, Landsat-TM/ETM+, are selected to build dense time-series images at Landsat-like spatial resolution, which can be served as the scientific and technical foundations for monitoring regional drought of winter wheat. The methods and results from this project will show the superiorities and potential applications of integrating satellite observations and field measurements, and also it will be use to ministries of agriculture and water resources for decision making in water resources management.