星-地联合的冬小麦区域干旱监测方法研究

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.

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.

干旱是影响全球生态和粮食安全的重大气象灾害。我国黄淮海平原冬春少雨，冬小麦生长季经常受到干旱的威胁，科学合理地监测冬小麦的干旱状况，是减少干旱造成的农业损失、实现粮食稳产高产的重要保障。本课题围绕冬小麦干旱问题，开展了如下研究工作：（1）利用中国气象局固城农业气象试验站的水分控制试验场，观测了冬小麦在不同水分胁迫条件下的生长发育状况，试验结果表明，随着干旱程度加重，冬小麦生长发育参数（植株密度、叶面积指数、株高、单株生物量、产量）降低，生育期缩短，实际蒸散量下降。（2）根据FAO关于作物系数的定义，研发了冬小麦关键发育阶段作物系数估算方法，并给出了华北冬小麦生长期内逐旬和各关键发育阶段作物系数。研究结果表明：无论是在关键发育阶段尺度，还是在逐旬尺度上，冬小麦作物系数均表现为“双峰型”曲线特征，体现了越冬作物的特性；与FAO推荐的“三点四段”式作物系数相比，本课题研发得到的作物系数能够更精细地刻画冬小麦的生长特性和需水规律。（3）提出了基于卫星遥感数据的作物最大可能蒸散估算方法，并结合双源能量平衡模型模拟的实际蒸散，构建了冬小麦干旱识别指数CDII（Crop Drought Identification Index for winter wheat）。该指数利用能够表达农业气象参数的作物最大可能蒸散，而非气候学上常用的潜在蒸散，可以更好地与农作物不同发育阶段的需水程度相关联，体现农作物在不同发育阶段的受旱状况。利用中国气象灾害年鉴和农业气象灾害记录，厘定冬小麦不同发育阶段干旱指数并验证，结果表明，CDII指标计算结果与历史灾情描述的吻合概率为86.2％，能够比较客观地体现出研究区域的实际受灾情况。（4）改进了遥感数据时空适应性反射率融合模型在输入数据的观测角度和几何位置不一致等方面的问题，提高了数据融合模型的模拟精度，实现了模型的半自动化运行，探索了一条基于数据融合模型提高冬小麦干旱监测的空间精度和时间频次的新途径。

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