利用遥感物候信息改进区域作物生长模拟的研究

G20 has noticed the progress of GEO's Global Agricultural Monitoring System of Systems and made a declaration at the G20 agriculture minister conference in Paris, France in June,2011.The declaration says "In order to improve crop production projections and weather forecasting, with the use of modern tools, in particular remote sensing tools, we decide to launch the Global Agricultural Geo-Monitoring Initiative, via the Group on Earth Observations, a useful input for FAO's Agricultural Market Information System concerning the provision of more accurate crop forecasts data." Therefore, how to provide more accurate crop forecast data for the global food security is challenging.The Chinese community of agriculture remote sensing is rising in this field and should play more important role in response to this initiative. The remote sensing data assimilation technology has been used to introduce remote sensing data into crop model. A bunch of satellite products retrieved from low resolution remotely sensed data has been generated and made freely available.NDVI,LAI and phenology are relevant to the crop growth.In particular, LAI and phenology are two key parametesrs in crop model. LAI influences the photosynthesis of the crop leaves and stems.Phenology may decide the crop stages.So, LAI and phenology may be used to link the crop modeland remote sensing products.Taking the advantages of the crop spectrum and the mechanism of the crop model, the assimilation of remote sensing data into crop model may improve the regional crop growth simulation..The proposal identifys the major winter wheat production area in North China Plain as the study area and will further improve the ordinary method of the assimilation of LAI into crop model by adding the crop phenology information, and finally achieve the more accurate crop yield estimation, using the methodologies of the experiment, the satellite data assimilation and the time series analysis. The parameters used in crop model will be collected from the in-situ experiments and then used to tune, validate and verify the crop model. The remote sensing products and the middle parameters output from the crop model will be calibrated, using the collected data in large area. A fast survey in large area will be conducted. The crop phenology model will be developed and the key phenology information will be retrieved from the reconstructed time series of remote sensing data. From the point of view of the crop phenophases matching, the simulation period will be decided bases on the crop phenology information and the meteorological data will be used accordingly. The methodology will be developed to assimilate the crop phenology and LAI into the crop model. The innovation of this proposal is to merge the phenology and LAI both into crop model to improve the crop simulation in the large area. The proposal is of scientific significance and feasibility for the regional crop production estimation and the crop quality forecast etc.

以数据同化技术把作物光谱信息融入作物模型机理过程是当前改进区域作物生长模拟方法的重要研究方向。项目拟以华北冬小麦主产区为研究区域，采用实验法、数据同化法和时间序列重构法，引入遥感物候信息，改进通常只同化遥感叶面积指数进入作物模型的方法，提高区域作物生长模拟的精度。项目通过布设地面实验，获取标定作物模型所需的参数，并用于检验和验证作物模型。通过在研究区快速调查，获取大范围观测数据来校准遥感数据产品和作物模型的中间参数。利用精准处理后的时间序列遥感数据，构建遥感物候模型，提取关键作物物候信息。以全生育期物候匹配的思路，根据遥感物候信息决定作物模型模拟作物生长的时间，并动态使用对应时间的气象数据，提出并实现同化两类遥感信息进入作物模型的技术方法。项目以物候匹配的思路，将遥感获取的物候信息和叶面积指数同化进入作物模型，在技术方法上有进步和创新，具有重要的科学意义，还可为作物产量预测、品质预报借鉴。

遥感监测和作物模型模拟是目前农作物生长监测的两个重要技术手段，然而，遥感技术利用的是光谱信息，不能很好的解释机理问题，作物模型是基于机理建立的，但是所需的现势性参数很难及时获取，模拟的结果与实际情况有较大差距。因此，遥感信息与作物模型的结合应用受到了农学、农业遥感甚至全球变化领域的研究者的广泛关注，他们倡导实现二者优势互补，提升应用潜力，利用遥感数据同化方法将遥感信息与作物模型结合起来是近年来研究的重要方向。项目以华北平原冬小麦主产区为研究区域，提出了利用自主卫星数据提取冬小麦种植区的技术方法，提出了利用自主卫星数据反演叶面积指数的方法，开发了遥感物候信息提取系统，引入遥感物候信息，以全生育期物候匹配的思路，根据遥感物候信息决定作物模型模拟作物生长的时间，动态优化相应模型参数，改进了通常只同化遥感叶面积指数进入作物模型的方法，提高了区域作物生长模拟的精度，经检验，使用同样参数的情况，单位面积产量与使用现成的叶面积指数和不使用的均方根误差分别降低750.20kg/ha和236.34kg/ha。项目实施过程中也发现遥感数据及产品的质量对监测结果有很大的影响，由于我国农区地块面积小且有插花种植现象，MODIS 数据反演的叶面积指数与真实情况差异较大，自主卫星的数据质量缺乏严格的控制，使用中必须进行检验和解决存在的问题，甚至需要持续跟踪和研发解决其中问题的技术。项目在技术方法上有所进步和集成创新，为区域作物产量预测、作物品质预报、作物变量施肥决策、作物精准灌溉决策等夯实了技术基础，奠定了理论、方法和试验基础。展望未来，遥感信息与作物生长模式结合的研究具有广阔的应用前景，项目的研究不仅具有很强的科学意义，还有重要的实用价值。