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Research of soil moisture retrieval in arid region on the moistureshed scale

机译：流域尺度上干旱区土壤水分反演研究

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摘要

Soil moisture is an indispensable parameter of water, heat and carbon cycle processes in the earth surface system, and plays a key role in the formation of run-off in arid areas. The retrieval of regional-scale soil moisture is significant in the monitoring of crop growth and drought in arid regions, and in the modeling of global climatic dynamic surface processes. The use of multi-source remote sensing data in the soil moisture retrieval can improve the accuracy of reversion and generally over perform the use of a single remote sensing data due to that the data acquired by different remote sensors can provide complementary information about the soil moisture. The co-reversion of multi-source remotely sensed data is a cutting-edge technique for soil moisture retrieval. This study tries to optimize and adjust the existing reversion models available for different land cover types. A co-reversion scheme model will be designed and used to retrieve soil moisture of different vegetation types of the arid area by using MODIS and AMSR-E remote sensing data. The downscaling strategy and field verification will be used to analyze the accuracy, uncertainty and sensitivity of the reversion models. The popularity and regionality of the model will be also examined to explore the possibility of the model used for large-scale and dynamic monitoring of soil moisture
机译：土壤水分是地表系统中水，热和碳循环过程必不可少的参数，在干旱地区径流的形成中起关键作用。区域尺度土壤水分的获取对于监测干旱地区的作物生长和干旱以及对全球气候动态地表过程进行建模具有重要意义。在土壤水分检索中使用多源遥感数据可以提高恢复的准确性，并且由于不同遥感器获取的数据可以提供有关土壤水分的补充信息，因此通常过度使用单个遥感数据。多源遥感数据的共转换是土壤水分获取的前沿技术。本研究试图优化和调整可用于不同土地覆盖类型的现有回归模型。将设计一个共逆方案模型，并利用MODIS和AMSR-E遥感数据检索干旱地区不同植被类型的土壤水分。缩减策略和现场验证将用于分析回归模型的准确性，不确定性和敏感性。该模型的受欢迎程度和区域性也将得到检验，以探索该模型用于土壤水分大规模动态监测的可能性。

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