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首页> 外文会议>Conference on Ecosystems' Dynamics, Agricultural Remote Sensing and Modeling, and Site-Specific Agriculture; Aug 7, 2003; San Diego, California, USA >Remote Sensing and Modeling the Dynamics of Soil Moisture and Vegetative Cover of Arid and Semiarid Areas
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Remote Sensing and Modeling the Dynamics of Soil Moisture and Vegetative Cover of Arid and Semiarid Areas

机译：干旱和半干旱地区土壤水分和植被覆盖度的遥感和模拟

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With the large volume of satellite remote sensing data of the earth terrestrial surface becoming available, precisely monitoring the dynamics of the land surface state variables for agricultural and land use management becomes possible. Currently, the moderate resolution imaging spectroradiometers on board NASA's Earth Observing Satellites (EOS) Terra and Aqua make it possible to derive a global coverage of land surface vegetation indices, leaf area index, and surface temperature data products at 1km spatial resolution every day. The advanced microwave scanning radiometers (AMSR) on board Aqua and Japan's ADEOS satellites start sending back a global coverage of rainfall and land surface soil moisture data products at up to 25km spatial resolution every two to three days. It is also well known that these land surface remote sensing products contain uncertainties due to imperfect instrument calibration and inversion algorithms, geophysical noise, representativeness error, communication breakdowns, and other sources while land surface model can continuously simulate these land surface state or storage variables for all time steps and all covered areas. Therefore a combination of satellite remote sensing products and land surface model simulations may provide more continuous, precise and comprehensive depiction of the dynamics of the land surface states. This paper introduces the state-of-the-arts technologies in the development of NASA's Land Data Assimilation System, and then proposes a procedure to combine the simulations of a simple land surface model and the remote sensing products from MODIS and AMSR. After the results of testing the procedure for an arid area in Southwest USA are presented, the application of the procedure for the oases in Fukang County of Xinjiang Autonomous Region is proposed.
机译：随着可获得大量的地球地面卫星遥感数据，为农业和土地利用管理精确监测土地表面状态变量的动态成为可能。目前，NASA地球观测卫星（EOS）Terra和Aqua上的中分辨率成像光谱仪可以每天以1km的空间分辨率获得全球覆盖的地表植被指数，叶面积指数和地表温度数据产品。 Aqua和日本的ADEOS卫星上的先进微波扫描辐射计（AMSR）开始每两到三天以高达25 km的空间分辨率向全球发送降雨和陆地表层土壤水分数据产品的报告。众所周知，由于仪器校准和反演算法不完善，地球物理噪声，代表性误差，通信故障和其他来源，这些陆面遥感产品存在不确定性，而陆面模型可以连续模拟这些陆面状态或存储变量，以用于所有时间步骤和所有覆盖区域。因此，卫星遥感产品和地表模型模拟的结合可以提供更连续，精确和全面的地表状态动态描述。本文介绍了NASA土地数据同化系统开发中的最新技术，然后提出了一种程序，将简单的地表模型的仿真与MODIS和AMSR的遥感产品相结合。介绍了美国西南干旱区绿洲绿化的试验结果，提出了绿洲绿化在新疆富康县的应用。

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