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首页> 外文会议>Conference on ecosystems' dynamics, agricultural remote sensing and modeling, and site-specific agriculture >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营养覆盖的遥感与建模

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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 1 km 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 instrumentation 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 Count of Xinjiang Autonomous Region is proposed.
机译：随着地球地面的大量卫星遥感数据变得可用，精确地监测农业和土地利用管理的土地表面状态变量的动态。目前，美国国家航空航天局的地球观测卫星（EOS）Terra和Aqua的中等分辨率成像分光剂仪使可以在每天1公里的空间分辨率下获得土地面植被指数，叶面积指数和表面温度数据产品的全球覆盖。先进的微波辐射计扫描（AMSR）在船上Aqua和日本的先进地球观测卫星的卫星以高达25公里的空间分辨率每隔两三天开始发回的降水和地表土壤水分数据产品的全球覆盖。众所周知，这些土地表面遥感产品由于仪器校准和反演算法，地球物理噪声，代表性误差，通信故障和其他来源而导致的不确定性，而陆地表面模型可以连续模拟这些陆地表面状态或存储变量所有时间步骤和所有覆盖区域。因此，卫星遥感产品和陆地面模拟的组合可以提供更加连续，精确和综合的土地表面状态的动态。本文介绍了NASA土地数据同化系统的发展中的最先进的技术，然后提出了一种组合简单陆地表面模型和遥感产品的模拟，从MODIS和AMSR结合起来。提出了在美国西南部干旱地区进行测试结果之后，提出了在新疆自治区福康计数中的粮农组织的应用。

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