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首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >Assimilation of Active and Passive Microwave Observations for Improved Estimates of Soil Moisture and Crop Growth
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Assimilation of Active and Passive Microwave Observations for Improved Estimates of Soil Moisture and Crop Growth

机译：吸收主动和被动微波观测值，以改善土壤水分和作物生长的估算

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An ensemble Kalman Filter-based data assimilation framework that links a crop growth model with active and passive (AP) microwave models was developed to improve estimates of soil moisture (SM) and vegetation biomass over a growing season of soybean. Complementarities in AP observations were incorporated to the framework, where the active observations were used to optimize surface roughness and update vegetation biomass, while passive observations were used to update SM. The framework was implemented in a rain-fed agricultural region of the southern La-Plata Basin during the 2011–2012 growing season, through a synthetic experiment and AP observations from the Aquarius mission. The synthetic experiment was conducted at a temporal resolution of 3 and 7 days to match the current AP missions. The assimilated estimates of SM in the root zone and dry biomass were improved compared to those from the cases without assimilation, during both 3- and 7-day assimilation scenarios. Particularly, the 3-day assimilation provided the best estimates of SM in the near surface and dry biomass with reductions in RMSEs of 41% and 42%, respectively. The absolute differences of assimilated LAI from Aquarius were compared to the MODIS LAI indicating that the performance of assimilation was similar to the MODIS product at a regional scale. This study demonstrates the potential of assimilation using AP observations at high temporal resolution such as those from soil moisture active passive (SMAP) for improved estimates of SM and vegetation parameters.
机译：建立了一个基于卡尔曼滤波器的集成数据同化框架，该框架将作物生长模型与主动和被动（AP）微波模型联系在一起，以改善大豆生长期土壤水分（SM）和植被生物量的估计。 AP观测中的互补性已纳入框架，其中主动观测用于优化表面粗糙度和更新植被生物量，而被动观测则用于更新SM。通过综合实验和水瓶座任务的AP观测，该框架在2011-2012年生长季节的南部拉普拉塔盆地雨养农业地区得到实施。合成实验的时间分辨率为3天和7天，以匹配当前的AP任务。在3天和7天的同化过程中，与未同化的情况相比，根区和干生物量中SM的同化估计值得到了改善。特别是，为期3天的同化过程提供了近地表生物和干燥生物质中SM的最佳估计值，RMSE分别降低了41％和42％。将来自水瓶座的同化LAI的绝对差异与MODIS LAI进行了比较，表明在区域范围内同化的性能与MODIS产品相似。这项研究证明了在高时间分辨率下使用AP观测进行同化的潜力，例如来自土壤水分主动无源（SMAP）的观测，以改善SM和植被参数的估计。

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