首页> 外文期刊>Remote Sensing of Environment: An Interdisciplinary Journal >Retrieval of soil moisture from passive and active L/S band sensor (PALS) observations during the Soil Moisture Experiment in 2002 (SMEX02)
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Retrieval of soil moisture from passive and active L/S band sensor (PALS) observations during the Soil Moisture Experiment in 2002 (SMEX02)

机译:在2002年的土壤水分实验中,通过被动和主动L / S带传感器(PALS)进行观测以检索土壤水分(SMEX02)

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The Soil Moisture Experiments in 2002 (SMEX02) were conducted in Iowa between June 25th and July 12th, 2002. A major aim of the experiments was examination of existing algorithms for soil moisture retrieval from active and passive microwave remote sensors under high vegetation water content conditions. The data obtained from the passive and active L and S band sensor (PALS) along with physical variables measured by in situ sampling have been used in this study to demonstrate the sensitivity of the instrument to soil moisture and perform soil moisture retrieval using statistical regression and physical modeling techniques. The land cover conditions in the region studied were predominantly soybean and corn crops with average vegetation water contents ranging from 0 to ~ 5 kg/m{sup}2, The PALS microwave sensitivity to soil moisture under these vegetation conditions was investigated for both passive and active measurements,. The performance of the PALS instrument and retrieval algorithms has been analyzed, indicating soil moisture retrieval errors of approximately 0.04 g/g gravimetric soil moisture. Statistical regression techniques have been shown to perform satisfactorily with soil moisture retrieval error of around 0.05 g/g gravimetric soil moisture. The retrieval errors were higher for the corn than for the soybean fields due to the higher vegetation water content of the com crops. However, the algorithms performed satisfactorily over the full range of vegetation conditions.
机译:2002年6月25日至7月12日在爱荷华州进行了2002年土壤水分实验(SMEX02)。该实验的主要目的是研究现有的用于在高植被含水量条件下从主动和被动微波遥感器中获取土壤水分的算法。 。从被动和主动L和S波段传感器(PALS)获得的数据,以及通过原位采样测量的物理变量,已用于本研究中,以证明该仪器对土壤水分的敏感性,并使用统计回归和物理建模技术。研究区域的土地覆盖条件主要为大豆和玉米作物,平均植被含水量为0至〜5 kg / m {sup} 2,在被动和被动条件下研究了PALS微波对土壤水分的敏感性。主动测量。对PALS仪器的性能和检索算法进行了分析,结果表明土壤重量的土壤水分检索误差约为0.04 g / g。统计回归技术已显示出令人满意的性能,其土壤水分反演误差约为0.05 g / g重量土壤水分。由于玉米作物的水分含量较高,玉米的取回误差高于大豆田。但是,该算法在整个植被条件范围内都能令人满意地执行。

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