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Calibration of IEM Model for the Soil Moisture Mapping of Non-Inundated Paddy Fields Using ALOS/PALSAR Data

机译:使用ALOS / PALSAR数据校准非淹没稻田土壤水分映射的IEM模型

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This paper reports calibration of model parameters to estimate soil moisture of non-inundated paddy fields using SAR (Synthetic Aperture Radar) data. The IEM (Integral Equation Method) model, theoretical model to represent surface scattering in the microwave region, was examined. Surface roughness parameters (standard deviation of surface height, autocorrelation function of the surface height, and autocorrelation length) are important parameters for microwave backscattering. However, the surface roughness of the paddy fields was found to change, and the autocorrelation length is quite difficult to accurately measure on the surface. Therefore, the two parameters, standard deviation of surface height, and autocorrelation length, were calibrated with actual soil moisture. As a result, it was found that the objective function to minimize the sum of residuals between estimated soil moisture and actual soil moisture produced the most optimal estimation results. In this case, The root mean square of errors between the estimated volumetric soil moisture and the actual soil moisture used for the calibration was 9.4%. This can be acceptable to consider the fluctuation of the surface roughness parameters during the non-inundated season.
机译:本文报告了模型参数的校准,使用SAR(合成孔径雷达)数据估算非淹没稻田的土壤水分。 IEM(整体式方法)模型,在微波区域中表示表面散射的理论模型。表面粗糙度参数(表面高度的标准偏差,表面高度的自相关函数,自相关长度)是微波反向散射的重要参数。然而,发现稻田的表面粗糙度改变,并且自相关长度非常难以准确地测量表面。因此,用实际土壤水分校准两种参数,表面高度和自相关长度的标准偏差。结果,发现目标函数最小化估计土壤水分和实际土壤水分之间的残留量产生了最佳的估计结果。在这种情况下,估计体积土壤水分和用于校准的实际土壤水分之间的误差的根均值为9.4%。这可以是可以接受的,以考虑在非淹没季节期间表面粗糙度参数的波动。

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