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Estimating daily evapotranspiration based on a model of evaporative fraction (EF) for mixed pixels

机译:基于混合像素的蒸发级分(EF)模型估算每日蒸散散

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Currently, applications of remote sensing evapotranspiration (ET) products are limited by the coarse resolution of satellite remote sensing data caused by land surface heterogeneities and the temporal-scale extrapolation of the instantaneous latent heat flux (LE) based on satellite overpass time. This study proposes a simple but efficient model (EFAF) for estimating the daily ET of remotely sensed mixed pixels using a model of the evaporative fraction (EF) and area fraction (AF) to increase the accuracy of ET estimate over heterogeneous land surfaces. To accomplish this goal, we derive an equation for calculating the EF of mixed pixels based on two key hypotheses. Hypothesis 1 states that the available energy (AE) of each sub-pixel is approximately equal to that of any other sub-pixels in the same mixed pixel within an acceptable margin of error and is equivalent to the AE of the mixed pixel. This approach simplifies the equation, and uncertainties and errors related to the estimated ET values are minor. Hypothesis 2 states that the EF of each sub-pixel is equal to that of the nearest pure pixel(s) of the same land cover type. This equation is designed to correct spatial-scale errors for the EF of mixed pixels; it can be used to calculate daily ET from daily AE data. The model was applied to an artificial oasis located in the midstream area of the Heihe River using HJ-1B satellite data with a 300m resolution. The results generated before and after making corrections were compared and validated using site data from eddy covariance systems. The results show that the new model can significantly improve the accuracy of daily ET estimates relative to the lumped method; the coefficient of determination (R-2) increased to 0.82 from 0.62, the root mean square error (RMSE) decreased to 1.60 from 2.47 MJm(-2)(decreased approximately to 0.64 from 0.99 mm) and the mean bias error (MBE) decreased from 1.92 to 1.18 MJm(-2) (decreased from approximately 0.77 to 0.47 mm). It is con
机译:目前,遥感蒸散(et)产品的应用受到由地表异质性引起的卫星遥感数据的粗略分辨率的限制,以及基于卫星立即时间的瞬时潜热通量(LE)的时间级推断。本研究提出了一种简单但有效的模型(EFAF),用于使用蒸发级分(EF)和面积分数(AF)的模型估计远程感测的混合像素的日常等,以提高异构陆地表面的ET估计的精度。为了实现这一目标,我们基于两个密钥假设来计算用于计算混合像素的EF的等式。假设1说明每个子像素的可用能量(AE)在可接受的误差边缘内近似等于与相同的混合像素中的任何其他子像素的差距。等同于混合像素的AE。这种方法简化了方程,与估计的ET值相关的不确定性和错误是较小的。假设2表示每个子像素的EF等于相同陆地覆盖类型的最接近纯像素的EF。该等式旨在纠正混合像素EF的空间尺度误差;它可用于从日常AE数据计算每日等。该模型应用于位于黑河河中地区的人工绿洲,使用HJ-1B卫星数据,分辨率为300米。使用来自EDDY协方差系统的现场数据进行比较和验证进行校正之前和校正的结果。结果表明,新型模型可以显着提高相对于集体方法的日常估计的准确性;测定系数(R-2)从0.62增加到0.82,根均方误差(RMSE)从2.47 MMM(-2)降低至1.60(从0.99 mm的约0.64减少)和平均偏置误差(MBE)从1.92降至1.18 MJM(-2)(从大约0.77〜0.47mm降低)。它是con

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  • 来源
    《Hydrology and Earth System Sciences 》 |2019年第2期| 共21页
  • 作者单位

    Inst Remote Sensing &

    Digital Earth State Key Lab Remote Sensing Sci Beijing 100101 Peoples R China;

    Inst Remote Sensing &

    Digital Earth State Key Lab Remote Sensing Sci Beijing 100101 Peoples R China;

    Inst Remote Sensing &

    Digital Earth State Key Lab Remote Sensing Sci Beijing 100101 Peoples R China;

    Inst Remote Sensing &

    Digital Earth State Key Lab Remote Sensing Sci Beijing 100101 Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 水文科学(水界物理学) ;
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