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Integrating two layers of soil moisture parameters into the MOD16 algorithm to improve evapotranspiration estimations

机译:将两层土壤水分参数集成到MOD16算法中,以改善蒸散量估算

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摘要

To integrate soil moisture into the algorithm of the Moderate Resolution Imaging Spectroradiometer (MODIS) global evapotranspiration (ET) project (MOD16), two improvements were implemented: two layers of relative soil moisture parameters were combined with a surface resistance model; and the complementary relationship was replaced with the Penman-Monteith (P-M) method to estimate the dry soil surface evaporation. In the vegetation surface resistance model, a multiplier R-sm1 was added, and the influence of the relative soil moisture in the root zone was accounted for. In the soil surface resistance model, an empirical exponential relationship was used. To calculate the relative soil moisture parameters, soil hydraulic parameters, such as field capacity (Fc), wilting point (Wp), and saturation point (Sp), were estimated according to the soil texture information; these parameters were used as critical values to estimate the relative soil moisture. Both the MOD16 method and improved method were validated using ET flux data collected at nine flux-tower sites in the USA from 2000 to 2009. The mean absolute BIAS and the root mean square error (RMSE) decreased from 0.36 to 0.30mmday(-1) and from 1.14 to 0.97mm day(-1), respectively, after integrating the soil moisture parameters. Meanwhile, the mean correlation coefficient (R) for the nine sites increased from 0.54 to 0.70. Therefore, the improved method performed better than the MOD16 method. Furthermore, the uncertainties associated with the MODIS leaf area index (LAI) products, flux-tower measurements, soil texture, soil moisture, and model parameters were analysed. The outlook for future modifications was also discussed.
机译:为了将土壤水分纳入中等分辨率成像光谱仪(MODIS)全球蒸散(ET)项目(MOD16)的算法中,实现了两个改进:将两层相对土壤水分参数与一个表面阻力模型结合在一起;互补关系被Penman-Monteith(P-M)方法代替,以估算干燥土壤表面的蒸发量。在植被表面阻力模型中,增加了乘数R-sm1,并考虑了根区相对土壤水分的影响。在土壤表面阻力模型中,使用了经验指数关系。为了计算土壤的相对湿度参数,根据土壤质地信息估算了土壤的水力参数,例如田间持水量(Fc),枯萎点(Wp)和饱和点(Sp)。这些参数被用作估计土壤相对湿度的临界值。 MOD16方法和改进方法均使用2000年至2009年在美国9个通量塔位置收集的ET通量数据进行了验证。平均绝对BIAS和均方根误差(RMSE)从0.36减少至0.30mmday(-1) )和整合土壤水分参数后的1.14至0.97mm日(-1)。同时,这9个位点的平均相关系数(R)从0.54增加到0.70。因此,改进的方法的性能优于MOD16方法。此外,分析了与MODIS叶面积指数(LAI)产品,通量塔测量,土壤质地,土壤湿度和模型参数有关的不确定性。还讨论了未来修改的前景。

著录项

  • 来源
    《International journal of remote sensing》 |2015年第20期|4953-4971|共19页
  • 作者单位

    Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China|Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China|Beijing Water Sci & Technol Inst, Dept Water Hazard Res, Beijing 100048, Peoples R China;

    Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China|Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Minist Agr, Key Lab Agri Informat, Beijing 100081, Peoples R China;

    Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China;

    Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China;

    Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China;

    Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
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