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A simple temperature domain two-source model for estimating agricultural field surface energy fluxes from Landsat images

机译:一种简单的温度域两种源模型,用于估算Landsat图像的农业领域表面能量通量

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摘要

A simple and robust satellite-based method for estimating agricultural field to regional surface energy fluxes at a high spatial resolution is important for many applications. We developed a simple temperature domain two-source energy balance (TD-TSEB) model within a hybrid two-source model scheme by coupling "layer" and "patch" models to estimate surface heat fluxes from Landsat thematic mapper/Enhanced Thematic Mapper Plus (TM/ETM+) imagery. For estimating latent heat flux (LE) of full soil, we proposed a temperature domain residual of the energy balance equation based on a simplified framework of total aerodynamic resistances, which provides a key link between thermal satellite temperature and subsurface moisture status. Additionally, we used a modified Priestley-Taylor model for estimating LE of full vegetation. The proposed method was applied to TM/ETM+ imagery and was validated using the ground-measured data at five crop eddy-covariance tower sites in China. The results showthat TD-TSEB yielded root-mean-square-error values between 24.9 (8.9) and 78.2 (21.4) W/m(2) and squared correlation coefficient (R-2) values between 0.60 (0.51) and 0.97 (0.90), for the estimated instantaneous (daily) surface net radiation, soil, latent, and sensible heat fluxes at all five sites. The TD-TSEBmodel shows good accuracy for partitioning LE into soil (LEsoil) and canopy (LEcanopy) components with an average bias of 11.1% for the estimated LEsoil/LE ratio at the Daman site. Importantly, the TD-TSEB model produced comparable accuracy but requires fewer forcing data (i.e., no wind speed and roughness length are needed) when compared with two other widely used surface energy balance models. Sensitivity analyses demonstrated that this accurate operational model provides an alternative method for mapping field surface heat fluxes with satisfactory performance.
机译:以高空间分辨率为区域表面能量通量估算农业领域的简单且坚固的基于卫星的方法对于许多应用来说是重要的。我们通过耦合“层”和“贴片”模型在混合双源模型方案中开发了一个简单的温度域两种源能量平衡(TD-TSEB)模型,以估算来自Landsat主题映射器/增强专题Mapper Plus的表面热通量( TM / ETM +)图像。为了估计满土的潜热通量(LE),我们基于总空气动力学电阻的简化框架提出了能量平衡方程的温度域残余,这提供了热卫星温度和地下水分状态之间的关键环节。此外,我们使用了修改的普利斯特利 - 泰勒模型,用于估算满植被的le。该提出的方法应用于TM / ETM +图像,并在中国的五个作物涡旋间塔网站上使用地面测量数据进行了验证。结果表明TD-TSEB产生的根平均方误差值24.9(8.9)和78.2(21.4)W / m(2)和平方相关系数(R-2)值0.60(0.51)和0.97(0.90 ),对于所有五个位点的估计的瞬时(每日)表面净辐射,土壤,潜在和明智的热量通量。 TD-TSEBMODEL对估计的LESOIL / LE比在DAMAN现场进行估计的LESOIL / LE比例,将LE分配到土壤(LESOIL)和树冠(卵膜复合)组分的良好准确性。重要的是,与两个其他广泛使用的表面能平衡模型相比,TD-TSEB模型产生了可比的精度,但需要较少的强制数据(即,不需要风速和粗糙度长度)。敏感性分析证明,这种精确的操作模型提供了一种替代方法,用于以满意的性能映射场表面热通量。

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    Beijing Normal Univ Fac Geog Sci Inst Remote Sensing Sci &

    Engn State Key Lab Remote Sensing Sci Beijing Peoples R China;

    Beijing Normal Univ Fac Geog Sci Inst Remote Sensing Sci &

    Engn State Key Lab Remote Sensing Sci Beijing Peoples R China;

    Beijing Normal Univ Fac Geog Sci Inst Remote Sensing Sci &

    Engn State Key Lab Remote Sensing Sci Beijing Peoples R China;

    Michigan State Univ CGCEO Geog E Lansing MI 48824 USA;

    Beijing Normal Univ Fac Geog Sci Sch Nat Resources State Key Lab Earth Surface Proc &

    Resource Ecol Beijing Peoples R China;

    Peking Univ Inst Remote Sensing Beijing Peoples R China;

    CALTECH Jet Prop Lab Pasadena CA 91125 USA;

    CSIRO Land &

    Water Canberra ACT Australia;

    Beijing Normal Univ Fac Geog Sci Inst Remote Sensing Sci &

    Engn State Key Lab Remote Sensing Sci Beijing Peoples R China;

    Beijing Normal Univ Fac Geog Sci Inst Remote Sensing Sci &

    Engn State Key Lab Remote Sensing Sci Beijing Peoples R China;

    Beijing Normal Univ Fac Geog Sci Inst Remote Sensing Sci &

    Engn State Key Lab Remote Sensing Sci Beijing Peoples R China;

    Beijing Normal Univ Fac Geog Sci Inst Remote Sensing Sci &

    Engn State Key Lab Remote Sensing Sci Beijing Peoples R China;

    Beijing Normal Univ Fac Geog Sci Inst Remote Sensing Sci &

    Engn State Key Lab Remote Sensing Sci Beijing Peoples R China;

    USDA ARS Hydrol &

    Remote Sensing Lab Beltsville MD USA;

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  • 正文语种 eng
  • 中图分类 生物分布与生物地理学;
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