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Estimating time series of land surface energy fluxes using optimized two source energy balance schemes: Model formulation, calibration, and validation

机译：Estimating time series of land surface energy fluxes using optimized two source energy balance schemes: model formulation, calibration, and validation

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Due to the limited availability of land surface temperature (LST) images, thermal-based evapotranspiration (ET) models can only provide instantaneous ET snapshots. In contrast, models that are based on near surface soil moisture (SM) and leaf area index (LAI) can operate at daily scales. However, their transpiration schemes need to be more physically realistic and their model parameters usually need to be calibrated by flux measurements. In this study, we incorporated a biophysical canopy conductance (Gc) model into a two source energy balance (TSEB) scheme to replace the original Priestly-Taylor (PT) approximation and then optimized both models (Gc-TSEB and PT-TSEB) at pixel scales using qualified MODIS LST data. The results show that using [ST is almost as effective in the calibration as using flux measurements. This is promising because unlike flux measurements, [ST can be acquired at various spatial resolutions by remote sensing, which makes model calibration feasible for any land pixel. In addition, ET and its partitioning between the canopy and soil layers were found to be reasonable at both validation sites. The day to day and diurnal variations of the predicted ET generally matched the trends and peaks of the flux measurements, although systematic biases were also found due to the decoupling effect of soil moisture at different depths. Furthermore, both models are robust with +/- 50% changes of SM or LAI because the parameters were automatically adjusted by the LST-calibration. The models are sensitive to LST. However, if the added noise of the LST is less significant than N(+/- 1, 2.5(2)), the medians of the RMSEs in the LE predictions from the LST-calibrated models were quite similar to those from the flux-calibrated models. Both models were found to be accurate, but Gc-TSEB provides slightly more precise and robust predictions than PT-TSEB. (C) 2015 Elsevier B.V. All rights reserved.
机译：由于地面温度（LST）图像的可用性有限，基于热的蒸散（ET）模型只能提供瞬时的ET快照。相比之下，基于近地表土壤水分（SM）和叶面积指数（LAI）的模型可以按日计算。但是，它们的蒸腾方案需要更加实际，并且其模型参数通常需要通过通量测量进行校准。在这项研究中，我们将生物物理冠层电导（Gc）模型合并到两源能量平衡（TSEB）方案中，以替代原始的Priestly-Taylor（PT）近似值，然后在750nm时优化了这两个模型使用合格的MODIS LST数据进行像素缩放。结果表明，使用[ST]在校准中几乎与使用磁通量测量一样有效。这是有希望的，因为与通量测量不同，[ST可以通过遥感以各种空间分辨率获取，这使得模型校准可用于任何陆地像素。此外，在两个验证地点都发现ET及其在冠层和土壤层之间的划分是合理的。尽管由于不同深度土壤水分的去耦合作用也发现了系统性偏差，但预测ET的日常变化和日变化通常与通量的趋势和峰值相吻合。此外，两个模型都具有鲁棒性，SM或LAI的变化为+/- 50％，因为参数是通过LST校准自动调整的。这些模型对LST敏感。但是，如果LST的附加噪声不如N（+/- 1，2.5（2））显着，则LST校准模型的LE预测中RMSE的中位数与通量-校准模型。两种模型都被认为是准确的，但是Gc-TSEB提供的预测比PT-TSEB更为精确和可靠。 （C）2015 Elsevier B.V.保留所有权利。

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    Gan G. J.; Gao, Y. C.;

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  • 年度 2015
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