Many satellite missions rely on modeling approaches to acquire global or regional evapotranspiration (ET) products. However, a current challenge in ET modeling lies in dealing with sub-pixel heterogeneity, as models often assume homogeneous conditions at the pixel level. This is particularly an issue for heterogeneous landscapes, such as tree-grass ecosystems (TGE). In these areas, while appearing homogeneous at larger spatial scales pertaining to a single land cover type, the separation of the spectral signals of the main landscape features (e.g. trees and grasses) may not be achieved at the conventional satellite sensor resolution (e.g. 10–1000 m). This leads to important heterogeneity within the pixel grid that may not be accounted for in traditional modeling frameworks. This study examined the effect of pixel heterogeneity on ET simulations over a complex TGE in central Spain. High resolution hyperspectral imagery from five airborne campaigns forced the two-source energy balance (TSEB) model at 1.5–1000 m spatial resolutions. Along with this, the sharpened (20 m) and original (1000 m) Sentinels for Evapotranspiration (Sen-ET) products were evaluated over the study site for 2017. Results indicated that TSEB accurately simulated ET (RMSD: ~60 W/m²) when the pixel scale was able to robustly discriminate between grass and tree pixels (<5 m). However, model uncertainty drastically increased at spatial resolution greater than 10 m (RMSD: ~115 W/m²). Model performance remains relatively constant between 30 and 1000 m spatial resolutions, with within pixel heterogeneity being similar at all these scales. For mixed pixels (≥30 m), forcing an effective landscape roughness into TSEB (RMSD: ~80 W/m²) or applying a seasonally changing TSEB (TSEB-2S; RMSD: ~65 W/m²) improved the modeling performance. The Sen-ET products behaved similarly at both scales with RMSD of ET roughly 80 W/m². The non-linear relationship between input parameters and flux output, along with the poor representation of aerodynamic surface roughness, were the main drivers for the increased uncertainties at coarser scales. These results suggest that care should be taken when using global ET products over TGE and similarly heterogeneous landscapes. The modeling procedure should inherently account for the presence of vastly different vegetation roughness elements within the pixel, to achieve reliable estimates of turbulent fluxes over a TGE.

许多卫星任务依靠建模方法来获取全球或区域蒸散量（ET）产品。但是，由于模型通常在像素级别假设均质条件，因此ET建模中的当前挑战在于处理子像素异质性。对于异质景观（例如树草生态系统（TGE））而言，这尤其是一个问题。在这些区域中，虽然在较大的空间尺度上似乎属于单一土地覆被类型，但在常规卫星传感器分辨率下（例如10– 1000 m）。这导致像素网格内的重要异质性，而传统建模框架可能无法解决。这项研究考察了西班牙中部复杂TGE上像素异质性对ET模拟的影响。来自五次空中战役的高分辨率高光谱图像迫使两源能量平衡（TSEB）模型处于1.5–1000 m的空间分辨率。同时，对2017年研究现场的蒸发蒸腾（Sen-ET）产品的锐化（20 m）和原始（1000 m）Sentinel进行了评估。结果表明，TSEB精确模拟了ET（RMSD：〜60 W / m²）当像素比例能够在草和树像素（<5 m）之间有力地区分时。但是，在空间分辨率大于10 m（RMSD：〜115 W / m ²）的情况下，模型不确定性急剧增加。在30到1000 m空间分辨率之间，模型性能保持相对恒定，而像素异质性在所有这些尺度上都相似。对于混合像素（≥30 m），将有效的景观粗糙度强制为TSEB（RMSD：〜80 W / m ²）或应用季节性变化的TSEB（TSEB-2S; RMSD：〜65 W / m ²）可改善建模性能。Sen-ET产品在两种规模上的表现都相似，ETRMS的RMSD约为80 W / m ²。输入参数与通量输出之间的非线性关系，以及空气动力学表面粗糙度的不良表示，是导致较粗尺度下不确定性增加的主要驱动力。这些结果表明，在TGE和类似异类景观上使用全球ET产品时应格外小心。建模过程应固有地考虑像素内存在极大不同的植被粗糙度元素，以实现TGE上湍流通量的可靠估计。