Improving our understanding of the energy and water exchanges between the land surface and the lower atmosphere (i.e. land–atmosphere interactions), and how climate change may affect them, is crucial to predict changes in temperature and precipitation extremes. Observations of energy and water fluxes at the land surface are typically retrieved from the eddy covariance method, which presents limitations related to spatial and temporal gaps, and the non-closure of the energy and water balances. Meanwhile, soil moisture (SM) products derived from satellite data have been widely used at regional and global scales, but they are limited to capture only surface soil water content and variations. The combination of remote sensing (RS) data and modelling frameworks is called to be the solution to improve the spatial coverage and vertical resolution of land–atmosphere interactions data, ensuring the energy and water balance closure. Here, we explore the combination of remote sensing and meteorological data with a physical-based modelling framework, the High resOlution Land Atmosphere Parameters from Space (HOLAPS). We used HOLAPS to produce hourly consistent estimates of energy and water fluxes over Europe at 5 km resolution. HOLAPS and other satellite-based evapotranspiration and sensible heat flux products from the literature are evaluated against the water balance method and eddy covariance measurements. HOLAPS SM estimates together with other RS-modelling products are also evaluated against ground-based measurements at the surface and in the root zone. The evaluation of HOLAPS ET estimates show similar performance to the other products with Kling–Gupta efficiency (KGE) ¿ -0.41 in comparison with eddy covariance measurements from FLUXNET in all seasons but in boreal winter. The simulation of H is more uncertain than for ET with KGE values ranging from -2.5 to 0.8 along the products and stations at monthly scales. HOLAPS reaches slightly better results than the rest of ET and H products at daily scales in summer (KGE ¿ 0.3 for ET and KGE ¿ 0.0 for H) and during hot conditions (KGE ¿ 0.2 for ET and KGE ¿-0.2 for H), while the state-of-the-art products show KGE ¿ 0.1 for ET and KGE ¿ -0.41 for H in summer and KGE ¿ -0.1 for ET and KGE ¿ -0.6 for H during hot conditions. All products evaluated here yield a reasonable performance (KGE ¿-0.41 at most sites) in simulating SM at the surface and in the root zone. Our results expose the need for further investigating and improving product performances during extreme conditions. The good performance of HOLAPS together with its inherent advantages (RS data driven, high temporal and spatial resolution, spatial and temporal continuity, soil moisture at different depths and long-term consistent evapotranspiration and sensible heat flux estimates) support its use for agricultural and forest management activities as well as to study land–atmosphere interactions based on Earth Observations.

中文翻译：

---

从 HOLAPS 框架生成和评估能量和水通量：在极端炎热天气下与卫星产品的比较


提高我们对陆地表面和低层大气之间的能量和水交换（即陆地-大气相互作用）以及气候变化如何影响它们的理解，对于预测温度变化和极端降水至关重要。对地表能量和水通量的观测值通常从涡度相关方法中检索，该方法存在与空间和时间间隙以及能量和水平衡不闭合相关的局限性。同时，来自卫星数据的土壤水分 （SM） 产品已在区域和全球范围内得到广泛使用，但它们仅限于捕获表层土壤含水量和变化。遥感 （RS） 数据和建模框架的结合被称为提高陆地-大气相互作用数据的空间覆盖范围和垂直分辨率的解决方案，确保能源和水平衡闭合。在这里，我们探索了遥感和气象数据与基于物理的建模框架的结合，即来自太空的高分辨率陆地大气参数 （HOLAPS）。我们使用 HOLAPS 以 5 公里的分辨率对欧洲上空的能量和水通量进行每小时一致的估计。根据水平衡法和涡度相关测量，对文献中的 HOLAPS 和其他基于卫星的蒸散和感热通量产品进行了评估。HOLAPS SM 估计值与其他 RS 建模产品一起，还根据地表和根区的地面测量进行评估。HOLAPS ET 估计值的评估显示，与 FLUXNET 在所有季节（北方冬季）的涡度相关测量相比，Kling-Gupta 效率 （KGE） ¿ -0.41 的其他产品的性能相似。 H 的模拟比 ET 更具不确定性，在月尺度上沿产品和站点的 KGE 值范围为 -2.5 至 0.8。HOLAPS在夏季（ET的KGE¿0.3，H的KGE¿0.0）和炎热的条件下（ET的KGE¿0.2，H的KGE¿-0.2）的日尺度上，效果略好于其他ET和H产品，而最先进的产品在夏季显示ET的KGE¿0.1，H的KGE¿-0.41，在炎热的条件下，ET的KGE¿-0.1和H的KGE¿-0.6。这里评估的所有产品在模拟表面和根区 SM 时都产生了合理的性能（大多数位点的 KGE ¿-0.41）。我们的结果揭示了在极端条件下进一步研究和改进产品性能的必要性。HOLAPS 的良好性能及其固有优势（RS 数据驱动、高时间和空间分辨率、空间和时间连续性、不同深度的土壤水分以及长期一致的蒸散和显热通量估计）支持其用于农业和森林管理活动以及基于地球观测研究陆地-大气相互作用。