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A recent review has suggested relatively simple contextual models would remain attractive in the estimation of evapotranspiration (ET). The surface temperature-vegetation index feature space model is a typical representative since it provides direct monitoring of ET using simply and effective contextual information. However, the temporally continuous estimation and large-scale application issues are not well identified at previous studies. Under this background, the main motivation for this study is to develop an operational new parameterization scheme within the feature space framework for large-scale temporally continuous ET estimation. Specifically, the spatial domain division strategy was first designed to obtain the optimal grid division size for large-scale ET estimation. The model boundaries of these sub-regions were parameterized using spatial domain based temporal domain information at seasonal scale. The applicability of new method was demonstrated over the entire contiguous United States (CONUS) in the year 2008 using Moderate Resolution Imaging Spectroradiometer (MODIS) and Global Land Surface Satellite (GLASS) products. The comparison between present method and traditional method indicates that the new parameterization scheme produced at different reference spatial domains has a slight accuracy improvement over the 14 FLUXNET sites. In addition, the new method holds unique advantages in its simplicity and continuity, especially superior in the temporally continuous estimation of ET over a large extended region. The validation results of 14 FLUXNET sites indicate the correlation coefficient (r), mean absolute error (MAE), root mean square error (RMSE) the mean bias (B) values for continuous estimation of daily ET were 0.62, 28.430 W/m2, 36.133 W/m2 and −0.059, respectively. The comparison results with existing ET product also demonstrated the comparable of ET distribution and estimates. Meanwhile, the boundary parameters obtained at seasonal scale also enhanced the physical meanings of feature space model. Consequently, the new parameterization scheme proposed in this study can be regarded as a suitable tool to provide large-scale temporally continuous estimation of ET.

Numerous studies have focused on the retrieval of soil moisture (SM) and evaporative fraction (EF) based on the surface temperature-vegetation index (TVX) framework. However, few literatures have investigated whether these two variables can be retrieved simultaneously under the same TVX framework. Besides, most previous studies only demonstrated the application of the TVX framework under clear sky conditions. The estimation of SM and EF under partially cloudy conditions is still a challenging task. The main motivation for this study is to develop an observation-driven optimization framework combined with TVX method for coupled and continuous estimation of SM and EF. Specifically, the optimization scheme was designed to construct the constraints and objective functions of the TVX framework at seasonal scale. In-situ SM and EF measurements were utilized to calibrate the theoretical boundaries of the TVX framework, respectively. The applicability of this new scheme was demonstrated over the Southern Great Plains (SGP) of the United States of America in the year 2005 using Moderate Resolution Imaging Spectroradiometer (MODIS) products. The comparisons between present method and previous traditional TVX models indicate that the accuracy of the optimization scheme produced using only one site for calibration has reached a comparable level over the whole study domain. However, the new scheme holds unique advantages in simplicity and continuity. It has not only circumvented the complex parameterization scheme of theoretical TVX framework, but also achieved estimation of SM and EF under partially cloudy conditions. The validation results of SM estimates show that the absolute value of correlation coefficient (r) under clear and partially cloudy sky conditions was 0.51 and 0.43, respectively. The validation results of EF estimates indicate that the r and root mean square error (RMSE) under clear sky conditions was 0.70 and 0.163. In contrast, the r and RMSE under partially cloudy conditions was 0.66 and 0.158. In addition, the coupled estimation of SM and EF means that these two objective variables can be interconverted into each other within the optimization framework. Thus either SM or EF can be used to calibrate the theoretical dry edge that applies to the estimation of both variables.