Evaluation of Remotely Sensed Evapotranspiration Over the CEOP EOP-1 Reference Sites

Abstract

In this paper, the Coordinated Enhanced Observing Period (CEOP) data during an Enhanced Observing Period (EOP-1) is used to assess the Surface Energy Balance System (SEBS) model. The purpose of this study is to evaluate the adaptability of SEBS to different climatic zones and land cover classifications at two different scales. The SEBS model was examined at the field (tower) scale based primarily on in-situ observations from CEOP sites. To examine a broader scale application, remotely sensed land surface temperature (LST) from the MODIS sensor and surface meteorology from the Global Land Data Assimilation System (GLDAS) were used for the required forcing datasets. Comparisons at tower scale show that the model predictions of the energy fluxes agree reasonably well with the observations. The root mean square error (RMSE) of the ET prediction based on MODIS Land Surface Temperature (LST) plus CEOP meteorological observations is about 61 W m À2 at a grassland site (Cabauw) and a needle leaf forest site (BERMS). The RMSE of ET predication at a corn site (Bondville) is 96 W m À2 and the corresponding percentage error is 28.9%. When GLDAS forcing was used instead of the CEOP tower observations, the RMSEs of ET prediction at Cabauw, BERMS and Bondville are increased to 82, 84 and 140 W m À2 respectively. The negative bias of surface downward radiative forcing from GLDAS contributed much to the larger deviation of the ET prediction when compared to tower based values. The innovative aspects of our study in this paper are: a) No similar work on evaluating remote sensing based ET model under a diverse climate and land cover condition has been done before; b) ET modeling was assessed in different scales ranging from site scale to GLDAS grid cell; c) The framework of estimating the spatial distribution of ET combining satellite data and available ground meteorology is tested.

Evaluation and Aggregation Properties of Thermal Infra-Red-Based Evapotranspiration Algorithms from 100 m to the km Scale over a Semi-Arid Irrigated Agricultural Area

“…The four aggregation rules of roughness length (linear, geometric i.e Equation (25), harmonic i.e., Equation 26 and conductance average i.e., Equation (30)) were implemented to derive input roughness lengths for SEBS at LR. The resulting ET calculated by SEBS was compared to the …”

“…For all dates except the 30 December 2007 and the 13 May 2008, it was possible to solve Equation (30) and to derive effective roughness values that reduced the difference between the HR and the LR conductances to zero. For the first and the last dates, no solution to Equation (30) was found using the numerical solver and only a minimum value for the first term of Equation (30) was found (effective roughnesses corresponding to these minimum were used).…”

“…For the first and the last dates, no solution to Equation (30) was found using the numerical solver and only a minimum value for the first term of Equation (30) was found (effective roughnesses corresponding to these minimum were used). Compared to the results obtained when roughness lengths were derived directly from LR remote sensing data (Section 5.2), significant reductions in relative biases and/or RMSE values were obtained for most dates when considering the first three types of aggregation methods (either with LR ASTER dataset, Table 2, or MODIS dataset, Table 3).…”

“…The fourth aggregation method relied on deriving the effective surface roughness lengths from the aggregation of aerodynamic conductance in neutral conditions (Section 4.5). Using the LR ASTER dataset, it was possible to solve Equation (30) for all dates, except for 30 December 2007, and 13 May 2008, and to derive effective roughness values that reduced the difference between the HR and the LR conductances to zero. For the first and the last dates, no solution to Equation (30) was found using the numerical solver and only a minimum value for the first term of Equation (30) was found (effective roughness lengths corresponding to these minimums were used).…”

“…The ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) sensor on board the Terra satellite provided a dataset made of four products and eight dates (30 (Table 1). All products are atmospherically corrected and include LST retrieval includes emissivity correction.…”