## 5 Discussion
### Assessment of PT-JPL-Thermal vs. MOD16 ET in the Donana Region
In the coniferous forest, (Figure 6f) maximum _ET_ values occurred later in the summer (i.e., July-August), which coincided with maximum _ETp_ values and showed their root capability to access deeper soil moisture layers [79]. On the contrary, MOD16 showed the maximum _ET_ values in April and the lowest _ET_ values in July for the three ecosystems equally since this model relies on large-scale meteorological variables to estimate _ET_ and is not able to capture ecosystems differences. In addition, the lowest _ET_ values identified by MOD16 in July indicate the low capability of this model to assess _ET_ fluxes in dry atmospheres with adequate soil moisture availability [33]. Since MOD16 bases the soil evaporation on _RH_ and _VPD_ alone, it does not take into account the availability of water in deep layers accessible for the shrublands' and forests' roots during part of the summer. This is especially important in the Donana region because of the presence of the aquifer Almonte-Marismas on which the region rests, which assures water availability for some natural vegetation. The _ET_ spatial patterns derived from both model estimations clearly depict the land use distribution in the region with a significant agreement between models except in the wetland (Figure 7) where MOD16 _ET_ values were lower than the average values in the region while the PT-JPL-thermal _ET_ values were higher. MOD16 uses a land cover map for parameterization and, therefore, a differentiation of land uses was expected. The fact that the land use map does not include a wetland class may partially explain the low \(ET\) rates in this area. In addition, the \(RH\) criterion used in MOD16 (MOD16 (MOD16, 2016) results in an erroneous elimination of the surface water cover fraction from April to October in the study region (\(RH\) below 70%). However, the PT-JPL-thermal is able to capture the distinct moisture dynamics among land cover classes accurately, which produces spatially consistent values within land uses and identifies large differences among them (Figure 7). This indicates that the physical concept of thermal inertia is applicable to different patterns of moisture availability such as areas with free water surfaces, irrigated crops, and natural vegetation extracting the water from deep layers. Within land uses, the correlation of the multi-year average \(ET\) between the models at the pixel level was relatively high (\(\rho\) = 0.74) in the irrigated areas (Figure 8a) while, in the natural ecosystems, the agreement was much lower (\(\rho\) = 0.17) (Figure 8b). In the irrigated areas, despite the \(ET\) underestimation by MOD16, both methods seem to differentiate pixels with more and less evaporation accordingly (Figure 8a). However, the two models do not coincide in identifying the pixels with higher and lower \(ET\) values in the natural ecosystems especially in the wetland (Figure 8b). The availability of water for natural ecosystems due to the underlying Almonte-Marismas aquifer might be better captured by the thermal inertia approach used by the PT-JPL-thermal than with the \(HR\) and \(VPD\) approach used by MOD16. In addition, higher correlation values (\(\rho\) > 0.80) were found in the months of maximum water demand and high evapotranspiration from July to September (Figure 10). In particular, the rice fields and mixed-irrigation left bank showed the highest agreement in these months (Figure 11) in contrast to the natural ecosystems (except for the shrubland). A possible explanation is that the land cover product used by MOD16 to constrain \(ET\) values depending on land use is correctly assigned to the shrublands but fails in the wetland (classified as grassland) and coniferous forest (classified as permanent wetland and grassland).