Why is WaPOR misrepresenting ETIa when ETIa is high in humid conditions?
ETIa-WPR is not representing ETa well in non-water limited conditions with high humidity. The PM method is not suitable for very low VPD (or high humidity) (Paw & Gao, 1988). Further, for tall crops, the VPD can have a considerable influence on the error (Rana & Katerji, 1998). It is not suitable in these conditions because of the linear assumption of saturated vapour pressure and air temperature. Paw, (1992) advised that the use of non-linear equations should be used in extreme conditions to maintain errors of less than 10-15%.
Quality of input data is likely affecting the quality of the ETIa-WPR in these regions. Low-quality data or missing RH data means VPD is calculated from Tmin. In humid climates condensation occurs during the night, which leads to an overestimation of VPD (Allen et al. , 1998), which is found when PM is applied without RH data in humid regions of Ecuador (Córdova, Carrillo-Rojas, Crespo, Wilcox, & Célleri, 2015). In non-water limited regions, the overestimation of VPD can lead to higher ETa, as it is easier for the flux to occur when there is less moisture in the air. Further, these regions frequently contain low-quality NDVI and LST layers in these regions. This is resulting for example, in overestimation of radiation at GH-ANK skewing results at this location. The NDVI and LST-quality layers are therefore a good indicator of the quality of the ETIa in these regions.
4.2. Product consistency
There is very high consistency between L1 and L2 products. The high consistency is partly explained by the SMC component, which is based on MODIS for both L1 and L2. The consistency between the L1 and L3 products is mixed. The Awash and ODN L3 areas show high consistency between L1 and L3. In the Koga, there is a strong positive bias for L1 ETIa-WPR, while the agreement between L1 and L3 in the Koga and in Zankalon is lower. These errors are likely largely attributed to the different input temporal and spatial resolutions available from the satellite platform combined with high spatial and temporal heterogeneity in the area (e.g. Koga and Zankalon have much smaller irrigated fields and higher crop diversity than the Awash and ODN–see Table 4). All levels have a dekadal time-step. However, the satellite revisit period varies; having revisits of 1-day, 2-days and 16 days for MODIS (L1), Proba-V (L2) and Landsat (L3), respectively, with daily meteorological data input. The variation in the revisit period can lead to differences when interpolating images to a dekadal timescale, particularly in rainy periods and during the growing season (Gao, Masek, Schwaller, & Hall, 2006). Uncertainty of up to 40% has attributed to the difference in a 16-day revisit as compared to 4-day revisit, depending on climate and season (Guillevic et al. , 2019), though this was without daily meteorological data as a tool for interpolation. Conversely, the L3 dataset can capture more spatial variability for a given image as compared to the L1 and L2 data, which is highly important when using non-linear models. Therefore the L3 dataset is expected to perform better in areas of higher spatial heterogeneity (Sharma, Kilic, & Irmak, 2016).