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).