Evapotranspiration Estimates from Remote Sensing for Irrigation Water Management - Satellite-based Applications on Climate Change - page 196

Monthly and seasonal evapotranspiration “maps” are derived from a series of
ET r F images by interpolating ET r F on a pixel-by-pixel basis between processed
images and multiplying, on a daily basis, by the ET r for each day. The interpolation
of ET r F between image dates is not unlike the construction of a seasonal K c curve
(Allen et al. 1998 ), where interpolation is done between discrete values for K c .
The METRIC approach assumes that the ET for the entire area of interest
changes in proportion to change in ET r at the weather station. This is a generally
valid assumption and is similar to the assumptions used in the conventional
application of K c ET r . This approach is effective in estimating ET for both
clear and cloudy days between the clear-sky satellite image dates. Tasumi et al.
( 2005 ) showed that the ET r F was consistent between clear and cloudy days using
lysimeter measurements at Kimberly, Idaho. ET r is computed at a specific weather
station location and therefore may not represent the actual condition at each pixel.
However, because ET r is used only as an index of the relative change in weather,
specific information at each pixel is retained through the ET r F .
Cumulative ET for any period, for example, month, season, or year is calculated as:

X

n

ET period ¼ ½ ð ET r F i Þ ET r24 i ð Þ (13.9)

i¼m

where ET period is the cumulative ET for a period beginning on day m and ending on
day n , ET r F i is the interpolated ET r F for day i , and ET r 24 i is the 24-h ET r for day i .
Units for ET period will be in mm when ET r24 is in mm d 1 . The interpolation
between values for ET r F is best made using a curvilinear interpolation function
(e.g., a spline function) to better fit the typical curvilinearity of crop coefficients
during a growing season (Wright 1982 ). Generally, one satellite image per month is
sufficient to construct an accurate ET r F curve to estimate seasonal ET (Allen et al.
2007a ). During periods of rapid vegetation change, a more frequent image interval
may be desirable. Examples of splining ET r F to estimate daily and monthly ET are
given in Allen et al. ( 2007a ) and Singh et al. ( 2008 ).
Moderately high-resolution satellites, such as Landsat, provide the opportunity to
view evapotranspiration on a field-by-field basis, which can be valuable for water
rights management, irrigation scheduling, and discrimination of ET among crop types
(Allen et al. 2007b ). The disadvantage of high-resolution imagery is less frequent
image acquisition. In the case of Landsat, the return interval is 16 days. As a result,
monthly ET estimates are based on one or two satellite images per month; however,
for areas influenced by clouds, there may be 32 or more days between high-quality
images. This can be rectified by combining multiple Landsats (5 with 7) or by using
data fusion techniques, where a more frequent but more coarse system like MODIS is
used as a carrier of information during periods without quality Landsat images (Gao
et al. 2006 ; Anderson et al. 2010 ).