**Conflict of interest**

*Advanced Evapotranspiration Methods and Applications*

**4. Conclusions**

or from water stress or cuttings, as in the case of alfalfa hay.

parts of the USA including the Midwestern states.

about 20% for the Nebraska study area.

or July, which emphasizes the impact of the timing of the images used. For path 30, image date June 28, 2002 had large areas of clouds masked out, which were filled in using the next available image date in time in the spline function. This underscores the importance of timing of images to identify key inflection points in the ETrF curves and to capture special events such as wetting events from irrigation and rain

In this study, monthly and growing season ET maps were derived by interpolating ETrF produced by METRIC for processed images and then multiplying, on a daily basis, by a reference ETr for each day to account for day-to-day variation in ET caused by weather fluctuations. The objective of the study was to explore the change in estimates for ET over complete growing seasons and for monthly periods when more frequent or less frequent Landsat imagery was available. The study was implemented by conducting a series of METRIC applications for a Landsat WRS path overlap area in southern Idaho (paths 39 and 40) during year 2000 and for a WRS path overlap area in central Nebraska (paths 29 and 30) during year 2002 when two fully functioning satellites, Landsat 5 and Landsat 7, were in orbit. During those years, Landsat 5 (L5) and Landsat 7 (L7) passed over the overlap areas twice, each, per 16 day period, providing four imaging opportunities every 16 days. The frequency of imagery was sparsened by removing imagery from one path or the other and by removing imagery from one satellite or the other. Monthly and seasonal ET were recomputed with the sparsened image series and compared with the baseline data. Idaho is a relatively 'clear' area, so that this analysis represents a somewhat 'optimistic' scenario, and Nebraska represents the more cloud-prone

The higher frequency imagery used in baseline run 1 was more able to capture the impacts of harvest and regrowth of alfalfa on the ETrF rate in the Idaho study area. Sparsened runs missed some of the alfalfa regrowth cycles. Run 4 that used only 7 image dates generated smoother ETrF curves due to the more sparse data points. The smoother curve tended to average out variation in ETrF caused by variation in water availability or variation in evaporation from soil following irrigation or precipitation wetting events. Time-integration runs 5, 6, and 7, which would have represented three additional replicates of a single satellite having 16-day revisit, via combinations of path 40 with Landsat 7 and path 39 with Landsat 5 and path 39 with Landsat 7, were not possible to implement in the Idaho study area due to too few images per combination to apply the ETrF interpolation process. This severe limitation on application of those scenarios emphasizes the need to maintain two

Similar results occurred for the Nebraska study area, where very large differences

Integrated ET from individual fields deviated relatively widely, which would be a concern for those individual water rights holders and managers of water rights or pumping permits. However, ET and ETrF averaged over a large number of fields yielded relatively similar and consistent values. Limiting the data source to one path with two satellites impacted the monthly integrations and growing season ET produced from one path only. ET based on a single path only underestimated ET according to the run 1 basis by about 8% on average for the Idaho study area and by

between runs occurred for the month of May. May is a period of very low-to-low vegetation amounts for many fields and is therefore more prone to varying wetness of

images caused by evaporation from bare soil following precipitation events.

Landsat satellites in orbit and ideally to have four-day revisit times.

**70**

The authors declare no conflict of interest.
