**3.2 Monthly comparisons**

**Idaho**: Example plots of ET integrated over months of April and July are shown in **Figure 7** for the Idaho study area, where ET from runs 2 and 3 is plotted against ET from run 1. Data for 1500 fields are shown. Limiting the image collection to one path for two satellites reduced the number of images available to the spline and impacted the monthly integrations.

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**Figure 7.**

*Influence of Landsat Revisit Frequency on Time-Integration of Evapotranspiration…*

Run 3 used images from path 40 and agreed closest with the two-path integration due to the stronger influence of path 40 in the two-path product. Images from path 39 exhibited more dryness for fields having relatively low amounts of vegetation cover in the July time frame, due to fewer rain events prior to those images. This manifested as lower ETrF for run 2 that was based on path 39 images versus the

*Monthly ETrF produced by time-integration from run 2 (left column) and run 3 (right column) versus ETrF* 

*produced from the baseline run 1 for the southern Idaho analysis area during year 2000.*

Monthly ET averaged over the 1500 sample points is plotted in **Figure 8** and monthly ETrF is plotted in **Figure 9**. In general, although ET and ETrF for some fields deviated relatively widely between runs, as shown in **Figure 7**, and which would be a concern for those individual water rights holders, ET and ETrF averaged over a large number of fields yielded relatively similar and consistent values.

**Nebraska**: Example plots of ET integrated over months of May, June, July, and August are shown in **Figures 9**–**12** for the Nebraska study area, where ET from runs 2, 3, 4, and 5 are plotted against ET from baseline run 1. Data for 1500 fields are shown. As with the Idaho analyses, limiting the image collection to one path for two satellites reduced the number of images available to the spline and substantially impacted the monthly integrations. For the month of May, ET estimated using only imagery from one path estimated as much as 40% higher

baseline run 1 for July for fields having low ETrF.

*DOI: http://dx.doi.org/10.5772/intechopen.80946*

*Influence of Landsat Revisit Frequency on Time-Integration of Evapotranspiration… DOI: http://dx.doi.org/10.5772/intechopen.80946*

**Figure 7.**

*Advanced Evapotranspiration Methods and Applications*

in Idaho. Other curves reflect behavior for alfalfa crops that are harvested three to five times per growing season so that the ETrF curves fluctuate up and down over time. The higher frequency imagery in run 1 was able to capture more of the impacts of harvest and regrowth of alfalfa on the ETrF values. Both runs 2 and 4 missed some of the alfalfa regrowth cycles, for example in the top right graph in **Figure 6**. Run 4 with only 7 image dates generated smoother ETrF curves due to the more sparse data points. The smoother curves tended to average out variation in ETrF caused by variation in water availability or variation in evaporation from soil

*About 10 additional representative ETrF curves for the southern Idaho analysis area during year 2000 created* 

**Idaho**: Example plots of ET integrated over months of April and July are shown in **Figure 7** for the Idaho study area, where ET from runs 2 and 3 is plotted against ET from run 1. Data for 1500 fields are shown. Limiting the image collection to one path for two satellites reduced the number of images available to the spline and

following irrigation or precipitation wetting events.

*by the cubic spline interpolation of ETrF for runs 1, 2, and 4.*

**3.2 Monthly comparisons**

impacted the monthly integrations.

**60**

**Figure 6.**

*Monthly ETrF produced by time-integration from run 2 (left column) and run 3 (right column) versus ETrF produced from the baseline run 1 for the southern Idaho analysis area during year 2000.*

Run 3 used images from path 40 and agreed closest with the two-path integration due to the stronger influence of path 40 in the two-path product. Images from path 39 exhibited more dryness for fields having relatively low amounts of vegetation cover in the July time frame, due to fewer rain events prior to those images. This manifested as lower ETrF for run 2 that was based on path 39 images versus the baseline run 1 for July for fields having low ETrF.

Monthly ET averaged over the 1500 sample points is plotted in **Figure 8** and monthly ETrF is plotted in **Figure 9**. In general, although ET and ETrF for some fields deviated relatively widely between runs, as shown in **Figure 7**, and which would be a concern for those individual water rights holders, ET and ETrF averaged over a large number of fields yielded relatively similar and consistent values.

**Nebraska**: Example plots of ET integrated over months of May, June, July, and August are shown in **Figures 9**–**12** for the Nebraska study area, where ET from runs 2, 3, 4, and 5 are plotted against ET from baseline run 1. Data for 1500 fields are shown. As with the Idaho analyses, limiting the image collection to one path for two satellites reduced the number of images available to the spline and substantially impacted the monthly integrations. For the month of May, ET estimated using only imagery from one path estimated as much as 40% higher

#### **Figure 8.**

*Monthly ET averaged over the 1500 sampled locations for the Idaho study area for the four time-integration runs that used all available images in both paths, images from path 39 only, images from path 40 only, and images from path 40 and Landsat 5, only.*

than the baseline ET. The cause of the differences was differential wetness of images due to rainfall for the collections on the two paths as well as longer spans between images in the spline integration and reliance on image information further away in time.

For example, large differences in ETrF existed between the May 2, 2002, path 30 image and the May 3, 2002, path 29 image due to rapid drying of soil between the two date and probable differences in calibration of the METRIC model for the two dates for low vegetation conditions (**Figure 5**). Comparison of golf courses and agricultural fields with full cover between the images yielded similar values, indicating similar calibration for those conditions. The Ord AWDN station, approximately 50 km north of the study area, recorded 22 mm of precipitation on May 27, 2002 and 5 mm on May 1, 2002. The Halsey AWDN station, approximately 100 km from the study area, recorded 19 mm on April 27, 2002 and 12 mm on May 1, 2002. The higher ETrF for the path 30 image caused time-integrated ET for the month of May to be higher than for path 29 when each path was processed alone.

The large difference in ETrF between the 5/2 and 5/3 image dates also may have affected the accuracy of the spline function when applied to the baseline run 1. The large differences in ETrF and the closeness in time between the images may have caused the spline function to produce overly high or low ETrF values for periods between image dates. This may have occurred even though all images that were only 1 day apart had their dates spaced 5 days apart during the splining process in an attempt to avoid the large slopes in the spline. The plot of ETrF for path 29 using only Landsat 5 had greatest deviation from the baseline run due to the lack of cloud-free imagery for Landsat 5 on path 29 in May. Therefore, the splining process relied on ETrF data from the synthetic images spaced in April and ETrF data from the month of June.

Comparisons of ETrF improved for June for the Nebraska study area, as shown in **Figure 12**, where the same runs as for **Figure 10** are shown. Poorest agreement in monthly ETrF values for June occurred for path 29 only using Landsat 7 only due to no available clear images in June, and therefore the need to interpolate across a large time span. Although comparisons approved between the various runs and the baseline runs for June, large differences still occurred, which is of concern for water accounting or ET sampling processes that require knowledge

**63**

section.

**Figure 10.**

**Figure 9.**

*images from path 40 and Landsat 5, only.*

*Influence of Landsat Revisit Frequency on Time-Integration of Evapotranspiration…*

*Monthly ETrF averaged over the 1500 sampled locations for the Idaho study area for the four time-integration runs that used all available images in both paths, images from path 39 only, images from path 40 only, and* 

of ET rates from individual fields. Statistical summaries are presented in a later

*from the baseline model run 1 for the central Nebraska analysis area for year 2002.*

*Plots of average integrated ETrF for May from model run 2 (two Landsats on path 29), run 3 (two satellites on path 30), run 4 (Landsat 5, only on path 29), and run 5 (Landsat 7, only on path 29) versus ETrF produced* 

Agreement between runs 2–5 and baseline run 1 were even more improved for the month of July (**Figure 13**) for the Nebraska study area. July is the month where most crops have attained full ground cover and ETrF rates are near their maximum

*DOI: http://dx.doi.org/10.5772/intechopen.80946*

*Influence of Landsat Revisit Frequency on Time-Integration of Evapotranspiration… DOI: http://dx.doi.org/10.5772/intechopen.80946*

#### **Figure 9.**

*Advanced Evapotranspiration Methods and Applications*

further away in time.

*from path 40 and Landsat 5, only.*

**Figure 8.**

than the baseline ET. The cause of the differences was differential wetness of images due to rainfall for the collections on the two paths as well as longer spans between images in the spline integration and reliance on image information

*Monthly ET averaged over the 1500 sampled locations for the Idaho study area for the four time-integration runs that used all available images in both paths, images from path 39 only, images from path 40 only, and images* 

For example, large differences in ETrF existed between the May 2, 2002, path 30 image and the May 3, 2002, path 29 image due to rapid drying of soil between the two date and probable differences in calibration of the METRIC model for the two dates for low vegetation conditions (**Figure 5**). Comparison of golf courses and agricultural fields with full cover between the images yielded similar values, indicating similar calibration for those conditions. The Ord AWDN station, approximately 50 km north of the study area, recorded 22 mm of precipitation on May 27, 2002 and 5 mm on May 1, 2002. The Halsey AWDN station, approximately 100 km from the study area, recorded 19 mm on April 27, 2002 and 12 mm on May 1, 2002. The higher ETrF for the path 30 image caused time-integrated ET for the month of

The large difference in ETrF between the 5/2 and 5/3 image dates also may have affected the accuracy of the spline function when applied to the baseline run 1. The large differences in ETrF and the closeness in time between the images may have caused the spline function to produce overly high or low ETrF values for periods between image dates. This may have occurred even though all images that were only 1 day apart had their dates spaced 5 days apart during the splining process in an attempt to avoid the large slopes in the spline. The plot of ETrF for path 29 using only Landsat 5 had greatest deviation from the baseline run due to the lack of cloud-free imagery for Landsat 5 on path 29 in May. Therefore, the splining process relied on ETrF data from the synthetic images spaced in April and ETrF data from

Comparisons of ETrF improved for June for the Nebraska study area, as shown in **Figure 12**, where the same runs as for **Figure 10** are shown. Poorest agreement in monthly ETrF values for June occurred for path 29 only using Landsat 7 only due to no available clear images in June, and therefore the need to interpolate across a large time span. Although comparisons approved between the various runs and the baseline runs for June, large differences still occurred, which is of concern for water accounting or ET sampling processes that require knowledge

May to be higher than for path 29 when each path was processed alone.

**62**

the month of June.

*Monthly ETrF averaged over the 1500 sampled locations for the Idaho study area for the four time-integration runs that used all available images in both paths, images from path 39 only, images from path 40 only, and images from path 40 and Landsat 5, only.*

#### **Figure 10.**

*Plots of average integrated ETrF for May from model run 2 (two Landsats on path 29), run 3 (two satellites on path 30), run 4 (Landsat 5, only on path 29), and run 5 (Landsat 7, only on path 29) versus ETrF produced from the baseline model run 1 for the central Nebraska analysis area for year 2002.*

of ET rates from individual fields. Statistical summaries are presented in a later section.

Agreement between runs 2–5 and baseline run 1 were even more improved for the month of July (**Figure 13**) for the Nebraska study area. July is the month where most crops have attained full ground cover and ETrF rates are near their maximum

**Figure 11.** *ETrF for May 2, 2002 path 30 (Left) and ETrF for May 3, 2002 path 29 (Right).*

#### **Figure 12.**

*Plots of average integrated ETrF for June from model run 2 (two Landsats on path 29), run 3 (two satellites on path 30), run 4 (Landsat 5, only on path 29), and run 5 (Landsat 7, only on path 29) versus ETrF produced from the baseline model run 1 for the central Nebraska analysis area for year 2002.*

values. July is also the month having the highest total ET amounts, as summarized later in the statistics section. For July, only run 3 had substantial disagreement, where images from both Landsats for path 30 only were utilized in the time integration. That disagreement may have stemmed from differences in evaporation amounts from fields having low vegetation cover due to differences in antecedent rainfall.

**65**

single field is of value.

**Figure 13.**

**3.3 Growing season ET**

*Influence of Landsat Revisit Frequency on Time-Integration of Evapotranspiration…*

Relatively, good agreement between runs 2–5 and baseline run 1 occurred for the month of August (**Figure 14**) for the Nebraska study area. As for July, August is a month where most crops have full ground cover and monthly ETrF rates are near their maximum values. The relatively good agreement between time-integrated ET using fewer available images and the baseline condition most likely stems from the relatively 'flat' nature of the ETrF curve during the July–early September period, where change in ETrF is gradual. Therefore, the spline function tended to produce similar spline shapes among the various collections of ETrF images and image dates. Total monthly ET averaged over the 1500 sample points is plotted in **Figure 15** and monthly ETrF is plotted in **Figure 16** for months of May through September for the Nebraska study area. Except for May and model run 5 (path 29 with only Landsat 7), values of ET and ETrF, when averaged over a large number of fields, produced relatively similar and consistent results. Differences in ET for the month of May have been previously discussed. The relatively good agreement in ET when averaged over a large area is of interest for ET data uses such as ground water depletion studies and river depletion studies, where ET integrated over areas larger than a

*Plots of average integrated ETrF for July from model run 2 (two Landsats on path 29), run 3 (two satellites on path 30), run 4 (Landsat 5, only on path 29), and run 5 (Landsat 7, only on path 29) versus ETrF produced* 

*from the baseline model run 1 for the central Nebraska analysis area for year 2002.*

Growing season (April–October) ET produced by the time-integration is plotted in **Figure 17** for the Idaho study area for runs 2, 3, and 4 versus run 1. Agreement was strongest between run 1 and runs 3 and 4. Growing season ET produced from path 39 images, only, tended to underestimate ET according to the run 1 basis by

about 8% on average. Statistics are summarized later in **Table 3**.

*DOI: http://dx.doi.org/10.5772/intechopen.80946*

*Influence of Landsat Revisit Frequency on Time-Integration of Evapotranspiration… DOI: http://dx.doi.org/10.5772/intechopen.80946*

#### **Figure 13.**

*Advanced Evapotranspiration Methods and Applications*

*ETrF for May 2, 2002 path 30 (Left) and ETrF for May 3, 2002 path 29 (Right).*

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rainfall.

**Figure 12.**

**Figure 11.**

values. July is also the month having the highest total ET amounts, as summarized later in the statistics section. For July, only run 3 had substantial disagreement, where images from both Landsats for path 30 only were utilized in the time integration. That disagreement may have stemmed from differences in evaporation amounts from fields having low vegetation cover due to differences in antecedent

*from the baseline model run 1 for the central Nebraska analysis area for year 2002.*

*Plots of average integrated ETrF for June from model run 2 (two Landsats on path 29), run 3 (two satellites on path 30), run 4 (Landsat 5, only on path 29), and run 5 (Landsat 7, only on path 29) versus ETrF produced* 

*Plots of average integrated ETrF for July from model run 2 (two Landsats on path 29), run 3 (two satellites on path 30), run 4 (Landsat 5, only on path 29), and run 5 (Landsat 7, only on path 29) versus ETrF produced from the baseline model run 1 for the central Nebraska analysis area for year 2002.*

Relatively, good agreement between runs 2–5 and baseline run 1 occurred for the month of August (**Figure 14**) for the Nebraska study area. As for July, August is a month where most crops have full ground cover and monthly ETrF rates are near their maximum values. The relatively good agreement between time-integrated ET using fewer available images and the baseline condition most likely stems from the relatively 'flat' nature of the ETrF curve during the July–early September period, where change in ETrF is gradual. Therefore, the spline function tended to produce similar spline shapes among the various collections of ETrF images and image dates.

Total monthly ET averaged over the 1500 sample points is plotted in **Figure 15** and monthly ETrF is plotted in **Figure 16** for months of May through September for the Nebraska study area. Except for May and model run 5 (path 29 with only Landsat 7), values of ET and ETrF, when averaged over a large number of fields, produced relatively similar and consistent results. Differences in ET for the month of May have been previously discussed. The relatively good agreement in ET when averaged over a large area is of interest for ET data uses such as ground water depletion studies and river depletion studies, where ET integrated over areas larger than a single field is of value.

#### **3.3 Growing season ET**

Growing season (April–October) ET produced by the time-integration is plotted in **Figure 17** for the Idaho study area for runs 2, 3, and 4 versus run 1. Agreement was strongest between run 1 and runs 3 and 4. Growing season ET produced from path 39 images, only, tended to underestimate ET according to the run 1 basis by about 8% on average. Statistics are summarized later in **Table 3**.

#### **Figure 14.**

*Plots of average integrated ETrF for August from model run 2 (two Landsats on path 29), run 3 (two satellites on path 30), run 4 (Landsat 5, only on path 29), and run 5 (Landsat 7, only on path 29) versus ETrF produced from the baseline model run 1 for the central Nebraska analysis area for year 2002.*

**Figure 15.**

*Monthly ET averaged from the 1500 sample pixels for the Nebraska study area for the five time-integration runs.*

Growing season (March–September) ET produced by the time-integration is plotted in **Figure 18** for the Nebraska study area for runs 3, 2, and 5 versus run 1. Agreement was strongest between baseline run 1 and run 2 that used images from both Landsats from path 29 only. Growing season ET produced from path 29 using only Landsat 7 images only had the worse correlation with r2 = 0.64. Growing season ET produced from path 29 images, tended to overestimate ET according to the baseline run 1 by about 19% on average.

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**Figure 17.**

*Influence of Landsat Revisit Frequency on Time-Integration of Evapotranspiration…*

**Table 3** summarizes monthly average ET for the four time-integration runs for the Idaho study area and root mean square error (RMSE) for the 1500 sampled fields. RMSE was relatively high for run 2 (both satellites for path 39 only),

*Monthly ETrF averaged over the 1500 sample pixels for the Nebraska study area for the five time-integration* 

*ET for April–October growing season for 1500 sampled locations for the Idaho study area for the timeintegration runs 2, 3, and 4 versus run 1 and (lower right) averages over all 1500 sampled fields. Also shown in* 

*the lower right is reference ET summed over the April–October period.*

*DOI: http://dx.doi.org/10.5772/intechopen.80946*

**3.4 Statistical summaries**

**Figure 16.**

*runs.*

*Influence of Landsat Revisit Frequency on Time-Integration of Evapotranspiration… DOI: http://dx.doi.org/10.5772/intechopen.80946*
