APPLICATION OF THE SURFACE ENERGY BALANCE USING LANDSAT THERMAL IMAGERY TO IMPROVE ON-FARM WATER MANAGEMENT IN THE IMPERIAL IRRIGATION DISTRICT

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1 APPLICATION OF THE SURFACE ENERGY BALANCE USING LANDSAT THERMAL IMAGERY TO IMPROVE ON-FARM WATER MANAGEMENT IN THE IMPERIAL IRRIGATION DISTRICT Deepak Lal, Remote Sensing Specialist Byron Clark, Water Management Specialist Bryan Thoreson, Water Management Specialist SEBAL North America, Inc Picasso Avenue, Suite E, Davis, CA John Eckhardt, Executive Program Manager QSA-IID/SDCWA Water Transfer Imperial Irrigation District 333 East Barioni Boulevard, Imperial, CA ABSTRACT This paper describes an analysis of evapotranspiration (ET) via remote sensing using the Surface Energy Balance Algorithm for Land (SEBAL ) and the development of crop coefficients derived from SEBAL results to support improved farm water management in Imperial Irrigation District of Southern California (IID). Spatially distributed ET was estimated using twelve Landsat 5 TM images acquired between October 1997 and September 1998 to encompass the 1998 water year (WY) for IID. The total ET in IID for WY 1998 was also quantified by an independent district-wide water balance study. The difference between the total ET quantified from water balance study and the total ET estimated from SEBAL for IID was within 1%, providing a strong validation of SEBAL in an arid, advective environment. SEBAL ET data were combined with ground-based weather and cropping data to develop crop water use coefficients that can be used to improve predictions of crop ET based on the crop grown and the evaporative demand of the atmosphere at the ground surface. The coefficients were provided to commercial irrigation scheduling service providers for use in a pilot irrigation scheduling program implemented in The goal of the program is to assist growers in selecting the optimum timing and amount of water to apply for irrigation in order to conserve water through improved irrigation efficiency. INTRODUCTION This paper presents an application of SEBAL to estimate spatially distributed actual evapotranspiration (ET a ) in the Imperial Irrigation District of Southern California (IID). SEBAL was applied to quantify district-wide annual ET a and to evaluate variability in ET a among individual fields over time. IID is situated in the agriculturally rich Imperial Valley of Southern California and provides both water and energy to more than 140,000 customers in its service area. IID is the largest irrigation district in the U.S. with respect to water use, delivering approximately 3.1 million acre-feet of Colorado River water per year to 450,000 acres of farmland via a system comprised of more than 3,000 miles of canals and drains. In 2003, IID entered into an agreement to transfer up to 303,000 acre-feet of conserved water per year to the San Diego County Water Authority and Coachella Valley Water District as part of the Quantification Settlement Agreement and related agreements. In planning for the transfer, SEBAL was applied to quantify actual ET (ET a ) at district and field scales. District-wide and field-specific information describing crop water use aided planners in quantifying flows within the system and in evaluating potential impacts of implementing water conservation measures on crop ET and yields. An independent water balance was conducted to estimate total consumptive use within IID and to identify potential for water conservation within the delivery system and on the farm. Results of the study are currently being used to support a pilot program in which commercial irrigation scheduling service providers are assisting growers in selecting the optimum timing and amount of water to apply for irrigation in order to conserve water through improved irrigation efficiency. The remotely sensed surface energy balance technique of SEBAL utilizes multispectral satellite data in the visible, near infrared, and thermal infrared regions to estimate actual evapotranspiration (ET a ) under field conditions. Net radiation, soil heat flux, and sensible heat flux are quantified at the Earth s surface, and the energy balance is

2 applied to solve for the latent heat flux, which is converted to ET a. SEBAL has been continuously applied, validated, and refined over the past two decades (Bastiaanssen et al., 2005). SEBAL is one of the most widely applied remotely sensed surface energy balance models, and since its advent, SEBAL has emerged as an effective remote sensing tool for estimation of ET and monitoring of other crop related parameters including root zone soil moisture content (Scott et al., 2003) and biomass production/crop yield (Bastiaanssen and Ali, 2003). Numerous studies (Roerink et al., 1995; Morse et al., 2001; Allen et al., 2003; Tasumi et al., 2005a and 2005b; Bastiaanssen et al., 2005) have supported and justified the use of SEBAL in irrigation management at various scales ranging from individual fields to entire basins. Most energy balance algorithms are based on estimating net radiation at the Earth surface and partitioning the available energy into soil, sensible, and latent heat fluxes. Often, the soil and sensible heat fluxes are estimated independently, and the latent heat flux (a measure of ET in energy units) is estimated as a closure term. Remotely sensed surface energy balance models include SEBAL (Bastiaanssen et al., 1998 and 2005), Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC; Allen et al., 2007), the Two-Source Model (TSM; Kustas and Norman 1996), Surface Energy Balance Index (SEBI, Menenti and Choudhury1993), Simplified Surface Energy Balance Index (S-SEBI, Roerink et al. 2000) and Surface Energy Balance System (SEBS, Su 2002). Various studies have been conducted over the years utilizing multispectral satellite data with a thermal band to evaluate water use and irrigation. A study was conducted by Thiruvengadachari (1981) where irrigation patterns in semiarid areas of India were identified using satellite data. In another study, Taconet et al. (1986) used NOAA- AVHRR imagery to develop a model to quantify ET over agricultural regions. Moran et al. (1989) utilized Landsat thematic mapper and ground-based meteorological data to map surface energy balance components. Vidal and Perrier (1989) analyzed a simplified relation using satellite thermal data to estimate daily ET. Results from initial remote sensing studies encouraged scientists to explore remote sensing techniques further for identification and monitoring of crop and irrigation related parameters more precisely with improved spatial resolution over traditional approaches. Such efforts resulted in the development of advanced surface energy balance algorithms to quantify actual ET at the Earth s surface using remotely sensed satellite and/or airborne data. Past remote sensing studies have focused on the development of crop water use coefficients (K c ), both with and without applying the surface energy balance. Neale et al. (1989) and Choudhury et al. (1994) utilized spectral indices to derive basal crop coefficients for agricultural crops. Remote sensing studies of crop coefficients involve estimation of actual ET as the residual term of the surface energy balance and is utilized in conjunction with reference ET (ET o ) estimated from ground-based weather observations to develop crop coefficients. Tasumi et al. (2005b) utilized an energy balance approach to estimate ET a in the Magic Valley of South Central Idaho to derive crop coefficients for various crops including alfalfa, beans, corn, peas, potatoes, sugar beets, and grain. Distributions of K c values were compared with traditional K c curves over the growing season. The authors concluded that the satellite based energy balance approach was useful in evaluating variations in K c and in developing mean K c curves that represent the impact of surface wetting by precipitation and irrigation on crop ET. The overall objectives of this paper are to describe the quantification of ET a in IID using SEBAL, to present a validation of SEBAL through comparison of annual ET a to the independent water balance study, and to present crop coefficients derived from SEBAL and discuss their potential use for improvement of on-farm water management through improved irrigation scheduling. METHODOLOGY SEBAL Model SEBAL combines the principles of radiative, aerodynamic and energy balance physics to estimate the amount of available energy at the Earth s surface available for latent heat transfer and corresponding evapotranspiration (ET). A comprehensive description of SEBAL is available from Bastiaanssen et al. (2005). A brief conceptual summary of SEBAL is provided herein Net available energy (net radiation flux, R n ) at the Earth s surface is first estimated from estimates of incoming short wave radiation from the Sun, reflected shortwave and emitted long wave radiation from the atmosphere, and outgoing shortwave and long wave radiation (reflected and emitted, respectively). Other primary energy balance components are then estimated including the amount of energy utilized to heat the air (sensible heat flux, H) and the amount of energy used to heat the soil surface (soil heat flux, G). Finally, the amount of energy contributing to evapotranspiration (latent heat flux, LE) is found by applying the surface energy balance. The latent heat flux, typically

3 expressed in Watts per square meter is converted to the depth of water undergoing evapotranspiration (ET a ) based on the latent heat of vaporization and density of water (Equation 1). ET = λρ 1 [ R ( G H )] a n + w, (1) where λ is the latent heat of vaporization of water, and ρ w is the density of water. ET a estimated from Equation 1 represents ET at the time of satellite image acquisition. The instantaneous ET a is extrapolated to daily and longer periods using average weather conditions from ground-based meteorological stations within the study area, evaporative fraction ( Λ ), and net available energy (R n -G). The relations used to estimate the evaporative fraction and daily ET are shown in Equations 2 and 3, respectively. Daily and period ET a values are additionally adjusted for advection effects using average daily and periodic weather conditions. LE LE = = R G LE H Λ, and (2) n , ET24 = ΛR λρ w n24, (3) where ET 24 and R n24 is the average daily estimates of evapotranspiration and net radiation respectively. Input Data A combination of satellite, ground based-meteorological, topographic, and land cover classification data are utilized to quantify spatially distributed ET a. For this study, these datasets were obtained from a variety of sources including the U.S. Geological Survey (USGS), the California Irrigation Management Information System (CIMIS), and IID. These data are described in greater detail in the following paragraphs. Satellite Images. Twelve Landsat 5TM multispectral images encompassing the period from October 1997 to September 1998 (Water Year 1998 or WY 1998 ) for Path 37/Row 39 were obtained from USGS. The selection of images was based on IID s interest in the WY 1998, clear sky or minimum cloud conditions in the study area on the image dates, and a temporal frequency of one image or more per month for the period of analysis. The selected images are listed in Table 1. During image selection, it was found that there was not a cloud-free image available for the month of February As a result, the image from January 30, 1998 was used to represent the period ET for this period. Table 1. Selected Landsat 5 TM Images (Path 37/Row 39) and Periods Represented. Image Date (mm/dd/yy) Periods No. of days in each period 10/26/97 10/01/97-10/31/ /27/97 11/01/97 11/30/ /13/97 12/01/97 12/31/ /14/98 1/01/98 1/31/ /30/98 2/01/98 2/28/ /03/98 3/01/98 3/31/ /04/98 4/01/98 4/30/ /22/98 5/01/98 5/31/ /07/98 6/01/98 6/30/ /09/98 7/01/98 7/31/ /26/98 8/01/98-8/31/ /27/98 9/01/98 9/30/98 30

4 Meteorological Data. For this study, weather data was available from a network of agricultural weather stations operated by CIMIS. Measurements of incoming solar radiation (R s ), relative humidity (RH), air temperature (T a ) and wind speed (WS) were available at instantaneous (hourly average for the time of image acquisition), daily (average for the image date), and period (average for the days represented by an individual image) time steps. Five stations falling within the study area were selected including Salton Sea West (Station 127), Salton Sea East (Station 128), Calipatria/Mulberry (Station 41), Meloland (Station 87), and Seeley (Station 68). Weather data were quality checked according to the guidelines specified in Appendix-D of the ASCE Task Committee Report on the Standardized Reference Evapotranspiration Equation (Allen et al., 2005). Landuse Data and Digital Elevation Model (DEM). A generalized landuse map from National Land Cover Dataset (NLCD) for 1992 was obtained from USGS and used to estimate obstacle heights for different types of surfaces within the study area. The NLCD data is of 30 meter pixel resolution and has been developed using a series of Landsat images along with aerial photographs and ground-surveys. Field boundary coverages for the study area were obtained from IID which included the physical information of each field (spatial location, field ID, Gate ID, etc.,) along with information on crop types, although information describing field boundaries and crop types from IID was not utilized in the SEBAL processing itself but rather in the evaluation of crop coefficients by crop. For citrus, fields located in the Coachella Valley to the north of the Imperial Valley were delineated based on high resolution aerial imagery and included in the analysis. Fields from the Coachella Valley were included to increase the number of fields in the analysis and are similar varieties grown on similar soils to the Imperial Valley. A DEM of 1 arc-second resolution (approximately 30 meter resolution) was obtained from USGS and was used in SEBAL to incorporate the effects of the slope, aspect and elevation into the energy balance. Buffering of Field Boundaries The native resolution of the thermal band for Landsat 5 TM images is 120 meters. Due to its coarser resolution relative to the visible and near infrared bands, thermal pixels along field edges can reflect long wave radiances of neighboring surfaces outside of the field. To compensate for field edge effects on estimated radiometric surface temperatures, the field boundaries were buffered inwards by a distance of 105 meters (3.5 pixels of thermal band from Landsat 5) based on the assumption that fields are typically rectangular in shape and that their boundaries align parallel to the edges of pixels from a Landsat image. The buffer distance of 105 meters was estimated so that only those thermal pixels falling completely within a given field are considered in extracting the data for subsequent analysis of crop coefficients (Figure 1, Clark et al., 2007). Field Boundary ETa Pixel Center 30m ETa Pixel Boundary 120m Thermal Pixel Boundary Minimum Inner Buffer, 3.5 Pixels = 105 meters Figure 1. Field Boundary Buffer Distance for Extraction of SEBAL ET a Grids Based on Landsat 5 TM Thermal Pixel Resolution (Clark et al., 2007). Extraction of Crop Coefficients for Selected Crops Crop coefficients (K c ) are utilized to predict and estimate crop ET over the course of a growing season by multiplying the crop-specific coefficient with weather-based reference ET. the reference ET is estimated for a specific reference crop such as grass or alfalfa based on weather observations. Published crop coefficients (Snyder et al., 1989a and 1989b; Allen et al., 1998) are often derived for relatively stress-free conditions, assuming crop growth under ideal moisture and nutrient conditions. In many cases it is difficult to attain stress-free growing conditions in the field over the course of an entire season due to differences in management practices, availability of

5 water, and other factors. Use of pristine crop coefficients under non-ideal conditions can induce error in the estimation of ET requirements for a given crop. The pristine crop coefficients can be adjusted to actual growing conditions to a certain extent; however, this procedure requires detailed field-specific information that may include irrigation and precipitation timing and amount, soil type, soil salinity, etc. which is often not available even to irrigation consultants. The remote sensing energy balance approach overcomes limitations in the estimation of crop coefficients by accounting for many of the stresses occurring in the field that reduce actual ET below potential ET; however, identification of the causal factors of stress remains a challenge. For this study, lumped crop coefficients representing the processes of surface evaporation and transpiration and accounting for crop related stress factors influencing crop ET at the instance of image acquisition were estimated using ET a and reference ET (ET o ) from the selected CIMIS stations (Equation 4). ET a K cs =, (4) ETo where K cs is the lumped crop coefficient accounting for the effects of various crop phenological stages and other management related stress factors (i.e., K cs = K c x K s ), ET a is the field average ET a, and ET o is the CIMIS ET o. For this study, crop coefficients were extracted for four crop-irrigation method combinations selected for a pilot program to test the implementation of a voluntary water conservation program and to evaluate conservation potential for improved irrigation scheduling. The four crop-irrigation method combinations selected were alfalfa irrigated with graded border strips, alfalfa irrigated with furrows, Bermuda hay irrigated with graded border strips, and citrus (primarily irrigated with micro irrigation). The lumped crop coefficients (K cs ) were estimated on a pixel by pixel basis by dividing daily ET a grids by daily ET o values from the selected CIMIS stations for ten of the twelve image dates. The image results for January 30, 1998 and June 7, 1998 were not included in the crop coefficient analysis based on professional judgement due to apparent anomalies in the individual image results. It was decided to exclude the results in the crop coefficients analysis rather than to reprocess the images due to time and resource constraints. RESULTS AND DISCUSSION Comparison of Annual ET a from SEBAL to Independent Water Balance As described previously, concurrent to the SEBAL analysis, an independent water balance was developed for IID to estimate district-wide ET for WY 1998 (Keller-Bliesner, 2007). Annual ET was estimated from the difference of inflows and outflows to IID other than ET, closing on the consumptive use. Inflows to IID include the All American Canal, Alamo and New Rivers from Mexico, rainfall, Mesa storm flows, and subsurface outflows. The upper boundary for the water balance was considered to be the top of the crop canopy, and the lower boundary was the soil profile beneath subsurface tile drains. Subsurface tile drains, installed under more than 95% of the irrigated area, intercept water that would have flowed through the bottom of the root zone. This water is discharged to surface drains and measured as drain outflow. This along with average annual rainfall less than 76 mm (53 mm during WY 1998) results in a water balance that closes on consumptive use with a high level of confidence. Based on the water balance, total ET within IID for WY 1998 was found to be 2,465 million cubic meters (MCM) with an estimated 95% confidence interval of 4.4% or 108 MCM. Based on the SEBAL analysis, total ET within IID for WY 1998 was found to be 2,479 MCM, a difference of 14 MCM or less than 1% (Soppe et. al, 2006). This difference falls well within the estimated confidence interval of 108 MCM. Field Scale SEBAL ET a and K cs Distributions for Selected Crops The distributions of daily ET a and K cs obtained for the selected crops are presented in Figures 4 through 6. In these figures, relative frequency distributions of ET a and K cs are presented along the axis of each image date along with their respective mean values, similar to Tasumi et al. (2005). Summary statistics of ET a and K cs values extracted for the selected crops are provided in Tables 3 through 10. The mean, 5 th percentile, and 95 th percentile values of remotely sensed K cs are compared to published K c values for each crop. These comparisons are discussed individually for each selected crop in the following paragraphs.

6 Alfalfa Irrigated with Graded Border Strips. Alfalfa grown in the Imperial Valley is typically planted in late September to October with some spring plantings. Alfalfa is harvested from February through September or October with fields typically producing for 3 to 5 years. The total area of border-irrigated alfalfa grown in IID in 1998 was 72,236 hectares with an average reported yield of 17.2 metric tons of yield per hectare (Imperial County Agricultural Commissioner, 1998). Figure 2 presents the ET a and K cs distributions of border-irrigated alfalfa for 263 fields within IID. High variability in ET a and K cs for a given image date may be attributed to differences in growth stage and cutting cycle effects; salinity, water, and pest stresses; stand density, or other factors. ET a values drop during fall and winter (October February) and increase in spring and summer as the evaporative demand increases and the crop grows. Peak ET a, as approximated by the 95 th percentile tracks closely with ET o (Table 3). Higher percentiles of ET a values (e.g., 97 th %tile) exceed ET o values, as expected for alfalfa with full cover under ideal growing conditions. Crop ET (mm/d) ETa Crop Coefficient, Kcs Kcs 0.2 9/1 10/1 11/1 12/1 1/1 1/31 3/3 4/2 5/3 6/2 7/3 8/2 9/2 10/2 11/2 9/1 10/1 11/1 12/1 1/1 1/31 3/3 4/2 5/3 6/2 7/3 8/2 9/2 10/2 11/2 Figure 2. ET a and K cs Distributions for Border-Irrigated Alfalfa, 10/97 9/98. Table 3. Summary Statistics of Actual ET (ET a ) and Reference ET (ET o ) for Border-Irrigated Alfalfa, 10/97 9/98. Actual Evapotranspiration, ET a (mm/d) Image Date (mm/d) Min 5%tile 10%tile Mean Median 90%tile 95%tile Max Std. Dev. 10/26/ /27/ /13/ /14/ /3/ /4/ /22/ /9/ /26/ /27/

7 Table 4. Summary Statistics of Crop Coefficient (K cs ) for Border-Irrigated Alfalfa, 10/97 9/98. Crop Coefficient, K cs Image Date Min 5%tile 10%tile Mean Median 90%tile 95%tile Max Std. Dev. 10/26/ /27/ /13/ /14/ /3/ /4/ /22/ /9/ /26/ /27/ The average 5 th percentile K cs across the peak growing season, calculated from Table 4 as 0.62 is substantially greater than the K c value of 0.40 for alfalfa following cutting reported by Snyder et al. (1989a) for the Imperial Valley. The average 95 th percentile K cs across the peak growing season, calculated from Table 4 as 4, is less than the K c value of 1.20 for midseason reported by Snyder et al. and could be attributed to difference between ideal conditions for which the standard K c values may have been developed and the actual field conditions in the Imperial Valley, which consist of high evaporative demand, heavy soils, and high water and soil salinity relative to other regions. Additionally, differences between K cs values estimated for this study and K c values reported in previous studies could result from differences in the estimation of ET 0 from which the crop coefficients are calculated. The relative frequency distributions of ET a and K cs for the peak growing season (April through August) clearly demonstrate the effects of cutting on water use by alfalfa. The distributions show a bell-shaped curve with the mode slightly greater than the mean due to strong skew to the right. Fields with the least ET a and K cs for a given image date have likely been recently cut while field at the upper end of the ET a and K cs distributions are likely at full cover and were cut shortly after the image date. The variability in daily crop coefficients provides useful insight into the effects of cutting on alfalfa water use. The wide variability in ET a and K cs for the March and September image date demonstrates the wide variability in stand density early and late in the season resulting from differences in stand development and from cultural practices. During March, variability in water use may result from differences in stand growth caused by the effects of grazing, the effects of different irrigation practices, or early season cutting. During September, variability in water use may result from differences in the timing of cultural practices such as disking and re-seeding to reestablish the stand for the following season, differences in late season cutting, or differences in irrigation practices resulting in variable stand growth due to water or salinity stress. There appears to be a trend of decreasing mean K cs values from April through July or August, possibly due to heat stress or water stress. Irrigators must be careful to avoid allowing water to stand on the soil surface during midsummer to prevent scalding of the alfalfa and permanent damage due to heat, anaerobic conditions, or other factors. As a result, additional water and salinity stress may occur. The trend may be representative of a combination of scalding effects and water and salinity stress during mid-summer. Alternatively, the trend could be indicative of decreased crop vigor resulting from mechanical damage or compaction occurring over the course of the cutting season. Furrow-Irrigated Alfalfa. Alfalfa grown on heavy clay soils in the Imperial Valley is often planted on raised beds and furrow-irrigated to help provide drainage of the root zone and avoid scalding during mid-summer as described previously. As described previously, approximately 72,236 hectares of alfalfa were grown in IID in It is estimated that approximately one-third of the alfalfa grown was irrigated using furrows. Results for the analysis of ET a and K cs for 77 fields of furrow-irrigated alfalfa within IID are presented in Figure 3 and in Tables 6 and 7.

8 Crop ET (mm/d) ETa Crop Coefficient, Kcs Kcs 0.2 9/1 10/1 11/1 12/1 1/1 1/31 3/3 4/2 5/3 6/2 7/3 8/2 9/2 10/2 11/2 9/1 10/1 11/1 12/1 1/1 1/31 3/3 4/2 5/3 6/2 7/3 8/2 9/2 10/2 11/2 Figure 3. ET a and K cs Distributions for Furrow-Irrigated Alfalfa, 10/97 9/98. The ET a and K cs distributions for furrow-irrigated alfalfa show similar trends as border-irrigated alfalfa. Namely, the effects of cutting are clearly shown for the April through August image dates, there is large, somewhat uniform variability in crop coefficients for the March and September image dates, and there appears to be a decreasing trend in K cs for the April to July image dates. With the exception of the decreasing trend in K cs for the April to July image dates similarities to crop coefficients for the border-irrigated alfalfa are not unexpected due to similar cultural practices. The apparent decline in K cs values in summer for both border-irrigated and furrow-irrigated alfalfa is somewhat surprising. Observation of this trend for both irrigation methods on alfalfa suggests that the trend could be due to decreased crop vigor resulting from mechanical damage or compaction because it is believed that planting alfalfa on raised beds and utilizing furrow irrigation helps to overcome the effects of scalding that can occur on border-irrigated fields. Table 5. Summary Statistics of Actual ET (ET a ) and Reference ET (ET o ) for Furrow-Irrigated Alfalfa, 10/97 9/98. Actual Evapotranspiration, ET a (mm/d) Image Date (mm/d) Min 5%tile 10%tile Mean Median 90%tile 95%tile Max Std. Dev. 10/26/ /27/ /13/ /14/ /3/ /4/ /22/ /9/ /26/ /27/

9 Table 6. Summary Statistics of Crop Coefficient (K cs ) for Furrow-Irrigated Alfalfa, 9/97 10/98. Crop Coefficient, K cs Image Date Min 5%tile 10%tile Mean Median 90%tile 95%tile Max Std. Dev. 10/26/ /27/ /13/ /14/ /3/ /4/ /22/ /9/ /26/ /27/ The average 5 th percentile K cs across the peak growing season (April through August), calculated from Table 6 as 0.54 is greater than the K c value of 0.40 for alfalfa following cutting reported by Snyder et al. (1989a) for the Imperial Valley. The average 95 th percentile K cs across the peak growing season, calculated from Table 6 as 6, is less than the K c value of 1.20 for midseason reported by Snyder et al. and could be attributed to differences between ideal conditions for which the standard K c values were developed and the actual field conditions in the Imperial Valley, consisting of high evaporative demand, heavy soils, and high water and soil salinity relative to other regions. Additionally, differences between K cs values estimated for this study and K c values reported in previous studies could result from differences in the estimation of ET 0 from which the crop coefficients are calculated. Although statistical analysis has not been performed to compare crop coefficients of border-irrigated and furrow-irrigated alfalfa, the average 5 th percentile values and 95 th percentile values are similar for the peak growing season. Bermuda Hay. Bermuda hay grown in the Imperial Valley is typically planted in late August to September. Once established, five to six cuttings are expected annually with a production life of 5 to 7 years per planting. Approximately 13,262 hectares of Bermuda hay were grown in IID in WY 1998 with an average yield of 10.2 metric tons per hectare (Imperial County Agricultural Commissioner, 1998). Results from the analysis of ET a and K cs for 154 fields of border-irrigated Bermuda hay are presented in Figure 4 and Tables 7 and 8, respectively ETa 1.4 Kcs Crop ET (mm/d) Crop Coefficient, Kcs /1 10/1 11/1 12/1 1/1 1/31 3/3 4/2 5/3 6/2 7/3 8/2 9/2 10/2 11/2 12/2 9/1 10/1 11/1 12/1 1/1 1/31 3/3 4/2 5/3 6/2 7/3 8/2 9/2 10/2 11/2 12/2 Figure 4. ET a and K cs Distributions for Bermuda Hay, 10/97 9/98. Similar to alfalfa, the relative frequency distributions of ET a and K cs show the effects of cutting, with the exception of the July image date, where there is wide, relatively uniform variability in ET a and the corresponding K cs values. This variability is likely due to the production of Bermuda seed in addition to the cutting effects seen in other months. To produce seed, growers may reduce irrigation to create moisture stress and trigger increased seed production.

10 Table 7. Summary Statistics of Actual ET (ET a ) and Reference ET (ET o ) for Bermuda Hay, 10/97 9/98. Actual Evapotranspiration, ET a (mm/d) Image Date (mm/d) Min 5%tile 10%tile Mean Median 90%tile 95%tile Max Std. Dev. 10/26/ /27/ /13/ /14/ /3/ /4/ /22/ /9/ /26/ /27/ Table 8. Summary Statistics of Crop Coefficient (K cs ) for Bermuda Hay, 10/97 9/98. Crop Coefficient, K cs Image Date Min 5%tile 10%tile Mean Median 90%tile 95%tile Max Std. Dev. 10/26/ /27/ /13/ /14/ /3/ /4/ /22/ /9/ /26/ /27/ The average K cs across the peak growing season (April through September), excluding the July image date, calculated from Table 8 as 0.87 is less than the K c value of 0 for Bermuda hay suggested by FAO Irrigation and Drainage Paper No. 56 (Allen et. al, 1998) for mid season to reflect the average effects of cutting. The lesser mean Kcs values than the FAO published values could be attributed to difference between ideal conditions for which the standard K c values were developed and the actual field conditions in the Imperial Valley, where high evaporative demand, heavy soils, and high water and soil salinity relative to other regions could lead to reduced crop water use. Another possible factor may be seed production on some fields that have been classified in this study as hay fields exclusively. Citrus. Citrus crops grown in the Imperial Valley include lemons, oranges (navel and Valencia), tangerines and other miscellaneous varieties. Citrus occupied a total area of 1,329 hectares in IID the WY In IID, citrus was dominated by lemons during WY 1998 which occupied approximately 721 hectares (approximately 55% of the total area under citrus crop) in Results from the analysis of ET a and K cs for 109 fields of citrus are presented in Figure 5 and Tables 9 and 10, respectively.

11 Crop ET (mm/d) ETa Crop Coefficient, Kcs Kcs 0.2 9/1 10/1 11/1 12/1 1/1 1/31 3/3 4/2 5/3 6/2 7/3 8/2 9/2 10/2 11/2 9/1 10/1 11/1 12/1 1/1 1/31 3/3 4/2 5/3 6/2 7/3 8/2 9/2 10/2 11/2 Figure 5. ET a and K cs Distributions for Citrus, 10/97 9/98. Estimated mean daily ET a values were consistently below ET o values with a difference ranging between 0.5 mm/d and mm/d for fall, winter, and spring image dates. This difference increased to 2.0 mm/d to 3.5 mm/d for the summer image dates. These large differences between ET a and ET o, particularly during the summer period could be due the limited supply of water, higher bulk surface resistances due to low soil moisture content and/or due to other, physiological responses of the citrus to preserve moisture and survive through extreme hot, arid conditions stomatal closure (Allen et. al, 1998). Variability in crop coefficients for a given image date may be due to differences in canopy cover or varietal differences as well as differences in environmental stresses among fields. Table 9. Summary Statistics of Actual ET(ET a ) and Reference ET (ET o ) for Citrus, 10/97 9/98. Actual Evapotranspiration, ET a (mm/d) Image Date (mm/d) Min 5%tile 10%tile Mean Median 90%tile 95%tile Max Std. Dev. 10/26/ /27/ /13/ /14/ /3/ /4/ /22/ /9/ /26/ /27/

12 Table 10. Summary Statistics of Crop Coefficient (K cs ) for Citrus, 10/97 9/98. Crop Coefficient, K cs Image Date Min 5%tile 10%tile Mean Median 90%tile 95%tile Max Std. Dev. 10/26/ /27/ /13/ /14/ /3/ /4/ /22/ /9/ /26/ /27/ The mean K cs values across all image dates of 0.71 is greater than the K c value of 0.56 (initial, mid and end periods) provided by Snyder et al. (1998b), for citrus grown in Imperial Valley but is similar to the K c value of 0.65 to 0.70 suggested by FAO for citrus with no ground cover and canopy cover of 70 percent. CONCLUSIONS Spatially distributed actual ET was estimated for 12 Landsat 5 TM images acquired during water year 1998 for the Imperial Irrigation District using the SEBAL remotely sensed surface energy balance approach. District-wide annual ET a from SEBAL was validated through comparison to an independent water balance study. The difference between District-wide ET a from SEBAL and the water balance study was found to be less than 1%, providing a strong validation of SEBAL in arid, advective environments. SEBAL results were used, in part, to evaluate the spatial and temporal variability in crop coefficients. Refined crop coefficients are being used to support the development of irrigation scheduling recommendations for a pilot voluntary water conservation program. Remotely sensed crop coefficients were evaluated for crops selected for participation in the pilot program, including border-irrigated and row-irrigated alfalfa, Bermuda hay, and citrus. High variability in crop coefficients was observed among field of the same crop for a given image date in many cases. This variability may result from a variety of factors, including differences in crop vigor, cutting effects, soil moisture content, salinity, crop physiological responses, or other management related factors. Differences between remotely sensed crop coefficients and published values may result from differences in actual and ideal field conditions. The availability of thermal infrared imagery from Landsat enables the surface energy balance to be applied at the field scale, which provides a valuable tool to improve understanding of crop evapotranspiration and enables improved management of scare freshwater supplies at scales ranging from individual fields to entire basins. REFERENCES Allen R. G., L. S. Pereira, D. Raes, and M. Smith, Crop Evapotranspiration. FAO Irrig. And Drain. Paper No. 56. Food and Agriculture Organization of the United Nations. Rome, Italy, 300 pp. Allen, R.G., A. Morse, and M. Tasumi, Application of SEBAL for Western US water rights regulation and Planning, In: Proc. ICID Int. Workshop on Remote Sensing, Montpellier, France. Allen, R. G., I.A. Walter, R. Elliot, T. Howell, D. Itenfisu, and M. Jensen, The ASCE Standardized Reference Evapotranspiration Equation, American Society of Civil Engineers, 59 pp. Allen, R.G., M. Tasumi, and R. Trezza, Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC): Model, ASCE J. Irrig. Drain. Eng., 133(4): Bastiaanssen, W.G.M., M. Menenti, R. A. Feddes, and A. A. M. Holtslag, A remote sensing surface energy balance algorithm for land (SEBAL): 1. Formulation, J. Hydrol., , Bastiaanssen, W. G. M. Ahman, and Y. Chemin, Satellite Surveillance of Evaporative Depletion Across Indus Basin, Water Resources Research, 38(12):

13 Bastiaanssen, W. G. M., and S. Ali, A new crop yield forecasting model on satellite measurements applied across the Indus Basin, Pakistan, Agriculture, Ecosystem and Environment, 94: Bastiaanssen, W. G.M., E. J. M. Noordman, H. Pelgrum, G. Davids, B.P. Thoreson, and R. G. Allen, SEBAL model with remotely sensed data to improve water-resources management under actual field conditions, ASCE J. Irrig. Drain Eng., 131(1): Choudhury, B. J., N.U. Ahmed, S. B. Idso, R. J. Reginato, and C.S.T. Daughtry, Relations between evaporation coefficients and vegetation indices studies by model simulations, Remote Sens. Environ., 50, Clark, B., R. Soppe, D. Lal, B. Thoreson, W. Bastiaanssen, and G. Davids, Variability of Crop Coefficients in space and time-examples from California, In: Proc. Of the USCID Fourth International Conference on Irrigation Conference on Irrigation and Drainage Engineering, October 3-6, 2007, Sacramento, California. Imperial Valley Agricultural Commissioner, Imperial County Crop and Livestock Report, El Centro, California. Keller-Bliesner Engineering IID Efficiency Conservation Definite Plan Technical Appendix 1.B. IID Water Balance Summary, Imperial Irrigation District, Imperial, California, pp Available at EfficiencyConservationProgram. Kustas, W. P., and J. M. Norman, Use of remote sensing for evapotranspiration monitoring over land Surfaces, Hydrol. Sci. J., 41(4): Menenti, M., and B.J. Choudhury, Parameterization of land surface evapotranspiration using a location dependent potential evapotranspiration and surface temperature range, In: Proc. Exchange Processes at Land Surface for a Range of Space and Time Scales, IAHS publication 212, International Association of Hydrological Sciences. Moran, M.S., R. D. Jackson, H. Raymond, W. Gay, and P.N. Slater, Mapping surface energy balance components by Combining Landsat Thematic Mapper, and ground-based meteorological data, Remote Sensing of Environment, 30: Morse, A., R. G. Allen, M. Tasumi, W. J. Kramber, T. Trezza, and J.L. Wright, Application of the SEBAL methodology for estimating evapotranspiration and consumptive use of water through remote sensing, Idaho Department of Water Resources, Idaho. Neale, C.M.U., W.C. Bausch and D. F. Heerman, Development of reflectance-based crop coefficients for Corn, Trans. ASAE, 32(6): Roerink, G. J, SEBAL estimates of the areal patterns of sensible and latent heat fluxes over the HAPEXsahel grid, a case study on 18 September 1992, DLE-Staring Centre Interne Mededeling 361, Alterra, Wageningen. Roerink, G.J., B. Su, and M. Menenti, S-SEBI: A simple remote sensing algorithm to estimate the surface energy balance, Physics and Chemistry of the Earth, Part B, 25(2): Scott, C. A., W.G.M. Bastiaanssen, and M. Ahmad, Mapping root zone moisture using remotely sensed optical imagery, J. Irrig. Drain Eng., 129(5): Snyder, R.L., B.J. Lanini, D.A. Shaw, and W.O. Pruitt, 1989a. Using Reference Evapotranspiration (ET o ) and Crop Coefficients to Estimate Crop Evapotranspiration (ET c ) for Agronomic Crops, Grasses and Vegetable Crops, Cooperative Extension, University of California, Berkeley, California, Leaflet No Snyder, R.L., B.J. Lanini, D.A. Shaw, and W.O. Pruitt, 1989b. Using Reference Evapotranspiration (ET o ) and Crop Coefficients to Estimate Crop Evapotranspiration (ET c ) for Trees and Vines. Cooperative Extension, University of California. Berkeley, California, Leaflet No Soppe, R.W., Bastiaanssen, W., Keller, A., Clark, B., Thoreson, B., Eckhardt, J., and Davids, G., Use of High Resolution Thermal Landsat Data to Estimate Evapotranspiration within the Imperial Irrigation District of Southern California, Oral Presentation, American Geophysical Union 2006 Fall Meeting, San Francisco, California. Su, Z, The surface energy balance system (SEBS) for estimation of turbulent fluxes, Hydrol. Earth Systems Sci., 6(1): Taconet, O.R., R. Bernard, and D. Vidal-Madjar, Evapotranspiration over an agricultural region using a surface flux/temperature model based on NOAA-AVHRR data, J. Climate Appl. Meteor., 25: Tasumi, M., R. G. Allen, R. Trezza, and J.L. Wright, 2005a. Satellite-based energy balance to assess withinpopulation variance of crop coefficient curves, J. Irrig. Drain.Eng., 131(1): Tasumi, M., R. G. Allen, R. Trezza, and J.L. Wright, 2005b. Operation aspects of satellite-based energy balance models for irrigated crops in the semi-arid U.S., Irrig. Drain. Syst., 19: Thiruvengadachari, S, Satellite sensing of irrigation pattern in semi-arid areas: An Indian Study,

14 Photogrammetric Eng. and Remote Sensing, 46(5): Vidal, A. and A. Perrier, Analysis of a simplified relation used to estimate daily evapotranspiration from satellite thermal IR data, International Journal of Remote Sensing, 10(8): 1,327-1,337.