assessing agricultural water productivity in desert...

Assessing Agricultural Water Productivity in DesertFarming System of Saudi Arabia

Virupakshagowda C. Patil, Khalid A. Al-Gaadi, Rangaswamy Madugundu, ElKamil H. M. Tola, Samy Marey,Ali Aldosari, Chandrashekhar M. Biradar, and Prasanna H. Gowda

Abstract—The primary objective of this study was to assess thewater productivity (WP) of the annual (wheat, barley, and corn) andbiennial (alfalfa and Rhodes grass) crops cultivated under center-pivot irrigation located over desert areas of the Al-Kharj region inSaudi Arabia. The Surface Energy Balance Algorithm for Land(SEBAL) was applied to Advanced Spaceborne Thermal Emissionand Reflection Radiometer (ASTER) images to obtain evapotrans-piration (ET) for assessing WP and irrigation performance (IP) ofcrops. Crop productivity (CP) was estimated using NormalizedDifference Vegetation Index (NDVI) crop productivity models. Thepredicted CP ( ) for corn varied from 12 690 to 14 060 t/ha andfrom 6000 to 7370 t/ha for wheat. The for alfalfa and Rhodesgrass was 42 450 and 58 210 (kg/ha/year), respectively. The highestpredicted WP was observed in wheat ( ) and thelowest was in alfalfa ( ). The deviation betweenSEBAL predicted ET ( ) and weather station recorded ET( ) was 10%. The performance of the prediction models wasassessed against the measured data. The overall mean bias/error ofthe predictions of CP, ET, andWPwas 9.4%, , and 9.65%,respectively; the root mean square error (RMSE) was 1996 (kg/ha),2107 ( ), and 0.09 ( ) for CP, ET, andWP, respectively.When CP was converted into variations between the actual andpredicted, the variations were 8% to 12% for wheat, 14% to 20%for corn, 17% to 35% for alfalfa, 3% to 38% for Rhodes grass, and4% for barley.

Index Terms—ASTER image, center-pivot irrigation system,crop productivity, evapotranspiration, water use.

I. INTRODUCTION

A GRICULTURE is the largest consumer of freshwater inthe world [1]. In arid and semiarid environments, compe-

tition for freshwater has been steadily increasing among

agricultural, domestic, and industrial sectors [2]. This competi-tion will further increase with ever growing concerns of climatechange and its variability, population growth, economic devel-opment, and environmental impacts. The demand for water inthe Middle East and North African (MENA) region that was

in 2000 is expected to increase to[3] or [4] in 2050.

The rapid dwindling of finite water resources and the steadyincrease in demand for food are the major obstacles for attainingagricultural sustainability in SaudiArabia. Agriculture in generaland irrigation in particular consume over 80% of the freshwaterused in Saudi Arabia. In 2012, freshwater consumption for theagricultural sector in Saudi Arabia was estimated at 86%—anincrease of 6% between 2008 and 2012 [5]. Water used forirrigation is pumped from deep aquifers (up to 1000 m) to feedcenter-pivot irrigation systems at enormous economic andenvironmental costs. This situation creates an urgent need forattaining agricultural sustainability, but it is extremely difficult tomaintain equilibrium between water and food securities. Thiscritical equilibrium emphasizes the Kingdom’s need for strategictechnologies and methods to drastically reduce the currentdepletion rate of groundwater resources and optimize waterconsumption without reducing agricultural production. This canonly be achieved through the efficient use of irrigation water.Therefore, increasing water productivity (WP) in the agriculturalsector is crucial forwater conservation efforts that can serve othercompetitive and critical needs such as domestic, industrial,environmental, and recreational purposes.

WP is determined from biological/economic yield of cropsand the quantity of water used to produce that yield. It is one ofthe key indicators for evaluating the efficiency of water use inagriculture. Any attempt to improve water use efficiency inirrigated agriculture must be based on reliable estimates ofseasonal/total evapotranspiration (ET), which has amajor impacton water management. ET varies regionally and seasonallyaccording to weather conditions [6]. Understanding the varia-tions in ET is essential for the management of water resources,particularly in hyper-arid regions of Saudi Arabia, where cropwater demand exceeds precipitation by several folds and requiresirrigation from groundwater resources to meet the deficit. ETvalues are not only useful for developing WP maps at field andregional scales, but are also useful for precision irrigationpurposes.

Satellite-based remote sensing is a robust, economic, andefficient tool for estimating ET, WP, and the assessment ofirrigation performance (IP). Monitoring the temporal changesof the key parameters used in these estimates through employing

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Manuscript received November 01, 2013; revised March 17, 2014; acceptedApril 17, 2014. This work was supported by NSTIP strategic technologiesprograms, under Grant 11 SPA 1503-02 in the Kingdom of Saudi Arabia.

V. C. Patil, K. A. Al-Gaadi, R. Madugundu, and E. H. M. Tola are with thePrecision Agriculture Research Chair, Department of Agricultural Engineering,College of Food and Agriculture Sciences, King Saud University, Riyadh 11451,Saudi Arabia (e-mail: [email protected]; [email protected]; [email protected]; [email protected]).

S. Marey was with Precision Agriculture Research Chair, Department ofAgricultural Engineering, College of Food and Agriculture Sciences, King SaudUniversity, Riyadh 11451, Saudi Arabia. He is now with the National Plan forScience and Technology, King Saud University, Riyadh 11451, Saudi Arabia(e-mail: [email protected]).

A. Aldosari is with the Department of Geography, College of Arts, King SaudUniversity, Riyadh 11451, Saudi Arabia (e-mail: [email protected]).

C.M. Biradar is with the International Center for Agricultural Research in DryAreas (ICARDA), Amman, Jordan (e-mail: [email protected]).

P. H. Gowda is with the USDA-ARS, Conservation and Production ResearchLab, Bushland, TX 79012 USA (e-mail: [email protected]).

Color versions of one ormore of the figures in this paper are available online athttp://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/JSTARS.2014.2320592

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 1

remote sensing techniques can significantly contribute to irriga-tionmanagement [7], [8]. A remote sensing approach overcomesconstraints such as data scarcity and scale limitations and reducesuncertainties by covering large spatial domains over time.

Various surface energy balance models such as SurfaceEnergy Balance Algorithm for Land (SEBAL) [9], SimplifiedSurface EnergyBalance Index (S-SEBI) [10], Simplified SurfaceEnergy Balance (SSEB) [11], and Mapping Evapotranspirationwith Internalized Calibration (METRIC) [12] have been widelyused around the globe for computing ET. The SEBALmodel hasbeen predominantly used to estimate ET [9], [13], WP, and IP[14]–[16]. SEBAL was applied to NOAA-AVHRR images tocompute the accumulated groundwater abstraction for a 30-yearperiod (1975–2004) in Saudi Arabia, and it was found to be833 mm per year on average [17]. In another study, census dataand crop water requirement models were used for estimating anationwide abstraction of ground water in Saudi Arabia, whichwas reported to be for 2010 [18].

In view of the very low WP of the majority of crops grownunder center-pivot irrigation systems in Saudi Arabia, there areample opportunities for significant improvement. One of theseopportunities was highlighted in a research study conducted toexplore the potential of adopting proper cropping pattern basedon water demand [19]. The results of that study showed greatpotential for enhancing food and water security in Saudi Arabiathrough producing alternate crops in regions where WP is high.In viewof the pressing need to assess theWPof agriculturalfieldsirrigated through center-pivot irrigation systems, this study wasundertaken with the goal of developing aWPM using AdvancedSpace borne Thermal Emission and Reflection Radiometer(ASTER) satellite imagery for alfalfa, Rhodes grass, corn, wheat,and barley crops across spatial–temporal domains. The outcomeof the study will be helpful in determining both the spatial andtemporal variability in WP and in selecting the right crop at theright cultivating season for the optimal use of groundwaterresources.

II. MATERIALS AND METHODS

A. Study Area

The studywas carried out on the Todhia Arable Farm (TAF), acommercial farm with 47 fields utilizing center-pivot irrigationsystems spread across an area of 6967 ha. The farm lieswithin thelatitudes of and and within thelongitudes of and (Fig. 1).Wheat, alfalfa, Rhodes grass, corn, and barley are cultivated tomeet the fodder demand of cattle farms. Table I shows thecropping patterns and acreage of the study area. The crop acreageunder alfalfa and corn was 580 and 227 ha, respectively, in 2012,and increasing to 900 and 640 ha, respectively, in 2013. Theacreage under Rhodes grass and wheat crops decreased from1430 and 437 ha, respectively, in 2012, to 542 and 100 ha,respectively, in 2013. However, barley was cultivated only in2013. The season-wide cropping periods of all the investigatedcrops is provided in Table II.

B. Ground Truth Data Collection

The geo-referenced data of the ground-measured NormalizedDifference Vegetation Index hereafter referred to asand the ground-measuredLeafArea Index hereafter referred to as

were collected synchronous to the dates of the satelliteoverpasses. The and ground truth data collec-tion schedules are presented in Table III.

Of the 47 pivots of the farm, 11 pivots (23%) were consideredas samples for modeling. About 270 ground-based “point data”of the and were collected from these 11 pivots;approximately, 25 random sample points from each pivot. Of the270 ground points, 162 points (60%) were used for the develop-ment of NDVI crop productivity (CP) models and the remaining108 (40%)were used for validation. The location of these samplepoints is depicted in Fig. 1.

The data on the quantity of water applied (WA) and the CP ofthe harvested crops for all the 47 pivots were obtained from the

Fig. 1. Location map of Todhia Arable Farm along with sample locations of ground measurements.

2 IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING

TAFManager. Of these, 11 pivots (23%)were used formodelingand the remaining 36 pivots were used for validation.

The was measured in the field on the dates of thesatellite overpass by using the crop circle (Model ACS-470)

of Holland Scientific, USA. The crop circle device was cali-brated through configuration of a 670-nm filter in channel 1,an NIR filter in channel 2 and a 550-nm filter in channel 3of the sensor socket for measuring . Map mode

TABLE IISOWING DATES OF CROPS IN TODHIA ARABLE FARM, SAUDI ARABIA

TABLE ICROPPING PATTERN IN TODHIA ARABLE FARM FOR THE YEAR 2012 AND 2013

TABLE IIISCHEDULE OF GROUND TRUTH DATA COLLECTION FOR THE YEAR 2012 AND 2013

PATIL et al.: ASSESSING AGRICULTURAL WATER PRODUCTIVITY IN DESERT FARMING SYSTEM OF SAUDI ARABIA 3

measurements with 2 samples/s were used for field datacollection. To determine the field data coordinates, anOmniSTAR GPS receiver (Model 9200-G2) was connected tothe Crop Circle at a baud rate of 9600. Field data measurementswere recorded with the Crop Circle positioned at 1 m above thecrop canopy.

The was determined on the dates of the satelliteoverpass by using the plant canopy analyser (Model PCA-2200) of LiCOR Biosciences, USA. To compute a single LAIvalue at each location, one above canopy and five below canopyreadings were recorded. The above and below canopy measure-ments were made by using a “fisheye” optical sensor with a 148angle of view. Respective geo-locations were collected usinga handheld Trimble GPS receiver (Model-Geo XH 600). Anazimuth mask of a 180 view cap was used on a PCA-2200sensor during data collection to obscure the bright sky andthus eliminate the shading effect of the instrument operator.The measured and were used to correct theASTER-derived NDVI and LAI and subsequently used in theselection of anchor pixels (cold and hot) for the SEBAL model.

Meteorological datawere collected from an automaticweatherstation (Vintage Pro2 wireless station) installed at the farm.Meteorological data such as wind speed, humidity, hourly solarradiation, and air temperature were used for processing the netradiation ( ), soil heat flux (G), and sensible heat flux (H) usedfor the SEBAL Model. The wind speed (u) at the time of thesatellite overpass was used for the computation of sensible heatflux (H) and humidity; which were utilized for the estimation ofreference ET ( ). Solar radiation data were used for theestimation of the image cloudiness and to adjust the atmospherictransmissivity ( ). A data assimilation approach was used tocomputeWP. CP and ET data were used to compute the finalWPmap (Fig. 2).

C. Processing of ASTER Images

Time seriesof level1B(ASTL1b)ASTER images (Appendix I)pertaining to Paths 164 and 165 procured from Japan SpaceSystems (Available: http://gds.ersdac.jspacesystems.or.jp) wereused in this study to generate the ET, CP, and WP map of the

TAF. Of the 15 procured images, 12 were from 2012 and theremaining 3 were from 2013. These images covered the entirerange of crop phenology for all crops. In the case of wheat,barley, and corn, the images covered the peak growth stage,whereas the images covered all the growth stages between twoharvests/cuts for alfalfa and Rhodes grass. The details of theASTER data, such as the date of acquisition, the sun elevationangle, the zenith angle, and the distance between sun and earth(which were used in radiometric calibration), are presented inTable IV.

The acquired images were georeferenced and radiometricallycalibrated by adopting a radiative transfer model using precali-brated coefficients [20]. At sensor, temperatures (K) were ob-tained from thermal bands, as described by Ghulam [21]. TheNDVI, which is widely used for the assessment of remotelysensed data, was derived from the visible and near-infraredbands [22].

D. Water Productivity Mapping

WP is defined as either the amount of yield produced per unitvolume ofwater (kg of ofwater) or as amonetary valueof the yield produced per unit volume of water [23]. WaterProductivity Mapping (WPM) was achieved in three steps:1) Crop Productivity Mapping (CPM), 2) Water Use (ET)Mapping (WUM) and 3) Water Productivity Mapping (WPM)[23]. Field-measured crop productivity data were related to theNDVI to obtain CP models. The best fit CP models wereextrapolated to larger areas by using remotely sensed data toobtain CPM. WUM was prepared by using crop ET. The(per day) was obtained from ASTER data by applying theSEBAL model [9]. WPM was generated for the entire TAF bydividing the CPM by the WUM.

1) Crop Productivity Mapping:CP is a very important end-of-season observation that integrates the cumulative effect ofweather and management practices over the entire cropgrowth season. A remote sensing approach provides both CPassessments and possible variations across fields. Linearrelationships between CP and the NDVI at the crop headingstagewere observed [24]. In this study, a scatter plot for each cropwas drawn between the corrected ASTER-derived NDVI( -axis) and CP ( -axis) for the development of CP predictionmodels. The NDVI at the ear-head emergence stage in wheat andbarley, the flag leaf stage in corn, and the flowering stage inalfalfa and Rhodes grass were considered for CP estimations.

The CP model was developed using (1)

where Y is the predicted CP (t/ha), X is theNDVI, and and arethe constants.

MeasuredCP ( ) datawere collected from theTAF recordsand correlated with the respective field’s NDVI derived fromASTER images. Remote sensing-based CP ( ) of corn,barley, and wheat was computed by multiplying the aboveground biomass (AGB) by the Harvest Index (HI), a functionof the NDVI [25].

Hay yield monitor was used to collect the hay yields foralfalfa and Rhodes grass. A hay yield monitor Model 880 of

Fig. 2. Water productivity prediction flow chart.


Harvest Tech., USA, was installed on a large square baler(Claas 3000) for recording the CP data at the time of baling.The hay yield maps were prepared by interpolating the filteredpoint data to grids using an ordinary kriging toolof ESRI GIS (ver. 2010) [26]. During the preparation of thehay yield maps, low- or high-yielding points associated withsignificant turning and manoeuvring of the baler were removed[27], as were the short segments, which were affected by start orend-pass delays [28].

2) Water Use Mapping: WUM was accomplished by usingcropET and assuming that the amount ofwater used by cropswasequal to seasonal ET (ETactual). The SEBAL method was usedto compute the ETactual on a pixel-by-pixel basis for theinstantaneous time of satellite image as the residual amount ofenergy remaining from the classical energy balance (2) [9]

where is the latent heat flux (an instantaneous value for thetime of the satellite overpass), is the net radiation at thesurface, G is the soil heat flux [calculated using (11)], and H isthe sensible heat flux to the air [calculated using (12)]. The unitfor all fluxes was . The SEBAL computes as a“residual” of net radiant energy after G and H are subtracted.

The first step in the SEBAL procedure is to compute byusing the surface radiation balance equation (3)

where is the net radiation at the surface, is the surfacealbedo, is the incoming short-wave radiance, is theincoming long-wave radiance, is the outgoing long-waveradiation, and is the surface emissivity. All of these parameterswere accomplished in a series of steps using the ERDAS Imaginemodel maker tool as described in the SEBAL manual [29].

Surface albedo: Georectified ASTER VNIR bands were sub-jected to Top-Of-Atmosphere (TOA) reflectance. This enabledthe conversion of image digital numbers (DN) to at-sensorradiance ( ) and subsequently to spectral reflectance ( ) byadopting the procedures of the ASTER User Manual [20]. Thespectral reflectance of each band was then used to compute theAlbedo-top of the atmosphere ( ) by utilizing the two visibleband albedos (4) of Liang [30] and the computed surface albedo( ) by correcting the (5)

where and are the VNIR 1 and VNIR 2 bands of theASTER image, respectively, and

where the path radiance is the average portion of the incomingsolar radiation across all bands that is back-scattered to thesatellite before it reaches the earth’s surface and is theatmospheric transmissivity. In this study, path-radiance valuewas considered as 0.03 [31]. The values assume clear sky andrelatively dry conditions and are obtained by using an elevation-based relationship: [32], where isthe elevation of the weather station above sea level (m).

Incoming short-wave radiation ( ): The incoming short-wave radiation is the direct and diffused solar radiation flux thatactually reaches the earth’s surface ( ); this was calculatedassuming clear sky conditions as a constant for the entireimage (6)

where is the solar constant ( ), is the cosineof the solar incidence angle, is the inverse squared relativeearth-sun distance, and is the atmospheric transmissivity.

Outgoing long-wave radiation ( ): The outgoing long-wave radiation is the thermal radiation flux emitted from theearth’s surface to the atmosphere ( ); this was computed byapplying the Stefan–Boltzmann equation (7)

where is the broad-band surface emissivity (dimensionless),is the Stefan–Boltzmann constant ( ), and

is the surface temperature (K). The surface emissivity ( ) wascomputed using an empirical equation where the NDVI > byinputting the NDVI and the LAI, as described in the SEBAL usermanual [29]. Subsequently, the surface temperature ( ) wascomputed from the ASTER TIR band 13 [21] and used in thecomputations.

Incoming long-wave radiation ( ): The incoming long-wave radiation is the downward thermal radiation flux fromthe atmosphere ( ) that was computed using the Stefan–Boltzmann equation (8)

TABLE IVCHARACTERISTICS OF ASTER DATA USED FOR THE STUDY


where is the atmospheric emissivity (dimensionless), is theStefan–Boltzmann constant ( ), and isthe near surface air temperature (K). was calculated using(9) [23]

where is the atmospheric transmissivity.Substituting (9) into (8) and using from the selected cold

pixel for provides the following equation (10):

Two anchor (hot and cold) pixels were selected [29] to fixboundary conditions for the energy balance. The cold pixel wasselected as a wet, well-irrigated crop surface having full groundcover by vegetation. The surface temperature and near-surfaceair temperature were assumed to be similar at this pixel. The hotpixel was selected as a dry, bare agricultural field where ET wasassumed to be zero.

Soil heat flux ( ): G is the rate of heat storage into thesoil and vegetation due to conduction. It was obtained by

using the empirical equation (11) representing values nearmidday [34]

where is the surface temperature ( ), is the surfacealbedo, the NDVI is the Normalized Difference VegetationIndex, and is the net surface radiation flux.

Sensible heat flux ( ): TheH is the rate of heat loss to the air byconvection and conduction due to a temperature difference. Itwas computed by using (12) for heat transport

where is the air density ( ), is the specific heat of air(1004 J/kg/K), (K) is the temperature difference, and isthe aerodynamic resistance to heat transport (s/m). Therefore, His a function of the temperature gradient, surface roughness, and

TABLE VSUMMARY OF GROUND MEASURED NDVI AND LAI

TABLE VINDVI MODELS USED FOR PREDICTING CROP PRODUCTIVITY


wind speed. Equation (12) is difficult to solve because of twounknowns and . To facilitate this computation, weutilized the two selected “anchor” pixels (where reliable valuesfor H can be predicted and a estimated) and thewind speed ata given height (which was obtained from the weather station) asdescribed in the SEBAL manual [29].

The SEBAL procedure (2) was completed by computing thenet surface radiation flux ( ) using the surface radiation balanceequation (3), the soil heat flux (11), and the sensible heat flux(12). After , G, and the final value of H were established, thelatter after an iterative process to consider atmospheric stabilityeffects, the Latent Heat (LE) was then calculated as a residual.

This is the energy equivalent of the instantaneous ET at the timeof satellite overpass. The evaporative fraction (EF) for each pixelwas calculated.

The , i.e., the rate of LE loss from the surface due to ET, isconsidered an instantaneous value for the time of satelliteoverpass used to compute instantaneous ET ( ) and refer-ence evaporative fraction ( ) values by applying (13)and (14)

where is the instantaneous ET (mm/h), 3600 is the timeconversion from seconds to hours, and is the latent heat ofvaporization or the heat observed when a kilogram of waterevaporates (J/kg); and

where derives from (13) (mm/h) and is the crop-coefficient ( ) of a known crop. This study considers the ofalfalfa as the at the time of the image overpass. The dailyvalues of ET ( ,mm/day)were computed using (15) and thenextrapolated to a growing season or for a particular periodemploying (16), as described in the SEBAL manual [29]

where is the cumulative 24-h for the day of theimage, which is calculated by adding the hourly values overthe day of the image and is the reference evaporativefraction

where the is the representative for the growingseason or period, is the daily , and is the number ofdays in the period. Units for are computed in mm,whereas the is in mm/day.

TABLE VIIPHENOLOGY OF BIENNIAL CROPS CORRESPONDING TO ASTER IMAGE

ACQUISITION DATES

TABLE VIIIPHENOLOGY OF ANNUAL CROPS CORRESPONDING TO ASTER IMAGE

ACQUISITION DATES


III. RESULTS

A. and

The and were examined across the cropsduring the study period (Table V). The values rangedbetween 0.12 (initial stage) and 0.82 (canopy closure stage). The

value of alfalfa was 0.62 ( ) and 0.51 ( ) in2012 and 2013, respectively. Meanwhile, Rhodes grass recordeda high value of 0.65 in 2012 and the leastvalue of 0.22 in 2013. The of seasonal crops rangedbetween 0.63 (wheat) and 0.71 (corn) in 2012; however, it variedfrom 0.53 (barley) to 0.63 (corn) in 2013. The variedbetween 0.21 (initial stage) and 6.04 (canopy closure stage). The

in the alfalfa crop was 4.51 ( ) and 4.02 ( ) in2012 and 2013, respectively, whereas the for Rhodesgrass was 4.50 ( ) and 1.00 ( ) in 2012 and 2013,respectively. In the annual crops, a higher was observedin wheat ( ), followed by corn ( ) duringthe year 2012. However, in 2013, corn recorded a higher( ) compared to barley ( ) and wheat( ).

B. CP Models

The best relationship between CP and NDVI was obtainedwhen the crops were in mid-season (growth stage), as presentedin Table VI. In the annual crops, the best response was observedon the Julian days of 43 (2013), 169 (2012), and 64 (2012) forbarley, corn, and wheat crops, respectively, when the crops wereat their peak growth stage. In the biennial crops (alfalfa andRhodes grass), which have a growth period of 30 to 45 daysbetween the two harvests, the best response was observed on the

Julian day 201 and 281 of 2012 for Rhodes grass and alfalfa,respectively (Tables VII and VIII).

The CP models were validated against the actual CP datarecorded on the farm; there was good correlation (Table VI). Theroot mean square error (RMSE) between the farm recorded andmodel predicted CP was high in Rhodes grass (24%), followedby alfalfa (21%), corn (18%), and barley (16%), with the leastRMSE observed in wheat (15%). However, similar RMSE valueswere observed in the cross-validation of the models (Table VI).

C. CP (kg/ha/season)

The results of the predicted and actual ET/crop water use CPand WP for both annual and biennial cops are presented inTable IX. It was observed that predicted CP ( ) variedsignificantly in both temporal and spatial scales. For annualcrops, the average for corn was 13 510 and 14 060 forseason 1 and 2, respectively, in 2012 and 12 690 in 2013.However, the actual recorded CP ( ) for corn was 10 930and 11 190 for season 1 and 2, respectively, in 2012, and 10 900in 2013. Meanwhile, the average for the wheat crop was6000 and 7370 for season 1 in 2012 and 2013, respectively. But,the for the wheat crop was 5530 and 6510 for season 1 in2012 and 2013, respectively. In the case of barley, the farmrecorded mean was 7210 kg/ha, whereas the was6910 kg/ha, resulting in a mean error of 4.16%.

For the biennial crops, the (kg/ha/year) for alfalfa was42 450 for 2012 and 15530 for 2013 (up toMay 19). The forthe alfalfa crop was 35 100 for 2012 and 21 000 for 2013.Although the average for Rhodes grass was 58 210 for 2012and 24 580 for 2013 (up to May 19), the for Rhodes grasswas 60 390 for 2012 and 15 140 for 2013.

TABLE IXPREDICTED AND ACTUAL CROP PRODUCTIVITY, EVAPOTRANSPIRATION/WATER USE AND WATER PRODUCTIVITY

, ASTER predicted crop productivity; , ASTER predicted evapotranspiration; , ASTER predicted water productivity; , TAFrecorded crop productivity; WA, Actual quantity of water applied; , actual water productivity.


D. Water Use (ET) Mapping

ET values were estimated in this study through the analysis ofASTER images using the SEBAL model. The accuracy of theASTER predicted ET ( ) data using the SEBAL model wastested against the weather station recorded ET ( ) data. Thedistribution pattern of and is illustrated in Fig. 3. Boththe and followed a similar pattern throughout the studyperiod. The correlation between and was furtherinvestigated through the regression analysis technique. Theresults showed a strong linear relationship between and

, with an of 0.78 (Fig. 4). Themean deviation of thefrom the was found to be 10.49%. The (Fig. 5) wasthen used to assess IP for all of the test crops. The mean values ofboth and the actual quantity of irrigation WA are presentedin Figs. 6 and 7.

During the 2011–2012 season, the WA to alfalfa, Rhodesgrass andwheat crops was lower than the required quantity as perthe . However, during the 2012–2013 season, alfalfa, wheat,and barley crops were irrigated with more than the requiredquantity of water. Conversely, corn received a higher thanrequired quantity of water during 2011–2012 and a lower thanrequired quantity during 2012–2013.

The deviation of from the WA to all of the crops wasdetermined in terms of overall mean error (Fig. 8). The results

Fig. 6. ASTERpredictedET( ) and thewater applied (WA) to biennial crops.

Fig. 5. SEBAL model-based ASTER predicted ET (mm/day) for the study site.

Fig. 4. Regression between ASTER predicted ( ) and weather stationrecorded ET ( ).

Fig. 3. Temporal variation in ASTER predicted ( ) and weather stationrecorded ET ( ).

Fig. 7. ASTER predicted ET ( ) and the water applied (WA) to annual crops.


indicated that the accuracy of was higher for alfalfa, corn,and Rhodes grass crops and lower for wheat and barley.

E. Water Productivity

As depicted in Fig. 9, the prediction of WP ( ) was moreaccurate for alfalfa andRhodes grass crops in 2012. In alfalfa, the

was versus an actual WP ( ) of. Meanwhile, the for Rhodes grass wasversus a of . In 2013 (Fig. 10),

the was 2.01, 1.07, 0.68, 0.55, and for wheat,Rhodes grass, barley, corn, and alfalfa, respectively, versus

values of 1.63, 0.69, 0.55, 0.51, and 0.43 for the samesequence of crops.

IV. DISCUSSION AND CONCLUSION

As indicated by the regression models, the CP of all the cropsshowed a visible and significant trend across a range of NDVIvalues. The values were moderate and ranged from 0.5211

(Rhodes grass) to 0.6214 (corn). This is one of the drawbacks ofstatistical expressions for relationships between the NDVI andthe CP [35]. This was also evident in the cross-validation of themodels, where the CP data were particularly inconsistent forforage crops due to bias in the selection of the proper growthstage (duration between two harvests) for the CP predictions.Conversely, the seasonal performance of different species,phenology, spectral response, and the establishment of time-dependent relationships with crop vigour and productivityplayed a major role in developing yield models [36], [37].Despite this, the obtained values concurred with the earlierreported values. Forexample,inthecaseofthecorncrop, thestrongrelationship of theNDVI and theCPestimates occurred in the June(i.e., Julian day, 169) image, with an value of 0.5647; this wasconsiderably higher than the reported values ( )in a previous study [38], but lower than that reported in anotherstudy ( ) [39]. This research gained further supportfrom previous studies on wheat crop, where the lowest CPprediction accuracy was obtained; accurate wheat CP predictionswere possible using only one image, provided the image wasacquired toward the middle of the growing season when mostwheat [40] and corn [41] crop canopies were fully developed.

The results of this study showed that spatial distribution of ETcould be predicted with an overall accuracy of 90%. The resultsof the concur with the results of another study that used theSEBAL model to estimate ET in the Philippines, where the ETvalues deviated by 9% from the ETc (Penman-Monteith) for theASTER sensor [42]. The obtained results were better thanexpected, as most remote sensing techniques used for estimatingevaporation (E) have accuracies of 70%–85% compared withground-based measurements [9]. In another study that summa-rized the accuracy of ET prediction using the SEBAL model(although under different climatic conditions), the accuracy atfield scale was 85% for 1 day and reached 95% on a seasonalbasis, whereas the average accuracy of annual ET for largewatersheds was observed to be 96% [14].

On an annual/seasonal basis, a deviation of approximately 19%was observed between the and . When focusing on afiner timescale, themodel resulted in a large deviation (i.e.,to ). The model overestimated the for the ASTERimages of June 1, 2012 (63%); June 17, 2012 (47%); July 3 (47%);February 13 (63%); and March 16, 2013 (34%), whereas under-estimated the for the March 20, 2012 (49%); April 21, 2012(4%); and August 4, 2012 (16%) images. This might be due to thegradation of individual pixels’ evaporative response, which canreflect upon the diversity of crops, growth stages, and gradients insoil moisture conditions across the fields [43], [44].

The WP of alfalfa observed in this study ( )concurred with the previously reported values (

) [45]. The harvests made in the cooler months ofJanuary–March 2012, November 2012, and February 2013recorded a higher WP ( ) than the harvestsmade inwarmermonths (i.e., 0.23–0.40). It is evident that alfalfa,being a plant, is adapted to cuttingsmade in cooler seasons butloses its efficiency during the summer season [46]. This largeamount of variation may be attributed to the influence of bothspatial and seasonal climatic variations on ET, alfalfa produc-tivity, and water use efficiency. Similar results were reported

Fig. 8. Accuracy assessment of predicted ET ( ) vs. Water applied (WA).

Fig. 9. ASTER predicted ( ) vs. Actual ( ) water productivity for 2012.

Fig. 10. ASTER predicted ( ) vs. Actual ( ) water productivity for 2013.


when comparisons were made between CP and ET for individualharvests, where the relationship varied across the growing seasonand changed depending on the harvest time [47]. A bettercorrelation between the and field recorded for theentire farm was observed, with an of 0.7967 ( < ) asshown in Fig. 11. Meanwhile, among the three annual crops,there were considerable differences between the andin wheat and barley ( plants) but not in corn ( plant). In thecase of wheat, the ( ) values of 0.80–2.01 closelyresembled the values of 0.1–2.07 [48], but were higherthan the values of 0.51–1.50 reported by others [49]–[52]. Thelower WP observed in the earlier studies could be due to thelower CP of 4.3–4.88 t/ha [49], [50] compared to 6.0 t/ha inthe present study. For corn, the ( )concurred with the WP ( ) reported earlier fromIran [53]. However, there were other reports with much higherWP values of [54], [55]. The WP values of

observed in Rhodes grass resembled earlierreported values of [56] and [57],but were lower than the values ( ) reported byICARDA [58]. In the case of barley, the and valueswere 0.68 ( ) and 0.55 ( ) , respectively. Thevalues were within the range of values reported in Mediterraneanenvironments [59], [60].

The performance of the prediction models was assessedagainst the recorded/measured data (Table X). The overallpercentage bias values for CP, ET, and WP were 9.4%,

, and 9.65%, respectively, whereas the RMSE valueswere 1996 (kg/ha), 2107 ( ), 0.087 ( ) for CP, ET,and WP, respectively. When converted into variations betweenthe actual and the predicted CP, the variations were 8%–12% forwheat, 14%–20% for corn, 4% for barley, 17%–35% for alfalfa,and 3%–38% for Rhodes grass. Although the performance of themodels varied among crops and across seasons/years, the pre-diction bias values were within acceptable limits.

Based on the results of this study, it can be inferred that WP ofwheat was consistently higher than all the other studied crops.Further, among the fodder crops, corn recorded higher WPcompared to extensively cultivated crops such as alfalfa andRhodes grass. The results of this research work were shared withfew agricultural companies. As a result, there was a shift in thecropping pattern on TAF in 2013, wherein the area under cornincreased and the area under Rhodes grass decreased as com-pared to the previous year.

This study also concludes that the SEBAL algorithm usingASTER images provided realistic estimates of ET, CP, and WPfor the crops (corn, wheat, barley, Rhodes grass, and alfalfa)cultivated under center-pivot irrigation system in Saudi Arabia.There was concurrence between the predicted and actual valuesof CP and WP. However, the predicted daily ET values signifi-cantly deviated from the meteorological data, particularly insummer months (June–September); this issue warrants furtherempirical research.

APPENDIX I

ACKNOWLEDGMENT

The unstinted cooperation and support extended by Mr. A.King and Mr. J. King are gratefully acknowledged. The assis-tance of the graduate students, M. E. Abbas, A. G. Kayad, andA. M. Zeyada was quite valuable.

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Virupakshagowda C. Patil was born in Rajankollurvillage, Karnataka, India on May 12, 1953. He re-ceived the B.Sc. and M.Sc. degrees in agronomy, andthe Ph.D. degree in agronomy from the University ofAgricultural Sciences (UAS), Bangalore, Karnataka,India, in 1973, 1975, and 1980, respectively.

During the academic year 1984–1985, he was aVisiting Scientist with the prestigiousCommonwealthForestry Institute, University of Oxford, Oxford, U.K.Currently, he is serving as Chair Professor with thePrecision Agriculture Research Chair, Department of

Agricultural Engineering, College of Food and Agriculture Sciences, King SaudUniversity (KSU), Riyadh, Saudi Arabia. Before joining KSU, he worked asDean of College of Agriculture and Special Officer (Technical) with UAS,Raichur, India, in 2008–2010. He served as Instructor with the Department ofAgronomy, UAS, Bangalore, India in 1980–1982, Assistant Professor in 1982–1986; and as Associate Professor with UAS, Dharwad, India in 1986–1994), andProfessor in 1994–2008. He has successfully implemented 17 extra-muralresearch projects. He has organized two international and five national confer-ences and edited the proceedings of all these conferences. He has so far published90 research papers in peer-reviewed Journals. He has mentored 24 M.Sc. and 8Ph.D. students. His research interests include precision agriculture and informa-tion and communication technologies for the conservation of natural resources.

Prof. Patil is a Fellow of the Indian Society of Agronomy. He is the FounderPresident of Indian Society of Agricultural Information Technology (INSAIT).From 2006 to 2008, he served as the President of the Asian Federation forInformation Technology in Agriculture (AFITA), Japan. Currently, he is Honor-ary Board Member of AFITA. He is a Member of International Society ofPrecision Agriculture, Life Member of Indian Society of Agronomy, IndianSociety of Weed Science, Indian Society of Remote Sensing, and Institute forStudies on Agriculture and Rural Development, Dharwad, India.

Khalid A. Al-Gaadi was born in Riyadh city, SaudiArabia, in 1966. He received the M.S. and Ph.D.degrees in agricultural engineering, power and ma-chinery from Colorado State University, Fort Collins,CO, USA, in 1992 and 1998, respectively.

From 1999 to 2002, he was a General Managerwith a limited company in Al-Khobar, Saudi Arabia.Between 2002 and 2003, he has been an AssistantProfessor with the Department of Agricultural Engi-neering, College of Agriculture and Veterinary Medi-cine, King Saud University, Gassim, Saudi Arabia.

From 2003 to 2004, he served as an Assistant Professor with the College ofEngineering, Al-Gassim University, Gassim. Since 2005, he has been with theDepartment of Agricultural Engineering, College of Food and AgricultureSciences, King Saud University, Riyadh, Saudi Arabia. He has been serving asa Full Professor from 2013 to date. He successfully established the PrecisionAgriculture Research Chair in King Saud University, Riyadh, Saudi Arabia, in2009 and has been working as a Chair Director from 2009 to date. He representsSaudi Arabia in the International Society of Precision Agriculture, USA. He haspresented research papers in International Conferences in USA, Canada, NewZealand, Czech Republic, Turkey, China, Egypt, and India.

Rangaswamy Madugundu was born in Yemmiga-nur, India, in 1978. He received the M.Sc. degree inbotany from Sri Venkateswara University, Tirupati,India, in 2002, and the Ph.D. degree in environmentalscience and technology from Jawaharlal Nehru Tech-nological University, Hyderabad, India, in 2009.

Since 2011, he has been working as an AssistantProfessor with the Precision Agriculture ResearchChair, King Saud University, Riyadh, Saudi Arabia.From2003 to 2008, hewas aResearch Fellowwith theNational Remote Sensing Agency, Hyderabad, India.

From2008 to 2011, he served as a Project Scientist (RS&GIS) for theWorldBanksponsored project “Monitoring and Evaluation of Watersheds” with the AntrixCorporation Ltd., Bangalore, India, and also served as a GIS expert for theIntegrated Watershed Management Programme (IWMP) at Watershed Develop-ment Department, Government of Karnataka, Bangalore, India. His researchinterests include forest biomass and carbon sequestration, evapotranspiration andwater productivity mapping of agricultural fields, and NPP of desert farmingsystems using Eddy covariance and remotely sensed data.

Dr. R. Madugundu was awarded with the Fellowship from IndianSpace Research Organization-Geosphere Biosphere Programme (ISRO-GBP)in 2003.

ElKamil H.M. Tolawas born in Sudan on January 1,1963. He received the B.Sc. degree in agriculturalengineering from the University of Khartoum, Sudan,in 1985, the M.Sc. degree in the area of conservationtillage in 1995, and the Ph.D. degree fromHohenheimUniversity, Stuttgart, Germany, in 2002, in the area ofzero-tillage technology, through DAAD fellowship.

He is an Agricultural Engineer with a teachingbackground and research work experience. He under-took a Postdoctoral research study in 2005–2007,funded by the JSPS, at the Crop Production Engineer-

ing Laboratory of Hokkaido University, Sapporo, Japan in the areas of PrecisionFarming and Zero Tillage. He was a Visiting Fellowwith the University of SouthAustralia in 2008–2009 and worked on GRDC-funded project on zero-till disccoulter mechanics. He worked as Teaching Assistant with the Department ofAgricultural Engineering, Faculty of Engineering, University of Khartoum, in1990–1995, Lecturer “Instructor” in 1996–2002, Assistant Professor in 2002–2009, and Associate Professor in 2009–2011. Since April 2011, he has beenworking as anAssociate Professorwith the PrecisionAgriculture ResearchChair,King Saud University, Riyadh, Saudi Arabia. His research interests includeconservation agriculture, precision agriculture, sustainable agriculture, and farmpower and machinery systems.


SamyMareywas born in Egypt, in 1966. He receivedthe B.Sc. and M.Sc. degrees in agricultural mechani-zation from Tanta University, Tanta, Egypt, in 1988and 1994, respectively, and the Ph.D. degree in agri-cultural engineering from Mansoura University, Da-qahlia, Egypt, in 2004.

From 1990 to 1994, he served as Head of the FarmMachinery Maintenance Department, AgriculturalMechanization Sector, Ministry of Agriculture,Egypt. He also worked as a Research Assistant in1994–2004 and as aResearcher in 2004–2010with the

Agricultural Research Institute, Agricultural Research Center, Dokki, Egypt. Hewas a Researcher in 2010–2012 with Precision Agriculture Research Chair(PARC), College of Food and Agriculture Sciences, King Saud University,Riyadh, Saudi Arabia. Since 2013, he has been serving as an Assistant Professorwith the National Plan for Science and Technology, King Saud University,Riyadh, Saudi Arabia.

Ali Aldosari was born in Wadi Aldwaser, SaudiArabia on July 10, 1975. He received the Ph.D. degreein geography from Exeter University, Exeter, U.K., in2006.

He was appointed as a Faculty Member withGeography Department, King Saud University(KSU), Saudi Arabia since 2006. From 2011 to2014, he served as Head of the Department. Heworked as Consultant to the Vice Rectorate for Edu-cational and Academic Affair, KSU, in 2011–2013and the Knowledge Exchange and Technology Trans-

fer at KSU in 2007–2009. He is also an Internal Consultant to PrecisionAgriculture Research Chair, KSU since 2010. He led or participated in severalfunded research projects: Flood assessment in AlQat area, Saudi Arabia, in 2014,Saudi’s Dams Evaluation Project in 2013, GIS web-based application in thecontrol of Screwworm disease in Jizan, Saudi Arabia in 2013, Drainage networkproject of Riyadh, Saudi Arabia in 2013, and the Atlas of Saudi Post Office in2012. His research interests include the applications of geospatial techniques inagriculture, habitats, population studies, and environmental modeling.

Dr. Aldosari is a Secretary General of Saudi Geographer Society since 2009.He also a member of Saudi Geographer Society, Association of AmericanGeographers, American Society for Photogrammetry and Remote Sensing, andThe Urban and Regional Information Systems Associations.

Chandrashekhar M. Biradar was born in Bijapur,India, in 1976.He received theB.Sc. degree in forestryfrom the University of Agricultural Sciences, Dhar-wad, India, in 2008, theM.Sc. degree in tree improve-ment and genetic engineering from the University ofHorticulture and Forestry, Solan, India, in 2002, andthe Ph.D. degree in remote sensing and environmentalsciences from the Department of Space, Indian Insti-tute of Remote Sensing, andUniversity of Pune, India,in 2005.

From 2005 to 2008, he was a Postdoctoral Fellowwith the InternationalWaterManagement Institute, CGIARCenter, then with theInstitute for the Study of Earth, Ocean, and Space (EOS), and with the Universityof New Hampshire, NH, USA. From 2008 to 2013, he served as a ResearchProfessor, Research Scientist, and Manager with the University of Oklahoma,Norman, USA. Currently, he is a Principal Agro-Ecosystems Scientist who headsthe Geoinformatics Unit with the International Centre for Agricultural Researchin Dry Areas (ICARDA), Amman, Jordan, one of the 15 CGIAR ConsortiumResearchCenters.He publishedmany research paperswhich include over 40 peerreviewed, 18 books/chapters, over 90 other publications. His research interestincludes developing geospatialmechanism for delivering better interventions andpackage of practices to reach out to smallholding farmers to improve food securityand livelihood in the dry areas of the world.

Dr. Biradar has been recognized with the number of international awards andhonors such as the Young Scientist of the Year, Outstanding Young Scientist,Best Team Initiative, Outstanding Team Member, Board Member, SteeringCommittee Member, and Panel Member of Geoinformatics for Agriculture.

Prasanna H. Gowda was born in 1965. He receivedthe M.S. degree in city and regional planning fromMysore University, Mysore, India, in 1990, and thePh.D. degree in agricultural engineering from OhioState University, Columbus, OH, USA, in 1996, andthe M.B.A degree in strategic management and entre-preneurship from the University of Minnesota, Min-neapolis, MN, USA, in 2004.

He served as Graduate Research Associate withthe Department of Food, Agricultural, and BiologicalEngineering, Ohio State University, in 1992–1996.

From1996 to 1998, hewas anAssociateResearcherwith theGISDivision,UpperMidwest Environmental Science Centre, USGS. Between 1998 and 2005, he wasa Senior Research Associate and Adjunct Senior Faculty Member with theDepartment of Soil, Water and Climate, University of Minnesota, St. Paul, MN,USA. Since 2005, he has been a Research Agricultural Engineer with USDA-ARS Conservation and Production Research Laboratory, Bushland, TX, USA.His research interests include evapotranspirationmeasurement andmappingwiththermal remote sensing, irrigation and groundwater management, and hydrologicmodeling.

Dr. Gowda’s awards and honors include the Outstanding Senior ScientistAward by the Association of Agricultural Scientists of Indian Origin (AASIO),Outstanding Performance Award for 2010 and 2011 (USDA-ARS), and Admin-istrator’s Postdoc Award 2010 (USDA-ARS).
