B satellite-based FAO-56 models for estimating winter wheat crop evapotranspiration during mid-season

Agricultural Water Management 189 (2017) 27–38

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Validation of two Huanjing-1A/B satellite-based FAO-56 models for estimating winter wheat crop evapotranspiration during mid-season Xiuliang Jin a,b,c,d,∗ , Guijun Yang a,b,c,∗ , Xuzhang Xue a,b,c , Xingang Xu a,b,c , Zhenhai Li a,b,c , Haikuan Feng a,b,c a Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture P. R. China, Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China b National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China c Beijing Engineering Research Center for Agriculture Internet of Things, Beijing 100097, China d Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Jilin 130102, China

a r t i c l e

i n f o

Article history: Received 22 June 2016 Received in revised form 2 April 2017 Accepted 29 April 2017 Keywords: Crop evapotranspiration FAO-56 model Crop coefficient Vegetation index Winter wheat

a b s t r a c t Crop evapotranspiration (ETc ) is an important indicator used in managing agriculture water and monitoring crop growth. The objectives of this study were to: (1) analyze the seasonal dynamics of crop coefficients (Kc ) and basal crop coefficient (Kcb ) derived from vegetation indices (VIs) based on a time series of Huanjing (HJ) satellite images during 2011 and 2013; (2) investigate daily and monthly variations of ETc at key growth stages of winter wheat using lysimeter or eddy covariance systems; (3) compare the performance of two Huanjing-1A/B satellite-based FAO-56 models (the FAO-56 dual-crop coefficient model and the vegetation indices-reference evapotranspiration (VIs-ETo ) method) to the ETc measurements; (4) select the best ETc model for estimating daily ETc (mm/day) at the Xiaotangshan experimental site and its surrounding farmland in conjunction with HJ satellite overpasses from March to May 2011. The VIs and concurrent ETc were acquired at the Xiaotangshan experimental site, Beijing, China, during the 2011 and 2013 winter wheat growing seasons. The results showed that the overall tendencies of crop coefficient patterns (Kcb and Kc ), ETc and ETo first increased and then decreased at key growth stages of winter wheat. The cumulative ETc of water consumption was highest at the heading-filling stage in May. Similar changes in cumulative ETc were found during April–May 2011 and 2013. The estimation accuracy of ETc was better based on FAO-56 dual-crop coefficient model (R2 = 0.88 and RMSE = 1.06 mm/day in 2011 and R2 = 0.84 and RMSE = 0.55 mm/day in 2013) than the VI-ETo method (R2 = 0.77 and RMSE = 1.22 mm/day in 2011 and R2 = 0.67 and RMSE = 0.81 mm/day in 2013). The results indicated that the FAO-56 dual-crop coefficient model and VI-ETo methods were used to estimate ETc in winter wheat. Two Huanjing-1A/B satellite-based FAO-56 models were used to timely estimate ETc during the winter wheat mid-season, and ETc was used to adjust agricultural water management practices. © 2017 Elsevier B.V. All rights reserved.

Abbreviations: ETc , crop evapotranspiration; Kc , crop coefficient; Kcb , basal crop coefficient; HJ, Huanjing satellite images; VIs-ETo , vegetation indices-reference evapotranspiration method; NCP, North China Plain; Ke , soil-water evaporation coefficient; Ks , stress coefficient; EC, Eddy covariance; LE, latent heat flux; ␭, latent heat of water vaporization; Rn , net radiation flux; H, sensible heat flux; G, soil heat flux; S, storage heat flux; EBR, energy balance ratio; CRESDA, China Center for Resources Satellite Data and Applications; Kr , dimensionless evaporation reduction coefficient dependent on topsoil water depletion; Kcmax , maximum value of Kc following rainfall or irrigation; few , fraction of soil surface that is both exposed and wet; Zr,max , maximum effective root depth; Zr,min , minimum effective root depth; SW, root zone water content; P, precipitation; IR, irrigation; R, surface runoff; D, downward flux below crop root zone; RZWD, root zone water deficit; RZWHC, root zone water holding capacity; p, fraction of RZWHC below which transpiration is reduced; TAW, total available water; RAW, the readily available water. ∗ Corresponding authors at: Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China. E-mail addresses: [email protected] (X. Jin), [email protected] (G. Yang). http://dx.doi.org/10.1016/j.agwat.2017.04.017 0378-3774/© 2017 Elsevier B.V. All rights reserved.

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X. Jin et al. / Agricultural Water Management 189 (2017) 27–38

1. Introduction Crop evapotranspiration (ETc ) constitutes a major component of regional and global hydrological cycles and therefore has important implications in the use of agriculture irrigation water, as well as in evaluating crop water stress in agricultural ecosystems (Telis and Koutsogiannis, 2007; Papadavid, 2012). The FAO-56 Penman-Monteith model for determining reference evapotranspiration (ETo ) was proposed by Food and Agriculture Organization (FAO) for agriculture water-management schedules in 1998 (Allen et al., 1998). Many scientists have evaluated the model under various climate conditions. (Kashyap and Panda, 2001; Allen, 2000; Suleiman et al., 2007; Bodner et al., 2007). In order to obtain an actual ETc , Allen et al. (1998) suggested models to calculate an actual ETc by multiplying the crop coefficient (Kc ) and ETo . Winter wheat (Triticum aestivum L.) is an important staple food for the majority of the North China Plain (NCP) population. Increasing industrial and domestic water use in the NCP has reduced the amount of water available for irrigating winter wheat (Jin et al., 2014). Thus it is necessary to develop appropriate agricultural water management scheduling, and then improve the water use efficiency for winter wheat. Agriculture water management and scheduling are currently carried out on the basis of an estimated ETc , which depends on Kc . Compared to conventional approaches, remote sensing technology has become a relatively useful tool in agriculture water management, especially given its capacity to acquire data rapidly at a relatively low cost (Thiruvengadachari and Sakthivadivel, 1997). Several studies have clearly demonstrated a relationship between ETc and vegetation indices based on the FAO-56 model (Heilman et al., 1982; Choudhury et al., 1994; Kustas et al., 2003; Hunsaker et al., 2005). Researchers have estimated ETc in wheat, cotton, grapes, corn, and barley using the spectral and satellite imaging data and the FAO-56 one-crop coefficient model (Heilman et al., 1982; Glenn et al., 2011; Farga et al., 2012; Er-Raki et al., 2013). Some of the studies used a combination of spectral data, satellite imaging data, and the FAO-56 dual-crop coefficient model to estimate ETc in various crops and improve irrigation scheduling (Hunsaker et al., 2003, 2005, 2007; Er-Raki et al., 2007; GonzalezDugo et al., 2009; Campos et al., 2010; Pôc¸as et al., 2015). The estimation results showed that the combination of spectral data, satellite imaging data, and the FAO-56 dual-crop coefficient model was better than the integration of the spectral data, satellite imaging data, and the FAO-56 one-crop coefficient model. The related estimations from the previous studies of ETc based on the remotely sensed the FAO-56 one- and dual-crop coefficient models are summarized in Table 1. In short, remote sensing has been applied successfully for Kc and ETc assessment of various crops under different environments based on the FAO-56 one- and dual-crop coefficient models. Currently, the problem of water resource shortage is serious for agricultural irrigation management in NCP, China. Estimation of the ETc of winter wheat based on remote sensing data and FAO-56 crop coefficient models is necessary for improving seasonal agricultural water management in the NCP. However, few studies have evaluated the feasibility of combining moderate-resolution satellite images (30 m) with shorter revisit periods (two days) and FAO-56 models for estimating daily ETc . The main objectives of this study were therefore to (1) analyze the seasonal dynamics of the Kc and basal crop coefficient (Kcb ) derived from VIs and based on a time series from Environmental Disaster Reduction Satellites (Huanjing1A/B) of 30 m spatial resolution (see Section 2.4), the soil-water evaporation coefficient (Ke ), and the stress coefficient (Ks ) during the study periods in 2011 and 2013, (2) investigate daily and monthly variations in ETc at key growth stages for winter wheat from lysimeter or eddy covariance system measurements, (3) com-

pare the performance of two ETc models (the FAO-56 dual-crop coefficient model and the vegetation indices-reference evapotranspiration (VIs-ETo ) method) to the ETc measurements that were used as calibration/validation datasets at the Xiaotangshan experimental site, (4) select the best ETc model to estimate ETc (mm/day) of winter wheat and its surrounding farmland by utilizing HJ satellite images. Our study may provide a reference for application the different FAO-56 models in crops with shorter revisit period remote sensing images to improve the agricultural water management. 2. Materials and methods 2.1. Study area This study was conducted from March through May of 2011 and from April through May of 2013, the winter wheat growing season. The experimental site is located at the China National Experimental Station for Precision Agriculture (Xiaotangshan), Changping District, Beijing, China (40◦ 10 31 N to 40◦ 11 18 N,116◦ 26 10 E to 116◦ 27 05 E, Fig. S1). Beijing has a typical continental climate. The annual average sunshine duration and precipitation are 2600 h and 543 mm, respectively. The annual average ground temperature is approximately 14.5 ◦ C. Local winter wheat cultivars and planting dates are shown in Table 2. 2.2. Field data measurement 2.2.1. Lysimeter data measurement A large lysimeter was constructed and used to obtain winter wheat evapotranspiration in the farmland from March through May 2011. It was of the suspended multiple-weighing type. It had a steel Box 3.0 m × 3.0 m × 2.5 m that weighed ∼12 ton. To represent field soil properties, the steel box was filled by cutting a monolith of original soil taken from a nearby farmland field. The precision of this lysimeter in measuring crop evapotranspiration (ETc ) can reach ± 0.05 mm. It is therefore suitable for measuring daily variations in evapotranspiration. The lysimeter was specifically described in the paper by Yang et al. (2014). The plot management at the lysimeter site and its surrounding field followed the same standard practices for wheat production in this region. The lysimeter and its surrounding fields were sown with the same winter wheat cultivars. In addition, the growth status of winter wheat at the lysimeter was in synchrony with winter wheat in the surrounding fields. It therefore represented the surrounding fields for which satellite vegetation data were collected. 2.2.2. Eddy covariance measurements An eddy covariance (EC) system was installed in the middle of the field (400 × 100 m). Winter wheat was planted in the surrounding region over a large planting area to provide an adequate fetch length for the EC system. The EC system measured ETc from April to May 2013, and the value was referenced for calibrating and validating the ETc model. The EC system consisted of a fast-response 3D sonic anemometer-thermometer (model GILL R3-50), an open-path CO2 -H2 O gas analyzer (model Licor 7500A), and a temperaturehumidity sensor (model SKH 2060). A data logger (model CR3000, Campbell Scientific Inc., USA) was used to record wind speed, air temperature, and water vapor density at 0.1 s intervals, and temperature and humidity at 30 min intervals. Data was collected every 0.5 h interval, and the crop evapotranspiration (ETc ) was calculated as follows: ET c = 1800 × LE/

(1)

where LE is the latent heat flux (W m−2 ),  is the latent heat of water vaporization, and ETc is crop evapotranspiration (mm 0.5 h−1 ).

X. Jin et al. / Agricultural Water Management 189 (2017) 27–38

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Table 1 Summary of ETc studies using the remotely sensed FAO-56 model. Not all papers performed ETc accuracy assessment and modelling studies; these are denoted in the relevant part of the column by N/A, representing ‘not applicable.’ In the “Accuracy/key findings” column, the abovementioned two components are identified by the following code: (1) estimation accuracy of ETc , (2) key findings. Remote sensing data/model used

Accuracy/key findings

Data used to validate models

4-band hand-held radiometer/FAO-56 dual-crop coefficient model 4-band hand-held radiometer/FAO-56 dual-crop coefficient model

(1) The ETc based on the NDVI-Kcb model provided close estimates of actual ETc . (1) The NDVI could provide real-time Kcb for determining the actual wheat ETc during the growing season. (1)N/A (2) Good relationship between NDVI and Kcb was found with good accuracy (15%).

ETo , NDVI, ke , ks , kcb , TAW, RAW

Reference

Study area

Crops

Latitude, Longitude

Hunsaker et al. (2003)

Arizona, USA

Cotton

N/A, N/A

Daily

Hunsaker et al. (2005)

Arizona, USA

Wheat

N/A, N/A

Daily

Duchemin et al. (2006)

Marrakesh, Morocco

Wheat

31◦ 68 N, 7◦ 38 W

Daily

Er-Raki et al. (2007)

Marrakesh, Morocco

Wheat

31◦ 68 N, 7◦ 38 W

Daily

Hunsaker et al. (2007)

Arizona,USA

Wheat

33◦ 04 N, 111◦ 58 W

Daily

Gonzalez-Dugo et al. (2009)

Iowa,USA

Soybean, corn

N/A, N/A

Daily

Campos et al. (2010)

Albacete, Spain

Grapes

39◦ 16 N, 1◦ 58 W

Daily

Landsat-5TM satellite images/FAO-56 dual-crop coefficient model

Glenn et al. (2011)

Arizona, USA

Wheat, Cotton

N/A, N/A

Daily

Farg et al. (2012)

Nile Delta, Egypt.

Wheat

N/A, N/A

Daily

Hand-held radiometers, high-resolution digital aerial and satellite images/FAO-56 one or dual-crop coefficient model SPOT-4 satellite images/FAO-56 one-crop coefficient model

Er-Raki et al. (2013)

Costa de Hermosillo, Mexico

Grapes

28◦ 56 N, 111◦ 21 W

Daily

MSR16 MultiSpectral Radiometer/FAO-56 one-crop coefficient model

Pôc¸as et al. (2015)

Viana do Alentejo, Portugal

38◦ 24 N, 7◦ 43 W

Daily

Landsat 5 TM and Landsat 7 ETM+ satellite images/FAO-56 dual-crop coefficient model

Corn, barley

Time extent

MSR87 MultiSpectral Radiometer and Landsat7-ETM satellite images/FAO-56 dual-crop coefficient model MSR87 MultiSpectral Radiometer/FAO-56 dual-crop coefficient model Exotech hand-held radiometer/FAO-56 dual-crop coefficient model Landsat 5 and 7 satellites imagery/FAO-56 dual-crop coefficient model

ETo , NDVI, ke , ks , kcb , TAW, RAW

ETo , NDVI, LAI, ke , ks , kcb , TAW, RAW

(1) RMSE = 0.82 mm (2) The Kcb NDVI relationship holds great potential for estimating crop water requirements. (1) RMSE = 0.53 mm (2)The NDVI appears to be a robust approach for ETc estimation of wheat. (1) RMSE = 0.4 mm (2)It tends to overestimate ETc from corn during a prolonged drydown period. (1) RMSE = 0.53 mm (2)NDVI appears to be a robust approach for Kcb estimation of wheat, able to reliably predict actual ETc. (1) N/A (2) These models were closely relationships with measured ETc .

ETo , NDVI, canopy cover, ke , ks , kcb , TAW, RAW

(1) Regression analysis was used to estimate Kcb for different growth stages from NDVI and OSAVI. (1) RMSE = 0.45 mm (2) The ETc was underestimated because of NDVI saturates at high values. (1) The proposed models are adequate for supporting irrigation management.

ETo , NDVI, SAVI, kc

ETo , NDVI, canopy cover, ke , ks , kcb , TAW, RAW ETo , NDVI, canopy cover, ke , ks , kcb , TAW, RAW, Zr

ETo , NDVI, SAVI, ke , ks , kcb , TAW, RAW

ETo , NDVI, SAVI, EVI, canopy cover, ke , kcb , kc

ETo , NDVI, canopy cover, kc

ETo , NDVI, canopy cover, ke , ks , kcb , TAW, RAW

Table 2 Winter wheat cultivars, planting dates, Harvesting dates, and length of growth stages for 2010 and 2012. Winter wheat cultivars

Planting dates

Harvesting dates

Length of growth stages (days)

Nongda 211 Jing 9843, Nongda 211,Zhongmai 175, Zhongyou 206

October 5, 2010 September 28, 2012

June 20, 2011 June 14, 2013

258 259

Daily ETc data was obtained as the sum of 0.5-h measurements over 24 h. The latent and sensible heat fluxes were calculated according to the EC method using standardized routines (Mauder and Foken, 2004) and EdiRe software (http://www.geos.ed.ac.uk/abs/

research/micromet/EdiRe). The steps for correcting the EC measurements included: (1) detection and elimination of raw peaks, (2) coordinate rotation using the planar fit method (Finnigan et al., 2003), (3) correction of oxygen following Tanner and Greene (1989), (4) correction for density following Webb et al. (1980), and

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(5) data gap filling using the MDV (mean diurnal variation) method (Falge et al., 2001). The closure error of the EC-based measurements at the daily scale was calculated based on the energy balance ratio (EBR) (Wilson et al., 2002; Oliphant et al., 2004) and used to correct the daily ETc values. The EBR was 0.86, and the coefficient of determination (R2 ) and root mean square error (RMSE) were 0.93 and 14.3 W/m2 , respectively.

 (LE + H) EBR = 

(Rn − G − S)

(2)

where Rn is the net radiation flux (W/m2 ), H is the sensible heat flux (W/m2 ), G is the soil heat flux (W/m2 ), and S is the storage heat flux (W/m2 ). Net radiation was measured with a net radiometer (NR-Lite2, Kipp and Zonen, Delft, Netherlands) at a height of 1.5 m from the ground. The soil heat flux was measured using two soil heat flux plates (HFP01, Hukseflux), which were buried at a depth of 80 mm. The soil heat flux at the surface was determined by correcting the flux at 80 mm depth for soil heat storage above the plates, which was calculated from temperature changes in the soil volume above the heat flux plates (combination approach). Flux plates were located about 1.5 m apart. The heat flux at 80 mm depth was calculated from the average outputs of the two plates. The storage heat flux was determined from the volumetric heat capacity of air and the measured change in air temperature between the surface and the height of the eddy covariance system. Because the lysimeter did not function in 2012 and the eddy covariance (EC) system was installed at the end of 2012, we did not record observations for the growth stages of winter wheat in 2012. Evapotranspiration of winter wheat was used to validate the Huanjing-1A/B satellite-based FAO-56 model during mid-season because agricultural water management is important to growth changes during this time period.

Table 3 Crop parameters used for estimating the crop coefficients and computing the water balance following the procedure described in FAO Irrigation and Drainage Paper No. 56 (Allen et al., 1998). Parameter

Winter wheat

Maximum crop height Maximum effective root depth (Zrmax ) Minimum effective root depth (Zrmin ) SAVImax SAVImin Maximum basal crop coefficient (Kcb ,max )a Ground cover fraction for Kcb , max (fc,mx )

0.90 m 1.10 m 0.20 m 0.86 0.08 1.09 0.90

Note: a Typical values adjusted for local relative humidity and wind speed.

et al. (2015). One historical Landsat-5 TM image with precise geometric correction was used as the reference image. The 40 ground control points of each HJCCD image were co-registered with this reference image using the registration model of ENVI 4.7. The root mean square error for each geometrically corrected scene was less than 0.5 pixels. 3. Methodology 3.1. FAO-56 dual-crop coefficient model Daily winter wheat ETc was calculated using the FAO-56 dualcrop coefficient model, which was developed by Allen et al. (1998). This method was based on the concepts of crop coefficient and reference evapotranspiration (ETo ). The FAO Penman-Monteith equation was used to estimate ETo using meteorological data from the experimental weather station in the study area. The crop coefficient (Kc ) was divided into two components: the basal crop coefficient (Kcb ) and the soil water evaporation coefficient (Ke ).

2.3. Selected irrigation schemes and meteorological data

ET c = (K cb K s + K e ) ET o

Meteorological data for the Xiaotangshan experimental site was obtained from the local field meteorological station (height = 2 m). The distance between the meteorological station and the lysimeter and EC systems was 10 m and 20 m, respectively. From February through July in 2011 and 2013, daily maximum and minimum temperatures, relative humidity, wind speed, rainfall, total sunshine hours, net short-wave radiation, net long-wave radiation, and atmospheric pressure were recorded directly at Xiaotangshan meteorological station. Sprinkler irrigation was applied four times during the 2011 and 2013 experimental periods. The specific information on rainfall, irrigation, crop evapotranspiration, and root zone water content are shown in Fig. S2.

where Kcb is the basal crop coefficient, Ks is the stress coefficient, expressed the reduction in ETc due to soil water deficit, and Ke is the soil water evaporation coefficient, obtained by estimating the amount of energy available at the soil surface as

2.4. Image preprocessing The Huanjing-1A/B were launched by the China Center for Resources Satellite Data and Applications (CRESDA) on September 6, 2008. The Huanjing CCD image (hereafter referred to as HJ-CCD) has a spatial resolution (30 m) and band setting similar to the commonly used Landsat-5 TM. Information on HJ-1A/B is shown in Table S1. The HJ-CCD has a much shorter revisit period (two days) than the Landsat-5 TM images, making it a good trade-off at both spatial and temporal resolutions. The HJ-CCD images were acquired for estimating ETc from March through May 2011 and from April through May 2013. After eliminating cloud-contaminated scenes, a total of 48 HJ-CCD scenes were obtained. The 30 HJ-CCD and 18 HJ-CCD scenes were obtained in 2011 and 2013, respectively (Table S2). The preprocessing of HJ-CCD images included a radiometric calibration, and atmospheric and geometric corrections. The specific information of images preprocessing was showed in the study of Jin

(3)

K e = K r (K cmax − Kcb )

(4)

where Kr is a dimensionless evaporation reduction coefficient dependent on topsoil water depletion (Allen et al., 1998) and Kcmax is the maximum value of Kc following rainfall or irrigation. The value of Ke cannot be greater than the product of few and Kcmax , where few is the fraction of the soil surface that is both exposed and wetted. Vegetation indices (VIs) consisting of two or more spectral bands were developed to estimate leaf area index (LAI), canopy cover, and phenology (Glenn et al., 2008). Neale et al. (1989) and Choudhury et al. (1994) found that both the Kcb and VIs are very sensitive to LAI and canopy cover (fc ). The results of those studies support the use of spectral data to estimate crop coefficients. Using the fact that Kcb reaches peak before full canopy cover, Gonzalez-Dugo and Mateos (2008) established a line equation SAVI (the soil adjusted vegetation index, Huete, 1988) and used SAVI to estimate Kcb . This equation was also modified to avoid the adjustment function of the crop height. It has been used here as follows: Kcb =

Kcb,max fc,max

 SAVI − SAVI  min SAVImax − SAVImin

Kcb = Kcb,max iffc ≥ fc,max

iffc < fc,max

(5a) (5b)

where the subscripts max and min correspond to the values of SAVI for the maximal LAI and bare soil, respectively, and fc ,max is the fc at which Kcb is maximal (Kcb ,max , Table 3). SAVImax and SAVImin values were obtained from HJ imagery data. The maximum crop height and

X. Jin et al. / Agricultural Water Management 189 (2017) 27–38

ground cover fraction for Kcb,max (fc,mx ) were measured using a steel ruler and camera, respectively. The value of the maximum basal crop coefficient (Kcb,max ) was calibrated using calibration datasets for local environmental conditions. A measurement of soil root zone water balance was carried out, and information regarding soil wetting by rainfall was obtained to compute Ke and Ks in Equation (3). Four Time domain reflectometry (TDR) was used to measure soil water content. The soil water content was calculated from the average outputs of four TDR measurements. The parameter Kcb was used to calculate the root zone depth (Zr ) according to a function of Kcb and Zr .



Zr = Zr,min + Zr,max − Zr,min

 Kcb Kcb,max

(6)

where Zr, max and Zr, min are the maximum effective root depth and the minimum effective root depth during the stage of crop growth (Table 3). The maximum effective root depth (Zrmax ) and minimum effective root depth (Zrmin ) were obtained from the FAO Irrigation and Drainage Paper No. 56 (Allen et al., 1998). The change in the root zone water content (SW) was computed as: SW = P + IR − R − D − ETc

(8)

where the subscript i indicates the day of concern, and RZWDi and RZWDi-1 are the root zone water deficits on days i and i-1, respectively. It is understood that the root zone is full of water, or RZWD = 0, when its water content is at field capacity, and that it is empty when the water content causes the crops to be at the wilting point. The root zone water holding capacity (RZWHC) is the quantity of water between those two extremes. The stress coefficient (Ks ) is computed based on the relative root zone water deficit as: Ks =

RZWHC − RZWDi if RZWDi < (1 − p)RZWHC (1 − p) RZWHC

Ks = 1if RZWDi ≥ (1 − p)RZWHC

3.2. Vegetation indices-reference evapotranspiration method To compare with the FAO-56 dual-crop coefficient model, the vegetation indices-reference evapotranspiration (VIs-ETo ) method was applied to evaluate the ETc . Remote sensing technology (satellite imagery) was developed to estimate ETc at a regional scale. The VIs was used in this study to combine the estimation of Kc with meteorological data to ETc . The Kc was ETc divided by ETo. The VI-ETo method (Neale et al., 1989; Hunsaker et al., 2007; Glenn et al., 2011) replaces Kc with VI in Eq. (5) and then calculates ETc based on locally measured data as follows: ET c = a ET o VIn

(10)

Where a is a coefficient determined by the measured regression ETc against a VI, and n is an exponent related to ETc by VIs (Choudhury et al., 1994). In this study, the calibrating data came from measurements of ETc (e.g., from lysimeters or the eddy covariance system). This method was further developed to more precisely estimate ETc by Nagler et al. (2005) and Guerschman et al. (2009). ET c = [a (1 − e(−bVIs) ) − c]ET o

(11)

(7)

where P is precipitation (mm) measured with a standard rain gauge at the weather station, IR is irrigation (mm), R is surface runoff (mm) (there were no major runoff events during the winter wheat growing seasons because the field was flat, so surface runoff was assumed to be insignificant), and D is the downward flux below the crop root zone (mm) (deep percolation was ignored because negligible drainage was found at the site). Eq. (6) may be expressed as the daily root zone water deficit: RZWDi = RZWDi−1 + ETi + Di − R i

31

(9a) (9b)

where p is the fraction of the RZWHC below which transpiration is reduced. The soil parameters used for computing water balance were obtained by identifying the description of the typical soil profile (Soil Survey Staff, NRCS, 2008) and selecting the appropriate values of soil water content at field capacity, and the wilting point for the experimental site, from Table 19 in Allen et al. (1998) (Table S3). The crop parameters (maximum crop height, Zrmax , Kcb ,max , fc,mx ) were calibrated using the calibration datasets from 2011 and 2013. The values of SAVI max and SAVI min were determined using HJ imaging data taken during the winter wheat growth stage in 2011 and 2013. The minimum and maximum values of SAVI corresponded to the tillering and anthesis stages, respectively. The values of SAVImax and SAVImin were fixed. The soil parameters (␪FC and ␪WP ) were measured by field investigation (Table S3).

Where a, b and c are fitting coefficients in Eq. (11), and (1 − e(−bVIs) ) is derived from the Beer-Lambert Law modified to predict the absorption of light by a canopy (Nagler et al., 2004). The selected vegetation indices based on the HJ-CCD images were summarized in Table S4. The coefficient c was used to account for VIs being not equal to zero at zero ETc , since bare soil has a low but positive VIs. The experimental site ETc was used to determine the coefficients a, b, and c using the nonlinear regression function of MatLab software (version 2015b, MathWorks, USA). The coefficients a, b, and c of Eq. (11) were calibrated using a calibration dataset from the HJ imaging data of 2011 and 2013, respectively.

3.3. Methods application The measured ETc (mm/day) dataset was randomly divided into two groups (calibration and validation) based on statistical analysis and the grouping function in SPSS software (16.0, SPSS, IBM, Chicago, USA). In 2011, there were 20 samples per calibration dataset and 10 samples per validation dataset; in 2013, there 12 samples per calibration dataset and 6 samples per validation dataset. The statistical summary of each dataset for the measured ETc is showed in Table S5. The ETc was calculated using the FAO-56 dual-crop coefficient model and the vegetation indices-reference evapotranspiration (VIs-ETo ) method. The calibrated and validated datasets were used to test different methods in the same year. First, the 2011 calibration dataset was used to calibrate the FAO56 dual-crop coefficient model and vegetation index-reference evapotranspiration method. Second, the 2011 validation dataset was used to test the performance of the calibrated FAO-56 dual-crop coefficient model and vegetation index-reference evapotranspiration methods. The estimated ETc was obtained from the FAO-56 dual-crop coefficient model and vegetation indexreference evapotranspiration methods. Third, the estimated and measured ETc was compared by dividing into the calibrated and validated results. Fourth, the regression equations for the measured and estimated ETc were obtained from the calibration and validation datasets, respectively. Fifth and finally, the measured and estimated crop evapotranspiration were combined in the total dataset, and then the regression relationship between the measured and estimated crop evapotranspiration was analyzed. The 2013 analysis method was the same as applied in 2011.

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Fig. 1. Seasonal dynamics of crop coefficients (Kc ), the basal crop coefficient (Kcb ), and the soil water evaporation coefficient (Ke ), and the stress coefficient (Ks ) at Xiaotangshan experimental site: (a) 2011 and (b) 2013.

3.4. Evaluation of model accuracy The relationship between the estimated and measured ETc was corroborated using prediction error statistics [coefficient of determination (R2 ) and root mean square error (RMSE)]. The R2 and RMSE were also used to evaluate the statistical error of calibration and validation results. 4. Results 4.1. Seasonal crop coefficient patterns for selected winter wheat The seasonal dynamics of crop coefficients (Kcb , Kc , Ke , and Ks ) from March to May 2011 (tillering, jointing, heading, anthesis, and filling stages) obtained from the FAO-56 dual-crop coefficient model are shown in Fig. 1a. Factors Kcb and Kc reached their lowest values on March 2, when they were 0.10 and 0.22, respectively. With onset of the winter wheat growth stage (tillering stage) from March 2 to March 9, the values of Kcb and Kc gradually increased. Factor ke reached a maximum because part of the soil had enough water content at this growth stage. The winter wheat was in the recovery phase, and the changes in Kcb and Kc were relatively less. In addition, the events of irrigation and rainfall did not occur at this growth stage (Fig. S2a). Thus, the values of Kcb and Kc were lowest. The results showed that the crop growth status was not influenced by soil water content at the root zone because the value of Ks was equal to 1 and the values of Kcb and Kc gradually increased from March 10 to March 24 (tillering stages). Ke gradually decreased from March 10 to March 25. This is because 85% and 70% of the soil was exposed from March 2–9 and from March 10–25, respectively, and increased canopy cover may further reduce soil evaporation. Events of rainfall and irrigation occurred from March 25 to April 4 (tillering stages). The value of ke increased at first and then gradually decreased while the values of Kcb and Kc gradually increased during this period. The results indicated that the winter wheat water requirements were satisfied. The events of rainfall and irrigation occurred between April 7 and April 27 (jointing stage). The value of ke increased at first and then gradually decreased while the values of Kcb and Kc sharply increased during this period. At this stage, the values for Kcb and Kc ranged from 0.60 to 0.98 and 0.60 to 1.04, respectively. The values of Kcb and Kc reached their highest values on May 10 because the growth status of winter wheat was most vigorous at this stage (anthesis). Values for ke remained almost unchanged from April 30 to May 13 (jointing-heading-anthesis

stages). These results suggested that the crop water requirement was satisfied by the rainfall and irrigation from April 30 to May 13. The value of ks was lower than 1 on May 22. A possible reason was that the root-zone water supplement of winter wheat was inadequate because of the winter wheat’s large water requirement between April 30 and May 13. The last irrigation occurred on the May 24 (filling stage). Factors Kcb and Kc gradually decreased because of winter wheat senescence. Factor ke increased at first and then remained unchanged from May 24 to May 30. The values of Kcb and Kc in 2011 were consistent with those of 2013.The values of Kcb and Kc exhibited an obvious peak on May 10, 2013 (Fig. 1b). Factors ke and ks were obviously influenced by rainfall and irrigation. The value of ks was lower than 1 because there was no rainfall or irrigation from April 18 to May 5 (Fig. S2b). Irrigation resulted in increasing values of ke on May 10.

4.2. Seasonal changes in daily ETc , and cumulative monthly ETc The minimum value of ETc (1.04 mm) was obtained on March 5, 2011 (tillering stage) because the product of the lowest kc (0.22, Fig. 1a) and ETo (Fig. 2b) resulted in the lowest ETc (Fig. 2a). The ETc gradually increased between April 4 and 18 (tillering and jointing stages), when the value of ETc rose from 1.85 mm to 3.00 mm. ETo decreased owing to low relative humidity (19.3%) and high temperature (24.1 ◦ C) during this growth stage. The value of ETo from April 4 to 18 was lower than from March 2 to March 5 (Fig. 2b), but the value of Kc from April 4 to 18 was relative higher than from March 2 to March 5 (Fig. 1a). The value of ETc therefore increased gradually from April 4 to 18 (Fig. 2a). The value of ETc sharply increased from April 19 to May 7 (jointing-heading stages). The maximum value of ETc was reached on May 7, when the corresponding ETc value was 7.96 mm. ETo sharply increased because of high relative humidity (52.4%), and Kc gradually increased at this growth stage. These reasons could explain why ETc sharply increased. The most vigorous growth of winter wheat occurred at this stage, as the plants of winter wheat maintained a better growth status than at the other growth stages. The value of Kc mostly remained unchanged. The occurrence of the dips in ETo and ETc on May 11 and 15 were likely due to continued high temperature (28.5 ◦ C) and low relative humidity (18.4%). From May 22 to 26 (filling stage), the value of ETc decreased rapidly and then slightly increased on May 31 (filling stage). From May 22 to 26, the value of ETc decreased sharply because the value of Kc of winter wheat began to decrease gradually (Fig. 1a), and ETo decreased sharply due to continued high

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Fig. 2. Seasonal changes of measured (a) crop evapotranspiration (ETc , mm/day) and (b) reference evapotranspiration (ETo , mm/day) at the Xiaotangshan experimental site in 2011 and 2013.

temperature (29.2 ◦ C, Fig. 2b). Rainfall and irrigation resulted in a slight increase of ETc . The dynamics of ETc were in agreement with those of ETo from April 27 to May 31 in 2011 and from April 18 to May 31 in 2013 (Fig. 2a and b). The seasonal change of ETc in 2013 was in agreement with that in 2011 (Fig. 2a). In 2013, however, the ETc displayed less variation than it did in 2011, as the trend line displayed only one peak and one trough between May 10 and 19, 2013 (headinganthesis stage). The value of ETc reached a maximum on May 11, 2013, when the corresponding ETc value was 7.62 mm. These dynamic changes were influenced mainly by rainfall, irrigation, temperature, and humidity. In short, the overall tendency of ETc first increased and then decreased at the key growth stages for winter wheat in 2011 and 2013. Changes in water consumption were found from March to May 2011 and from April to May 2013. Fig. 3a shows that the changes of monthly ETc and the cumulative ETc of winter wheat at key growth stages from March to May of 2011 was 342.46 mm. The percentage of the total ETc for March, April, and May was 8% (28.14), 30% (103.21), and 62% (211.11), respectively (Fig. 3a). The field irrigation and precipitation were comprehensively analyzed (Fig. S2). The sum of irrigation and precipitation was 62 mm in March, 87 mm in April, and 158 mm in May 2011. The total amount of field irrigation and precipitation was greater than the lysimeter measurement of ETc in March 2011 (Fig. S2). On the contrary, the total amount of

field irrigation and precipitation was less than the lysimeter measurement of ETc in April and May 2011. The results indicated that the excessive water in March was stored in the soil and then used during April and May. The water consumption by winter wheat was highest at the heading-filling stage in May; this stage accounted for 62% of the total water consumption at the key growth stages. The changes of the cumulative ETc displayed a sharp linear increase in May, light linear growth in March, and gradual linear growth in April. The changes of cumulative monthly ETc were 211.11 mm in May and 55.92 mm between April 18 and 31, 2011, and the changes of monthly cumulative ETc were 190.69 mm in May and 55.25 mm in between April 18 and 31, 2013. The results demonstrated that the change of cumulative monthly ETc in 2013 was consistent with ETc in 2011 (Fig. 3a and b). The highest values of cumulative monthly ETc occurred in May 2011 and 2013. The results indicated that more water was consumed during May because that period encompasses the most important growth stages (heading and anthesis stages) for winter wheat. 4.3. Comparison of HJ satellite-based FAO-56 dual-crop coefficient model and the VIs-ETo method Values of Kc and Kcb from Section 4.1 were used in the HJ satellite-based FAO-56 dual-crop coefficient model for estimating the ETc of winter wheat. In 2011 and 2013, the FAO-56 dual-crop

Fig. 3. Monthly and total values of ETc as determined by: (a) lysimeter in 2011, and (b) the eddy covariance system in 2013.

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Fig. 4. Relationship between the measured and estimated crop evapotranspiration (ETc , mm/day) using FAO-56 dual-crop coefficient model in 2011(a), 2013(b) and 2011 and 2013. Table 4 Relationships between the estimated and measured crop evapotranspiration (ETc ) for winter wheat based on the FAO-56 dual-crop coefficient model at the experimental site in 2011 and 2013. Years 2011 2013 2011, 2013

Dataset

Equation

R2

RMSE (mm/day)

Calibration Validation Calibration Validation Calibrationa Validationb

y = 1.0696x − 0.8992 y = 0.9857x − 0.5767 y = 0.8927x + 0.532 y = 0.9949x − 0.2222 y = 1.0279x − 0.8401 y = 0.8810x + 0.4268

0.89 0.88 0.86 0.78 0.88 0.86

1.08 1.01 0.57 0.54 1.12 0.58

Note: a Calibration dataset form 2011; b Validation dataset from 2013; R2 , determination coefficient; RMSE, root mean square of error; x represents estimated ETc , y represents measured ETc .

coefficient model was calibrated using the calibration datasets. In those same years, the estimated ETc was consistent with the measured ETc from the calibration datasets (Table 4 and Fig. 4). The R2 of the estimated and measured ETc in 2011 was 0.89, and in 2013 the value was 0.86 (Table 4 and Fig. 4a). In 2011 and 2013, the FAO-56 dual-crop coefficient model was further evaluated using the validation datasets. Similarly, the results showed a consistent relationship between the estimated and measured ETc from the validation datasets in 2011 (RMSE = 1.01 mm/day) and 2013 (RMSE = 0.54 mm/day, Table 4 and Fig. 4b). The 2011 and 2013 datasets were used to calibrate and validate the FAO-56 dual-crop coefficient model, respectively. Estimated ETc agreed well with the

measured value (R2 = 0.88 and RMSE = 0.58 mm/day, Table 4 and Fig. 4c). In short, the results in 2011 and 2013 indicated that a reliable relationship between the estimated and measured ETc was found using the FAO-56 dual-crop coefficient model at the experimental site. The FAO-56 dual-crop coefficient model was also used to estimate ETc during the winter wheat growth season. The coefficients a, b, and c of the VIs-ETo method are shown in Table S6 based on the calibration dataset from HJ vegetation indices (NDVI, SAVI, OSAVI and EVI) in 2011 and 2013. These coefficients of the VIs-ETo method were further used to estimate ETc in 2011 and 2013, respectively. The results showed that the estimation accuracy of ETc based on the EVI (R2 = 0.81 and RMSE = 0.72 mm/day) was better than the others vegetation indices from the calibration and validation datasets (Table 5, Figs. 5 , S3, and S4). The estimation accuracy of ETc based on the NDVI was worst using the VIs-ETo method. The descending order of estimation accuracy was EVI, OSAVI, SAVI and NDVI (Table 5). Estimated ETc based on the VIsETo method did not agree well with the measured value of ETc . The coefficient c used in the VIs-ETo method were slightly above zero in 2011, but negative in 2013. This may be due to the range of the ETc dataset (Table S5), which were 1.04–7.96 mm/day in 2011 and 3.39–7.62 mm/day in 2013. In previous studies, ETc ranged from 0.1 to 8.00 mm/day, and reported values of c were similar to our value in 2011 (Nagler et al., 2005, 2013; Guerschman et al., 2009). Therefore, for the value of c to be slightly above zero, the range of ETc should be set to 0.1–8.00 mm/day. Similarly, coefficients a and b are expected to fall within certain boundaries. We plan to ana-

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Table 5 Relationships between the estimated and measured crop evapotranspiration (ETc ) for winter wheat based on the Vegetation indices-reference evapotranspiration (VIs-ETo ) method at the experimental site in 2011 and 2013. Years

Vegetation indices

Dataset

Equation

R2

RMSE (mm/day)

NDVI

Calibration Validation Calibration Validation Calibration Validation Calibration Validation Calibration Validation Calibration Validation Calibration Validation Calibration Validation Calibrationa Validationb Calibrationa Validationb Calibrationa Validationb Calibrationa Validationb

y = 0.879x + 0.0374 y = 0.8323x + 0.21 y = 0.92x + 0.2873 y = 0.9142x − 0.2104 y = 0.9754x − 0.3404 y = 1.0458x − 0.7119 y = 0.9925x − 0.0779 y = 0.9744x − 0.3953 y = 0.816x + 0.9183 y = 0.7866x + 1.203 y = 0.7891x + 0.8763 y = 0.6818x + 1.5856 y = 0.8492x + 0.549 y = 0.6278x + 1.703 y = 0.8514x + 0.7093 y = 0.6683x + 1.6283 y = 0.8999x − 0.1072 y = 0.8329x + 0.8184 y = 0.8952x + 0.0092 y = 0.7855x + 1.0353 y = 1.0238x − 0.7143 y = 0.8583x + 0.5638 y = 1.0182x − 0.3023 y = 0.8107x + 0.9857

0.72 0.74 0.73 0.75 0.74 0.76 0.76 0.77 0.60 0.55 0.61 0.56 0.63 0.57 0.65 0.61 0.74 0.57 0.73 0.71 0.79 0.72 0.81 0.75

1.51 1.38 1.35 1.32 1.37 1.29 1.24 1.28 0.96 0.63 1.03 0.63 0.97 0.69 0.89 0.61 1.41 0.90 1.38 0.83 1.32 0.77 1.21 0.72

SAVI 2011 OSAVI EVI NDVI SAVI 2013 OSAVI EVI NDVI SAVI 2011, 2013 OSAVI EVI

Note: a Calibration dataset from 2011; b Validation dataset from 2013; R2 , determination coefficient; RMSE, root mean square of error; x represents estimated ETc , y represents measured ETc .

Fig. 5. Relationship between the measured and estimated crop evapotranspiration (ETc , mm/day) based on the Vegetation indices-reference evapotranspiration (VIs-ETo ) method in 2011 and 2013, (a) NDVI, (b) SAVI, (c) OSAVI, and (d) EVI. Note: Calibration dataset form 2011, Validation dataset from 2013.

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lyze the effects of the range of ETc on values of coefficients (a, b and c) in future studies. The results showed that the estimation accuracy of ETc with the FAO-56 dual-crop coefficient model was better than with the VIs-ETo method in 2011 and 2013 (Tables 4 and 5; Figs. 4 and 5, Figs. S3, and S4). In summary, the results demonstrated that the two methods are useful for estimating ETc during the winter wheat growth season. 4.4. Application of the times series of HJ satellite images for estimating ETc The results supported the use of FAO-56 dual-crop coefficient model for estimating ETc for winter wheat in preference to the VIsETo method (Tables 4 and 5; Figs. 4 and 5, Figs. S3, and S4). The authors therefore applied HJ satellite images to FAO-56 dual-crop coefficient model for daily estimates of ETc at the Xiaotangshan experimental site and its surrounding farmland during the period March through May 2011 (Fig. S5). The results showed that the estimated daily ETc first increased and then decreased during the period from March through May 2011. The daily ETc ranged from 0.03 to 9.00 at key growth stages of winter wheat. 5. Discussion In this paper, the closure error of the energy balance equation was analyzed based on the EBR, R2 , and RMSE for the entire half-hourly dataset. In a comprehensive study across 22 FLUXNET sites, Wilson et al. (2002) reported that the values for slopes and intercepts ranged from 0.53 to 0.99 and from −33 to 37 W/m2 , respectively, and EBR ranged from 0.39 to 1.69. Liu et al. (2010) reported an EBR value for an alfalfa crop of 0.85 in a semi-arid region in China. Similarly, an EBR value of 0.80 was reported by Xin and Liu (2010) in a maize crop under similar environmental conditions. Other authors (Foken et al., 2006; Cava et al., 2008; Wilson et al., 2002) have discussed a possible error or uncertainty concerning the energy imbalance. Three primary factors that may have affected the energy balance closure at the experimental site are as follows: 1) instrument bias, inaccurate calibration, and data processing errors, 2) loss of low and/or high frequency contributions to turbulent fluxes, 3) horizontal and/or vertical advection of heat and water vapor. Previous studies have shown that the ETc provided by EC systems differs from the value measured by lysimeters (Ding et al., 2010; Gebler et al., 2015). However, it is difficult to analyze the relationship between these values because they were measured during different years. We plan to investigate the relationship in future studies to better evaluate their differences. The results concerned seasonal dynamics of the crop coefficient patterns (Kcb , Kc , Ke , and Ks ), ETo , and ETc for the key growth stages of winter wheat (March through May) in 2011 and 2013 (Figs. 1 and 2). Over the course of winter wheat growth stages in 2011 and 2013, the overall tendency of Kcb , Kc , ETo , and ETc was first to increase and then decrease. Kc increased sharply, from 0.1 to 1.1, as plants grew, followed by a slow decline at the end of May. There was a strong relationship between Kc and key growth stages of winter wheat. Kc increased sharply and decreased slowly in response to the rapid increase in plant biomass during the jointing, heading, and anthesis stages; wheat plants became senescent during the filling stage. ETo increased at the end of April and declined rapidly at the end of May owing to changes in relative humidity and temperature. High relative humidity increased ETo at the end of April, whereas high temperature decreased values at the end of May. Relatively small changes of ke and ks were found. The results demonstrated that the seasonal dynamics of these parameters were influenced by rainfall, irrigation, air temperature, and relative humidity. Factors ke and ks displayed a particularly close

relationship with rainfall and irrigation (Fig. 1). Factor ETo was influenced by air temperature and relative humidity. The dynamic changes of Kcb , Kc , ETc were strongly correlated with the growth and development of winter wheat. These findings agreed with those of Hunsaker et al. (2005), Er-Raki et al. (2007, 2013), Kamble et al. (2013), and Pôc¸as et al. (2015). Cumulative ETc of winter wheat indicated that winter wheat water consumption reached its maximum in May, during the heading and filling stages. The results thus suggested that winter wheat requires more energy to maintain normal growth and grain production at the heading and filling stages. Precipitation patterns differed somewhat between 2011 and 2013, but field irrigation made up for the water requirements of the crops. The differences in cumulative monthly ETc in 2013 were therefore in agreement with ETc values from 2011 (Fig. 3a and b). The FAO-56 dual-crop coefficient model and the VIs-ETo method were used to estimate ETc in winter wheat. The parameters of the FAO-56 dual-crop coefficient model were calibrated based on the measured data (Table 3). The results showed that the calibrated parameters agreed with the parameter ranges in FAO Irrigation and Drainage Paper No. 56 (Allen et al., 1998). The results indicated that the calibrated parameters in the FAO-56 dual-crop coefficient model were acceptable. The results indicated that the estimation accuracy of ETc with the FAO-56 dual-crop coefficient model was better than with the VIs-ETo method (Tables 4 and 5; Figs. 4 and 5, Figs. S3, and S4). Two possible reasons for this are as follows. First, the soil water status of the root zone and soil water evaporation were contained to obtain a more accurate kc in the FAO-56 dualcrop coefficient model. Second, the parameter Kc was obtained in the VI-ETo method by dividing ETc by ETo ; the effects of environmental factors on Kc were not considered. ETc well estimated based on the Huanjing-1A/B satellite-based FAO-56 dual-crop coefficient model. We plan to compare the differences between values of kc determined by the FAO-dual crop coefficient model based on satellite data with values not based on satellite data. The results indicated that the estimated ETc was very consistent with the measured ETc in winter wheat (Tables 4 and 5). Previous results demonstrated that a good relationship between the estimated ETc and the measured ETc in winter wheat was obtained based on the FAO-56 dual- or single-crop coefficient model with the satellite imaging data (Duchemin et al., 2006; Gonzalez-Dugo et al., 2009; Glenn et al., 2011; Farga et al., 2012). In this study, the VIs-ETo method was similar to the FAO-56 single-crop coefficient model. Our results further confirmed their results. The previous results and ours suggested that it is feasible to estimate ETc in winter wheat using the FAO-56 dual- or single-crop coefficient model with satellite imaging data. Compared with Landsat-5 or 7 and SPOT-4 imaging data, the HJ imaging data had a much shorter revisit period (2 days), providing it with a good balance of spatial and temporal resolution. The results demonstrated that the combination HJ imaging data and the FAO-56 dual-crop coefficient model was very useful in timely estimating ETc in winter wheat and in improving field-water management at the field or regional scale. To improve the availibity of this method for estimating ETc , we plan to conduct more experiments to build a stabile model that does not require independent calibration based on larger datasets. The results indicated a good regression relationship between the measured and estimated ETc using the VIs-ETo method (Table 5 and Fig. 5). This method, however, be calibrated and validated to maintain estimation accuracy before application at each new site. The best estimation result of ETc was obtained using the FAO-56 dual-crop coefficient model. Soil moisture data is needed, however, when using the FAO-56 dualcrop coefficient model if a good result is to be obtained. But it could result in reduced accuracy of estimation due to the low availability of soil moisture data when this method is applied over wide areas. The VIs-ETo method should therefore be considered when

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the study is carried out over such areas. Good estimation results were obtained using the FAO-56 dual-crop coefficient model and the VIs-ETo method, but the error and uncertainty of VI-derived estimates models from ground ETc data still existed. The model may mainly include as follows. First, the illumination conditions of selected HJ imaging data varied, which resulted in deviations from the obtained VIs. Second, the VIs were influenced by the saturation phenomenon during anthesis and the sensitivity of VIs to soil at the tillering stage, producing an error between the estimated and measured kc . The estimation accuracy of ETc was influenced by these differences, which were inputs for the FAO-56 dual-crop coefficient model and the VIs-ETo method. Many researchers have employed HJ satellite images and compared differences between Landsat-5 TM and HJ-CCD data for various applications (Li et al., 2016; Hao et al., 2016; Wang and Sun, 2014; Yang et al., 2017). Landsat-5 TM and HJ-CCD data have been shown to be very consistent. Previous studies have also suggested that HJ-CCD data can be applied to the fields of agriculture, forestry, ecology, etc. Our results further confirm previous results. The combination HJ-CCD data and the FAO-56 dual-crop coefficient model or the VIs-ETo method can be used to estimate ETc for winter wheat. We used a simple root zone water deficit equation because the focus of this study was on the application of the satellite-based FAO-56 model for estimating winter wheat evapotranspiration (ETc ). We will consider more detailed root zone equations to estimate changes in ETc in the future. In order to obtain daily high-resolution ETc data for precise agriculture and water irrigation management, a combination of HJ satellite image data and high-resolution satellite image data should be considered in the future. The present work supports the use of the VI-ETo method for simple, rapid, and relatively accurate estimation of ETc at regional scales. One advantage of FAO-56 dual-crop coefficient model and VI-ETo methods that they require only near-infrared, red, and blue spectral band reflectance as remotely sensed inputs. For more accurate estimates of ETc , however, researchers should rely on accurate precipitation reports and ETo , which account for heterogeneity in the soil moisture distribution at regional scales. This study ignored spatial weather differences, using a weather site to support all pixels. The spatial distribution characteristics of spectral indices were used to estimate ETc over a large area. In addition, the measured ETc was from the eddy covariance or the lysimeter systems. The results showed that the estimated ETc was very consistent with the measured ETc from the lysimeter systems. The lysimeter, however, measured only the very small-scaled ETc for winter wheat. Compared to the lysimeter, the eddy covariance system could measure large-range ETc for winter wheat. We will therefore carry out related studies to better test our results using the eddy covariance system at the regional scale. The study results therefore must still be verified for other crops and in other ecological areas, as this study was limited to winter wheat near Beijing, PR China.

6. Conclusion In this study, two methods (the FAO-56 dual-crop coefficient model and the VI-ETo method) were evaluated for the estimation of ETc in winter wheat based on HJ imaging data. The results showed that the overall tendencies of crop coefficient patterns (Kcb and Kc ), ETc , and ETo was to first increase and then decrease at key growth stages of winter wheat. Factor ETo was influenced by air temperature and relative humidity. The Kcb , Kc , and ETc correlated well with the growth status of winter wheat. The overall tendencies of ke and ks were not obvious at key growth stages of winter wheat. Factors ke and ks were influenced mainly by rainfall and irrigation. The cumulative ETc indicated that water consumption was highest at the heading-filling stage in May. The cumulative

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ETc from April 18-31 through May 2011 was consistent with ETc during the same period in 2013. In 2011 and 2013, the estimation accuracy of ETc was better when based on FAO-56 dual-crop coefficient model than the VI-ETo method. The results indicated that the FAO-56 dual-crop coefficient model and the VI-ETo method could be used to improve the estimation accuracy of ETc further. During the period March through May 2011, the HJ satellite images were applied to FAO-56 dual-crop coefficient model for daily estimates of ETc at the Xiaotangshan experimental site and its surrounding farmland. The results suggested that the combination of the FAO-56 dual-crop coefficient model and HJ imaging data or the integration of the VI-ETo method and HJ imaging data could be used for timely estimates of ETc during the growth season of winter wheat. Acknowledgments This study was supported by the Natural Science Foundation of China (61661136003, 41601369, 41601346, 41471285, 41471351), the UK Science and Technology Facilities Council through the PAFiC project (Ref: ST/N006801/1), the National Key Research and Development Program (2016YFD0300602), and the Special Funds for Technology innovation capacity building sponsored by the Beijing Academy of Agriculture and Forestry Sciences(KJCX20170423, KJCX20150409). Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.agwat.2017.04. 017. References Allen, R.G., Pereira, L.S., Raes, D., Smith, M., 1998. Chapter 5. In: Crop Evapotranspiration-Guidelines for Computing Crop Water Requirements, Irrigation and Drain; Paper No. 56. Food and Agriculture Organization, Rome, Italy (p. 300). Allen, R.G., 2000. Using the FAO-56 dual crop coefficient method over an irrigated region as part of an evapotranspiration inter comparison study. J. Hydrol. 229, 27–41. Bodner, G., Loiskandl, W., Kaulm, H., 2007. Cover crop evapotranspiration under semi-arid conditions using FAO dual crop coefficient method with water stress compensation. Agric. Water Manage. 93, 85–98. Campos, I., Neale, C.M.U., Calera, A., Balbontin, C., González-Piqueras, J., 2010. Assessing satellite-based basal crop coefficients for irrigated grapes (Vitis vinifera L.). Agric. Water Manage. 97, 1760–1768. Cava, D., Contini, D., Donateo, A., Martano, P., 2008. Analysis of short-term closure of the surface energy balance above short vegetation. Agric. For. Meteorol. 148, 82–93. Choudhury, B.J., Ahmed, N.U., Idso, S.B., Reginato, R.J., Daughtry, C.S.T., 1994. Relations between evaporation coefficients and vegetation indices studied by model simulations. Remote Sens. Environ. 50, 1–17. Ding, R., Kang, S., Li, F., Zhang, Y., Tong, L., Sun, Q., 2010. Evaluating eddy covariance method by large-scale weighing lysimeter in a maize field of northwest China. Agric. Water Manage. 98, 87–95. Duchemin, B., Hadria, R., Erraki, S., Boulet, G., Maisongrande, P., Chehbouni, A., Escadafal, R., Ezzahar, J., Hoedjes, J.C.B., Kharrou, M.H., Khabba, S., Mougenot, B., Olioso, A., Rodriguez, J.C., Simonneaux, V., 2006. Monitoring wheat phenology and irrigation in Central Morocco: on the use of relationships between evapotranspiration, crops coefficients, leaf area index and remotely-sensed vegetation indices. Agric. Water Manage. 79, 1–27. Er-Raki, S., Chehbouni, G., Guemouria, N., Duchemin, B., Ezzahar, J., Hadria, R., 2007. Combining FAO-56 model and ground-based remote sensing to estimate water consumptions of wheat crops in a semi-arid region. Agric. Water Manage. 87, 41–54. Er-Raki, S., Rodriguez, J.C., Garatuza-Payan, J., Watts, C.J., Chehbouni, A., 2013. Determination of crop evapotranspiration of table grapes in a semi-arid region of Northwest Mexico using multi-spectral vegetation index. Agric. Water Manage. 122, 12–19. Falge, E., Baldocchi, D.D., Olson, R., Anthoni, P., Aubinet, M., Bernhofer, C., Burba, G., Ceulemans, R., Clement, R., Dolman, H., Granier, A., Gross, P., Grunwald, T., Hollinger, D., Jensen, N.O., Katul, G., Keronen, P., Kowalski, A., Ta Lai, C., Law, B.E., Meyers, T., Moncrieff, J., Moors, E., Munger, J.W., Pilegaard, K., Rannik, U., Rebmann, C., Suyker, A., Tenhunen, J., Tu, K., Verma, S., Vesala, T., Wilson, K., Wofsy, S., 2001. Gap filling strategies for long term energy flux data sets. Agric. For. Meteorol. 107, 71–77.

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