Abstract
Crop evapotranspiration (ET) is the largest water consumer of agriculture water in an irrigation district. Remote sensing (RS) technique has provided an effective way to map regional ET using various RS-based ET models over the past several decades. To map growing season ET of different crops and partition ET into evaporation (E) and transpiration (T) at regional scale, appropriate ET models should be further integrated with crop distribution maps in different years and crop growing seasons determined for each crop pixel. In this study, a hybrid dual-source scheme and trapezoid framework-based ET Model (HTEM) fed with HJ-1A/1B data was applied in Hetao Irrigation District (HID) of China from 2009 to 2015 to map crop growing season ET and T at 30 m resolution. The HTEM model with HJ-1A/1B data performed well in estimating ET in HID, and the finer spatial resolution of model input data can improve the estimation accuracy of ET. Combined with the annual crop planting map identified in previous study, and crop growing seasons determined from fitted Normalized Difference Vegetation Index (NDVI) curves for crop pixels, the spatial and temporal variations of growing season ET and T of major crops (maize and sunflower) were examined. The results indicate that ET and T of maize and sunflower reach their minimum values in the southwest HID with smaller crop planting density, and reach their maximum values in northwest HID with higher crop planting density. Over the study period with a decreasing trend of available irrigation water, ET and T in maize and sunflower growing seasons show decreasing trends, while ratios of T/ET show increasing trends, which implies that the adverse effect of decreased irrigation water diversion on crop growth is diminished due to the favorable portioning of E and T in cropland of HID. In addition, the calculation results of crop coefficients show that there is water stress to crop growth in the study area. The present results are helpful to better understand the spatial pattern of crop water consumption and water stress of different crops during crop growing season, and provide the basis for optimizing the spatial distribution of crop planting with less water consumption and more crop yield.
Highlights
Agriculture is a major water consumer in China and around the world, and agricultural water use accounts for about 70% of the total water uses in the world
The annual precipitation in the study area is much less than pote4nofti2a2l evaporation, which results in the high dependence of the agriculture on water diversion from the Yellow River for irrigation
The HTEM model has been validated with ground measurements of ET for different crops in previous studies, including the Soil Moisture-Atmosphere Coupling Experiment (SMACEX) campaign conducted in the summer of 2002 in Iowa, USA, with the main crops of corn and soybean [28], the Weishan flux site in the North China Plain during the growing season of winter wheat and summer corn in 2007 [28], and the MUlti-Scale Observation EXperiment on Evapotranspiration over heterogeneous land surfaces 2012 (MUSOEXE-12) campaign in the Heihe River Basin in Northwest China [29]
Summary
Agriculture is a major water consumer in China and around the world, and agricultural water use accounts for about 70% of the total water uses in the world. The third group is physically based simulation models that estimate crop ET from simulation models for water flow in the soil-plant-atmosphere continuum (SPAC) and/or crop growth [18,19,20,21] These models require a large number of in situ measured data as parameters and inputs [22]. Compared with the measured data of EC flux tower, HTEM performed better than TSEB in arid area [29] These RS-based ET models usually give instantaneous ET estimates at the satellite overpassing time or ET on specified periods after temporal upscaling. WAthsilme aatllrsepgaiotinaallssccaallee,,tthhee ccrroopp ggrroowwiinngg sseeaassoonnccaannbbeeeosbtitmaianteedd tbhyrothueghPofileylfdits–uMrvaxeiym[1u8m]. mWehthiloeda,t wrehgiicohnaclosncsaidlee,rtshtehceroppergiorodwoifnggrseeaatesostndceacnrebaeseesitnimthaetesdeabsyotnhael PNoDlyVfiIt–tiMmaexsimeruiems ams eththeobde,gwinhnicinhgcoonf sviedgeerstatthioenpderoiromd aonfcgyre[a3t8e–s4t0d]e. crease in the seasonal NDVI time seriesTahsethmeabinegoinbnjeicntgivoef vofegtehtiastisotnuddyorwmaasnctyo [s3t8u–d4y0].the growing season evapotranspiration and transTphireatmioaninofombajejoctrivcreoposf atht iirsrisgtuatdioynwdiasstrtioct sstcuadleybythceomgrboiwniinnggasReaSs-obnaseedvadpuoatlrsaonusprcieraEtiTomn oadnedl t(rHaTnsEpMir)a, tiaonnnuoaf lmcarjoopr cdriosptrsibauttiiorrnigmataiopn, danisdtrigcrtoswcainlegbsyeacsoomnboinfinmgaaizeRSa-nbdasesdundfluoawl esrouinrceHEIDT mdeotdereml (iHneTdEbMy),aasnimnupalel carlogporditihstmribfruotmionNmDaVpI, saenrdiesg.rCowominpgasreeadswonitohfpmreavizioeuasnsdtusduineflsoowneRrSi-nbHasIeDd dEeTteersmtiminaetdesby[6a,3s0i,m37p],lethailsgostruitdhymafirmoemdNtoDeVstIimseariteesp. iCxoelm-spcaalreedETwiatnhdpTrevfoior udsifsfteurednietscoronpRsSi-nbathseeidr EgrToewsitnimg asteeasso[6n,s3,0i,n3s7t]e, athdios fstpuixdeyl-asicmaleedEtToaensdtimT ainteapwixheol-lsecraelegiEoTn oanr dinTalflocrrdopifflaenrednstfcorroapsspinectihfieeidr gpreorwioidngofsetaimsoen. sT, hinessteeardesoufltpsixcaenl-spcarolevEidTeatnhde Tbaisnisa fworhoolpetrimegiizoinngorthine aslplactrioapl ldainsdtrsibfuortiaonspoefcicfiroedp ppelarniotdingofwtiimthel.esTshwesaeterrecsounltssucmapntipornovainddemthoerebacrsoispfyoirelodp.timizing the spatial distribution of crop planting with less water consumption and more crop yield
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