@article{c02c02454226495483158e0d9eb0a439,
title = "Estimating Actual Evapotranspiration over Croplands Using Vegetation Index Methods and Dynamic Harvested Area",
abstract = "Advances in estimating actual evapotranspiration (ETa) with remote sensing (RS) have contributed to improving hydrological, agricultural, and climatological studies. In this study, we evaluated the applicability of Vegetation-Index (VI)-based ETa (ET-VI) for mapping and monitoring drought in arid agricultural systems in a region where a lack of ground data hampers ETa work. To map ETa (2000–2019), ET-VIs were translated and localized using Landsat-derived 3-and 2-band Enhanced Vegetation Indices (EVI and EVI2) over croplands in the Zayandehrud River Basin (ZRB) in Iran. Since EVI and EVI2 were optimized for the MODerate Imaging Spectroradiometer (MODIS), using these VIs with Landsat sensors required a cross-sensor transformation to allow for their use in the ET-VI algorithm. The before-and after-impact of applying these empirical translation methods on the ETa estimations was examined. We also compared the effect of cropping patterns{\textquoteright} interannual change on the annual ETa rate using the maximum Normalized Difference Vegetation Index (NDVI) time series. The performance of the different ET-VIs products was then evaluated. Our results show that ETa estimates agreed well with each other and are all suitable to monitor ETa in the ZRB. Compared to ETc values, ETa estimations from MODIS-based continuity corrected Landsat-EVI (EVI2) (EVIMccL and EVI2MccL) performed slightly better across croplands than those of Landsat-EVI (EVI2) without transformation. The analysis of harvested areas and ET-VIs anomalies revealed a decline in the extent of cultivated areas and a loss of corresponding water resources downstream. The findings show the importance of continuity correction across sensors when using empirical algorithms designed and optimized for specific sensors. Our comprehensive ETa estimation of agricultural water use at 30 m spatial resolution provides an inexpensive monitoring tool for cropping areas and their water consumption.",
keywords = "Actual evapotranspiration, Cross-sensor transformation, Enhanced vegetation index, Google earth engine, Harvested area",
author = "Neda Abbasi and Hamideh Nouri and Kamel Didan and Armando Barreto-Mu{\~n}oz and Borujeni, {Sattar Chavoshi} and Hamidreza Salemi and Christian Opp and Stefan Siebert and Pamela Nagler",
note = "Funding Information: Funding:This research wasfunded bythe German Academic Exchange Service(DAAD, funding S.C.B., S.S., C.O., supervision: H.N., S.S., C.O. All authors have read and agreed to the published number 57399578) to support the Ph.D. degree. Some parts of the data analysis associated with this version of the manuscript. work were partially supported by NASA EOS-MODIS grant number 80NSSC18K0617 (K Didan, PI). Funding: This research was funded by the German Academic Exchange Service (DAAD, funding Data Anuvmaiblaebri5l739it9y5 7S8ta)tteomsuenppt: ortDthaetaP ehi.tDhe.rd aerger ee.nSootm fue pllayrtasvoafitlhaebdlea otar ahnaavleysisliamssiotecdia taedvawiliatbh tilhiitsy, due to restricwtioornk ws.ere Ppaurbtilaiclllyysupapvoariteldabblye NAdSaAtaEsOeSt-sMOwDeIrSegranatnnaulymbzeerd8 0NinSSC1t8hKi0s 617s(tKu dDyid:an,MPIO). D09GQ (https://lpdaac.usgs.gov/products/mod09gqv006/, accessed on 10 February 2021), MOD09GA (https://lpdaac.usgs.gov/products/mod09gav006/, accessed on 10 February 2021), Landsat (httpsu:/s/gwsw.gwov.u/spgrso.gdouvc/tcso/rme-od09sgcqievn0c0e6/-s,yasctceemssesd/nolin/L1a0nFdesbatr,u aryac2c0e2s1s)e,dM oOnD091G5A (Jahnttupasr:/y /l2p0d21a)a c.or on Googlues egasr.gtho ven/gpirnoed pulcattsf/omrmo.d E0a9rgthav/M00a6p/s, iamcacegsesse d on 10aFnedb Eruaratryh2 E0n2g1),inLea nredssuatlt(hs tatrpes :/co/vwerwewd.uisngtsh. e terms of service.GCWM isavailable on requestdue to privacy/ethical restrictions. platform. Earth/Maps images and Earth Engine results are covered in the terms of service. GCWM Acknisoawvlaeidlagbmleeonntsr:equOeustr dsupeetocipalr itvhaacnyk/se thtioca tlhrees tfriincatinocnisa.l support (Ph.D. scholarship) given by the German Academic ExchangeService(DAAD). Wewouldlike togratefullyacknowledge NASAand Acknowledgments: Our special thanks to the financial support (Ph.D. scholarship) given by the the USGSfor providingtheopen-accesssatellite data (Landsat andMODIS),Google for their Earth/Maps images and EarthEngine platform, andtheIANREC, IRIMO,and IAOforthe provision of fieElda rdtha/taM. aApsniym uasgee sofantdraEdaret,h fiErmng, ionrepplartofdorumc,ta nnadmtheesIAisNfRorECd,eIsRcIrMipOt,iavnedpIuArOpofosersthoenplryovainsidondoes not implyo efnfidelodrsdeatam. Aennty buys ethoef tUra.Sd.e ,Gfoirvme,rnomr pernotd. uWcte nameswisofuolrdd eliskcer iptot ivtheapnukr pthoes es oUnSlyGaSn rdevdioeews noetr, Hatim Geli, for providing an external review forus. We alsothank our colleagues atthe University of Arizona for thefor providingir scanientifiexternalc inpureviewts and for us.collabWe alsooration. thank our colleagues at the University of Arizona for their scientific inputs and collaboration. Conflicts of Interest: The authors declare no conflict of interest. Conflicts of Interest: The authors declare no conflict of interest. Funding Information: This research was funded by the German Academic Exchange Service (DAAD, funding number 57399578) to support the Ph.D. degree. Some parts of the data analysis associated with this work were partially supported by NASA EOS-MODIS grant number 80NSSC18K0617 (K Didan, PI). Publisher Copyright: {\textcopyright} 2021 by the authors. Licensee MDPI, Basel, Switzerland.",
year = "2021",
month = dec,
day = "1",
doi = "10.3390/rs13245167",
language = "English (US)",
volume = "13",
journal = "Remote Sensing",
issn = "2072-4292",
publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",
number = "24",
}