Abstract
The chapter presents an overview of various methods and model approaches that can be used to derive crop evapotranspiration and agricultural yield state from remote sensing data. The overview is based on an extensive literature review. The studied literature reveals that many valuable techniques have been developed both for the retrieval of evapotranspiration and crop yield from reflective remote sensing data as for the integration of the retrieved variables into crop models. However, for crop modelling and remote sensing data assimilation to be commonly employed on a global operational basis, emphasis will have to be put on bridging the mismatch between data availability and accuracy on one hand, and model and user requirements on the other. This could be achieved by the integration of images with different spatial, temporal, spectral and angular resolutions, and the fusion of optical data with data from different sources.
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References
Alexandrov V, Eitzinger J, Cajic V, Oberforster M (2002) Potential impact of climate change on selected agricultural crops in northeastern Austria. Glob Change Biol 8:372–389
Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration-guidelines for computing crop water requirements-FAO irrigation and drainage paper 56, vol 300. FAO, Rome, p 6541
Allen R, Tasumi M, Trezza R (2007) Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)-model. J Irrig Drain Eng 133:380–394
Allen R-G, Pereira L-S, Howell T, Jensen M-E (2011) Evapotranspiration information reporting: I. Factors governing measurement accuracy. Agric Water Manag 98:899–920
ASCE (2005) The ASCE standardized reference evapotranspiration equation. ASCE-EWRI, Reston, VA
Atkinson P-M, Jeganathan C, Dash J, Atzberger C (2012) Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology. Remote Sens Environ 123:400–417
Bacsi Z, Hunkar M (1994) Assessment of the impacts of climate change on the yields of winter wheat and maize, using crop models. J Agric Sci 149:145–157. https://doi.org/10.1017/s0021859610000808
Balaghi R, Tychon B, Eerens H, Jlibene M (2005) Empirical regression models using NDVI, rainfall and temperature data for the early prediction of wheat grain yields in Morocco. Int J Appl Earth Obs Geoinf 10:438–452. https://doi.org/10.1016/j.jag.2006.12.001
Bastiaanssen W-G-M, Menenti M, Feddes R-A, Holtslag A-A-M (1998a) A remote sensing surface energy balance algorithm for land (SEBAL) 1. Formulation. J Hydrol 212:198–212. https://doi.org/10.1016/S0022-1694(98)00253-4
Bastiaanssen W-G-M, Pelgrum H, Wang J, Ma Y, Moreno J, Roerink G-J, van der Wal T (1998b) The surface energy balance algorithm for land (SEBAL): Part 2 validation. J Hydrol 212–213:213–229
Batchelor W-D, Basso B, Paz J-O (2002) Examples of strategies to analyze spatial and temporal yield variability using crop models. Eur J Agron 18:141–158
Bausch W-C, Neale C-M-U (1989) Spectral inputs improve corn crop coefficients and irrigation scheduling. Trans ASAE 32:1901–1908
Bellvert J, Adeline K, Baram S, Pierce L, Sanden B-L, Smart D-R (2018) Monitoring crop evapotranspiration and crop coefficients over an almond and pistachio orchard throughout remote sensing. Remote Sens 10. https://doi.org/10.3390/rs10122001
Bhattarai N, Quackenbush L-J, Dougherty M, Marzen L-J (2015) A simple Landsat–MODIS fusion approach for monitoring seasonal evapotranspiration at 30 m spatial resolution. Int J Remote Sens 36:115–143. https://doi.org/10.1080/01431161.2014.990645
Boulet G, Mougenot B, Lhomme J-P, Fanise P, Lili-Chabaane Z, Olioso A, Bahir M, Rivalland V, Jarlan L, Merlin O, Coudert B, Er-Raki S, Lagouarde J-P (2015) The SPARSE model for the prediction of water stress and evapotranspiration components from thermal infra-red data and its evaluation over irrigated and rainfed wheat. Hydrol Earth Syst Sci 19:4653–4672. https://doi.org/10.5194/hess-19-4653-2015
Bradley A-B, Jacob W-R, Hermance F-J, Mustard F-J (2007) A curve fitting procedure to derive inter-annual phenologies from time series of noisy satellite NDVI data. Remote Sens Environ 106:137–145
Bregaglio S, Frasso N, Pagani V, Stella T, Francone C, Cappelli G, Acutis M, Ballaghi R, Ouabbou H, Paleari L, Confalonieri R (2014) New multi-model approach gives good estimations of wheat yield under semi-arid climate in Morocco. Agron Sustain Dev 35(1):157–167
Calera A, Campos I, Osann A, D’Urso G, Menenti M (2017) Remote sensing for crop water management: from ET modelling to services for the end users. Sensors 17:1104
Carletto C, Jolliffe D, Banerjee R (2015) From tragedy to renaissance: improving agricultural data for better policies. J Dev Stud 51:133–148. https://doi.org/10.1080/00220388.2014.968140
Chávez J, Neale C-M-U, Hipps L-E, Prueger J-H, Kustas W-P (2005) Comparing aircraft-based remotely sensed energy balance fluxes with eddy covariance tower data using heat flux source area functions. J Hydrometeorol 6:923–940. https://doi.org/10.1175/jhm467.1(2005)
Chehbouni A, Lo Seen D, Njoku EG, Monteny BM (1996) Examination of the difference between radiative and aerodynamic surface temperatures over sparsely vegetated surfaces. Remote Sens Environ 58:177–186. https://doi.org/10.1016/s0034-4257(96)00037-5
Chiesi M, Battista P, Fibbi L, Gardin L, Pieri M, Rapi B, Romani M, Maselli F (2018) A semiempirical method to estimate actual evapotranspiration in mediterranean environments. Adv Meteorol 2018, Article No. 9792609
Choudhury BJ, Ahmed NU, Idso SB, Reginato RJ, Daughtry C-S-T (1994) Relations between evaporation coefficients and vegetation indices studied by model simulations. Remote Sens Environ 50:1–17
Clevers J-G-P-W (1997) A simplified approach for yield prediction of sugar beet based on optical remote sensing data. Remote Sens Environ 61:221–228. https://doi.org/10.1016/s0034-4257(97)00004-7
Clevers J-G-P-W, Kooistra L, Van den Brande M-M (2017) Using Sentinel-2 data for retrieving LAI and leaf and canopy chlorophyll content of a potato crop. Remote Sens 9:405. https://doi.org/10.3390/rs9050405
Corbari C, Ravazzani G, Galvagno M, Cremonese E, Mancini M (2017) Assessing crop coefficients for natural vegetated areas using satellite data and eddy covariance stations. Sensors 17(11), Article No. 2664. https://doi.org/10.3390/s17112664
Crago R, Friedl M, Kustas W, Wang Y (2004) Investigation of aerodynamic and radiometric land surface temperatures. NASA Scientific and Technical Aerospace Reports (STAR) 42
Dadhwal V-K, Ray S-S (2000) Crop assessment using remote sensing—Part-II: crop condition assessment and yield forecasting. Indian J Agric Econ 55(2):55–67
Dalezios N-R, Dercas N, Spyropoulos N, Psomiadis E (2019) Remotely sensed methodologies for crop water availability and requirements in precision farming of vulnerable agriculture. Water Resour Manage. https://doi.org/10.1007/s11269-018-2161-8
Delécolle R, Maas S-J, Guérif M, Baret F (1992) Remote sensing and crop production models: present trends. 1991/01/14-18. ISPRS J Photogr Remote Sens 47(2–3):145–161
Dhungel R, Allen R-G, Trezza R, Robison C-W (2014) Comparison of latent heat flux using aerodynamic methods and using the penman-monteith method with satellite-based surface energy balance. Remote Sens 6:8844–8877. https://doi.org/10.3390/rs6098844
Dhungel S, Barber M-E (2018) Estimating calibration variability in evapotranspiration derived from a satellite-based energy balance model. Remote Sens 10:1695
Domenikiotis C, Spiliotopoulos M, Tsiros E, Dalezios NR (2004) Early cotton yield assessment by the use of the NOAA/AVHRR derived vegetation condition index (VCI) in Greece. Int J Remote Sens 25(14):2807–2819
Doorenbos J, Pruitt W-O (1976) Guidelines for predicting crop water requirements, FAO irrigation and drainage paper 24, Second ed. Rome, 156 pp
Doorenbos J, Pruitt W-O (1977) FAO irrigation and drainage paper 24, (Rev.) Rome, 156 p
Fischer A, Kergoat L, Dedieu G (1997) Coupling satellite data with vegetation functional models: review of different approaches and perspectives suggested by the assimilation strategy. Remote Sens Rev 15:283–303
Fuentes-Peñailillo F, Ortega-FarÃas S, Acevedo-Opazo C, Fonseca-Luengo D (2018) Implementation of a two-source model for estimating the spatial variability of olive evapotranspiration using satellite images and ground-based climate data. Water 10(3)
Funk C, Budde E-M (2009) Phenologically-tuned MODIS NDVI-based production anomaly estimates for Zimbabwe. Remote Sens Environ 113:115–125
Glenn E-P, Neale C-M-U, Hunsaker D-J, Nagler P-L (2011) Vegetation index-based crop coefficients to estimate evapotranspiration by remote sensing in agricultural and natural ecosystems. Hydrol Process 25:4050–4062
González A-R, Kjaersgaard J, Trooien T, Hay C, Ahiablame L (2018) Estimation of crop evapotranspiration using satellite remote sensing-based vegetation index. Adv Meteorol 2018, Article No. 4525021. https://doi.org/10.1155/2018/4525021
Goward S-N, Cruickshanks G-D, Hope A-S (1985) Observed relation between thermal emission and reflected spectral radiance of a complex vegetated landscape. Remote Sens Environ 18:137–146. https://doi.org/10.1016/0034-4257(85)90044-6
Gowda P-H, Chávez J-L, Colaizzi P-D, Evett S-R, Howell T-A, Tolk J-A (2007) Remote sensing based energy balance algorithms for mapping ET: current status and future challenges. Trans ASABE 50:1639–1644
Guérif M, Hollecker D, Beaudoin N, Bruchou C, Clastre1 P, Houlès V, Machet J-M, Mary B, Moulin S, Nicoullaud B (2003) Conference information: 4th European conference on precision agriculture, 2003 Berlin, Germany, 253–258
Hayes J-T, O’Rourke P-A, Terjung W-E, Todhunter P-E (1982) YIELD: a numerical crop yield model of irrigated and rainfed agriculture. Publications in Climatology, p 35
Hayes M-J, Decker W-L (1996) Using NOAA AVHRR data to estimate maize production in the United States Corn Belt. Int J Remote Sens 17:3189–3200
Hong S, Hendrickx J-M-H, Borchers B (2011) Down-scaling of SEBAL derived evapotranspiration maps from MODIS (250 m) to Landsat (30 m) scales. Int J Remote Sens 32:6457–6477
Hunsaker D-J, Barnes E-M, Clarke T-R, Fitzgerald G-J, Pinter PJ (2005) Cotton irrigation scheduling using remotely sensed and FAO-56 basal crop coefficients. Trans ASAE 48(4):1395–1407. https://doi.org/10.13031/2013.19197
Jaafar H-H, Ahmad F-A (2019) Time series trends of Landsat-based ET using automated calibration in METRIC and SEBAL: the Bekaa Valley, Lebanon. Remote Sens Environ (in press)
Jaber H-S, Mansor S, Pradhan B, Ahmad N (2016) Evaluation of SEBAL model for evapotranspiration mapping in Iraq using remote sensing and GIS. Int J Appl Eng Res 11:3950–3955
Jiang L, Islam S (1999) A methodology for estimation of surface evapotranspiration over large areas using remote sensing observations. Geophys Res Lett 26:2773–2776. https://doi.org/10.1029/1999gl006049
Jones DR (1982) A statistical inquiry into crop–weather dependence. Agric Meteorol 26:91–104
Kalma J, McVicar T, McCabe M (2008) Estimating land surface evaporation: a review of methods using remotely sensed surface temperature data. Surv Geophys 29:421–469. https://doi.org/10.1007/s10712-008-9037-z
Kamble B, Kilic A, Hubbard K (2013) Estimating crop coefficients using remote sensing-based vegetation index. Remote Sens 5(4):1588–1602. https://doi.org/10.3390/rs5041588
Ke Y, Im J, Park S, Gong H (2016) Downscaling of MODIS one kilometer evapotranspiration using Landsat-8 data and machine learning approaches. Remote Sens 8(3), Article No. 215. https://doi.org/10.3390/rs8030215
Koksal E-S, Artik C, Tasan M (2019) Crop evapotranspiration estimations of red pepper using field level remote sensing data and energy balance. Polish J Environ Stud 28:165–175
Kullberg E-G, DeJonge K-C, Chavez J-L (2017) Evaluation of thermal remote sensing indices to estimate crop evapotranspiration coefficients. Agric Water Manag 179:64–73
Kustas W-P, Daughtry C-S-T (1990) Estimation of the soil heat flux/net radiation ratio from spectral data. Agric Forest Meteorol 49:205–223. https://doi.org/10.1016/0168-1923(90)90033-3
Kustas W-P, Norman J-M (1996) Use of remote sensing for evapotranspiration monitoring over land surfaces. Hydrol Sci J 41:495–516
Lagouarde J-P, Brunet Y (1991) A simple model for estimating the daily upward longwave surface radiations from NOAA–AVHRR data. Int J Remote Sens 12:1853–1864
Launay M, Guerif M (2005) Assimilating remote sensing data into a crop model to improve predictive performance for spatial applications. Agr Ecosyst Environ 111:321–339
Li Z-L, Tang R, Wan Z, Bi Y, Zhou C, Tang B, Yan G, Zhang X (2009) A review of current methodologies for regional evapotranspiration estimation from remotely sensed data. Sensors 9:3801–3853
Lian J, Huang M (2016) Comparison of three remote sensing based models to estimate evapotranspiration in an oasis-desert region. Agric Water Manag 165:153–162. https://doi.org/10.1016/j.agwat.2015.12.001
Liaqat U-W, Choi M (2015) Surface energy fluxes in the Northeast Asia ecosystem: SEBS and METRIC models using Landsat satellite images. Agric Forest Meteorol 214–215:60–79. https://doi.org/10.1016/j.agrformet.2015.08.245
Linacre E-T (1977) A simple formula for estimating evaporation rates in various climates, using temperature data alone. Agric Meteorol 18:409–424. https://doi.org/10.1016/0002-1571(77)90007-3
Liou Y-A, Kar S-K (2014) Evapotranspiration estimation with remote sensing and various surface energy balance algorithms: a review. Energies 7:2821–2849. https://doi.org/10.3390/en7052821
Loheide S-P, Gorelick S-M (2005) A local-scale, high-resolution evapotranspiration mapping algorithm (ETMA) with hydroecological applications at riparian meadow restoration sites. Remote Sens Environ 98:182–200. https://doi.org/10.1016/j.rse.2005.07.003
Matese A, Baraldi R, Berton A, Cesaraccio C, Di Gennaro S-F, Duce P, Facini O, Mameli M-G, Piga A, Zaldei A (2018) Estimation of water stress in grapevines using proximal and remote sensing methods. Remote Sens 10:114
Menenti M, Choudhury B (1993) Parameterization of land surface evapotranspiration using a location dependent potential evapotranspiration and surface temperature range. IAHS Publ 212:561–568
Metochis C, Eliades G, Papachristodoulou S (1995) Technoeconomic analysis of irrigation in Cyprus’ (in Greek). Agricultural Research Institute, Nicosia, Cypru
Minacapilli M, Iovino M, D’Urso G (2008) A distributed agro-hydrological model for irrigation water demand assessment. Agric Water Manag 95:123–132
Moran M-S, Clarke T-R, Inoue Y, Vidal A (1994) Estimating crop water deficit using the relation between surface-air temperature and spectral vegetation index. Remote Sens Environ 49:246–263. https://doi.org/10.1016/0034-4257(94)90020-5
Navarro A, Rolim J, Miguel I, Catalão J, Silva J, Painho M, Vekerdy Z (2016) Crop monitoring based on SPOT-5 Take-5 and sentinel-1A data for the estimation of crop water requirements. Remote Sens 8(6), Article No. 525. https://doi.org/10.3390/rs8060525
Olioso A, Chauki H, Courault D, Wigneron J-P (1999) Estimation of evapotranspiration and photosynthesis by assimilation of remote sensing data into SVAT Models. Remote Sens Environ 68:341–356. https://doi.org/10.1016/s0034-4257(98)00121-7
Olioso A, Inoue Y, Ortega-Farias S, Demarty J, Wigneron J-P, Braud I, Jacob F, Lecharpentier P, Ottlé C, Calvet J, Brisson N (2003) Assimilation of remote sensing data into crop simulation models and SVAT models. In: Proceedings of the international workshop on use of remote sensing of crop evapotranspiration for large regions, 54th IEC meeting of the ICID, Montpellier, France
Oroda A (2001) The international archives of the photogrammetry. Remote Sens Spatial Inform Sci XXXIV:66–72, Part 6/W6
Ortega-FarÃas S, Ortega-Salazar S, Poblete T, Kilic A, Allen R, Poblete-EcheverrÃa C, Ahumada-Orellana L, Zuñiga M, Sepúlveda D (2016) Estimation of energy balance components over a drip-irrigated olive orchard using thermal and multispectral cameras placed on a helicopter-based unmanned aerial vehicle (UAV). Remote Sens 8(8), Article No. 638
Papadavid G, Hadjimitsis D-G (2009) Spectral signature measurements during the whole life cycle of annual crops and sustainable irrigation management over Cyprus using remote sensing and spectro-radiometric data: the cases of spring potatoes and peas. In: Proceedings of SPIE, remote sensing for agriculture, ecosystems, and hydrology XI, vol 7472, 747215. https://doi.org/10.1117/12.830552
Papadavid G, Hadjimitsis D-G (2014) An image based method for crop yield prediction using remotely sensed and crop canopy data: The case of Paphos district, western Cyprus. In: 2nd international conference on remote sensing and geoinformation of the environment, RSCy 2014, Paphos, Cyprus, 7–10 April 2014
Papadavid G, Hadjimitsis D, Themistocleous K, Toulios L (2010) Spectral vegetation indices from field spectroscopy intended for evapotranspiration purposes for spring potatoes in Cyprus. Proc SPIE 7824:782410
Papadavid G, Hadjimitsis D, Toulios L, Michaelides S (2011) Mapping Potatoes crop height and LAI through vegetation indices using remote sensing, in Cyprus. J Appl Remote Sens 5:053526. https://doi.org/10.1117/1.3596388
Papadavid G, Hadjimitsis D, Toulios L, Michailides S (2013) A modified SEBAL modeling approach for estimating crop evapotranspiration in semi-arid conditions. Water Resour Manag 27:3493–3506
Papadavid G, Neocleous D, Kountios G, Markou M, Michailidis A, Ragkos A, Hadjimitsis D (2017) Using SEBAL to investigate how variations in climate impact on crop evapotranspiration. J Imag 3(3):30. https://doi.org/10.3390/jimaging3030030
Papadavid G, Toulios L (2018) The use of earth observation methods for estimating regional crop evapotranspiration and yield for water footprint accounting. J Agric Sci 156(5):599–617. https://doi.org/10.1017/s0021859617000594 © Cambridge University Press
Pereira L-S, Allen R-G, Smith M, Raes D (2015) Crop evapotranspiration estimation with FAO56: past and future. Agric Water Manag 147:4–20. https://doi.org/10.1016/j.agwat.2014.07.031
Petropoulos G-P, Srivastava P-K, Piles M (2018) Earth observation-based operational estimation of soil moisture and evapotranspiration for agricultural crops in support of sustainable water management. Sustainability 10(1)
Piedelobo L, Ortega-Terol D, Pozo S-D, Hernández-López D, Ballesteros R, Moreno M-A, Molina J-L, Aguilera DG (2018) HidroMap: a new tool for irrigation monitoring and management using free satellite imagery. ISPRS Int J Geo-Inf 7(6):220. https://doi.org/10.3390/ijgi7060220
Poon P-K, Kinoshita A-M (2018) Estimating evapotranspiration in a post-fire environment using remote sensing and machine learning. Remote Sens 10:1728. https://doi.org/10.3390/rs10111728
Prasad A, Chai L, Ramesh P, Kafatos M (2006) Crop yield estimation model for Iowa using remote sensing and surface parameters. Int J Appl Earth Obs Geoinf 8:26–33
Prueger JH, Hatfield JL, Aase JK, Pikul JL (1997) Bowen-ratio comparisons with Lysimeter evapotranspiration. Agron J 89:730–736
Quarmby N-A, Milnes M, Hindle T-L, Silleos N (1993) The use of multitemporal NDVI measurements from AVHRR data for crop yield estimation and prediction. Int J Remote Sens 14:199–210
Reyes-González A, Kjaersgaard J, Trooien T, Hay C, Ahiablame L (2017) Comparative analysis of METRIC model and atmometer methods for estimating actual evapotranspiration. Int J Agronomy. Article No. 3632501
Roerink G-J, Su Z, Menenti M (2000) S-SEBI: A simple remote sensing algorithm to estimate the surface energy balance. Phys Chem Earth, Part B Hydrol Oceans Atmos 25:147–157. https://doi.org/10.1016/S1464-1909(99)00128-8
Romaguera M, Toulios L, Stancalie G, Nertan A, Spiliotopoulos M, Struzik P, Calleja E, Papadavid G (2014) Identification of the key variables that can be estimated using remote sensing data and needed for water footprint (WF) assessment. In: Presented, during the second international conference on remote sensing and geoinformation of environment, RSCy 2014, Paphos, Cyprus 7–10 April 2014
Saadi S, Boulet G, Bahir M, Brut A, Delogu É, Fanise P, Mougenot B, Simonneaux V, Chabaane Z-L (2018) Assessment of actual evapotranspiration over a semiarid heterogeneous land surface by means of coupled low-resolution remote sensing data with an energy balance model: comparison to extra-large aperture scintillometer measurements. Hydrol Earth Syst Sci 22:2187–2209
Sakamoto T, Yokozawa M, Toritani H, Shibayama M, Ishitsuka N, Ohno H (2005) A crop phenology detection method using time-series MODIS data. Remote Sens Environ 96:366–374
Shanahan J-F, Schepers S, Francis D, Varvel G, Wilhelm W (2001) Use of remote-sensing imagery to estimate corn grain yield. Agron J 93:583–589
Smith M, Allen R, Monteith J-L, Perrier L-A, Segeren A (1991) Report on the expert consultation for the revision of FAO methodologies for crop water requirements. FAO/AGL, Rome
Smith M (1992) CROPWAT. A computer program for irrigation planning and management, FAO irrigation and drainage paper, p 46
Smith M (1993) CLIMWAT for CROPWAT, a climatic data base for irrigation planning and management. FAO irrigation and drainage paper 49, Rome, 113 pp
Smith M, Kivumbi D, Heng L-K (2002) Use of the FAO CROPWAT model in deficit irrigation studies. Water Reports FAO 22, ISSN: 1020-1203
Spiliotopoulos M, Adaktylou N, Loukas A, Michalopoulou H, Mylopoulos N, Toulios L (2013) A spatial downscaling procedure of MODIS derived actual evapotranspiration using Landsat images at central Greece. In: Proceedings of SPIE—the international society for optical engineering, vol 8795, pp 296–299
Spiliotopoulos M, Holden N-M, Loukas A (2017) Mapping evapotranspiration coefficients in a temperate maritime climate using the METRIC model and Landsat TM. Water 9:23
Spiliotopoulos M, Loukas A (2019) Hybrid methodology for the estimation of crop coefficients based on satellite imagery and ground-based measurements. Water 11:1364
Stancalie G, Marica A, Toulios L (2010) Using earth observation data and CROPWAT model for estimation the actual crop evapotranspiration. Phys Chem Earth Parts A/B/C 35:25–30
Stoikos G (1995) Sugar beet crop yield prediction using artificial neural networks (in Greek). In: Proceedings of the modern technologies conference in automatic control, Athens, Greece, pp 120–122
Struzik P, Toulios L, Stancalie G, Danson M, Mika J, Domenikiotis C (2008) Satellite remote sensing as a tool for monitoring climate and its impact on the environment–possibilities and limitations. In: Nejedlik, Orlandini (eds) Survey of agrometeorological practices and applications in Europe regarding climate change impacts. COST 734, ESF, pp 205–236
Su Z (2002) the surface energy balance system (SEBS) for estimation of turbulent heat fluxes. Hydrol Earth Syst Sci 6:85–99
Taghvaeian S, Chávez J-L, Bausch W-C, De Jonge K-C, Trout T-J (2014) Minimizing instrumentation requirement for estimating crop water stress index and transpiration of maize. Irrig Sci 32:53–65
Toulios L, Stancalie G, Struzik P, Danson M, Mika J, Dunkel Z, Tsiros (2008) Satellite spectral climatic and biophysical data for warning purposes for European agriculture. In: Nejedlik, Orlandini (eds) Survey of agrometeorological practices and applications in Europe regarding climate change impacts. COST 734, ESF, pp 163–203
Trezza R, Allen R-G, Tasumi M (2013) Estimation of actual evapotranspiration along the middle rio grande of new Mexico using MODIS and Landsat imagery with the METRIC model. Remote Sens 5:5397–5423. https://doi.org/10.3390/rs5105397
Vanino S, Nino P, De Michele C, Falanga Bolognesi S, D’Urso G, Di Bene C, Pennelli B, Vuolo F, Farina R, Pulighe G, Napoli R (2018) Capability of Sentinel-2 data for estimating maximum evapotranspiration and irrigation requirements for tomato crop in Central Italy. Remote Sens Environ 215:452–470
Veysi S, Naseri A-A, Hamzeh S, Bartholomeus H (2017) A satellite based crop water stress index for irrigation scheduling in sugarcane fields. Agric Water Manag 189:70–86
Wendroth O, Reuter H-I, Kersebaum K-C (2003) Predicting yield of barley across a landscape: a state-space modeling approach. J Hydrol 272:250–263
Wilhite D-A (1993) Drought assessment, management and planning: theory and case studies. Kluwer Academic Publishers, Hingham, MA, p 293
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Toulios, L., Spiliotopoulos, M., Papadavid, G., Loukas, A. (2020). Observation Methods and Model Approaches for Estimating Regional Crop Evapotranspiration and Yield in Agro-Landscapes: A Literature Review. In: Mirschel, W., Terleev, V., Wenkel, KO. (eds) Landscape Modelling and Decision Support. Innovations in Landscape Research. Springer, Cham. https://doi.org/10.1007/978-3-030-37421-1_5
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