Abstract
Evapotranspiration (ET) is a vital process involving the transfer of water from the Earth's surface to the atmosphere through soil evaporation and plant transpiration. Accurate estimation of ET is important for a variety of applications, including irrigation management and water resource planning. The two-source energy balance (TSEB) model is a commonly used method for estimating ET using remotely sensed data. This study used the TSEB model and high-resolution unmanned aerial vehicle (UAV) imagery to estimate sorghum ET under four different irrigation regimes over two growing seasons in 2020 and 2021. The study also validated net radiation (Rn) flux through hand-held radiometer measurements and compared the estimated ET with a soil water balance model. The study outcomes revealed that that the TSEB model capably estimated Rn values, aligning well with ground-based Rn measurements for all irrigation treatments (RMSE = 32.9–39.8 W m−2 and MAE = 28.1–35.2 W m−2). However, the TSEB model demonstrated robust performance in estimating ET for fully irrigated conditions (S1), while its performance diminished with increasing water stress (S2, S3, and S4). The R2, RMSE, and MAE values range from 0.64 to 0.06, 10.94 to 17.04 mm, and 7.09 to 11.43 mm, respectively, across the four irrigation treatments over a 10-day span. These findings not only suggest the potential of UAVs for ET mapping at high-resolution over large areas under various water stress conditions, but also highlight the need for further research on ET estimation under water stress conditions.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abd El-Mageed TA, El- Samnoudi IM, Ibrahim AE-AM, Abd El Tawwab AR (2018) Compost and mulching modulates morphological, physiological responses and water use efficiency in sorghum (bicolor L. Moench) under low moisture regime. Agric Water Manag 208:431–439. https://doi.org/10.1016/j.agwat.2018.06.042
Abioye EA, Hensel O, Esau TJ et al (2022) Precision irrigation management using machine learning and digital farming solutions. AgriEngineering 4:70–103. https://doi.org/10.3390/agriengineering4010006
Acorsi MG, Gimenez LM, Martello M (2020) Assessing the performance of a low-cost thermal camera in proximal and aerial conditions. Remote Sens 12:3591. https://doi.org/10.3390/rs12213591
Aguirre-García S-D, Aranda-Barranco S, Nieto H et al (2021) Modelling actual evapotranspiration using a two source energy balance model with Sentinel imagery in herbaceous-free and herbaceous-cover Mediterranean olive orchards. Agric for Meteorol 311:108692. https://doi.org/10.1016/j.agrformet.2021.108692
Allen RG, Masahiro T, Ricardo T (2007) Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)—Model. J Irrig Drain Eng 133:380–394. https://doi.org/10.1061/(ASCE)0733-9437(2007)133:4(380)
Asadi M, Kamran KV (2022) Comparison of SEBAL, METRIC, and ALARM algorithms for estimating actual evapotranspiration of wheat crop. Theor Appl Climatol 149:327–337. https://doi.org/10.1007/s00704-022-04026-3
Aubrecht DM, Helliker BR, Goulden ML et al (2016) Continuous, long-term, high-frequency thermal imaging of vegetation: Uncertainties and recommended best practices. Agric for Meteorol 228:315–326. https://doi.org/10.1016/j.agrformet.2016.07.017
Awais M, Li W, Hussain S et al (2022) Comparative evaluation of land surface temperature images from unmanned aerial vehicle and satellite observation for agricultural areas using in situ data. Collect FAO Agric 12:184. https://doi.org/10.3390/agriculture12020184
Aydinsakir K, Buyuktas D, Dinç N et al (2021) Yield and bioethanol productivity of sorghum under surface and subsurface drip irrigation. Agric Water Manag 243:106452. https://doi.org/10.1016/j.agwat.2020.106452
Bastiaanssen WGM, Menenti M, Feddes RA, Holtslag AAM (1998) A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation. J Hydrol 212–213:198–212. https://doi.org/10.1016/S0022-1694(98)00253-4
Bellvert J, Pelechá A, Pamies-Sans M et al (2023) Assimilation of sentinel-2 biophysical variables into a digital twin for the automated irrigation scheduling of a vineyard. Water 15(14):2506. https://doi.org/10.3390/w15142506
Ben-Asher J, Fuchs M, Goldberg D (1978) Radiation and energy balance of sprinkler and trickle irrigated fields1. Agron J 70:415–417. https://doi.org/10.2134/agronj1978.00021962007000030012x
Burchard-Levine V, Nieto H, Riaño D et al (2021) The effect of pixel heterogeneity for remote sensing based retrievals of evapotranspiration in a semi-arid tree-grass ecosystem. Remote Sens Environ 260:112440. https://doi.org/10.1016/j.rse.2021.112440
Cáceres G, Millán P, Pereira M, Lozano D (2021) Smart farm irrigation: model predictive control for economic optimal irrigation in agriculture. Agronomy 11:1810. https://doi.org/10.3390/agronomy11091810
Campi P, Navarro A, Palumbo AD et al (2016) Energy of biomass sorghum irrigated with reclaimed wastewaters. Eur J Agron 76:176–185. https://doi.org/10.1016/j.eja.2016.01.015
Carpintero E, Andreu A, Gómez-Giráldez PJ et al (2020) Remote-sensing-based water balance for monitoring of evapotranspiration and water stress of a Mediterranean Oak-Grass Savanna. Water 12:1418. https://doi.org/10.3390/w12051418
Cemek B, Ünlükara A, Kurunç A, Küçüktopcu E (2020) Leaf area modeling of bell pepper (Capsicum annuum L.) grown under different stress conditions by soft computing approaches. Comput Electron Agric 174:105514. https://doi.org/10.1016/j.compag.2020.105514
Chávez JL, Gowda PH, Howell TA et al (2009) Estimating hourly crop ET using a two-source energy balance model and multispectral airborne imagery. Irrig Sci 28:79–91. https://doi.org/10.1007/s00271-009-0177-9
Chen D, Zhuang Q, Zhang W et al (2022) Estimation of Landsat-like daily evapotranspiration for crop water consumption monitoring using TSEB model and data fusion. PLoS ONE 17(5):e0267811. https://doi.org/10.1371/journal.pone.0267811
Choi M, Kustas WP, Anderson MC et al (2009) An intercomparison of three remote sensing-based surface energy balance algorithms over a corn and soybean production region (Iowa, US) during SMACEX. Agric for Meteorol 149:2082–2097. https://doi.org/10.1016/j.agrformet.2009.07.002
Colaizzi PD, Agam N, Tolk JA et al (2014) Two-source energy balance model to calculate E, T, and ET: comparison of Priestley-Taylor and penman-Monteith formulations and two time scaling methods. Trans ASABE. https://doi.org/10.13031/trans.57.10423
Cosentino SL, Mantineo M, Testa G (2012) Water and nitrogen balance of sweet sorghum (Sorghum bicolor moench (L.)) cv. Keller under semi-arid conditions. Ind Crops Prod 36:329–342. https://doi.org/10.1016/j.indcrop.2011.10.028
de Teixeira AHC, Bastiaanssen WGM, Ahmad BMG (2009) Reviewing SEBAL input parameters for assessing evapotranspiration and water productivity for the Low-Middle São Francisco River basin, Brazil: part a: calibration and validation. Agric for Meteorol 149:462–476. https://doi.org/10.1016/j.agrformet.2008.09.016
Deus D, Gloaguen R, Krause P (2013) Water balance modeling in a semi-arid environment with limited in situ data using remote sensing in Lake Manyara, East African Rift, Tanzania. Remote Sens 5:1651–1680. https://doi.org/10.3390/rs5041651
Feng J, Wang W, Xu F, Sun S (2020) Estimating surface heat and water vapor fluxes by combining two-source energy balance model and back-propagation neural network. Sci Total Environ 729:138724. https://doi.org/10.1016/j.scitotenv.2020.138724
French AN, Hunsaker DJ, Thorp KR (2015) Remote sensing of evapotranspiration over cotton using the TSEB and METRIC energy balance models. Remote Sens Environ 158:281–294. https://doi.org/10.1016/j.rse.2014.11.003
French AN, Hunsaker DJ, Sanchez CA et al (2020) Satellite-based NDVI crop coefficients and evapotranspiration with eddy covariance validation for multiple durum wheat fields in the US Southwest. Agric Water Manag 239:106266. https://doi.org/10.1016/j.agwat.2020.106266
Gan G, Gao Y (2015) Estimating time series of land surface energy fluxes using optimized two source energy balance schemes: model formulation, calibration, and validation. Agric for Meteorol 208:62–75. https://doi.org/10.1016/j.agrformet.2015.04.007
Gano B, Dembele JSB, Ndour A et al (2021) Using UAV borne, multi-spectral imaging for the field phenotyping of shoot biomass, leaf area index and height of West African Sorghum varieties under two contrasted water conditions. Agronomy 11:850. https://doi.org/10.3390/agronomy11050850
Gao R, Torres-Rua A, Nassar A et al (2021) Evapotranspiration partitioning assessment using a machine-learning-based leaf area index and the two-source energy balance model with sUAV information. In: Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VI. SPIE, pp 106–129
Garofalo P, Rinaldi M (2013) Water-use efficiency of irrigated biomass sorghum in a Mediterranean environment. Span J Agric Res 11:1153–1169. https://doi.org/10.5424/sjar/2013114-4147
GhassemiSahebi F, Mohammadrezapour O, Delbari M et al (2020) Effect of utilization of treated wastewater and seawater with Clinoptilolite-Zeolite on yield and yield components of sorghum. Agric Water Manag 234:106117. https://doi.org/10.1016/j.agwat.2020.106117
Gonzalez-Dugo MP, Neale CMU, Mateos L et al (2009) A comparison of operational remote sensing-based models for estimating crop evapotranspiration. Agric for Meteorol 149:1843–1853. https://doi.org/10.1016/j.agrformet.2009.06.012
Guzinski R, Anderson MC, Kustas WP et al (2013) Using a thermal-based two source energy balance model with time-differencing to estimate surface energy fluxes with day–night MODIS observations. Hydrol Earth Syst Sci 17:2809–2825. https://doi.org/10.5194/hess-17-2809-2013
Guzinski R, Nieto H, Jensen R, Mendiguren G (2014) Remotely sensed land-surface energy fluxes at sub-field scale in heterogeneous agricultural landscape and coniferous plantation. Biogeosciences 11:5021–5046. https://doi.org/10.5194/bg-11-5021-2014
Hao B, Xue Q, Bean BW et al (2014) Biomass production, water and nitrogen use efficiency in photoperiod-sensitive sorghum in the Texas High Plains. Biomass Bioenergy 62:108–116. https://doi.org/10.1016/j.biombioe.2014.01.008
Hoffmann H, Nieto H, Jensen R et al (2015) Estimating evapotranspiration with thermal UAV data and two source energy balance models. Hydrol Earth Syst Sci Discuss 12:7469–7502. https://doi.org/10.5194/hessd-12-7469-2015
Huang J, Ma H, Su W et al (2015) Jointly Assimilating MODIS LAI and ET Products Into the SWAP Model for Winter Wheat Yield Estimation. IEEE J Select Top Appl Earth Observ Remote Sens 8:4060–4071. https://doi.org/10.1109/JSTARS.2015.2403135
Huete A, Didan K, Miura T et al (2002) Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens Environ 83:195–213. https://doi.org/10.1016/S0034-4257(02)00096-2
Jackson RD, Hatfield JL, Reginato RJ et al (1983) Estimation of daily evapotranspiration from one time-of-day measurements. Agric Water Manag 7:351–362. https://doi.org/10.1016/0378-3774(83)90095-1
Jofre-Čekalović C, Nieto H, Girona J et al (2022) Accounting for almond crop water use under different irrigation regimes with a two-source energy balance model and copernicus-based inputs. Remote Sens 14:2106. https://doi.org/10.3390/rs14092106
Kalita PK, Kanwar RS (1992) Energy balance concept in the evaluation of water table management effects on corn growth: experimental investigation. Water Resour Res 28:2753–2764. https://doi.org/10.1029/92wr01430
Kelly J, Kljun N, Olsson P-O et al (2019) Challenges and best practices for deriving temperature data from an uncalibrated UAV thermal infrared camera. Remote Sens 11:567. https://doi.org/10.3390/rs11050567
Khan MS, Baik J, Choi M (2021) A physical-based two-source evapotranspiration model with Monin-Obukhov similarity theory. Gisci Remote Sens 58:88–119. https://doi.org/10.1080/15481603.2020.1857625
Khosa FV, Feig GT, van der Merwe MR et al (2019) Evaluation of modeled actual evapotranspiration estimates from a land surface, empirical and satellite-based models using in situ observations from a South African semi-arid savanna ecosystem. Agric for Meteorol 279:107706. https://doi.org/10.1016/j.agrformet.2019.107706
Knipper KR, Kustas WP, Anderson MC et al (2019) Evapotranspiration estimates derived using thermal-based satellite remote sensing and data fusion for irrigation management in California vineyards. Irrig Sci 37:431–449. https://doi.org/10.1007/s00271-018-0591-y
Köksal ES, Cemek B, Artik C, Temizel KE, Taşan M (2011) A new approach for neutron moisture meter calibration: artificial neural network. Irrig Sci 29(2011):369–377. https://doi.org/10.1007/s00271-010-0246-0
Köksal ES, Tasan M, Artik C, Gowda P (2017) Evaluation of financial efficiency of drip-irrigation of red pepper based on evapotranspiration calculated using an iterative soil water-budget approach. Sci Hortic 226:398–405. https://doi.org/10.1016/j.scienta.2017.08.025
Köksal ES, Artik C, Tasan M (2018) Crop evapotranspiration estimations of red pepper using field level remote sensing data and energy balance. Pol J Environ Stud 28:165–175. https://doi.org/10.15244/pjoes/85351
Küçüktopcu E, Cemek B, Simsek H (2022) Application of spatial analysis to determine the effect of insulation thickness on energy efficiency and cost savings for cold storage. Processes 10:2393. https://doi.org/10.3390/pr10112393
Kustas WP, Norman JM (1999) Evaluation of soil and vegetation heat flux predictions using a simple two-source model with radiometric temperatures for partial canopy cover. Agric for Meteorol 94:13–29. https://doi.org/10.1016/S0168-1923(99)00005-2
Lamm FR, AbouKheira AA, Trooien TP (2010) Sunflower, soybean, and grain sorghum crop production as affected by dripline depth. Appl Eng Agric 26:873–882. https://doi.org/10.13031/2013.34952
Li Y, Huang C, Hou J et al (2017) Mapping daily evapotranspiration based on spatiotemporal fusion of ASTER and MODIS images over irrigated agricultural areas in the Heihe River Basin, Northwest China. Agric for Meteorol 244–245:82–97. https://doi.org/10.1016/j.agrformet.2017.05.023
Li C, Li Z, Gao Z, Sun B (2021) Estimation of evapotranspiration in sparse vegetation areas by applying an optimized two-source model. Remote Sens 13:1344. https://doi.org/10.3390/rs13071344
Liang W-Z, Possignolo I, Qiao X et al (2021) Utilizing digital image processing and two-source energy balance model for the estimation of evapotranspiration of dry edible beans in western Nebraska. Irrig Sci 39:617–631. https://doi.org/10.1007/s00271-021-00721-7
Liaqat UW, Choi M (2015) Surface energy fluxes in the Northeast Asia ecosystem: SEBS and METRIC models using landsat satellite images. Agric for Meteorol 214–215:60–79. https://doi.org/10.1016/j.agrformet.2015.08.245
Long D, Singh VP (2012) A Two-source Trapezoid Model for Evapotranspiration (TTME) from satellite imagery. Remote Sens Environ 121:370–388. https://doi.org/10.1016/j.rse.2012.02.015
Malbéteau Y, Parkes S, Aragon B et al (2018) Capturing the diurnal cycle of land surface temperature using an unmanned aerial vehicle. Remote Sens 10:1407. https://doi.org/10.3390/rs10091407
Mecikalski JR, Diak GR, Anderson MC, Norman JM (1999) Estimating fluxes on continental scales using remotely sensed data in an atmospheric–land exchange model. J Appl Meteorol Climatol 38:1352–1369. https://doi.org/10.1175/1520-0450(1999)038%3c1352:EFOCSU%3e2.0.CO;2
Meier F, Scherer D, Richters J et al (2011) Atmospheric correction of thermal-infrared imagery of the 3-D urban environment acquired in oblique viewing geometry. Atmosp Meas Techn 4(5):909–922. https://doi.org/10.5194/amt-4-909-2011
Mesas-Carrascosa FJ, Pérez-Porras F, Meroño de Larriva JE et al (2018) Drift correction of lightweight microbolometer thermal sensors on-board unmanned aerial vehicles. Remote Sens-Basel 10:615. https://doi.org/10.3390/rs10040615
Mokhtari A, Noory H, Pourshakouri F et al (2019) Calculating potential evapotranspiration and single crop coefficient based on energy balance equation using Landsat 8 and Sentinel-2. ISPRS J Photogramm Remote Sens 154:231–245. https://doi.org/10.1016/j.isprsjprs.2019.06.011
Mokhtari A, Ahmadi A, Daccache A, Drechsler K (2021) Actual evapotranspiration from UAV images: a multi-sensor data fusion approach. Remote Sens 13:2315. https://doi.org/10.3390/rs13122315
Moorhead JE, Marek GW, Colaizzi PD et al (2017) Evaluation of sensible heat flux and evapotranspiration estimates using a surface layer scintillometer and a large weighing lysimeter. Sensors. https://doi.org/10.3390/s17102350
Morillas L, Villagarcía L, Domingo F et al (2014) Environmental factors affecting the accuracy of surface fluxes from a two-source model in Mediterranean drylands: upscaling instantaneous to daytime estimates. Agric for Meteorol 189–190:140–158. https://doi.org/10.1016/j.agrformet.2014.01.018
Mutanga O, Skidmore AK (2004) Narrow band vegetation indices overcome the saturation problem in biomass estimation. Int J Remote Sens 25:3999–4014. https://doi.org/10.1080/01431160310001654923
Nassar A, Torres-Rua A, Kustas W et al (2021) Assessing daily evapotranspiration methodologies from one-time-of-day sUAS and EC information in the GRAPEX project. Remote Sens (Basel) 13:2887. https://doi.org/10.3390/rs13152887
Nassar A, Torres-Rua A, Hipps L et al (2022) Using remote sensing to estimate scales of spatial heterogeneity to analyze evapotranspiration modeling in a natural ecosystem. Remote Sens 14:372. https://doi.org/10.3390/rs14020372
Norman JM, Kustas WP, Humes KS (1995) Source approach for estimating soil and vegetation energy fluxes in observations of directional radiometric surface temperature. Agric for Meteorol 77:263–293. https://doi.org/10.1016/0168-1923(95)02265-Y
Norman JM, Kustas WP, Prueger JH, Diak GR (2000) Surface flux estimation using radiometric temperature: a dual-temperature-difference method to minimize measurement errors. Water Resour Res 36:2263–2274. https://doi.org/10.1029/2000wr900033
Olbrycht R, Więcek B, De Mey G (2012) Thermal drift compensation method for microbolometer thermal cameras. Appl Opt 51(11):1788–1794. https://doi.org/10.1364/AO.51.001788
Peng J, Nieto H, Andersen MN et al (2023) Accurate estimates of land surface energy fluxes and irrigation requirements from UAV-based thermal and multispectral sensors. ISPRS J Photogramm Remote Sens 198:238–254. https://doi.org/10.1016/j.isprsjprs.2023.03.009
Phasinam K, Kassanuk T, Shinde PP et al (2022) Application of IoT and cloud computing in automation of agriculture irrigation. J Food Qual. https://doi.org/10.1155/2022/8285969
Potgieter AB, George-Jaeggli B, Chapman SC et al (2017) Multi-spectral imaging from an unmanned aerial vehicle enables the assessment of seasonal leaf area dynamics of sorghum breeding lines. Front Plant Sci 8:1532. https://doi.org/10.3389/fpls.2017.01532
Sakellariou-Makrantonaki M, Papalexis D, Nakos N, Kalavrouziotis IK (2007) Effect of modern irrigation methods on growth and energy production of sweet sorghum (var. Keller) on a dry year in Central Greece. Agric Water Manag 90:181–189. https://doi.org/10.1016/j.agwat.2007.03.004
Sánchez JM, Kustas WP, Caselles V, Anderson MC (2008) Modelling surface energy fluxes over maize using a two-source patch model and radiometric soil and canopy temperature observations. Remote Sens Environ 112:1130–1143. https://doi.org/10.1016/j.rse.2007.07.018
Sánchez JM, López-Urrea R, Rubio E, Caselles V (2011) Determining water use of sorghum from two-source energy balance and radiometric temperatures. Hydrol Earth Syst Sci 15:3061–3070. https://doi.org/10.5194/hess-15-3061-2011
Sánchez JM, López-Urrea R, Rubio E et al (2014) Assessing crop coefficients of sunflower and canola using two-source energy balance and thermal radiometry. Agric Water Manag 137:23–29. https://doi.org/10.1016/j.agwat.2014.02.002
Sánchez JM, López-Urrea R, Doña C et al (2015) Modeling evapotranspiration in a spring wheat from thermal radiometry: crop coefficients and E/T partitioning. Irrig Sci 33:399–410. https://doi.org/10.1007/s00271-015-0476-2
Sau F, Boote KJ, McNair Bostick W et al (2004) Testing and improving evapotranspiration and soil water balance of the DSSAT crop models. Agron J 96:1243–1257. https://doi.org/10.2134/agronj2004.1243
Senay GB, Budde M, Verdin JP, Melesse AM (2007) A coupled remote sensing and simplified surface energy balance approach to estimate actual evapotranspiration from irrigated fields. Sensors 7:979–1000. https://doi.org/10.3390/s7060979
Senay GB, Bohms S, Singh RK et al (2013) Operational evapotranspiration mapping using remote sensing and weather datasets: a new parameterization for the SSEB approach. J Am Water Resour Assoc 49:577–591. https://doi.org/10.1111/jawr.12057
Shafian S, Rajan N, Schnell R et al (2018) Unmanned aerial systems-based remote sensing for monitoring sorghum growth and development. PLoS ONE 13:e0196605. https://doi.org/10.1371/journal.pone.0196605
Simpson JE, Holman F, Nieto H et al (2021) High spatial and temporal resolution energy flux mapping of different land covers using an off-the-shelf unmanned aerial system. Remote Sens 13:1286. https://doi.org/10.3390/rs13071286
Singh RK, Ayse I, Suat I, Martin DL (2008) Application of SEBAL model for mapping evapotranspiration and estimating surface energy fluxes in South-Central Nebraska. J Irrig Drain Eng 134:273–285. https://doi.org/10.1061/(ASCE)0733-9437(2008)134:3(273)
Song B, Park K (2020) Verification of accuracy of unmanned aerial vehicle (UAV) land surface temperature images using in-situ data. Remote Sens 12:288. https://doi.org/10.3390/rs12020288
Song L, Kustas WP, Liu S et al (2016) Applications of a thermal-based two-source energy balance model using Priestley-Taylor approach for surface temperature partitioning under advective conditions. J Hydrol 540:574–587. https://doi.org/10.1016/j.jhydrol.2016.06.034
Taheri M, Mohammadian A, Ganji F et al (2022) Energy-based approaches in estimating actual evapotranspiration focusing on land surface temperature: a review of methods, concepts, and challenges. Energies 15:1264. https://doi.org/10.3390/en15041264
Tang R, Li Z-L, Tang B (2010) An application of the Ts–VI triangle method with enhanced edges determination for evapotranspiration estimation from MODIS data in arid and semi-arid regions: Implementation and validation. Remote Sens Environ 114:540–551. https://doi.org/10.1016/j.rse.2009.10.012
Todd RW, Evett SR, Howell TA (2000) The Bowen ratio-energy balance method for estimating latent heat flux of irrigated alfalfa evaluated in a semi-arid, advective environment. Agric for Meteorol 103:335–348. https://doi.org/10.1016/S0168-1923(00)00139-8
Togneri R, Kamienski C, Dantas R et al (2019) Advancing IoT-based smart irrigation. IEEE Internet of Things Mag 2:20–25. https://doi.org/10.1109/IOTM.0001.1900046
Tunca E, Köksal ES, Çetin S et al (2018) Yield and leaf area index estimations for sunflower plants using unmanned aerial vehicle images. Environ Monit Assess 190:682. https://doi.org/10.1007/s10661-018-7064-x
Tunca E, Köksal ES, Torres-Rua AF et al (2022) Estimation of bell pepper evapotranspiration using two-source energy balance model based on high-resolution thermal and visible imagery from unmanned aerial vehicles. JARS 16:022204. https://doi.org/10.1117/1.JRS.16.022204
Vinukollu RK, Meynadier R, Sheffield J, Wood EF (2011) Multi-model, multi-sensor estimates of global evapotranspiration: climatology, uncertainties and trends. Hydrol Process 25:3993–4010. https://doi.org/10.1002/hyp.8393
Wandera L, Mallick K, Kiely G et al (2017) Upscaling instantaneous to daily evapotranspiration using modelled daily shortwave radiation for remote sensing applications: an artificial neural network approach. Hydrol Earth Syst Sci 21:197–215. https://doi.org/10.5194/hess-21-197-2017
Zhuang Q, Wu B (2015) Estimating evapotranspiration from an improved two-source energy balance model using ASTER satellite imagery. Water 7:6673–6688. https://doi.org/10.3390/w7126653
Zou Y, Saddique Q, Ali A et al (2021) Deficit irrigation improves maize yield and water use efficiency in a semi-arid environment. Agric Water Manag 243:106483. https://doi.org/10.1016/j.agwat.2020.106483
Acknowledgements
This study was supported by the Scientific and Technical Research Council of Turkey (TUBITAK, Project Number: 118O831).
Author information
Authors and Affiliations
Contributions
The author conducted the study, drafted the manuscript, and analyzed the data. The figures and tables of the manuscript were made by the author.
Corresponding author
Ethics declarations
Conflict of interest
The author declares that no conflicts of interest relevant to this study.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendix
Appendix
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Tunca, E. Evaluating the performance of the TSEB model for sorghum evapotranspiration estimation using time series UAV imagery. Irrig Sci (2023). https://doi.org/10.1007/s00271-023-00887-2
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s00271-023-00887-2