Abstract
Reference evapotranspiration (ET0) is the representation of real-time crop-specific measurement of evapotranspiration and could be used for measuring the available water for agriculture. Accurate estimation of reference evapotranspiration (ET0) is required for irrigation management and water resource allocation. Satellite remote sensing provides an opportunity to estimate its quantity and map the spatio-temporal distribution of evapotranspiration in an efficient way. There are several methods developed for estimating ET0 but most of them are mainly based on daily meteorological data provided by weather station networks. This paper aims to estimate the monthly reference evapotranspiration (ET0) by the FAO-56 Penman-Monteith method using the remote sensing data (LANDSAT 8-OLI and LANDSAT 7-ETM+) of 2014, 2015, 2016 and weather data (Maximum and minimum temperature, Dew point temperature, wind speed, relative humidity) over the Dwarakeswar river basin. Input parameters required for this model are emissivity, land surface temperature (LST), net radiation, soil heat flux (G), air temperature, actual and saturation vapor pressure and wind speed. This study indicates that evapotranspiration variation in this area is closely related to crop growth. Evapotranspiration values were found low (66–120 mm/month) when paddy fields were empty and the fields were covered by very sparse vegetation. Whereas, the estimated values were high (120–180 mm/month) in cropping season and in monsoon, when vegetation cover was dense. Furthermore, the evapotranspiration estimation results were analyzed and validated with MODIS data which shows a good agreement between them.
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References
Allen RG, Pruitt WO (1991) FAO-24 reference evapotranspiration factors. J Irrig Drain Eng ASCE 117(5):758–773
Bastiaanssen WGM et al (1998) A remote sensing surface energy balance algorithm for land (SEBAL): 2. Validation. J Hydrol 212–213(1–4):213–229. https://doi.org/10.1016/S0022-1694(98)00254-6
Chen JM, Liu J (2020) Evolution of evapotranspiration models using thermal and shortwave remote sensing data. Remote Sens Environ 237:111594. https://doi.org/10.1016/j.rse.2019.111594
Chiew FHS et al (1995) Penman-Monteith, FAO-24 reference crop evapotranspiration and class-A pan data in Australia. Agric Water Manage 28(1):9–21. https://doi.org/10.1016/0378-3774(95)01172-F
Cruz-Blanco M et al (2014) Assessment of reference evapotranspiration using remote sensing and forecasting tools under semi-arid conditions. Int J Appl Earth Observ Geoinf 33(1):280–289. https://doi.org/10.1016/j.jag.2014.06.008
Da Rocha HR, Manzi AO, Shuttleworth J (2009) Evapotranspiration. Geophys Monogr Ser 186:261–272. https://doi.org/10.4324/9781315081069-13
FAO (2017) Chapter 2—FAO Penman-Monteith equation, pp 1–12
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
Hatfield JL et al (2005) Soil Heat Flux (January). https://doi.org/10.2134/agronmonogr47.c7
He T et al (2015) Land surface albedo estimation from Chinese HJ satellite data based on the direct estimation approach. Remote Sens 7(5):5495–5510. https://doi.org/10.3390/rs70505495
Krishna SVSS, Manavalan P, Rao PVN (2014) Estimation of net radiation using satellite based data inputs. Int Arch Photogramm Remote Sens Spatial Inf Sci-ISPRS Arch XL–8(1):307–313. https://doi.org/10.5194/isprsarchives-XL-8-307-2014
Liang S et al (2003) Narrowband to broadband conversions of land surface albedo: II. Validation. Remote Sens Environ 84(1):25–41. https://doi.org/10.1016/S0034-4257(02)00068-8
López-Urrea R et al (2006) Testing evapotranspiration equations using lysimeter observations in a semiarid climate. Agric Water Manage 85(1–2):15–26. https://doi.org/10.1016/j.agwat.2006.03.014
Monteith JL, Unsworth MH (2013) Principles of environmental physics: plants, animals, and the atmosphere. 4th Edition, pp 422
Monteith JL (1973) Principles of environmental physics, Edward Arnold, London, pp 241
Penman HL (1948) Natural evaporation from open water, hare soil and grass. Proc R Soc Lond Ser A Math Phys Sci 193(1032):120–145. https://doi.org/10.1098/rspa.1948.0037
Rouse JW, Haas RH, Schell JA, Deering DW (1974) Monitoring vegetation systems in the Great Plains with ERTS, In: Freden SC, Mercanti EP, Becker M (eds) Third Earth Resources Technology Satellite–1 Syposium. Volume I: Technical Presentations, NASA SP-351, NASA, Washington, DC, pp 309–317
Seenu N et al (2019) Reference evapotranspiration assessment techniques for estimating crop water requirement. Int J Recent Technol Eng 8(4):1094–1100. https://doi.org/10.35940/ijrte.d6738.118419
Smith M, Allen R, Pereira L (1998) Revised FAO methodology for crop-water requirements. International Atomic Energy Agency (IAEA), pp 51–58
Verstraeten WW, Veroustraete F, Feyen J (2008) Assessment of evapotranspiration and soil moisture content across different scales of observation. Sensors 8(1):70–117. https://doi.org/10.3390/s8010070
Wang YQ et al (2016) Is scale really a challenge in evapotranspiration estimation? A multi-scale study in the Heihe oasis using thermal remote sensing and the three-temperature model. Agric For Meteorol 230–231:128–141. https://doi.org/10.1016/j.agrformet.2016.03.012
Xiong YJ et al (2015) An evapotranspiration product for arid regions based on the three-temperature model and thermal remote sensing. J Hydrol 530:392–404. https://doi.org/10.1016/j.jhydrol.2015.09.050
Xu C-Y, Chen D (2005) Comparison of seven models for estimation of evapotranspiration and groundwater recharge using lysimeter measurement data in Germany. Hydrol Process 19(18):3717–3734. https://doi.org/10.1002/hyp.5853
Zeng X (2003) The common land model experience. Global Change Newsletter 55:19–20
Zotarelli L, Dukes MD, Romero CC, Migliaccio KW, Morgan KT (2014) Step by step calculation of the Penman-Monteith evapotranspiration. Institute of Food and …, pp 1–14. Available at: https://edis.ifas.ufl.edu/ae459
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Basu Roy, T., Dutta, D., Chakrabarty, A. (2021). Spatio-temporal Variation of Evapotranspiration Derived from Multi-temporal Landsat Datasets using FAO-56 Penman-Monteith Method. In: Shit, P.K., Pourghasemi, H.R., Das, P., Bhunia, G.S. (eds) Spatial Modeling in Forest Resources Management . Environmental Science and Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-56542-8_17
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