A baseline estimate of regional agricultural water demand from GEO-LEO satellite observations

References

• Allen RG. 2000. Using the FAO-56 dual crop coefficient method over an irrigated region as part of an evapotranspiration inter-comparison study. J Hydrol. 229(1-2):27–41.
   Web of Science ®Google Scholar
• Allen RG, Clemmens AJ, Burt CM, Solomon K, O’Halloran T. 2005. Prediction accuracy for project wide evapotranspiration using crop coefficients and reference evapotranspiration. J Irrig Drain Eng. 13:24–36.
   Google Scholar
• Allen RG, Jensen ME, Wright JL, Burman RD. 1989. Operational estimates of reference evapotranspiration. Agron J. 81 (4):650–662.
   Web of Science ®Google Scholar
• Allen RG, Pereira LS, Raes D, Smith M. 1998. Crop evapotranspiration: guideline for computing crop water requirements. FAO Irrigation and drainage paper No. 56. Rome: Food and Agriculture Organization of the United Nations; p. 26–40.
   Google Scholar
• Allen RG, Pruitt WO, Wright JL, Howell TA, Ventura F, Snyder R, Itenfisu D, Steduto P, Berengena J, Yrisarry JB, et al. 2006. A recommendation on standardized surface resistance for hourly calculation of reference ET0 by the FAO 56 Penman Monteith method. Agric Water Manage. 81(1-2):1–22.
   Web of Science ®Google Scholar
• Allen RG, Tasumi M, Morse A, Trezza R. 2005. A Landsat-based energy balance and evapotranspiration model in Western US water rights regulation and planning. Irrig Drainage Syst. 19(3-4):251–268.
   Google Scholar
• Andales A. 2014. Effects of Weather on Irrigation Requirements. Soil and crop sciences, Colorado State University, Fact Sheet No. 4.721, 12/2014. https://extension.colostate.edu/docs/pubs/crops/04721.pdf.
   Google Scholar
• Avola G, Gennaro SFD, Cantini C, Riggi E, Muratore F, Tornambè C, Matese A. 2019. Remotely sensed vegetation indices to discriminate field-grown olive cultivars. Remote Sens. 11(10):1242. https://doi.org/http://dx.doi.org/10.3390/rs11101242.
   Google Scholar
• Bashir MA, Hata T, Tanakamaru H, Abdelhadi AW, Tada A. 2007. Remote sensing derived crop coefficient for estimating crop water requirements for irrigated sorghum in the Gezira scheme. J Env Inform. 10(1):47–54.
   Web of Science ®Google Scholar
• Bastiaanssen WGM, Pelgrum H, Wang J, Ma Y, Moreno JF, Roerink GJ, Van der Wal T. 1998b. A remote sensing surface energy balance algorithm for land (SEBAL): part 2: validation. J Hydrol. 212-213:213–229.
   Web of Science ®Google Scholar
• Bausch WC, Neale CMU. 1987. Crop coefficients derived from reflected canopy radiation: a concept. Trans Atmos Soc Agric Eng. 30(3):703–709.
   Google Scholar
• Bausch WC, Neale CMU. 1989. Spectral inputs improve corn crop coefficients and irrigation scheduling. Trans Atmos Soc Agric Eng. 32:1901–1908.
   Google Scholar
• Belaqziz S, Khabba S, Er-Raki S, Jarlan L, Le Page M, Kharrou MH, Adnani ME, Chehbouni A. 2013. A new irrigation priority index based on remote sensing data for assessing the networks irrigation scheduling. Agric Water Manage. 119:1–9.
   Web of Science ®Google Scholar
• Berg A, Findell K, Lintner B, Giannini A, Seneviratne S, Hurk BVD, Lorenz R, Pitman A, Hagemann S, Meier A, et al. 2016. Land–atmosphere feedbacks amplify aridity increase over land under global warming. Nat Clim Change. 6(9):869–874.
   Web of Science ®Google Scholar
• Bhattacharya BK, Nigam R, Dorjee N, Patel NK, Panigrahy S. 2008. Normalized difference vegetation index-towards development of operational product from INSAT 3A CCD. National Symposium ISRS, Dec 18–20.
   Google Scholar
• Bhattacharya BK, Padmanabhan N, Mahammed S, Ramakrishnan R, Parihar JS. 2013. Assessing solar energy potential using diurnal remote-sensing observations from Kalpana-1 VHRR and validation over the Indian landmass. Int J Remote Sens. 34(20):7069–7090.
   Web of Science ®Google Scholar
• Bois B, Pieri P, Van Leeuwen C, Wald L, Huard F, Gaudillere J-P, Saur E. 2008. Using remotely sensed solar radiation data for reference evapotranspiration estimation at a daily time Step. Agric for Meteorol. 148(4):619–630. agrformet.2007.11.005.
   Web of Science ®Google Scholar
• Bruin HD, Trigo FI, Lorite IJ, Blanco MC. 2012. Reference crop evapotranspiration obtained from the geostationary satellite MSG (METEOSAT). Geophys Res Abstr. 14:11453.
   Google Scholar
• Burman RD, Nixon PR, Wright JL, Pruitt WO. 1980b. Water requirements. In: Jensen ME, editor. Design of farm irrigation systems, ASAE Mono. St. Joseph (MI): American Society of Agricultural Engineering; p. 189–232.
   Google Scholar
• Burman RD, Wright JL, Nixon PR, Hill RW. 1980a. Irrigation management-water requirements and water balance. In Irrigation, challenges of the 80’s, proceedings of the second national irrigation symposium. St. Joseph (MI): American Society of Agricultural Engineering; p. 141–153.
   Google Scholar
• Campos I, Villodre J, Carrara A, Calera A. 2013. Remote sensing-based soil water balance to estimate mediterranean Holm Oak Savanna (Dehesa) evapotranspiration under water stress conditions. J Hydrol. 494:1–9.
   Web of Science ®Google Scholar
• Chaudhari KN, Sarkar C, Patel NK, Parihar JS. 2006. An inter-comparison of satellite based NOAA CPC rainfall estimates and gauge observations over selected stations in India. Proceedings of the ISPRS Symposium on Geospatial Databases for Sustainable Development, XXXVI.
   Google Scholar
• Chen Y, Li Z, Li W, Deng H, Shen Y. 2016. Water and ecological security: dealing with hydroclimatic challenges at the heart of China’s Silk Road. Environ Earth Sci. 75(10):881.
   Web of Science ®Google Scholar
• De Bruin HAR, Stricker JNM. 2000. Evaporation of grass under non-restricted soil moisture conditions. Hydrol Sci. 45 (3):391–406.
   Web of Science ®Google Scholar
• Deardorff JW. 1978. Efficient prediction of ground surface-temperature and moisture with inclusion of a layer of vegetation. J Geophys Res. 83(C4):1889–1903.
   Google Scholar
• Department of Agriculture, Ministry of Agriculture and Farmers Welfare, Government of India. (2019). http://www.agriculture.gov.in.
   Google Scholar
• Dhawan V. 2017. Water and agriculture in India background paper for the South Asia expert panel during the Global Forum for Food and Agriculture (GFFA) 2017. [accessed 2020 Apr 23]. https://www.oav.de/fileadmin/user_upload/5_Publikationen/5_Studien/170118_Study_Water_Agriculture_India.pdfWebsite.
   Google Scholar
• Directorate of Economics and Statistics. ( 2018, 2019). https://www.eands.dacnet.nic.in.
   Google Scholar
• Doorenbos J, Pruitt WO. 1975. Guidelines for predicting crop water requirements. Irrigation and Drainage Paper No. 24. Rome (Italy): Food and Agriculture Organization of the United Nations, 168 pp.
   Google Scholar
• Doorenbos J, Pruitt WO. 1977. Guidelines for predicting crop water requirements. Irrigation and Drainage Paper No. 24, 2nd ed. Rome (Italy): Food and Agriculture Organization of the United Nations, 144 pp.
   Google Scholar
• Eitzinger J, Marinkovic D, HöSch J. 2002. Sensitivity of different evapotranspiration calculation methods in different crop-weather models. In: Rizzoli AE, Jakeman AJ, editors. Integrated Assessment and Decision Support. Proceedings of the First Biennial Meeting of the International Environmental Modeling and Software Society (IEMSS), Jun 24–27; Lugano, Switzerland, Vol. 2. p. 395–400.
   Google Scholar
• Er-Raki S, Chehbouni A, Guemouria N, Duchemin B, Ezzahar J, Hadria R. 2007. Combining FAO-56 model and ground-based remote sensing to estimate water consumptions of wheat crops in a semi-arid region. Agric Water Manage. 87(1):41–54.
   Web of Science ®Google Scholar
• Evett SR, Howell TA, Schneider AD, Tolk JA. 1995. Crop coefficient based evapotranspiration estimates compared with mechanistic model results. In: Espey WH, Combs PG, editors. Water Resources Engineering, Proceedings of the First International Conference, Aug 14–18; San Antonio, TX, Vol. 2.
   Google Scholar
• Ezzahar J, Chehbouni A, Hoedjes JCB, Er-Raki S, Chehbouni A, Boulet G, Bonnefond J-M, De Bruin HAR. 2007. The use of the scintillation technique for estimating and monitoring water consumption of olive orchards in a semi-arid region. Agric Water Manage. 89(3):173–184.
   Web of Science ®Google Scholar
• Famiglietti JS. 2014. The global groundwater crisis. Nature Clim Change. 4(11):945–948.
   Web of Science ®Google Scholar
• FAO. 2016. Food and agriculture: key to achieving the 2030 agenda for sustainable development. Rome: Food and Agriculture Organization.
   Google Scholar
• Farahani HJ, Howell TA, Shuttleworth WJ, Bausch WC. 2007. Evapotranspiration: progress in measurement and modeling in agriculture. Trans Am Soc Agric Biol Eng. 50(5):1627–1638.
   Web of Science ®Google Scholar
• Fensholt R, Sandholt I, Stisen S, Tucker C. 2006. Analysing NDVI for the African continent using the geostationary meteosat second generation SEVIRI sensor. Remote Sens Environ. 101(2):212–229.
   Web of Science ®Google Scholar
• Frédéric F, Ramillien G. 2018. Monitoring groundwater storage changes using the gravity recovery and climate experiment (GRACE) satellite mission: a review. Remote Sens. 10(6):829.
   Google Scholar
• Gabriel BS, Stefanie B, Singh RK, Gowda PH, Velpuri NM, Alemu H, Verdin JP. 2013. Operational evapotranspiration mapping using remote sensing and weather datasets: a new parameterization for the SSEB approach. USGS Staff-Published Research Paper 739. http://digitalcommons.unl.edu/usgsstaffpub/739.
   Google Scholar
• Ghilain N, Arboleda A, Sepulcre-Canto G, Batelaan O, Ardo J, Gellens-Meulenberghs F. 2011. Improving evapotranspiration in land surface models by using biophysical parameters derived from MSG/SEVIRI satellite. Hydrol Earth Syst Sci Discuss. 8(5):9113–9171.
   Google Scholar
• Government of India, Ministry of Irrigation, Water Management Division. 1984. A guide for estimating irrigation water requirement. http://cwc.gov.in/sites/default/files/A%20Guide%20for%20Estimating%20Irrigation%20Water%20Requirement%20(Technical%20Series%20No.%202). 2.pdf
   Google Scholar
• Hargreaves GH, Samani ZA. 1985. Reference crop evapotranspiration from temperature. Appl Eng Agric. 1:96–99.
   Google Scholar
• Herman A, Kumar VB, Arkin PA, Kousky JV. 1997. Objectively determined 10-day African rainfall estimates created for famine early warning systems. Int J Remote Sens. 18(10):2147–2159.
   Web of Science ®Google Scholar
• Hoedjes JCB, Chehbouni A, Ezzahar J, Escadafal R, De Bruin HAR. 2007. Comparison of large aperture scintillometer and eddy covariance measurements: can thermal infrared data be used to capture footprint induced differences? J Hydrometeorol. 8(2):144–159.
   Web of Science ®Google Scholar
• Hussein M. Al-Ghobari 2000. Estimation of reference evapotranspiration for southern region of Saudi Arabia. Irrig Sci. 19 (2):81–86.
   Web of Science ®Google Scholar
• IAI and FICCI. 2016. Accelerating growth of Indian agriculture: micro irrigation an efficient solution. http://ficci.in/study-page.asp?spid=20735&sectorid=1.
   Google Scholar
• [IMD] India Meteorological Department. 2012. Southwest Monsoon, End-of-season report, 1 –14. India Meteorological Department. www.imd.gov.in.
   Google Scholar
• IMD. 2014. Annual report. Department of Agriculture & Co-operation, Ministry of Agriculture, Government of India, India Meteorological Department. http://metnet.imd.gov.in/imdnews/ar2014.pdf.
   Google Scholar
• IMD. 2015. Annual report. Department of Agriculture & Co-operation, Ministry of Agriculture, Government of India, India Meteorological Department. www.imd.gov.in.
   Google Scholar
• IMD. 2010. Annual report. Department of Agriculture & Co-operation, Ministry of Agriculture, Government of India, India Meteorological Department. www.agricoop.nic.in.
   Google Scholar
• Irmak S. 2010. Nebraska water and energy flux measurement, modeling, and research network (NEBFLUX). Trans Am Soc Agric Biol Eng. 53(4):1097–1115.
   Web of Science ®Google Scholar
• Iyer R. 2003. Water, perspectives, issues, concern. New Delhi: Sage Publications.
   Google Scholar
• Jackson RD, Idso SB, Reginato RJ, Pinter PJ. 1980. Remotely sensed crop temperatures and reflectances as inputs to irrigation scheduling. In Proceedings of the Special Conference on Irrigation and Drainage, Jul 23–25, Boise, Idaho, ASCE New York. p. 390–397.
   Google Scholar
• Jayanthi H, Neale CMU, Wright JL. 2000. Seasonal evapotranspiration estimation using canopy reflectance: a case study involving pink beans. Proceedings of Remote Sensing and Hydrology 2000; Apr 2–7; Santa Fe, NM. p. 302–305.
   Google Scholar
• Jensen ME. 1968. Water consumption by agricultural plants. In: Kozlowski TT, editor. Water deficits and plant growth. Vol II. New York: Academic Press, Inc; p. 1–22.
   Google Scholar
• Jensen ME, Burman RD, Allen RG. 1990. Evaporation and irrigation water requirements. ASCE Manuals and Reports on Engineering Practices No. 70. New York: American Society of Civil Engineering. 360 pp.
   Google Scholar
• Jongyoun K, Terri SH. 2008. Evaluation of a MODIS-based potential evapotranspiration product at the point scale. J Hydrometeorol. 9:444–460.
   Web of Science ®Google Scholar
• Kang J, Zi X, Wang S, He L. 2019. Evaluation and optimization of agricultural water resources carrying capacity in Haihe river basin, China. Water. 11(5):999.
   Web of Science ®Google Scholar
• Kashyap PS, Panda RK. 2001. Evaluation of evapotranspiration estimation methods and development of crop coefficients for potato crop in a sub-humid region. Agric Water Manage. 50(1):9–25.
   Web of Science ®Google Scholar
• Khole M, De US. 2003. A study on the north-east monsoon rainfall over India. Mausam. 54(2):419–426.
   Google Scholar
• Kidd C, Kniveton DR, Todd MC, Bellerby TJ. 2003. Satellite rainfall estimation using combined passive microwave and infrared algorithms. J Hydrometeor. 4(6):1088–1104.
   Web of Science ®Google Scholar
• Kite GW, Droogers P. 2000. Comparing evapotranspiration estimates from satellites, hydrological models and field data. J Hydrol. 209:3–18.
   Google Scholar
• Knipper K, Hogue T, Scott R, Franz K. 2017. Evapotranspiration estimates derived using multi-platform remote sensing in a semiarid region. Remote Sens. 9(3):184–122. www.mdpi.com/journal/remotesensing., pp. 1-
   Google Scholar
• Kumar P, Bhattacharya BK, Nigam R, Kishtawal CM, Pal PK. 2014. Impact of Kalpana-1 derived land surface albedo on short-range weather forecasting over the Indian subcontinent. J Geophys Res Atmos. 119(6):2764–2717.
   Web of Science ®Google Scholar
• Kumari M, Patel NR, Khayruloevich PY. 2013. Estimation of crop water requirement in rice-wheat system from multi-temporal AWIFS satellite data. Int J Geomat Geosci. 4(1):61–74.
   Google Scholar
• Lascano RJ. 2000. A general system to measure and calculate daily crop water use. Agron J. 92(5):821–832.
   Web of Science ®Google Scholar
• Makkink GF. 1957. Testing the Penman formula by means of lysimeters. J Inst Water Eng. 11:277–288.
   Google Scholar
• Matthew R, Isabella V, Famiglietti JS. 2009. Satellite-based estimates of groundwater depletion in India. Nature 460:999–1002.
   PubMed Web of Science ®Google Scholar
• Miller SW, Arkin PA, Joyce R. 2001. A combined microwave/infrared rain rate algorithm. Int J Remote Sens. 22(17):3285–3307.
   Web of Science ®Google Scholar
• Miralles DG, Holmes TRH, De Jeu RAM, Gash JH, Meesters AGCA, Dolman AJ. 2011. Global land-surface evaporation estimated from satellite-based observations. Hydrol Earth Syst Sci. 15(2):453–469. www.hydrol-earth-syst-sci.net/15/453/2011/.
   Web of Science ®Google Scholar
• Monteith JL. 1965. Evaporation and environment. In 19th symposia of the society for experimental biology. Vol. 19. Cambridge: University Press; p. 205–234.
   Google Scholar
• Nigam R, Bhattacharya BK, Gunjal KR, Padmanabhan N, Patel NK. 2011. Continental scale vegetation index from Indian geostationary satellite: algorithm definition and validation. Curr Sci. 100 (8)
   PubMed Web of Science ®Google Scholar
• Pakhale G, Gupta P, Jyoti N. 2010. Crop and irrigation water requirement estimation by remote sensing and GIS: a case study of Karnal District, Haryana, India. Int J Eng Technol. 2(4):207–211.
   Google Scholar
• Panda SS, Ames DP, Panigrahi S. 2010. Application of vegetation indices for agricultural crop yield prediction using neural network techniques. Remote Sens. 2(3):673–696.
   Google Scholar
• Patel NR, Rakhesh D, Mohammed AJ. 2006. Mapping of regional evepotranspiration in wheat using Terra/MODIS satellite data. IAHS Hydrol Sci J. 51(2):325–333.
   Web of Science ®Google Scholar
• Penman HL. 1948. Natural evaporation from open water, bare soil, and grass. Proc R Soc Lond A193:116–140.
   Google Scholar
• Pongpinyopap S, Mungcharoen T. 2012. Comparative study of green water footprint estimation methods for Thailand: a case study of Cassava-based ethanol. Environ Nat Res J. 10 (2):66–72.
   Google Scholar
• Priestley CHB, Taylor RJ. 1972. On the assessment of surface heat flux and evaporation using large scale parameters. Mon Wea Rev. 100(2):81–92.
   Web of Science ®Google Scholar
• Rind D, Goldberg R, Hansen J, Rosenzweig C, Ruedy R. 1990. Potential evapotranspiration and the likelihood of future drought. J Geophys Res. 95(D7):9983–10004.
   Web of Science ®Google Scholar
• Rodell M, Velicogna I, Famiglietti JS. 2009. Satellite-based estimates of groundwater depletion in India. Nature. 460(7258):999–1002.
   PubMed Web of Science ®Google Scholar
• Sharma S, Kumar P, Vaishnav R, Lal S. 2016. Evaluation of clouds simulated by a weather model over western India. Remote Sens Lett. 7(9):905–913.
   Web of Science ®Google Scholar
• Shideed K, Oweis T, Gabr M, Osman M. 1995. Assessing on-farm water use efficiency: a new approach. ICARDA/ESCWA, editor. Aleppo: Syria, 86 pp.
   Google Scholar
• Silveira Junior CR, Ferreira Junior LG, Silva BB. 2019. Characteristics and challenges of NDVI generation to Brazil from Meteosat-10 geostationary orbit satellite data. Revista Brasileira de Geografia Física. 12(03):1142–1155. https://periodicos.ufpe.br/revistas/rbgfe.
   Google Scholar
• Snyder RL, Lanini BJ, Shaw DA, Pruit WO. 1987. Using reference evapotranspiration (ET0) and crop co-efficients to estimate crop evapotranspiration (ETc) for agronomic crops, grasses, and vegetable crops. University of California, Division of Agriculture and Natural Resources Leaflet 21427, 12 p.
   Google Scholar
• Snyder RL, Orang M, Bali K, Eching S. 2004. Basic irrigation scheduling. [accessed 2017 Dec 26] https://www.waterplan.water.ca.gov/landwateruse/wateruse/Ag/CUP/Californi/Climate_.Data_010804.xls
   Google Scholar
• Surendran U, Sushanth CM, George M, & Joseph, E J. 2014. Modeling the impacts of increase in temperature on irrigation water requirements in Palakkad district – a case study in humid tropical Kerala. J Water Clim Change. 5(3):472–487.
   Google Scholar
• Surendran U, Sushanth CM, George M, Joseph EJ. 2015. Modeling the crop water requirement using FAO-CROPWAT and assessment of water resources for sustainable water resource management: a case study in Palakkad district of humid tropical Kerala, India. Aquatic Procedia 4 (2015), International Conference on Water Resources, Coastal and Ocean Engineering (ICWRCOE 2015). p. 1211–1219.
   Google Scholar
• Surendran U, Sushanth CM, George M, Joseph EJ. 2017. FAO-CROPWAT model-based estimation of crop water need and appraisal of water resources for sustainable water resource management: pilot study for Kollam district – humid tropical region of Kerala, India. Curr Sci. 112(1):76–86.
   Web of Science ®Google Scholar
• UN. 2015. Transforming our world: the 2030 agenda for sustainable development. New York: United Nations, Department of Economic and Social Affairs.
   Google Scholar
• Usda Soil Conservation Services. 1993. Irrigation water requirements. In National Engineering Handbook NEH. National Technical Information Service, Part 623, chapter 2.
   Google Scholar
• Vishal K, Mehtaa VR, Hadenb BA, Joycea DR, Purkeya LE, Jackson C. 2013. Irrigation demand and supply, given projections of climate and land-use change, in Yolo County, California. Agric Water Manage. 117:70–82.
   Web of Science ®Google Scholar
• Vyas S, Nigam R, Bhattacharya BK, Kumar P. 2016. Development of real-time reference evapotranspiration at the regional scale using satellite-based observations. Int J Remote Sens. 37(24):6108–6126.
   Web of Science ®Google Scholar
• Xiao J, Moody A. 2005. Comparison of methods for estimating fractional green vegetation cover within a desert-to-upland transition zone in central New Mexico, USA. Remote Sens Environ. 98(2-3):237–250.
   Web of Science ®Google Scholar
• Xie P, Arkin PA. 1996. Analysis of global monthly precipitation using gauge observations, satellite estimates, and numerical model prediction. J Clim. 9(4):840–858. > 2.0.CO;2.
   Web of Science ®Google Scholar
• Xie P, Yarosh Y, Love T, Janowiak JE, Arkin PA. 2002. A real-time daily precipitation analysis over South Asia. Proceedings of the 16th Conference on Hydrology, American Meteorological Society; Orlando, FL. [accessed 2017 Sep 23]. http://www.cpc.ncep.noaa.gov/products/fews/sasia_rfe.pdf.
   Google Scholar
• Xu C, Gong L, Jiang T, Chen D, Singh VP. 2006. Analysis of spatial distribution and temporal trend of reference evapotranspiration and pan evaporation in Changjiang (Yangtze River) catchment. J Hydrol. 327(1-2):81–93.
   Web of Science ®Google Scholar
• Xue J, Su B. 2017. Significant remote sensing vegetation indices: a review of developments and applications. J Sens. 2017:1–17..
   Web of Science ®Google Scholar
• Zhao J, Chen X, Zhang J, Zhao H, Song Y. 2019. Higher temporal evapotranspiration estimation with improved SEBS model from geostationary meteorological satellite data. Sci Rep. 9(1):14981.
   PubMedGoogle Scholar