Estimating irrigation water requirement in rice by integration of satellite data and agrometeorological indices in Palakkad, Kerala
DOI:
https://doi.org/10.54386/jam.v25i2.2051Keywords:
crop coefficient, crop evapotranspiration, irrigation scheduling, Normalized Difference Vegetation Index, rice, water requirementAbstract
The sustainability of irrigated agriculture is jeopardized by catastrophic climate change, with projected forecasts indicating that by 2025, one out of every four people on the planet will be experiencing extreme water scarcity. In this context, an attempt was made for scheduling irrigation at a regional scale combining satellite data and agrometeorological indices over major rice growing tracts of Palakkad district in Kerala. Normalized Difference Vegetation Index (NDVI) product of MODIS (MOD13Q1) with a temporal resolution of 16 days and a spatial resolution of 250 m was utilized to establish a relationship with crop coefficient (Kc) of rice during the mundakan rice season of 2020-21 and 2021-22 in 30 ground truth locations. The results revealed that NDVI values have strong relationship with Kc values with an R2 value of 0.81. Crop coefficient (Kc) maps developed using satellite derived NDVI provided Kc values at a regional scale during different stages of crop growth and it helped to estimate crop evapotranspiration with greater accuracy. Based on this crop water demands maps depicting the spatial and temporal distribution of irrigation requirement were generated for the whole study area. These maps can be used as a tool for the estimation of the crop water requirement of a rice field if the geographical coordinates of the location are known. The total crop water requirements estimated during mundakan season 2020-21 and 2021-22 in Palakkad district were in the range of 700-975 mm and 560-897 mm respectively. Integration of remote sensing & agrometeorological techniques has scope for regional-scale crop water requirement estimation in a cost-effective and time-bound manner.
References
Allen, R. G., Pereira, L. S., Raes, D. and Smith, M. (1998). Crop evapotranspiration: guidelines for computing crop water requirements, Irrigation and drainage Paper No. 56, FAO, Rome, Italy, p.300
Anil Kumar, D., Neelima, T. L., Srikanth, P., Uma devi, M., Suresh, K. and Murthy, C. S. (2022). Maize yield prediction using NDVI derived from Sentinel 2 data in Siddipet district of Telangana state. J. Agrometeorol., 24(2): 165–168. https://doi.org/10.54386/jam.v24i2.1635
Campos, I., Neale, C. M. U., Suyker, A. E., Arkebauer, T. J. and Goncalves, I. Z. (2017). Reflectance-based crop coefficients REDUX: For operational evapotranspiration estimates in the age of high producing hybrid varieties, Agri. Wat. Manag., 187: 140-153. https://doi.org/10.1016/j.agwat.2017.03.022
Capelli, G., Mazza, R., and Gazzetti, C. (2005). Tools and strategies for the compatible use of water resources in Lazio, Pythagoras, Italy, p.216
González, A., Kjaersgaard, J., Trooien, T., Hay, C., and Ahiablame, L. (2018). Estimation of crop evapotranspiration using satellite remote sensing-based vegetation index, Adv. Meteorol., 1-12. https://doi.org/10.1155/2018/4525021
Hossain, M. B., Yesmin, S., Maniruzzaman, M., and Biswas, J. C. (2017). Irrigation scheduling of rice (Oryza sativa L.) using CROPWAT model in the western region of Bangladesh, Agriculturists, 15(1): 19-27. DOI: 10.3329/agric.v15i1.33425
FAO (2021). United Nations social and economic council, Rome, Italy. https://www.fao.org/3/X5560E/x5560e03.htm,
Hunsaker, D. J., Pinter, P. J., and Kimball, B. A. (2005). Wheat basal crop coefficients determined by normalized difference vegetation index, Irrig. Sci., 24: 1-14. DOI: https://doi.org/10.1007/s00271-005-0001-0
Javed, M. A., and Ahmad, S. R. (2020). A decision support system for crop water requirement estimation using advanced geospatial techniques, Pak. J. Agri. Sci., 57(4): 981-991. DOI: 10.21162/PAKJAS/20.9482
Jayanthi, H., Nealea, C. M. U., and Wright, J. L. (2007). Development and validation of canopy reflectance-based crop coefficient for potato, Agri. Wat. Manag., 88(3): 235-246. DOI: https://doi.org/10.1016/j.agwat.2006.10.020
Kamble, B., Kilic, A., and Hubbard, K. (2013). Estimating crop coefficients using remote sensing-based vegetation index, Remote Sens., 5(4): 1588-1602. DOI: https://doi.org/10.3390/rs5041588
KAU [ Kerala Agricultural University]. (2016). Package of Practices Recommendation, Crops (15th Ed.). Kerala Agricultural University, Thrissur, P.22.
Kaushalya R., Gayatri, M., Praveen, V., and Satish., J. (2014). Use of NDVI variations to analyse the length of growing period in Andhra Pradesh. J. Agrometeorol., 16(1):112-115. https://doi.org/10.54386/jam.v16i1.1494
Kuo, S., Ho, S., and Liu, C. (2006). Estimation irrigation water requirements with derived crop coefficients for upland and paddy crops in China irrigation association, Taiwan, Agri. Wat. Manag., 82: 433-445. https://doi.org/10.1016/j.agwat.2005.08.002
Mushtaq, R., Sharma, M. K., Ahmad, L., Krishna, B., Mushtaq, K. and Mir, J. I. (2020). Crop water requirement estimation using pan evaporimeter for high density apple plantation system in Kashmir region of India. J. Agrometeorol., 22(1): 86-88. https://doi.org/10.54386/jam.v22i1.133
Olsen, J. L., Stisen, S., Proud, S. R., and Fensholt, R. (2015). Evaluating EO-based canopy water stress from seasonally detrended NDVI and SIWSI with modeled evapotranspiration in the Senegal River Basin. Remote Sens. Environ., 159: 57–69. https://doi.org/10.1016/j.rse.2014.11.029
Rao A. N., Wani S. P., Ramesha M. S. and Ladha J. K. (2017). Rice production systems. In: Chauhan B. S. (eds), Rice production worldwide. Springer international publishing, pp. 185-205. DOI 10.1007/978-3-319-47516-5_8
Rouse, J., Haas, R., Schell, J. and Deering, D. (1973). Monitoring vegetation systems in the Great Plains with ERTS. In Proceedings of the Third ERTS-1 Symposium NASA SP-351, Washington, DC, USA, 10–14 December 1973; pp. 309–317.
Shanmugapriya, P., Latha, K. R., Pazhanivelan, S., Kumaraperumal, R., Karthikeyan, G. and Sudarmanian, N. S. (2022). Cotton yield prediction using drone derived LAI and chlorophyll content. J. Agrometeorol., 24(4): 348-352. https://doi.org/10.54386/jam.v24i4.1770
Singh, R. and Irmak, A. (2008). A modified approach for estimation of crop coefficients using satellite remote sensing data. ASABE Annual International Meeting, Providence, 2008, Rhode Island, Paper Number: 083542. DOI:10.13031/2013.24593
Spanu, A., Murtas, A., and Ballone, F. (2009). Water use and crop coefficients in sprinkler irrigated rice, Ital. J. Agron., 4(2): 47-58. https://doi.org/10.4081/ija.2009.2.47
Shouqin, Z., Weihua, Z., Jaike, L. V. and Chaofu, W. (2014). Temporal variation of soil water and its influencing factors in hilly area of Chongqing, China, Intl J. Agric. & Biol. Eng., 7(4): 47-59. DOI: 10.3965/j.ijabe.20140704.006
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 CHINNU RAJU, AJITH K., AJITHKUMAR B., ANITHA S., DIVYA VIJAYAN V.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This is a human-readable summary of (and not a substitute for) the license. Disclaimer.
You are free to:
Share — copy and redistribute the material in any medium or format
Adapt — remix, transform, and build upon the material
The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
NonCommercial — You may not use the material for commercial purposes.
ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation.
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.