Time-series analysis of evapotranspiration using normalized difference vegetation index over north-eastern and north-western agro-climatic zones of Tamil Nadu
DOI:
https://doi.org/10.14719/pst.5571Keywords:
actual evapotranspiration, agroclimatic zones, fractional vegetation cover, normalized difference vegetation index, reference evapotranspiration, splineAbstract
Estimation of evapotranspiration spatially is mandatory for water budgeting and water resource monitoring based on crop water demand. The amount of evapotranspiration can be estimated spatially from reference evapotranspiration and fractional vegetation cover over the region. The incorporation of specific characteristics like spatial variability over the land cover produced highly reliable results. The AquaCrop model gives reference evapotranspiration based on climatic parameters like temperature, rainfall, solar radiation, relative humidity and elevation. Normalized Difference Vegetation Index (NDVI) depicts the health of vegetation spatially ranging from -1 to +1. The dimidiate pixel model converts NDVI to Fractional Vegetation Cover (FVC) which was then used as a substitute for crop coefficient value. The products of these two parameters produce actual evapotranspiration spatially over the region with high spatial resolution. The average amount of actual evapotranspiration varies for each Land Use Land Cover (LULC) type over different agroclimatic zones as the climatic parameters and water usage patterns vary for each land cover type.
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Gao H, Liu J, Wang H, Mei C, Wang J. Estimation of irrigated crop artificial irrigation evapotranspiration in China. Scientific Reports. 2024;14(1). https://doi.org/10.1038/s41598-024-67042-5
Pavithran P, Pazhanivelan S, Sivamurugan AP, Ragunath KP, Selvakumar S, et al. Estimation of actual evapotranspiration using surface energy balance algorithm for land in the lower Bhavani basin. Plant Science Today. 2024. https://doi.org/10.14719/pst.4576
Wang L, Caylor KK, Villegas JC, Barron-Gafford GA, Breshears DD, Huxman TE. Partitioning evapotranspiration across gradients of woody plant cover: Assessment of a stable isotope technique. Geophysical Research Letters. 2010;37(9). https://doi.org/10.1029/2010GL043228
Gamal R, El-Shirbeny M, Abou-Hadid A, Swelam A, El-Gindy A-G, Arafa Y, et al. Identification and quantification of actual evapotranspiration using integrated satellite data for sustainable water management in dry areas. Agronomy. 2022;12(9):2143. https://doi.org/10.3390/agronomy12092143
Cammalleri C, Anderson MC, Gao F, Hain CR, Kustas WP. Mapping daily evapotranspiration at field scales over rainfed and irrigated agricultural areas using remote sensing data fusion. Agricultural and Forest Meteorology. 2014;186:1-11. https://doi.org/10.1016/j.agrformet.2013.11.001
Carlson TN, Capehart WJ, Gillies RR. A new look at the simplified method for remote sensing of daily evapotranspiration. Remote Sensing of Environment. 1995;54(2):161-7. https://doi.org/10.1016/0034-4257(95)00139-R
Rajkumar R, Magesh Kumaran M, Mathan Prasath RK, Senthilshanmugavelayutham SP. Agro-climatic zonation for Tamil Nadu using GIS and AHP techniques. IOP Conference series: Materials science and engineering. 2020;1006(1):012015. https://doi.org/10.1088/1757-899X/1006/1/012015
Arumugam S, K.R A, Kulshreshtha SN, Vellangany I, Govindasamy R. Yield variability in rainfed crops as influenced by climate variables. International Journal of Climate Change Strategies and Management. 2015;7(4):442-59. https://doi.org/10.1108/IJCCSM-08-2013-0096
Vinothkanna S, Geethalakshmi V, Lakshmanan A, Bhuvaneswari K, Raja AB, editors. Analysis of current trends of temperature and rainfall for different Agro Climatic Zones of Tamil nadu. Conference paper in National Seminar on Geo informatics in Land and Water Resources Studies At: Bharathidasan University, Tiruchirappalli; 2013.
Chinnadurai M. Situation Analysis of water resources in Tamil Nadu. International Journal of Agriculture Sciences, ISSN. 2018:0975-3710.
Chatterjee S, Stoy PC, Debnath M, Nayak AK, Swain CK, Tripathi R, et al. Actual evapotranspiration and crop coefficients for tropical lowland rice (Oryza sativa L.) in eastern India. Theoretical and Applied Climatology. 2021;146:155-71. https://doi.org/10.1007/s00704-021-03710-0
Kim SJ, Bae SJ, Jang MW. Linear regression machine learning algorithms for estimating reference evapotranspiration using limited climate data. Sustainability. 2022;14(18):11674. https://doi.org/10.3390/su141811674
Raja P, Sona F, Surendran U, Srinivas CV, Kannan K, Madhu M, et al. Performance evaluation of different empirical models for reference evapotranspiration estimation over Udhagamandalm, The Nilgiris, India. Scientific Reports. 2024;14(1):12429. https://doi.org/10.1038/s41598-024-60952-4
Busschaert L, de Roos S, Thiery W, Raes D, De Lannoy GJ. Net irrigation requirement under different climate scenarios using AquaCrop over Europe. Hydrology and Earth System Sciences. 2022;26(14):3731-52. https://doi.org/10.5194/hess-26-3731-2022
Rodrigues GC, Braga RP. Evaluation of NASA POWER reanalysis products to estimate daily weather variables in a hot summer mediterranean climate. Agronomy. 2021;11(6):1207. https://doi.org/10.3390/agronomy11061207
Wu H, Yue Q, Guo P, Xu X, Huang X. Improving the AquaCrop model to achieve direct simulation of evapotranspiration under nitrogen stress and joint simulation-optimization of irrigation and fertilizer schedules. Agricultural Water Management. 2022;266:107599. https://doi.org/10.1016/j.agwat.2022.107599
Rehana S, Monish N. Characterization of regional drought over water and energy limited zones of India using potential and actual evapotranspiration. Earth and Space Science. 2020;7(10):e2020EA001264. https://doi.org/10.1029/2020EA001264
Yan K, Gao S, Chi H, Qi J, Song W, Tong Y, et al. Evaluation of the vegetation-index-based dimidiate pixel model for fractional vegetation cover estimation. IEEE Transactions on Geoscience and Remote Sensing. 2021;60:1-14. https://doi.org/10.1109/TGRS.2020.3048493
Eisfelder C, Asam S, Hirner A, Reiners P, Holzwarth S, Bachmann M, et al. Seasonal vegetation trends for europe over 30 Years from a novel Normalised Difference Vegetation Index (NDVI) Time-Series-The TIMELINE NDVI Product. Remote Sensing. 2023;15(14):3616. https://doi.org/10.3390/rs15143616
Maselli F, Battista P, Chiesi M, Rapi B, Angeli L, Fibbi L, et al. Use of Sentinel-2 MSI data to monitor crop irrigation in Mediterranean areas. International Journal of Applied Earth Observation and Geoinformation. 2020;93:102216. https://doi.org/10.1016/j.jag.2020.102216
Er-Raki S, Bouras E, Rodriguez J, Watts C, Lizarraga-Celaya C, Chehbouni A. Parameterization of the AquaCrop model for simulating table grapes growth and water productivity in an arid region of Mexico. Agricultural Water Management. 2021;245:106585. https://doi.org/10.1016/j.agwat.2020.106585.
Zhang X, Yan G, Li Q, Li ZL, Wan H, Guo Z. Evaluating the fraction of vegetation cover based on NDVI spatial scale correction model. International Journal of Remote Sensing. 2006;27(24):5359-72. https://doi.org/10.1080/01431160600658107.
Purevdorj TS, Tateishi R, Ishiyama T, Honda Y. Relationships between percent vegetation cover and vegetation indices. International Journal of Remote Sensing. 1998;19(18):3519-35. https://doi.org/10.1080/014311698213795
McFeeters SK. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing. 1996;17(7):1425-32. https://doi.org/10.1080/01431169608948714
Xiao Q, Tao J, Xiao Y, Qian F. Monitoring vegetation cover in Chongqing between 2001 and 2010 using remote sensing data. Environmental Monitoring and Assessment. 2017;189:493. https://doi.org/10.1007/s10661-017-6210-1
Salimi Kouchi H, Sahebi M, Abkar A, Valadan Zoej M. Fractional vegetation cover estimation in urban environments. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2013;40:357-60. https://doi.org/10.5194/isprsarchives-XL-1-W3-357-2013
Cho J, Lee Y, Han K-S. The effect of fractional vegetation cover on the relationship between EVI and soil moisture in non-forest regions. Remote Sensing Letters. 2014;5:37-45. https://doi.org/10.1080/2150704X.2013.866288
Wang X-G, Kang Q, Chen X-H, Wang W, Fu Q-H. Wind speed-independent two-source energy balance model based on a theoretical trapezoidal relationship between land surface temperature and fractional vegetation cover for evapotranspiration estimation. Advances in Meteorology. 2020;2020(1):6364531. https://doi.org/10.1155/2020/6364531
Anwar SA, Mamadou O, Diallo I, Sylla MB. On the influence of vegetation cover changes and vegetation-runoff systems on the simulated summer potential evapotranspiration of tropical Africa using RegCM4. Earth Systems and Environment. 2021;5(4):883-97. https://doi.org/10.1007/s41748-021-00252-3
Pervin R, Robeson SM, MacBean N. Fusion of airborne hyperspectral and LiDAR canopy-height data for estimating fractional cover of tall woody plants, herbaceous vegetation, and other soil cover types in a semi-arid savanna ecosystem. International Journal of Remote Sensing. 2022;43(10):3890-926. https://doi.org/10.1080/01431161.2022.2105176
Saikrishnan K, Anand KV, Aglian V. Coastal vulnerability assessment along the coast of Kerala, India, based on physical, geological, and socio-economic parameters. Marine Geodesy. 2024;47(2):119-49. https://doi.org/10.1080/01490419.2023.2285944
Gong JQ, Liu Z, Yan Q, Xu Q. Object-Oriented island land cover change detection by iteratively reweighted multivariate statistical analysis. Marine Geodesy. 2017;40(2-3):87-103. https://doi.org/10.1080/01490419.2017.1304472
Kumar L, Agrawal N, Pandey VK, Rai AK, Mishra SK, Pandey VS. Era-interim forced simulation of the indian summer monsoon. Marine Geodesy. 2019;42(6):558-74. https://doi.org/10.1080/01490419.2019.1654050
Meiaraj C, Jeyapriya S. Comparative study of evapo-transpiration and rainfall analysis of Pollachi watershed in Coimbatore, Tamil Nadu, India. 2019.
Ghorbanian A, Mohammadzadeh A, Jamali S. Linear and non-linear vegetation trend analysis throughout Iran using two decades of MODIS NDVI imagery. Remote Sensing. 2022;14(15):3683. https://doi.org/10.3390/rs14153683
Lunetta RS, Knight JF, Ediriwickrema J, Lyon JG, Worthy LD. Land-cover change detection using multi-temporal MODIS NDVI data. Geospatial Information Handbook for Water Resources and Watershed Management, Volume II: CRC Press; 2022: 65-88. https://doi.org/10.1201/9781003175025-5
Singh D, editor Estimation of surface vapour pressure deficits using satellite derived land surface temperature data2010.
Cao R, Zhang Y, Yang C, Chen T, editors. Estimation of vegetation coverage based on dimidiate pixel model using remote sensing data in Songhua Lake basin, Jilin Province. International Conference on Remote Sensing Technology and Survey Mapping (RSTSM 2024); 2024: SPIE. https://doi.org/10.1117/12.3029281
Hu X, Zhao J, Sun S, Jia C, Zhang F, Ma Y, et al. Evaluation of the temporal reconstruction methods for MODIS-based continuous daily actual evapotranspiration estimation. Agricultural Water Management. 2023;275:107991. https://doi.org/10.1016/j.agwat.2022.107991
Shankar S, Dharanirajan K, Manahoran K. Quantification of Hydro-meteorological balance around Port Blair, South Andaman, India, using soil water balance model. Indian Journal of Geo-Marine Sciences. 2018;47:456-63.
Gebremedhin MA, Lubczynski MW, Maathuis BH, Teka D. Deriving potential evapotranspiration from satellite-based reference evapotranspiration, Upper Tekeze Basin, Northern Ethiopia. Journal of Hydrology: Regional Studies. 2022;41:101059. https://doi.org/10.1016/j.ejrh.2022.101059
Satpathi A, Danodia A, Nain AS, Dhyani M, Vishwakarma DK, Dewidar AZ, et al. Estimation of crop evapotranspiration using statistical and machine learning techniques with limited meteorological data: a case study in Udham Singh Nagar, India. Theoretical and Applied Climatology. 2024;155(6):5279-96. https://doi.org/10.1007/s00704-024-04953-3
Adeyeri OE, Ishola KA. Variability and trends of actual evapotranspiration over West Africa: The role of environmental drivers. Agricultural and Forest Meteorology. 2021; 308:108574. https://doi.org/10.1016/j.agrformet.2021.108574
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Copyright (c) 2024 M Sabthapathy, KP Ragunath, S Pazhanivelan, S Selvakumar, AP Sivamurugan, R Kumaraperumal, Ahamed J Mohammed, K Chandrasekar, S Thiruvarassan
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