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Plant Science Today (PST)

Research Articles

Vol. 13 No. sp5 (2026): Recent Advances in Agriculture

Multi-scale drought monitoring using meteorological and vegetation indices over different agro-climatic zones of Tamil Nadu, India

DOI
https://doi.org/10.14719/pst.16329
Submitted
24 June 2026
Published
21-09-2026

Abstract

Drought episodes pose severe dangers to agricultural productivity and disrupt global socio-economic situations. Climate change and global warming are predicted to intensify drought risks and their impacts on natural resources and agricultural systems. A study was conducted at the Agro-Climate Research Centre, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India, to monitor and assess the multi-scale drought dynamics across different agro-climatic zones of Tamil Nadu during 1991–2024. In this study, the standardised precipitation index (SPI) was combined with multi-source weighted ensemble precipitation (MSWEP) data, the normalised difference vegetation index (NDVI) and vegetation condition index (VCI) anomalies derived from Moderate Resolution Imaging Spectroradiometer (MODIS) cropland images from 2002–24. The lagged Pearson correlation was used to quantify the relationship between aridity and vegetation in 38 districts and seven agro-climatic zones (ACZs). Standardised precipitation index classified 2002, 2003, 2013 and 2016 as state-wide drought years. Deficiencies in the Northeast monsoon (NEM) led to far more severe agricultural stress than deficiencies in the Southwest monsoon (SWM). The Southern and Northeastern zones suffered disproportionate damage. Crop canopies responded to moisture loss instantaneously, with 95 % of ideal responses showing a zero-month lag. The NDVI anomaly and NDVI (r = 0.46–0.49) were better than the raw NDVI (r ≈ 0.29) compared with the short- to medium-term SPI (SPI-3 and SPI-6). The link was strongest in the interior agricultural areas (mean r = 0.48), but the crop response to vegetative indices was minimised across the irrigated coastal regions. This research finally provides farmers and policymakers with useful zone-specific early warning information for efficient deficit irrigation management, planting window adjustments and the deployment of drought-tolerant crop types to further improve crop productivity.

References

  1. 1. Disasa KN, Yan H, Zhang J, Wang G, Zhang C, Zhang D, et al. Comprehensive review of drought characteristics and intensification under climate change: implications for agriculture and water resources. J Hydrol. 2026;664:134571. https://doi.org/10.1016/j.jhydrol.2025.134571
  2. 2. Hua L, Wang H, Sui H, Wardlow B, Hayes MJ, Wang J. Mapping the spatial-temporal dynamics of vegetation response lag to drought in a semi-arid region. Remote Sens. 2019;11(16):1873. https://doi.org/10.3390/rs11161873
  3. 3. Mishra AK, Singh VP. A review of drought concepts. J Hydrol. 2010;391(1–2):202–16. https://doi.org/10.1016/j.jhydrol.2010.07.012
  4. 4. Wilhite DA, Glantz MH. Understanding: the drought phenomenon: the role of definitions. Water Int. 1985;10(3):111–20. https://doi.org/10.1080/02508068508686328
  5. 5. Huang M, Lu R, Zhang Z, Zhou Y, Li P, Du P, et al. Fine-scale analysis of the cumulative and time-lagged effects of drought on vegetation in the Ili River Basin, Central Asia. J Environ Manage. 2025;392:126670. https://doi.org/10.1016/j.jenvman.2025.126670
  6. 6. Cammalleri C, McCormick N, Spinoni J, Nielsen-Gammon JW. An analysis of the lagged relationship between anomalies of precipitation and soil moisture and its potential role in agricultural drought early warning. J Appl Meteorol Climatol. 2024;63(2):339–50. https://doi.org/10.1175/JAMC-D-23-0077.1
  7. 7. Tanakanti VM, Bharghavi K, Lokeswara Reddy T, Krishna Reddy K. Comparative assessment of IMD and CHIRPS rainfall products over Southern Peninsular India: A multi-scale validation study (2001-2020). Evolving Earth. 2026;4:100125. https://doi.org/10.1016/j.eve.2026.100125
  8. 8. Krishna TM, Ravikumar G, Krishnaveni M. Remote sensing based agricultural drought assessment in Palar basin of Tamil Nadu state, India. J Indian Soc Remote Sens. 2009;37(1):9–20. https://doi.org/10.1007/s12524-009-0008-8
  9. 9. Santhoshkumar B, Nk S, Geethalakshmi V, Ganesan D, Kovilpillai B, Manikandan N, et al. Prediction of drought – Risk zones in Tamil Nadu using historical and global climate model data. Curr Sci. 2024;127:340. https://doi.org/10.18520/cs/v127/i3/340-351
  10. 10. Mahalingam V, Geethalakshmi V, Bhuvaneswari K, Jagannathan R, Panneerselvam S. District level drought assessment over Tamil Nadu. Madras Agric J. 2019;106:225–7. https://doi.org/10.29321/MAJ.2019.000250
  11. 11. Samui RP, Kamble M, Sabale JP. Northeast monsoon rainfall and agricultural production in Tamil Nadu and Andhra Pradesh I - Rainfall variability and its significance in agricultural production. Mausam. 2013;64:309–16. https://doi.org/10.54302/mausam.v64i2.687
  12. 12. Pazhanivelan S, Geethalakshmi V, Samykannu V, Kumaraperumal R, Kancheti M, Kaliaperumal R, et al. Evaluation of SPI and rainfall departure based on multi-satellite precipitation products for meteorological drought monitoring in Tamil Nadu. Water. 2023;15(7):1435. https://doi.org/10.3390/w15071435
  13. 13. Kasera R, Minocha VK. Spatial variability of ENSO-based monsoon rainfall in the coastal region of southeastern India. J Water Clim Change. 2025;16(2):547–62. https://doi.org/10.2166/wcc.2025.589
  14. 14. Sreekala P, Rao SVB, Rajeevan K, Arunachalam M. Combined effect of MJO, ENSO and IOD on the intraseasonal variability of northeast monsoon rainfall over south peninsular India. Clim Dyn. 2018;51(9):3865–82. https://doi.org/10.1007/s00382-018-4117-3
  15. 15. Vijaya Bhaskara Rao S, Pradhan PK, Raman MR, Kumar V, Sunilkumar K, Jena B, et al. Regional and remote influences of ocean-atmospheric processes on northeast monsoon rainfall during 2021 over India. Int J Climatol. 2024;44(1):17–38. https://doi.org/10.1002/joc.8313
  16. 16. McKee TB, Doesken NJ, Kleist J. The relationship of drought frequency and duration to time scales. Proc 8th Conf Appl Climatol. 1993;179–84.
  17. 17. Svoboda M, Hayes M, Wood D. Standardised Precipitation Index: User Guide. WMO. 2012.
  18. 18. Vicente-Serrano SM, Beguería S, López-Moreno JI. A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. J Clim. 2010;23(7):1696–718. https://doi.org/10.1175/2009JCLI2909.1
  19. 19. Zhang Y, Liu Y, Chen L, Sun J, Sun Y, Peng C, et al. Characterising drought patterns and vegetation responses in Northeast China: A multi-temporal-scale analysis using the SPI and NDVI. Sustainability. 2025;17(12):5288. https://doi.org/10.3390/su17125288
  20. 20. Rouse W. Monitoring vegetation system in the great plain with ERTS. 3rd ERTS Symp. 1973;309–17.
  21. 21. Tucker CJ. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ. 1979;8(2):127–50. https://doi.org/10.1016/0034-4257(79)90013-0
  22. 22. Kogan FN. Application of vegetation index and brightness temperature for drought detection. Adv Space Res. 1995;15(11):91–100. https://doi.org/10.1016/0273-1177(95)00079-T
  23. 23. Shi X, Chen F, Ding H, Shi M, Li Y. Assessing vegetation ecosystem resistance to drought in the middle reaches of the Yellow River Basin, China. Int J Environ Res Public Health. 2022;19(7):4180. https://doi.org/10.3390/ijerph19074180
  24. 24. Zhou Z, Ding Y, Fu Q, Wang C, Wang Y, Cai H, et al. Comprehensive evaluation of vegetation responses to meteorological drought from both linear and nonlinear perspectives. Front Earth Sci. 2022;10:953805. https://doi.org/10.3389/feart.2022.953805
  25. 25. Zhao A, Zhang A, Cao S, Liu X, Liu J, Cheng D. Responses of vegetation productivity to multi-scale drought in Loess Plateau, China. Catena. 2018;163:165–71. https://doi.org/10.1016/j.catena.2017.12.016
  26. 26. Murthy RVR. Mapping spatio-temporal cropland changes due to water stress in Krishna River basin using temporal satellite data [dissertation]. Andhra University; 2020.
  27. 27. Naresh C, Mathew A. Deep learning-based agricultural drought monitoring and prediction using vegetation health index in the Papagni River Basin, India. Earth Syst Environ. 2026;1–18. https://doi.org/10.1007/s41748-026-01097-4
  28. 28. Didan K. MOD13Q1 MODIS/Terra vegetation indices 16-day L3 global 250m SIN grid V006. NASA EOSDIS Land Processes DAAC. 2015. https://doi.org/10.5067/MODIS/MOD13Q1.006
  29. 29. Beck HE, Wood EF, Pan M, Fisher CK, Miralles DG, Van Dijk AI, et al. MSWEP V2 global 3-hourly 0.1 precipitation: methodology and quantitative assessment. Bull Am Meteorol Soc. 2019;100(3):473–500. https://doi.org/10.1175/BAMS-D-17-0138.1
  30. 30. Friedl M, Sulla-Menashe D. MODIS/Terra+ Aqua land cover type yearly L3 global 0.05 Deg CMG V061. NASA EOSDIS Land Processes DAAC. 2022. https://doi.org/10.5067/MODIS/MCD12C1.061
  31. 31. Bhatia U, Poonia H, Mansoor Tantary D, Mishra V, Kumar R. Regional responses to oceanic variability constrain global drought synchrony. Commun Earth Environ. 2026;7(1):86. https://doi.org/10.1038/s43247-025-03111-5
  32. 32. Subramanian KK, Sreevidhya V, Vivek S, Priya V. Drought vulnerability assessment using GIS and remote sensing techniques: A case study in part of Coimbatore, Tamil Nadu, India. J Indian Soc Remote Sens. 2024;53. https://doi.org/10.1007/s12524-024-02038-2
  33. 33. Ehsan MA, Esmaili E, Robertson A, Saharwardi MS, Yuan J, Zampieri M, et al. ENSO Teleconnections and seasonal forecasts of wet season droughts in the Arabian Peninsula. 2025. https://doi.org/10.21203/rs.3.rs-8307914/v1
  34. 34. Tiwari A, Nanjundan P, Sharma T, Kumar RR, Ratna SB. Regional drought modulation by ENSO and IOD as Indicated by the standardised precipitation index. Northeast J Complex Syst. 2026;8(1). https://doi.org/10.63562/2577-8439.1140
  35. 35. Lalmuanzuala B, Sathyamoorthy N, Dheebakaran G, Jagadeeswaran R, Ravikumar R, Sumathi C. A comprehensive review of meteorological and remote sensing indices for drought monitoring and their in situ application in India. Environ Rev. 2025;33:1–15. https://doi.org/10.1139/er-2025-0077
  36. 36. Venkadesh S, Pazhanivelan S, Kaliaperumal R, Ramalingam K, Panneerselvam S, Sathy R. Assessment of agricultural drought using MODIS NDVI based vegetation status for different agro climatic zones of Tamil Nadu. Int J Curr Microbiol App Sci. 2019;8:2204–12. https://doi.org/10.20546/ijcmas.2019.805.260
  37. 37. Ghobadi M, Badehian Z. Assessment of agricultural drought severity using multi-temporal remote sensing data in Lorestan region. Sci Rep. 2025;15(1):18528. https://doi.org/10.1038/s41598-025-03087-4
  38. 38. Qiao M, Hong C, Jiao Y, Hou S, Gao H. Impacts of drought on photosynthesis in major food crops and the related mechanisms of plant responses to drought. Plants. 2024;13(13):1808. https://doi.org/10.3390/plants13131808
  39. 39. Santhoshkumar B, Sathyamoorthy NK, Dheebakaran G, Geethalakshmi V, Boomiraj K, Manikandan N, et al. Future projections of monsoon droughts in Tamil Nadu using the Standardised Precipitation Index. J Atmos Sol-Terr Phys. 2026;282:106783. https://doi.org/10.1016/j.jastp.2026.106783
  40. 40. Liu Y, Zhou R, Wen Z, Khalifa M, Zheng C, Ren H, et al. Assessing the impacts of drought on net primary productivity of global land biomes in different climate zones. Ecol Indic. 2021;130:108146. https://doi.org/10.1016/j.ecolind.2021.108146
  41. 41. Pachore AB, Remesan R, Kumar R. Multifractal characterisation of meteorological to agricultural drought propagation over India. Sci Rep. 2024;14(1):18889. https://doi.org/10.1038/s41598-024-68534-0
  42. 42. Zhou J, Gou A, Xu S, Wu Y, Yang X, Wei W, et al. Propagation characteristics and influencing factors of meteorological drought to soil drought in the upper reaches of the Shiyang River based on the copula function. Land. 2024;13(12):2050. https://doi.org/10.3390/land13122050
  43. 43. Ji L, Peters AJ. Assessing vegetation response to drought in the northern Great Plains using vegetation and drought indices. Remote Sens Environ. 2003;87(1):85–98. https://doi.org/10.1016/S0034-4257(03)00174-3
  44. 44. Janarth S, Jagadeeswaran R, Pazhanivelan S, Kannan B, Ragunath K, Sathiyamoorthy N. Assessment of agricultural drought in Tamil Nadu using remote sensing techniques. Agric Sci Dig. 2024:1–9. https://doi.org/10.18805/ag.D-6007

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