Assessing Drought Vulnerability in Pakistan (2001-2022) Using EVI-Based Standardized Vegetation Index in Google Earth Engine
DOI:
https://doi.org/10.53560/PPASA(62-3)843Keywords:
Standardized Vegetation Index (SVI), Drought Vulnerability, Vegetation Stress, Remote Sensing, Google Earth Engine (GEE), Drought in PakistanAbstract
This study examines vegetation dynamics and drought risk in Pakistan from 2001 to 2022 using MODIS Enhanced Vegetation Index (EVI) and Standardized Vegetation Index (SVI) processed in Google Earth Engine. EVI highlights high greenness in the irrigated plains of Punjab and Sindh, while SVI exposes widespread stress in these same areas, revealing a “greening paradox” where apparent productivity masks underlying vulnerability. Monthly SVI patterns follow strong seasonal cycles, with positive anomalies peaking during the monsoon driven kharif season (June-October) and persistent deficits occurring in the rabi season (December–May). Spatially, high EVI values (>0.4) were concentrated along the Indus River corridor, while arid zones such as Balochistan and the Thar Desert exhibited low EVI (<0.2). Mean SVI maps contradicted these patterns, showing negative anomalies in high EVI regions. Long term analysis indicated stable EVI until 2018, followed by a modest upward trend, while SVI shifted from chronic negative anomalies in the early 2000s (mean = –0.21) to sustained positive values after 2020 (mean = +0.58). At the provincial scale, Punjab showed a post 2015 decline, Sindh demonstrated recovery after drought episodes in 2010-2012 and 2017-2018, Khyber Pakhtunkhwa displayed high variability without a clear trend, and Balochistan recorded the strongest improvement since 2005. Overall, EVI captured absolute greenness, while SVI provided anomaly based insights into drought conditions, detecting hidden stress in intensively irrigated areas and identifying genuine recovery in marginal regions. By integrating EVI and SVI, this study offers a robust framework for spatiotemporal drought monitoring in Pakistan. The results provide a scientific basis for climate smart agriculture, early warning systems, and sustainable land and water management strategies aimed at safeguarding food security in the face of rising drought frequency.
References
1. S.J. Scherr, S. Shames, and R. Friedman. From climate smart agriculture to climate smart landscapes. Agriculture & Food Security 1: 12 (2012).
2. H. Azadi, S.M. Moghaddam, S. Burkart, H. Mahmoudi, S. Van Passel, A. Kurban, and D.L. Carr. Rethinking resilient agriculture: From climate smart agriculture to vulnerable smart agriculture. Journal of Cleaner Production 319: 128602 (2021).
3. R. Singh and G.S. Singh. Traditional agriculture: a climate smart approach for sustainable food production. Energy, Ecology and Environment 2: 296-316 (2017).
4. K. Chandra, K.E. McNamara, and P. Dargusch. Climate smart agriculture: perspectives and framings. Climate Policy 18(4): 526-541 (2018).
5. M. Shelestov, M. Lavreniuk, N. Kussul, A. Novikov, and S. Skakun. Exploring Google Earth Engine platform for big data processing: Classification of multi temporal satellite imagery for crop mapping. Frontiers in Earth Science 5: 17 (2017).
6. S.S. Ray, S.M. Neetu, and S. Gupta. Use of remote sensing in crop forecasting and assessment of impact of natural disasters: operational approaches in India. In: Crop Monitoring for Improved Food Security. M.K. Srivastava (Ed.). Food and Agriculture Organization of the United Nations and the Asian Development Bank, Bangkok pp. 111 (2015).
7. V.K. Dadhwal, R.P. Singh, S. Dutta, and J.S. Parihar. Remote sensing-based crop inventory: A review of Indian experience. Tropical Ecology 43(1): 107-122 (2002).
8. I. Ahmad, A. Ghafoor, M.I. Bhatti, I.U.H. Akhtar, M. Ibrahim, and O. Rehmann. Satellite remote sensing and GIS based crops forecasting & estimation system in Pakistan. In: Crop Monitoring for Improved Food Security. M.K. Srivastava (Ed.). Food and Agriculture Organization of the United Nations and the Asian Development Bank, Bangkok pp. 95 (2015).
9. X. Wang, D. P. Lettenmaier, and J. Sheffield. Soil moisture drought in China, 1950–2006. Journal of Climate 24(13): 3257-3271 (2011).
10. S. Beguería, S.M.V. Serrano, F. Reig, and B. Latorre. Standardized precipitation evapotranspiration index (SPEI) revisited: parameter fitting, evapotranspiration models, tools, datasets and drought monitoring. International Journal of Climatology 34(10): 3001-3023 (2014).
11. J.H. Stagge, L.M. Tallaksen, L. Gudmundsson, A.F. Van Loon, and K. Stahl. Candidate distributions for climatological drought indices (SPI and SPEI). International Journal of Climatology 35(13): 4027-4040 (2015).
12. L. Li, D. She, H. Zheng, P. Lin, and Z.L. Yang. Elucidating diverse drought characteristics from two meteorological drought indices (SPI and SPEI) in China. Journal of Hydrometeorology 21(7): 1513-1530 (2020).
13. J. Tucker and B.J. Choudhury. Satellite remote sensing of drought conditions. Remote Sensing of Environment 23(2): 243-251 (1987).
14. Z. Li, Y. Han, and T. Hao. Assessing the consistency of remotely sensed multiple drought indices for monitoring drought phenomena in continental China. IEEE Transactions on Geoscience and Remote Sensing 58(8): 5490-5502 (2020).
15. L. Zhang, W. Jiao, H. Zhang, C. Huang, and Q. Tong. Studying drought phenomena in the Continental United States in 2011 and 2012 using various drought indices. Remote Sensing of Environment 190: 96-106 (2017).
16. A. Bégué, D. Arvor, B. Bellon, J. Betbeder, D. De Abelleyra, R.P.D. Ferraz, and S.R. Verón. Remote sensing and cropping practices: A review. Remote Sensing 10(1): 99 (2018).
17. Y. Guo, H. Xia, L. Pan, X. Zhao, and R. Li. Mapping the northern limit of double cropping using a phenology-based algorithm and Google Earth Engine. Remote Sensing 14(4): 1004 (2022).
18. T. Rotjanakusol and T. Laosuwan. Remote Sensing Based Drought Monitoring in the Middle Part of Northeast Region of Thailand. Studia Universitatis Vasile Goldis Arad, Seria Stiintele Vietii 28(1): 14-21 (2018).
19. J. Veneros and L. García. Application of the Standardized Vegetation Index (SVI) and Google Earth Engine (GEE) for Drought Management in Peru. Tropical and Subtropical Agroecosystems 25: 27 (2022).
20. S. Sangpradid, Y. Uttaruk, T. Rotjanakusol, and T. Laosuwan. Forecasting Time Series Change of the Average Enhanced Vegetation Index to Monitoring Drought Condition by Using Terra/Modis Data. Agriculture and Forestry 67(4): 115-129 (2021).
21. H. Sun, W. Liu, Y. Wang, and S. Yuan. Evaluation of typical spectral vegetation indices for drought monitoring in cropland of the north China plain. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10(12): 5404-5411 (2017).
22. O. Mikaili and M. Rahimzadegan. Investigating remote sensing indices to monitor drought impacts on a local scale (case study: Fars province, Iran). Natural Hazards 111(3): 2511-2529 (2022).
23. S. Ali and M. Imran. The Challenge of Spatial Information Accessibility for Agricultural Policies: Case of Pakistan. Journal of Regional Socio-Economic Issues 10(3): 62 (2020).
24. S. Aziz, M. Umar, M. Mansha, M.S. Khan, M.N. Javed, H. Gao, and S. Abdullah. Assessment of drought conditions using HJ 1A/1B data: a case study of Potohar region, Pakistan. Geomatics, Natural Hazards and Risk 9(1): 1019-1036 (2018).
25. M.Z. Zakar and R. Zakar. Diffusion of information technology for agricultural development in rural Punjab: Challenges and opportunities. Pakistan Vision 9(2): 136-174 (2021).
26. A. Hameed, I.U.H. Padda, and S. Karim. Spatial analysis of food and nutrition security in Pakistan: a holistic pathway towards zero hunger policies. GeoJournal 88(6): 2563-2585 (2023).
27. M.A. Imran, A. Ali, M. Ashfaq, S. Hassan, R. Culas, and C. Ma. Impact of Climate Smart Agriculture (CSA) practices on cotton production and livelihood of farmers in Punjab, Pakistan. Sustainability 10(6): 2101 (2018).
28. S. Bhandari, S. Phinn, and T. Gill. Assessing viewing and illumination geometry effects on the MODIS vegetation index (MOD13Q1) time series: Implications for monitoring phenology and disturbances in forest communities in Queensland, Australia. International Journal of Remote Sensing 32(22): 7513-7538 (2011).
29. S. Testa, E.C.B. Mondino, and C. Pedroli. Correcting MODIS 16-day composite NDVI time-series with actual acquisition dates. European Journal of Remote Sensing 47(1): 285-305 (2014).
30. S. Panigrahi, K. Verma, and P. Tripathi. Review of MODIS EVI and NDVI data for data mining applications (Chapter 12). In: Data Deduplication Approaches. T.T. Thwel and G.R. Sinha (Eds.). Academic Press pp. 231-253 (2021).
31. X. Peng, X. Zhang, B. Zhang, L. Liu, X. Liu, A.R. Huete, and H. Zhang. Scaling effects on spring phenology detections from MODIS data at multiple spatial resolutions over the contiguous United States. ISPRS Journal of Photogrammetry and Remote Sensing 132: 185-198 (2017).
32. L.D. Fiore, G. Piovesan, M. Baliva, and A.D. Filippo. Combined analysis of tree rings and MODIS images to evaluate beech forest productivity along geographic gradients. EGU General Assembly Conference, Abstracts pp. 5335 (2020). https://doi.org/10.5194/egusphere-egu2020-5335.
33. T. Phompila, M. Lewis, B. Ostendorf, and K. Clarke. MODIS EVI and LST temporal response for discrimination of tropical land covers. Remote Sensing 7(5): 6026-6040 (2015).
34. K.A. Uyeda, D.A. Stow, D.A. Roberts, and P.J. Riggan. Combining ground-based measurements and MODIS-based spectral vegetation indices to track biomass accumulation in post-fire chaparral. International Journal of Remote Sensing 38(3): 728-741 (2017).
35. C. Fernando, K.C. Mo, R. Fu, B. Pu, A. Bowerman, B.R. Scanlon, and K. Zhang. What caused the spring intensification and winter demise of the 2011 drought over Texas? Climate Dynamics 47: 3077-3090 (2016).
36. R. McLeman, S. Herold, Z. Reljic, M. Sawada, and D. McKenney. GIS based modeling of drought and historical population change on the Canadian Prairies. Journal of Historical Geography 36(1): 43-56 (2010).
37. P. Chopra. Drought Risk Assessment Using Remote Sensing and GIS: A Case Study of Gujarat. Master Thesis. International Institute for Geo-Information Science and Earth Observation, Enschede, The Netherlands (2006).
38. M.R. Rahman and H. Lateh. Meteorological drought in Bangladesh: assessing, analysing and hazard mapping using SPI, GIS and monthly rainfall data. Environmental Earth Sciences 75: 1026 (2016).
39. J. Peters, E.A.W. Shea, L. Ji, A. Vina, M. Hayes, and M.D. Svoboda. Drought monitoring with NDVI based standardized vegetation index. Photogrammetric Engineering and Remote Sensing 68(1): 71-75 (2002).
40. C. Pachanaparn, P. Jeefoo and P. Rojanavasu. Application of Remote Sensing for Drought Monitoring with NDVI-based Standardized Vegetation Index in Nan Province, Thailand. 2022 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT & NCON), Chiang Rai, Thailand pp. 323-328 (2022).
41. S. Aksoy, O. Gorucu, and E. Sertel. Drought monitoring using MODIS derived indices and Google Earth Engine platform. 8th International Conference on Agro Geoinformatics (Agro Geoinformatics) (16th – 19th July 2019), IEEE, Istanbul, Turkey (2019).
42. O. Mutanga and L. Kumar. Google earth engine applications. Remote Sensing 11(5): 591 (2019).
43. E. Roteta, A. Bastarrika, A. Ibisate, and E. Chuvieco. A preliminary global automatic burned area algorithm at medium resolution in Google Earth Engine. Remote Sensing 13(21): 4298 (2021).
44. R. Tabassum, I.A. Khan, and M.H. Arsalan. Monitoring drought events and vegetation conditions in Pakistan: Implications for drought management and food security. Proceedings of the Pakistan Academy of Sciences 60(4): 13-28 (2023).
45. S.S. Hassan, M.A. Goheer, H. Farah, F. Hafeez, K. Sheraz, J. Fahad, and A. Tariq. Geospatial assessment of climate variability and drought patterns: a case study from Pakistan. Theoretical and Applied Climatology 156(3): 185 (2025).
46. K. Ahmed, G. Shabbir, and M. Ahmed. Exploring drought tolerance for germination traits of diverse wheat genotypes at seedling stage: a multivariate analysis approach. BMC Plant Biology 25(1): 390 (2025).
47. Z.M. Yaseen, B. Halder, M.L. Tan, H.C. Kilinc, I. Ahmadianfar, S.I. Abba, K.N.A. Maulud, V. Demir, A.M. Al-Areeq and S. Heddam. Climate change impact analysis on seasonal drought and landforms using meteorological and remote sensing derived indices. Acta Geophysica 73: 4849-4881(2025).
48. I. Nazeer. Optimizing Agricultural Inputs and Cropped Area Dynamics: A Decade Long Analysis in Pakistan. International Journal of Agriculture and Sustainable Development 6(2): 117-126 (2024).
49. A. Khan, M. Jawad, S. Ali, A. Ali, Z.U.D. Khan, W. Murad, and M.B. Baig. Impact of Climate Change on Biodiversity and Food Security in Khyber Pakhtunkhwa: Challenges, Vulnerabilities, and Strategies for Sustainable Development. In: Food Systems and Biodiversity in the Context of Environmental and Climate Risks: Dynamics and Evolving Solutions. M. Behnassi, M.B. Baig, H. Gupta, R. Sabbahi, G.N. Gill, and M.E. Haiba (Eds.). Springer Nature Switzerland pp. 101-140 (2025).
50. S. Ahmad, M. Islam, and S.N. Mirza. Rangeland degradation and management approaches in Balochistan, Pakistan. Pakistan Journal of Botany 44: 127-136 (2012).
51. A. Hussain, S. Majeed, A. Hassan, M.A. Khathian, M.Z.U. Khan, I. Raza, and J.K. Bajkani. Adoption and Cost benefit Analysis of Drip Irrigation for Production of High Value Crops in Pakistan. Proceedings of the Pakistan Academy of Sciences: Part B 59(1): 57-68 (2022).
52. A. Huete, K. Didan, T. Miura, E.P. Rodriguez, X. Gao, and L.G. Ferreira. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment 83(1-2): 195-213 (2002).
53. M.A. Watto, A.W. Mugera, R. Kingwell, and M.M. Saqab. Re thinking the unimpeded tube well growth under the depleting groundwater resources in the Punjab, Pakistan. Hydrogeology Journal 26(7): 2411-2425 (2018).
54. A.K. Sikka, M.F. Alam, and P. Pavelic. Managing groundwater for building resilience for sustainable agriculture in South Asia. Irrigation and Drainage 70(3): 560-573 (2021).
55. M. Aleem, S.N. Khan, M. U. Akbar, A. Arshad, Y. Alsubhi, M. Pandey, A. Nasir, and et al. Ten Billion Tree Tsunami Project Reveals Climate Change Mitigation and Precipitation Increase in Khyber Pakhtunkhwa Province, Pakistan. Earth Systems and Environment in press (2025). https://doi.org/10.1007/s41748-024-00533-7.
56. C. Srinivasarao, R. Lal, S. Kundu, and P.B. Thakur. Conservation agriculture and soil carbon sequestration. In: Conservation Agriculture. M. Farooq and K.H.M. Siddique (Eds.). Springer, Cham, Heidelberg, New York, Dordrecht, London pp. 479- 524 (2014).
57. Z. Wan, P. Wang, and X. Li. Using MODIS land surface temperature and normalized difference vegetation index products for monitoring drought in the southern Great Plains, USA. International Journal of Remote Sensing 25(1): 61-72 (2004).
58. A. Abbas, S. Ullah, W. Ullah, C. Zhao, A. Karim, M. Waseem, and et al. Characteristics of winter precipitation over Pakistan and possible causes during 1981-2018. Water 15(13): 2420 (2023).
59. T.R. Saha, P.K. Shrestha, O. Rakovec, S. Thober, and L. Samaniego. A drought monitoring tool for South Asia. Environmental Research Letters 16(5): 054014 (2021).
60. E. Fisher, J. Hellin, H. Greatrex, and N. Jensen. Index insurance and climate risk management: Addressing social equity. Development Policy Review 37(5): 581-602 (2019).
61. S.A. Memon, F. Osanami, and T. Kondo. Economic Impact Assessment of the Left Bank Out fall Drainage Project: A Case Study. In: New Challenges Facing Asian Agriculture under Globalisation, Volume II. J. Sulaiman, F.M. Arshad, and M.N. Shamsudin (Eds.) AgEcon Search pp. 665-673 (2005).
62. M.A. Kahlown and A. Majeed. Water resources situation in Pakistan: challenges and future strategies. In: Water Resources in the South: Present Scenario and Future Prospects. Commission on Science and Technology for Sustainable Development in the South (COMSTECH), Islamabad, Pakistan pp. 21 (2003). https://comsats.org/Publications/Books_SnT_Series/03.%20Water%20Resources%20in%20the%20South%20-%20Present%20Scenario%20and%20Future%20Prospects%20(Nov.%202003).pdf.
63. M. Usman, M.U. Qamar, R. Becker, M. Zaman, C. Conrad, and S. Salim. Numerical modelling and remote sensing-based approaches for investigating groundwater dynamics under changing land use and climate in the agricultural region of Pakistan. Journal of Hydrology 581: 124408 (2020).
64. T. Tanaka. Groundwater governance in Asia: present state and barriers to implementation of good governance. Proceedings of the International Association of Hydrological Sciences 364: 470-474 (2014).
65. R.S.D. Fries, M. Hansen, J.R.G. Townshend, and R. Sohlberg. Global land cover classifications at 8 km spatial resolution: The use of training data derived from Landsat imagery in decision tree classifiers. International Journal of Remote Sensing 19(16): 3141-3168 (1998).
66. M. Casazza. Climate Parameter Variability. In: Handbook of Drought and Water Scarcity, (1st Edition). S. Eslamian and F.A. Eslamian (Eds.). CRC Press, Taylor & Francis Group, Boca Raton, Florida, USA pp. 65-80 (2017).
67. A.W. Rana and S. Gill. Pakistan: Strategy to promote climate smart agriculture practices. International Food Policy Research Institute (2025). https://cgspace.cgiar.org/server/api/core/bitstreams/17bec81f-dbd3-4a2d-b2bb-750a484f93e4/content.
68. X. Zhang, M.A. Friedl, C.B. Schaaf, A.H. Strahler, J.C. Hodges, F. Gao, B.C. Reed, and A. Huete. Monitoring vegetation phenology using MODIS. Remote Sensing of Environment 84(3): 471-475 (2003).
69. S. Khan and S.H. Maqsoom. Elaborating on the Water Wars: The Politics of a Thirsty World. Roshni Analysis and Report (2025). https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5394527.
70. S. Sato. Water security in South Asia: Transboundary water politics between India, Pakistan, and Bangladesh. Authorea pp. 1-4 (2024). https://d197for5662m48.cloudfront.net/documents/publicationstatus/229174/preprint_pdf/f790bfc3b5b097e8957dcfc831c26fca.pdf
