Abstract
Drought is an intricate weather phenomenon; it directly affects food security and agricultural productivity. Accurate prediction of agricultural drought helps to take mitigation steps for reducing production losses. In the present study, agricultural drought was assessed by using the Normalized Difference Vegetation Index (NDVI), Vegetation Condition Index (VCI), Temperature Condition Index (TCI), and Vegetation Health Index (VHI) based on Landsat 8 and 9 data from 2013–2022. The LULC maps were also prepared using the supervised classification based on the maximum likelihood algorithm by the semi-automatic classification plugin (SCP) in QGIS from Sentinel-2 images. The remote sensing indices were calculated using a raster calculator in ArcGIS software. The results of VCI indicate that 2014 and 2017 years were highly affected by drought, whereas 2016 was the most vulnerable year according to TCI. In 2017, the entire district was badly affected by VCI and TCI. The VHI results showed that 2015, 2016, and 2018 were the most drought-prone years. The spatial agricultural drought result shows that Chattna, Bankura I, Onda, and Ranibudh were extreme drought-affected blocks. Drought greatly impacts agriculture, so satellite-based drought data would benefit the understanding of the drought of Bankura district risk within the entire geographical area.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alahacoon N, Edirisinghe M, Ranagalage M (2021) Satellite-based meteorological and agricultural drought monitoring for agricultural sustainability in Sri Lanka. Sustainability 13(6):3427. https://doi.org/10.3390/su13063427
Alam J, Saha P, Mitra R, Das J (2023) Investigation of spatio-temporal variability of meteorological drought in the Luni River Basin, Rajasthan, India. Arab J Geosci 16(3):201. https://doi.org/10.1007/s12517-023-11290-8
Apurv T, Cai X (2021) Regional drought risk in the contiguous United States. Geophys Res Lett 48(5):e2020GL092200-1–e2020GL092200-12. https://doi.org/10.1029/2020GL092200
Ayugi B, Eresanya EO, Onyango AO, Ogou FK, Okoro EC, Okoye CO, Ongoma V et al (2022) Review of meteorological drought in Africa: historical trends, impacts, mitigation measures, and prospects. Pure Appl Geophys 179(4):1365–1386
Basak A, Rahman ATMS, Das J, Hosonod T, Kisi O (2022) Drought forecasting using the Prophet Model in semi-arid climate region of western India. Hydrol Sci J 67(9):1397–1417. https://doi.org/10.1080/02626667.2022.2082876
Bhunia P, Das P, Maiti R (2020) Meteorological drought study through SPI in three drought prone districts of West Bengal, India. Earth Syst Environ 4(1):43–55. https://doi.org/10.1007/s41748-019-00137-6
Census of India (2011) District census handbook, Bankura, Government of India
Cunha APM, Alvalá RC, Nobre CA, Carvalho MA (2015) Monitoring vegetative drought dynamics in the Brazilian semiarid region. Agric for Meteorol 214–215:494–505
Das S, Choudhury MR, Nanda S (2013) Geospatial assessment of agricultural drought (a case study of Bankura District, West Bengal). Int J Agric Sci Res (IJASR) 3(2):1–27
Das J, Gayen A, Saha P, Bhattacharya SK (2020) Meteorological drought analysis using Standardized Precipitation Index over Luni River Basin in Rajasthan, India. SN Appl Sci 2(9):1–17. https://doi.org/10.1007/s42452-020-03321-w
Dutta D, Kundu A, Patel NR, Saha SK, Siddiqui AR (2015) Assessment of agricultural drought in Rajasthan (India) using remote sensing derived Vegetation Condition Index (VCI) and Standardized Precipitation Index (SPI). Egypt J Remote Sens Space Sci 18(1):53–63. https://doi.org/10.1016/j.ejrs.2015.03.006
Gidey E, Dikinya O, Sebego R, Segosebe E, Zenebe A (2018) Analysis of the long-term agricultural drought onset, cessation, duration, frequency, severity and spatial extent using Vegetation Health Index (VHI) in Raya and its environs, Northern Ethiopia. Environ Syst Res 7(1):1–18
Glenn DM, Tabb A (2019) Evaluation of five methods to measure normalized difference vegetation index (NDVI) in apple and citrus. Int J Fruit Sci 19(2):191–210
Guo H, Wang R, Garfin GM, Zhang A, Lin D (2021) Rice drought risk assessment under climate change: based on physical vulnerability a quantitative assessment method. Sci Total Environ 751:141481. https://doi.org/10.1016/j.scitotenv.2020.141481
Hadri A, Saidi MEM, Boudhar A (2021) Multiscale drought monitoring and comparison using remote sensing in a Mediterranean arid region: a case study from west-central Morocco. Arab J Geosci 14(2):1–18. https://doi.org/10.1007/s12517-021-06493-w
Hoque M, Pradhan B, Ahmed N, Alamri A (2021a) Drought vulnerability assessment using geospatial techniques in Southern Queensland, Australia. Sensors 21(20):6896. https://doi.org/10.3390/s21206896
Hoque MAA, Pradhan B, Ahmed N, Sohel MSI (2021b) Agricultural drought risk assessment of Northern New South Wales, Australia using geospatial techniques. Sci Total Environ 756:143600. https://doi.org/10.1016/j.scitotenv.2020.143600
Kim JE, Yu J, Ryu JH, Lee JH, Kim TW (2021) Assessment of regional drought vulnerability and risk using principal component analysis and a Gaussian mixture model. Nat Hazards 109(1):707–724. https://doi.org/10.1007/s11069-021-04854-y
Kogan FN (1995) Application of vegetation index and brightness temperature for drought detection. Adv Space Res 15(11):91–100. https://doi.org/10.1016/0273-1177(95)00079-T
Kogan FN (1997) Global drought watch from space. Bull Am Meteor Soc 78(4):621–636
Liu Q, Zhang J, Zhang H, Yao F, Bai Y, Zhang S, Liu Q (2021) Evaluating the performance of eight drought indices for capturing soil moisture dynamics in various vegetation regions over China. Sci Total Environ 789:147803. https://doi.org/10.1016/j.scitotenv.2021.147803
Moisa MB, Merga BB, Gemeda DO (2022) Multiple indices-based assessment of agricultural drought: a case study in Gilgel Gibe Sub-basin, Southern Ethiopia. Theor Appl Climatol 148(1):455–464. https://doi.org/10.1007/s00704-022-03962-4
Nath R, Nath D, Li Q, Chen W, Cui X (2017) Impact of drought on agriculture in the Indo-Gangetic Plain, India. Adv Atmos Sci 34(3):335–346
Nejadrekabi M, Eslamian S, Zareian MJ (2022) Spatial statistics techniques for SPEI and NDVI drought indices: a case study of Khuzestan Province. Int J Environ Sci Technol 19:6573–6594
Orlovsky L, Kogan F, Eshed E, Dugarjav C (2011) Monitoring droughts and pastures productivity in Mongolia using NOAA-AVHRR data. In: Use of satellite and in-situ data to improve sustainability. Springer, Dordrecht, pp 69–79
Patil MB, Desai CG, Umrikar BN (2012) Image classification tool for land use/land cover analysis: a comparative study of maximum likelihood and minimum distance method. Int J Geol Earth Environ Sci 2(3):189–196
Pei F, Wu C, Liu X, Li X, Yang K, Zhou Y, Wang K, Xu L, Xia G (2018) Monitoring the vegetation activity in China using vegetation health indices. Agric for Meteorol 248:215–227
Peng Y, Gitelson AA (2011) Application of chlorophyll-related vegetation indices for remote estimation of maize productivity. Agric For Meteorol 151(9):1267–1276. https://doi.org/10.1016/j.agrformet.2011.05.005
Raha S, Gayen SK (2020) Simulation of meteorological drought using exponential smoothing models: a study on Bankura District, West Bengal, India. SN Appl Sci 2(5):909
Seiler RA, Kogan F, Sullivan J (1998) AVHRR-based vegetation and temperature condition indices for drought detection in Argentina. Adv Space Res 21(3):481–484
Sultana MS, Gazi MY, Mia MB (2021) Multiple indices based agricultural drought assessment in the northwestern part of Bangladesh using geospatial techniques. Environ Challenges 4:100120-1–100120-17
Swain S, Wardlow BD, Narumalani S, Tadesse T, Callahan K (2011) Assessment of vegetation response to drought in Nebraska using Terra-MODIS land surface temperature and normalized difference vegetation index. Gisci Remote Sens 48(3):432–455
Wan Z (2006) MODIS land surface temperature products users’guide. Institute for Computational Earth System Science, University of California, SantaBarbara
Wang JL, Yu YH (2021) Comprehensive drought monitoring in Yunnan Province, China using multisource remote sensing data. J Mt Sci 18(6):1537–1549. https://doi.org/10.1007/s11629-020-6333-7
Zambrano F, Lillo-Saavedra M, Verbist K, Lagos O (2016) Sixteen years of agricultural drought assessment of the BioBío region in Chile using a 250 m resolution Vegetation Condition Index (VCI). Remote Sens 8(6):530
Zeng J, Zhang R, Qu Y, Bento VA, Zhou T, Lin Y, Wang Q (2022) Improving the drought monitoring capability of VHI at the global scale via ensemble indices for various vegetation types from 2001 to 2018. Weather Clim Extremes 35:100412. https://doi.org/10.1016/j.wace.2022.100412
Zhang B, Wu P, Zhao X, Wang Y, Gao X, Cao X (2013) A drought hazard assessment index based on the VIC–PDSI model and its application on the Loess Plateau, China. Theor Appl Climatol 114(1):125–138. https://doi.org/10.1007/s00704-012-0826-4
Zhao X, Xia H, Liu B, Jiao W (2022) Spatiotemporal comparison of drought in Shaanxi–Gansu–Ningxia from 2003 to 2020 using various drought indices in Google Earth Engine. Remote Sens 14(7):1570. https://doi.org/10.3390/rs14071570
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Ali, S.S., Mukherjee, K., Kundu, P., Saha, P. (2023). Spatio-Temporal Agricultural Drought Monitoring Using Remote Sensing Indices. In: Das, J., Halder, S. (eds) Advancement of GI-Science and Sustainable Agriculture. GIScience and Geo-environmental Modelling. Springer, Cham. https://doi.org/10.1007/978-3-031-36825-7_4
Download citation
DOI: https://doi.org/10.1007/978-3-031-36825-7_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-36824-0
Online ISBN: 978-3-031-36825-7
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)