Abstract
The agricultural and meteorological droughts are prominent natural hazards, and the risk associated with them has adversely affected rainfed agriculture with a more prominent impact in arid and semiarid regions. In the present study, two drought indices, namely vegetation condition index (VCI) for spatial and temporal monitoring of agricultural droughts and standardized precipitation index (SPI) for spatial and temporal monitoring of meteorological droughts, were utilized derived considering the 30 years of dataset between 1986 and 2015. The current study aims to use geospatial techniques to classify the onset and spatial extent of the VCI used in this study to monitor agricultural drought and assess the suitability of NOAA-AVHRR, derived VCI to monitor an agricultural drought at the regional scale. The SPI drought index has gained momentum in assessing drought severity, magnitude, and its geographical extent. One of the major advantages of using the SPI is that it can identify and monitor various types of dryness conditions at various time scales. The SPI index can consider daily, weekly, and monthly variations in precipitation across multiple time scales. The present study computes the meteorological drought indices in terms of SPI using rainfall data from the year 1986 to 2015 using a 3-month time scales. SPI at 3-month time scale was interpolated to show how drought patterns are spatially distributed and how severe they are during droughts and wet years. A correlation analysis was conducted to evaluate its effectiveness in quantifying the effects of drought on crop production. In addition, the SPI 3-month time series was used to assess drought risk in Gujarat. In this study, the crop yield anomaly index was also applied for the analysis of the association between precipitation and crop yield throughout wet and dry years. In this study, a significant correlation between NDVI and rainfall (r = 0.51), NDVI and crop yield anomaly (0.42), and SPI and crop yield anomaly (0.65) was observed. The findings show that the frequency of drought events varied on a complex drought during the study period. As a result of the combined drought risk map, 21.68%, 22.66%, and 30.26% of the study area were at risk for very severe, severe, and moderate drought conditions, respectively.
Similar content being viewed by others
References
Aboelghar, M., Arafat, S., Saleh, A., Naeem, S., Shirbeny, M., & Belal, A. (2010). Retrieving leaf area index from SPOT4 satellite data. The Egyptian Journal of Remote Sensing and Space Science, 13(2), 121–127. https://doi.org/10.1016/j.ejrs.2010.06.001.
Akbari, H., Rakhshandehroo, G., Sharifloo, A. H., Ostadzadeh, E. (2015). Drought analysis based on standardized precipitation index (SPI) and streamflow drought index (SDI) in Chenar Rahdar river basin, Southern Iran. Southern Iran, American Society of Civil Engineers (pp. 11–22). https://doi.org/10.1061/9780784479322.002.
Alahacoon, N., Edirisinghe, M., & Ranagalage, M. (2021). Satellite-based meteorological and agricultural drought monitoring for agricultural sustainability in Sri Lanka. Sustainability (switzerland), 13, 3427. https://doi.org/10.3390/su13063427.
Albarakat, R., & Lakshmi, V. (2019). Comparison of normalized difference vegetation index derived from Landsat, MODIS, and AVHRR for the Mesopotamian marshes between 2002 and 2018. Remote Sensing, 11(10), 1245. https://doi.org/10.3390/rs11101245.
Alley, W. M. (1984). The Palmer drought severity index: Limitations and assumptions. Journal of Applied Meteorology and Climatology, 23(7), 1100–1109. https://doi.org/10.1175/1520-0450(1984)023%3C1100:TPDSIL%3E2.0.CO;2.
Anctil, F., Larouche, W., & Viau, A. A. (2002). Exploration of the standardized precipitation index with regional analysis. Canadian Journal of Soil Science, 82(1), 115–125.
Awchi, T. A., & Kalyana, M. M. (2017). Meteorological drought analysis in northern Iraq using SPI and GIS. Sustainable Water Resources Management, 3(4), 451–463. https://doi.org/10.1007/s40899-017-0111-x.
Bajgiran, P. R., Darvishsefat, A. A., Khalili, A., & Makhdoum, M. F. (2008). Using AVHRR-based vegetation indices for drought monitoring in the Northwest of Iran. Journal of Arid Environments, 72(6), 1086–1096. https://doi.org/10.1016/j.jaridenv.2007.12.004.
Barati, S., Rayegani, B., Saati, M., Sharifi, A., & Nasri, M. (2011). Comparison the accuracies of different spectral indices for estimation of vegetation cover fraction in sparse vegetated areas. The Egyptian Journal of Remote Sensing and Space Science, 14(1), 49–56. https://doi.org/10.1016/j.ejrs.2011.06.001.
Bhuiyan, C., Saha, A. K., Bandyopadhyay, N., & Kogan, F. N. (2017a). Advances in remote sensing and GIS-based drought monitoring Analyzing the impact of thermal stress on vegetation health and agricultural drought: A case study from Gujarat, India. Geoscience & Remote Sensing, 54(5), 678–699. https://doi.org/10.1080/15481603.2017.1309737.
Bhuiyan, C., Singh, R. P., & Kogan, F. N. (2006). Monitoring drought dynamics in the Aravalli region (India) using different indices based on ground and remote sensing data. International Journal of Applied Earth Observation and Geoinformation, 8(4), 289–302. https://doi.org/10.1016/j.jag.2006.03.002.
Byun, H. R., & Wilhite, D. A. (1999). Objective quantification of drought severity and duration. Journal of Climate, 12(9), 2747–2756. https://doi.org/10.1175/15200442(1999)012%3C2747:OQODSA%3E2.0.CO;2.
Chaudhari, K. N., & Dadhwal, V. K. (2004). Assessment of impact of drought-2002 on the production of major Kharif and Rabi crops using standardized precipitation index. Journal of Agrometeorology, 6, 10–15.
Chopra, P. (2006). Drought risk assessment using remote sensing and GIS: A case study of Gujarat. Master thesis submitted to the International Institute for Geo-Information Science and Earth Observation, Enschede, The Netherlands.
Daba, M., & Oromia, E. (2016). Studies on temporal relationship between normalized difference vegetation index and rainfall in the Southern Part of Ethiopia. Journal of Environment and Earth Science, 6(6), 93–97. http://hdl.handle.net/20.500.12424/729548.
Danandeh Mehr, A., Sorman, A. U., Kahya, E., & Hesami Afshar, M. (2020). Climate change impacts on meteorological drought using SPI and SPEI: Case study of Ankara. Turkey. Hydrological Sciences Journal, 65(2), 254–268. https://doi.org/10.1080/02626667.2019.1691218.
Dhakar, R., Sehgal, V. K., & Pradhan, S. (2013). Study on inter-seasonal and intra-seasonal relationships of meteorological and agricultural drought indices in the Rajasthan State of India. Journal of Arid Environments, 97, 108–119. https://doi.org/10.1016/j.jaridenv.2013.06.001.
Dubey, S. K., Pranuthi, G., & Tripathi, S. K. (2012). Relationship between NDVI and rainfall relationship over India. International Journal of Water Resources and Environmental Sciences, 1(4), 102–108. https://doi.org/10.5829/idosi.ijwres.2012.1.4.11117.
Dutta, D., Kundu, A., & Patel, N. R. (2013). Predicting agricultural drought in eastern Rajasthan of India using NDVI and standardized precipitation index. Geocarto International, 28(3), 192–209. https://doi.org/10.1080/10106049.2012.679975.
Eyoh, A., Okeke, F., & Ekpa, A. (2019). Assessment of the effectiveness of the vegetation condition index (VCI) as an indicator for monitoring drought condition across the Niger Delta region of Nigeria using AVHRR/MODIS NDVI. European Journal of Earth and Environment, 6(1), 12–18.
Ghulam, A., Qin, Q., & Zhan, Z. (2007). Designing of the perpendicular drought index. Environmental Geology, 52(6), 1045–1052. https://doi.org/10.1007/s00254-006-0544-2.
Gibbs, W. J., & Maher, J. V. (1967). Rainfall Deciles as Drought Indicators. Bureau of Meteorology Bulletin No. 48, Melbourne: Bureau of Meteorology.
Gómez Flores, J. L., Ramos Rodríguez, M., González Jiménez, A., Farzamian, M., Herencia Galán, J. F., Salvatierra Bellido, B., Cermeño Sacristan, P., & Vanderlinden, K. (2022). Depth-specific soil electrical conductivity and NDVI elucidate salinity effects on crop development in reclaimed marsh soils. Remote Sensing, 14, 3389. https://doi.org/10.3390/rs14143389.
Gore, P. G., & Ray, K. S. (2002). Droughts and aridity over districts of Gujarat. Journal of Agrometeorology, 4(1), 75–85. https://doi.org/10.54386/jam.v4i1.428.
Guttman, N. B. (1999). Comparing the palmer drought index and the standardized precipitation index. Journal of the American Water Resources Association, 34(1), 113–121. https://doi.org/10.1111/j.1752-1688.1998.tb05964.x.
Hayes, M. J., Svoboda, M. D., Wardlow, B., Anderson, M. C., & Kogan, F. (2012). Drought monitoring: Historical and current perspectives. In Remote sensing for drought: Innovative monitoring approaches (pp. 1–19). Taylor and Francis Group.
Jain, V. K., Pandey, R. P., Jain, M. K., & Byun, H. R. (2015). Comparison of drought indices for appraisal of drought characteristics in the Ken River Basin. Weather Climate Extreme, 8(1), 1–11. https://doi.org/10.1016/j.wace.2015.05.002.
Ji, L., & Peters, A. J. (2004). A spatial regression procedure for evaluating the relationship between AVHRR-NDVI and climate in the northern Great Plains. International Journal of Remote Sensing, 25(2), 297–311. https://doi.org/10.1080/0143116031000102548.
Jiménez-Donaire, M. P., Tarquis, A., & Giráldez, J. M. (2020). Evaluation of a combined drought indicator and its potential for agricultural drought prediction in southern Spain. Natural Hazards and Earth System Sciences, 20(1), 21–33. https://doi.org/10.5194/nhess-20-21-2020.
Kalubarme, M., Acharya, M., & Shukla, S. H. (2019). Monitoring drought and its impact on agriculture using drought indices and geo-informatics technology in Patan District, Gujarat. International Journal of Environment and Geoinformatics, 6(2), 153–162. https://doi.org/10.30897/ijegeo.554465.
Kogan, F. N. (1995). Application of vegetation index and brightness temperature for drought detection. Advances in Space Research, 15(11), 91–100. https://doi.org/10.1016/0273-1177(95)00079-T.
Kourouma, J. M., Eze, E., Negash, E., Phiri, D., Vinya, R., Girma, A., & Zenebe, A. (2021). Assessing the spatio-temporal variability of NDVI and VCI as indices of crops productivity in Ethiopia: A remote sensing approach. Geomatics, Natural Hazards and Risk, 12(1), 2880–2903. https://doi.org/10.1080/19475705.2021.1976849.
Kumar, C. K., Chandola, V. K., & Kumar, R. (2018). Meteorological drought characterization using effective drought index (EDI) for Banswara District (Rajasthan). International Journal of Current Microbiology and Applied Sciences, 7(9), 3407–3416. https://doi.org/10.20546/ijcmas.2018.709.423.
Kumar, M., Desai, V. R., & Manekar, V. (2009). An examination of relationships between vegetation and rainfall using maximum value composite AVHRR-NDVI data. Journal of Atmosphere, 27(2), 93–101.
Kundu, A., & Dutta, D. (2011). Monitoring desertification risk through climate change and human interference using Remote sensing and GIS. International Journal of Geomatics and Geosciences, 2(1), 21–33. https://doi.org/10.1007/s12524-019-01048-9.
Kundu, A., Patel, N. R., Saha, S. K., & Dutta, D. (2015). Monitoring the extent of desertification processes in western Rajasthan (India) using geo-information science. Arabian Journal of Geosciences, 8(8), 5727–5737. https://doi.org/10.1007/s12517-014-1645-y.
Kundu, A., Patel, N. R., Denis, D. M., & Dutta, D. (2019). An estimation of hydrometeorological drought stress over the central part of India using geo-information technology. Journal of the Indian Society of Remote Sensing, 48(1), 1–9.
Lana, X., Serra, C., & Burgueno, A. (2001). Patterns of monthly rainfall shortage and excess in terms of the standardized precipitation index for Catalonia (NE Spain). International Journal of Climatology: A Journal of the Royal Meteorological Society, 21(13), 1669–1691. https://doi.org/10.1002/joc.697.
Leasor, Z. T., Quiring, S. M., & Svoboda, M. D. (2020). Utilizing objective drought severity thresholds to improve drought monitoring. Journal of Applied Meteorology and Climatology, 59(3), 455–475. https://doi.org/10.1175/JAMC-D-19-0217.1.
Lee, S. H., Yoo, S. H., Choi, J. Y., & Bae, S. (2017). Assessment of the impact of climate change on drought characteristics in the Hwanghae Plain, North Korea using time series SPI and SPEI: 1981–2100. Water, 9(8), 579. https://doi.org/10.3390/w9080579.
Li, C., Li, H., Li, J., Lei, Y., Li, C., Manevski, K., & Shen, Y. (2019). Using NDVI percentiles to monitor real-time crop growth. Computers and Electronics in Agriculture, 162, 357–363.
Liou, Y. A., & Mulualem, G. M. (2019). Spatio-temporal assessment of drought in Ethiopia and the impact of recent intense droughts. Remote Sensing, 11(15), 1828. https://doi.org/10.3390/rs11151828.
Liu, W. T., & Kogan, F. N. (1996). Monitoring regional drought using the vegetation condition index. International Journal of Remote Sensing, 17(14), 2761–2782. https://doi.org/10.1080/01431169608949106.
Lloyd-Hughes, B., & Saunders, M. A. (2002). A drought climatology for Europe. International Journal of Climatology: A Journal of the Royal Meteorological Society, 22(13), 1571–1592. https://doi.org/10.1002/joc.846.
McKee, T. B., Doesken, N. J., & Kleist, J. (1993). The relationship of drought frequency and duration to time scales. In Proceedings of the 8th Conference on Applied Climatology (Vol. 17(22), pp. 179–183).
Mishra, A. K., & Desai, V. R. (2005). Drought forecasting using stochastic models. Stochastic Environmental Research and Risk Assessment, 19(5), 326–339. https://doi.org/10.1007/s00477-005-0238-4.
Mohamed, E. S., Schütt, B., & Belal, A. (2013). Assessment of environmental hazards in the north western coast-Egypt using RS and GIS. The Egyptian Journal of Remote Sensing and Space Science, 16(2), 219–229. https://doi.org/10.1016/j.ejrs.2013.11.003.
Mohammed, S., Alsafadi, K., Daher, H., Gombos, B., Mahmood, S., & Harsányi, E. (2020). Precipitation pattern changes and response of vegetation to drought variability in the eastern Hungary. Bulletin of the National Research Centre, 44(55), 1–10. https://doi.org/10.1186/s42269-020-00306-y.
Mondol, M., Haque, A., Ara, I., & Das, S. C. (2017). Meteorological drought index mapping in Bangladesh using standardized precipitation index during 1981–2010. Advances in Meteorology, 2017, 4642060. https://doi.org/10.1155/2017/4642060.
Murad, H., & Islam, A. K. M. S. (2011). Drought assessment using remote sensing and GIS in North-West Region of Bangladesh. In 3rd International conference on water and flood management (pp. 861–877).
Nicholson, S. E., & Farrar, T. J. (1994). The influence of soil type on the relationships between NDVI, rainfall, and soil moisture in semiarid Botswana. I. NDVI response to rainfall. Remote Sensing of Environment, 50(2), 107–120. https://doi.org/10.1016/0034-4257(94)90038-8.
Palmer, W. C. (1965). Meteorological Drought. Research Paper No. 45. US, Department of Commerce Weather Bureau, Washington DC.
Pandya, P. A., Gontia, N. K., & Parmar, H. V. (2022). Development of PCA-based composite drought index for agricultural drought assessment using remote sensing. Journal of Agrometeorology, 24(4), 384–392. https://doi.org/10.54386/jam.v24i4.1738.
Pani, P., Alahacoon, N., Amarnath, G., Bharani, G., Mondal, S., & Jeganathan, C. (2016). Comparison of SPI and IDSI applicability for agriculture drought monitoring in Sri Lanka. In 37th Asian conference on remote sensing (Vol. 201(1), pp. 530–537).
Patel, N. R., Chopra, P., & Dadhwal, V. K. (2007). Analyzing spatial patterns of meteorological drought using standardized precipitation index. Meteorological Applications: A Journal of Forecasting, Practical Applications, Training Techniques and Modelling, 14(4), 329–336.
Patel, N. R., & Yadav, K. (2015). Monitoring spatio-temporal pattern of drought stress using integrated drought index over Bundelkhand region, India. Natural Hazards, 77(2), 663–677. https://doi.org/10.1007/s11069-015-1614-0.
Pei, Z., Fang, S., Yang, W., Wang, L., Wu, M., Zhang, Q., Han, W., & Khoi, D. N. (2019). The relationship between NDVI and climate factors at different monthly time scales: A case study of grasslands in inner Mongolia, China (1982–2015). Sustainability, 11(24):7243. https://doi.org/10.3390/su11247243.
Priya, A., Sharma, M., Jhajharia, D., & Yurembam, G. S. (2021). Assessment of meteorological drought with application of standardized precipitation evapotranspiration index (SPEI) for Tripura, Northeast India. International Journal of Environment and Climate Change, 11(3), 126–135. https://doi.org/10.9734/IJECC%2F2021%2FV11I330383.
Quiring, S. M. (2009). Developing objective operational definitions for monitoring drought. Journal of Applied Meteorology and Climatology, 48(6), 1217–1229. https://doi.org/10.1175/2009JAMC2088.1.
Quiring, S. M., & Ganesh, S. (2010). Evaluating the utility of the Vegetation Condition Index (VCI) for monitoring meteorological drought in Texas. Agricultural and Forest Meteorology, 150(3), 330–339. https://doi.org/10.1016/j.agrformet.2009.11.015.
Rimkus, E., Stonevicius, E., Kilpys, J., Maciulytė, V., & Valiukas, D. (2017). Drought identification in the eastern Baltic region using NDVI. Earth System Dynamics, 6(3), 627–637. https://doi.org/10.5194/esd-2017-5.
Rulinda, C. M., Dilo, A., Bijker, W., & Stein, A. (2012). Characterising and quantifying vegetative drought in East Africa using fuzzy modelling and NDVI data. Journal of Arid Environments, 78, 169–178. https://doi.org/10.1016/j.jaridenv.2011.11.016.
Sahoo, A. K., Sheffield, J., Pan, M., & Wood, E. F. (2015). Evaluation of the tropical rainfall measuring mission multi-satellite precipitation analysis (TMPA) for assessment of large-scale meteorological drought. Remote Sensing of Environment., 159, 181–193. https://doi.org/10.1016/J.RSE.2014.11.032.
Sandeep, P., Reddy, G. O., Jegankumar, R., & Kumar, K. A. (2021). Monitoring of agricultural drought in semi-arid ecosystem of Peninsular India through indices derived from time-series CHIRPS and MODIS datasets. Ecological Indicators, 121, 107033. https://doi.org/10.1016/j.ecolind.2020.107033.
Seiler, R. A., Hayes, M., & Bressan, L. (2002). Using the standardized precipitation index for flood risk monitoring. International Journal of Climatology A Journal of the Royal Meteorological Society, 22(11), 1365–1376. https://doi.org/10.1002/joc.799.
Senamaw, A., Addisu, S., & Suryabhagavan, K. V. (2021). Mapping the spatial and temporal variation of agricultural and meteorological drought using geospatial techniques, Ethiopia. Environmental Systems Research, 15(10), 1–17. https://doi.org/10.1186/s40068-020-00204-2.
Shafer, B. A., & Dezman, L. E. (1982). Development of surface water supply index (SWSI) to assess the severity of drought condition in snowpack runoff areas. In Proceeding of the western snow conference. https://westernsnowconference.org/sites/westernsnowconference.org/PDFs/1982Shafer.pdf.
Shamim, A., Ahmed, M. I., Ahmad, R., & Roy, P. S. (2021). Responses of NDVI anomaly to SPI for vegetation over the years 2000–2019: A case study of Raichur district. International Journal of Scientific & Engineering Research, 12(8), 329.
Shukla, S., & Wood, A. W. (2008). Use of a standardized runoff index for characterizing hydrologic drought. Geophysical Research Letters, 35(2), L0240. https://doi.org/10.1029/2007GL032487.
Srivastava, S. K., Jayarama, V., Nageswara Rao, P. P., Manikiam, B., & Chandrasekhar, M. G. (1997). Interlinkages of NOAA/AVHRR derived integrated NDVI to seasonal precipitation and transpiration in dryland tropics. International Journal of Remote Sensing, 18(14), 2931–2952. https://doi.org/10.1080/014311697217134.
Sur, C., Kim, D., Lee, J. H., Iqbal, M. M., & Choi, M. (2019). Hydrological drought assessment of energy-based water deficit index (EWDI) at different geographical regions. Advances in Meteorology. https://doi.org/10.1155/2019/8512727.
Thenkabail, P. S., & Gamage, M. S. D. N. (2004). The use of remote sensing data for drought assessment and monitoring in Southwest Asia. 85.
Tsakiris, G., Pangalou, D., & Vangelis, H. (2007). Regional drought assessment based on the Reconnaissance Drought Index (RDI). Water Resources Management, 21(5), 821–833. https://doi.org/10.1007/s11269-006-9105-4.
Van Rooy, M. P. (1965). A rainfall anomaly index independent of time and space. Notos, 14, 43–48.
Vaani, N., & Porchelvan, P. (2017). GIS based agricultural drought assessment for the state of Tamilnadu, India using vegetation condition index (VCI). International Journal of Civil Engineering and Technology, 8(5), 1185–1194.
Vangelis, H., Tigkas, D., & Tsakiris, G. (2013). The effect of PET method on reconnaissance drought index (RDI) calculation. Journal of Arid Environments, 88, 130–140. https://doi.org/10.1016/J.JARIDENV.2012.07.020.
Vrieling, A., Meroni, M., Shee, A., Mude, A. G., Woodard, J., de Bie, C. K., & Rembold, F. (2014). Historical extension of operational NDVI products for livestock insurance in Kenya. International Journal of Applied Earth Observation and Geoinformation, 28, 238–251. https://doi.org/10.1016/j.jag.2013.12.010.
Wable, P. S., Jha, M. K., & Shekhar, A. (2018). Comparison of drought indices in a semi-arid river basin of India. Water Resources Management, 33(1), 75–102. https://doi.org/10.1007/s11269-018-2089-z.
Wu, H., Hayes, M. J., Welss, A., & Hu, Q. (2005). An evaluation the standardized precipitation index, the china-z index and the statistical z-score. International Journal of Climatology, 21, 745–758. https://doi.org/10.1002/joc.658.
Yu, H., Zhang, Q., Xu, C. Y., Du, J., Sun, P., & Hu, P. (2019). Modified Palmer Drought Severity Index: Model improvement and application. Environment International. https://doi.org/10.1016/j.envint.2019.104951.
Acknowledgements
The authors are thankful to various organizations such as to National Oceanic and Atmospheric Administration (NOAA) and the University of Maryland Global Land Cover Facility Data Distribution Centre for providing the NOAA-AVHRR-derived NDVI dataset. Gujarat Ecological Commission (http://www.gec.gov.in), State Water Data Centre, Gandhinagar, and the Director of Agriculture, Gandhinagar, for sharing the historical archive of precipitation and crop statistical data. The authors are also thankful to Mr. Gangadhar Namwade, Ph.D. research scholar, College of Agricultural Engineering and Technology, AAU, Godhra, for the technical assistance provided during the collection of the different meteorological datasets.
Funding
The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors have no relevant financial or non-financial interests to disclose.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Bhukya, S., Tiwari, M.K. & Patel, G.R. Assessment of Spatiotemporal Variation of Agricultural and Meteorological Drought in Gujarat (India) Using Remote Sensing and GIS. J Indian Soc Remote Sens 51, 1493–1510 (2023). https://doi.org/10.1007/s12524-023-01715-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12524-023-01715-y