Agricultural drought monitoring in Tamil Nadu in India using Satellite-based multi vegetation indices
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Abstract
Drought being an insidious hazard, is considered to have one of the most complex phenomenons. The proposed study identifies remote sensing-based indices that could act as a proxy indicator in monitoring agricultural drought over Tamil Nadu's region India. The satellite data products were downloaded from 2000 to 2013 from MODIS, GLDAS – NOAH, and TRMM. The intensity of agricultural drought was studied using indices viz., NDVI, NDWI, NMDI, and NDDI. The satellite-derived spectral indices include raw, scaled, and combined indices. Comparing satellite-derived indices with in-situ rainfall data and 1-month SPI data was performed to identify exceptional drought to no drought conditions for September month. The additive combination of NDDI showed a positive correlation of 0.25 with rainfall and 0.23 with SPI, while the scaled NDDI and raw NDDI were negatively correlated with rainfall and SPI. Similar cases were noticed with raw LST and raw NMDI. Indices viz., LST, NDVI, and NDWI performed well; however, it was clear that NDWI performed better than NDVI while LST was crucial in deciding NDVI coverage over the study area. These results showed that no single index could be put forward to detect agricultural drought accurately; however, an additive combination of indices could be a successful proxy to vegetation stress identification.
Article Details
Article Details
Agricultural Drought, Land Use, MODIS, SPI, Vegetation Indices
Aghakouchak, A., Farahmand, A., Melton, F.S., Teixeira, J., Anderson, M.C., Wardlow, B.D., & Hain, C.R. (2015). Reviews of geophysics remote sensing of drought: Progress, challenges. Review of Geophysics, 53, 1–29. https://doi.org/10.1002/2014RG000456
Ceccato, P., Flasse, S., Tarantola, S., Jacquemoud, S., & Grégoire, J.M. (2001). Detecting vegetation leaf water content using reflectance in the optical domain. Remote Sensing of Environment, 77, 22–33. https://doi.org/1 0.1016/S0034-4257(01)00191-2
DOE. (2011). Season & crop report Tamil Nadu, Department of Economics and Statistics (DOE) Chennai.
Di, L., Rundquist, D.C., & Han, L. (1994). Modelling relationships between NDVI and precipitation during vegetative growth cycles. International Journal of Remote Sensing, 15, 2121–2136. https://doi.org/10.1080/0143 1169408954231
Farrar, T.J., & Nicholson, S.E., Lare, A.R. (1994). The influence of soil type on the relationship between NDVI, rainfall, and soil moisture in semiarid Botswana. II. NDVI response to soil moisture. Remote Sensing of Environment, 50, 121–133. https://doi.org/10.1016/0034-4257(94)90038-8
Gao, B.C. (1996). NDWI- A normalised difference water index for remote sensing of vegetation liquid from space. Remote Sensing of Environment, 58, 257–266. https://doi.org/10.1016/S0034-4257(96)00067-3
Ge, Y., Cai, X., Zhu, T., & Ringler, C. (2016). Drought frequency change: An assessment in northern India plains. Agricultural Water Management, 176, 111-121. https://doi.org/10.1016/j.agwat.2016.05.015
GOI (2018). Regional Report on Southwest monsoon over southern peninsular India, Government of India (GOI), I.M.D.
Gu, Y., Brown, J.F., Verdin, J.P., & Wardlow, B. (2007). A five year analysis of MODIS NDVI and NDWI for grassland drought assessment over the central Great Plains of the United States. Geophysics Research Letter, 34, 1–6. https://doi.org/10.1029/2006GL029127
Gupta, V., Kumar Jain, M., & Singh, V. P. (2020). Multivariate modeling of projected drought frequency and hazard over India. Journal of Hydrologic Engineering, 25(4), 04020003.
Ji, L. & Peters, A.J. (2003). Assessing vegetation response to drought in the northern Great Plains using vegetation and drought indices. Remote Sensing of Environment, 87, 85–98. https://doi.org/10.1016/S0034-4257(03)00174-3
Kogan, F.N. (1995). Application of vegetation index and brightness temperature for drought detection. Advances in Space Research, 15, 91–100. https://doi.org/10.10 16/0273-1177(95)00079-T
Le Houerou, H.N., Bingham, R.L., & Skerbek, W. (1988). Relationship between the variability of primary production and the variability of annual precipitation in world arid lands. Journal of Arid Environment, 15, 1–18. https://doi.org/10.1016/S0140-1963(18)31001-2
McKee, T.B. (1995). Drought monitoring with multiple time scales, In: 9th Conference on Applied Climatology,
Boston.
McKee, T.B., Doesken, N.J., & Kleist, J. (1993). The relationship of drought frequency and duration to time scales, In: Proceedings of 8th Conference on Applied Climatology, Boston, p.179–183.
Palmer, W.C. (1965). Meteorological Drought. U.S. Department of Commerce, Weather Bureau, Washington
Rhee, J., Im, J., & Carbone, G.J. (2010). Monitoring agricultural drought for arid and humid regions using multi-sensor remote sensing data. Remote Sensing of Environment, 114, 2875–2887. https://doi.org/10.1016/j.rse.20 10.07.005
Rouse, J.W., Haas, R.H., Schell, J.A., & Deering, D.W. (1974). Monitoring vegetation systems in the Great Plains with ERTS. In: Proceedings of the Third Earth Resources Technology Satellite-1 Symposium, Washington: NASA, Scientific and Technical Information Office, p.309-317.
Rousta, I., Olafsson, H., & Zhang, H. (2020). Impact of Drought on Vegetation Assessed by Vegetation Indices and Meteorological Factors in Afghanistan. Remote Sensing, 12(15), 2433. https://doi.org/10.3390/rs12152433
Wang, J., Price, K.P., & Rich, P.M. (2001). Spatial patterns of NDVI in response to precipitation and temperature in central great plains. International Journal of Remote Sensing, 22(18), 3827-3844. https://doi.org/10.108 0/01431160010007033
Wang, L. & Qu, J.J. (2007). NMDI: A normalised multi-band drought index for monitoring soil and vegetation moisture with satellite remote sensing. Geophysics. Research Letter, 34(20). https://doi.org/10.1029/200 7GL031021
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