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Agricultural drought early warning from geostationary meteorological satellites: concept and demonstration over semi-arid tract in India

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Abstract

Remote sensing data from Indian geostationary satellites (Kalapana-1, INSAT 3A) were used for the first time for early warning of agricultural drought and forewarning of crop vigour. An Early warning indicator (EWI) was developed from operational product of rainfall and reference evapotranspiration from observations of Kalpana-1 very high resolution radiometer (VHRR). The effectiveness of EWI was evaluated for the two drought years (2009 and 2012). The positive correlation (r = 0.66–0.68 for 2009 and r = 0.64–0.70 for 2012) between the EWI in the month of June–July and standardized precipitation index-1 (SPI-1) averaged over administrative unit (called district) indicates that EWI can be used successfully for drought early warning. Lag-response behaviour between EWI and crop vigour in terms of normalized difference vegetation index (NDVI) and LAI (leaf area index) over cropland was studied. Systematic patterns emerged for 30 days lag period between negative EWI and NDVI at both grid-scale (0.25°) and at district level. Linear relations were found between 10-day EWI and NDVI or LAI at 30 days lag during June–July period. Linear models were developed to forewarn crop vigour which was validated with realized NDVI from INSAT 3A charge-coupled device (CCD) observations within 95% accuracy. The EWI is recommended as potential indicator for early-season agricultural drought assessment and can be used for sub-district scale with finer scale rainfall and evaporation products from advanced next-generation geostationary meteorological satellites.

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References

  • Aadhar, S., & Mishra, V. (2017). High-resolution near real-time drought monitoring in South Asia. Scientific Data, 4, 170145.

    Google Scholar 

  • Agrawal, S. (2003). Spot-vegetation multi temporal data for classifying vegetation in South Central Asia. Current Science, 84(11), 1440–1448.

    Google Scholar 

  • Akwango, D., Obaa, B. B., Turyahabwe, N., Baguma, Y., & Egeru, A. (2017). Effect of drought early warning system on household food security in Karamoja sub-region, Uganda. Agriculture & Food Security, 6(1), 43.

    Google Scholar 

  • Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Guidelines for computing crop water requirements. Irrigation and drainage Paper, 56, 300.

  • Bhattacharya, B. K., Dutt, C. B. S., & Parihar, J. S. (2009). INSAT uplinked Agromet Station -a scientific tool with a network of automated micrometeorological measurements for soil-canopy-atmosphere feedback studies. In ISPRS Archives XXXVIII-8/W3 Workshop Proceedings: Impact of Climate Change on Agriculture (pp. 72–77).

  • Bhattacharya, B. K., Padmanabhan, N., Mahammed, S., Ramakrishnan, R., & Parihar, J. S. (2013). Assessing solar energy potential using diurnal remote-sensing observations from Kalpana-1 VHRR and validation over the Indian landmass. International Journal of Remote Sensing, 34(20), 7069–7090.

    Google Scholar 

  • Brown, J. F., Wardlow, B. D., Tadesse, T., Hayes, M. J., & Reed, B. C. (2008). The vegetation drought response index (VegDRI): A new integrated approach for monitoring drought stress in vegetation. GIScience & Remote Sensing, 45(1), 16–46.

    Google Scholar 

  • Cai, G., Du, M., & Liu, Y. (2010). Regional drought monitoring and analyzing using MODIS data—A case study in Yunnan Province. In International conference on computer and computing technologies in agriculture (pp. 243–251). Springer, Berlin, Heidelberg.

  • Chakraborty, A., Seshasai, M. V. R., Murthy, C. S., & Rao, S. K. (2013). Assessing early season drought condition using AMSR-E soil moisture product. Geomatics, Natural Hazards and Risk, 4(2), 164–186.

    Google Scholar 

  • Department of Agriculture, Cooperation (DoA). (2010). Annual Report 2009–10, Department of Agriculture & Co-operation, Ministry of Agriculture, Government of India. http://www.agricoop.nic.in.

  • Department of Agriculture, Cooperation (DoA). (2013). Annual Report 2012–13, Department of Agriculture & co-operation, Ministry of Agriculture, Government of India. http://www.agricoop.nic.in.

  • Department of Agriculture, Cooperation (DoA). (2018). Agricultural Statistics at a Glance 2017. Department of Agriculture, Cooperation& Farmers Welfare, Directorate of Economics & Statistics. Ministry of Agriculture, Government of India. http://www.agricoop.nic.in.

  • Ghulam, A., Li, Z., Qin, Q., & Tong, Q. (2007). Exploration of the spectral space based on vegetation index and albedo for surface drought estimation. Journal of Applied Remote Sensing, 1(1), 013529.

    Google Scholar 

  • Guhathakurta, P., Koppar, A. L., Krishan, U., & Menon, P. (2011). New rainfall series for the districts, meteorological sub-divisions and country as whole of India. National Climate Centre Research Report, (2). http://imdpune.gov.in/Clim_Pred_LRF_New/Reports/NCCResearchReports/research_report 16.pdf.

  • Guhathakurta, P., Menon, P., Inkane, P. M., Krishnan, U., & Sable, S. T. (2017). Trends and variability of meteorological drought over the districts of India using standardized precipitation index. Journal of Earth System Science, 126(8), 120.

    Google Scholar 

  • Guttman, N. B. (1999). Accepting the standardized precipitation index: A calculation algorithm 1. JAWRA Journal of the American Water Resources Association, 35(2), 311–322.

    Google Scholar 

  • Hao, Z., AghaKouchak, A., Nakhjiri, N., & Farahmand, A. (2014). Global integrated drought monitoring and prediction system. Scientific data, 1, 140001.

    Google Scholar 

  • Hao, C., Zhang, J., & Yao, F. (2015). Combination of multi-sensor remote sensing data for drought monitoring over Southwest China. International Journal of Applied Earth Observation and Geoinformation, 35, 270–283.

    Google Scholar 

  • Hewitt, K. (2014). Regions of risk: A geographical introduction to disasters. London: Routledge. [ISBN 9781315844206]. https://doi.org/10.4324/9781315844206.

    Book  Google Scholar 

  • Hobbins, M. T., Wood, A., McEvoy, D. J., Huntington, J. L., Morton, C., Anderson, M., & Hain, C. (2016). The evaporative demand drought index. Part I: Linking drought evolution to variations in evaporative demand. Journal of Hydrometeorology, 17(6), 1745–1761.

    Google Scholar 

  • India Meteorological Department (IMD). (2009). Southwest monsoon, end-of-season report (pp. 11–12). Pune: India Meteorological Department https://www.tropmet.res.in/~kolli/MOL/Monsoon/year2009/Monsoon-2009.pdf.

    Google Scholar 

  • India Meteorological Department (IMD). (2012). Southwest monsoon, end-of-season report (pp. 12–14). Pune: India Meteorological Department http://www.tropmet.res.in/~kolli/MOL/Monsoon/year2012/Monsoon-2012-NEW.pdf.

    Google Scholar 

  • India Meteorological Department (IMD). (2015). 110 years (1901–2010) monthly rainfall data series for districts, states and met sub-divisions and all India (pp. 171–295). Pune: India Meteorological Department http://www.imdpune.gov.in/library/public/e-book110.pdf.

    Google Scholar 

  • India Meteorological Department (IMD). (2016). Southwest monsoon, end-of-season report (pp. 16–17). Pune: India Meteorological Department https://www.tropmet.res.in/~kolli/MOL/Monsoon/year2015/Monsoon-2015-NEW.pdf.

    Google Scholar 

  • Kang, W., Wang, T., & Liu, S. (2018). The response of vegetation phenology and productivity to drought in semi-arid regions of Northern China. Remote Sensing, 10(5), 727.

    Google Scholar 

  • Kavitha, M., Ramteke, G., Reddy, A. K., & Narender, N. (2019). Integrated Approach for Local Level Drought Assessment and Risk Reduction. In Proceedings of International Conference on Remote Sensing for Disaster Management (pp. 265–279). Cham: Springer.

    Google Scholar 

  • Kogan, F. N. (1990). Remote sensing of weather impacts on vegetation in non-homogeneous areas. International Journal of Remote Sensing, 11(8), 1405–1419.

    Google Scholar 

  • Kogan, F. N. (1997). Global drought watch from space. Bulletin of the American Meteorological Society, 78(4), 621–636.

    Google Scholar 

  • Kogan, F. N. (2000). Contribution of remote sensing to drought early warning. Early warning systems for drought preparedness and drought management, (pp. 75–87).

  • Kumar, P., Bhattacharya, B. K., & Pal, P. K. (2015). Evaluation of weather research and forecasting model predictions using micrometeorological tower observations. Boundary-Layer Meteorology, 157(2), 293–308.

    Google Scholar 

  • Liu, W. T., & Kogan, F. N. (1996). Monitoring regional drought using the vegetation condition index. International Journal of Remote Sensing, 17(14), 2761–2782.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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, no. 22, pp. 179–183).

  • Ministry of Rural Development (MoRD). (1994). Report of the technical committee on drought prone areas programme and desert development programme. Technical report,. New Delhi: Ministry of Rural Development.

    Google Scholar 

  • Murthy, C. S., SeshaSai, M. V. R., Kumari, V. B., & Roy, P. S. (2007). Agricultural drought assessment at disaggregated level using AWiFS/WiFS data of Indian remote sensing satellites. Geocarto International, 22(2), 127–140.

    Google Scholar 

  • Nagarajan, R. (2010). Drought assessment. New Delhi: Springer Science & Business Media ISBN 9789048124992.

    Google Scholar 

  • Niemeyer, S. (2008). New drought indices. Options Méditerranéennes. Série A: Séminaires Méditerranéens, 80, 267–274.

    Google Scholar 

  • Nigam, R., Bhattacharya, B. K., Gunjal, K. R., Padmanabhan, N., & Patel, N. K. (2011). Continental-scale vegetation index from Indian geostationary satellite: Algorithm definition and validation. Current Science, 1184–1192.

  • Obasi, G. O. P. (1994). WMO's role in the international decade for natural disaster reduction. Bulletin of the American Meteorological Society, 75(9), 1655–1662.

    Google Scholar 

  • Otkin, J. A., Anderson, M. C., Hain, C., Mladenova, I. E., Basara, J. B., & Svoboda, M. (2013). Examining rapid onset drought development using the thermal infrared–based evaporative stress index. Journal of Hydrometeorology, 14(4), 1057–1074.

    Google Scholar 

  • Pai, D. S., & Bhan, S. C. (2014). Monsoon 2013: A report. IMD Met. Monograph: ESSO Document No.: ESSO/IMD/SYNOPTIC MET/01 (2014)/15. https://www.tropmet.res.in/~kolli/MOL/Monsoon/year2013/Monsoon-2013-NEW.pdf.

  • Pai, D. S., & Bhan, S. C. (2015). Monsoon 2014: A report. IMD Met. Monograph: ESSO Document No.: ESSO/IMD/SYNOPTIC MET/01 (2015)/17. http://www.imd.gov.in/section/nhac/dynamic/monsoon_report_2014.pdf.

  • Peters, A. J., Walter-Shea, E. A., Ji, L., Vina, A., Hayes, M., & Svoboda, M. D. (2002). Drought monitoring with NDVI-based standardized vegetation index. Photogrammetric Engineering and Remote Sensing, 68(1), 71–75.

    Google Scholar 

  • Pozzi, W., Sheffield, J., Stefanski, R., Cripe, D., Pulwarty, R., Vogt, J. V., et al. (2013). Toward global drought early warning capability: Expanding international cooperation for the development of a framework for monitoring and forecasting. Bulletin of the American Meteorological Society, 94(6), 776–785.

    Google Scholar 

  • Prakash, S., Mahesh, C., Gairola, R. M., & Pal, P. K. (2010). Estimation of Indian summer monsoon rainfall using Kalpana-1 VHRR data and its validation using rain gauge and GPCP data. Meteorology and Atmospheric Physics, 110(1–2), 45–57.

    Google Scholar 

  • Pulwarty, R. S., & Sivakumar, M. V. (2014). Information systems in a changing climate: Early warnings and drought risk management. Weather and Climate Extremes, 3, 14–21.

    Google Scholar 

  • Raju, B. M. K., Rao, K. V., Venkateswarlu, B., Rao, A. V. M. S., Rao, C. R., Rao, V. U. M., et al. (2013). Revisiting climatic classification in India: a district-level analysis. Current Science, 492–495.

  • Rao, C. H. (2000). Watershed development in India: Recent experience and emerging issues. Economic and Political Weekly, (pp. 3943–3947).

  • Ray, K. S. (2000). Role of drought early warning systems for sustainable agricultural research in India. Early Warning Systems for Drought Preparedness and Drought Management, 131.

  • Ray, S. S., Sai, M. S., & Chattopadhyay, N. (2015). Agricultural drought assessment: Operational approaches in India with special emphasis on 2012. In High-impact weather events over the SAARC region (pp. 349–364). Cham: Springer.

    Google Scholar 

  • Reddy, G. & Prabhu, C.N. (2016). Natural Disaster Monitoring System – Karnataka Model.178-187. https://doi.org/10.17491/cgsi/2016/95964.

  • 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(12), 2875–2887.

    Google Scholar 

  • Saha, S. (2000). Management of drought in Gujarat case of Banaskantha district. In Ph.D Thesis. https://shodhganga.inflibnet.ac.in/bitstream/10603/49033/8/08_chapter%201.pdf.

  • Sánchez, N., González-Zamora, Á., Piles, M., & Martínez-Fernández, J. (2016). A new soil moisture agricultural drought index (SMADI) integrating MODIS and SMOS products: A case of study over the Iberian Peninsula. Remote Sensing, 8(4), 287.

    Google Scholar 

  • Sánchez, N., González-Zamora, Á., Martínez-Fernández, J., Piles, M., & Pablos, M. (2018). Integrated remote sensing approach to global agricultural drought monitoring. Agricultural and Forest Meteorology, 259, 141–153.

    Google Scholar 

  • Schwabe, K., Albiac, J., Connor, J. D., Hassan, R. M., & González, L. M. (Eds.). (2013). Drought in arid and semi-arid regions: A multi-disciplinary and cross-country perspective. Berlin: Springer Science & Business Media.

    Google Scholar 

  • Seager, R., Ting, M., Held, I., Kushnir, Y., Lu, J., Vecchi, G., et al. (2007). Model projections of an imminent transition to a more arid climate in South-Western North America. Science, 316(5828), 1181–1184.

    CAS  Google Scholar 

  • Senay, G. B., Velpuri, N. M., Bohms, S., Budde, M., Young, C., Rowland, J., & Verdin, J. P. (2015). Drought monitoring and assessment: Remote sensing and modeling approaches for the famine early warning systems network. In Hydro-meteorological hazards, risks and disasters (pp. 233–262). Amsterdam: Elsevier.

    Google Scholar 

  • Shahabfar, A., & Eitzinger, J. (2011). Agricultural drought monitoring in semi-arid and arid areas using MODIS data. The Journal of Agricultural Science, 149(4), 403–414.

    Google Scholar 

  • Shaw, R. H., & Laing, D. R. (1966). Moisture stress and plant response. In W. H. Pierre, D. Kirkham, J. Pesek, & R. Shaw (Eds.), Plant Environment and Efficient Water Use (pp. 73–94). Madison: ASA, SSSA. https://doi.org/10.2134/1966.plantenvironment.c5.

    Chapter  Google Scholar 

  • Sheffield, J., & Wood, E. F. (2012). Drought: Past problems and future scenarios. New York: Routledge ISBN 9781849710824.

    Google Scholar 

  • Svoboda, M., LeComte, D., Hayes, M., Heim, R., Gleason, K., et al. (2002). The drought monitor. Bulletin of the American Meteorological Society, 83(8), 1181–1190.

    Google Scholar 

  • Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127–150.

    Google Scholar 

  • Tucker, C. J., & Choudhury, B. J. (1987). Satellite remote sensing of drought conditions. Remote Sensing of Environment, 23(2), 243–251.

    Google Scholar 

  • Udmale, P. D., Ichikawa, Y., Kiem, S. A., & Panda, N. S. (2014). Drought impacts and adaptation strategies for agriculture and rural livelihood in the Maharashtra State of India. The Open Agriculture Journal, 8(1).

  • Venkateswarlu, B., Raju, B. M. K., Rao, K. V., & Rao, C. R. (2014). Revisiting drought-prone districts in India. Economic and Political Weekly, 49(25), 71–75.

    Google Scholar 

  • Vyas, S. (2017). Regional Agricultural drought characterization using remote sensing based observations from geostationary satellites. In PhD Thesis. https://shodhganga.inflibnet.ac.in/handle/10603/188037.

  • Vyas, S. S., Bhattacharya, B. K., Nigam, R., Guhathakurta, P., Ghosh, K., Chattopadhyay, N., & Gairola, R. M. (2015). A combined deficit index for regional agricultural drought assessment over semi-arid tract of India using geostationary meteorological satellite data. International Journal of Applied Earth Observation and Geoinformation, 39, 28–39.

    Google Scholar 

  • Vyas, S. S., Nigam, R., Bhattacharya, B. K., & Kumar, P. (2016). Development of real-time reference evapotranspiration at the regional scale using satellite-based observations. International Journal of Remote Sensing, 37(24), 6108–6126.

    Google Scholar 

  • Wan, Z., Wang, P., & Li, X. (2004). 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.

    Google Scholar 

  • Wang, L., & Qu, J. J. (2007). NMDI: A normalized multi-band drought index for monitoring soil and vegetation moisture with satellite remote sensing. Geophysical Research Letters, 34(20).

  • Wilhite, D. A. (2000). Drought as a natural hazard: Concepts and definitions. In D. A. Wilhite (Ed.), Drought: A Global Assessment (pp. 3–18). London: Routledge.

    Google Scholar 

  • Zhang, A., & Jia, G. (2013). Monitoring meteorological drought in semiarid regions using multi-sensor microwave remote sensing data. Remote Sensing of Environment, 134, 12–23.

    Google Scholar 

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Acknowledgements

The authors wish to acknowledge the generous support from project ‘Energy and Mass Exchange in Vegetative Systems’ (EMEVS) under ISRO-GBP programme to conduct this study. We would also like to thank Director, Space Applications Centre (ISRO) for his constant encouragement towards the development of satellite-based product for agricultural meteorological applications.

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  1. Department of Geography (Geoinformatics), University of Pune, Pune, Maharashtra, 41107, India

    Swapnil S. Vyas

  2. Agriculture and Land Ecosystem Division (AED), Space Applications Centre, ISRO, Ahmedabad, Gujarat, 380015, India

    Bimal K. Bhattacharya

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Vyas, S.S., Bhattacharya, B.K. Agricultural drought early warning from geostationary meteorological satellites: concept and demonstration over semi-arid tract in India. Environ Monit Assess 192, 311 (2020). https://doi.org/10.1007/s10661-020-08272-8

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