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Satellite Remote Sensing for Monitoring Agriculture Growth and Agricultural Drought Vulnerability Using Long-Term (1982–2015) Climate Variability and Socio-economic Data set

  • P. Bhavani
  • P. S. Roy
  • V. ChakravarthiEmail author
  • Vijay P. Kanawade
Research Article

Abstract

Climate variability significantly impact the agricultural growth, stress, cropping pattern, phenophase and its vulnerability. Satellite derived indices, climate and socio-economic data sets have been used to study the time series trend of agricultural NDVI and agriculture drought vulnerability for two states of India namely Andhra Pradesh and Telangana. The study uses NOAA AVHRR GIMMS NDVI.3g. v1 (1982–2015) data set. The trend analysis of climate and soil moisture was carried out to understand their impact on the agriculture growth/stress, length of the growing period (LGP) and projected agriculture NDVI for IPCC climate AR5 2050 RCP 2.6 scenario. A novel approach is applied to the integrated data sets i.e. satellite and climate variables including socio-economic to assess the agricultural drought vulnerability at the district level, and at the tehsil level of united Telangana and Andhra Pradesh states for the recent-past. We further projected the vulnerability using IPCC AR5 2050 and 2070 climate RCP 2.6 scenario. The study has revealed that climate and soil moisture have a significant impact on LGP and agriculture condition. The predicted agricultural NDVI are near like normal years (2007 and 2013) indicating climate change signatures are not expected in near future. There is a need to improve the understanding using higher resolution soil moisture data to plan appropriate adaptive and mitigation strategies for the agricultural drought conditions in changing climate scenario.

Keywords

GIMMS Trend LGP Climate change Agricultural drought vulnerability 

Notes

Acknowledgements

PSR would like to acknowledge the National Academy of Sciences India (NASI) for the support to research work. BP thanks Departmental Research Committee (DRC) members, University Hyderabad for guidance. The Authors are thankful to Dr. D.S. Pai, Scientist, India Meteorological Department (IMD) for providing climate data (Temperature and Precipitation).

Supplementary material

40010_2017_445_MOESM1_ESM.docx (15 kb)
Supplementary material 1 (DOCX 15 kb)

References

  1. 1.
    Udmale P, Ichikawa Y, Manandhar S, Ishidaira H, Kiem AS (2014) Farmers' perception of drought impacts, local adaptation and administrative mitigation measures in Maharashtra State, India. Int J Disaster Risk Reduct 10:250–269.  https://doi.org/10.1016/j.ijdrr.2014.09.011 CrossRefGoogle Scholar
  2. 2.
    Kaushalya R, Venkateshwarlu B, Ramarao C, Rao V, Raju B, Rao A, Saikia U, Thilagavathi N, Gayatri M, Satish J (2013) Assessment of vulnerability of Indian agriculture to rainfall variability—use of NOAA-AVHRR (8 km) and MODIS (250 m) time-series NDVI data products. Clim Change Environ Sustain 1(1):37–52.  https://doi.org/10.5958/j.2320-6411.1.1.005 CrossRefGoogle Scholar
  3. 3.
    Ahmad S, Abbas Q, Abbas G, Fatima Z, Atique-ur-Rehman Naz S, Younis H, Khan RJ, Nasim W et al (2017) Quantification of climate warming and crop management impacts on cotton phenology. Plants 6(1):7.  https://doi.org/10.3390/plants6010007 CrossRefGoogle Scholar
  4. 4.
    IPCC (2007) Climate change 2007: impacts, adaptation and vulnerability. In: Parry ML, Canziani OF, Palutikof JP, van der Linden PJ, Hanson CE (eds) Contribution of working group II to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, UK, p 976Google Scholar
  5. 5.
    Shukla R, Chakraborty A, Joshi PK (2017) Vulnerability of agro-ecological zones in India under the earth system climate model scenarios. Mitig Adapt Strat Glob Change 22(3):399–425.  https://doi.org/10.1007/s11027-015-9677-5 CrossRefGoogle Scholar
  6. 6.
    Upadhyay G, Ray SS, Panigrahi S (2008) Derivation of crop phenological parameters using multi-date SPOT-VGTNDVI data: a case study for Punjab. J Indian Soc Remote Sens 36:37–50CrossRefGoogle Scholar
  7. 7.
    Chakraborty A, Das PK, Sesha Sai MVR, Behera G (2011) Spatial pattern of temporal trend of crop phenology matrices over India using timeseries GIMMS NDVI data (1982–2006). Int Arch Photogramm Remote Sens Spat Inf Sci XXXVIII-8/W20:113–118.  https://doi.org/10.5194/isprsarchives-XXXVIII-8-W20-113-2011 CrossRefGoogle Scholar
  8. 8.
    Ramachandran K, Gayatri M, Praveen V, Satish J (2014) Use of NDVI variations to analyse the length of growing period in Andhra Pradesh. J Agrometeorol 16(1):112Google Scholar
  9. 9.
    Berry PM, Rounsevell MDA, Harrison PA, Audsley E (2006) Assessing the vulnerability of agricultural land use and species to climate change and the role of policy in facilitating adaptation. Environ Sci Policy 9(2):189–204.  https://doi.org/10.1016/j.envsci.2005.11.004 CrossRefGoogle Scholar
  10. 10.
    Chandrasekar K, Sesha Sai MVR, Roy PS, Jayaraman V, Krishnamurthy RR (2009) Identification of agricultural drought vulnerable areas of Tamil Nadu, India-using GIS based multi criteria analysis. Asian J Environ Disaster Manag 1(1):40–61.  https://doi.org/10.3850/S17939240200900009X CrossRefGoogle Scholar
  11. 11.
    Murthy CS, Laxman B, Sesha Sai MVR (2015) Geospatial analysis of agricultural drought vulnerability using a composite index based on exposure, sensitivity and adaptive capacity. Int J Disaster Risk Reduct 12:163–171.  https://doi.org/10.1016/j.ijdrr.2015.01.004 CrossRefGoogle Scholar
  12. 12.
    Sehgal VK, Singh MR, Chaudhary A, Jain N, Pathak H (2013) Vulnerability of agriculture to climate change: district level assessment in the Indo-Gangetic Plains. Indian Agric Res Inst, New DelhiGoogle Scholar
  13. 13.
    Bhavani P, Chakravarthi V, Roy PS, Joshi PK, Chandrasekar K (2017) Long-term agricultural performance and climate variability for drought assessment: a regional study from Telangana and Andhra Pradesh states. Geomat Nat Hazards Risk, India.  https://doi.org/10.1080/19475705.2016.1271831 Google Scholar
  14. 14.
    Vamsi V (2004) Agricultural growth and irrigation in Telangana: a review of evidence. Econ Polit Wkly 39:1421–1426Google Scholar
  15. 15.
    Revadekar JV, Tiwari Yogesh K, Ravi Kumar K (2012) Impact of climate variability on NDVI over the Indian region during 1981–2010. Int J Remote Sens 33:7132–7150CrossRefGoogle Scholar
  16. 16.
    Pai DS, Sridhar L, Badwaik MR, Rajeevan M (2015) Analysis of the daily rainfall events over India using a new long period (1901–2010) high resolution (0.25° × 0.25°) gridded rainfall data set. Clim Dyn 45(3–4):755–776CrossRefGoogle Scholar
  17. 17.
    IPCC (2014) Climate change 2014: impacts, adaptation, and vulnerability. In: Barros VR, Field CB, Dokken DJ, Mastrandrea MD, Mach KJ, Bilir TE, Chatterjee M, Ebi KL, Estrada YO, Genova RC, Girma B, Kissel ES, Levy AN, MacCracken S, Mastrandrea PR, White LL (eds) Part B: regional aspects. contribution of working group II to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp 688Google Scholar
  18. 18.
    Klosterman ST, Hufkens K, Gray JM, Melaas E, Sonnentag O, Lavine I, Mitchell L, Norman R, Friedl MA, Richardson AD (2014) Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery. Biogeosciences 11:4305–4320ADSCrossRefGoogle Scholar
  19. 19.
    FilippaG Edoardo C, Mirco M, Marta G, Matthias F, Lisa W, Enrico T, di Umberto Morra C, Andrew DR (2016) Phenopix: a R package for image-based vegetation phenology. Agric For Meteorol 220:141–150ADSCrossRefGoogle Scholar
  20. 20.
    Kogan FN (1997) Global drought watch from space. Bull Am Meteorol Soc 78:621–636CrossRefGoogle Scholar
  21. 21.
    Murthy CS, Laxman B, Sesha Sai MVR, Diwakar PG (2014) Analysing agricultural drought vulnerability at sub-district level through exposure, sensitivity and adaptive capacity based composite index. Int Arch Photogramm Remote Sens Spat Inf Sci XL-8:65–70.  https://doi.org/10.5194/isprsarchives-XL-8-65-2014 CrossRefGoogle Scholar
  22. 22.
    Singh NP, Bantilan C, Byjesh K (2014) Vulnerability and policy relevance to drought in the semi-arid tropics of Asia—a retrospective analysis. Weather Clim Extrem 3:54–61.  https://doi.org/10.1016/j.wace.2014.02.002 CrossRefGoogle Scholar
  23. 23.
    Saaty TL (1980) Decision making with the analytic hierarchy process. Int J Serv Sci 1:83–98Google Scholar
  24. 24.
    Cheng J, Tao JP (2010) Fuzzy comprehensive evaluation of drought vulnerability based on the analytic hierarchy process. Agric Agric Sci Procedia 1:126–135.  https://doi.org/10.1016/j.aaspro.2010.09.015 CrossRefGoogle Scholar
  25. 25.
    Miura ABSF (2013) Remote sensing, GIS, and AHP for assessing physical vulnerability to tsunami hazard. Int J Environ Chem Ecol Geol Geophys Eng 7:670–679Google Scholar
  26. 26.
    FAO (2003) Food and agriculture organization of the United Nations. http://www.fao.org/about/en/. Accessed 18 Jan 2016
  27. 27.
    Modarresi Mostafa, Nikpey Mohammad Ali, Mikpey Mehdi (2015) Assessing the impact of climate variability on rice phenology. Res J Environ Sci 9:296–301.  https://doi.org/10.3923/rjes.2015.296.301 CrossRefGoogle Scholar
  28. 28.
    Hatfield JL, Prueger JH (2015) Temperature extremes: effect on plant growth and development. Weather Clim Extrem 10:4–10.  https://doi.org/10.1016/j.wace.2015.08.001 CrossRefGoogle Scholar
  29. 29.
    Propastin P, Kappas M (2008) Spatio-temporal drifts in AVHRR/NDVI-precipitation relationships and their linkage to land use change in central Kazakhstan. EARSeL eProceedings 7(1):30–45Google Scholar
  30. 30.
    Liu Y, Li Y, Li S, Motesharrei S (2015) Spatial and temporal patterns of global NDVI trends: correlations with climate and human factors. Remote Sens 7(10):13233ADSCrossRefGoogle Scholar

Copyright information

© The National Academy of Sciences, India 2017

Authors and Affiliations

  • P. Bhavani
    • 1
  • P. S. Roy
    • 1
  • V. Chakravarthi
    • 1
    Email author
  • Vijay P. Kanawade
    • 1
  1. 1.Centre for Earth and Space SciencesUniversity of HyderabadHyderabadIndia

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