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Monitoring of late-season agricultural drought in cotton-growing districts of Andhra Pradesh state, India, using vegetation, water and soil moisture indices

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Abstract

Persistent soil moisture deficits during flowering and yield formation stage are referred to as late-season agricultural drought. This study aims to assess late-season agricultural drought in cotton- and millet-growing districts of Andhra Pradesh, India, during summer cropping season 2011. Satellite-based indices like the Normalized Difference Vegetation Index, Normalized Difference Water Index and their Vegetation Condition Index from MODIS were analyzed. The root zone Soil Moisture Index (SMI) using soil water balance model for cotton and millet (sorghum and pearl millet) crops was derived to evaluate the soil moisture status. The analysis was carried out by comparing the satellite-derived indices with the previous normal years, and the assessments were made. The satellite-based indices clearly brought out the stress that the crop endured during late October and November, while SMI indicated soil water stress in early October. The soil- and crop-specific SMI’s were able to clearly indicate the exact period of water stress. The results show that millet crop was able to escape drought due to sufficient rainfall and its shorter duration, while cotton crop did not have enough soil moisture during the critical stage of flowering and boll formation and suffered severe yield loss due to the late-season agricultural drought.

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References

  • Agricultural statistics at a glance: 2011–12, Directorate of Economics and Statistics, Government of Andhra Pradesh, Hyderabad

  • Allen RG, Pereira L, Raes D, Smith M (1998) Crop Evapotranspiration. Food and Agriculture Organization of the United Nations, Rome, Italy, FAO publication 56. ISBN 92-5-104219-5

  • Beljaars ACM, Viterbo P, Miller MJ, Betts AK (1996) The anomalous rainfall over the United States during July 1993: sensitivity to land surface parameterization and soil anomalies. Mon Weather Rev 124:362–383

    Article  Google Scholar 

  • Capehart WJ, Carlson TN (1997) Decoupling of surface and near surface soil water content: a remote sensing perspective. Water Resour Res 33(6):1383–1395

    Article  Google Scholar 

  • Ceccato P, Flassee S, Tarantola S, Gregoire JM (2001) Detecting vegetation leaf water content using reflectance in the optical domain. Remote Sens Environ 77:22–33

    Article  Google Scholar 

  • Ceccato P, Gobron N, Lassee S, Pinty B, Tarantola S (2002a) Designing a spectral index to estimate vegetation water content from remote sensing data: part 1, theoretical approach. Remote Sens Environ 82:188–197

    Article  Google Scholar 

  • Ceccato P, Flassee S, Gregoire JM (2002b) Designing a spectral index to estimate water content from remote sensing data: part 2, validation and applications. Remote Sens Environ 82:198–207

    Article  Google Scholar 

  • Chandrasekar K, Sesha Sai MVR, Jeyaseelan AT, Dwivedi RS, Roy PS (2006) Vegetation response to rainfall as monitored by NOAA-AVHRR. Curr Sci 91:1626–1633

    Google Scholar 

  • Chen D, Jackson TJ, Li F, Cosh MH, Walthall C, Anderson M (2003) Estimation of vegetation water content for corn and soybeans with a Normalized Difference Water Index (NDWI) using landsat thematic mapper data. Proceeding of Int. Geosciences and Remote Sens. Symposium, New York, USA

  • Chen D, Huang J, Jackson TT (2005) Vegetation water content estimation for corn and soybeans using spectral indices derived from MODIS near and short-wave infrared bands. Remote Sens Environ 98:225–236

    Article  Google Scholar 

  • Curran PJ (1980) Relative reflectance data from preprocessed multispectral photography. Int J Remote Sens 1:77–83

    Article  Google Scholar 

  • Devenport ML, Nicholson SE (1993) On the relation between rainfall and Normalized Difference Vegetation Index for diverse vegetation types of East Africa. Int J Remote Sens 12:2369–2389

    Article  Google Scholar 

  • Gao BC (1996) NDWI-A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens Environ 58:257–266

    Article  Google Scholar 

  • Gao BC, Goetz AFH (1995) Retrieval of equivalent water thickness and information related to biochemical components of vegetation canopies from AVIRIS data. Remote Sens Environ 52:155–162

    Article  Google Scholar 

  • Hielkema JU, Prince SD, Astle WL (1986) Rainfall and vegetation monitoring in the Savanna zone of the Democratic Republic of Sudan using the NOAA-AVHRR. Int J Remote Sens 7:1499–1514

    Article  Google Scholar 

  • Hojin K, Huete AR, Pamela N, Ed Glenn, Emmerich W, Scott RL (2004) Monitoring riparian and semi-arid upland vegetation using vegetation and water indices from the MODIS satellite sensor. Research insights in semiarid ecosystems (RISE) Symposium, 13th November, 2004, University of Arizona, Tucson, Marley Building

  • Hunt ER Jr, Running SW (1990) Problem with leaf water relations to regional scales. Proceedings of IGRASS 1990, Institute of Electrical and Electronic Engineers, New York, USA

  • Hunt ER Jr, Rock RN (1989) Detection of changes in leaf water content using near and middle-infrared reflectance. Remote Sens Environ 30:43–54

    Article  Google Scholar 

  • Jackson TJ, Chen D, Cosh M, Li F, Anderson M, Walthall C (2004) Vegetation water content mapping using Landsat data derived normalized difference water index for corn and soybeans. Remote Sens Environ 92:475–482

    Article  Google Scholar 

  • Jürgens C (1997) The modified normalized difference vegetation index (mNDVI)—a new index to determine frost damages in agriculture based on Landsat TM data. Int J Remote Sens 18:3583–3594

    Article  Google Scholar 

  • Kogan FN (1987) Vegetation index for areal analysis of crop conditions. Proceedings of the 18th Conference on Agricultural and Forest Meteorology, AMS held in W. Lafayette, Indiana

  • Kogan FN (1990) Remote Sens. of weather impacts on vegetation on non-homogeneous area. Int J Remote Sens 11:1405–1419

    Article  Google Scholar 

  • Kogan FN (1997) Global drought watch from space. Bull Am Meteorol Soc 78:621–636

    Article  Google Scholar 

  • Kogan FN (2000) Global drought detection and impact assessment from space. In: DA Wilhite (ed) Drought: a global assessment, Vol. 1, Routledge, London, pp. 196–210

  • Mandal UK, Sundara Sarma KS, Victor US, Rao NH (2002) Soil water dynamics, profile water balance model under irrigated and rainfed systems. Agron J 94:1204–1211

    Article  Google Scholar 

  • McVicar TR, Jupp DLB, Reece PH, Williams NA (1996) Relating LANDSAT TM Vegetation Indices to in situ Leaf Area Index measurements. CSIRO Technical Memorandum, Division of Water Resources 96.14. Canberra, ACT: CSIRO

  • Ministry of Agriculture (1972) Handbook of hydrology. Government of India, New Delhi

    Google Scholar 

  • Mishra PK (2005) Guidelines for rainfed production system and management. In: Sharma KD, Ramasastri KS (eds) Drought management, Allied Publishers Private Limited, pp 387–398

  • Neitsch SL, Arnold JG, Kiniry J.R, Williams JR (2011) Soil and water assessment tool-theoretical documentation, Version 2009, Texas Water Resources Institute, pp 98–121 (swat.tamu.edu/documentation/)

  • Oza MP, Rajak DR, Bhagia N, Dadhwal VK (2001) Monitoring the impact of drought on agriculture using multi-temporal IRS WiFS data. Proceedings of the ISRS 2001 Symposium. Ahmedabad, India: Indian Society of Remote Sens., Ahmedabad

  • Raju S, Chanzy A, Wigneron J, Calvet J, Kerr Y, Laguerre L (1995) Soil moisture and temperature profile effects on microwave emission at low frequencies. Remote Sens Environ 54:85–97

    Article  Google Scholar 

  • Rasmussen MS (1997) Operational yield forecast using AVHRR NDVI data: reduction of environmental and inter-annual variability. Int J Remote Sens 18:1059–1077

    Article  Google Scholar 

  • Richard Y, Poccard I (1998) A statistical study of NDVI sensitivity to seasonal and inter annual rainfall variation in southern Africa. Int J Remote Sens 15:2907–2920

    Article  Google Scholar 

  • Roberts DA, Green RO, Adams JB (1997) Temporal and spatial pattern in vegetation and atmospheric properties from AVIRIS. Remote Sens Environ 62:223–240

    Article  Google Scholar 

  • Rollin EM, Milton EJ (1998) Processing of high spectral resolution reflectance data for the retrieval of canopy water content information. Remote Sens Environ 65:86–92

    Article  Google Scholar 

  • Sahu D (1990) Land forms hydrology and sedimentation. Naya Prakash Publ, Calcutta

    Google Scholar 

  • Saxton KE, Rawls WJ, Romberger JS, Papendick RI (1986) Estimating generalized soil–water characteristics from texture. Soil Sci Soc Am J 50(4):1031–1036

    Article  Google Scholar 

  • Season and Crop Report—Andhra Pradesh, 2009–10, Directorate of Economics and Statistics, Government of Andhra Pradesh, Hyderabad

  • Senay GB, Verdin JP, Lietzow R, Melesse AM (2008) Global daily reference evapotranspiration modeling and evaluation. J Am Water Resour Assoc (JAWARA). 44:969–979

    Article  Google Scholar 

  • Sesha Sai MVR, Murthy CS, Chandrasekar K, Raghavaswamy V (2013) National Agricultural Drought Assessment & Monitoring System (NADASM): Genesis & Way Forward. NNRMS Bulletin, ISRO. NNRMS(B)-37:139–145

  • Seth SM (1998) Drought characterization in sub-humid climatic region. CS(AR)-13/98-99, National institute of Hydrology, Jalvigyan Bhawan, Roorkee

  • Sharpley AN, Williams JR (ed.) (1990) EPIC—erosion/productivity impact calculator: I. Model documentation. USDA Tech. Bull. 1768. USDA-ARS, Grassl., Soil and Water Res. Lab., Temple, TX

  • Shrestha MS, Artan GA, Bajracharya SR, Sharma RR (2008) Using satellite based rainfall estimate for streamflow modelling: Bagmati Basin. J Flood Risk Manag 1:89–99

    Article  Google Scholar 

  • Shuttleworth WJ (1992) Evaporation. In: Maidment D (ed) Handbook of hydrology. McGraw-Hill, New York

    Google Scholar 

  • Tucker CJ (1980) Remote sensing of leaf water content in the near infrared. Remote Sens Environ 10:23–32

    Article  Google Scholar 

  • Tucker CJ, Sellers PJ (1986) Satellite remote sensing of primary production. Int J Remote Sens 7:1395–1416

    Article  Google Scholar 

  • Tucker CJ, Vanpract C, Sharman MJ, Van Ittersum G (1985) Satellite remote sensing of total herbaceous biomass production in the Senegalese Sahel: 1980–1984. Remote Sens Environ 17:233–249

    Article  Google Scholar 

  • Vermote EF, Saleous NZ, Justice CO, Kaufman YJ, Privette J, Remer L, Roger JC, Tanré D (1997) Atmospheric correction of visible to middle infrared EOS-MODIS data over land surface, background, operational algorithm and validation. J Geophys Res 102:17131–17141

    Article  Google Scholar 

  • Verstraeten WW, Veroustraete F, van der Sande CJ, Grootaers I, Feyen J (2006) Soil moisture retrieval using thermal intertia, determined with visible and thermal spaceborne data, validated for European forests. Remote Sens Environ 101:299–314

    Article  Google Scholar 

  • Vischel T, Pegram GGS, Sinclair S, Wagner W, Bartsch A (2008) Comparison of soil moisture fields estimated by catchment modelling and remote sensing: a case study in South Africa. Hydrology and Earth System Sciences. Hydrol Earth Syst Sci 12:751–767

    Article  Google Scholar 

  • Wagner W, Bloschl G, Pampaloni P, Calvet J-C, Bizzarri B, Wigneron J-P, Kerr Y (2007) Operational readiness of microwave remote sensing of soil moisture for hydrologic applications. Nord Hydrol 38(1):1–20

    Article  Google Scholar 

  • Walker JP, Houser PR (2001) A methodology for initializing soil moisture in a global climate model: assimilation of near-surface soil moisture observations. J Geophys Res 106:761–774

    Google Scholar 

  • Western AW, Blöschl G (1999) On the spatial scaling of soil moisture. J Hydrol 217:203–224

    Article  Google Scholar 

  • Wilhite DA, Glantz MH (1985) Understanding the drought phenomenon: the role of definitions. Water Int 10:111–120

    Article  Google Scholar 

  • Xiao X, Zhang Q, Braswell B, Urbanski S, Boles S, Wofsy SC et al (2004) Modeling gross primary production of a deciduous broadleaf forest using satellite image and climate data. Remote Sens Environ 91:256–270

    Article  Google Scholar 

Download references

Acknowledgments

The author is indebted to Dr. V.K. Dadhwal, Director, National Remote Sensing Centre for his encouragement and guidance, without whom this endeavor would not have happened. Deep gratitude is due to Dr. P.G. Diwakar, Deputy Director (RSA), NRSC who gave unstinted cooperation toward this endeavor. Grateful thanks also go to all my colleagues and the administrative staffs for their support and cooperation.

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  1. Agricultural Sciences Applications Group, National Remote Sensing Centre, ISRO, Balanagar, Hyderabad, 500 037, Andhra Pradesh, India

    K. Chandrasekar & M. V. R. Sesha Sai

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  1. K. Chandrasekar
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  2. M. V. R. Sesha Sai
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Correspondence to K. Chandrasekar.

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Chandrasekar, K., Sesha Sai, M.V.R. Monitoring of late-season agricultural drought in cotton-growing districts of Andhra Pradesh state, India, using vegetation, water and soil moisture indices. Nat Hazards 75, 1023–1046 (2015). https://doi.org/10.1007/s11069-014-1364-4

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