Mapping drought-induced changes in rice area in India

References

• Amalo, L. F., and R. Hidayat. 2017. “Comparison between Remote-Sensing-Based Drought Indices in East Java.” Paper Presented at the IOP Conference Series: Earth and Environmental Science 54 (1). doi:10.1088/1755-1315/54/1/012009.
   Google Scholar
• Anderson, J. H., K. T. Weber, B. Gokhale, and F. Chen. 2011. “Intercalibration and Evaluation of ResourceSat-1 and Landsat-5 NDVI.” Canadian Journal of Remote Sensing 37 (2): 213–219. doi:10.5589/m11-032.
   Web of Science ®Google Scholar
• Anderson, L. O., Y. Malhi, L. E. O. C. Aragão, R. Ladle, E. Arai, N. Barbier, and O. Phillips. 2010. “Remote Sensing Detection of Droughts in Amazonian Forest Canopies.” New Phytologist 187 (3): 733–750. doi:10.1111/j.1469-8137.2010.03355.x.
   PubMed Web of Science ®Google Scholar
• Bajgain, R., X. Xiao, P. Wagle, J. Basara, and Y. Zhou. 2015. “Sensitivity Analysis of Vegetation Indices to Drought over Two Tallgrass Prairie Sites.” ISPRS Journal of Photogrammetry and Remote Sensing 108: 151–160. doi:10.1016/j.isprsjprs.2015.07.004.
   Web of Science ®Google Scholar
• Behrenfeld, M. J., J. T. Randerson, C. R. McClain, G. C. Feldman, S. O. Los, C. J. Tucker, P. G. Falkowski, et al. 2001. “Biospheric Primary Production during an ENSO Transition.” Science 291 (5513): 2594–2597. doi:10.1126/science.1055071.
   PubMed Web of Science ®Google Scholar
• Benedetti, R., and P. Rossini. 1993. “On the Use of NDVI Profiles as a Tool for Agricultural Statistics: The Case Study of Wheat Yield Estimate and Forecast in Emilia Romagna.” Remote Sensing of Environment 45 (3): 311–326. doi:10.1016/0034-4257(93)90113-C.
   Web of Science ®Google Scholar
• Bhat, G. S. 2006. “The Indian Drought of 2002–A Sub-Seasonal Phenomenon?” Quarterly Journal of the Royal Meteorological Society 132 (621): 2583–2602. doi:10.1256/qj.05.13.
   Web of Science ®Google Scholar
• Bhuiyan, C., R. P. Singh, and F. N. Kogan. 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. doi:10.1016/j.jag.2006.03.002.
   Web of Science ®Google Scholar
• Bhuvan-NRSC. 2011. Accessed 17 October 2011. http://bhuvan.nrsc.gov.in/bhuvan/In
   Google Scholar
• Biggs, T. W., P. Gangadhara Rao, and L. Bharati. 2010. “Mapping Agricultural Responses to Water Supply Shocks in Large Irrigation Systems, Southern India.” Agricultural Water Management 97 (6): 924–932. doi:10.1016/j.agwat.2010.01.027.
   Web of Science ®Google Scholar
• CBIP. 2007. “Water Resources Map of India (Map No. 27).” In Central Board of Irrigation and Power. New Delhi - 110, 021. New Delhi: Maps of India.
   Google Scholar
• Census. “Government of India (2001). Census of India. Accessed 02 May 2012. http://www.censusindia.gov.in
   Google Scholar
• Chandrasekar, K., M. V. R. Sesha Sai, P. S. Roy, and R. S. Dwevedi. 2010. “Land Surface Water Index (LSWI) Response to Rainfall and NDVI Using the MODIS Vegetation Index Product.” International Journal of Remote Sensing 31 (15): 3987–4005. doi:10.1080/01431160802575653.
   Web of Science ®Google Scholar
• CHIRP. 2016. “Climate Hazard Group InfraRed Precipitation (CHIRP) Data Archive. Accessed 29 July 2017. http://chg.ucsb.edu/data/In
   Google Scholar
• Cihlar, J., Q. Xiao, J. Chen, J. Beaubien, K. Fung, and R. Latifovic. 1998. “Classification by Progressive Generalization: A New Automated Methodology for Remote Sensing Multichannel Data.” International Journal of Remote Sensing 19 (14): 2685–2704. doi:10.1080/014311698214451.
   Web of Science ®Google Scholar
• Congalton, R. G. 1991. “A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data.” Remote Sensing of Environment 37 (1): 35–46. doi:10.1016/0034-4257(91)90048-B.
   Web of Science ®Google Scholar
• Daughtry, C. S. T., K. P. Gallo, S. N. Goward, S. D. Prince, and W. P. Kustas. 1992. “Spectral Estimates of Absorbed Radiation and Phytomass Production in Corn and Soybean Canopies.” Remote Sensing of Environment 39 (2): 141–152. doi:10.1016/0034-4257(92)90132-4.
   Web of Science ®Google Scholar
• De Bie, C. A. J. M., M. R. Khan, V. U. Smakhtin, V. Venus, M. J. C. Weir, and E. M. A. Smaling. 2011. “Analysis of Multi-Temporal SPOT NDVI Images for Small-Scale Land-Use Mapping.” International Journal of Remote Sensing 32 (21): 6673–6693. doi:10.1080/01431161.2010.512939.
   Web of Science ®Google Scholar
• DRMS. 2010. “Drought Risk Management in South Asia, SAARC Workshop, 8-9th August 2010, Kabul, Afganistan. Accessed 25 December 2012. http://saarc-sdmc.nic.in/pdf/Publications/Drought/drought.pdf.In
   Google Scholar
• Eriyagama, N., V. Smakhtin, and N. Gamage. 2009. Mapping Drought Patterns and Impacts: A Global Perspective. Colombo. Sri Lanka: International Water Management Institute. 31. (IWMI Research Report 133).
   Google Scholar
• Gao, B. C. 1996. “NDWI—A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water from Space.” Remote Sensing of Environment 58 (3): 257–266. doi:10.1016/S0034-4257(96)00067-3.
   Web of Science ®Google Scholar
• Gaur, A., T. W. Biggs, M. K. Gumma, G. Parthasaradhi, and H. Turral. 2008. “Water Scarcity Effects on Equitable Water Distribution and Land Use in a Major Irrigation Project—Case Study in India.” Journal of Irrigation and Drainage Engineering 134 (1): 26–35. doi:10.1061/(ASCE)0733-9437.
   Web of Science ®Google Scholar
• Goward, S. N., C. J. Tucker, and D. G. Dye. 1985. “North American Vegetation Patterns Observed with the NOAA-7 Advanced Very High Resolution Radiometer.” Plant Ecology 64 (1): 3–14. doi:10.1007/BF00033449.
   Web of Science ®Google Scholar
• Gu, Y., F. B. Jesslyn, P. V. James, and W. Brian. 2007. “A Five Year Analysis of MODIS NDVI and NDWI for Grassland Drought Assessment over the Central Great Plains of the United States.” Geophysical Research Letters 34: 6. doi:10.1029/2006GL029127.
   Web of Science ®Google Scholar
• Gumma, M. K., N. Andrew, P. S. Thenkabail, and N. S. Amrendra. 2011a. “Mapping Rice Areas of South Asia Using MODIS Multitemporal Data.” Journal of Applied Remote Sensing 5: 053547. doi:10.1117/1.3619838.
   Web of Science ®Google Scholar
• Gumma, M. K., D. Gauchan, A. Nelson, S. Pandey, and A. Rala. 2011b. “Temporal Changes in Rice-Growing Area and Their Impact on Livelihood over A Decade: A Case Study of Nepal.” Agriculture, Ecosystems & Environment 142 (3–4): 382–392. doi:10.1016/j.agee.2011.06.010.
   Web of Science ®Google Scholar
• Gumma, M. K., K. Kajisa, I. A. Mohammed, A. M. Whitbread, A. Nelson, A. Rala, and K. Palanisami. 2015a. “Temporal Change in Land Use by Irrigation Source in Tamil Nadu and Management Implications.” Environmental Monitoring and Assessment 187 (1): 1–17. doi:10.1007/s10661-014-4155-1.
   PubMed Web of Science ®Google Scholar
• Gumma, M. K., S. Mohanty, A. Nelson, R. Arnel, I. A. Mohammed, and S. R. Das. 2015b. “Remote Sensing Based Change Analysis of Rice Environments in Odisha, India.” Journal of Environmental Management 148: 31–41. doi:10.1016/j.jenvman.2013.11.039.
   PubMed Web of Science ®Google Scholar
• Gumma, M. K., P. S. Thenkabail, A. Maunahan, S. Islam, and A. Nelson. 2014. “Mapping Seasonal Rice Cropland Extent and Area in the High Cropping Intensity Environment of Bangladesh Using MODIS 500m Data for the Year 2010.” ISPRS Journal of Photogrammetry and Remote Sensing 91 (5): 98–113. doi:10.1016/j.isprsjprs.2014.02.007.
   Web of Science ®Google Scholar
• Gumma, M. K., P. S. Thenkabail, I. V. Muralikrishna, M. N. Velpuri, P. T. Gangadhararao, V. Dheeravath, C. M. Biradar, S. Acharya Nalan, and A. Gaur. 2011c. “Changes in Agricultural Cropland Areas between a Water-Surplus Year and a Water-Deficit Year Impacting Food Security, Determined Using MODIS 250 M Time-Series Data and Spectral Matching Techniques, in the Krishna River Basin (India).” International Journal of Remote Sensing 32 (12): 3495–3520. doi:10.1080/01431161003749485.
   Web of Science ®Google Scholar
• Gumma, M. K., P. S. Thenkabail, P. Teluguntla, M. N. Rao, I. A. Mohammed, and A. M. Whitbread. 2016. “Mapping Rice-Fallow Cropland Areas for Short-Season Grain Legumes Intensification in South Asia Using MODIS 250 M Time-Series Data.” International Journal of Digital Earth 9 (10): 981–1003. doi:10.1080/17538947.2016.1168489.
   Web of Science ®Google Scholar
• Hansen, P. M., and J. K. Schjoerring. 2003. “Reflectance Measurement of Canopy Biomass and Nitrogen Status in Wheat Crops Using Normalized Difference Vegetation Indices and Partial Least Squares Regression.” Remote Sensing of Environment 86 (4): 542–553. doi:10.1016/S0034-4257(03)00131-7.
   Web of Science ®Google Scholar
• Huete, A. R. 1988. “A Soil-Adjusted Vegetation Index (SAVI).” Remote Sensing of Environment 25 (3): 295–309. doi:10.1016/0034-4257(88)90106-X.
   Web of Science ®Google Scholar
• IMD. 2015. “Rainfall Statistics of India 2015, India Meteorological Department.” Accessed 24 July 2017. http://hydro.imd.gov.in/hydrometweb/(S(djyq3in5ulrjdgurswauypnm))/landing.aspx#
   Google Scholar
• INDIASTAT. 2015. “State-Wise Net Area Irrigated by Source in India and State-Wise Irrigated Area under Crops in India.” Accessed 30 May 2015. (www.indiastat.com)
   Google Scholar
• Jensen, J. R. 1996. Introductory Digital Image Processing: A Remote Sensing Perspective. Upper Saddle River, New Jersey: Prentice Hall.
   Google Scholar
• Justice, C. O., and P. H. Y. Hiernaux. 1986. “Monitoring the Grasslands of the Sahel Using NOAA AVHRR Data: Niger 1983.” International Journal of Remote Sensing 7 (11): 1475–1497. doi:10.1080/01431168508948281.
   Web of Science ®Google Scholar
• Justice, C. O., J. R. G. Townshend, B. N. Holben, and C. J. Tucker. 1985. “Analysis of the Phenology of Global Vegetation Using Meteorological Satellite Data.” International Journal of Remote Sensing 6 (8): 1271–1318. doi:10.1080/01431168508948281.
   Web of Science ®Google Scholar
• King, M. D., J. Closs, S. Spangler, and R. Greenstone. (Eds.) 2003. EOS Data Products Handbook Version 1. Greenbelt, Maryland: NASA Goddard Space Flight Center.
   Google Scholar
• Kneis, D., C. Chatterjee, and R. Singh. 2014. “Evaluation of TRMM Rainfall Estimates over a Large Indian River Basin (Mahanadi).” Hydrology and Earth System Sciences 18 (7): 2493–2502. doi:10.5194/hess-18-2493-2014.
   Web of Science ®Google Scholar
• Kumar, L., K. Schmidt, S. Dury, and A. Skidmore. 2002. “Imaging Spectrometry and Vegetation Science.” In Imaging Spectrometry, edited by  Meer F. D., S. M. D. Jong,  111–155. Remote Sensing and Digital Image Processing 4. Dordrecht: Springer.
   Google Scholar
• Kumar, S., B. M. K. Raju, C. A. Rama Rao, K. Kareemulla, and B. Venkateswarlu. 2011. “Sensitivity of Yields of Major Rainfed Crops to Climate in India.” Indian Journal of Agricultural Economics 66 (3): 340.
   Google Scholar
• Kummerow, C., W. Barnes, T. Kozu, J. Shiue, and J. Simpson. 1998. “The Tropical Rainfall Measuring Mission (TRMM) Sensor Package.” Journal of Atmospheric and Oceanic Technology 15 (3): 809–817. doi:10.1175/1520-0426.
   Web of Science ®Google Scholar
• Manjunath, K. R., and S. Panigrahy. 2009. “Spatial Database Generation of the Rice-Cropping Pattern of India Using Satellite Remote Sensing Data.” Paper presented at the International Workshop on Impact of Climate Change on Agriculture-2009 Workshop, ISPRS Ahmedabad, Ahmedabad.
   Google Scholar
• Mohanty, S., and T. Yamano. “Rice Food Security in India: Emerging Challenges and Opportunities.” In The Future Rice Strategy for India, edited by Mohanty, S., P.G. Chengappa, Mruthyunjaya, J. K. Ladha, S. Baruah, E. Kannan, A. V. Manjunatha, 1–13. Academic Press.
   Google Scholar
• Murthy, C. S., M. V. R. Sesha Sai, K. Chandrasekar, and P. S. Roy. 2009. “Spatial and Temporal Responses of Different Crop-Growing Environments to Agricultural Drought: A Study in Haryana State, India Using NOAA AVHRR Data.” International Journal of Remote Sensing 30 (11): 2897–2914. doi:10.1080/01431160802558626.
   Web of Science ®Google Scholar
• Nair, S., G. Srinivasan, and R. Nemani. 2009. “Evaluation of Multi-Satellite TRMM Derived Rainfall Estimates over a Western State of India.” Journal of the Meteorological Society of Japan. Ser. II 87 (6): 927–939. doi:10.2151/jmsj.87.927.
   Web of Science ®Google Scholar
• Nguyen, T. T. H., C. A. J. M. De Bie, A. Ali, E. M. A. Smaling, and T. H. Chu. 2012. “Mapping the Irrigated Rice Cropping Patterns of the Mekong Delta, Vietnam, through Hyper-Temporal SPOT NDVI Image Analysis.” International Journal of Remote Sensing 33 (2): 415–434. doi:10.1080/01431161.2010.532826.
   Web of Science ®Google Scholar
• Niranjan Kumar, K., M. Rajeevan, D. S. Pai, A. K. Srivastava, and B. Preethi. 2013. “On the Observed Variability of Monsoon Droughts over India.” Weather and Climate Extremes 1: 42–50. doi:10.1016/j.wace.2013.07.006.
   Google Scholar
• Pandey, S., and H. Bhandari. 2009. “Drought, Coping Mechanisms and Poverty, the Seventh in a Series of Discussion Papers Produced by the Asia and the Pacific Division, IFAD.” Accessed 01 May 2014. (http://www.ifad.org/operations/projects/regions/pi/paper/7.pdf)
   Google Scholar
• Peel, M. C., G. G. S. Pegram, and T. A. McMahon. 2004. “Global Analysis of Runs of Annual Precipitation and Runoff Equal to or below the Median: Run Length.” International Journal of Climatology 24 (7): 807–822. doi:10.1002/joc.1041.
   Web of Science ®Google Scholar
• Pinjarla, B., V. Chakravarthi, P. S. Roy, P. K. Joshi, and K. Chandrasekar. 2017. “Long-Term Agricultural Performance and Climate Variability for Drought Assessment: A Regional Study from Telangana and Andhra Pradesh States, India.” Geomatics, Natural Hazards and Risk 8 (2): 822–840. doi:10.1080/19475705.2016.1271831.
   Web of Science ®Google Scholar
• Potter, C. S., J. T. Randerson, C. B. Field, P. A. Matson, P. M. Vitousek, H. A. Mooney, and S. A. Klooster. 1993. “Terrestrial Ecosystem Production: A Process Model Based on Global Satellite and Surface Data.” Global Biogeochemical Cycles 7 (4): 811–841. doi:10.1029/93GB02725.
   Web of Science ®Google Scholar
• Running, S. W., R. Nemani, J. M. Glassy, and P. E. Thornton. 1999. “MODIS Daily Photosynthesis (PSN) and Annual Net Primary Production (NPP) Product (MOD17) Algorithm Theoretical Basis Document.” In University of Montana, SCF At-Launch Algorithm ATBD Documents. (www. ntsg. umt. edu/modis/ATBD/ATBD_MOD17_v21. pdf)
   Google Scholar
• Singh, R. P., S. Roy, and F. Kogan. 2003. “Vegetation and Temperature Condition Indices from NOAA AVHRR Data for Drought Monitoring over India.” International Journal of Remote Sensing 24 (22): 4393–4402. doi:10.1080/0143116031000084323.
   Web of Science ®Google Scholar
• Steven, M. D., T. J. Malthus, H. Xu, and M. J. F. Baret Chopping. 2003. “Intercalibration of Vegetation Indices from Different Sensor Systems.” Remote Sensing of Environment 88 (4): 412–422. doi:10.1016/j.rse.2003.08.010.
   Web of Science ®Google Scholar
• Teng, W. L. 1990. “AVHRR Monitoring of US Crops during the 1988 Drought.” Photogrammetric Engineering and Remote Sensing 56: 1143–1146.
   Web of Science ®Google Scholar
• Thenkabail, P., P. GangadharaRao, T. Biggs, M. K. Gumma, and H. Turral. 2007. “Spectral Matching Techniques to Determine Historical Land-Use/Land-Cover (LULC) and Irrigated Areas Using Time-Series 0.1-Degree AVHRR Pathfinder Datasets.” Photogrammetric Engineering & Remote Sensing 73 (10): 1029–1040.
   Google Scholar
• Thenkabail, P. S., C. M. Biradar, P. Noojipady, V. Dheeravath, Y. Li, M. Velpuri, M. K. Gumma, et al. 2009. “Global Irrigated Area Map (GIAM), Derived from Remote Sensing, for the End of the Last Millennium.” International Journal of Remote Sensing 30 (14): 3679–3733. doi:10.1080/01431160802698919.
   Web of Science ®Google Scholar
• Thenkabail, P. S., M. N. Gamage, and V. U. Smakhtin. 2004. “The Use of Remotesensing Data for Drought Assessment and Monitoring in Southwest Asia.” Research Report 85. Colombo, Sri Lanka: International Water Management Institute. 1–34.
   Google Scholar
• TRMM. 2016. “Tropical Rainfall Measuring Mission.” Accessed 10 Jan 2016. http://trmm.gsfc.nasa.gov/
   Google Scholar
• Verbesselt, J., R. Hyndman, G. Newnham, and D. Culvenor. 2010. “Detecting Trend and Seasonal Changes in Satellite Image Time Series.” Remote Sensing of Environment 114 (1): 106–115. doi:10.1016/j.rse.2009.08.014.
   Web of Science ®Google Scholar
• Vermote, E. F., N. Z. El Saleous, and C. O. Justice. 2002. “Atmospheric Correction of MODIS Data in the Visible to Middle Infrared: First Results.” Remote Sensing of Environment 83 (1–2): 97–111. doi:10.1016/s0034-4257(02)00089-5.
   Web of Science ®Google Scholar
• Vermote, E. F., and A. Vermeulen. 1999. “MODIS Algorithm Technical Background Document, Atmospheric Correction Algorithm: Spectral Reflectances (MOD09).” NASA contract NAS5-96062.
   Google Scholar
• Wan, Z., P. Wang, and X. Li. 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. doi:10.1080/0143116031000115328.
   Web of Science ®Google Scholar
• Wang, J., P. M. Rich, K. P. Price, and W. D. Kettle. 2005. “Relations between NDVI, Grassland Production, and Crop Yield in the Central Great Plains.” Geocarto International 20 (3): 5–11. doi:10.1080/10106040508542350.
   Google Scholar
• WorldBank. “Agriculture, Value Added (% of GDP).” ( Accessed 14 June 2016). http://data.worldbank.org/indicator/NV.AGR.TOTL.ZS
   Google Scholar
• Xiao, X., S. Boles, S. Frolking, C. Li, J. Y. Babu, W. Salas, and B. Moore. 2006. “Mapping Paddy Rice Agriculture in South and Southeast Asia Using Multi-Temporal MODIS Images.” Remote Sensing of Environment 100 (1): 95–113. doi:10.1016/j.rse.2005.10.004.
   Web of Science ®Google Scholar
• Zampieri, M., G. Carmona Garcia, F. Dentener, M. K. Gumma, P. Salamon, L. Seguini, and A. Toreti. 2018. “Surface Freshwater Limitation Explains Worst Rice Production Anomaly in India in 2002.” Remote Sensing 10 (2): 244. doi:10.3390/rs10020244.
   Web of Science ®Google Scholar
• Zhang, C., and X. Guo. 2008. “Monitoring Northern Mixed Prairie Health Using Broadband Satellite Imagery.” International Journal of Remote Sensing 29 (8): 2257–2271. doi:10.1080/01431160701408378.
   Web of Science ®Google Scholar