Satellite imagery and household survey for tracking chickpea adoption in Andhra Pradesh, India

References

• Anderson, J. R. 1976. A Land Use and Land Cover Classification System for Use with Remote Sensor Data. Vol. 964. Washington, DC: US Government Printing Office.
   Google Scholar
• Aykroyd, W. R., J. Doughty, and A. Walker. 1982. Legumes in Human Nutrition. Vol. 20. London: Food & Agriculture Org.
   Google Scholar
• Badhwar, G. D. 1984. “Automatic Corn-Soybean Classification Using Landsat MSS Data. I. Near-Harvest Crop Proportion Estimation.” Remote Sensing of Environment 14 (1–3): 15–29. doi:10.1016/0034-4257(84)90004-x.
   Web of Science ®Google Scholar
• Bantilan, C., D. Kumara Charyulu, P. M. Gaur, D. Moses Shyam, and J. Davis. 2014. “Short-duration Chickpea Technology: Enabling Legumes Revolution in Andhra Pradesh.” Research Report no.23, Research Program on -Markets, institutions and policies, Patancheru: ICRISAT. Accessed 14 May 2015. http://www.icrisat.org/what-we-do/mip/SPIA.pdf
   Google Scholar
• Biggs, T. W., P. S. Thenkabail, M. K. Gumma, C. A. Scott, G. R. Parthasaradhi, and H. N. Turral. 2006. “Irrigated Area Mapping in Heterogeneous Landscapes with MODIS Time Series, Ground Truth and Census Data, Krishna Basin, India.” International Journal of Remote Sensing 27 (19): 4245–4266. doi:10.1080/01431160600851801.
   Web of Science ®Google Scholar
• Biradar, C. M., P. S. Thenkabail, P. Noojipady, L. Yuanjie, V. Dheeravath, H. Turral, M. Velpuri, M. K. Gumma, O. R. P. Gangalakunta, X. L. Cai, X. Xiao, M. A. Schull, R. D. Alankara, S. Gunasinghe, and S. Mohideen. 2009. “A Global Map of Rainfed Cropland Areas (GMRCA) at the End of Last Millennium Using Remote Sensing.” International Journal of Applied Earth Observation and Geoinformation 11 (2): 114–129. doi:10.1016/j.jag.2008.11.002.
   Web of Science ®Google Scholar
• Congalton, R. G. 2001. “Accuracy Assessment and Validation of Remotely Sensed and Other Spatial Information.” International Journal of Wildland Fire 10 (4): 321–328. doi:10.1071/WF01031.
   Web of Science ®Google Scholar
• Congalton, R. G., and K. Green. 1999. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices Lewis. Boca Raton, FL: Lewis Publisher.
   Google Scholar
• Congalton, R. G. 1991. “Remote Sensing and Geographic Information System Data Integration: Error Sources and.” Photogrammetric Engineering & Remote Sensing 57 (6): 677–687.
   Web of Science ®Google Scholar
• Congalton, R. G., and K. Green. 2008. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. Boca Raton, FL: CRC Press, Taylor & Francis Group.
   Google Scholar
• Coppin, P., I. Jonckheere, K. Nackaerts, B. Muys, and E. Lambin. 2004. “Digital Change Detection Methods in Ecosystem Monitoring: A Review.” International Journal of Remote Sensing 25 (9): 1565–1596. doi:10.1080/0143116031000101675.
   Web of Science ®Google Scholar
• Deb, U. K. 2013. Raising Hope and Nurturing Options for Agricultural Development: Essays in Honor of Cynthia Bantilan. Friends and Admirers n Students, Hyderabad, India. ISBN 978-92-9066-558-8, Accessed 14 September 2014. http://oar.icrisat.org/7100
   Google Scholar
• Dheeravath, V., P. S. Thenkabail, G. Chandrakantha, P. Noojipady, G. P. O. Reddy, C. M. Biradar, M. K. Gumma, and M. Velpuri. 2010. “Irrigated Areas of India Derived Using MODIS 500 M Time Series for the Years 2001–2003.” ISPRS Journal of Photogrammetry and Remote Sensing 65 (1): 42–59. doi:10.1016/j.isprsjprs.2009.08.004.
   Web of Science ®Google Scholar
• DOES. 2011. “Directorate of Ecomomics and Satatistics, Government of Andhra Pradesh.” Accessed 18 September 2014. http://www.apdes.ap.gov.in/agri_stat.htm
   Google Scholar
• FAO. 2012. “Food and Agriculture Organization data base.” Accessed 14 September 2014. www.fao.org
   Google Scholar
• Gaur, A., T. W. Biggs, M. K. Gumma, P. Gangadhara Rao, and H. Turral. 2008. “Water Scarcity Effects on Equitable Water Distribution and Land Use in Major Irrigation Project - A Case Study in India.” Journal of Irrigation and Drainage Engineering 134 (1): 26–35. doi:10.1061/(ASCE)0733-9437(2008)134:1(26).
   Web of Science ®Google Scholar
• Geil, P. B., and J. W. Anderson. 1994. “Nutrition and Health Implications of Dry Beans: A Review.” Journal of the American College of Nutrition 13 (6): 549–558. doi:10.1080/07315724.1994.10718446.
   PubMed Web of Science ®Google Scholar
• Goetz, S. J., D. Varlyguin, A. J. Smith, R. K. Wright, S. D. Prince, M. E. Mazzacato, J. Tringe, C. Jantz, and B. Melchoir. 2004. “Application of Multitemporal Landsat Data to Map and Monitor Land Cover and Land Use Change in the Chesapeake Bay Watershed.” In Analysis of Multi-temporal Remote Sensing Images, edited by P. Smits and L. Bruzzone, 223–232. Singapore: World Scientific Publishers.
   Google Scholar
• Gumma, M. K., A. Nelson, P. S. Thenkabail, and A. N. Singh. 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., S. Mohanty, A. Nelson, R. Arnel, I. A. Mohammed, and S. R. Das. 2015. “Remote Sensing Based Change Analysis of Rice Environments in Odisha, India.” Journal of Environmental Management 148 (0): 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. A. 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
• Jensen, J. R. 1996. Introductory Digital Image Processing: A Remote Sensing Perspective. Upper Saddle River, NJ: Prentice Hall.
   Google Scholar
• Knight, J. F., R. L. Lunetta, J. Ediriwickrema, and S. Khorram. 2006. “Regional Scale Land-Cover Characterization using MODIS-NDVI 250 m Multi-Temporal Imagery: A Phenology Based Approach.” GIScience and Remote Sensin 43 (1): 1–23. doi:10.2747/1548-1603.43.1.1.
   Web of Science ®Google Scholar
• Lobell, D. B., G. P. Asner, J. Ivan Ortiz-Monasterio, and T. L. Benning. 2003. “Remote Sensing of Regional Crop Production in the Yaqui Valley, Mexico: Estimates and Uncertainties.” Agriculture, Ecosystems & Environment 94 (2): 205–220. doi:10.1016/s0167-8809(02)00021-x.
   Web of Science ®Google Scholar
• Lu, D., P. Mausel, E. Brondízio, and E. Moran. 2004. “Change Detection Techniques.” International Journal of Remote Sensing 25 (12): 2365–2401. doi:10.1080/0143116031000139863.
   Web of Science ®Google Scholar
• Mundia, C. N., and M. Aniya. 2005. “Analysis of Land Use/Cover Changes and Urban Expansion of Nairobi City Using Remote Sensing and GIS.” International Journal of Remote Sensing 26 (13): 2831–2849. doi:10.1080/01431160500117865.
   Web of Science ®Google Scholar
• Rouse, J. W., R. H. Haas, J. A. Schell, and D. W. Deering. 1973. “Monitoring Vegetation Systems in the Great Plains with ERTS.” Third ERTS Symposium, NASA SP-351, Vol. 1, Washington, DC: NASA. 309–317.
   Google Scholar
• Sakamoto, T., M. Yokozawa, H. Toritani, M. Shibayama, N. Ishitsuka, and H. Ohno. 2005. “A Crop Phenology Detection Method Using Time-Series MODIS Data.” Remote Sensing of Environment 96 (3–4): 366–374. doi:10.1016/j.rse.2005.03.008.
   Web of Science ®Google Scholar
• Sharma, D., and N. S. Jodha. 1982. “Pulses Production in Semi-arid Regions of India: Constraints and Opportunities.” Economic and political weekly:A135-A48.
   Google Scholar
• Singh, A. 1989. “Review Article Digital Change Detection Techniques Using Remotely-Sensed Data.” International Journal of Remote Sensing 10 (6): 989–1003. doi:10.1080/01431168908903939.
   Web of Science ®Google Scholar
• Singh, U., and B. Singh. 1992. “Tropical Grain Legumes as Important Human Foods.” Economic Botany 46 (3): 310–321. doi:10.1007/BF02866630.
   Web of Science ®Google Scholar
• Teillet, P. M., K. Staenz, and D. J. William. 1997. “Effects of Spectral, Spatial, and Radiometric Characteristics on Remote Sensing Vegetation Indices of Forested Regions.” Remote Sensing of Environment 61 (1): 139–149. doi:10.1016/s0034-4257(96)00248-9.
   Web of Science ®Google Scholar
• Thenkabail, P. S. 2010. “Global Croplands and their Importance for Water and Food Security in the Twenty-first Century: Towards an Ever Green Revolution that Combines a Second Green Revolution with a Blue Revolution.” Remote Sensing 2 (9): 2305–2312. doi:10.3390/rs2092305.
   Web of Science ®Google Scholar
• Thenkabail, P. S., C. M. Biradar, P. Noojipady, V. Dheeravath, L. Yuanjie, M. Velpuri, M. Gumma, O. R. P. Gangalakunta, H. Turral, X. Cai, J. Vithanage, M. A. Schull, and R. Dutta. 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. Schull, and H. Turral. 2005. “Ganges and Indus River Basin Land Use/Land Cover (LULC) and Irrigated Area Mapping Using Continuous Streams of MODIS Data.” Remote Sensing of Environment 95 (3): 317–341. doi:10.1016/j.rse.2004.12.018.
   Web of Science ®Google Scholar
• Thenkabail, P. S., 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 AVHRR Pathfinder Datasets in the Krishna River Basin, India.” Photogrammetric Engineering and Remote Sensing 73 (10): 1029–1040.
   Google Scholar
• Thiruvengadachari, S., and R. Sakthivadivel. 1997. “Satellite Remote Sensing for Assessment of Irrigation System Performance: A Case Study in India.” Research Report 9. Colombo, Sri Lanka: International Irrigation Management Institute.
   Google Scholar
• Tucker, C. J. 1979. “Red and Photographic Infrared Linear Combinations for Monitoring Vegetation.” Remote Sensing of Environment 8 (2): 127–150. doi:10.1016/0034-4257(79)90013-0.
   Web of Science ®Google Scholar
• Varlyguin, D., R. Wright, S. J. Goetz, and S. D. Prince. 2001. “Advances in Land Cover Classification: A Case Study from the Mid-Atlantic Region.” American Society for Photogrammetry and Remote Sensing, Proceedings, St. Louis, MO. www.geog.umd.edu/resac, 7.
   Google Scholar
• Velpuri, N. M., P. S. Thenkabail, M. K. Gumma, C. B. Biradar, V. Dheeravath, P. Noojipady, and L. Yuanjie. 2009. “Influence of Resolution in Irrigated Area Mapping and Area Estimations.” Photogrammetric Engineering & Remote Sensing 75 (12): 1383–1395. doi:10.14358/PERS.75.12.1383.
   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