Global food security support analysis data at nominal 1 km (GFSAD1km) derived from remote sensing in support of food security in the twenty-first century: Current achievements and future possibilities

Land Resources Monitoring, Modeling, and Mapping with Remote Sensing

Volumn , Issue , 2015, Pages 131-159

  Teluguntla, Pardhasaradhi ^a,b   Thenkabail, Prasad S ^a   Xiong, Jun ^c   Gumma, Murali Krishna ^d   Giri, Chandra ^a   Milesi, Cristina ^e   Ozdogan, Mutlu ^f   Congalton, Russell G ^g   Tilton, James ^h  

^a U S GEOLOGICAL SURVEY   (United States)

^b BAY AREA ENVIRONMENTAL RESEARCH INSTITUTE   (United States)

^c NORTHERN ARIZONA UNIVERSITY   (United States)

^d INTERNATIONAL CROPS RESEARCH INSTITUTE FOR THE SEMI ARID TROPICS ICRISAT   (India)

^e NASA AMES RESEARCH CENTER   (United States)

^f UNIVERSITY OF WISCONSIN MADISON   (United States)

^g UNIVERSITY OF NEW HAMPSHIRE   (United States)

^h NASA GODDARD SPACE FLIGHT CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CROPS; DEVELOPING COUNTRIES; FOOD SUPPLY; GAS EMISSIONS; GREENHOUSE GASES; NUTRITION; OBSERVATORIES; ORBITS; PHOTOMAPPING; PIXELS; POPULATION DYNAMICS; RADIOMETERS; REMOTE SENSING; SATELLITE IMAGERY; SPACE-BASED RADAR; SYNTHETIC APERTURE RADAR; VEGETATION;

GLOBAL EARTH OBSERVATION SYSTEM OF SYSTEMS; JAPANESE EARTH RESOURCES SATELLITES; MODERATE RESOLUTION IMAGING SPECTRORADIOMETER; NATIONAL AGRICULTURAL STATISTICS SERVICES; NATIONAL OCEANIC AND ATMOSPHERIC ADMINISTRATION; SPECTRAL MATCHING TECHNIQUES; UNITED STATES DEPARTMENT OF AGRICULTURES; UNITED STATES GEOLOGICAL SURVEYS;

CHEMICAL CONTAMINATION;

EID: 85051140400     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1201/b19322     Document Type: Chapter

References (77)

1. Biggs, T., Thenkabail, P. S., Krishna, M., GangadharaRao, P., and Turral, H. (2006). Vegetation phenology and irrigated area mapping using combined MODIS time-series, ground surveys, and agricultural census data in Krishna River Basin, India. International Journal of Remote Sensing, 27 (19), 4245-4266.
2. Bindraban, P. S., Bulte, E. H., and Conijn, S. G. (2009). Can large-scale biofuel production be sustained by 2020? Agricultural Systems, 101, 197-199.
3. Biradar, C. M., Thenkabail, P. S., Noojipady, P., Li, Y., Dheeravath, V., Turral, H., Velpuri, M. et al. (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.
4. Chander, G., Markham, B. L., and Helder, D. L. (2009). Summary of current radiometric calibration coeffici ts for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sensing of Environment, 13 (5), 893-903. ISSN 0034-4257, http://dx.doi.org/10.1016Aj.rse.2009.01.007. http://www.sciencedirect.com/science/article/pii/S0034425709000169. Last accessed March 15th, 2014.
5. Cohen, W. B. and Goward, S. N. (2004). Landsat’s role in ecological applications of remote sensing. BioScience, 54, 535-545.
6. Congalton, R. (1991). A review of assessing the accuracy of classifi ations of remotely sensed data. Remote Sensing of Environment, 37, 35-46.
7. Congalton, R. (2009). Accuracy and error analysis of global and local maps: Lessons learned and future considerations. In: Remote Sensing of Global Croplands for Food Security, P. Thenkabail, J. Lyon, H. Turral, and C. Biradar (eds). CRC/ Taylor & Francis, Boca Raton, FL, pp. 441-458.
8. Congalton, R. and Green, K. (2009). Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, 2nd edn. CRC/Taylor & Francis, Boca Raton, FL, 183pp.
9. Crist, E. P. and Cicone, R. C. (1984). Application of the tasseled cap concept to simulated Thematic Mapper data. Photogrammetric Engineering and Remote Sensing, 50, 343-352.
10. DeFries, R., Hansen, M., Townsend, J. G. R., and Sohlberg, R. (1998). Global land cover classifi ations at 8 km resolution: The use of training data derived from Landsat imagery in decision tree classifie s. International Journal of Remote Sensing, 19, 3141-3168.
11. Dheeravath, V., Thenkabail, P. S., Chandrakantha, G, Noojipady, P., Biradar, C. B., Turral, H., Gumma, M. I., Reddy, G. P. O., and Velpuri, M. (2010). Irrigated areas of India derived using MODIS 500m data for years 2001-2003. ISPRS Journal of Photogrammetry and Remote Sensing, 65 (1), 42-59. http://dx.doi.org/10.1016/.isprsjprs.2009.08.004.
12. EPW (2008). Food Security Endangered: Structural changes in global grain markets threaten India’s food security. Economic and Political Weekly, 43, 5.
13. FAO (2009). Food Outlook: Outlook Global Market Analysis. Rome: FAO.
14. Foley, J. A., DeFries, R., Asner, G. P., Barford, C., Bonan, G., Carpenter, S. R., Chapin, F. S. et al. (2011). Solutions for a cultivated planet. Nature, 478 (7369), 337-342.
15. Foley, J. A., Monfreda, C., Ramankutty, N., and Zaks, D. (2007). Our share of the planetary pie. PNAS, 104, 12585-12586.
16. Falkenmark, M. and Rockstrom, J. (2006). The new blue and green water paradigm: Breaking new ground for water resources planning and management. Journal of Water Resource Planning and Management, 132, 1-15.
17. Friedl, M. A., McIver, D. K., Hodges, J. C. F., Zhang, X. Y., Muchoney, D., and Strahler, A. H. (2002). Global land cover mapping from MODIS: Algorithms and early results. Remote Sensing of Environment, 83, 287-302.
18. Friedl, M. A., Sulla-Menashe, D., Tan, B., Schneider, A., Ramankutty, N. S., and Huang, X. M. (2010). MODIS Collection 5 global land cover: Algorithm refi ements and characterization of new datasets. Remote Sensing of Environment, 114 (1), 168-182.
19. Funk, C. and Brown, M. (2009). Declining global per capita agricultural production and warming oceans threaten food security. Food Security, 1 (3), 271-289. doi:10.1007/ s12571-009-0026-y.
20. Gibbs, H. K., Johnston, M., Foley, J. A., Holloway, T., Monfreda, C., Ramankutty, N., and Zaks, D. (2008). Carbon payback times for crop-based biofuel expansion in the tropics: The effects of changing yield and technology. Environment Research Letters, 3(2008), 034001, 10pp. doi:10.1088/1748-9326/3/3/034001.
21. Goldewijk, K., Beusen, A., de Vos, M., and van Drecht, G. (2011). The HYDE 3.1 spatially explicit database of human induced land use change over the past 12,000 years. Global Ecology and Biogeography, 20 (1), 73-86. doi:10.1111/j.1466-8238.2010.00587.x.
22. Goodchild, M. and Gopal, S. (eds.) (1989). The Accuracy of Spatial Databases. Taylor & Francis, New York, 290pp.
23. Gordon, L. J., Finlayson, C. M., and Falkenmark, M. (2009). Managing water in agriculture for food production and other ecosystem services. Agricultural Water Management, 97, 512-519. doi: 10.1016/j.agwat.2009.03.017.
24. Gumma, M. K., Nelson, A., Thenkabail, P. S., and Singh, A. N. (2011). Mapping rice areas of South Asia using MODIS multi temporal data. Journal of Applied Remote Sensing, 5, 053547 (September 1, 2011). doi:10.1117/1.3619838.
25. Gutman, G., Byrnes, R., Masek, J., Covington, S., Justice, C., Franks, S., and Headley, R. (2008). Towards monitoring Land-cover and land-use changes at a global scale: The global land survey 2005. Photogrammetric Engineering and Remote Sensing, 74 (1): 6-10.
26. Hansen, M. C., DeFries, R. S., Townshend, J. R. G., Sohlberg, R., Dimiceli, C., and Carroll, M. (2002). Towards an operational MODIS continuous fi ld of percent tree cover algorithm: Examples using AVHRR and MODIS data. Remote Sensing of Environment, 83, 303-319.
27. Hossain, M., Janaiah, A., and Otsuka, K. (2005). Is the productivity impact of the Green Revolution in rice vanishing? Economic and Political Weekly 5595-9600.
28. Jiang, Y. (2009). China’s water scarcity. Journal of Environmental Management, 90, 3185-3196.
29. Johnson, D. M. and Mueller, R. (2010). The 2009 cropland data layer. Photogrammetric Engineering and Remote Sensing, 76 (11), 1201-1205.
30. Khan, S. and Hanjra, M. A. (2008). Sustainable land and water management policies and practices: A pathway to environmental sustainability in large irrigation systems. Land Degradation and Development, 19, 469-487.
31. Kumar, M. D. and Singh, O. P. (2005). Virtual water in global food and water policy making: Is there a need for rethinking? Water Resources Management, 19, 759-789.
32. Kurz, B. and Seelan, S. K. Use of remote sensing to map irrigated agriculture in areas overlying the Ogallala Aquifer, U.S. In Remote Sensing of Global Croplands for Food Security; Thenkabail, P. S., Turral, H., Lyon, J. G., Biradar, C., Eds.; CRC Press: Boca Raton, FL, 2009.
33. Lal, R. and Pimentel, D. (2009). Biofuels from crop residues. Soil and Tillage Research, 93, 237-238.
34. Loveland, T. R., Reed, B. C., Brown, J. F., Ohlen, D. O., Zhu, Z., and Yang, L. (2000). Development of global land cover characteristics database and IGBP DISCover from 1 km AVHRR data. International Journal of Remote Sensing, 21, 1303-1330.
35. McIntyre, B. D. (2008). International assessment of agricultural knowledge, science and technology for development (IAASTD): Global report. Includes Bibliographical References and Index. ISBN 978-1-59726-538-6, Oxford, U.K.
36. Masek, J. G., Huang, C., Wolfe, R., Cohen, W., Hall, F., Kutler, J., and Nelson, P. (2008). North American forest disturbance mapped from a decadal Landsat record. Remote Sensing of Environment, 112, 2914-2926.
37. Masek, J. G., Vermote, E. F., Saleous, N., Wolfe, R., Hall, F. G., Huemmrich Gao, F., Kutler, J., and Lim, T. K. (2006). A Landsat surface refl ctance data set for North America, 1990-2000. Geoscience and Remote Sensing Letters, 3, 68-72.
38. Monfreda, C., Patz, J. A., Prentice, I. C., Ramankutty, N., and Snyder, P. K. (2005). Global consequences of land use. Science July 22; 309 (5734), 570-574.
39. Monfreda, C., Ramankutty, N., and Foley, J. A. (2008). Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000. Global Biogeochemical Cycles 22 (1), GB1022.
40. Olofsson, P., Stehman, S. V., Woodcock, C. E., Sulla-Menashe, D., Sibley, A.M., Newell, J.D. et al. (2012). A global land cover validation dataset, I: Fundamental design principles. International Journal of Remote Sensing, 33, 5768-5788.
41. Ozdogan, M. and Gutman, G. (2008). A new methodology to map irrigated areas using multi-temporal MODIS and ancillary data: An application example in the continental US. Remote Sensing of Environment, 112 (9), 3520-3537.
42. Ozdogan, M. and Woodcock, C. E. (2006). Resolution dependent errors in remote sensing of cultivated areas. Remote Sensing of Environment, 103, 203-217.
43. Pennisi, E. (2008). The Blue revolution, drop by drop, gene by gene. Science, 320 (5873): 171-173. doi:10.1126/ science.320.5873.171
44. Pittman, K., Hansen, M. C., Becker-Reshef, I., Potapov, P. V., and Justice, C. O. (2010). Estimating global cropland extent with multi-year MODIS data. Remote Sensing 2 (7), 1844-1863.
45. Portmann, F., Siebert, S., and Doll, P. (2010). MIRCA2000— Global monthly irrigated and rainfed crop areas around the year 2000: A new high-resolution data set for agricultural and hydrological modelling. Global Biogeochemical Cycles, 24 GB0003435. 24 pp.
46. Ramankutty, N., Evan, A. T., Monfreda, C., and Foley, J. A. (2008). Farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000. Global Biogeochemical Cycles, 22. 19pp. doi:10.1029/2007GB002952.
47. Ramankutty, N. and Foley, J. A. (1998). Characterizing patterns of global land use: An analysis of global croplands data. Global Biogeochemical Cycles 12 (4), 667-685.
48. Rodell, M., Velicogna, I., and Famiglietti, J. S. (2009). Satellit-based estimates of groundwater depletion in India. Nature, 460, 999-1002. doi:10.1038/nature08238.
49. Sakamoto, T., Yokozawa, M., Toritani, H., Shibayama, M., Ishitsuka, N., Ohno, H. (2005), A crop phenology detection method using time-series MODIS data. Remote Sensing of Environment, 96, 366-374.
50. Searchinger, T., Heimlich, R., Houghton, R. A., Dong, F., Elobeid, A., Fabiosa, J., Tokgoz, S., Hayes, D., and Yu, T. H. (2008). Use of U.S. croplands for biofuels increases greenhouse gases through emissions from land-use change. Science, 319, 1238-1240.
51. Siebert, S. and Döll, P. (2010). Quantifying blue and green virtual water contents in global crop production as well as potential production losses without irrigation. Journal of Hydrology. 384(3-4), 198-217. doi:10.1016/j.jhydrol.2009.07.031.
52. Siebert, S., Hoogeveen, J., and Frenken, K. (2006). Irrigation in Africa, Europe and Latin America—Update of the digital global map of irrigation areas to version 4, Frankfurt Hydrology Paper 05, 134 pp. Institute of Physical Geography, University of Frankfurt, Frankfurt am Main, Germany and Rome, Italy.
53. Thenkabail, P. S., Biradar, C. M., Noojipady, P., Cai, X. L., Dheeravath, V., Li, Y. J., Velpuri, M., Gumma, M., and Pandey, S. (2007b). Sub-pixel irrigated area calculation methods. Sensors Journal (special issue: Remote Sensing of Natural Resources and the Environment Remote Sensing Sensors Edited by Assefa M. Melesse). 7: 2519-2538. http://www.mdpi.org/sensors/papers/s7112519.pdf.
54. Thenkabail, P. S., Biradar, C. M., Noojipady, P., Dheeravath, V., Li, Y. J., Velpuri, M., Gumma, M. et al. (2009b). 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. July, 20, 2009.
55. Thenkabail, P. S., Enclona, E. A., Ashton, M. S., Legg, C., Jean De Dieu, M. (2004). Hyperion, IKONOS, ALI, and ETM+ sensors in the study of African rainforests. Remote Sensing of Environment, 90, 23-43.
56. Thenkabail, P. S., GangadharaRao, P., Biggs, T., Krishna, M., and Turral, H., (2007a). Spectral matching techniques to determine historical land use/land cover (LULC) and irrigated areas using time-series AVHRR pathfi der datasets in the Krishna river Basin, India. Photogrammetric Engineering and Remote Sensing, 73 (9), 1029-1040. (Second Place Recipients of the 2008 John I. Davidson ASPRS President’s Award for Practical papers).
57. Thenkabail, P. S., Hanjra, M. A., Dheeravath, V., Gumma, M. (2011). Global croplands and their water use remote sensing and non-remote sensing perspectives. In: Weng, Q. (ed.), Advances in Environmental Remote Sensing: Sensors, Algorithms, and Applications, Taylor & Francis, pp. 383-419.
58. Thenkabail, P. S., Hanjra, M. A., Dheeravath, V., Gumma, M. A. (2010). A holistic view of global croplands and their water use for ensuring global food security in the 21st century through advanced remote sensing and non-remote sensing approaches. Journal Remote Sensing, 2 (1), 211-261. doi:10.3390/rs2010211. http://www.mdpi.com/2072-4292/2/1/211.
59. Thenkabail, P. S., Knox, J. W., Ozdogan, M., Gumma, M. K., Congalton, R. G., Wu, Z., Milesi, C. et al. (2012). Assessing future risks to agricultural productivity, water resources and food security: How can remote sensing help? Photogrammetric Engineering and Remote Sensing, August 2012 Special Issue on Global Croplands: Highlight Article. 78(8), 773-782.
60. Thenkabail, P. S., Lyon, G. J., Turral, H., and Biradar, C. M. (2009a). Remote Sensing of Global Croplands for Food Security. CRC Press-Taylor & Francis group, Boca Raton, FL, London, New York. pp. 556 (48 pages in color). Published in June, 2009.
61. Thenkabail, P. S., Lyon, G. J., Turral, H., and Biradar, C. M. (2009c). Remote Sensing of Global Croplands for Food Security. Boca Raton, FL, London, New York: CRC Press/Taylor & Francis Group, Published in June.
62. Thenkabail, P. S., Mariotto, I., Gumma, M. K., Middleton, E., M. Landis, and D. R., Huemmrich, F. K. (2013). Selection of hyperspectral narrowbands (HNBs) and composition of hyperspectral twoband vegetation indices (HVIs) for biophysical characterization and discrimination of crop types using fi ld refl ctance and Hyperion/EO-1 data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 6 (2), 427-439, April 2013. doi:10.1109/JSTARS.2013.2252601.
63. Thenkabail, P. S., Schull, M., and Turral, H. 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.
64. Thenkabail, P. S. and Wu, Z. (2012). An automated cropland clas-sifi ation algorithm (ACCA) for Tajikistan by combining landsat, MODIS, and secondary data. Remote Sensing, 4 (10), 2890-2918.
65. Tillman, D., C. Balzer, J. Hill, and B. L. Befort, 2011. Global food demand and the sustainable intensifi ation of agriculture, Proceedings of the National Academy of Sciences of the United States of America, November 21. doi:10.1073/pnas.1116437108.
66. Tomlinson, R. 2003. Thi king about Geographic Information Systems Planning for Managers, 283 ESRI Press.
67. Turral, H., Svendsen, M., and Faures, J. (2009). Investing in irrigation: Reviewing the past and looking to the future. Agricultural Water Management. 97(2010), 551-560. doi:101.1016/j.agwt.2009.07.012.
68. UNDP (2012). Human Development Report 2012: Overcoming Barriers: Human Mobility and Development. New York: United Nations.
69. Velpuri, M., Thenkabail, P. S., Gumma, M. K., Biradar, C. B., Dheeravath, V., Noojipady, P., and Yuanjie, L. (2009). Influence of resolution or scale in irrigated area mapping and area estimations. Photogrammetric Engineering and Remote Sensing (PE&RS). 75 (12), December 2009, 1383-1395.
70. Vinnari, M. and Tapio, P. (2009). Future images of meat consumption in 2030. Futures, 41 (5), 269-278. doi:10.1016/j.futures.2008.11.014.
71. Wada, Y., van Beek, L. P. H., and Bierkens, M. F. P. (2012). Nonsustainable groundwater sustaining irrigation: A global assessment. Water Resources Research, 48, W00L06. doi:10.1029/2011WR010562.
72. Wardlow, B. D. and Egbert, S. L. (2008). Large-area crop mapping using time-series MODIS 250 m NDVI data: An assessment for the U.S. Central Great Plains. Remote Sensing of Environment, 112, 1096-1116.
73. Wardlow, B. D., Egbert, S. L., and Kastens, J. H. (2007). Analysis of time-series MODIS 250 m vegetation index data for crop classifi ation in the U.S. Central Great Plains. Remote Sensing of Environment, 108, 290-310.
74. Wardlow, B. D., Kastens, J. H., and Egbert, S. L. (2006). Using USDA crop progress data for the evaluation of greenup onset date calculated from MODIS 250-meter data. Photogrammetric Engineering and Remote Sensing, 72, 1225-1234.
75. Wu, Z., Thenkabail, P. S., and Verdin, J. (2014b). An automated cropland classification algorithm (ACCA) using Landsat and MODIS data combination for California. Photogrammetric Engineering and Remote Sensing, 80 (1): 81-90.
76. Wu, Z., Thenkabail, P. S., Zakzeski, A., Mueller, R., Melton, F., Rosevelt, C., Dwyer, J., Johnson, J., and Verdin, J. P. (2014a). Seasonal cultivated and fallow cropland mapping using modis-based automated cropland classifi ation algorithm. Journal of Applied Remote Sensing 0001;8 (1), 083685. doi:10.1117/1.JRS.8.083685.
77. Yu, L., Wang, J., Clinton, N., Xin, Q., Zhong, L., Chen, Y., and Gong, P. (2013). FROM-GC: 30 m global cropland extent derived through multisource data integration. International Journal of Digital Earth. 12 pp. doi:10.1080/17538947.2013.822574.