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Journal of Applied Remote Sensing / Volume 5 / Research Papers

Mapping rice areas of South Asia using MODIS multitemporal data

J. Appl. Remote Sens. 5, 053547 (Sep 01, 2011); http://dx.doi.org/10.1117/1.3619838

Murali Krishna Gumma, Andrew Nelson, and Amrendra N. Singh

International Rice Research Institute, Social Sciences Division, DAPO Box 7777, Metro Manila, 7777 Philippines m.gumma@cgiar.org; muraligk5@gmail.com

Prasad S. Thenkabail

Southwest Geographic Science Center, U.S. Geological Survey, Flagstaff, Arizona 86001

Our goal is to map the rice areas of six South Asian countries using moderate-resolution imaging spectroradiometer (MODIS) time-series data for the time period 2000 to 2001. South Asia accounts for almost 40% of the world's harvested rice area and is also home to 74% of the population that lives on less than $2.00 a day. The population of the region is growing faster than its ability to produce rice. Thus, accurate and timely assessment of where and how rice is cultivated is important to craft food security and poverty alleviation strategies. We used a time series of eight-day, 500-m spatial resolution composite images from the MODIS sensor to produce rice maps and rice characteristics (e.g., intensity of cropping, cropping calendar) taking data for the years 2000 to 2001 and by adopting a suite of methods that include spectral matching techniques, decision trees, and ideal temporal profile data banks to rapidly identify and classify rice areas over large spatial extents. These methods are used in conjunction with ancillary spatial data sets (e.g., elevation, precipitation), national statistics, and maps, and a large volume of field-plot data. The resulting rice maps and statistics are compared against a subset of independent field-plot points and the best available subnational statistics on rice areas for the main crop growing season (kharif season). A fuzzy classification accuracy assessment for the 2000 to 2001 rice-map product, based on field-plot data, demonstrated accuracies from 67% to 100% for individual rice classes, with an overall accuracy of 80% for all classes. Most of the mixing was within rice classes. The derived physical rice area was highly correlated with the subnational statistics with R2 values of 97% at the district level and 99% at the state level for 2000 to 2001. These results suggest that the methods, approaches, algorithms, and data sets we used are ideal for rapid, accurate, and large-scale mapping of paddy rice as well as for generating their statistics over large areas.

© 2011 Society of Photo-Optical Instrumentation Engineers (SPIE)

History
Received Dec 22, 2010
Accepted Jul 12, 2011
Revised Jul 07, 2011
Published online Sep 01, 2011
Digital Object Identifier
http://dx.doi.org/10.1117/1.3619838
Citation
Murali Krishna Gumma, Andrew Nelson, Prasad S. Thenkabail and Amrendra N. Singh, "Mapping rice areas of South Asia using MODIS multitemporal data", J. Appl. Remote Sens. 5, 053547 (Sep 01, 2011); http://dx.doi.org/10.1117/1.3619838

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