Mapping rice areas of South Asia using MODIS multitemporal data
Open Access
- 1 January 2011
- journal article
- research article
- Published by SPIE-Intl Soc Optical Eng in Journal of Applied Remote Sensing
- Vol. 5 (1), 053547-053547-26
- https://doi.org/10.1117/1.3619838
Abstract
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.Keywords
This publication has 18 references indexed in Scilit:
- Temporal changes in rice-growing area and their impact on livelihood over a decade: A case study of NepalAgriculture, Ecosystems & Environment, 2011
- 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, 2011
- Mapping paddy rice with multi-date moderate-resolution imaging spectroradiometer (MODIS) data in ChinaJournal of Zhejiang University-SCIENCE A, 2009
- Global irrigated area map (GIAM), derived from remote sensing, for the end of the last millenniumInternational Journal of Remote Sensing, 2009
- Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS imagesRemote Sensing of Environment, 2006
- Ganges and Indus river basin land use/land cover (LULC) and irrigated area mapping using continuous streams of MODIS dataRemote Sensing of Environment, 2005
- Mapping paddy rice agriculture in southern China using multi-temporal MODIS imagesRemote Sensing of Environment, 2005
- The shuttle radar topography mission—a new class of digital elevation models acquired by spaceborne radarISPRS Journal of Photogrammetry and Remote Sensing, 2003
- Changes in CH4 emission from rice fields from 1960 to 1990s: 1. Impacts of modern rice technologyGlobal Biogeochemical Cycles, 2000
- Global land cover classifications at 8 km spatial resolution: The use of training data derived from Landsat imagery in decision tree classifiersInternational Journal of Remote Sensing, 1998