Mapping paddy rice agriculture in southern China using multi-temporal MODIS images

Abstract

Information on the area and spatial distribution of paddy rice fields is needed for trace gas emission estimates, management of water resources, and food security. Paddy rice fields are characterized by an initial period of flooding and transplanting, during which period open canopy (a mixture of surface water and rice crops) exists. The Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the NASA EOS Terra satellite has visible, near infrared and shortwave infrared bands; and therefore, a number of vegetation indices can be calculated, including Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI) that is sensitive to leaf water and soil moisture. In this study, we developed a paddy rice mapping algorithm that uses time series of three vegetation indices (LSWI, EVI, and NDVI) derived from MODIS images to identify that initial period of flooding and transplanting in paddy rice fields, based on the sensitivity of LSWI to the increased surface moisture during the period of flooding and rice transplanting. We ran the algorithm to map paddy rice fields in 13 provinces of southern China, using the 8-day composite MODIS Surface Reflectance products (500-m spatial resolution) in 2002. The resultant MODIS-derived paddy rice map was evaluated, using the National Land Cover Dataset (1:100,000 scale) derived from analysis of Landsat ETM+ images in 1999/2000. There were reasonable agreements in area estimates of paddy rice fields between the MODIS-derived map and the Landsat-based dataset at the provincial and county levels. The results of this study indicated that the MODIS-based paddy rice mapping algorithm could potentially be applied at large spatial scales to monitor paddy rice agriculture on a timely and frequent basis.