Mapping paddy rice in northeastern Asia based on Landsat 8 and Google Earth Engine

Jinwei Dong, University of Oklahoma, jinwei.dong@ou.edu (Presenter)
Xiangming Xiao, University of Oklahoma, xiangming.xiao@ou.edu
Michael Angelo Menarguez, University of Oklahoma, mamenarguez@ou.edu
Geli Zhang, University of Oklahoma, geli.zhang@ou.edu
Yuanwei Qin, University of Oklahoma, yuanwei.qin@ou.edu

Area and distribution information of paddy rice are important for understanding of food security, water use, greenhouse gas emission, and disease transmission. Due to climatic warming and increasing food demand, paddy rice has been expanding rapidly in northeastern Asia, especially northeast China. However, the knowledge about area and spatial distribution of paddy rice fields in these high latitude regions is still limited. The phenology- and pixel-based paddy rice mapping algorithm, by identifying the flooding and transplanting phase, has been proved effective in the tropical zones. However, the application of the algorithm with Landsat TM/ETM data in high latitude areas of Asia is still challenging due to the limited data availability, cloud and cloud shadow issues, huge data size, spatial and temporal variations of rice plantings. The enhanced temporal and geographic coverage afforded by Landsat 8 operations provide an opportunity to acquire the phenology information and map paddy rice, even on the yearly scale. This study examined and evaluated the potential of Landsat 8 images on annual paddy rice mapping in the cold temperate zone where paddy rice was dominated by single cropping system, including Japan, North Korea, South Korea, and Northeast China. The results indicated that the phenology- and pixel-based algorithm and the Google Earth Engine can effectively support the mapping of the paddy rice in 2014 by using all the available Landsat 8 images. The resultant 30-m paddy rice map expect to provide unprecedented details about the paddy rice pattern and magnitude in northeastern Asia, which will contribute to food security assessment, water resource management, estimation of greenhouse gas emissions, and disease transmission.

Presentation Type:  Poster

Session:  General Contributions   (Tue 4:35 PM)

Associated Project(s): 

• Xiao, Xiangming: Mapping industrial forest plantations in tropical monsoon Asia through integration of Landsat and PALSAR imagery ...details
• Xiao, Xiangming: Quantifying changes in agricultural intensification and expansion in monsoon Asia during 2000-2010 ...details

Poster Location ID: 142