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Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine

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Title Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine
 
Creator Dong, Jinwei
 
Contributor Xiao, Xiangming
Menarguez, Michael Angelo
Zhang, Geli
Qin, Yuanwei
Thau, David
Biradar, Chandrashekhar
Moore III, Berrien
 
Subject paddy rice
phenology- and pixel-based algorithm
cloud computing
google earth engine
landsat 8
 
Description Area and spatial 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 high latitude areas in the last decade, particularly in northeastern (NE) Asia. Current knowledge about paddy rice fields in these cold regions is limited. The phenology- and pixel-based paddy rice mapping (PPPM) algorithm, which identifies the flooding signals in the rice transplanting phase, has been effectively applied in tropical areas, but has not been tested at large scale of cold regions yet. De- spite the effects from more snow/ice, paddy rice mapping in high latitude areas is assumed to be more encouraging due to less clouds, lower cropping intensity, and more observations from Landsat sidelaps. Moreover, the enhanced temporal and geographic coverage from Landsat 8 provides an opportunity to acquire phenology information and map paddy rice. This study evaluated the potential of Landsat 8 images on annual paddy rice mapping in NE Asia which was dominated by single cropping system, including Japan, North Korea, South Korea, and NE China. The cloud computing approach was used to process all the available Landsat 8 imagery in 2014 (143 path/rows, ~3290 scenes) with the Google Earth Engine (GEE) platform. The results indicated that the Landsat 8, GEE, and improved PPPM algorithm can effectively support the yearly mapping of paddy rice in NE Asia. The resultant paddy rice map has a high accuracy with the producer (user) accuracy of 73% (92%), based on the validation using very high resolution images and intensive field photos. Geographic characteristics of paddy rice distribution were analyzed from aspects of country, elevation, latitude, and climate. The resultant 30-m paddy rice map is expected to provide unprecedented details about the area, spatial distribution, and landscape pattern of paddy rice fields in NE Asia, which will contribute to food security assessment, water resource management, estimation of greenhouse gas emissions, and disease control.
 
Date 2017-02-23T13:16:56Z
2017-02-23T13:16:56Z
 
Type Journal Article
 
Identifier https://mel.cgiar.org/dspace/limited
https://www.sciencedirect.com/science/article/pii/S003442571630044X#!
https://www.researchgate.net/publication/296690405_Mapping_paddy_rice_planting_area_in_northeastern_Asia_with_Landsat_8_images_phenology-based_algorithm_and_Google_Earth_Engine
Jinwei Dong, Xiangming Xiao, Michael Angelo Menarguez, Geli Zhang, Yuanwei Qin, David Thau, Chandrashekhar Biradar, Berrien Moore III. (30/11/2016). Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine. Remote Sensing of Environment (, 185, pp. 142-154.
https://hdl.handle.net/20.500.11766/5913
Limited access
 
Language en
 
Format PDF
 
Publisher Elsevier
 
Source Remote Sensing of Environment (;185,(2016) Pagination 142-154