Mapping paddy rice planting area in cold temperate climate region through analysis of time series Landsat 8 (OLI), Landsat 7 (ETM+) and MODIS imagery.
Accurate and timely rice paddy field maps with a fine spatial resolution would greatly improve our understanding of the effects of paddy rice agriculture on greenhouse gases emissions, food and water security, and human health. Rice paddy field maps were developed using optical images with high temporal resolution and coarse spatial resolution (e.g., Moderate Resolution Imaging Spectroradiometer (MODIS)) or low temporal resolution and high spatial resolution (e.g., Landsat TM/ETM+). In the past, the accuracy and efficiency for rice paddy field mapping at fine spatial resolutions were limited by the poor data availability and image-based algorithms. In this paper, time series MODIS and Landsat ETM+/OLI images, and the pixel- and phenology-based algorithm are used to map paddy rice planting area. The unique physical features of rice paddy fields during the flooding/open-canopy period are captured with the dynamics of vegetation indices, which are then used to identify rice paddy fields. The algorithm is tested in the Sanjiang Plain (path/row 114/27) in China in 2013. The overall accuracy of the resulted map of paddy rice planting area generated by both Landsat ETM+ and OLI is 97.3%, when evaluated with areas of interest (AOIs) derived from geo-referenced field photos. The paddy rice planting area map also agrees reasonably well with the official statistics at the level of state farms (R2= 0.94). These results demonstrate that the combination of fine spatial resolution images and the phenology-based algorithm can provide a simple, robust, and automated approach to map the distribution of paddy rice agriculture in a year.
Department of Microbiology and Plant Biology, Center for Spatial Analysis, University of Oklahoma, Norman, OK 73019, USA.
Yuanwei
Y
Qin
Department of Microbiology and Plant Biology, Center for Spatial Analysis, University of Oklahoma, Norman, OK 73019, USA; Institute of Biodiversity Science, Fudan University, Shanghai 200433, China.
Xiangming
X
Xiao
Department of Microbiology and Plant Biology, Center for Spatial Analysis, University of Oklahoma, Norman, OK 73019, USA.
Jinwei
J
Dong
Department of Microbiology and Plant Biology, Center for Spatial Analysis, University of Oklahoma, Norman, OK 73019, USA.
Yuting
Y
Zhou
Center for Remote Sensing, Department of Geography and Environment, Boston University, Boston, MA 02215, USA.
Zhe
Z
Zhu
Department of Microbiology and Plant Biology, Center for Spatial Analysis, University of Oklahoma, Norman, OK 73019, USA.
Geli
G
Zhang
College of Resources and Environment, Northeast Agricultural University, Harbin, Heilongjiang 150030, China.
Guoming
G
Du
Department of Microbiology and Plant Biology, Center for Spatial Analysis, University of Oklahoma, Norman, OK 73019, USA.
Cui
C
Jin
Department of Computer and Information Science, Southwest Forestry University, Kunming, Yunnan 650224, China; Department of Microbiology and Plant Biology, Center for Spatial Analysis, University of Oklahoma, Norman, OK 73019, USA.
Weili
W
Kou
Department of Microbiology and Plant Biology, Center for Spatial Analysis, University of Oklahoma, Norman, OK 73019, USA.
Jie
J
Wang
Institute of Biodiversity Science, Fudan University, Shanghai 200433, China.
Xiangping
X
Li
Cropland; Data availability; Observation frequency; Rice paddy; Sanjiang Plain; Vegetation indices
Int J Biometeorol. 2001 Nov;45(4):170-7
11769316
Glob Chang Biol. 2013 Jan;19(1):19-32
23504718
PLoS One. 2012;7(1):e29156
22235268
Proc Natl Acad Sci U S A. 2008 Mar 25;105(12):4769-74
18362346
Agric Ecosyst Environ. 2007;119:409-415
18418464
Science. 2010 Feb 12;327(5967):825-8
20150491
J Zhejiang Univ Sci B. 2013 Oct;14(10):934-46
24101210
Ying Yong Sheng Tai Xue Bao. 2011 Jun;22(6):1653-8
21941772