Mapping paddy rice planting area in rice-wetland coexistent areas through analysis of Landsat 8 OLI and MODIS images.
Accurate and up-to-date information on the spatial distribution of paddy rice fields is necessary for the studies of trace gas emissions, water source management, and food security. The phenology-based paddy rice mapping algorithm, which identifies the unique flooding stage of paddy rice, has been widely used. However, identification and mapping of paddy rice in rice-wetland coexistent areas is still a challenging task. In this study, we found that the flooding/transplanting periods of paddy rice and natural wetlands were different. The natural wetlands flood earlier and have a shorter duration than paddy rice in the Panjin Plain, a temperate region in China. We used this asynchronous flooding stage to extract the paddy rice planting area from the rice-wetland coexistent area. MODIS Land Surface Temperature (LST) data was used to derive the temperature-defined plant growing season. Landsat 8 OLI imagery was used to detect the flooding signal and then paddy rice was extracted using the difference in flooding stages between paddy rice and natural wetlands. The resultant paddy rice map was evaluated with in-situ ground-truth data and Google Earth images. The estimated overall accuracy and Kappa coefficient were 95% and 0.90, respectively. The spatial pattern of OLI-derived paddy rice map agrees well with the paddy rice layer from the National Land Cover Dataset from 2010 (NLCD-2010). The differences between RiceLandsatand RiceNLCDare in the range of ±20% for most 1-km grid cell. The results of this study demonstrate the potential of the phenology-based paddy rice mapping algorithm, via integrating MODIS and Landsat 8 OLI images, to map paddy rice fields in complex landscapes of paddy rice and natural wetland in the temperate region.
Department of Microbiology and Plant Biology, Center for Spatial Analysis, University of Oklahoma, Norman, OK73019, USA.
Yuting
Y
Zhou
Department of Microbiology and Plant Biology, Center for Spatial Analysis, University of Oklahoma, Norman, OK73019, 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, OK73019, USA.
Yuanwei
Y
Qin
Department of Microbiology and Plant Biology, Center for Spatial Analysis, University of Oklahoma, Norman, OK73019, USA.
Jinwei
J
Dong
Department of Microbiology and Plant Biology, Center for Spatial Analysis, University of Oklahoma, Norman, OK73019, USA.
Geli
G
Zhang
Southwest Forestry University, Kunming, Yunnan 650224, China.
Weili
W
Kou
Department of Microbiology and Plant Biology, Center for Spatial Analysis, University of Oklahoma, Norman, OK73019, USA.
Cui
C
Jin
Department of Microbiology and Plant Biology, Center for Spatial Analysis, University of Oklahoma, Norman, OK73019, USA.
Jie
J
Wang
Institute of Biodiversity Science, Fudan University, Shanghai 200433, China.
Xiangping
X
Li
Landsat 8; MODIS; Paddy rice; Phenology-based algorithm; Rice-wetland coexistent areas
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