A Rice Mapping Method Based on Time-Series Landsat Data for the Extraction of Growth Period Characteristics

The rapid and accurate acquisition of rice cultivation information is very important for the management and assessment of rice agriculture and for research on food security, the use of agricultural water resources, and greenhouse gas emissions. Rice mapping methods based on phenology have been widely used but further studies are needed to clearly quantify the rice characteristics during the growth cycle. This paper selected the area where rice agriculture has undergone tremendous changes as the observation object. The rice areas were mapped in three time periods during the period from 1993 to 2016 by combining the characteristics of the harvested areas, flooded areas, and the time interval when harvesting and flooding occurred. An error matrix was used to determine the mapping accuracy. After exclusion of clouds and cloud shadows, the overall accuracy of the paddy fields was higher than 90% (90.5% and 93.5% in period 1 and period 3, respectively). Mixed pixels, image quality, and image acquisition time are important factors affecting the accuracy of rice mapping. The rapid economic development led to an adjustment of people’s diets and presumably this is the main reason why rice cultivation is no longer the main agricultural production activity in the study area.

[1]  Shao Yun,et al.  Compact polarimetry assessment for rice and wetland mapping , 2013 .

[2]  A. Schneider,et al.  Mapping rice paddy extent and intensification in the Vietnamese Mekong River Delta with dense time stacks of Landsat data , 2015 .

[3]  Chongcheng Chen,et al.  Mapping paddy rice areas based on vegetation phenology and surface moisture conditions , 2015 .

[4]  Changsheng Li,et al.  Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images , 2006 .

[5]  Jiefang Dong,et al.  A sub-pixel method for estimating planting fraction of paddy rice in Northeast China , 2018 .

[6]  Emily Elert,et al.  Rice by the numbers: A good grain , 2014, Nature.

[7]  Chiharu Hongo,et al.  Using variance analysis of multitemporal MODIS images for rice field mapping in Bali Province, Indonesia , 2012 .

[8]  T. L. Toan,et al.  Use of ENVISAT/ASAR wide-swath data for timely rice fields mapping in the Mekong River Delta , 2011 .

[9]  Shi-lun Yang,et al.  Delta changes in the Pearl River estuary and its response to human activities (1954–2008) , 2016 .

[10]  Juying Li,et al.  Spatial distribution and partition of perfluoroalkyl acids (PFAAs) in rivers of the Pearl River Delta, southern China. , 2015, The Science of the total environment.

[11]  R. Lopez,et al.  What Drives China’s New Agricultural Subsidies? , 2017 .

[12]  Aris P. Georgakakos,et al.  Land cover classification and wetland inundation mapping using MODIS , 2018 .

[13]  Zhe Zhu,et al.  Object-based cloud and cloud shadow detection in Landsat imagery , 2012 .

[14]  Jinwei Dong,et al.  Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine. , 2016, Remote sensing of environment.

[15]  C. Woodcock,et al.  Classification and Change Detection Using Landsat TM Data: When and How to Correct Atmospheric Effects? , 2001 .

[16]  H. Fang,et al.  Using NOAA AVHRR and landsat TM to estimate rice area year-by-year , 1998 .

[17]  Hanqiu Xu.  Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery , 2006 .

[18]  Catherine Linard,et al.  Predicting the risk of avian influenza A H7N9 infection in live-poultry markets across Asia , 2014, Nature Communications.

[19]  Jiyuan Liu,et al.  Tracking the dynamics of paddy rice planting area in 1986–2010 through time series Landsat images and phenology-based algorithms , 2015 .

[20]  Quazi K. Hassan,et al.  Development of a Remote Sensing-Based "Boro" Rice Mapping System , 2014, Remote. Sens..

[21]  L. Zhong,et al.  Formation of particulate sulfate and nitrate over the Pearl River Delta in the fall: Diagnostic analysis using the Community Multiscale Air Quality model , 2015 .

[22]  Giles M. Foody,et al.  Good practices for estimating area and assessing accuracy of land change , 2014 .

[23]  Damien Sulla-Menashe,et al.  Winter wheat area estimation from MODIS-EVI time series data using the Crop Proportion Phenology Index , 2012 .

[24]  C. Tucker.  Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[25]  Jie Wang,et al.  Mapping paddy rice planting area in rice-wetland coexistent areas through analysis of Landsat 8 OLI and MODIS images , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[26]  Pak Wai Chan,et al.  Process analysis of regional aerosol pollution during spring in the Pearl River Delta region, China , 2015 .

[27]  Richard R. Irish,et al.  Landsat 7 automatic cloud cover assessment , 2000, SPIE Defense + Commercial Sensing.

[28]  Guanxing Huang,et al.  A regional scale investigation on factors controlling the groundwater chemistry of various aquifers in a rapidly urbanized area: A case study of the Pearl River Delta. , 2018, The Science of the total environment.

[29]  Thomas Blaschke,et al.  Measuring urban agglomeration using a city-scale dasymetric population map: A study in the Pearl River Delta, China , 2017 .

[30]  E. Zeng,et al.  Assessing the effects of urbanization on the environment with soil legacy and current-use insecticides: a case study in the Pearl River Delta, China. , 2015, The Science of the total environment.

[31]  Lalit Kumar,et al.  Monitoring the coastline change of Hatiya Island in Bangladesh using remote sensing techniques , 2015 .

[32]  R. Cicerone,et al.  Photosynthate allocations in rice plants: Food production or atmospheric methane? , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[33]  Jinwei Dong,et al.  Spatiotemporal patterns of paddy rice croplands in China and India from 2000 to 2015. , 2017, The Science of the total environment.

[34]  J. Jiao,et al.  Enrichment and mechanisms of heavy metal mobility in a coastal quaternary groundwater system of the Pearl River Delta, China. , 2016, The Science of the total environment.

[35]  Changsheng Li,et al.  Mapping paddy rice agriculture in southern China using multi-temporal MODIS images , 2005 .

[36]  Qihao Weng,et al.  An evaluation of monthly impervious surface dynamics by fusing Landsat and MODIS time series in the Pearl River Delta, China, from 2000 to 2015 , 2017 .

[37]  Changsheng Li,et al.  Observation of flooding and rice transplanting of paddy rice fields at the site to landscape scales in China using VEGETATION sensor data , 2002 .

[38]  Thi Thu Ha Nguyen,et al.  Mapping the irrigated rice cropping patterns of the Mekong delta, Vietnam, through hyper-temporal SPOT NDVI image analysis , 2012 .