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Remote Sensing for Land & Resources    2020, Vol. 32 Issue (2) : 177-185     DOI: 10.6046/gtzyyg.2020.02.23
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Extraction and analysis of spatiotemporal variation of rice planting area in Hunan Province based on MODIS time-series data
Gang DENG1,2, Zhiguang TANG1(), Chaokui LI1, Hao CHEN1, Huanhua PENG1, Xiaoru WANG1,2
1. National-Local Joint Engineering Laboratory of Geo-spatial Information Technology, Hunan University of Science and Technology, Xiangtan 411201, China
2. School of Resource,Environment and Safety Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
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Abstract  

It is essential to obtain a wide range of spatial distribution and dynamic change information of paddy rice timely and accurately for scientific guidance of rice production, rational utilization of water resources, and monitoring of atmospheric environmental changes. In this study, the decision tree extraction model of rice planting area in Hunan Province was constructed on the basis of time series variation characteristics of MODIS-derived LSWI and EVI in rice planting area, and the accuracy of the model was evaluated. In addition, the spatiotemporal variation characteristics of the rice planting areas in Hunan Province from 2000 to 2016 were investigated. The results are as follows: the total classification accuracy of rice planting area extraction model in the research area is 90.2%, the Kappa coefficient is 0.74 and; in comparison with agriculture statistics, the mean relative error is 13.6%. The proposed extraction model can be applied to the efficient extraction of rice planting area on a wide range and long time series basis. The average annual rice planting area in Hunan Province is 3 441.2 thousand hectares, of which 1 024.1 thousand hectares are single-cropping rice, mainly distributed in the Dongting Lake plain, and 2 417.1 thousand hectares are double-cropping rice, mainly distributed in the north-central part of Hunan Province such as Yueyang, Yiyang, Changde, Changsha, Zhuzhou, Xiangtan and Loudi City. the rice planting area in Hunan Province was reduced by 732 thousand hectares from 2000 to 2004, reached a relatively stable level in 2005—2010 and increased by 295.5 thousand hectares in 2011—2016. Overall, there existed a decreasing trend from 2000 to 2016, with the rice planting area reduced by 582.2 thousand hectares.
Keywords rice planting area      MODIS      LSWI      EVI      decision tree classifier     
:  TP79  
Corresponding Authors: Zhiguang TANG     E-mail: tangzhg11@hnust.edu.cn
Issue Date: 18 June 2020
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Gang DENG
Zhiguang TANG
Chaokui LI
Hao CHEN
Huanhua PENG
Xiaoru WANG
Cite this article:   
Gang DENG,Zhiguang TANG,Chaokui LI, et al. Extraction and analysis of spatiotemporal variation of rice planting area in Hunan Province based on MODIS time-series data[J]. Remote Sensing for Land & Resources, 2020, 32(2): 177-185.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020.02.23     OR     https://www.gtzyyg.com/EN/Y2020/V32/I2/177
Fig.1  Overall technical route
Fig.2  Distribution of training area for different land types in study area
Fig.3  Variation of Characteristic index of different land types
Fig.4  Decision tree model of rice information extraction
Fig.5  Comparison of rice extractionin verification sample area one
验证样区(5个)MODIS Landsat+Google Earth 目视解译
类型 水稻/
像元		 非水稻/
像元		 Kappa
系数		 总体分类
精度/%
MODIS 水稻 1 572 625 0.74 90.2
提取水稻 非水稻 143 5 460
Tab.1  Total classification accuracy evaluation of five verification sample areas
Fig.6  Rice area extraction results in Hunan Province during 2000—2016
年份 MODIS提取
结果/(万hm2)		 统计数据/
(万hm2)		 相对误差/% 年份 MODIS提取
结果/(万hm2)		 统计数据/
(万hm2)		 相对误差/%
2000年 408.78 389.61 4.92 2009年 353.54 404.72 -12.65
2001年 375.79 369.16 1.80 2010年 325.03 403.05 -19.36
2002年 380.68 354.15 7.49 2011年 321.01 406.63 -21.06
2003年 352.17 341.00 3.28 2012年 342.58 409.51 -16.34
2004年 335.58 371.68 -9.71 2013年 311.95 408.51 -23.64
2005年 338.09 379.52 -10.92 2014年 330.35 412.07 -19.83
2006年 340.88 393.12 -13.29 2015年 317.18 411.41 -22.90
2007年 341.93 389.72 -12.26 2016年 350.56 408.55 -14.19
2008年 323.93 393.20 -17.62
Tab.2  Comparison of rice area extraction results and agricultural statistics data in Hunan Province during 2000—2016
Fig.7  Variation of rice area in Hunan Province during 2000—2016
[1] 陈晓华. 中国农业统计资料(2013)[M].中国农业出版社, 2014.
[1] Chen X H. Agricultural statistics in China (2013)[M].China Agriculture Press, 2014.
[2] Brenkert A L, Smith S J, Kim S H, et al. Model documentation for the MiniCAM[R].Pacific Northwest National Lab.(PNNL),Richland,WA(United States), 2003.
[3] Masuoka P M, Claborn D M, Andre R G, et al. Use of IKONOS and Landsat for malaria control in the Republic of Korea[J]. Remote Sensing of Environment, 2003,88(1-2):187-194.
doi: 10.1016/j.rse.2003.04.009 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425703002402
[4] 徐新刚, 李强子, 周万村, 等. 应用高分辨率遥感影像提取作物种植面积[J]. 遥感技术与应用, 2008,23(1):17-25.
url: http://www.rsta.ac.cn/CN/abstract/abstract661.shtml
[4] Xu X G, Li Q Z, Zhou W C, et al. Classification application of QuickBird imagery to obtain crop planting area[J]. Remote Sensing Technology and Application, 2008,23(1):17-25.
[5] Yoshikawa N, Shiozawa S. Estimating variable acreage of cultivated paddy fields from preceding precipitation in a tropical watershed utilizing Landsat TM/ETM[J]. Agricultural water management, 2006,85(3):296-304.
[6] 黄振国, 陈仲新, 刘芳清, 等. 利用SPOT5影像提取水稻种植面积的研究——以湖南株洲市区为例[J]. 湖南农业大学学报(自科版), 2013,39(2):137-140.
[6] Huang Z G, Chen Z X, Liu F Q, et al. Mapping rice planting area using SPOT5 imagery:Taking Zhuzhou,Hunan as an example[J]. Journal of Hunan Agricultural University, 2013,39(2):137-140.
[7] 郑长春, 王秀珍, 黄敬峰. 基于特征波段的SPOT-5卫星影像水稻面积信息自动提取的方法研究[J]. 遥感技术与应用, 2008,23(3):294-299.
[7] Zheng C C, Wang X Z, Huang J F. Decision tree algorithm of automatically extracting paddy rice information from SPOT-5 images based on characteristic bands[J]. Remote Sensing Technology and Application, 2008,23(3):294-299.
[8] 钟仕全, 莫建飞, 陈燕丽, 等. 基于HJ-1B卫星遥感数据的水稻识别技术研究[J]. 遥感技术与应用, 2010,25(4):464-468.
[8] Zhong S Q, Mo J F, Chen Y L, et al. Study on rice identification technology using HJ-1B data[J]. Remote Sensing Technology and Applications, 2010,25(4):464-468.
[9] Xiao X, Boles S, Frolking S, et al. Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images[J]. Remote Sensing of Environment, 2006,100(1):95-113.
[10] Xiao X, Boles S, Frolking S, et al. Observation of flooding and rice transplanting of paddy rice fields at the site to landscape scales in China using Vegetation sensor data[J]. International Journal of Remote Sensing, 2002,23(15):3009-3022.
doi: 10.1080/01431160110107734 url: https://www.tandfonline.com/doi/full/10.1080/01431160110107734
[11] 李郁竹, 曾燕. 应用NOAA/AVHRR数据测算局地水稻种植面积方法研究[J]. 遥感学报, 1998,2(2):125-130.
doi: 10.11834/jrs.19980209 url: http://www.jors.cn/jrs/ch/reader/view_abstract.aspx?file_no=19980209&flag=1
[11] Li Y Z, Zeng Y. Study on methods of rice planting area estimation at regional scale using NOAA/AVHRR data[J]. Journal Of Remote Sensing, 1998,2(2):125-130.
[12] Bouvet A, Toan T L. Use of ENVISAT/ASAR wide-swath data for timely rice fields mapping in the Mekong River Delta[J]. Remote Sensing of Environment, 2011,115(4):1090-1101.
[13] Ribbes F, Toan T L. Mapping and monitoring rice crop with RADARSAT data [C]// IEEE International Geoscience & Remote Sensing Symposium, 1998.
[14] Zhang Y, Wang C, Wu J, et al. Mapping paddy rice with multitemporal ALOS/PALSAR imagery in southeast China[J]. International Journal of Remote Sensing, 2009,30(23):6301-6315.
doi: 10.1080/01431160902842391 url: https://www.tandfonline.com/doi/full/10.1080/01431160902842391
[15] Gumma M K, Nelson A, Thenkabail P S, et al. Mapping rice areas of South Asia using MODIS multitemporal data[J]. Journal of applied remote sensing, 2011,5(1):053547.
[16] Son N T, Chen C F, Chen C R, et al. A comparative analysis of multitemporal MODIS EVI and NDVI data for large-scale rice yield estimation[J]. Agricultural and forest meteorology, 2014,197(6):52-64.
[17] Zhang G L, Xiao X M, Dong J W, et al. Mapping paddy rice planting areas through time series analysis of MODIS land surface temperature and vegetation index data[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2015,106:157-171.
[18] 苗翠翠, 江南, 彭世揆, 等. 基于NDVI时序数据的水稻种植面积遥感监测分析——以江苏省为例[J]. 地球信息科学学报, 2011,13(2):273-280.
[18] Miao C C, Jiang N, Peng S K, et al. Extraction of paddy land area based on NDVI time-series data:Taking Jiangsu Province as an example[J]. Journal of Geo-Information Science, 2011,13(2):273-280.
[19] 邬明权, 王长耀, 牛铮. 利用多源时序遥感数据提取大范围水稻种植面积[J]. 农业工程学报, 2010,26(7):240-244.
[19] Wu M Q, Wang C Y, Niu Z. Mapping paddy fields in large areas, based on time series multi-sensors data[J]. Transactions of the Chinese Society of Agricultural Engineering, 2010,26(7):240-244.
[20] 杨沈斌, 景元书, 王琳, 等. 基于MODIS时序数据提取河南省水稻种植分布[J]. 大气科学学报, 2012,35(1):113-120.
[20] Yang S B, Jing Y S, Wang L, et al. Mapping rice paddy distribution in Henan Province based on multi-temporal MODIS imagery[J]. Transactions of Atmospheric Sciences, 2012,35(1):113-120.
[21] 张莉, 吴文斌, 左丽君, 等. 基于EOS/MODIS数据的南方水稻面积提取技术[J]. 中国农业资源与区划, 2011,32(4):39-44.
[21] Zhang L, Wu W B, Zuo L J. Extracting planting area of paddy rice in southern China by using EOS/MODIS data[J]. Journal of China Agricultural Resources and Regional Planning, 2011,32(4):39-44.
[22] 郑长春, 王秀珍, 黄敬峰. 多时相MODIS影像的浙江省水稻种植面积信息提取方法研究[J]. 浙江大学学报(农业与生命科学版), 2009,35(1):98-104.
url: http://www.journals.zju.edu.cn/agr/CN/abstract/abstract8380.shtml
[22] Zheng C C, Wang X Z, Huang J F. Mapping paddy rice planting area in Zhejiang Province using multi-temporal MODIS images[J]. Journal of Zhejiang University(Agriculture and Life Sciences), 2009,35(1):98-104.
url: http://www.journals.zju.edu.cn/agr/CN/abstract/abstract8380.shtml
[23] 曹丹, 白林燕, 冯建中, 等. 东北三省水稻种植面积时空变化监测与分析[J]. 江苏农业科学, 2018,46(10):260-265,271.
[23] Cao D, Bai L Y, Feng J Z, et al. Monitoring and analysis of temporal and spatial variation of rice planting area in northeastern China[J]. Jiangsu Agricultural Sciences, 2018,46(10):260-265,271.
[24] 顾娟, 李新, 黄春林. NDVI时间序列数据集重建方法述评[J]. 遥感技术与应用, 2006,21(4):391-395.
url: http://www.rsta.ac.cn/CN/abstract/abstract610.shtml
[24] Gu J, Li X, Huang C L. Research on the Reconstructing of Time-series NDVI Data[J]. Remote Sensing Technology and Application, 2006,21(4):391-395.
url: http://www.rsta.ac.cn/CN/abstract/abstract610.shtml
[25] 田波. 湖南水稻的比较优势与竞争力分析[J].新疆农垦经济, 2006(1):35-37.
url: http://d.wanfangdata.com.cn/Periodical_xjnkjj200601008.aspx
[25] Tian B. Analysis on the Comparative Advantage and Competition Dynamics of Rice in Hunan[J].Xinjiang State Farms Economy, 2006(1):35-37.
url: http://d.wanfangdata.com.cn/Periodical_xjnkjj200601008.aspx
[26] Huete A, Didan K, Miura T, et al. Overview of the radiometric and biophysical performance of the MODIS vegetation indices[J]. Remote Sensing of Environment, 2002,83(1-2):195-213.
doi: 10.1016/S0034-4257(02)00096-2 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425702000962
[27] Sakamoto T, Yokozawa M, Toritani H, et al. A crop phenology detection method using time-series MODIS data[J]. Remote Sensing of Environment, 2005,96(3-4):366-374.
[28] Xiao X, Boles S, Liu J, et al. Mapping paddy rice agriculture in southern China using multi-temporal MODIS images[J]. Remote Sensing of Environment, 2005,95(4):480-492.
doi: 10.1016/j.rse.2004.12.009 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425705000209
[29] Jonsson P, Eklundh L. TIMESAT—a program for analyzing time-series of satellite sensor data[J]. Computers & Geosciences, 2004,30(8):833-845.
[30] Jonsson P, Eklundh L. Seasonality extraction by function fitting to time-series of satellite sensor data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002,40(8):1824-1832.
[31] 孙华生. 利用多时相MODIS数据提取中国水稻种植面积和长势信息[D]. 杭州:浙江大学, 2009.
[31] Sun H S. Extracting Planting Area and Growth Information of Paddy Rice using Multi-temporal MODIS Data in China[D]. Hangzhou:Zhejiang University, 2009.
[32] 吴文斌, 杨鹏, 唐华俊, 等. 两种NDVI时间序列数据拟合方法比较[J]. 农业工程学报, 2009,25(11):183-188.
[32] Wu W B, Yang P, Tang H J, et al. Comparison of two fitting methods of NDVI time series datasets[J]. Transactions of the Chinese Society of Agricultural Engineering, 2009,25(11):183-188.
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