Research on rice acreage estimation in fragmented area based on decomposition of mixed pixels

Rice acreage estimation is a key aspect to guarantee food security and also important to support government agricultural subsidy system. In this paper, we explored a sophisticated method to improve rice estimation accuracy at county scale and we developed our approach with China Environment Satellite HJ-1A/B data in Hunan Province, a fragmented area with complex rice cropping patterns. Our approach improved the estimation accuracy by combing supervised and unsupervised classification upon decomposition of mixed pixels model, and the rice estimation results, validated by ground survey data, showed a close relationship (RMSE≈3.40) with survey figures, the estimated accuracy (EA) reached 83.74% at county level according to the sub-pixel method, and the accuracy can be increased about 12% compared to the pure-pixel method. The results suggest that decomposition of mixed pixels method has great significance to the improvement of rice acreage estimation accuracy, and can be used in mountainous and broken planting area.

[1]  Hongling Fang,et al.  Rice crop area estimation of an administrative division in China using remote sensing data , 1998 .

[2]  D. Lobell,et al.  Cropland distributions from temporal unmixing of MODIS data , 2004 .

[3]  Chen Zhongxin,et al.  Crop discrimination in Northern China with double cropping systems using Fourier analysis of time-series MODIS data , 2008 .

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

[5]  Alan H. Strahler,et al.  Global land cover mapping from MODIS: algorithms and early results , 2002 .

[6]  V. Wuwongse,et al.  Discrimination of irrigated and rainfed rice in a tropical agricultural system using SPOT VEGETATION NDVI and rainfall data , 2005 .

[7]  Jiyuan Liu,et al.  Characterization of forest types in Northeastern China, using multi-temporal SPOT-4 VEGETATION sensor data , 2002 .

[8]  Jinxing Hu,et al.  Mapping rice planting areas in southern China using the China Environment Satellite data , 2011, Math. Comput. Model..

[9]  Huadong Guo.  China's Earth observing satellites for building a Digital Earth , 2012, Int. J. Digit. Earth.

[10]  Christoph Hütt,et al.  Rice monitoring with multi-temporal and dual-polarimetric TerraSAR-X data , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[11]  Qing Li,et al.  Chinese HJ-1A/B satellites and data characteristics , 2010 .

[12]  K. Okamoto.  Estimation of rice-planted area in the tropical zone using a combination of optical and microwave satellite sensor data , 1999 .

[13]  J. Maclean,et al.  Rice Almanac: source book for the most important economic activity on earth. , 2002 .

[14]  Maurizio Migliaccio,et al.  Dual-Polarimetric TerraSAR-X SAR Data for Target at Sea Observation , 2013, IEEE Geoscience and Remote Sensing Letters.

[15]  Dailiang Peng,et al.  Detection and estimation of mixed paddy rice cropping patterns with MODIS data , 2011, Int. J. Appl. Earth Obs. Geoinformation.

[16]  Liu Jun.  Crop Extraction Based on Multi-temporal HJ Satellite CCD Data in Jiaxiang County , 2012 .

[17]  Thuy Le Toan,et al.  Rice crop mapping and monitoring using ERS-1 data based on experiment and modeling results , 1997, IEEE Trans. Geosci. Remote. Sens..

[18]  T. Sakamoto,et al.  Assessment of digital camera-derived vegetation indices in quantitative monitoring of seasonal rice growth , 2011 .

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

[20]  Didier Tanré,et al.  Second Simulation of the Satellite Signal in the Solar Spectrum, 6S: an overview , 1997, IEEE Trans. Geosci. Remote. Sens..

[21]  W. Dulaney,et al.  Normalized difference vegetation index measurements from the Advanced Very High Resolution Radiometer , 1991 .

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