Crop discrimination in Northern China with double cropping systems using Fourier analysis of time-series MODIS data

Crop identification is the basis of crop monitoring using remote sensing. Remote sensing the extent and distribution of individual crop types has proven useful to a wide range of users, including policy-makers, farmers, and scientists. Northern China is not merely the political, economic, and cultural centre of China, but also an important base for grain production. Its main grains are wheat, maize, and cotton. By employing the Fourier analysis method, we studied crop planting patterns in the Northern China plain. Then, using time-series EOS-MODIS NDVI data, we extracted the key parameters to discriminate crop types. The results showed that the estimated area and the statistics were correlated well at the county-level. Furthermore, there was little difference between the crop area estimated by the MODIS data and the statistics at province-level. Our study shows that the method we designed is promising for use in regional spatial scale crop mapping in Northern China using the MODIS NDVI time-series.

[1]  D. Legates,et al.  Crop identification using harmonic analysis of time-series AVHRR NDVI data , 2002 .

[2]  Jesslyn F. Brown,et al.  Seasonal Land‐Cover Regions of the United States , 1995, Annals of the Association of American Geographers.

[3]  Wout Verhoef,et al.  Mapping agroecological zones and time lag in vegetation growth by means of Fourier analysis of time series of NDVI images , 1993 .

[4]  W. Verhoef,et al.  Reconstructing cloudfree NDVI composites using Fourier analysis of time series , 2000 .

[5]  Massimo Menenti,et al.  Mapping vegetation-soil-climate complexes in southern Africa using temporal Fourier analysis of NOAA-AVHRR NDVI data , 2000 .

[6]  C. Justice,et al.  Characterization and classification of South American land cover types using satellite data , 1987 .

[7]  C. Justice,et al.  A global 1° by 1° NDVI data set for climate studies derived from the GIMMS continental NDVI data , 1994 .

[8]  Jiyuan Liu,et al.  Study on spatial pattern of land-use change in China during 1995–2000 , 2003, Science in China Series D Earth Sciences.

[9]  P. Sellers.  Canopy reflectance, photosynthesis and transpiration , 1985 .

[10]  Jesslyn F. Brown,et al.  Measuring phenological variability from satellite imagery , 1994 .

[11]  C. Justice,et al.  Global land cover classification by remote sensing: present capabilities and future possibilities , 1991 .

[12]  J. Townshend,et al.  Global discrimination of land cover types from metrics derived from AVHRR pathfinder data , 1995 .

[13]  J. Townshend,et al.  African Land-Cover Classification Using Satellite Data , 1985, Science.

[14]  G. Gutman,et al.  The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models , 1998 .

[15]  J. Eastman,et al.  Long sequence time series evaluation using standardized principal components , 1993 .

[16]  A. J. Stern,et al.  Application of MODIS derived parameters for regional crop yield assessment , 2005 .

[17]  Thomas R. Loveland,et al.  USING MULTISOURCE DATA IN GLOBAL LAND-COVER CHARACTERIZATION: CONCEPTS, REQUIREMENTS, AND METHODS , 1993 .

[18]  Jin Chen,et al.  A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter , 2004 .

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

[20]  A. Strahler,et al.  Monitoring vegetation phenology using MODIS , 2003 .

[21]  T. Williams,et al.  OBTAINING SPATIAL AND TEMPORAL VEGETATION DATA FROM LANDSAT MSS AND AVHRR/NOAA SATELLITE IMAGES FOR A HYDROLOGIC MODEL , 1997 .