Next Article in Journal
Spatio-Temporal Assessment of Tuz Gölü, Turkey as a Potential Radiometric Vicarious Calibration Site
Previous Article in Journal
Response to Sheppard, C. Atoll Rim Expansion or Erosion in Diego Garcia Atoll, Indian Ocean? Comment on Hamylton, S.; East, H. A Geospatial Appraisal of Ecological and Geomorphic Change on Diego Garcia Atoll, Chagos Islands (British Indian Ocean Territory). Remote Sens. 2012, 4, 3444–3461
Remote Sens. 2014, 6(3), 2473-2493; doi:10.3390/rs6032473
Article

Mapping Crop Cycles in China Using MODIS-EVI Time Series

1,2,* , 2,* , 3
, 2
, 4
 and 5
Received: 30 December 2013; in revised form: 6 March 2014 / Accepted: 10 March 2014 / Published: 20 March 2014
View Full-Text   |   Download PDF [1551 KB, uploaded 19 June 2014]   |   Browse Figures
Abstract: As the Earth’s population continues to grow and demand for food increases, the need for improved and timely information related to the properties and dynamics of global agricultural systems is becoming increasingly important. Global land cover maps derived from satellite data provide indispensable information regarding the geographic distribution and areal extent of global croplands. However, land use information, such as cropping intensity (defined here as the number of cropping cycles per year), is not routinely available over large areas because mapping this information from remote sensing is challenging. In this study, we present a simple but efficient algorithm for automated mapping of cropping intensity based on data from NASA’s (NASA: The National Aeronautics and Space Administration) MODerate Resolution Imaging Spectroradiometer (MODIS). The proposed algorithm first applies an adaptive Savitzky-Golay filter to smooth Enhanced Vegetation Index (EVI) time series derived from MODIS surface reflectance data. It then uses an iterative moving-window methodology to identify cropping cycles from the smoothed EVI time series. Comparison of results from our algorithm with national survey data at both the provincial and prefectural level in China show that the algorithm provides estimates of gross sown area that agree well with inventory data. Accuracy assessment comparing visually interpreted time series with algorithm results for a random sample of agricultural areas in China indicates an overall accuracy of 91.0% for three classes defined based on the number of cycles observed in EVI time series. The algorithm therefore appears to provide a straightforward and efficient method for mapping cropping intensity from MODIS time series data.
Keywords: cropping intensity; phenology cycles; land cover; land use; gross sown area; planted area cropping intensity; phenology cycles; land cover; land use; gross sown area; planted area
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Export to BibTeX |
EndNote


MDPI and ACS Style

Li, L.; Friedl, M.A.; Xin, Q.; Gray, J.; Pan, Y.; Frolking, S. Mapping Crop Cycles in China Using MODIS-EVI Time Series. Remote Sens. 2014, 6, 2473-2493.

AMA Style

Li L, Friedl MA, Xin Q, Gray J, Pan Y, Frolking S. Mapping Crop Cycles in China Using MODIS-EVI Time Series. Remote Sensing. 2014; 6(3):2473-2493.

Chicago/Turabian Style

Li, Le; Friedl, Mark A.; Xin, Qinchuan; Gray, Josh; Pan, Yaozhong; Frolking, Steve. 2014. "Mapping Crop Cycles in China Using MODIS-EVI Time Series." Remote Sens. 6, no. 3: 2473-2493.


Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert