Mapping Crop Cycles in China Using MODIS-EVI Time Series

Remote Sensing

Le Li⁴  Mark A. Friedl³  Qinchuan Xin²  Josh Gray³  Yaozhong Pan¹ 

[1] State Key Laboratory of Earth Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China; E-Mail:;Ministry of Education Key Laboratory for Earth System Modeling, Tsinghua University, Beijing 100084, China; E-Mail:;Department of Earth and Environment, Boston University, Boston, MA 02215, USA; E-Mail:;State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China

关键词: cropping intensity;    phenology cycles;    land cover;    land use;    gross sown area;    planted area;   

DOI  :  10.3390/rs6032473

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland