Spatial Exploration of Multiple Cropping Efficiency in China Based on Time Series Remote Sensing Data and Econometric Model

This study explored spatial explicit multiple cropping efficiency （MCE） of China in 2005 by coupling time series remote sensing data with an econometric model - stochastic frontier analysis （SFA）. We firstly extracted multiple cropping index （MCI） on the basis of the close relationship between crop phenologies and moderate-resolution imaging spectroradiometer （MODIS） enhanced vegetation index （EVI） value. Then, SFA model was employed to calculate MCE, by considering several indicators of meteorological conditions as inputs of multiple cropping systems and the extracted MCI was the output. The result showed that 46% of the cultivated land in China in 2005 was multiple cropped, including 39% double- cropped land and 7% triple-cropped land. Most of the multiple cropped land was distributed in the south of Great Wall. The total efficiency of multiple cropping in China was 87.61% in 2005. Southwestern China, Ganxin Region, the middle and lower reaches of Yangtze River and Huanghuaihai Plain were the four agricultural zones with the largest rooms for increasing MCI and improving MCE. Fragmental terrain, soil salinization, deficiency of water resources, and loss of labor force were the obstacles for MCE promotion in different zones. The method proposed in this paper is theoretically reliable for MCE extraction, whereas further studies are need to be done to investigate the most proper indicators of meteorological conditions as the inputs of multiple cropping systems.

Monitoring paddy rice phenology using time series MODIS data over Jiangxi Province,China

Paddy rice is one of the most important crops in the world.Accurate estimation and monitoring of paddy rice phenology is necessary for management and yield prediction.Remotely sensed time-series data are essential for estimation of crop phenology stages across large areas.Here,the paddy rice phenological stages(i.e.,transplanting,tillering,heading,and harvesting)were detected in Jiangxi Province,China.A comparison study was conducted using ground observation data from 10 agricultural meteorological stations,collected between 2006 and 2008.The phenological stages were detected using Moderate Resolution Imaging Spectroradiometer(MODIS)time-series enhanced vegetation index(EVI)data.Savitzky-Golay filter and wavelet transform were used to reduce the noise in the time-series EVI data and reconstruct the smoothed EVI time-series profile.Key phenological stages of double-cropping rice were detected using the characteristics of the smoothed EVI profile.The root mean square errors(RMSEs)for each stage were ±10 days around the ground observation data.The results suggest that Savitzky-Golay filter and wavelet transform are promising approaches for reconstructing high-quality EVI time-series data.Moreover,the phenological stages of double-cropping rice could be detected using time-series MODIS EVI data smoothed by Savitzky-Golay filter and wavelet transform.