Spatial and Temporal Distribution of Multiple Cropping Indices in the North China Plain Using a Long Remote Sensing Data Time Series.
Multiple cropping provides China with a very important system of intensive cultivation, and can effectively enhance the efficiency of farmland use while improving regional food production and security. A multiple cropping index (MCI), which represents the intensity of multiple cropping and reflects the effects of climate change on agricultural production and cropping systems, often serves as a useful parameter. Therefore, monitoring the dynamic changes in the MCI of farmland over a large area using remote sensing data is essential. For this purpose, nearly 30 years of MCIs related to dry land in the North China Plain (NCP) were efficiently extracted from remotely sensed leaf area index (LAI) data from the Global LAnd Surface Satellite (GLASS). Next, the characteristics of the spatial-temporal change in MCI were analyzed. First, 2162 typical arable sample sites were selected based on a gridded spatial sampling strategy, and then the LAI information was extracted from the samples. Second, the Savizky-Golay filter was used to smooth the LAI time-series data of the samples, and then the MCIs of the samples were obtained using a second-order difference algorithm. Finally, the geo-statistical Kriging method was employed to map the spatial distribution of the MCIs and to obtain a time-series dataset of the MCIs of dry land over the NCP. The results showed that all of the MCIs in the NCP showed an increasing trend over the entire study period and increased most rapidly from 1982 to 2002. Spatially, MCIs decreased from south to north; also, high MCIs were mainly concentrated in the relatively flat areas. In addition, the partial spatial changes of MCIs had clear geographical characteristics, with the largest change in Henan Province.
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China. zhaoyanjun_1230@sina.com.|Key Lab of Agri-information Service Technology, Ministry of Agriculture of China, Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China. zhaoyanjun_1230@sina.com.
Yan
Y
Zhao
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China. baily@radi.ac.cn.
Linyan
L
Bai
Key Lab of Agri-information Service Technology, Ministry of Agriculture of China, Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China. fengjianzhong@caas.cn.
Jianzhong
J
Feng
College of architecture and urban planning, Chongqing Jiaotong University, Chongqing 400074, China. lxsgis@163.com.
Xiaosong
X
Lin
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China. wl_6887@163.com.
Li
L
Wang
Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China. xulijun@caas.cn.
Lijun
L
Xu
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China. qiyunran123@sina.com.|Key Lab of Agri-information Service Technology, Ministry of Agriculture of China, Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China. qiyunran123@sina.com.
Qiyun
Q
Ran
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China. wkrealmadrid@hotmail.com.|Key Lab of Agri-information Service Technology, Ministry of Agriculture of China, Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China. wkrealmadrid@hotmail.com.
Kui
K
Wang
D000383:Agriculture; D002681:China; D057231:Climate Change; D017753:Ecosystem; D004784:Environmental Monitoring; D040362:Geographic Information Systems; D006801:Humans; D018515:Plant Leaves / Q000254:growth & development; D058998:Remote Sensing Technology
GLASS LAI; NCP; multiple cropping index; remote sensing; spatial and temporal changes