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EXTRACTING CROPPING INDEX VARIATIONS IN NORTHERN CHINA BASED ON 
NDVI TIME-SERIES 
ZHU Xiaolin, WU Jin, CHEN Jin * 
College of Resources Science and Technology, Beijing Normal University, Beijing, 100875, China-(zhuxiaolin, 
wujinl985, chenjin)@ires.cn 
Commission VII, WG VII/5 
KEY WORDS: Remote Sensing; Agriculture; Change Detection; Indicators; Multitemporal; Spatial 
ABSTRACT: 
Multiple cropping system, characterized by cropping index, is of significant importance to Chinese food production and security. 
Owing to the changing nature conditions and human activity, cropping index could show remarkable inter-annual fluctuations, 
which in turn reflects arable land use intensity and indicates climate change impacts on agriculture system. NDVI (Normalized 
Difference Vegetation Index) time-series is an effective indicator of vegetation status at regional scale. We developed a new method 
for extracting cropping index from NDVI time-series, and employed it to extract cropping index and its inter-annual variations of 
northern China from 1982 to 2003. The results show: remotely sensed cropping index is high consistent with statistical data at 
province scale (J? 2 =0.9213, /><0.001, slope= 1.0775), demonstrating that this method can extract cropping index effectively and 
correctly. The average cropping index of northern China increased from 87.27 in 1982 to 115.98 in 2003, with an average change 
rate 1.3275 per year(R"=0.7955, P<0.001). The areas displayed different changes of cropping index, with Huang-Huai-Hai drainage 
area experiencing a clear cropping index increase and other regions relatively less cropping index change. 
1. INTRODUCTION 
Multiple cropping system, characterized by cropping index, is 
important to Chinese food production, which feeds 22% of the 
whole world population by using 8.6% arable land of the whole 
world (Liu, 1992; Ma, 2003). Cropping index refers to the times 
of sequential crop planting in the same arable land in one year, 
usually defined as the ratio of the total seeding area to the 
arable land area (Liu, 1993), which reflects the using efficiency 
of soil, water, light and other natural resources. Owing to the 
changing nature conditions and human activity, cropping index 
could show remarkable inter-annual fluctuations, which in turn 
reflects the using intensity of arable land and indicates the 
impacts of climate change on agriculture system. It is desired to 
extract the cropping index and its change information by 
remotely sensed data for agriculture sustainable development 
and assessing the impacts of climate change on agriculture 
system. 
Traditionally, cropping index is calculated by statistical data at 
local administration unit, which is time-lagged, labor 
consuming, poor in creditability, and lack of details of spatial 
distribution. On the other hand, remote sensing technology has 
been widely applied to agriculture and crop growing status 
monitoring (Harris, 2003; Seelan et al., 2003). The 
development of remote sensing technology makes it possible to 
obtain actual cropping index information efficiently and 
reliably. Peak of the NDVI (Normalized Difference Vegetation 
Index) time series curve could reveal the time when above 
ground biomass of crops reaches the maximum and tone of the 
curve fluctuates with the crops growing processes such as 
sowing, seeding, heading, ripeness, and harvesting within a year. 
Therefore, the cropping index could be defined as the number 
of peaks of NDVI time-series per year (Fan and Wu, 2004). 
However, since NDVI data are easy to suffer from cloud and the 
other poor atmospheric conditions, the curve of NDVI time 
series may include much noise, turning out to be small peaks 
and valleys in one cycle, which makes it more difficult to 
extract the cropping index effectively. Some methods have been 
developed to extract cropping index by reconstructing high 
quality NDVI curve, such as Harmonic Analysis of Time Series 
(HANTS) (Fan and Wu, 2004) and so on (Yan et al., 2005). 
Nevertheless, these methods depend too much on prior 
knowledge, and can not get rid of all the disturbing noise 
effectively. 
This study aimed to (1) develop a new method to eliminate the 
atmospheric effects and other contaminations and improve 
accuracy of cropping index extracting form NDVI time-series 
and (2) extract the cropping index and the Cropping Index 
Variation (VCI) of 17 provinces of northern China from 1982 to 
2003 by using 8km 15-day Maximum Value Composite 
NOAA/AVHRR GIMMS NDVI time-series data. 
2. STUDY AREA AND DATA 
The cropping index exaction was conducted on the 17 provinces 
of northern China (Figure. 1), of which the arable land area was 
identified according to Chinese vegetation type map. 
The Global Inventory Monitoring and Modelling Study 
(GIMMS) AVHRR 8 km resolution NDVI 15-day composite 
dataset covering the period of 1982 to 2003 (Tucker et al., 2005. 
Available at http://gimms.gsfc.nasa.gov/) was used to exact the 
cropping index. This dataset was produced by Maximum Value 
Composition (MVC) technique which selects the highest value 
of NDVI during every 15-day period for each pixel to remove 
* Corresponding author: Chen Jin, email: chenjin@ires.cn