Paper
15 September 2011 The extraction of multiple cropping index of China based on NDVI time-series
Haitao Huang, Zhiqiang Gao
Author Affiliations +
Abstract
Multiple cropping index reflects the intensity of arable land been used by a certain planting system. The bond between multiple cropping index and NDVI time-series is the crop cycle rule, which determines the crop process of seeding, jointing, tasseling, ripeness and harvesting and so on. The cycle rule can be retrieved by NDVI time-series for that peaks and valleys on the time-series curve correspond to different periods of crop growth. In this paper, we aim to extract the multiple cropping index of China from NDVI time-series. Because of cloud contamination, some NDVI values are depressed. MVC (Maximum Value Composite) synthesis is used to SPOT-VGT data to remove the noise, but this method doesn't work sufficiently. In order to accurately extract the multiple cropping index, the algorithm HANTS (Harmonic Analysis of Time Series) is employed to remove the cloud contamination. The reconstructed NDVI time-series can explicitly characterize the biophysical process of planting, seedling, elongating, heading, harvesting of crops. Based on the reconstructed curve, we calculate the multiple cropping index of arable land by extracting the number of peaks of the curve for that one peak represents one season crop. This paper presents a method to extracting the multiple cropping index from remote sensing image and then the multiple cropping index of China is extracted from VEGETATION decadal composites NDVI time series of year 2000 and 2009. From the processed data, we can get the spatial distribution of tillage system of China, and then further discussion about cropping index change between the 10 years is conducted.
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Haitao Huang and Zhiqiang Gao "The extraction of multiple cropping index of China based on NDVI time-series", Proc. SPIE 8156, Remote Sensing and Modeling of Ecosystems for Sustainability VIII, 81560Z (15 September 2011); https://doi.org/10.1117/12.891895
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KEYWORDS
Remote sensing

Vegetation

Clouds

Agriculture

Composites

Contamination

Environmental sensing

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