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Proceedings Paper

A method of estimating crop acerage in large scale by unmixing of MODIS data
Author(s): Wenbo Xu; Yichen Tian; Jun Qing; Jianxi Huang; Yong Zhang
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Paper Abstract

Crop acreage monitoring is basic information necessary for wise management of plant natural resources. Recent developments in remote sensing technologies have created promising opportunities for improving agricultural statistics systems. The Moderate Resolution Imaging Spectroradiometer (MODIS) is one detector board on Terra's (EOS-AM1), which was lunched on December 18, 1999 by NASA. It offers a unique combination of spectral, temporal, and spatial resolution compared to previous global sensors, making it a good candidate for large-scale crop acreage estimating. However, because of subpixel heterogeneity, the application of traditional hard classification approaches to MODIS data may result in significant errors in crop area estimation, especially in China. This paper developed and tested an unmixing approach with MODIS data that estimates subpixel fractions of crop area based on the temporal signature of reflectance throughout the growing season. A zone that can get LANDSAT/TM data was chosen to be train dataset in this method. The paper assumes that the crop area estimating from LANDSAT/TM data is correct; in the training zone the crop area based on MODIS data can get from the classification result of LANDSAT/TM data. Then we can extend the result to a large-scale; finally we compare the result to national statistic data. The results of this study demonstrate the importance of subpixel heterogeneity in cropland systems, and the potential of temporal unmixing to provide accurate and rapid assessments of crop distributions using MODIS data.

Paper Details

Date Published: 2 December 2005
PDF: 7 pages
Proc. SPIE 6045, MIPPR 2005: Geospatial Information, Data Mining, and Applications, 604538 (2 December 2005); doi: 10.1117/12.651855
Show Author Affiliations
Wenbo Xu, Univ. of Electronic Science and Technology of China (China)
Yichen Tian, Institute of Remote Sensing Applications, CAS (China)
Jun Qing, Beijing Normal Univ. (China)
Jianxi Huang, Institute of Remote Sensing Applications, CAS (China)
Yong Zhang, China Academy of Transportation Sciences (China)


Published in SPIE Proceedings Vol. 6045:
MIPPR 2005: Geospatial Information, Data Mining, and Applications

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