A method of estimating crop acerage in large scale by unmixing of MODIS data

Wenbo Xu; Yichen Tian; Jun Qing; Jianxi Huang; Yong Zhang

doi:10.1117/12.651855

2 December 2005 A method of estimating crop acerage in large scale by unmixing of MODIS data

Wenbo Xu, Yichen Tian, Jun Qing, Jianxi Huang, Yong Zhang

Proceedings Volume 6045, MIPPR 2005: Geospatial Information, Data Mining, and Applications; 604538 (2005) https://doi.org/10.1117/12.651855
Event: MIPPR 2005 SAR and Multispectral Image Processing, 2005, Wuhan, China

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.

Citation Download Citation

Wenbo Xu, Yichen Tian, Jun Qing, Jianxi Huang, and Yong Zhang "A method of estimating crop acerage in large scale by unmixing of MODIS data", Proc. SPIE 6045, MIPPR 2005: Geospatial Information, Data Mining, and Applications, 604538 (2 December 2005); https://doi.org/10.1117/12.651855

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KEYWORDS

MODIS

Earth observing sensors

Reflectivity

Landsat

Remote sensing

Agriculture

Image classification

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