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Proceedings Paper

Estimating winter wheat biomass based on LANDSAT TM and MODIS data
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Paper Abstract

Biomass can indicate plant growth status, so it is an important index for plant growth monitoring. This paper focused on the methodology of estimating winter wheat biomass based on LANDSAT TM and EOS MODIS images. In order to develop the method of retrieving wheat biomass from remote sensing data, field measurements were conducted when LANDSAT satellite passed over the study region. In the experiments, five LANDSAT TM images were acquired respectively at early erecting stage, jointing stage, earing stage, flowering stage and grain-filling stage of winter wheat, and experiment sites' wheat biomass was measured at each stage. Based on the TM and MODIS images, spectral indices such as NDVI, RDVI, EVI, MSAVI, SIPI and NDWI were calculated. Then the correlation coefficients between wheat biomass and spectral indices of the experiment sites were computed. According to the correlation coefficients, the optimal spectral indices for estimating wheat biomass were determined. The best-fitting method was employed to build the relationship models between wheat biomass and the optimal spectral indices. Finally, the models were used to estimate wheat biomass based on TM and MODIS data. The RMSE of estimated biomass was not more than 66.403 g/m2.

Paper Details

Date Published: 10 September 2008
PDF: 9 pages
Proc. SPIE 7083, Remote Sensing and Modeling of Ecosystems for Sustainability V, 70831L (10 September 2008); doi: 10.1117/12.806210
Show Author Affiliations
Yansong Bao, Nanjing Univ. of Information Science & Technology (China)
Colorado State Univ. (United States)
East China Normal Univ. (China)
Wei Gao, Colorado State Univ. (United States)
East China Normal Univ. (China)
Zhiqiang Gao, Colorado State Univ. (United States)
East China Normal Univ. (China)
Institute of Geographical Sciences and Natural Resources Research (China)


Published in SPIE Proceedings Vol. 7083:
Remote Sensing and Modeling of Ecosystems for Sustainability V
Wei Gao; Hao Wang, Editor(s)

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