Multiscale approach for fusing leaf area index estimates from multiple sensors

Zhiqiang Xiao; Jindi Wang; Huawei Wan

doi:10.1117/12.748313

14 November 2007 Multiscale approach for fusing leaf area index estimates from multiple sensors

Zhiqiang Xiao, Jindi Wang, Huawei Wan

Proceedings Volume 6790, MIPPR 2007: Remote Sensing and GIS Data Processing and Applications; and Innovative Multispectral Technology and Applications; 679013 (2007) https://doi.org/10.1117/12.748313
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China

Abstract

With the rapid development of the technique of remote sensing, many vegetation biophysical variables are estimated from remote sensing data. However, the biophysical variable products are limited to certain resolutions and some products are incomplete in space. Therefore these products can not meet the needs of many operational applications. Therefore, more and more attentions have been paid to fusing or assimilating multi-sensor and multi-temporal data to improve the biophysical variable products with precision as high as possible and high temporal resolution and variable spatial resolutions in recent years. In this paper, the multiscale Kalman filter (MKF) is introduced to fuse the biophysical products from different kinds of remote sensing data. The multiscale Kalman filter allows us to model explicitly and very efficiently the spatial dependence and scaling properties of remote sensing data, and can be used to produce optimal estimation of biophysical variables at any desired spatial scale given uncertain and sparse observations at different scales. Taking leaf area index as an example, our method is tested by fusing LAI products from MODIS and Landsat ETM+, and the results show that the method can be used to fuse effectively different biophysical variables inverted from different sensors.

Citation Download Citation

Zhiqiang Xiao, Jindi Wang, and Huawei Wan "Multiscale approach for fusing leaf area index estimates from multiple sensors", Proc. SPIE 6790, MIPPR 2007: Remote Sensing and GIS Data Processing and Applications; and Innovative Multispectral Technology and Applications, 679013 (14 November 2007); https://doi.org/10.1117/12.748313

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