Lingjia Gu, Kai Zhao, and Bormin Huang

Abstract- Passive microwave sensors have better capability of penetrating forest layers to obtain more information from forest canopy and ground surface. For forest management, it is useful to study passive microwave signals from forests. Passive microwave sensors can detect signals from needleleaf, broadleaf, and mixed forests. The observed brightness temperature of a mixed forest can be approximated by a linear combination of the needleleaf and broadleaf brightness temperatures weighted by their respective abundances. For a mixed forest observed by an N-band microwave radiometer with horizontal and vertical polarizations, there are 2N observed   brightness temperatures.  It is desirable to infer4 N +2 unknowns: 2 N broadleaf brightness   temperatures, 2 N needleleaf brightness temperatures, 1 broadleaf  abundance, and 1 needleleaf abundance. This is a challenging underdeter- mined problem. In this paper, we devise a novel method that com- bines microwave unmixing with video segmentation for inferring broadleaf and needleleaf brightness temperatures and abundances from mixed forests. We propose an improved Otsu method for video segmentation to infer broadleaf and needleleaf abundances. The brightness temperatures of needleleaf and broadleaf trees can then be solved by the nonnegative least squares solution. For our mixed forest unmixing problem, it turns out that the ordinary least squares solution yields the desired positive brightness temperatures. The  experimental   results  demonstrate that the proposed method is able to unmix broadleaf and necdleleaf brightness temperatures and abundances well. The absolute  differences betweenthe reconstructed and  observed brightness temperatures of themixed forest are well within 1K.

Index Terms—Abundance, brightness temperature, broadleaf, mixed forest, needleleaf, pa,ssive microwave, unmixing method.