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Journal of Applied Remote Sensing

Synergistic retrieval model of forest biomass using the integration of optical and microwave remote sensing
Author(s): Linjing Zhang; Zhenfeng Shao; Chunyuan Diao
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Paper Abstract

Accurate estimation of forest aboveground biomass is crucial for monitoring ecosystem responses to environmental change. Passive optical and active microwave remote sensing plays an important role in retrieving the forest biomass. However, optical spectral reflectance gets saturated in the relatively high-density vegetation area and microwave backscattering is largely influenced by the soil underneath when the vegetation coverage is relatively low. Both of these conditions affect the biomass retrieval accuracy. A synergistic biomass retrieval model through the integration of optical (PROSAIL) and microwave (MIMICS) radiative transfer models was put forward. The proposed model unified the vegetation and soil conditions of PROSAIL and MIMICS models, and determined the optical-alone model, microwave-alone model, and the contributions of key optical and microwave factors to biomass retrieval with the simulated database. The database consisted of the optical bidirectional reflectance and full polarization microwave backscattering of the broad-leaved forest canopy under various conditions. The synergistic model was verified by comparing with the ground measurements and the results of the optical-alone and microwave-alone models. The results indicated that the proposed synergistic retrieval model was more effective than the optical-alone or microwave-alone model, and showed considerable potential in forest aboveground biomass retrieval by integrating passive optical and active microwave remote sensing.

Paper Details

Date Published: 26 March 2015
PDF: 18 pages
J. Appl. Remote Sens. 9(1) 096069 doi: 10.1117/1.JRS.9.096069
Published in: Journal of Applied Remote Sensing Volume 9, Issue 1
Show Author Affiliations
Linjing Zhang, Wuhan Univ. (China)
Collaborative Innovation Ctr. for Geospatial Technology (China)
Zhenfeng Shao, Wuhan Univ. (China)
Collaborative Innovation Ctr. for Geospatial Technology (China)
Chunyuan Diao, Univ. at Buffalo (United States)


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