@inproceedings{6ef34daf13934d7eb46e0befe9973cc8,
title = "Water Cloud Model for above Ground Biomass Retrival in Savanna Woodlands",
abstract = "Statistical relationship between above ground biomass and radar backscatter described by an empirical model, even it is quite strong, failed to include the physics of scattering from the target area, which hinders widespread implementation of this model for biomass retrieval. To account for soil moisture in woody biomass estimation, Water Cloud Model, a semi-empirical model based on physics of radar scattering both from vegetation and soil moisture is suggested. However, application of WCM is hindered by the lack of a generally agreed theoretical background allowing definition of model descriptors and parameters. In this study, we analyse the effectiveness of WCM to improve the accuracy of biomass retrieval by proposing a suitable WCM formulation for biomass retrieval using L-band SAR at HV polarisation in miombo woodlands. Results indicated that WCM reduce biomass estimation uncertainties by correcting soil moisture effects compared to linear and exponential statistical models.",
keywords = "Biomass, L-band SAR, Savanna, Water Cloud Model",
author = "Yaqing Gou and Casey Ryan and Heiko Balzter",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 ; Conference date: 28-07-2019 Through 02-08-2019",
year = "2019",
month = jul,
doi = "10.1109/IGARSS.2019.8898012",
language = "English",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "6011--6014",
booktitle = "2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings",
address = "United States",
}