气候变化领域集成服务门户(CCPortal): Integration of allometric equations in the water cloud model towards an improved retrieval of forest stem volume with L-band SAR data in Sweden

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DOI	10.1016/j.rse.2020.112235
	Integration of allometric equations in the water cloud model towards an improved retrieval of forest stem volume with L-band SAR data in Sweden
	Santoro M.; Cartus O.; Fransson J.E.S.
发表日期	2021
ISSN	00344257
卷号	253
英文摘要	Much attention is paid to the estimation of forest biomass-related variables (stem volume and above-ground biomass) with synthetic aperture radar (SAR) backscatter images because of the increasing number of sensors in space providing global and repeated coverage and the sensitivity of the backscattered intensity to forest properties. One of the most popular models used to estimate a biomass-related variable from SAR backscatter observations is the Water Cloud Model (WCM) because of its simplicity allowing for a straightforward retrieval. Nonetheless, a common feature of these estimates is the tendency to over- or underestimate specific ranges due to simplifying assumptions in the model. In this study, the WCM has been revisited by exploring pathways for a physically-based, Light Detection and Ranging (LiDAR)-aided, model parameterization at larger scale with the overall aim to reduce systematic retrieval errors associated with empirical assumptions in the model. The study was undertaken in Sweden where repeated observations of backscatter by the Advanced Land Observing Satellite (ALOS) Phased Array-type L-band Synthetic Aperture Radar (PALSAR) were available. The integration was prototyped in Sweden thanks to detailed allometries relating forest variables in the WCM. These were derived from spatially dense estimates of canopy density and vegetation height from observations by the Ice, Cloud and land Elevation Satellite (ICESat) Geoscience Laser Altimeter System (GLAS) and measurements of height and stem volume from the Swedish National Forest Inventory (NFI). The SAR backscatter predicted by the revisited WCM was in strong agreement with the observations. When evaluated against stem volumes estimated from the NFI data, the SAR-based stem volumes presented strong dispersion at the pixel level. Average stem volume at the level of five or more pixels, i.e., for an area larger than 0.3 ha, were instead unbiased and similar to the average values obtained from the NFI data (relative root mean square error: 21.4%, estimation bias: 0.9 m3/ha and coefficient of determination: 0.67). This study demonstrates that the integration of allometries in the WCM effectively reduces estimation errors. The method here prototyped in Sweden qualifies to provide large-scale estimates of biomass-related variables using multiple observations of L-band backscatter with potential application worldwide. © 2020 Elsevier Inc.
英文关键词	Allometry; ALOS PALSAR; Backscatter; Biomass; Forest; ICESat GLAS; LiDAR; SAR; Stem volume; Sweden
语种	英语
scopus关键词	Aneroid altimeters; Backscattering; Biomass; Cloud computing; Integration; Mean square error; Optical radar; Pixels; Radar imaging; Radio altimeters; Space-based radar; Synthetic aperture radar; Systematic errors; Advanced land observing satellites; Backscattered intensity; Coefficient of determination; Geoscience laser altimeter systems; Ice , cloud and land elevation satellites; Light detection and ranging; National forest inventories; Phased array type l-band synthetic aperture radars; Forestry; aboveground biomass; backscatter; canopy architecture; forest ecosystem; GLAS; lidar; parameterization; population density; synthetic aperture radar; Sweden
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179013
作者单位	GAMMA Remote Sensing, Gümligen, 3073, Switzerland; Department of Forest Resource Management, Swedish University of Agricultural Sciences, Umeå, 901 83, Sweden
推荐引用方式 GB/T 7714	Santoro M.,Cartus O.,Fransson J.E.S.. Integration of allometric equations in the water cloud model towards an improved retrieval of forest stem volume with L-band SAR data in Sweden[J],2021,253.
APA	Santoro M.,Cartus O.,&Fransson J.E.S..(2021).Integration of allometric equations in the water cloud model towards an improved retrieval of forest stem volume with L-band SAR data in Sweden.Remote Sensing of Environment,253.
MLA	Santoro M.,et al."Integration of allometric equations in the water cloud model towards an improved retrieval of forest stem volume with L-band SAR data in Sweden".Remote Sensing of Environment 253(2021).