气候变化领域集成服务门户(CCPortal): Biomass Estimation with GNSS Reflectometry Using a Deep Learning Retrieval Model

CCPortal

DOI	10.3390/rs16071125
	Biomass Estimation with GNSS Reflectometry Using a Deep Learning Retrieval Model
	Pilikos, Georgios; Clarizia, Maria Paola; Floury, Nicolas
发表日期	2024
EISSN	2072-4292
起始页码	16
结束页码	7
卷号	16 期号:7
英文摘要	GNSS Reflectometry (GNSS-R) is an emerging technique for the remote sensing of the environment. Traditional GNSS-R bio-geophysical parameter retrieval algorithms and deep learning models utilize observables derived from only the peak power of the delay-Doppler maps (DDMs), discarding the rest. This reduces the data available, which potentially hinders estimation accuracy. In addition, reflections from water bodies dominate the signal amplitude, and using only the peak power in those areas is challenging. Motivated by all the above, we propose a novel deep learning retrieval model for biomass estimation that uses the full DDM of surface reflectivity. Experiments using CYGNSS data have illustrated the improvements achieved when using the full DDM of surface reflectivity. Our proposed model was able to estimate biomass, trained using the ESA Climate Change Initiative (CCI) biomass map, outperforming the model that used peak reflectivity. Global and regional analysis is provided along with an illustration of how biomass estimation is achieved when using the full DDM around water bodies. GNSS-R could become an efficient method for biomass monitoring with fast revisit times. However, an elaborate calibration is necessary for the retrieval models, to associate GNSS-R data with bio-geophysical parameters on the ground. To achieve this, further developments with improved training data are required, as well as work using in situ validation data. Nevertheless, using GNSS-R and deep learning retrieval models has the potential to enable fast and persistent biomass monitoring and help us better understand our changing climate.
英文关键词	biomass; GNSS-R; deep learning; delay-Doppler map
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:001200822300001
来源期刊	REMOTE SENSING
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/298675
作者单位	European Space Agency
推荐引用方式 GB/T 7714	Pilikos, Georgios,Clarizia, Maria Paola,Floury, Nicolas. Biomass Estimation with GNSS Reflectometry Using a Deep Learning Retrieval Model[J],2024,16(7).
APA	Pilikos, Georgios,Clarizia, Maria Paola,&Floury, Nicolas.(2024).Biomass Estimation with GNSS Reflectometry Using a Deep Learning Retrieval Model.REMOTE SENSING,16(7).
MLA	Pilikos, Georgios,et al."Biomass Estimation with GNSS Reflectometry Using a Deep Learning Retrieval Model".REMOTE SENSING 16.7(2024).