干旱区生态环境知识资源中心(Arid): A Pixel Dichotomy Coupled Linear Kernel-Driven Model for Estimating Fractional Vegetation Cover in Arid Areas From High-Spatial-Resolution Images

Arid

DOI	10.1109/TGRS.2023.3289093
	A Pixel Dichotomy Coupled Linear Kernel-Driven Model for Estimating Fractional Vegetation Cover in Arid Areas From High-Spatial-Resolution Images
	Ma, Xu; Ding, Jianli; Wang, Tiejun; Lu, Lei; Sun, Hui; Zhang, Fei; Cheng, Xiao; Nurmemet, Ilyas
通讯作者	Ma, X
来源期刊	IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
ISSN	0196-2892
EISSN	1558-0644
出版年	2023
卷号	61
英文摘要	With the increased use of high-spatial-resolution (HSR) images for vegetation monitoring in arid areas, more details of the low vegetation coverage and interference from the land background are captured in the corresponding images. From computational time and accuracy, the multiangle method (MAM) in the pixel dichotomy model is a potential algorithm to apply in arid areas, but MAM needs the multiangle vegetation index (VI) as the driver parameters. However, most HSR images are obtained in nadir mode, and the multiangle information of reflectance is difficult to obtain, which limits the estimation of multiangle VI from HSR images. To address this issue, this study used a graphical method to modify the radiation influence caused by the canopy structure and land background. We developed an inversion method of the linear kernel-driven model (KDM) and designed a random sampling method to estimate multiangle VI from HSR images. Then, we proposed a new pixel dichotomy coupled linear KDM (PDKDM), validated using simulated, field-measured, and reference data. The results showed that the FVC in arid areas estimated by PDKDM was highly consistent with true data, with root-mean-square error (RMSE) < 0.062, RMSE < 1.125, and RMSE < 0.027 for comparison with simulated, field-measured, and reference data, respectively. PDKDM addressed the issue with the previous MAMs to estimate FVC from HSR images in arid areas. This study provides a useful algorithm with high computational efficiency for producing HSR FVCs in arid areas.
英文关键词	Fractional vegetation cover (FVC) of the arid areas high-spatial-resolution (HSR) image linear kernel-driven model (KDM) modified linear pixel dichotomy model (PDM) multiangle method (MAM)
类型	Article
语种	英语
开放获取类型	Green Published, hybrid
收录类别	SCI-E
WOS记录号	WOS:001036188600021
WOS关键词	SPECTRAL MIXTURE ANALYSIS ; SEMIARID ENVIRONMENTS ; REFLECTANCE ; INDEX ; SOIL ; CLASSIFICATION ; PREDICTION ; DERIVATION ; SCATTERING ; LAYER
WOS类目	Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/396908
推荐引用方式 GB/T 7714	Ma, Xu,Ding, Jianli,Wang, Tiejun,et al. A Pixel Dichotomy Coupled Linear Kernel-Driven Model for Estimating Fractional Vegetation Cover in Arid Areas From High-Spatial-Resolution Images[J],2023,61.
APA	Ma, Xu.,Ding, Jianli.,Wang, Tiejun.,Lu, Lei.,Sun, Hui.,...&Nurmemet, Ilyas.(2023).A Pixel Dichotomy Coupled Linear Kernel-Driven Model for Estimating Fractional Vegetation Cover in Arid Areas From High-Spatial-Resolution Images.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,61.
MLA	Ma, Xu,et al."A Pixel Dichotomy Coupled Linear Kernel-Driven Model for Estimating Fractional Vegetation Cover in Arid Areas From High-Spatial-Resolution Images".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 61(2023).