气候变化领域集成服务门户(CCPortal): An Effective Method for Canopy Chlorophyll Content Estimation of Marsh Vegetation Based on Multiscale Remote Sensing Data

CCPortal

DOI	10.1109/JSTARS.2021.3081565
	An Effective Method for Canopy Chlorophyll Content Estimation of Marsh Vegetation Based on Multiscale Remote Sensing Data
	Lou, Peiqing; Fu, Bolin; He, Hongchang; Chen, Jianjun; Wu, Tonghua; Lin, Xingchen; Liu, Lilong; Fan, Donglin; Deng, Tengfang
通讯作者	Fu, BL (通讯作者)，Guilin Univ Technol, Coll Geomat & Geoinformat, Guilin 541004, Peoples R China. ; Wu, TH (通讯作者)，Chinese Acad Sci, Northwest Inst Ecoenvironm & Resource, State Key Lab Cryospher Sci, Cryosphere Res Stn Qinghai Tibet Plateau, Lanzhou 730000, Peoples R China.
发表日期	2021
ISSN	1939-1404
EISSN	2151-1535
起始页码	5311
结束页码	5325
卷号	14
英文摘要	High-precision canopy chlorophyll content (CCC) inversion for marsh vegetation is of great significance for marsh protection and restoration. However, it is difficult to collect the CCC measured data for marsh vegetation that matches the pixel scale of remote sensing image. This article proposes a new method based on unmanned aerial vehicle (UAV) multispectral images to obtain multiscale marsh vegetation CCC sample data. A random forest (RF) regression algorithm was used to evaluate the application performance of GF-1 wide field view (WFV), Landsat-8 Operational Land Imager (OLI), and Sentinel-2 multispectral instrument (MSI) satellite remote sensing data in marsh vegetation CCC inversion. In addition, parameter optimization of the RF regression model was used to construct an optimization algorithm suitable for marsh vegetation, and the importance of input variables was quantitatively evaluated. The results showed that the UAV multispectral images assisted in the acquisition of marsh vegetation CCC sample data, as the method expanded the number of CCC samples while quantifying the CCC sample data collection accuracy [R-2 >= 0.86, root mean square error (RMSE) <= 6.98 SPAD], which improved the CCC inversion accuracy compared with traditional sampling methods. Extracting pure vegetation pixels through binary classification reduces the uncertainty of the UAV-scale CCC inversion results. Parameter optimization of the RF regression model further improves the CCC inversion accuracy at GF-1 WFV, Landsat-8 OLI, and Sentinel-2 MSI scales. Among the three satellite remote sensing data, Sentinel-2 MSI achieved the highest CCC inversion accuracy for marsh vegetation (R-2 = 0.79, RMSE = 10.96 SPAD) due to the inclusion of red-edge bands that are more sensitive to vegetation properties. Red-edge Chlorophyll Index (Clred-edge) and Green Chlorophyll Index (Cl-green) have the highest influence on the CCC inversion accuracy among input variables.
关键词	LEAF-AREA INDEX RANDOM FOREST REGRESSION SPECTRAL REFLECTANCE BIOPHYSICAL PARAMETERS WORLDVIEW-2 IMAGERY CONTENT PREDICTION ZOIGE PLATEAU WETLAND RETRIEVAL SENTINEL-2
英文关键词	Vegetation mapping; Remote sensing; Biomedical monitoring; Unmanned aerial vehicles; Radio frequency; Spatial resolution; Earth; Canopy chlorophyll content (CCC); multiscale remote sensing data; random forest (RF) regression; scale matching; unmanned aerial vehicle (UAV)
语种	英语
WOS研究方向	Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Engineering, Electrical & Electronic ; Geography, Physical ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000660636600004
来源期刊	IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
来源机构	中国科学院西北生态环境资源研究院
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/254665
作者单位	[Lou, Peiqing; Fu, Bolin; He, Hongchang; Lin, Xingchen; Liu, Lilong; Fan, Donglin; Deng, Tengfang] Guilin Univ Technol, Coll Geomat & Geoinformat, Guilin 541004, Peoples R China; [Lou, Peiqing; Wu, Tonghua] Chinese Acad Sci, Northwest Inst Ecoenvironm & Resource, State Key Lab Cryospher Sci, Cryosphere Res Stn Qinghai Tibet Plateau, Lanzhou 730000, Peoples R China; [Lou, Peiqing] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Chen, Jianjun] Guilin Univ Technol, Coll Geomat & Geoinformat, Guangxi Key Lab Spatial Informat & Geomat, Guilin 541004, Peoples R China
推荐引用方式 GB/T 7714	Lou, Peiqing,Fu, Bolin,He, Hongchang,et al. An Effective Method for Canopy Chlorophyll Content Estimation of Marsh Vegetation Based on Multiscale Remote Sensing Data[J]. 中国科学院西北生态环境资源研究院,2021,14.
APA	Lou, Peiqing.,Fu, Bolin.,He, Hongchang.,Chen, Jianjun.,Wu, Tonghua.,...&Deng, Tengfang.(2021).An Effective Method for Canopy Chlorophyll Content Estimation of Marsh Vegetation Based on Multiscale Remote Sensing Data.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,14.
MLA	Lou, Peiqing,et al."An Effective Method for Canopy Chlorophyll Content Estimation of Marsh Vegetation Based on Multiscale Remote Sensing Data".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 14(2021).