干旱区生态环境知识资源中心(Arid): A Simplified and Robust Surface Reflectance Estimation Method (SREM) for Use over Diverse Land Surfaces Using Multi-Sensor Data

Arid

DOI	10.3390/rs11111344
	A Simplified and Robust Surface Reflectance Estimation Method (SREM) for Use over Diverse Land Surfaces Using Multi-Sensor Data
	Bilal, Muhammad 1; Nazeer, Majid 2,3; Nichol, Janet E.4; Bleiweiss, Max P.5; Qiu, Zhongfeng 1; Jaekel, Evelyn 6; Campbell, James R.7; Atique, Luqman 8; Huang, Xiaolan 1; Lolli, Simone 9,10
通讯作者	Qiu, Zhongfeng
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2019
卷号	11 期号:11
英文摘要	Surface reflectance (SR) estimation is the most critical preprocessing step for deriving geophysical parameters in multi-sensor remote sensing. Most state-of-the-art SR estimation methods, such as the vector version of the Second Simulation of the Satellite Signal in the Solar Spectrum (6SV) radiative transfer (RT) model, depend on accurate information on aerosol and atmospheric gases. In this study, a Simplified and Robust Surface Reflectance Estimation Method (SREM) based on the equations from 6SV RT model, without integrating information of aerosol particles and atmospheric gasses, is proposed and tested using Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper plus (ETM+), and Landsat 8 Operational Land Imager (OLI) data from 2000 to 2018. For evaluation purposes, (i) the SREM SR retrievals are validated against in situ SR measurements collected by Analytical Spectral Devices (ASD) from the South Dakota State University (SDSU) site, USA; (ii) cross-comparison between the SREM and Landsat spectral SR products, i.e., Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) and Landsat 8 Surface Reflectance Code (LaSRC), are conducted over 11 urban (2013-2018), 13 vegetated (2013-2018), and 11 desert/arid (2000 to 2018) sites located over different climatic zones at a global scale; (iii) the performance of the SREM spectral SR retrievals for low to high aerosol loadings is evaluated; (iv) spatio-temporal cross-comparison is conducted for six Landsat paths/rows located in Asia, Africa, Europe, and the United States of America from 2013 to 2018 to consider a large variety of land surfaces and atmospheric conditions; (v) cross-comparison is also performed for the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Soil Adjusted Vegetation Index (SAVI) calculated from both the SREM and Landsat SR data; (vi) the SREM is also applied to the Sentinel-2A and Moderate Resolution Imaging Spectrometer (MODIS) data to explore its applicability; and (vii) errors in the SR retrievals are reported using the mean bias error (MBE), root mean squared deviation (RMSD), and mean systematic error (MSE). Results depict significant and strong positive Pearson's correlation (r), small MBE, RMSD, and MSE for each spectral band against in situ ASD data and Landsat (LEDAPS and LaSRC) SR products. Consistency in SREM performance against Sentinel-2A (r = 0.994, MBE = -0.009, and RMSD = 0.014) and MODIS (r = 0.925, MBE = 0.007, and RMSD = 0.014) data suggests that SREM can be applied to other multispectral satellites data. Overall, the findings demonstrate the potential and promise of SREM for use over diverse surfaces and under varying atmospheric conditions using multi-sensor data on a global scale.
英文关键词	Landsat 8 surface reflectance LEDAPS LaSRC 6SV SREM NDVI
类型	Article
语种	英语
国家	Peoples R China ; Pakistan ; England ; USA ; Germany ; Italy
开放获取类型	Green Published, gold, Green Accepted
收录类别	SCI-E
WOS记录号	WOS:000472648000087
WOS关键词	RETRIEVAL ALGORITHM SARA ; C6 DARK TARGET ; ATMOSPHERIC CORRECTION ; AEROSOL RETRIEVAL ; SATELLITE SIGNAL ; SOLAR SPECTRUM ; VALIDATION ; PRODUCT ; 6S ; PERFORMANCE
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
来源机构	南京信息工程大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/218375
作者单位	1.Nanjing Univ Informat Sci & Technol, Sch Marine Sci, Nanjing 210044, Jiangsu, Peoples R China; 2.East China Univ Technol, Key Lab Digital Land & Resources, Nanchang 330013, Jiangxi, Peoples R China; 3.COMSATS Univ Islamabad, Dept Meteorol, Earth & Atmospher Remote Sensing Lab EARL, Islamabad 45550, Pakistan; 4.Univ Sussex, Sch Global Studies, Dept Geog, Brighton BN19RH, E Sussex, England; 5.NMSU, Dept Entomol Plant Pathol & Weed Sci, Las Cruces, NM 88003 USA; 6.Univ Leipzig, LIM, Stephanstr 3, D-04103 Leipzig, Germany; 7.Naval Res Lab, Monterey, CA 93943 USA; 8.Zhejiang Univ, Sch Earth Sci, Hangzhou 310027, Zhejiang, Peoples R China; 9.Natl Res Council CNR, Inst Methodol Environm Anal, I-85050 Tito, PZ, Italy; 10.Sci Syst & Applicat Inc, SSAI NASA, Lanham, MD 20706 USA
推荐引用方式 GB/T 7714	Bilal, Muhammad,Nazeer, Majid,Nichol, Janet E.,et al. A Simplified and Robust Surface Reflectance Estimation Method (SREM) for Use over Diverse Land Surfaces Using Multi-Sensor Data[J]. 南京信息工程大学,2019,11(11).
APA	Bilal, Muhammad.,Nazeer, Majid.,Nichol, Janet E..,Bleiweiss, Max P..,Qiu, Zhongfeng.,...&Lolli, Simone.(2019).A Simplified and Robust Surface Reflectance Estimation Method (SREM) for Use over Diverse Land Surfaces Using Multi-Sensor Data.REMOTE SENSING,11(11).
MLA	Bilal, Muhammad,et al."A Simplified and Robust Surface Reflectance Estimation Method (SREM) for Use over Diverse Land Surfaces Using Multi-Sensor Data".REMOTE SENSING 11.11(2019).