{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T15:18:55Z","timestamp":1783005535350,"version":"3.54.5"},"reference-count":55,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2019,6,4]],"date-time":"2019-06-04T00:00:00Z","timestamp":1559606400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Special Project of Jiangsu Distinguished Professor","award":["1421061801003"],"award-info":[{"award-number":["1421061801003"]}]},{"name":"the Startup Foundation for Introduction Talent of NUIST","award":["2017r107"],"award-info":[{"award-number":["2017r107"]}]},{"name":"the National Key Research and Development Program of China by Jiangsu Chair Professorship","award":["2016YFC1400901"],"award-info":[{"award-number":["2016YFC1400901"]}]},{"name":"Jiangsu Provincial Programs for Marine Science and Technology Innovation","award":["HY2017-5"],"award-info":[{"award-number":["HY2017-5"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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\u20132018), 13 vegetated (2013\u20132018), 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\u2019s 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 = \u22120.009, 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.<\/jats:p>","DOI":"10.3390\/rs11111344","type":"journal-article","created":{"date-parts":[[2019,6,5]],"date-time":"2019-06-05T09:37:58Z","timestamp":1559727478000},"page":"1344","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":77,"title":["A Simplified and Robust Surface Reflectance Estimation Method (SREM) for Use over Diverse Land Surfaces Using Multi-Sensor Data"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1022-3999","authenticated-orcid":false,"given":"Muhammad","family":"Bilal","sequence":"first","affiliation":[{"name":"School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7631-1599","authenticated-orcid":false,"given":"Majid","family":"Nazeer","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Land and Resources, East China University of Technology, Nanchang 330013, China"},{"name":"Earth and Atmospheric Remote Sensing Lab (EARL), Department of Meteorology, COMSATS University Islamabad, Islamabad 45550, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4015-893X","authenticated-orcid":false,"given":"Janet E.","family":"Nichol","sequence":"additional","affiliation":[{"name":"Department of Geography, School of Global Studies, University of Sussex, Brighton BN19RH, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0723-6350","authenticated-orcid":false,"given":"Max P.","family":"Bleiweiss","sequence":"additional","affiliation":[{"name":"Department of Entomology, Plant Pathology and Weed Science, New Mexico State University (NMSU), Las Cruces, NM 88003, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1167-3991","authenticated-orcid":false,"given":"Zhongfeng","family":"Qiu","sequence":"additional","affiliation":[{"name":"School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Evelyn","family":"J\u00e4kel","sequence":"additional","affiliation":[{"name":"Leipzig Institute for Meteorology (LIM), University of Leipzig, Stephanstr. 3, 04103 Leipzig, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0251-4550","authenticated-orcid":false,"given":"James R.","family":"Campbell","sequence":"additional","affiliation":[{"name":"Naval Research Laboratory, Monterey, CA 93943, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luqman","family":"Atique","sequence":"additional","affiliation":[{"name":"School of Earth Sciences, Zhejiang University, Hangzhou 310027, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaolan","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6111-152X","authenticated-orcid":false,"given":"Simone","family":"Lolli","sequence":"additional","affiliation":[{"name":"Institute of Methodologies for Environmental Analysis, National Research Council (CNR), 85050 Tito Scalo (PZ), Italy"},{"name":"SSAI-NASA, Science Systems and Applications Inc, Lanham, MD 20706, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,6,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the radiometric and biophysical performance of the MODIS vegetation indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. 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