Evaluation of atmospheric correction models and Landsat surface reflectance product in an urban coastal environment

Abstract

Precise atmospheric correction is important for applications where small differences in surface reflectance (SR) are significant, such as biomass estimation, crop phenology, and retrieval of water quality parameters. It also enables direct comparison between different image dates and different sensors. As a precursor to monitoring different parameters of water quality around the coastline of Hong Kong using medium-resolution sensors Landsat TM/ETM, and HJ-1A/B, this study evaluated the performance of five atmospheric correction methods. The estimated SR of the first four reflective bands of Landsat 7 ETM+ and of the identical bands of the HJ-1A/B satellites was compared with in situ multispectral radiometer (MSR) SR measurements over sand, artificial turf, grass, and water surfaces for the five atmospheric correction methodssecond simulation of the satellite signal in the solar spectrum (6S), fast line-of-sight atmospheric analysis of spectral hypercubes (FLAASH), atmospheric correction (ATCOR), dark object subtraction (DOS), and the empirical line method (ELM). Among the five methods, 6S was observed to be consistently more precise for SR estimation, with significantly less difference from the in-situ-measured SR, especially over lower reflective water surfaces. Of the two image-based methods, DOS performed well over the darker surfaces of water and artificial turf, although still inferior to 6S, while ELM worked well for grass sites as compared to the DOS and equalled the good performance of 6S over the high reflective sand surfaces.The study also evaluated the new standard Landsat SR product Landsat ecosystem disturbance adaptive processing system (LEDAPS) using the in situ measured SR data for the three land surface types -sand, artificial turf, and grass. For the highly and moderately reflecting bright sand and artificial turf, LEDAPS performed poorly, while for the darker grass site it performed better, although still inferior to 6S and ELM methods. This is probably due to the variable aerosol types and atmospheric conditions of Hong Kong, as LEDAPS was mainly compiled with reference to larger continental landmass areas.

Evaluation of Radiometric and Atmospheric Correction Algorithms for Aboveground Forest Biomass Estimation Using Landsat 5 TM Data

“…This finding is similar to that reported by Nazeer et al [68], who observed that, among five algorithms tested, 6S produced the smallest difference in field-measured surface reflectance and that obtained using the Landsat ETM sensor.…”

Quantifying Urban Land Sprawl and its Driving Forces in Northeast China from 1990 to 2015

“…To address inconsistencies among image datasets, Landsat TM images were geo-rectified against the Landsat OLI images, and then all images were projected onto the Albers equal-area conic coordinate system to accurately extract land cover area. A module in ENVI software [40], i.e., the FLAASH module was used Sustainability 2018, 10, 188 4 of 18 for atmospheric corrections of Landsat images [41]. The point layers, including the center positions of cities, counties, towns and villages, and the polygon layers of different levels of administrative boundaries from the Resource Environmental Science Data Center of Chinese Academy of Sciences (http://www.resdc.cn) and National Earth System Science Data Sharing Infrastructure, National Science and the Technology Infrastructure of China (http://www.geodata.cn) were also used in this study.…”

Automated Extraction of Urban Water Bodies from ZY‐3 Multi‐Spectral Imagery

“…We used FLAASH (Fast Line-of-Sight Atmospheric correction model Analysis of Spectral Hypercubus) for atmospheric correction [40]. All of the above steps were completed in ENVI5.2 software.…”