Optimal Atmospheric Correction for Above-Ground Forest Biomass Estimation with the ETM+ Remote Sensor

Abstract

The reflectance of the Earth’s surface is significantly influenced by atmospheric conditions such as water vapor content and aerosols. Particularly, the absorption and scattering effects become stronger when the target features are non-bright objects, such as in aqueous or vegetated areas. For any remote-sensing approach, atmospheric correction is thus required to minimize those effects and to convert digital number (DN) values to surface reflectance. The main aim of this study was to test the three most popular atmospheric correction models, namely (1) Dark Object Subtraction (DOS); (2) Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) and (3) the Second Simulation of Satellite Signal in the Solar Spectrum (6S) and compare them with Top of Atmospheric (TOA) reflectance. By using the k-Nearest Neighbor (kNN) algorithm, a series of experiments were conducted for above-ground forest biomass (AGB) estimations of the Gongju and Sejong region of South Korea, in order to check the effectiveness of atmospheric correction methods for Landsat ETM+. Overall, in the forest biomass estimation, the 6S model showed the bestRMSE’s, followed by FLAASH, DOS and TOA. In addition, a significant improvement of RMSE by 6S was found with images when the study site had higher total water vapor and temperature levels. Moreover, we also tested the sensitivity of the atmospheric correction methods to each of the Landsat ETM+ bands. The results confirmed that 6S dominates the other methods, especially in the infrared wavelengths covering the pivotal bands for forest applications. Finally, we suggest that the 6S model, integrating water vapor and aerosol optical depth derived from MODIS products, is better suited for AGB estimation based on optical remote-sensing data, especially when using satellite images acquired in the summer during full canopy development.

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

“…It should be taken into account that the GCV criterion considers both the residual error and also the model complexity, penalizing those models with a high number of parameters. The results are similar to those obtained by Nguyen et al [71] in a study carried out in South Korea in which it was confirmed that the 6S algorithm fitted by the kNN technique (RMSE = 22.5 Mg¨ha´1) produced better results for estimating AGB than the other algorithms tested (DOS, FLAASH and ToA), especially for Landsat ETM bands in the infrared region. Furthermore, various studies have concluded that the MARS technique is a flexible method that yields robust predictions [72,73].…”

Atmospheric and Radiometric Correction Algorithms for the Multitemporal Assessment of Grasslands Productivity

“…In comparison with other multitemporal studies [23,49,50], we have tested different CMs with a change of platform from Landsat 5 to Landsat 8 in grassland areas. In the site Eden, we found that the deviations between the estimated and measured biomass were high for 2013 and 2014.…”

“…Martins et al [47] compared the 6S, ACOLITE, and Sen2Cor methods applied to the new platform Sentinel 2-MSI. However, these studies lack the multitemporal component and only a few studies [48][49][50] have been developed to compare the methods of radiometric correction on different dates.…”

Comparing Atmospheric Correction Methods for Landsat Oli Data

“…Lee et al (2000) who examined Landsat 5 Data with FLAASH, Foster and 6S models found that FLAASH was the best model. Nguyen et al (2015) who reviewed QUAC, FLAASH and 6S models with Landsat 5 Data stated that 6S was the best model which resembled on ground condition. Afterall, this research aimed to analyze a suitable atmospheric correction model for Landsat OLI data in order to retrieve Indonesian land cover information which corresponded to actual coverage.…”