A Comparison of Four Common Atmospheric Correction Methods

Mahiny, Abdolrassoul Salman; Turner, B. J.

doi:10.14358/pers.73.4.361

Cited by 124 publications

(60 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…However, the bands in the visible and near-infrared regions indicated a statistically-significant difference in the configuration of the groups of algorithms. These findings are consistent with those reported by Mahiny and Turner [69], who compared the first four bands of the Landsat TM sensor under five radiometric correction methods (two relative approaches: pseudo invariant features (PIF) and radiometric control sets (RCS); and three absolute approaches: COST, 6S and ToA), showing that in most cases, the four bands produced significantly different results.…”

Section: Discussionsupporting

confidence: 90%

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

et al. 2016

View full text Add to dashboard Cite

Solar radiation is affected by absorption and emission phenomena during its downward trajectory from the Sun to the Earth's surface and during the upward trajectory detected by satellite sensors. This leads to distortion of the ground radiometric properties (reflectance) recorded by satellite images, used in this study to estimate aboveground forest biomass (AGB). Atmospherically-corrected remote sensing data can be used to estimate AGB on a global scale and with moderate effort. The objective of this study was to evaluate four atmospheric correction algorithms (for surface reflectance), ATCOR2 (Atmospheric Correction for Flat Terrain), COST (Cosine of the Sun Zenith Angle), FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes) and 6S (Second Simulation of Satellite Signal in the Solar), and one radiometric correction algorithm (for reflectance at the sensor) ToA (Apparent Reflectance at the Top of Atmosphere) to estimate AGB in temperate forest in the northeast of the state of Durango, Mexico. The AGB was estimated from Landsat 5 TM imagery and ancillary information from a digital elevation model (DEM) using the non-parametric multivariate adaptive regression splines (MARS) technique. Field reference data for the model training were collected by systematic sampling of 99 permanent forest growth and soil research sites (SPIFyS) established during the winter of 2011. The following predictor variables were identified in the MARS model: Band 7, Band 5, slope (β), Wetness Index (WI), NDVI and MSAVI2. After cross-validation, 6S was found to be the optimal model for estimating AGB (R 2 = 0.71 and RMSE = 33.5 Mg¨ha´1; 37.61% of the average stand biomass). We conclude that atmospheric and radiometric correction of satellite images can be used along with non-parametric techniques to estimate AGB with acceptable accuracy.

show abstract

Section: Discussionsupporting

confidence: 90%

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

et al. 2016

View full text Add to dashboard Cite

show abstract

“…Raw at-sensor data has been modified by a combination of effects that include surface conditions, atmospheric effects, topographic effects and sensor characteristics [22,23]. These effects obscure the true surface reflectance properties and diminish the capacity to extract accurate quantitative information from remotely sensed imagery.…”

Section: Uav Multispectral Sensorsmentioning

confidence: 99%

Sensor Correction of a 6-Band Multispectral Imaging Sensor for UAV Remote Sensing

2012

View full text Add to dashboard Cite

Unmanned aerial vehicles (UAVs) represent a quickly evolving technology, broadening the availability of remote sensing tools to small-scale research groups across a variety of scientific fields. Development of UAV platforms requires broad technical skills covering platform development, data post-processing, and image analysis. UAV development is constrained by a need to balance technological accessibility, flexibility in application and quality in image data. In this study, the quality of UAV imagery acquired by a miniature 6-band multispectral imaging sensor was improved through the application of practical image-based sensor correction techniques. Three major components of sensor correction were focused upon: noise reduction, sensor-based modification of incoming radiance, and lens distortion. Sensor noise was reduced through the use of dark offset imagery. Sensor modifications through the effects of filter transmission rates, the relative monochromatic efficiency of the sensor and the effects of vignetting were removed through a combination of spatially/spectrally dependent correction factors. Lens distortion was reduced through the implementation of the Brown-Conrady model. Data post-processing serves dual roles in data quality improvement, and the identification of platform limitations and sensor idiosyncrasies. The proposed corrections improve the quality of the raw multispectral imagery, facilitating subsequent quantitative image analysis.

show abstract

“…This method uses only the cosine of the sun zenith angle (cos(TZ)) as an acceptable parameter for approximating the effects of atmospheric gas absorption and Rayleigh scattering, hence the name COST [44]. Similar to Landsat TM sensors, the COST method is effective for HJ-1A/1B satellite CCD images.…”

Section: The Cost (Cosine Of the Sun Zenith Angle (Cos(tz))) Modelmentioning

confidence: 99%

Assessment of Total Suspended Sediment Distribution under Varying Tidal Conditions in Deep Bay: Initial Results from HJ-1A/1B Satellite CCD Images

et al. 2014

View full text Add to dashboard Cite

Using Deep Bay in China as an example, an effective method for the retrieval of total suspended sediment (TSS) concentration using HJ-1A/1B satellite images is proposed. The factors driving the variation of the TSS spatial distribution are also discussed. Two field surveys, conducted on August 29 and October 26, 2012, showed that there was a strong linear relationship (R 2 = 0.9623) between field-surveyed OBS (optical backscatter) measurements (5-31NTU) and laboratory-analyzed TSS concentrations (9.89-35.58 mg/L). The COST image-based atmospheric correction procedure and the pseudo-invariant features (PIF) method were combined to remove the atmospheric effects from the total radiance measurements obtained with different CCDs onboard the HJ-1A/1B satellites. Then, a simple and practical retrieval model was established based on the relationship between the satellite-corrected reflectance band ratio of band 3 and band 2 (Rrs3/Rrs2) and in-situ TSS measurements. The R 2 of the regression relationship was 0.807, and the mean relative error (MRE) was 12.78%, as determined through in-situ data validation. Finally, the influences of tide cycles, wind factors (direction and speed) and other factors on the OPEN ACCESSRemote Sens. 2014, 6 9912 variation of the TSS spatial pattern observed from HJ-1A/1B satellite images from September through November of 2008 are discussed. The results show that HJ-1A/1B satellite CCD images can be used to estimate TSS concentrations under different tides in the study area over synoptic scales without using simultaneous in-situ atmospheric parameters and spectrum data. These findings provide strong informational support for numerical simulation studies on the combined influence of tide cycles and other associated hydrologic elements in Deep Bay.

show abstract

A Comparison of Four Common Atmospheric Correction Methods

Cited by 124 publications

References 14 publications

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

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

Sensor Correction of a 6-Band Multispectral Imaging Sensor for UAV Remote Sensing

Assessment of Total Suspended Sediment Distribution under Varying Tidal Conditions in Deep Bay: Initial Results from HJ-1A/1B Satellite CCD Images

Contact Info

Product

Resources

About