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Impact of Atmospheric Correction Methods Parametrization on Soil Organic Carbon Estimation Based on Hyperion Hyperspectral Data

Mruthyunjaya, P and Shetty, A and Umesh, P and Gomez, C (2022) Impact of Atmospheric Correction Methods Parametrization on Soil Organic Carbon Estimation Based on Hyperion Hyperspectral Data. In: Remote Sensing, 14 (20).

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Official URL: https://doi.org/10.3390/rs14205117


Visible Near infrared and Shortwave Infrared (VNIR/SWIR, 400–2500 nm) remote sensing data is becoming a tool for topsoil properties mapping, bringing spatial information for environmental modeling and land use management. These topsoil properties estimates are based on regression models, linking a key topsoil property to VNIR/SWIR reflectance data. Therefore, the regression model’s performances depend on the quality of both topsoil property analysis (measured on laboratory over-ground soil samples) and Bottom-of-Atmosphere (BOA) VNIR/SWIR reflectance which are retrieved from Top-Of-Atmosphere radiance using atmospheric correction (AC) methods. This paper examines the sensitivity of soil organic carbon (SOC) estimation to BOA images depending on two parameters used in AC methods: aerosol optical depth (AOD) in the FLAASH (Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes) method and water vapor (WV) in the ATCOR (ATmospheric CORrection) method. This work was based on Earth Observing-1 Hyperion Hyperspectral data acquired over a cultivated area in Australia in 2006. Hyperion radiance data were converted to BOA reflectance using seven values of AOD (from 0.2 to 1.4) and six values of WV (from 0.4 to 5 cm), in FLAASH and ATCOR, respectively. Then a Partial Least Squares regression (PLSR) model was built from each Hyperion BOA data to estimate SOC over bare soil pixels. This study demonstrated that the PLSR models were insensitive to the AOD variation used in the FLAASH method, with R2cv and RMSEcv of 0.79 and 0.4%, respectively. The PLSR models were slightly sensitive to the WV variation used in the ATCOR method, with R2cv ranging from 0.72 to 0.79 and RMSEcv ranging from 0.41 to 0.47. Regardless of the AOD values, the PLSR model based on the best parametrization of the ATCOR model provided similar SOC prediction accuracy to PLSR models using the FLAASH method. Variation in AOD using the FLAASH method did not impact the identification of bare soil pixels coverage which corresponded to 82.35% of the study area, while a variation in WV using the ATCOR method provided a variation of bare soil pixels coverage from 75.04 to 84.04%. Therefore, this work recommends (1) the use of the FLAASH AC method to provide BOA reflectance values from Earth Observing-1 Hyperion Hyperspectral data before SOC mapping or (2) a careful selection of the WV parameter when using ATCOR.

Item Type: Journal Article
Publication: Remote Sensing
Publisher: MDPI
Additional Information: The copyright for this article belongs to the Authors.
Keywords: Infrared devices; Land use; Least squares approximations; Optical remote sensing; Organic carbon; Pixels; Quality control; Reflection; Regression analysis; Sensitivity analysis; Soil surveys; Soils; Spectroscopy, Atmospheric analysis; Atmospheric corrections; Fast line-of-sight atmospheric analyse of spectral hypercube; Hyper-cubes; Hyper-spectral imageries; Hyperion; Line of Sight; Lines-of-sight; Soil organic carbon, Mapping
Department/Centre: Division of Biological Sciences > Centre for Ecological Sciences
Division of Mechanical Sciences > Centre for Atmospheric & Oceanic Sciences
Date Deposited: 20 Nov 2022 06:52
Last Modified: 20 Nov 2022 06:52
URI: https://eprints.iisc.ac.in/id/eprint/77890

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