Faculty Publications

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Impact of Atmospheric Correction Methods Parametrization on Soil Organic Carbon Estimation Based on Hyperion Hyperspectral Data
(MDPI, 2022) Mruthyunjaya, P.; Shetty, A.; Umesh, P.; Gomez, C.
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. © 2022 by the authors.
Integrating Soil Spectral Library and PRISMA Data to Estimate Soil Organic Carbon in Crop Lands
(Institute of Electrical and Electronics Engineers Inc., 2024) Reddy, B.S.; Shwetha, H.R.
The increasing demand for precise soil organic carbon (SOC) monitoring in croplands is crucial for food security (SDG 2), and has led to the exploration of fusing soil spectral libraries (SSLs) with hyperspectral sensing data for SOC estimation. However, the widespread adoption of SSL for SOC estimation faces challenges, particularly in developing nations, due to inconsistent calibration libraries and reliable estimation models. Furthermore, SSL rely on regular soil sample collection and spectral data recording using spectroradiometers, which is impractical in agricultural-predominant countries, such as India, with limited time for sample collection between crop rotations. To address this challenge, we developed synthesized SSL in laboratory conditions and integrated it with hyperspectral data using machine learning (ML) algorithms to bridge the gap between synthesized SSL and hyperspectral data for local-scale SOC mapping. This approach was tested by mapping SOC in Mysore, India, using spectroradiometer hyperspectral measurements and PRISMA sensor data. The proposed approach and synthesized SSL exhibited better performance prediction accuracies, R2 of 0.92 and 0.79, and the RMSE values of 2.31 and 9.91 g/kg, respectively, for PRISMA and laboratory spectroscopy data. These results highlight the potential of synthesized SSL for SOC prediction in alluvial soils, leveraging local datasets, and hyperspectral data. Our future work will expand the synthesis approach to other study areas, particularly those with alluvial soil origins, further enhancing the applicability of this methodology for SOC estimation and aiding food security efforts. © 2004-2012 IEEE.
Enhancing soil organic carbon estimation accuracy: Integrating spatial vegetation dynamics and temporal analysis with Sentinel 2 imagery
(Elsevier B.V., 2024) Mruthyunjaya, P.; Shetty, A.; Umesh, P.
This article introduces an improved method for estimating Soil Organic Carbon (SOC) using Sentinel 2 images, with a specific emphasis on the Dakshina Kannada area in India. By examining 364 soil samples, SOC estimation models were constructed using Random forests (RF) and Partial Least Squares Regression (PLSR), focusing on the impact of nearby vegetation pixels. The approach consisted of classifying soil samples by the presence of plant pixels at distances of 0, 10, and 20 m, and evaluating the influence of dry vegetation by the use of the Normalised Burn Ratio 2 (NBR2). The findings demonstrated a significant improvement in the precision of the model (by up to 20 %) when vegetation pixels within a 20-meter radius of the sample locations were omitted. The research also included a temporal analysis utilizing Sentinel-2 images from April 2017 to May 2023. This analysis showed strong relationships between the amount of exposed soil and the accuracy of predicting soil organic carbon (SOC) levels. These results emphasize the need to take into account both the spatial dynamics of vegetation and the temporal variations in bare soil covering to get an accurate estimate of soil organic carbon (SOC). This study improves the accuracy and dependability of SOC evaluations by including geographical and temporal aspects, providing useful insights for agricultural and ecological applications. © 2024 The Author(s)

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