ReflectGAN: Modeling Vegetation Effects for Soil Carbon Estimation from Satellite Imagery

--Soil organic carbon (SOC) is a critical indicator of soil health, but its accurate estimation from satellite imagery is hindered in vegetated regions due to spectral contamination from plant cover, which obscures soil reflectance and reduces model reliability. This study proposes the Reflectance Transformation Generative Adversarial Network (ReflectGAN), a novel paired GAN-based framework designed to reconstruct accurate bare soil reflectance from vegetated soil satellite observations. Using the LUCAS 2018 dataset and corresponding Landsat 8 imagery, we trained multiple learning-based models on both original and ReflectGAN-reconstructed reflectance inputs. Models trained on ReflectGAN outputs consistently outperformed those using existing vegetation correction methods. The performance of the models with ReflectGAN is also better compared to their counterparts when applied to another dataset, i.e., Sentinel-2 imagery. These findings demonstrate the potential of ReflectGAN to improve SOC estimation accuracy in vegetated landscapes, supporting more reliable soil monitoring. OIL organic carbon (SOC) is a fundamental indicator of soil health, influencing agricultural productivity, carbon sequestration, improved soil moisture retention and overall ecosystem sustainability. Accurate estimation of SOC is essential for promoting sustainable agriculture, improving soil management practices, and monitoring environmental changes [1], [2]. Traditional methods for estimating SOC rely on laboratory-based soil analyses, which, although precise, are labor-intensive, costly, and limited in spatial coverage [3], [4]. D. Datta and M. Paul are with the School of Computing, Mathematics, and Engineering, Charles Sturt University, Bathurst, NSW 2795, Australia, and also with the Cooperative Research Centre for High Performance Soils, Callaghan, NSW 2308, Australia (e-mail: ddatta@csu.edu.au; M. Murshed is with the School of Information Technology, Deakin University, Burwood, VIC 3125, Australia (e-mail: manzur.murshed@deakin.edu.au). S. W . Teng is with the Institute of Innovation, Science and Sustainability, Federation University, Mount Helen, VIC 3350, Australia, and also with the Cooperative Research Centre for High Performance Soils, Callaghan, NSW 2308, Australia (e-mail: s.w.teng@federation.edu.au). Laboratory-based hyperspectral imaging (HSI) provides a powerful tool for SOC estimation by offering high spatial and spectral resolution, enabling detailed analysis of soil properties without the need for destructive sampling [5]-[7]. Numerous studies have validated the effectiveness of HSI in accurately estimating SOC levels [7], [8]. However, the widespread deployment of HSI is constrained by the high cost of equipment and limited accessibility, making it impractical for large-scale applications.