Soil Organic Carbon Mapping Using Hyperspectral Remote Sensing and ANN

Soil organic carbon (SOC) is an important and reliable indicator of soil quality. In this study, soil spectra were characterized and analyzed to predict the spatial SOC content, using multivariate predictive modeling technique-artificial neural network (ANN). EO1-Hyperion (400 - 2500 nm) hyper-spectral image, field and laboratory scale data sets (350 - 2500 nm) were generated, consisting of laboratory estimated SOC content of collected soil samples (dependent variable) and their corresponding reflection data of SOC sensitive spectral bands (predictive variables). For each data set, ANN predictive models were developed and three data set (image-scale, field-scale and lab-scale) revealed significant network performances for training, testing and validation, indicating a good network generalization for SOC content. ANN based analysis showed high prediction of SOC content at image (R2 = 0.93, and RPD = 3.19), field (R2 = 0.92 and RPD = 3.17), and lab scale (R2 = 0.95 and RPD = 3.16). Validation results of ANN indicated that predictive models performed well (R2 = 0.90) with RMSE 0.070. The result showed that ANN methods have a great potential for estimating SOC content.