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Article citations


Ben-Dor, E., Chabrillat, S., Demattê, J.A.M., Taylor, G.R., Hill, J., Whiting, M.L. and Sommer, S. (2009) Using Imaging Spectroscopy to Study Soil Properties. Remote Sensing of Environment, 113, S38-S55.

has been cited by the following article:

  • TITLE: Prediction Modeling and Mapping of Soil Carbon Content Using Artificial Neural Network, Hyperspectral Satellite Data and Field Spectroscopy

    AUTHORS: Sudheer Kumar Tiwari, Sudip Kumar Saha, Suresh Kumar

    KEYWORDS: Soil Carbon, Artificial Neural Network, Hyperspectral, Imagine Spectroscopy, Hyperion

    JOURNAL NAME: Advances in Remote Sensing, Vol.4 No.1, March 23, 2015

    ABSTRACT: Soil organic carbon (SOC) is an important and reliable indicator of soil quality. In this study, soil spectra were characterized and analysed to predict the spatial soil organic carbon (SOC) content using multivariate predictive modeling technique-artificial neural network (ANN). EO1-Hyperion (400 - 2500 nm) hyperspectral image, field and laboratory scale data sets (350 - 2500 nm) were generated which consisted of laboratory estimated SOC content of collected soil samples (dependent variable) and their corresponding reflectance data of SOC sensitive spectral bands (predictive variables). For each data set, ANN predictive models were developed and all three datasets (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 had a great potential for estimating and mapping spatial SOC content. The study concluded that ANN model was potential tools in predicting SOC distribution in agricultural field using hyper-spectral remote sensing data at image-scale, field-scale and lab-scale.