@article{8667301,
  abstract     = {{Estimating soil organic carbon (SOC) in topsoil can help improve soil quality and food production. This study aimed to explore the potential of airborne hyperspectral image to estimate the SOC of bare topsoil at an agricultural site located in the southeast part of Iowa State, United States. To magnify the subtle spectral signals concerning SOC, and accelerate calibration and improve predictive ability, we developed a framework to combine two advanced spectral algorithms, namely, fractional-order derivative (FOD) and optimal band combination algorithm for SOC predicting. Our case was based on 49 soil samples and a scattered airborne hyperspectral image. Random forest (RF) was utilized to establish SOC estimation models by incorporating the optimal spectral indices processed by different FOD transformations on the basis of the optimal band combination algorithm. Results indicated that when the fractional order increased, overlapping peaks and baseline drifts were gradually removed. However, the magnitude of spectral strength decreased concurrently. More detailed and abundant spectral variability was captured by FOD as compared with those by original reflectance and first and second derivatives. The estimation accuracies developed from the optimal band combination algorithm (cross validation R-2, 0.36-0.66) were generally better than those from full-spectrum data (cross-validation R-2, 0.32-0.54). The RF model based on the combination of 0.75-order reflectance and optimal band combination algorithm obtained the highest estimation accuracy for SOC with cross-validation R-2 of 0.66. This research provides guidance for future studies in selecting the most appropriate FOD transformation to preprocess spectral data and in using the optimal band combination algorithm to determine the spectral index. Airborne hyperspectral image-based modeling can be further used to map agricultural topsoil SOC to support local-scale agricultural planning.}},
  articleno    = {{114228}},
  author       = {{Hong, Yongsheng and Guo, Long and Chen, Songchao and Linderman, Marc and Mouazen, Abdul and Yu, Lei and Chen, Yiyun and Liu, Yaolin and Liu, Yanfang and Cheng, Hang and Liu, Yi}},
  issn         = {{0016-7061}},
  journal      = {{GEODERMA}},
  keywords     = {{Soil Science,Airborne hyperspectral image,Soil organic carbon,Fractional-order derivative,Optimal band combination algorithm,Random forest,NEAR-INFRARED-SPECTROSCOPY,REMOTE-SENSING TECHNIQUES,NATURE-RESERVE ELWNNR,SOIL PROPERTIES,MATTER CONTENT,CLAY CONTENT,HEAVY-METALS,PREDICTION,SEQUESTRATION,CALIBRATION}},
  language     = {{eng}},
  pages        = {{12}},
  title        = {{Exploring the potential of airborne hyperspectral image for estimating topsoil organic carbon : effects of fractional-order derivative and optimal band combination algorithm}},
  url          = {{http://doi.org/10.1016/j.geoderma.2020.114228}},
  volume       = {{365}},
  year         = {{2020}},
}

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