Data mining of urban soil spectral library for estimating organic carbon

Author:

Hong Yongsheng,Chen YiyunORCID,Chen SongchaoORCID,Shen Ruili,Hu BifengORCID,Peng Jie,Wang Nan,Guo Long,Zhuo ZhiqingORCID,Yang Yuanyuan,Liu Yaolin,Mouazen Abdul Mounem,Shi ZhouORCID

Funder

National Natural Science Foundation of China

China Postdoctoral Science Foundation

Fonds Wetenschappelijk Onderzoek

National Key Research and Development Program of China

Publisher

Elsevier BV

Subject

Soil Science

Reference104 articles.

1. Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R., Moore, S., Murray, D.G., Steiner, B., Tucker, P., Vasudevan, V., Warden, P., Wicke, M., Yu, Y., Zheng, X., 2019. TensorFlow: large-scale machine learning on heterogeneous systems. Software available from https://www.tensorflow.org/.

2. Quantification of soil organic carbon stock in urban soils using visible and near infrared reflectance spectroscopy (VNIRS) in situ or in laboratory conditions;Allory;Sci. Total Environ.,2019

3. Improving the prediction performance of a large tropical vis-NIR spectroscopic soil library from Brazil by clustering into smaller subsets or use of data mining calibration techniques;Araújo;Eur. J. Soil Sci.,2014

4. Global land cover mapping using Earth observation satellite data: Recent progresses and challenges;Ban;ISPRS-J. Photogramm. Remote Sens.,2015

5. Agricultural Chemistry Analysis;Bao,2005

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