Spectroscopy and Spectral Analysis, Volume. 43, Issue 9, 2942(2023)
A Novel Hyperspectral Prediction Model of Organic Matter in Red Soil Based on Improved Temporal Convolutional Network
[1] [1] Munson S A, Carey A E. Applied Geochemistry, 2004, 19(7): 1111.
[2] [2] Yan L, Escobar M S, Kaneko H, et al. Chemometrics and Intelligent Laboratory Systems, 2017, 167: 139.
[3] [3] Lin Z D, Wang Y B, Wang R J, et al. Journal of Applied Spectroscopy, 2017, 84(3): 529.
[5] [5] Shen L, Gao M, Yan J, et al. Remote Sensing, 2020, 12(7): 1206.
[6] [6] Padarian J, Minasny B, McBratney A B. Geoderma Regional, 2019, 16: e00198.
[10] [10] Galvao R K H, Araujo M C U, José G E, et al. Talanta, 2005, 67(4): 736.
[11] [11] Li R, Zheng S, Duan C, et al. Remote Sensing, 2020, 12(3): 582.
[12] [12] Mei X, Pan E, Ma Y, et al. Remote Sensing, 2019, 11(8): 963.
[13] [13] Li R, Yin B, Cong Y, et al. Sensors, 2020, 20(21): 6271.
Get Citation
Copy Citation Text
DENG Yun, NIU Zhao-wen, FENG Qi-yao, WANG Yu. A Novel Hyperspectral Prediction Model of Organic Matter in Red Soil Based on Improved Temporal Convolutional Network[J]. Spectroscopy and Spectral Analysis, 2023, 43(9): 2942
Received: May. 16, 2022
Accepted: --
Published Online: Jan. 12, 2024
The Author Email: