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Article

The Simultaneous Prediction of Soil Properties and Vegetation Coverage from Vis-NIR Hyperspectral Data with a One-Dimensional Convolutional Neural Network: A Laboratory Simulation Study

by 1,2, 1,2,*, 1,2, 1,2, 1,2, 1,2, 1,2 and 1,2
1
State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
2
College of Advanced Agricultural Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Academic Editors: Saeid Homayouni and Dino Ienco
Remote Sens. 2022, 14(2), 397; https://doi.org/10.3390/rs14020397
Received: 29 November 2021 / Revised: 31 December 2021 / Accepted: 12 January 2022 / Published: 15 January 2022
(This article belongs to the Section AI Remote Sensing)
Remote sensing of land surface mostly obtains a mixture of spectral information of soil and vegetation. It is thus of great value if soil and vegetation information can be acquired simultaneously from one model. In this study, we designed a laboratory experiment to simulate land surface compositions, including various soil types with varying soil moisture and vegetation coverage. A model of a one-dimensional convolutional neural network (1DCNN) was established to simultaneously estimate soil properties (organic matter, soil moisture, clay, and sand) and vegetation coverage based on the hyperspectral data measured in the experiment. The results showed that the model achieved excellent predictions for soil properties (R2 = 0.88–0.91, RPIQ = 4.01–5.78) and vegetation coverage (R2 = 0.95, RPIQ = 7.75). Compared with the partial least-squares regression (PLSR), the prediction accuracy of 1DCNN improved 42.20%, 45.82%, 43.32%, and 36.46% in terms of the root-mean-squared error (RMSE) for predicting soil organic matter, sand, clay, and soil moisture, respectively. The improvement might be caused by the fact that the spectral preprocessing and spectral features useful for predicting soil properties were successfully identified in the 1DCNN model. For the prediction of vegetation coverage, although the prediction accuracy by 1DCNN was excellent, its performance (R2 = 0.95, RPIQ = 7.75, RMSE = 3.92%) was lower than the PLSR model (R2 = 0.98, RPIQ = 12.57, RMSE = 2.41%). These results indicate that 1DCNN can simultaneously predict soil properties and vegetation coverage. However, the factors such as surface roughness and vegetation type that could affect the prediction accuracy should be investigated in the future. View Full-Text
Keywords: convolutional neural network; multitask learning; soil properties; vegetation coverage convolutional neural network; multitask learning; soil properties; vegetation coverage
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MDPI and ACS Style

Zhang, F.; Wang, C.; Pan, K.; Guo, Z.; Liu, J.; Xu, A.; Ma, H.; Pan, X. The Simultaneous Prediction of Soil Properties and Vegetation Coverage from Vis-NIR Hyperspectral Data with a One-Dimensional Convolutional Neural Network: A Laboratory Simulation Study. Remote Sens. 2022, 14, 397. https://doi.org/10.3390/rs14020397

AMA Style

Zhang F, Wang C, Pan K, Guo Z, Liu J, Xu A, Ma H, Pan X. The Simultaneous Prediction of Soil Properties and Vegetation Coverage from Vis-NIR Hyperspectral Data with a One-Dimensional Convolutional Neural Network: A Laboratory Simulation Study. Remote Sensing. 2022; 14(2):397. https://doi.org/10.3390/rs14020397

Chicago/Turabian Style

Zhang, Fangfang, Changkun Wang, Kai Pan, Zhiying Guo, Jie Liu, Aiai Xu, Haiyi Ma, and Xianzhang Pan. 2022. "The Simultaneous Prediction of Soil Properties and Vegetation Coverage from Vis-NIR Hyperspectral Data with a One-Dimensional Convolutional Neural Network: A Laboratory Simulation Study" Remote Sensing 14, no. 2: 397. https://doi.org/10.3390/rs14020397

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