气候变化领域集成服务门户(CCPortal): Vis-NIR Spectroscopy Combined with GAN Data Augmentation for Predicting Soil Nutrients in Degraded Alpine Meadows on the Qinghai-Tibet Plateau

CCPortal

DOI	10.3390/s23073686
	Vis-NIR Spectroscopy Combined with GAN Data Augmentation for Predicting Soil Nutrients in Degraded Alpine Meadows on the Qinghai-Tibet Plateau
	Jiang, Chuanli; Zhao, Jianyun; Ding, Yuanyuan; Li, Guorong
发表日期	2023
EISSN	1424-8220
卷号	23 期号:7
英文摘要	Soil nutrients play vital roles in vegetation growth and are a key indicator of land degradation. Accurate, rapid, and non-destructive measurement of the soil nutrient content is important for ecological conservation, degradation monitoring, and precision farming. Currently, visible and near-infrared (Vis-NIR) spectroscopy allows for rapid and non-destructive monitoring of soil nutrients. However, the performance of Vis-NIR inversion models is extremely dependent on the number of samples. Limited samples may lead to low prediction accuracy of the models. Therefore, modeling and prediction based on a small sample size remain a challenge. This study proposes a method for the simultaneous augmentation of soil spectral and nutrient data (total nitrogen (TN), soil organic matter (SOM), total potassium oxide (TK2O), and total phosphorus pentoxide (TP2O5)) using a generative adversarial network (GAN). The sample augmentation range and the level of accuracy improvement were also analyzed. First, 42 soil samples were collected from the pika disturbance area on the QTP. The collected soils were measured in the laboratory for Vis-NIR and TN, SOM, TK2O, and TP2O5 data. A GAN was then used to augment the soil spectral and nutrient data simultaneously. Finally, the effect of adding different numbers of generative samples to the training set on the predictive performance of a convolutional neural network (CNN) was analyzed and compared with another data augmentation method (extended multiplicative signal augmentation, EMSA). The results showed that a GAN can generate data very similar to real data and with better diversity. A total of 15, 30, 60, 120, and 240 generative samples (GAN and EMSA) were randomly selected from 300 generative samples to be included in the real data to train the CNN model. The model performance first improved and then deteriorated, and the GAN was more effective than EMSA. Further shortening the interval for adding GAN data revealed that the optimal ranges were 30-40, 50-60, 30-35, and 25-35 for TK2O, TN, TP2O5, and SOM, respectively, and the validation set accuracy was maximized in these ranges. Therefore, the above method can compensate to some extent for insufficient samples in the hyperspectral prediction of soil nutrients, and can quickly and accurately estimate the content of soil TK2O, TN, TP2O5, and SOM.
关键词	Vis-NIR spectroscopy data augmentation soil nutrients generative adversarial networks Qinghai-Tibet Plateau
英文关键词	NEAR-INFRARED SPECTROSCOPY; TOTAL NITROGEN; DEGRADATION; RIVERS; REGIME; CARBON
WOS研究方向	Chemistry, Analytical ; Engineering, Electrical & Electronic ; Instruments & Instrumentation
WOS记录号	WOS:000970244400001
来源期刊	SENSORS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/283180
作者单位	Qinghai University
推荐引用方式 GB/T 7714	Jiang, Chuanli,Zhao, Jianyun,Ding, Yuanyuan,et al. Vis-NIR Spectroscopy Combined with GAN Data Augmentation for Predicting Soil Nutrients in Degraded Alpine Meadows on the Qinghai-Tibet Plateau[J],2023,23(7).
APA	Jiang, Chuanli,Zhao, Jianyun,Ding, Yuanyuan,&Li, Guorong.(2023).Vis-NIR Spectroscopy Combined with GAN Data Augmentation for Predicting Soil Nutrients in Degraded Alpine Meadows on the Qinghai-Tibet Plateau.SENSORS,23(7).
MLA	Jiang, Chuanli,et al."Vis-NIR Spectroscopy Combined with GAN Data Augmentation for Predicting Soil Nutrients in Degraded Alpine Meadows on the Qinghai-Tibet Plateau".SENSORS 23.7(2023).