干旱区生态环境知识资源中心(Arid): A new method to estimate soil organic matter using the combination model based on short memory fractional order derivative and machine learning model

Arid

DOI	10.1016/j.infrared.2023.104922
	A new method to estimate soil organic matter using the combination model based on short memory fractional order derivative and machine learning model
	Fu, Chengbiao; Gan, Shu; Xiong, Heigang; Tian, Anhong
通讯作者	Gan, S
来源期刊	INFRARED PHYSICS & TECHNOLOGY
ISSN	1350-4495
EISSN	1879-0275
出版年	2023
卷号	134
英文摘要	In order to improve the quantitative inversion accuracy of soil organic matter in arid areas, the soils affected by different human disturbances in Xinjiang were used as research objects (Regions I, II and III). This study compared and analyzed the preprocessing hyperspectral data effects of GMFOD (global memory fractional order derivative) with 1759 memory length and SMFOD (short memory fractional order derivative) with different memory lengths (L = 1500, 1200, 1000, 800, 500, 300, 250, 200, 150, 100), machine learning models based on RBF (radial basis function) and ELM (extreme learning machine) were used to explore the best model for predicting soil organic matter content. Simulation results showed that (1) SMFOD had shorter computational runtime compared to GMFOD, and the 100 SMFOD had the shortest running time. When all orders of fractional order derivative were calculated, the time required for 100 SMFOD was 115.27 s, while the time required for GMFOD was 1025.32 s. These indicated that the running time of the former was 789.49% shorter than that of GMFOD. (2) Comparing the correlation coefficients of hyperspectral and soil organic matter in all fractional orders calculated based on GMFOD and SMFOD, it was found that the maximum correlation coefficient of 100 SMFOD and GMFOD differed by 0.0103, 0.0058 and 0.0287 in Regions I, II and III, which means that GMFOD only increased by 0.94%, 1.71% and 4.25%. These indicated that the maximum correlation coefficient of SMFOD and GMFOD was very similar at each fractional order. (3) The best model for inversion of soil organic matter in all three regions was SMFOD-CC1-RBF (CC1 denotes bands passed the 0.01 significance test), which was located at the fractional orders of 1.4, 1.8 and 1.1, respectively. Meanwhile, R2 (coefficient of determination), RMSE (root mean square error), RPD (ratio of the performance to deviation) were 0.8249, 0.7656, 1.9256 in Region I; were 0.7630, 1.4422, 1.9561 in Region II; and were 0.6799, 1.5343,1.8513 in Region III, which indicated that the higher-order SMFOD had better performance in inverting organic matter than the lower-order SMFOD, and the prediction ability of this model for soil organic matter in the three regions was good.
英文关键词	Soil organic matter Visible and near infrared spectroscopy Short memory fractional order derivative Global memory fractional order derivative Machine learning model
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001097519100001
WOS关键词	NEAR-INFRARED-SPECTROSCOPY ; IDENTIFICATION ; ACCURACY ; FIELD ; LEAD
WOS类目	Instruments & Instrumentation ; Optics ; Physics, Applied
WOS研究方向	Instruments & Instrumentation ; Optics ; Physics
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/396942
推荐引用方式 GB/T 7714	Fu, Chengbiao,Gan, Shu,Xiong, Heigang,et al. A new method to estimate soil organic matter using the combination model based on short memory fractional order derivative and machine learning model[J],2023,134.
APA	Fu, Chengbiao,Gan, Shu,Xiong, Heigang,&Tian, Anhong.(2023).A new method to estimate soil organic matter using the combination model based on short memory fractional order derivative and machine learning model.INFRARED PHYSICS & TECHNOLOGY,134.
MLA	Fu, Chengbiao,et al."A new method to estimate soil organic matter using the combination model based on short memory fractional order derivative and machine learning model".INFRARED PHYSICS & TECHNOLOGY 134(2023).