干旱区生态环境知识资源中心(Arid): Coupled Machine Learning and Unmanned Aerial Vehicle Based Hyperspectral Data for Soil moisture Content Estimation

Arid

DOI	10.3788/LOP57.093002
	Coupled Machine Learning and Unmanned Aerial Vehicle Based Hyperspectral Data for Soil moisture Content Estimation
	Tian Meiling; Ge Xiangyu; Ding Jianli; Wang Jingzhe; Zhang Zhenhua
通讯作者	Ding, JL
来源期刊	LASER & OPTOELECTRONICS PROGRESS
ISSN	1006-4125
出版年	2020
卷号	57 期号:9
英文摘要	Accurate estimation of soil moisture content (SMC) is of great significance for precision agriculture and water resources management in arid areas. Traditional estimation methods and field measurements arc time consuming and labor intensive. Therefore, we obtain hyperspectral image data of winter wheat plots in Fukang City, Xinjiang by unmanned aerial vehicle platform, and the original hyperspectral data arc preprocessed through first derivative, second derivative, absorbance, first derivative of absorbance ( FDA), and second derivative of absorbance. Random forest (RF), gradient boosted regression tree (GERT), and extreme gradient boost (XGBoost) arc used to select the importance of feature variables. A model is established based on geographical weighted regression (GWR). The results show that the pretreatment effect of FDA is the best. The model based on FDA-GERT is optimal. The determination coefficient (R-2) of the modeling set and the verification set arc 0.890 and 0.891, respectively, and the quartile interval reaches 3.490. Compared with RF and XGBoost algorithms, the advantages of the GERT algorithm arc more prominent. The R-2 of most of the model modeling set and the verification set arc greater than 0.600. This indicates that the GWR model is effective in predictive modeling of SMC and can provide theoretical support for the management and protection of agro ecosystem in arid regions.
英文关键词	soil moisture content unmanned aerial vehicle hyperspectral data machine learning geographical weighted regression model
类型	Article
语种	中文
收录类别	ESCI
WOS记录号	WOS:000549480400031
WOS类目	Engineering, Electrical & Electronic ; Optics
WOS研究方向	Engineering ; Optics
Scopus学科分类	Xinjiang Univ, Key Lab Oasis Ecol, Urumqi 830046, Xinjiang, Peoples R China. ; Ding, JL
CSCD记录号	CSCD:Xinjiang Univ, Key Lab Smart City & Environm Modelling, Higher Educ Inst, Urumqi 830046, Xinjiang, Peoples R China.
来源机构	新疆大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/334482
作者单位	[Tian Meiling; Ge Xiangyu; Ding Jianli; Wang Jingzhe; Zhang Zhenhua] Xinjiang Univ, Coll Resource & Environm Sci, Urumqi 830046, Xinjiang, Peoples R China; [Tian Meiling; Ge Xiangyu; Ding Jianli; Wang Jingzhe; Zhang Zhenhua] Xinjiang Univ, Key Lab Oasis Ecol, Urumqi 830046, Xinjiang, Peoples R China; [Tian Meiling; Ge Xiangyu; Ding Jianli; Wang Jingzhe; Zhang Zhenhua] Xinjiang Univ, Key Lab Smart City & Environm Modelling, Higher Educ Inst, Urumqi 830046, Xinjiang, Peoples R China
推荐引用方式 GB/T 7714	Tian Meiling,Ge Xiangyu,Ding Jianli,et al. Coupled Machine Learning and Unmanned Aerial Vehicle Based Hyperspectral Data for Soil moisture Content Estimation[J]. 新疆大学,2020,57(9).
APA	Tian Meiling,Ge Xiangyu,Ding Jianli,Wang Jingzhe,&Zhang Zhenhua.(2020).Coupled Machine Learning and Unmanned Aerial Vehicle Based Hyperspectral Data for Soil moisture Content Estimation.LASER & OPTOELECTRONICS PROGRESS,57(9).
MLA	Tian Meiling,et al."Coupled Machine Learning and Unmanned Aerial Vehicle Based Hyperspectral Data for Soil moisture Content Estimation".LASER & OPTOELECTRONICS PROGRESS 57.9(2020).