干旱区生态环境知识资源中心(Arid): Estimating Agricultural Soil Moisture Content through UAV-Based Hyperspectral Images in the Arid Region

Arid

DOI	10.3390/rs13081562
	Estimating Agricultural Soil Moisture Content through UAV-Based Hyperspectral Images in the Arid Region
	Ge, Xiangyu; Ding, Jianli; Jin, Xiuliang; Wang, Jingzhe; Chen, Xiangyue; Li, Xiaohang; Liu, Jie; Xie, Boqiang
通讯作者	Ding, JL (corresponding author), Xinjiang Univ, Key Lab Smart City & Environm Modelling, Higher Educ Inst, Coll Resources & Environm Sci, Urumqi 830046, Peoples R China. ; Ding, JL (corresponding author), Xinjiang Univ, Key Lab Oasis Ecol, Urumqi 830046, Peoples R China.
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2021
卷号	13 期号:8
英文摘要	Unmanned aerial vehicle (UAV)-based hyperspectral remote sensing is an important monitoring technology for the soil moisture content (SMC) of agroecological systems in arid regions. This technology develops precision farming and agricultural informatization. However, hyperspectral data are generally used in data mining. In this study, UAV-based hyperspectral imaging data with a resolution o 4 cm and totaling 70 soil samples (0-10 cm) were collected from farmland (2.5 x 10(4) m(2)) near Fukang City, Xinjiang Uygur Autonomous Region, China. Four estimation strategies were tested: the original image (strategy I), first- and second-order derivative methods (strategy II), the fractional-order derivative (FOD) technique (strategy III), and the optimal fractional order combined with the optimal multiband indices (strategy IV). These strategies were based on the eXtreme Gradient Boost (XGBoost) algorithm, with the aim of building the best estimation model for agricultural SMC in arid regions. The results demonstrated that FOD technology could effectively mine information (with an absolute maximum correlation coefficient of 0.768). By comparison, strategy IV yielded the best estimates out of the methods tested (R-val(2) = 0.921, RMSEP = 1.943, and RPD = 2.736) for the SMC. The model derived from the order of 0.4 within strategy IV worked relatively well among the different derivative methods (strategy I, II, and III). In conclusion, the combination of FOD technology and the optimal multiband indices generated a highly accurate model within the XGBoost algorithm for SMC estimation. This research provided a promising data mining approach for UAV-based hyperspectral imaging data.
英文关键词	fractional-order derivatives ensemble learning hyperspectral data precision agriculture
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000644669000001
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
来源机构	兰州大学 ; 新疆大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/351509
作者单位	[Ge, Xiangyu; Ding, Jianli; Li, Xiaohang; Liu, Jie; Xie, Boqiang] Xinjiang Univ, Key Lab Smart City & Environm Modelling, Higher Educ Inst, Coll Resources & Environm Sci, Urumqi 830046, Peoples R China; [Ge, Xiangyu; Ding, Jianli; Li, Xiaohang; Liu, Jie; Xie, Boqiang] Xinjiang Univ, Key Lab Oasis Ecol, Urumqi 830046, Peoples R China; [Jin, Xiuliang] Minist Agr, Inst Crop Sci, Chinese Acad Agr Sci, Sci Lab Crop Physiol & Ecol, Beijing 100081, Peoples R China; [Wang, Jingzhe] Shenzhen Univ, MNR Key Lab Geoenvironm Monitoring Great Bay Area, Shenzhen 518060, Peoples R China; [Wang, Jingzhe] Shenzhen Univ, Guangdong Key Lab Urban Informat, Shenzhen 518060, Peoples R China; [Wang, Jingzhe] Shenzhen Univ, Shenzhen Key Lab Spatial Smart Sensing & Serv, Shenzhen 518060, Peoples R China; [Chen, Xiangyue] Lanzhou Univ, Coll Atmospher Sci, Lanzhou 730000, Peoples R China
推荐引用方式 GB/T 7714	Ge, Xiangyu,Ding, Jianli,Jin, Xiuliang,et al. Estimating Agricultural Soil Moisture Content through UAV-Based Hyperspectral Images in the Arid Region[J]. 兰州大学, 新疆大学,2021,13(8).
APA	Ge, Xiangyu.,Ding, Jianli.,Jin, Xiuliang.,Wang, Jingzhe.,Chen, Xiangyue.,...&Xie, Boqiang.(2021).Estimating Agricultural Soil Moisture Content through UAV-Based Hyperspectral Images in the Arid Region.REMOTE SENSING,13(8).
MLA	Ge, Xiangyu,et al."Estimating Agricultural Soil Moisture Content through UAV-Based Hyperspectral Images in the Arid Region".REMOTE SENSING 13.8(2021).