干旱区生态环境知识资源中心(Arid): A New Method for Predicting Soil Moisture Based on UAV Hyperspectral Image

Arid

DOI	10.3964/j.issn.1000-0593(2020)02-0602-08
	A New Method for Predicting Soil Moisture Based on UAV Hyperspectral Image
	Ge Xiang-yu 1,2,3; Ding Jian-li1,2,3 ; Wang Jing-zhe 4; Sun Hui-lan 5; Zhu Zhi-qiang 6
通讯作者	Ding Jian-li
来源期刊	SPECTROSCOPY AND SPECTRAL ANALYSIS
ISSN	1000-0593
出版年	2020
卷号	40 期号:2 页码:602-609
英文摘要	Soil moisture content (SMC) is a key factor in biogeochemical and atmospheric coupling processes. It plays an important role in areas such as agriculture, ecology and environment in arid region. Compared to the spaceborne remote sensing system, UAV platform with hyperspectral sensors possess higher spatial resolution and maneuverability. With UAV (Unmanned Aerial Vehicle) being increasingly popular, it offers brand new platform of remote sensing. This platform realizes the goal that quickly and quantificationally monitor object in the area. Moreover, hyperspectral sensors contribute to remote sensing when they enrich high dimensional and nanoscale data source. However, there still lacks a standardized research scheme for estimation of UAV by hyperspectral Remote Sensing. In this study, we obtained UAV hyperspectral image from a typically dry-farming region lying in Xinjiang Uygur Autonomous Region. Hyperspectral image was pretreated using six methods of pretreatment, including first-derivative (FDR) , second-derivative (SDR) , continuum removal (CR), absorbance (A) , first-derivative absorbance (FDA) and second-derivative absorbance (SDA). From pretreatment foundation, four types spectral indices were proposed containing the Difference Index (DI) , the Ratio Index (RI), the Normalization Index (NDI) and the Perpendicular Index (PI). And the rationality of the spectral index was discussed from the spectral mechanism. Considering the superiority of ensemble learning algorithm rising in recent years, the SMC estimation model was constructed via Gradient Boosted Regression Tree (GBRT) Random Forest (RF) and eXtreme Gradient Boosting (XGBoost). In these models, 28 appropriate spectral indices were used as independent variables and 70 SMC measured values as response variables. Spectral indices were ranked via importance based on ensemble learning model analyzed and compared to make a more comprehensive evaluation. The result indicated that : (1) atmospheric disturbance and soil background were eliminated effectively throughvarious pretreatment schemes and spectral indices. Pretreatment scheme A highlighted more spectral information and PI correlation was significant. (2) Optimum spectral index was A_PI (vertical bar r vertical bar =0. 773) that the ranking of importance ranks first, and the correlation coefficient vertical bar r vertical bar is the highest, and it had excellent performance in both linear and nonlinear relationships. (2) XGBoost prediction model was outstanding in three ensemble learning models, and it yielded the highest R-val(2), the lowest RMSP and the best RPD (R-val(2 ) =0. 926 , RMSEP=1. 943 and RPD =2. 556). The ranking of the predictive performance was XGBoost>RF>GBRT. This proved that this scheme was effective in digital mapping in arid region. In conclusion, there is potential high accuracy for UAV imagery based on hyperspectral imagery. This study afforded an effective method for predicting SMC in arid regions, and it provided a new perspective for quickly and easily monitoring object attributes and it proposed an alternative solution for predicting soil moisture. Ultimately, our program is supporting better management and conservation strategies for precision agriculture and ecosystems in arid regions.
英文关键词	UAV Remote Sensing Hyperspectral image Machine learning Ensemble learning
类型	Article
语种	中文
国家	Peoples R China
收录类别	SCI-E
WOS记录号	WOS:000528834600045
WOS关键词	RANDOM FOREST ; SPECTROSCOPY
WOS类目	Spectroscopy
WOS研究方向	Spectroscopy
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/2XILL650/318500
作者单位	1.Xinjiang Univ, Coll Resources & Environm Sci, Urumqi 830046, Peoples R China; 2.Xinjiang Univ, Key Lab Oasis Ecol, Urumqi 830046, Peoples R China; 3.Xinjiang Univ, Key Lab Smart City & Environm Modelling Higher Ed, Urumqi 830046, Peoples R China; 4.Shenzhen Univ, Key Lab Geoenvironm Monitoring Coastal Zone, Minist Nat Resoures, Shenzhen 518060, Peoples R China; 5.Xinjiang Normal Univ, Sch Geog Sci & Tourism, Urumqi 830054, Peoples R China; 6.Beijing Univ Chem Technol, China Coll Mat Sci & Engn, Beijing 100029, Peoples R China
推荐引用方式 GB/T 7714	Ge Xiang-yu,Ding Jian-li,Wang Jing-zhe,et al. A New Method for Predicting Soil Moisture Based on UAV Hyperspectral Image[J]. 新疆大学,2020,40(2):602-609.
APA	Ge Xiang-yu,Ding Jian-li,Wang Jing-zhe,Sun Hui-lan,&Zhu Zhi-qiang.(2020).A New Method for Predicting Soil Moisture Based on UAV Hyperspectral Image.SPECTROSCOPY AND SPECTRAL ANALYSIS,40(2),602-609.
MLA	Ge Xiang-yu,et al."A New Method for Predicting Soil Moisture Based on UAV Hyperspectral Image".SPECTROSCOPY AND SPECTRAL ANALYSIS 40.2(2020):602-609.