干旱区生态环境知识资源中心(Arid): Possibility of Zhuhai-1 Hyperspectral Imagery for Monitoring Salinized Soil Moisture Content Using Fractional Order Differentially Optimized Spectral Indices

Arid

DOI	10.3390/w12123360
	Possibility of Zhuhai-1 Hyperspectral Imagery for Monitoring Salinized Soil Moisture Content Using Fractional Order Differentially Optimized Spectral Indices
	Kahaer, Yasenjiang; Tashpolat, Nigara; Shi, Qingdong; Liu, Suhong
通讯作者	Tashpolat, N ; Shi, QD (corresponding author), Xinjiang Univ, Coll Resources & Environm Sci, Urumqi 830046, Peoples R China. ; Tashpolat, N ; Shi, QD (corresponding author), Xinjiang Univ, Key Lab Oasis Ecol, Minist Educ, Urumqi 830046, Peoples R China.
来源期刊	WATER
EISSN	2073-4441
出版年	2020
卷号	12 期号:12
英文摘要	The possibility of quantitative inversion of salinized soil moisture content (SMC) from Zhuhai-1 hyperspectral imagery and the application effect of fractional order differentially optimized spectral indices were discussed, which provided new research ideas for improving the accuracy of hyperspectral remote sensing inversion. The hyperspectral data from indoor and Zhuhai-1 remote sensing imagery were resampled to the same spectral scale. The soil hyperspectral data were processed by fractional order differential preprocessing method and optimized spectral indices method, and the Pearson correlation coefficient (PCC/r) analysis was made with SMC data. The sensitive optimized spectral indices were used to establish the ground hyperspectral estimation model, and a variety of modeling methods were used to select the best SMC inversion model. The results were as follows: the maximum one-dimensional r between SMC and the 466-938 nm band was -0.635, the maximum one-dimensional r with the 0.5-order absorbance spectrum was 0.665, and the maximum two-dimensional r with the difference index (DI) calculated by the 0.5-order absorbance spectrum was +/- 0.72. The maximum three-dimensional r with the triangle vegetation index (TVI) calculated from the 0.5-order absorbance spectrum reached 0.755, which exceeded the one-dimensional r extreme value of 400-2400 nm. The TreeNet gradient boosting machine (TGBM) regression model had the highest modeling accuracy, with a calibration coefficient of determination (R-C(2)) = 0.887, calibration root mean square error (RMSEC) = 2.488%, standard deviation (SD) = 6.733%, and r = 0.942. However, the partial least squares regression (PLSR) model had the strongest predictive ability, with validation coefficient of determination (R-V(2)) = 0.787, validation root mean square error (RMSEV) = 3.247%, and relative prediction deviation (RPD) = 2.071. The variable importance in projection (VIP) method could not only improve model efficiency but also increased model accuracy. R-C(2) of the optimal PLSR model was 0.733, RMSEC was 3.028%, R-V(2) was 0.805, RMSEV was 3.100%, RPD was 1.976, and Akaike information criterion (AIC) was 151.050. The three-band optimized spectral indices with fractional differential pretreatment could to a certain extent break through the limitation of visible near-infrared spectrum in SMC estimation due to the lack of shortwave infrared spectra, which made it possible to quantitatively retrieve saline SMC on the basis of Zhuhai-1 hyperspectral imagery.
英文关键词	Zhuhai-1 hyperspectral imagery salinized soil moisture content fractional order differential optimized spectral indices Ugan– Kuqa Oasis
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:000603056000001
WOS关键词	ORGANIC-MATTER CONTENT ; NATURE-RESERVE ELWNNR ; CLAY CONTENT ; PREDICTION ; SPECTROSCOPY ; REFLECTANCE ; MODEL ; CHINA
WOS类目	Environmental Sciences ; Water Resources
WOS研究方向	Environmental Sciences & Ecology ; Water Resources
来源机构	北京师范大学 ; 新疆大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/349225
作者单位	[Kahaer, Yasenjiang; Tashpolat, Nigara; Shi, Qingdong] Xinjiang Univ, Coll Resources & Environm Sci, Urumqi 830046, Peoples R China; [Kahaer, Yasenjiang; Tashpolat, Nigara; Shi, Qingdong] Xinjiang Univ, Key Lab Oasis Ecol, Minist Educ, Urumqi 830046, Peoples R China; [Liu, Suhong] Beijing Normal Univ, Beijing Key Lab Environm Remote Sensing & Digital, Beijing 100875, Peoples R China
推荐引用方式 GB/T 7714	Kahaer, Yasenjiang,Tashpolat, Nigara,Shi, Qingdong,et al. Possibility of Zhuhai-1 Hyperspectral Imagery for Monitoring Salinized Soil Moisture Content Using Fractional Order Differentially Optimized Spectral Indices[J]. 北京师范大学, 新疆大学,2020,12(12).
APA	Kahaer, Yasenjiang,Tashpolat, Nigara,Shi, Qingdong,&Liu, Suhong.(2020).Possibility of Zhuhai-1 Hyperspectral Imagery for Monitoring Salinized Soil Moisture Content Using Fractional Order Differentially Optimized Spectral Indices.WATER,12(12).
MLA	Kahaer, Yasenjiang,et al."Possibility of Zhuhai-1 Hyperspectral Imagery for Monitoring Salinized Soil Moisture Content Using Fractional Order Differentially Optimized Spectral Indices".WATER 12.12(2020).