干旱区生态环境知识资源中心(Arid): Application of Fractional Differential Technique in Estimating Soil Water Content from Airborne Hyperspectral Data

Arid

DOI	10.3964/j.issn.1000-0593(2022)11-3559-09
	Application of Fractional Differential Technique in Estimating Soil Water Content from Airborne Hyperspectral Data
	Wang Jin-jie; Ding Jian-li; Ge Xiang-yu; Zhang Zhe; Han Li-jing
通讯作者	Ding, JL
来源期刊	SPECTROSCOPY AND SPECTRAL ANALYSIS
ISSN	1000-0593
出版年	2022
卷号	42 期号:11 页码:3559-3567
英文摘要	UAV-based remote sensing technique provides a new perspective and platform for precision agriculture and agricultural information monitoring. The hyperspectral sensor has centimeter-level spatial and fine spectral resolution, allowing for the acquisition of high-quality hyperspectral data. However, hyperspectral data often bring question on noise, data redundancy and inefficient use of hyperspectral information, whereas conventional preprocessing is difficult to estimate withhigh-precision. Therefore, data mining for UAV-based hyperspectral images is essential to solve the above problems. Here we used fractional order differential (FOD) to process UAV-based hyperspectral data (with a step length of 0. 1). The optimal FOD order is explored at the spectral level by comparing the ability of the FOD technique with the integer order technique to improve the hyperspectral data. Soil moisture content (SMC) estimation models were constructed under the Gradient-Boosted Regression Tree (GBRT) algorithm, and the spatial distribution of SMC was finally evaluated under the best model. The results found that the correlation between the spectrum and SMC alsowas increased (absolute maximum correlation coefficient, r(max) = 0. 768). Compared with the original image and processed images via first and second order derivatives, r(max) increased by 0. 168, 0. 157 and 0. 158, respectively. The main reason for the FOD technique to enhance the accuracy of model estimation is to highlight the role of effective spectral information, especially chlorophyll, plant structure and water response bands closely sensitive to drought stress. (430, 460, 640, 660 and 970 nm). By comparison, the low-order FOD (order<1) is more effective in the image quality, correlation and model accuracy than high-order FOD (orderer 1). The higher order FOD adds a certain amount of noise to the image, though the FOD technology achieves the desired result. Estimated model achieved the best results in the 0. 4 -order model (R-p(2), =0. 874, RMSEP= 1. 458, RPIQ= 3. 029). In addition, the SMC estimation models of 0. 1-0. 9 order and 1. 6-1. 9 order outperformed the integer-order models (R-p(2), improvement of 0. 8%similar to 13. 8%) but the lower-order FOD models were found to be stronger in terms of model predictive power based on the RPIQ of the models. The spatial distribution of inverse farmland soil moisture under the 0. 4 order model indicated significant spatial heterogeneity of farmland SMC in the arid regions. In conclusion, the low-order FOD technique effectively enables the mining of hyperspectral data to accurately estimate agricultural SMC. This study proposes a new approach to airborne hyperspectral image processing that provides a new strategy for precision agriculture implementation and management in arid regions.
英文关键词	Hyperspectral UAV FOD Precision farming Soil moisture content
类型	Article
语种	中文
收录类别	SCI-E
WOS记录号	WOS:000891902300036
WOS关键词	ORGANIC-MATTER ; CALIBRATION
WOS类目	Spectroscopy
WOS研究方向	Spectroscopy
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/394536
推荐引用方式 GB/T 7714	Wang Jin-jie,Ding Jian-li,Ge Xiang-yu,et al. Application of Fractional Differential Technique in Estimating Soil Water Content from Airborne Hyperspectral Data[J],2022,42(11):3559-3567.
APA	Wang Jin-jie,Ding Jian-li,Ge Xiang-yu,Zhang Zhe,&Han Li-jing.(2022).Application of Fractional Differential Technique in Estimating Soil Water Content from Airborne Hyperspectral Data.SPECTROSCOPY AND SPECTRAL ANALYSIS,42(11),3559-3567.
MLA	Wang Jin-jie,et al."Application of Fractional Differential Technique in Estimating Soil Water Content from Airborne Hyperspectral Data".SPECTROSCOPY AND SPECTRAL ANALYSIS 42.11(2022):3559-3567.