干旱区生态环境知识资源中心(Arid): Developing a Hyperspectral Remote Sensing-Based Algorithm to Diagnose Potato Moisture for Water-Saving Irrigation

Arid

DOI	10.3390/horticulturae10080811
	Developing a Hyperspectral Remote Sensing-Based Algorithm to Diagnose Potato Moisture for Water-Saving Irrigation
	Suyala, Qiqige; Li, Zhuoling; Zhang, Zhenxin; Jia, Liguo; Fan, Mingshou; Sun, Youping; Xing, Haifeng
通讯作者	Xing, HF
来源期刊	HORTICULTURAE
EISSN	2311-7524
出版年	2024
卷号	10 期号:8
英文摘要	Appropriate water supply is crucial for high-yield and high-quality potato tuber production. However, potatoes are mainly planted in arid and semi-arid regions in China, where the precipitation usually cannot meet the water demand throughout the growth period. In view of the actual situation of water shortage in these areas, to monitor the water status of potato plants timely and accurately and thus precisely control the irrigation are of significance for water-saving management of potatoes. Hyperspectral remote sensing has unique advantages in diagnosing crop water stress. In this paper, the canopy spectral reflectance and plant water content were measured under five irrigation treatments. The spectral parameters that respond to plant water content were selected, and a hyperspectral water diagnosis model for leaf water content (LWC) and aboveground water content (AGWC) of potato plants was established. It was found that potato tuber yield was the highest during the entire growth period under sufficient irrigation, and the plant water content showed a downward trend as the degree of drought intensified. The peak hyperspectral reflectance of potato plant canopies appeared in the red wavelength, where the reflectance varied significantly under different water treatments and decreased with decreasing irrigation. Six models with sensitive bands, first-order derivatives, and moisture spectral indices were established to monitor water content of potato plants. The R2 values of partial least squares regression (PLSR), support vector machine (SVM), and BP neural network (BP) models are 0.8418, 0.9020, and 0.8926, respectively, between LWC and hyperspectral data; and 0.8003, 0.8167, and 0.8671, respectively, between the AGWC and hyperspectral data. These six models can all predict the water content of potato plants, but SVM is the best model for predicting LWC of potato plants. These results are of great significance for guiding precision irrigation of potato plants at different growth stages.
英文关键词	potato water diagnosis models hyperspectral precision irrigation
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:001305309100001
WOS关键词	LEAF-AREA INDEX ; SPECTRAL REFLECTANCE ; YIELD ; LAI
WOS类目	Horticulture
WOS研究方向	Agriculture
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/404069
推荐引用方式 GB/T 7714	Suyala, Qiqige,Li, Zhuoling,Zhang, Zhenxin,et al. Developing a Hyperspectral Remote Sensing-Based Algorithm to Diagnose Potato Moisture for Water-Saving Irrigation[J],2024,10(8).
APA	Suyala, Qiqige.,Li, Zhuoling.,Zhang, Zhenxin.,Jia, Liguo.,Fan, Mingshou.,...&Xing, Haifeng.(2024).Developing a Hyperspectral Remote Sensing-Based Algorithm to Diagnose Potato Moisture for Water-Saving Irrigation.HORTICULTURAE,10(8).
MLA	Suyala, Qiqige,et al."Developing a Hyperspectral Remote Sensing-Based Algorithm to Diagnose Potato Moisture for Water-Saving Irrigation".HORTICULTURAE 10.8(2024).