Rapid determination of leaf water content for monitoring waterlogging in winter wheat based on hyperspectral parameters

Journal of Integrative Agriculture

Ming-zhu DU¹  Qi-yuan WANG²  Tao LIU³  Da-zhong LIU³  Fei-fei YANG⁴  Tian-le YANG⁴  Sheng-ping LIU⁴  Shi-juan LI⁴ 

[1] Technology, Beijing 100083, P.R.China;;College of Geoscience and Surveying Engineering, China University of Mining &Jiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops/Agricultural College, Yangzhou University, Yangzhou 225009, P.R.China;Key Laboratory of Agri-information Service Technology, Ministry of Agriculture and Rural Affairs/Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, P.R.China;

关键词: winter wheat;    hyperspectral remote sensing;    leaf water content;    new vegetation index;    BP neural network;   

DOI  :  

来源: DOAJ

【 摘 要 】

Waterlogging is becoming an obvious constraint on food production due to the frequent occurrence of extremely high-level rainfall events. Leaf water content (LWC) is an important waterlogging indicator, and hyperspectral remote sensing provides a non-destructive, real-time and reliable method to determine LWC. Thus, based on a pot experiment, winter wheat was subjected to different gradients of waterlogging stress at the jointing stage. Leaf hyperspectral data and LWC were collected every 7 days after waterlogging treatment until the winter wheat was mature. Combined with methods such as vegetation index construction, correlation analysis, regression analysis, BP neural network (BPNN), etc., we found that the effect of waterlogging stress on LWC had the characteristics of hysteresis and all waterlogging stress led to the decrease of LWC. LWC decreased faster under severe stress than under slight stress, but the effect of long-term slight stress was greater than that of short-term severe stress. The sensitive spectral bands of LWC were located in the visible (VIS, 400–780 nm) and short-wave infrared (SWIR, 1 400–2 500 nm) regions. The BPNN Model with the original spectrum at 648 nm, the first derivative spectrum at 500 nm, the red edge position (λr), the new vegetation index RVI (437, 466), NDVI (437, 466) and NDVI’ (747, 1 956) as independent variables was the best model for inverting the LWC of waterlogging in winter wheat (modeling set: R2=0.889, RMSE=0.138; validation set: R2=0.891, RMSE=0.518). These results have important theoretical significance and practical application value for the precise control of waterlogging stress.

【 授权许可】

Unknown