麦类耕作栽培合辑Triticeae Crops Physiology · Biochemistry · Cultivation · Tillage
|Rapid determination of leaf water content for monitoring waterlogging in winter wheat based on hyperspectral parameters
YANG Fei-fei1, LIU Tao2, WANG Qi-yuan3, DU Ming-zhu1, YANG Tian-le2, LIU Da-zhong1, LI Shi-juan1, LIU Sheng-ping1
1 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
2 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
3 College of Geoscience and Surveying Engineering, China University of Mining & Technology, Beijing 100083, P.R.China
摘要 于极端降雨量事件的频繁发生，涝渍胁迫已成为粮食生产的明显制约因素。叶片含水量是一个重要的指标，且高光谱遥感为测定它提供了一种无损、实时且可靠的方法。因此，本文基于盆栽试验，于拔节期对冬小麦进行不同涝渍胁迫梯度处理。涝渍胁迫后每7天采集一次叶片高光谱数据、叶片含水量（leaf water content, LWC）数据，直至小麦成熟。结合植被指数构建、相关分析、回归分析、BP神经网络（BP neural network, BPNN）等方法，我们发现：（1）涝渍胁迫对叶片含水量的影响具有滞后性。（2）所有涝渍胁迫均会导致叶片含水量的降低。重度渍水下叶片含水量的下降速度比轻度渍水快，但长期轻度渍水比短期重度渍水对叶片含水量的影响程度更深。（3）叶片含水量的光谱敏感波段位于可见光（VIS, 400-780 nm）和短波红外（SWIR, 1400-2500 nm）波段。（4）以648 nm处原始光谱值，500 nm处一阶微分值，红边位置，新植被指数RVI (437, 466), NDVI (437, 466) 和NDVI' (747, 1956) 作为自变量建立的BPNN模型最适合反演涝渍胁迫冬小麦叶片含水量（建模集：R2=0.889, RMSE=0.138；验模集：R2=0.891, RMSE=0.518）。研究结果对涝渍胁迫精确防控具有重要的理论意义和实际应用价值。
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.
Received: 22 February 2020
|Fund: This work was supported by the National Key Research and Development Program of China (2016YFD0200600, 2016YFD0200601), the Key Research and Development Program of Hebei Province, China (19227407D); the Central Public-interest Scientific Institution Basal Research Fund (JBYW-AII-2020-29, JBYW-AII-2020-30); and the Technology Innovation Project Fund of Chinese Academy of Agricultural Sciences (CAAS-ASTIP-2020-AII).
Correspondence LI Shi-juan, Tel: +86-10-82109916, E-mail: firstname.lastname@example.org;
LIU Sheng-ping, Tel: +86-10-82109348, E-mail: email@example.com
|About author: YANG Fei-fei, E-mail: firstname.lastname@example.org;
Cite this article:
YANG Fei-fei, LIU Tao, WANG Qi-yuan, DU Ming-zhu, YANG Tian-le, LIU Da-zhong, LI Shi-juan, LIU Sheng-ping.
Rapid determination of leaf water content for monitoring waterlogging in winter wheat based on hyperspectral parameters. Journal of Integrative Agriculture, 20(10): 2613-2626.
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