Effects of the Depth of Shallow Groundwater Table During Post-anthesis Stage on Hyperspectral Characteristics of Winter Wheat as well as Model for Predicting Leaf Chlorophyll Content
WU Qixia;YAN Jun;ZHU Jianqiang;LI Dongwei;ZHOU Xinguo;GUO Shulong;Farmland Irrigation Research Institute, Chinese Academy of Agricultural Sciences;CAAS/Agricultural Environmental Science Observation Experiment Stations of Shangqiu, Ministry of Agriculture;College of Agriculture, Yangtze University;
【Objective】The monsoon spring in Jianghan Plain of China often results in waterlogging in wheat field. This study aimed to investigate the feasibility of using hyperspectral remote sensing to monitor the physiological traits of the wheat under different depth of shallow groundwater table in attempts to provide a non-destructive and rapid method to monitor waterlogged stress.【Method】We designed three shallow groundwater depths at0, 20 and 40 cm after the anthesis stage. In the experiment, the spectral reflectance of the wheat canopy and the flag leaf chlorophyll content were measured after 8 days, 17 days and 28 days of the onset of the experiment. The effects of the shallow groundwater depth on the hyperspectral characteristics were analyzed and a model was proposed to calculate the chlorophyll content.【Result】When the subsurface waterlogging continued for about 17 days at groundwater depth of 0 cm and 20 cm, the spectral reflectance of the canopy in the two absorption-valleys in the blue-purple wave band and the infrared wave band increased and the reflection-valley became flat, and the peak between the two absorption valleys became steep. The shallower of groundwater table was, the longer it continued, and the rising of reflectivity in the two absorption valleys became steeper and the peak became flatter. Waterlogging caused an reduction in red absorption and the red edge"blue shifts". The longer of duration that crop stayed under the shallow groundwater, the more obvious of"blue shifts"was. The liner and quadratic regression models were selected to stimulate the relationship between the position of red edge(λr), the skewness of red edge(Sr), the kurtosis of red edge(Kr) and the Chla, Chlb and Chl(a + b) content of wheat flag leaf under shallow groundwater depth stress. The R2 of the λr, Sr, Kr-based BP neural network model was used to estimate Chla, Chlb and Chl(a+b) content of wheat flag leaf under shallow groundwater depth stress. The R2 were 0.842 5, 0.700 2,0.850 8, and the RMSE were 0.146, 0.048 and 0.173 respectively.【Conclusion】The BP neural network model can be used to estimate the dynamics of the chlorophyll content the flag leaf under the shallow groundwater stress.
【Fund】： 中国农业科学院农田灌溉研究所开放课题(SQZ2015-02);; 公益性行业(农业)科研专项(201203032)
【CateGory Index】： S512.1
【CateGory Index】： S512.1