| In order to solve the problems of low precision and low universality of the model for estimating the chlorophyll content of winter wheat leafin the field, an accurate and efficient method was proposed by combining multiple remote sensing spectral indices and neural networks. Based on the red band reflectance (BRred) and near infrared band reflectance (BRnir) of winter wheat canopy at jointing and heading stages, the normalized difference vegetation index (NDVI), differential vegetation index (DVI), ratio vegetation index (RVI), soil adjusted vegetation index (SAVI), modified simple ratio vegetation index (MSR), renormalization difference vegetation index (RDVI), enhanced vegetation index of type II(EVI2) and nonlinear vegetation index (NLI) were calculated. After statistical analysis, five remote sensing spectral indicators (NDVI, MSR, NLI, BRred, and RVI) well correlated with leaf chlorophyll content were selected as input variables to establish a BP neural network estimation model (WWLCCBP) for winter wheat leaf chlorophyll content, and the accuracy of the estimation model was verified. The results showed that the determination coefficient (r2), root mean square error (RMSE), and average relative error (ARE) of WWLCCBP estimation model at jointing stage were 0.84, 5.39, and 9.87%, respectively. The estimation effect of heading stage was consistent with that of jointing stage. The spatial distribution information of chlorophyll content in winter wheat leaf in the study area was monitored by combining WWLCCBP and GF-6 image. The winter wheat with leaf SPAD value between 43.2 and 53.7 grew normally, and the planting area was 25 483 hm2, accounting for 69.81% of the total planting area of winter wheat. The neural network estimation model based on multiple remote sensing spectral indices can effectively estimate the chlorophyll content of winter wheat leaf in the field. |
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