多遥感光谱指标结合进行大田冬小麦叶片叶绿素含量估测研究

为解决大田冬小麦叶片叶绿素含量估测模型精度低、通用性弱的问题，在获取冬小麦拔节期和抽穗期冠层红光波段反射率(BR_red)和近红外波段反射率(BR_nir)的基础上，计算归一化差值植被指数(NDVI)、差值植被指数(DVI)、比值植被指数(RVI)、土壤调节植被指数(SAVI)、改进型比值植被指数(MSR)、重归一化植被指数(RDVI)、II型增强植被指数(EVI2)和非线性植被指数(NLI)等8个植被指数。经统计分析，选择与叶片叶绿素含量(SPAD值)相关性较好的5个遥感光谱指标(NDVI、MSR、NLI、BR_red和RVI)作为输入变量，建立了冬小麦叶片叶绿素含量的BP神经网络估测模型(WWLCC_BP),并对估测模型进行精度验证。结果表明，WWLCC_BP估测模型在拔节期估测的决定系数(r²)为0.84,均方根误差(RMSE)为5.39,平均相对误差(ARE)为9.87%。抽穗期的估测效果与拔节期较为一致。将WWLCC_BP和高分六号影像...

Study on Chlorophyll Content Estimation of Winter Wheat Leaf Based on Multiple Remote Sensing Spectral Indices

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 (BR_red) and near infrared band reflectance (BR_nir) 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, BR_red, and RVI) well correlated with leaf chlorophyll content were selected as input variables to establish a BP neural network estimation model (WWLCC_BP) 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 WWLCC_BP 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 WWLCC_BP 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.