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Remote sensing technology has many advantages in real-time estimating of crop growth. Crop growing estimation primarily establishes the direct statistical model between spectral data and crop biophysical variables. However, the model accuracy rarely improves with the increase in the number of independent variables owing to the multicollinearity; hence, the addition of a data set different from spectral variables may solve this problem. Considering the strong correlation between crop growth and soil environment, this study aims to investigate whether and how the addition of important soil variables can improve estimation model accuracy. This study collected LAI and Chlorophyll substitution index (SPAD value) of wheat canopy and canopy spectral data under different soil environments to quantify the correspondent relationship. Important spectral parameters (IPs) and important soil variables (ISVs) were selected by least absolute shrinkage and selection operator (LASSO) to establish linear and nonlinear models for wheat growth estimation and the effect of multiple soil variables in enhancing wheat growth estimation was tested. The results indicated LASSO can effectively reduce feature dimensionality for wheat growth estimation with maintaining model accuracy; the extra ISVs can improve the model accuracy due to the high collinearity of spectral parameters. The optimal models of wheat LAI estimation (R 2  = 0.83, RMSE = 0.500) and SPAD estimation (R 2  = 0.75, RMSE = 1.835) were constructed based on orthogonal partial least squares analysis (OPLS) by IPs and IPs+ISVs, respectively. Finally, we discussed the applicability of spectral parameters and soil variables. This research combines remote sensing features of crops with crucial growth variables to obtain an efficient and mechanical crop growth estimation.

Estimating canopy parameters of winter wheat at different stages using hyperspectral data combined with soil variables