Correlation between soil apparent electroconductivity and plant hyperspectral reflectance in a managed wetland

Abstract

The apparent electrical conductivity (σa) of soil is influenced by a complex combination of soil physical and chemical properties. For this reason, σa is proposed as an indicator of plant stress and potential community structure changes in an alkaline wetland setting. However, assessing soil σa is relatively laborious and difficult to accomplish over large wetland areas. This work examines the feasibility of using the hyperspectral reflectance of the vegetation canopy to characterize the σa of the underlying substrate in a study conducted in a Central California managed wetland. σa determined by electromagnetic (EM) inductance was tested for correlation with in-situ hyperspectral reflectance measurements, focusing on a key waterfowl forage species, swamp timothy (Crypsis schoenoides). Three typical hyperspectral indices, individual narrow-band reflectance, first-derivative reflectance and a narrow-band normalized difference spectral index (NDSI), were developed and related to soil σa using univariate regression models. The coefficient of determination (R 2) was used to determine optimal models for predicting σa, with the highest value of R 2 at 2206 nm for the individual narrow bands (R 2 = 0.56), 462 nm for the first-derivative reflectance (R 2 = 0.59), and 1549 and 2205 nm for the narrow-band NDSI (R 2 = 0.57). The root mean squared error (RMSE) and relative root mean squared error (RRMSE) were computed using leave-one-out cross-validation (LOOCV) for accuracy assessment. The results demonstrate that the three indices tested are valid for estimating σa, with the first-derivative reflectance performing better (RMSE = 30.3 mS m−1, RRMSE = 16.1%) than the individual narrow-band reflectance (RMSE = 32.3 mS m−1, RRMSE = 17.1%) and the narrow-band NDSI (RMSE = 31.5 mS m−1, RRMSE = 16.7%). The results presented in this paper demonstrate the feasibility of linking plant–soil σa interactions using hyperspectral indices based on in-situ spectral measurements.