VIS-NIR Reflectance Spectroscopy for Assessment of Soil Microbiological Properties

Abstract

Soil microbes play an important role in major processes of soil system. Rapid, reliable and cost effective assessment of soil microbiological properties is highly essential for monitoring soil health. Traditional chemical-based soil analysis is laborious, hazardous and time consuming. Visible near-infrared (VIS-NIR) reflectance spectroscopy can be a viable alternative to ensure rapid and environmentally safe soil test results. With this objective, two key soil microbiological parameters microbial biomass carbon (MBC) and dehydrogenase activity (DHA) were examined for their amenability to diffuse reflectance spectroscopy assessment. Geo referenced surface soil (0-15cm) samples (n=170) were collected from different fields for spectral analysis. Partial least square regression (PLSR) technique was employed for developing spectral models. The different model evaluation indices like coefficient of determination (R 2 ), root mean square error of prediction (RMSE) and ratio of performance deviation (RPD) were used to evaluate the predictive performance of model. The RPD values for MBC and DHA were 1.15 and 1.13. Poor predictive performance of our model as opposed to substantial evidence in literature can be ascribed to range of factors like limited variability in spectra, relatively smaller sample size, use of entire spectra instead of selective regions and use of samples collected under natural farm environment as opposed to those generated under controlled conditions. K e y w o r d sDehydrogenase, Land use, Microbial biomass carbon, Reflectance spectroscopy, VIS-NIR models.

Spectral assessment of soil properties in semi-arid tropical regions of southern Karnataka Plateau

“…The development of regional-specific soil spectral libraries is, therefore, a priority for soil research worldwide (Brown et al 2006) Several successful VNIR spectral models have been developed to predict soil properties of agronomic importance in India (Dwivedi et al 1981;Singh et al 2014). For example, the soil properties linked to nutrient management in precision farming (Vibhute et al 2018); assessment of soil salinity properties (Srivastava et al 2017); assessment of functional soil properties (Saxena et al 2003;Srivastava et al 2004;Kadupitiya et al 2010;Solanke et al 2021); hydraulic property prediction (Santra et al 2009); soil moisture assessment (Gulfo et al 2012); aggregate size segregation (Sarathjith et al 2014); soil microbiological properties (Mondal et al 2017) have been assessed spectroscopically using different models.…”