Journal Article

Impact of Spectral Resolution and Signal-to-Noise Ratio in Vis–NIR Spectrometry on Soil Organic Matter Estimation

Yu, Bo, Dai, Hu, Yuan, Jing, Xu, Jiawei, Ma, Chaoran, Yan, Changxiang

2023

Overview

Recently, considerable efforts have been devoted to the estimation of soil properties using optical payloads mounted on drones or satellites. Nevertheless, many studies focus on diverse pretreatments and modeling techniques, while there continues to be a conspicuous absence of research examining the impact of parameters related to optical remote sensing payloads on predictive performance. The main aim of this study is to evaluate how the spectral resolution and signal-to-noise ratio (SNR) of spectrometers affect the precision of predictions for soil organic matter (SOM) content. For this purpose, the initial soil spectral library was partitioned into to two simulated soil spectral libraries, each of which were individually adjusted with respect to the spectral resolutions and SNR levels. To verify the consistency and generality of our results, we employed four multiple regression models to develop multivariate calibration models. Subsequently, in order to determine the minimum spectral resolution and SNR level without significantly affecting the prediction accuracy, we conducted ANOVA tests on the RMSE and R2 obtained from the independent validation dataset. Our results revealed that (i) the factors significantly affecting SOM prediction performance, in descending order of magnitude, were the SNR levels > spectral resolutions > estimation models, (ii) no substantial difference existed in predictive performance when the spectral resolution fell within 100 nm, and (iii) when the SNR levels exceeded 15%, altering them did not notably affect the SOM predictive performance. This study is expected to provide valuable insights for the design of future optical remote sensing payloads aimed at monitoring large-scale SOM dynamics.

• Publisher: MDPI AG
• Subject: Accuracy / Artificial satellites in remote sensing / Carbon / data collection / Humus / Libraries / Methods / multiple regression model / Multiple regression models / Optical properties / optical remote sensing / Organic matter / Payloads / Performance prediction / prediction / Predictions / Regression analysis / Remote sensing / Sensors / Signal processing / Signal to noise ratio / soil / Soil organic matter / Soil properties / Soils / spectral analysis / Spectral resolution / Spectrometers / Spectrometry / spectroscopy / Variance analysis