Application of Low-Cost MEMS Spectrometers for Forest Topsoil Properties Prediction

Abstract

Increasing temperatures and drought occurrences recently led to soil moisture depletion and increasing tree mortality. In the interest of sustainable forest management, the monitoring of forest soil properties will be of increasing importance in the future. Vis-NIR spectroscopy can be used as fast, non-destructive and cost-efficient method for soil parameter estimations. Microelectromechanical system devices (MEMS) have become available that are suitable for many application fields due to their low cost as well as their small size and weight. We investigated the performance of MEMS spectrometers in the visual and NIR range to estimate forest soil samples total C and N content of Ah and Oh horizons at the lab. The results were compared to a full-range device using PLSR and Cubist regression models at local (2.3 ha, n: Ah = 60, Oh = 50) and regional scale (State of Saxony, Germany, 184,000 km2, n: Ah = 186 and Oh = 176). For each sample, spectral reflectance was collected using MEMS spectrometer in the visual (Hamamatsu C12880MA) and NIR (NeoSpetrac SWS62231) range and using a conventional full range device (Veris Spectrophotometer). Both data sets were split into a calibration (70%) and a validation set (30%) to evaluate prediction power. Models were calibrated for Oh and Ah horizon separately for both data sets. Using the regional data, we also used a combination of both horizons. Our results show that MEMS devices are suitable for C and N prediction of forest topsoil on regional scale. On local scale, only models for the Ah horizon yielded sufficient results. We found moderate and good model results using MEMS devices for Ah horizons at local scale (R2≥ 0.71, RPIQ ≥ 2.41) using Cubist regression. At regional scale, we achieved moderate results for C and N content using data from MEMS devices in Oh (R2≥ 0.57, RPIQ ≥ 2.42) and Ah horizon (R2≥ 0.54, RPIQ ≥2.15). When combining Oh and Ah horizons, we achieved good prediction results using the MEMS sensors and Cubist (R2≥ 0.85, RPIQ ≥ 4.69). For the regional data, models using data derived by the Hamamatsu device in the visual range only were least precise. Combining visual and NIR data derived from MEMS spectrometers did in most cases improve the prediction accuracy. We directly compared our results to models based on data from a conventional full range device. Our results showed that the combination of both MEMS devices can compete with models based on full range spectrometers. MEMS approaches reached between 68% and 105% of the corresponding full ranges devices R2 values. Local models tended to be more accurate than regional approaches for the Ah horizon. Our results suggest that MEMS spectrometers are suitable for forest soil C and N content estimation. They can contribute to improved monitoring in the future as their small size and weight could make in situ measurements feasible.

Evaluation of two miniaturized FT‐NIR spectrometers for rapid soil property analysis

“…Results for predicting TN in other studies with low-cost spectrometers have been mixed. A study using Indonesian soils had an R 2 of 0.52 (Ng, Husnain, et al, 2020), compared to a value of 0.85 for a study in Germany (Thomas et al, 2021). Reflectance spectroscopy has been successfully used to predict TN (Li et al, 2019) and further testing with low-cost spectrometers is warranted.…”

Estimating Forest Soil Properties for Humus Assessment—Is Vis-NIR the Way to Go?

“…The predictions can further be improved by creating several models ("committees"), where each new model corrects the previous one to reduce the error [63]. It was already used for soil property estimations in previous studies [19].…”

“…This approach has already been applied, e.g., for mixed samples of whole soil profiles [22] as well as modelling samples from both Oh and A horizon with the same model [71]. On the other hand, this could imply further difficulties as the wider data range for C and N contents could result in overoptimistic error measures [19]. Another reason against combined modeling is the different spectral behaviour of the investigated horizons.…”

“…They used partial least squares regression (PLSR) approaches and confirmed the usefulness for SOC and N contents, but had variable results for pH-values dependent on the data range. Comparing different sensors for acquiring spectral data, Thomas et al [19] found useful results for C and N content in Saxon forests.…”

Micro-Electro-Mechanical Systems in Light Stabilization

“…However, the progress of miniaturization has not stopped at the mini-spectrometer level, because optical sensors using micro-electro-mechanical systems (MEMS) technology have been developed. In the meantime, a number of scientific publications have been produced on research in various areas of these sensors’ applications [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ]. Unfortunately, there have also been critical opinions about the MEMS technology applications in spectral analysis.…”