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

Abstract

Recently, forest management faces new challenges resulting from increasing temperatures and drought occurrences. For sustainable, site-specific management strategies, the availability of up to date soil information is crucial. Proximal soil sensing techniques are a promising approach for rapid and inexpensive collection of data, and could facilitate the provision of the necessary information. This study evaluates the potential of visual and near-infrared spectroscopy (vis-NIRS) for estimating soil parameters relevant for humus mapping in Saxon forests. Therefore, soil samples from the organic layer are included. So far there is little knowledge about the applicability of vis-NIRS in the humus layer of forests. We investigate the spectral behaviour of samples from organic (Oh) and mineral (0–5 cm, Ah) horizons, pointing out differences in the occurring absorption features. Further, we identify and assess the accuracy of selected soil properties based on vis-NIRS for forest sites, compare the outcome of different regression methods, investigate the implications for forest soils due to the presence and different composition of the humus layer and organic horizons and interpret the results regarding their usefulness for soil mapping and monitoring purposes. For this, we used retained humus soil samples of forests from Saxony. Regression models were built with Partial Least Squares Regression, Support Vector Machine and Cubist. Investigated properties were carbon (C) and nitrogen (N) content, C/N ratio, pH value, cation exchange capacity (CEC) and base saturation (BS) due to their importance for assessing humus conditions in forests. In organic Oh horizons, prediction results for C and N content achieved R² values between 0.44 and 0.58, with corresponding RPIQ ranging from 1.58 to 2.06 depending on the used algorithm. Estimations of C/N ratio were more precise with R² = 0.65 and RMSE = 2.16. Best results were reported for pH value, with R² = 0.90 and RMSE = 0.20. Regarding BS, the best model accuracy was R² = 0.71, with RMSE = 13.97. In mineral topsoil, C and N content models achieved higher values of R² = 0.59 to 0.72, with RPIQ values between 2.22 and 2.54. However, prediction accuracy was lower for C/N ratio (R² = 0.50, RMSE = 3.52) and pH values (R² = 0.62, RMSE = 0.29). Models for CEC achieved R² = 0.65, with RPIQ = 2.81. In general, prediction precision varied dependent on the used algorithm, without showing clear tendencies. Classification into pH classes was exemplified since this offers a new perspective for humus mapping on forest soils. Balanced accuracy for the defined classes ranged from 0.50 to 0.87. We show that vis-NIR spectroscopy is suitable for assessing humus conditions in Saxon forests (Germany), in particular not only for mineral horizons but also for organic Oh horizons.

Multinational prediction of soil organic carbon and texture via proximal sensors

“…This is the visible range of the spectrum related to chromophores (Yang et al., 2019), which consists of compounds that infer color to soils. Absorption features in this region are commonly related to soil OC (Thomas et al., 2022).…”

“…This is the visible range of the spectrum related to chromophores (Yang et al, 2019), which consists of compounds that infer color to soils. Absorption features in this region are commonly related to soil OC (Thomas et al, 2022). F I G U R E 6 Hold-out validation (right, 20% of datasets) and 10-fold cross-validation (left, 80% of datasets) results of the best Random Forest (RF) prediction models of soil organic carbon (OC), sand, silt, and clay using portable X-ray fluorescence (pXRF) spectrometry and visible near-infrared (Vis-NIR) data from four countries (1545 samples, Mozambique data not included).…”

Estimating Soil Organic Matter (SOM) Using Proximal Remote Sensing: Performance Evaluation of Prediction Models Adjusted at Local Scale in the Brazilian Cerrado

“…The quality classification of the models considering the RPIQ was adopted following the criteria defined by Veum et al [44] and Thomas et al [45], where RPIQ ≥ 2.70 represents models with good performance, 2.69 > RPIQ ≥ 1.89 represents models with moderate performance and RPIQ < 1.88 represents models with low performance. The BIAS was obtained by calculating the difference between the reference and predicted values through the spectral curves for the Vis/NIR/SWIR regions [46].…”

Remote Sensing for Soil Organic Carbon Mapping and Monitoring

“…Forests are particularly difficult environments as they show a strong vertical gradient in SOC content from organic to mineral horizons. Thomas et al [12] developed a large spectral library for forests and demonstrated that vis-NIR spectroscopy is suitable for assessing humus conditions in Saxon forests (Germany), not only for mineral horizons but also for organic Oh horizons in particular.…”