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.

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.

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<p>Area-wide high resolution information of organic layer properties is required for assessing the current nutrient availability in forest stands. Together with climate, location, parent material and terrain predictors, vegetation is known to have a direct impact on the characteristics of the organic surface layer of forest soils and therefore plays an important role in predicting its chemical properties.</p><p>Here, we use multi-temporal Sentinel-2 (S2) Earth Observation (EO) data as a proxy for various vegetation characteristics to spatially predict forest organic layer properties at 10 m spatial resolution in Saxony/Germany. To ensure full data coverage of the study area, we used multi-temporal statistical measures of S2 data generated by the FORCE algorithm. Complementary to ancillary predictors (climate, location, parent material, terrain), we compared three different sets of vegetation related data as predictors: (1) categorical tree species groups from a field survey, (2) multi-temporal statistical measures derived from S2 data and (3) S2 multi-temporal statistical measures and additional S2 multi-temporal spectral indices.</p><p>We used random forest regression models to estimate pH value, base saturation, C/N ratio and effective cation exchange capacity of the organic surface layer and the upper 0-5 cm of the first mineral soil horizon. For model evaluation 5-times 10-fold cross-validation was applied. For predictor evaluation and selection, we used recursive feature elimination.</p><p>The results indicate that S2 data can serve as a vegetation proxy when predicting forest organic layer properties. For example, the cross-validation estimate of the prediction error in scenario (3) for C/N ratio (organic surface layer) is about 7.3 % lower than in scenario (1). In some cases the explanatory power is higher compared to the field survey data of the tree species groups, probably due to the high local variability of EO based data. This may help to reveal short range variability of chemical properties. We conclude that the three scenarios show comparable results and thus multi-temporal S2 data can be used as a vegetation proxy to spatially predict chemical properties of the organic surface layer of forest soils.</p>

<p>There is a high demand for information about soil conditions in forests stands as it is crucial to ensure sustainable management, to maintain ecosystem services, to preserve timber production and establish proper pest management. Nowadays, the main drivers for changes in soil conditions are element input, forest conversion, subsoil liming and changing climate. These drivers influence nutrients and water availability and are challenging current site mapping methods. However, for impact assessment high-resolution and up-to-date information is needed. As laboratory analysis is time consuming and expensive, alternative approaches are preferred.</p><p>The project DIGI-Humus uses methods of reflectance spectroscopy in the visual and near-infrared-region of the electromagnetic spectrum for indirect measurement and prediction of physical and chemical soil properties in forest stands. For this purpose, spectral data were collected under laboratory conditions to build a database of forest soils. We used retained samples from Saxony soil survey, measuring both Oh and Ah horizons. To ensure data quality, we developed our own protocol based on literature review and self-conducted test measurements. The data has been used to successfully calibrate regression models based on different forest types and soil horizons to predict the soil parameters C and N content, C/N ratio and pH-value.</p><p>To improve model performance and test its generalization capability, the created library has been extended with new samples from a field campaign conducted in 2019 at an additional local test site. Using this data, the impact of adding new information to the modelling process and the robustness of the models could be evaluated.</p><p>The results of this research will be used to assess forest sites regarding nutrients availability, as basis for the development of site specific management strategies and to enhance and improve current methods of periodic site mapping of forest stands.</p>