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Forests cover one-third of the global land and are important components of carbon and nitrogen cycling. Anthropogenic disturbances, such as forest road systems or skid trails for timber harvesting, can dramatically change the nutrient cycling in these ecosystems. Skid trails increase soil erosion and thus the displacement of soil organic carbon (SOC) and total nitrogen (Nt). Additionally, runoff transports high amounts of dissolved organic carbon (DOC), which can have a negative impact on aquatic ecosystems. One of the most important countermeasures against soil erosion is the quick recolonization of vegetation. To date, the extent to which natural vegetation succession influences the relocation of SOC and Nt, and in particular the role of mosses in this context, has not been well investigated. This study investigates the influence of natural vegetation succession and in particular of mosses on the displacement process of SOC and Nt as well as DOC caused by soil erosion. To this end, we combine the results of a field study using in-situ rainfall simulations with small-scale runoff plots in skid trails of the Schönbuch Nature Park in southwestern Germany with the results of ex-situ rainfall simulation experiments with infiltration boxes containing the substrate from the respective skid trails. The eroded sediments of skid trails were on average enriched in SOC by 16% and in Nt by 35% compared to the original soil, which lead to a decrease of the C/N ratio in sediments. As vegetation succession progressed, the displacement of SOC and Nt was reduced, confirmed by a negative correlation between the enrichment ratios of SOC (ERSOC), Nt and total vegetation cover. However, mosses tended to reduce ERSOC more than vascular plants. Additionally, mosses significantly decreased DOC concentration in surface runoff compared to bare soils, while no difference in DOC concentration in percolated water could be observed. Future research should explore the role of mosses in the storage of SOC and Nt in the soil and their impact on soil stability. Thus, utilizing mosses could potentially minimize environmental impacts from soil disturbances in forests. [ABSTRACT FROM AUTHOR]

Mosses are often overlooked; however, they are important for soil-atmosphere interfaces with regard to water exchange. This study investigated the influence of moss structural traits on maximum water storage capacities (WSCmax) and evaporation rates, and species-specific effects on water absorption and evaporation patterns in moss layers, moss-soil-interfaces and soil substrates using biocrust wetness probes. Five moss species typical for Central European temperate forests were selected: field-collected Brachythecium rutabulum, Eurhynchium striatum, Oxyrrhynchium hians and Plagiomnium undulatum; and laboratory-cultivated Amblystegium serpens and Oxyrrhynchium hians.

Rainfall redistribution through the vegetation canopy plays a key role in the hydrological cycle. Although there have been studies on the heterogeneous patterns of rainfall redistribution in some ecosystems, the understanding of this process in different stages of forest succession remains insufficient. Therefore, this study investigated the change tendency in rainfall redistribution and rainwater chemistry in a subtropical forest succession in South China, based on 22 years (2001–2022) of rainfall monitoring (740 valid events). Results showed that, at the event scale, both the throughfall ratio and the stemflow ratio in pine forest (PF) were higher than in mixed forest (MF) and broadleaf forest (BF). At the interannual scale, throughfall and stemflow of forests experienced an initial decrease followed by a subsequent increase over the entire measurement period (except stemflow of the pine forest), which reflects the trend in open rainfall. The variability in throughfall showed an increase from MF to PF to BF, and the variability in stemflow likewise showed an increase from MF to PF to BF. Changes in throughfall and stemflow in the BF are thus higher than those in the MF and PF over time. Furthermore, important differences in rainwater chemistry fluxes among the three forest types were found, changing in varying order over time. On average, total nitrogen (TN) and total phosphorus (TP) fluxes of throughfall increased from BF to MF to PF, while the potassium (K+) flux of throughfall showed a decrease from BF to MF to PF. Stemflow chemical fluxes varied less among forest types and over time, although tree species most importantly affected varying stemflow chemistry. These results show important changes in patterns of rainfall redistribution over time and that characteristic variations are driven by rainfall and forest factors. Therefore, this study provides insight into long-term rainfall redistribution processes by linking changes in rainfall spectra with a typical subtropical forest succession sequence. [ABSTRACT FROM AUTHOR]

(1) Background: The use of multiscale prediction or the optimal scaling of predictors can enhance soil maps by applying pixel size in digital soil mapping (DSM). (2) Methods: A total of 200, 50, and 129 surface soil samples (0–30 cm) were collected by the CLHS method in three different areas, namely, the Marvdasht, Bandamir, and Lapuee plains in southwest Iran. Then, four soil properties—soil organic matter (SOM), bulk density (BD), soil shear strength (SS), and mean weighted diameter (MWD)—were measured at each sampling point as representative attributes of soil physical and chemical quality. This study examined different-scale scenarios ranging from resampling the original 30 m digital elevation model and remote sensing indices to various pixel sizes, including 60 × 60, 90 × 90, 120 × 120, and up to 2100 × 2100 m. (3) Results: After evaluating 22 environmental covariates, 11 of them were identified as the most suitable candidates for predicting soil properties based on recursive feature elimination (RFE) and expert opinion methods. Furthermore, among different pixel size scenarios for SOM, BD, SS, and MWD, the highest accuracy was achieved at 1200 × 1200 m (R2 = 0.35), 180 × 180 m (R2 = 0.67), 1200 × 1200 m (R2 = 0.42), and 2100 × 2100 m (R2 = 0.34), respectively, in Marvdasht plain. (4) Conclusions: Adjusting the pixel size improves the capture of soil property variability, enhancing mapping precision and supporting effective decision making for crop management, irrigation, and land use planning. [ABSTRACT FROM AUTHOR]

Soil organic matter (SOM) and the ratio of soil organic carbon to total nitrogen (C/N ratio) are fundamental to the ecosystem services provided by soils. Therefore, understanding the spatial distribution and relationships between the SOM components mineral-associated organic matter (MAOM), particulate organic matter (POM), and C/N ratio is crucial. Three ensemble machine learning models were trained to obtain spatial predictions of the C/N ratio, MAOM, and POM in German agricultural topsoil (0–10 cm). Parameter optimization and model evaluation were performed using nested cross-validation. Additionally, a modification to the regressor chain was applied to capture and interpret the interactions among the C/N ratio, MAOM, and POM. The ensemble models yielded mean absolute percent errors (MAPEs) of 8.2% for the C/N ratio, 14.8% for MAOM, and 28.6% for POM. Soil type, pedo-climatic region, hydrological unit, and soilscapes were found to explain 75% of the variance in MAOM and POM, and 50% in the C/N ratio. The modified regressor chain indicated a nonlinear relationship between the C/N ratio and SOM due to the different decomposition rates of SOM as a result of variety in its nutrient quality. These spatial predictions enhance the understanding of soil properties' distribution in Germany. [ABSTRACT FROM AUTHOR]

Land suitability assessment, as an important process in modern agriculture, involves the evaluation of numerous aspects such as soil properties, climate, relief, hydrology and socio-economic aspects. The aim of this study was to evaluate the suitability of soils for wheat cultivation in the Gavshan region, Iran, as the country is facing the task of becoming self-sufficient in wheat. Various methods were used to evaluate the land, such as multi-criteria decision-making (MCDM), which is proving to be important for land use planning. MCDM and machine learning (ML) are useful for decision-making processes because they use complicated spatial data and methods that are widely available. Using a geomorphological map, seventy soil profiles were selected and described, and ten soil properties and wheat yields were determined. Three MCDM approaches, including the technique of preference ordering by similarity to the ideal solution (TOPSIS), gray relational analysis (GRA), and simple additive weighting (SAW), were used and evaluated. The criteria weights were extracted using Shannon's entropy method. Random forest (RF) model and auxiliary variables (remote sensing data, terrain data, and geomorphological maps) were used to represent the land suitability values. Spatial autocorrelation analysis as a statistical method was applied to analyze the spatial variability of the spatial data. Slope, CEC (cation exchange capacity), and OC (organic carbon) were the most important factors for wheat cultivation. The spatial autocorrelation between the key criteria (slope, CEC, and OC) and wheat yield confirmed these results. These results also showed a significant correlation between the land suitability values of TOPSIS, GRA, and SAW and wheat yield (0.74, 0.72, and 0.57, respectively). The spatial distribution of land suitability values showed that the areas classified as good according to TOPSIS and GRA were larger than those classified as moderate and weak according to the SAW approach. These results were also confirmed by the autocorrelation of the MCDM techniques with wheat yield. In addition, the RF model showed its effectiveness in processing complex spatial data and improved the accuracy of land suitability assessment. In this study, by integrating advanced MCDM techniques and ML, an applicable land evaluation approach for wheat cultivation was proposed, which can improve the accuracy of land suitability and be useful for considering sustainability principles in land management. [ABSTRACT FROM AUTHOR]

Accurate spatiotemporal monitoring and modeling of soil moisture (SM) is of paramount importance for various applications ranging from food production to climate change adaptation. This study deals with modeling SM with the random forest (RF) algorithm using datasets comprising multispectral data from Sentinel-2, Landsat-8/9, and hyperspectral data from the CoSpectroCam sensor (CSC, licensed to AgriWatch BV, Enschede, The Netherlands) mounted on an unmanned aerial vehicle (UAV) in Iran. The model included nine bands from Landsat-8/9, 11 bands from Sentinel-2, and 1252 bands from the CSC (covering the wavelength range between 420 and 850 nm). The relative feature importance and band sensitivity to SM variations were analyzed. In addition, four indices, including the perpendicular index (PI), ratio index (RI), difference index (DI), and normalized difference index (NDI) were calculated from the different bands of the datasets, and their sensitivity to SM was evaluated. The results showed that the PI exhibited the highest sensitivity to SM changes in all datasets among the four indices considered. Comparisons of the performance of the datasets in SM estimation emphasized the superior performance of the UAV hyperspectral data (R2 = 0.87), while the Sentinel-2 and Landsat-8/9 data showed lower accuracy (R2 = 0.49 and 0.66, respectively). The robust performance of the CSC data is likely due to its superior spatial and spectral resolution as well as the application of preprocessing techniques such as noise reduction and smoothing filters. The lower accuracy of the multispectral data from Sentinel-2 and Landsat-8/9 can also be attributed to their relatively coarse spatial resolution compared to the CSC, which leads to pixel non-uniformities and impurities. Therefore, employing the CSC on a UAV proves to be a valuable technology, providing an effective link between satellite observations and ground measurements. [ABSTRACT FROM AUTHOR]