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Digital soil mapping (DSM) is an effective mapping technique that supports the increased need for quantitative soil data. In DSM, soil properties are correlated with environmental characteristics using statistical models such as regression. However, many of these relationships are explicitly described in mechanistic simulation models. Therefore, the mechanistic relationships can, in theory, replace the statistical relationships in DSM. This study aims to develop a mechanistic model to predict soil organic matter (SOM) stocks in Natura2000 areas of the Cantabria region (Spain). The mechanistic model is established in four steps: (a) identify major processes that influence SOM stocks, (b) review existing models describing the major processes and the respective environmental data that they require, (c) establish a database with the required input data, and (d) calibrate the model with field observations. The SOM stocks map resulting from the mechanistic model had a mean error (ME) of −2 t SOM ha−1 and a root mean square error (RMSE) of 66 t SOM ha−1. The Lin's concordance correlation coefficient was 0.47 and the amount of variance explained (AVE) was 0.21. The results of the mechanistic model were compared to the results of a statistical model. It turned out that the correlation coefficient between the two SOM stock maps was 0.8. This study illustrated that mechanistic soil models can be used for DSM, which brings new opportunities. Mechanistic models for DSM should be considered for mapping soil characteristics that are difficult to predict by statistical models, and for extrapolation purposes. Highlights: Theoretically, mechanistic models can replace the statistical relationships in digital soil mapping.Mechanistic soil models were used to develop a mechanistic model for digital soil mapping that predicted SOM stocks.The applicability of the mechanistic approach needs to be explored for different soil properties and regions. [ABSTRACT FROM AUTHOR]

• DSOLMap is a new high-resolution global digital soil property map for the SWAT + model. • Soil data play a crucial role in the hydrological cycle and its modelling. • An impact on streamflow performance using different global soil maps was observed. • The use of DSOLMap allows for an improved daily flow simulation. • DSOLMap can be useful for hydrological modelling in regions with limited soil data. This research paper addresses the ongoing challenge of developing fine-resolution global digital soil property maps for hydrological modelling applications. Hydrological models are essential for understanding watershed dynamics and the impact of human activities on water resources. Soil data, which plays a crucial role in the hydrological cycle, is a requisite model input. Global digital soil property maps usually have coarse spatial resolutions, adding considerable uncertainty to hydrological models despite calibration efforts. To address this issue, a new global digital soil property map with 250 m spatial resolution, known as Digital Soil Open Land Map (DSOLMap), was developed and evaluated in this study. The DSOLMap has a finer spatial resolution than existing global soil maps and a more detailed soil profile divided into six soil horizons. This new high-resolution global digital soil property map was tailored to the SWAT + model format. SWAT + is the latest released version of the Soil and Water Assessment Tool (SWAT), one of the most comprehensive hydrological models, and is widely used worldwide. A hydrological evaluation was conducted with the DSOLMap and its results were compared to two other global soil databases using the SWAT + model in a basin located in the north of Spain. The findings showed that using more detailed, finer-resolution soil data, such as those that the DSOLMap offers, improved the hydrological performance of the SWAT + model on a daily scale before and after calibration and validation procedures. The DSOLMap represents a global step forward in hydrological modelling, notably for regions with scarce or unavailable soil information. This new digital soil property map can help decision-makers address global challenges related to water resources and environmental issues through hydrological modelling. [ABSTRACT FROM AUTHOR]

The modeling and mapping of soil organic carbon (SOC) has advanced through the rapid growth of Earth observation data (e.g., Sentinel) collection and the advent of appropriate tools such as the Google Earth Engine (GEE). However, the effects of differing optical and radar sensors on SOC prediction models remain uncertain. This research aims to investigate the effects of different optical and radar sensors (Sentinel-1/2/3 and ALOS-2) on SOC prediction models based on long-term satellite observations on the GEE platform. We also evaluate the relative impact of four synthetic aperture radar (SAR) acquisition configurations (polarization mode, band frequency, orbital direction and time window) on SOC mapping with multiband SAR data from Spain. Twelve experiments involving different satellite data configurations, combined with 4027 soil samples, were used for building SOC random forest regression models. The results show that the synthesis mode and choice of satellite images, as well as the SAR acquisition configurations, influenced the model accuracy to varying degrees. Models based on SAR data involving cross-polarization, multiple time periods and "ASCENDING" orbits outperformed those involving copolarization, a single time period and "DESCENDING" orbits. Moreover, combining information from different orbital directions and polarization modes improved the soil prediction models. Among the SOC models based on long-term satellite observations, the Sentinel-3-based models (R2 = 0.40) performed the best, while the ALOS-2-based model performed the worst. In addition, the predictive performance of MSI/Sentinel-2 (R2 = 0.35) was comparable with that of SAR/Sentinel-1 (R2 = 0.35); however, the combination (R2 = 0.39) of the two improved the model performance. All the predicted maps involving Sentinel satellites had similar spatial patterns that were higher in northwest Spain and lower in the south. Overall, this study provides insights into the effects of different optical and radar sensors and radar system parameters on soil prediction models and improves our understanding of the potential of Sentinels in developing soil carbon mapping. • Our results revealed that the composite mode, radar data utilization strategies and sensor selection influenced outputs. • The mixed information of different orbital directions and polarization modes improved the mapping accuracy. • The predictive performance of Sentinel-1 and Sentinel-2 was comparable. • The accuracy rankings based on satellite observations were as follows: Sentinel-3 > Sentinel-2 > Sentinel-1 > ALOS-2. • The synergistic utilization of the Sentinel series gave better prediction models. [ABSTRACT FROM AUTHOR]

• 10 new geomorphic variables for non-continuous hillslopes are presented. • These variables express position relative to, and properties, of hillslope discontinuities. • Data from three study sites are used to test the value of the new variables for digital soil mapping. • The new variables are only weakly correlated with 19 existing geomorphometric variables for the same sites. • The new variables improve model fit and performance in each site. We present a set of new geomorphometric variables that express landscape position relative to breaklines in hillslopes, and test whether these variables are of value in explaining soil property variation in three study sites in the United States, the Netherlands, and Spain. Underlying this work is the recognition that slope breaks, such as cliff lines, lynchets, and large slumped blocks, are associated with processes that affect soil formation around them. For each study site, we digitized slope breaks and calculated vertical and horizontal distance to the nearest lower and higher slope breaks, as well as the relative position between multiple slope breaks, the slope increase at the next higher slope break, and the slope decrease at the next lower slope break. We then assessed the value of these geomorphometric variables in the prediction of slope properties by simple linear regression. At each study site, models were fitted to existing soil observations using a traditional set of geomorphometric variables, and the traditional set plus our newly developed variables. Model comparison indicated that the new variables substantially improved model fit and reduced model error for the site in the United States (Kansas, n = 100), improved model fit but did not reduce model error for the site in the Netherlands (Limburg, n = 192), and did not solve model overfitting issues for the small dataset in Spain (Malaga, n = 66). [ABSTRACT FROM AUTHOR]

Soil protection and the increase and intensification of agricultural production require detailed knowledge of soil properties and their variability. On the other hand, the complexity associated with traditional soil mapping processes can lead to the implementation of inappropriate agricultural practices that degrade this resource. Therefore, it is necessary to use mapping techniques to provide more detailed information to farmers and managers. In this study, the geostatistical technique ordinary kriging was used to map the distribution of the most important edaphic properties (texture, nutrients content -N, P, K-, pH, organic carbon, water retention, COLE, carbonate content, and cation exchange capacity) from known sampled points, which allows inferring the value and distribution of the different edaphic parameters studied along the agricultural fields. The results obtained show after validation that the analysis of semivariograms is suitable for evaluating the distribution of the main soil parameters on a large scale, since it faithfully reflects their distribution and makes the ordinary kriging tool a suitable method for optimizing the resources available in soil mapping processes. In addition, the knowledge of these distributions made it possible to establish different recommendations for improving the management of the agricultural ecosystem, which will guarantee a higher agricultural yield as well as a better protection of the analyzed soils. [ABSTRACT FROM AUTHOR]

The objective of this study was to compare two different methods for the calculation of the soil quality index for agricultural land in the province of Salamanca in the semiarid ecosystem of the Northern Plateau, Spain. The integrated quality index (IQI) and Nemoro quality index (NQI) were applied using the following indicator selection methods: total data set (TDS) and minimum data set (MDS). A total of 16 soil parameters were used for the TDS method. The evaluation of the soil quality index using only the properties of surface soil provides incomplete information because crop productivity is influenced by both the surface and subsoil properties [e.g. lithic contact, petrocalcic horizon, clayey horizon (Bt)]. The quality indexes were calculated using data from 75 soil profiles considering the soil surface properties (between 0 and 25 cm depth) and control section properties (between 0 and 100 cm). The results show that the consideration of soil properties of both the surface and subsurface horizons is very important to establish a good relationship between soil quality, soil functions, and agricultural management. The results based on the IQI index provide a better estimate of the soil quality compared with the NQI index; higher values were obtained with the TSD than with the MSD. However, the results obtained from the IQIMSD method provide an adequate evaluation of the soil quality. This is relevant because the use of a limited number of indicators reduces the analysis cost and increases the sampling density. • The Integrated and Nemoro Quality Indexes were applied to assess soil quality. • A factorial analysis was used to define minimum data set from total data set. • Digital maps of the soil quality indexes and the soil loss rate have been made. • Quality indexes have been linked to the vertical distribution of soil properties. • Soil quality indexes based on the properties of all soil horizons (0–100 cm). [ABSTRACT FROM AUTHOR]