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Soil pH affects food production, pollution control and ecosystem services. Mapping soil pH levels, therefore, provides policymakers with crucial information for developing sustainable soil use and management policies. In this study, we used the LUCAS 2015 TOPSOIL data to map soil pH at a European level. We used random forest kriging (RFK) to build a predictive model of spatial variability of soil pH, as well as random forest (RF) without co‐kriging and boosted regression trees (BRT) modelling techniques. Model accuracy was evaluated using a ten‐fold cross‐validation procedure. While we found that all methods accurately predicted soil pH, the accuracy of the RFK method was best with regression performance metrics of: R2 = 0.81 for pH (H2O) and pH (CaCl2); RMSE = 0.59 for pH (H2O) and RMSE = 0.61 in pH (CaCl2); MAE = 0.41 for pH (H2O) and MAE = 0.43 in pH (CaCl2). Dominant explanatory variables in the RF and BRT modelling were topography and remote sensing variables, respectively. The generated maps broadly depicted similar spatial patterns of soil pH, with an increasing gradient of soil pH from north to south Europe, with the highest values mainly concentrated along the Mediterranean coast. The mapping could provide spatial reference for soil pH assessment and dynamic monitoring. [ABSTRACT FROM AUTHOR]

Spatial information on the soil carbon-to-nitrogen (C:N) ratio is essential for sustainable soil use and management. The unprecedented availability of Sentinel optical and radar data on cloud computing platforms, such as the Google Earth Engine (GEE), has created new possibilities for developing soil prediction models from the local scale to the planetary scale. However, there is a paucity of literature on the effects of Sentinel sensor selection and integration and radar data utilization strategies on mapping the C:N ratio. In this study, we explored the use of multiyear Sentinel-1 radar and Sentinel-2 optical data obtained from the GEE platform combined with the digital soil mapping (DSM) technique to map the soil C:N ratio at the European scale. The performance of soil prediction models, which were constructed using two modeling techniques (random forest and support vector machine), derived under multiple scenarios based on optical, radar and commonly used auxiliary data (climatic and topographic variables) combined with the LUCAS 2018 soil dataset, was evaluated by a cross-validation technique. The results showed that the modeling performance varied with the selection and integration of Sentinel observations, as well as the configuration of the radar data. Models based on single polarization performed the worst across all scenarios related to Sentinel-1, with cross-polarization performing better than copolarization. Models that utilized Sentinel-1 data from ascending orbits outperformed those that utilized data from descending orbits. The application of Sentinel-1 backscatter information derived from different orbits and polarization modes resulted in improved prediction accuracy. Our study also demonstrated the potential of integrating multiyear Sentinel satellite data via the GEE to map the continental-scale C:N ratio. The model based on Sentinel-1 data outperformed the one built on Sentinel-2 data, whereas combining Sentinel-2 optical data with Sentinel-1 radar data led to more accurate predictions. The variable importance results indicated that optical data and backscattering information from Sentinel observations are the most important groups of variables for soil C:N ratio mapping compared to the other variable groups (terrain and climate data). The digital soil maps generated under the different scenarios exhibited detailed patterns with significant spatial variation, with similar overall trends but slightly different details. • Soil C:N ratios were mapped at the European scale using Sentinel radar and optical data on GEE. • The strategy for using Sentinel observations greatly impacted the prediction accuracy. • Predictions can benefit from information on different orbits and polarizations. • Optical and backscatter information are the most important groups of variables. • Our results indicated that the GEE platform is a promising tool for developing DSM products. [ABSTRACT FROM AUTHOR]

• The selection of satellite sensor and radar system parameters greatly affected the model output. • The model was improved when more polarizations, orbital directions and frequencies were involved. • Models built using multiband radar datasets achieved a comparable accuracy to models based on optical data. • The model built by fusing SAR-optical data achieved better results than the model without SAR data. • The development of this continental-scale DSM work largely benefits from GEE. The advent of cloud computing platforms (e.g., Google Earth Engine (GEE)) and the massive amounts of optical and radar Earth Observation (EO) data hosted by these platforms present new opportunities for mapping soil pH at large scales. However, existing studies generally lack consensus on the effects of satellite and radar sensor parameters on GEE-based soil pH prediction models. In this study, we assessed the suitability of long-term radar (C-band Sentinel-1 and L-band PALSAR-1/2) and optical (Sentinel-2) EO data on GEE for the digital mapping of soil pH on a continental (Europe) scale and determined the most appropriate radar sensor parameters. Thirteen scenarios with different data configurations were simulated and combined with the 2018 LUCAS soil database and two machine learners (boosted regression trees and extreme gradient boosting) to develop soil prediction models. Results showed that the selection of modeling techniques, satellite sensors and radar system parameters largely affected the model output. Models involving a single polarization mode of PALSAR-1/2 data performed the worst (RPD = 1.24). Models based on Sentinel-1 data performed better than those built using PALSAR-1/2 data. The model performance was improved when a model involved more polarization bands, orbital directions, and band frequencies. The multiband model built using the two radar datasets achieved a comparable accuracy to the model based on optical data. Moreover, the model that fused radar-optical data achieved better results, with RPD values of 1.56 and 1.46 for the models with and without radar data, respectively; its performance was comparable to that of models built with commonly used variables (topography and climate). The analysis of importance indicated that long-term optical and radar EO data on GEE were important in our model. The modelling of soil pH at the continental scale largely benefits from GEE. The predicted maps exhibited strong spatial heterogeneity among different biogeographic regions, with similar spatial patterns under different modelling scenarios. [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]

Soil organic carbon (SOC) and soil carbon-to-nitrogen ratio (C:N) are the main indicators of soil quality and health and play an important role in maintaining soil quality. Together with Landsat, the improved spatial and temporal resolution Sentinel sensors provide the potential to investigate soil information on various scales. We analyzed and compared the potential of satellite sensors (Landsat-8, Sentinel-2 and Sentinel-3) with various spatial and temporal resolutions to predict SOC content and C:N ratio in Switzerland. Modeling was carried out at four spatial resolutions (800 m, 400 m, 100 m and 20 m) using three machine learning techniques: support vector machine (SVM), boosted regression tree (BRT) and random forest (RF). Soil prediction models were generated in these three machine learners in which 150 soil samples and different combinations of environmental data (topography, climate and satellite imagery) were used as inputs. The prediction results were evaluated by cross-validation. Our results revealed that the model type, modeling resolution and sensor selection greatly influenced outputs. By comparing satellite-based SOC models, the models built by Landsat-8 and Sentinel-2 performed the best and the worst, respectively. C:N ratio prediction models based on Landsat-8 and Sentinel-2 showed better results than Sentinel-3. However, the prediction models built by Sentinel-3 had competitive or better accuracy at coarse resolutions. The BRT models constructed by all available predictors at a resolution of 100 m obtained the best prediction accuracy of SOC content and C:N ratio; their relative improvements (in terms of R2) compared to models without remote sensing data input were 29.1% and 58.4%, respectively. The results of variable importance revealed that remote sensing variables were the best predictors for our soil prediction models. The predicted maps indicated that the higher SOC content was mainly distributed in the Alps, while the C:N ratio shared a similar distribution pattern with land use and had higher values in forest areas. This study provides useful indicators for a more effective modeling of soil properties on various scales based on satellite imagery. Unlabelled Image • The performance of Landsat-8, Sentinel-2 and 3 data in mapping SOC and C:N ratio was compared. • Our results revealed that the model type, modeling resolution and sensor selection greatly influenced outputs. • National scale SOC content and C:N ratio were mapped in Switzerland. • Remote sensing variables were the best predictors for our soil prediction models. [ABSTRACT FROM AUTHOR]

Soil organic carbon (SOC) and soil total nitrogen (STN) are important indicators of soil health and play a key role in the global carbon and nitrogen cycles. High-resolution radar Sentinel-1 and multispectral Sentinel-2 images have the potential to investigate soil spatial distribution information over a large area, although Sentinel-1 and Sentinel-2 data have rarely been combined to map either SOC or STN content. In this study, we applied machine learning techniques to map both SOC and STN content in the southern part of Central Europe using digital elevation model (DEM) derivatives, multi-temporal Sentinel-1 and Sentinel-2 data, and evaluated the potential of different remote sensing sensors (Sentinel-1 and Sentinel-2) to predict SOC and STN content. Four machine-learners including random forest (RF), boosted regression trees (BRT), support vector machine (SVM) and Bagged CART were used to construct predictive models of SOC and STN contents based on 179 soil samples and different combinations of environmental covariates. The performance of these models was evaluated based on a 10-fold cross-validation method by three statistical indicators. Overall, the BRT model performed better than RF, SVM and Bagged CART, and these models yielded similar spatial distribution patterns of SOC and STN. Our results showed that multi-source sensor methods provided more accurate predictions of SOC and STN contents than individual sensors. The application of radar Sentinel-1 and multispectral Sentinel-2 images proved useful for predicting SOC and STN. A combination of Sentinel-1/2-derived predictors and DEM derivatives yielded the highest prediction accuracy. The prediction accuracy changed with and without the Sentinel-1/2-derived predictors, with the R2 for estimating both SOC and STN content using the BRT model increasing by 12.8% and 18.8%, respectively. Topographic variables were the main explanatory variables for SOC and STN predictions, where elevation was assigned as the variable with the most importance by the models. The results of this study illustrate the potential of free high-resolution radar Sentinel-1 and multispectral Sentinel-2 data as input when developing SOC and STN prediction models. Unlabelled Image • Multi-source sensor methods achieved more accurate SOC and STN predictions than single sensors. • The potential of Sentinel-1 and 2 data in predicting SOC and STN was explored. • Boosted regression trees model performed best in predicting SOC and STN. [ABSTRACT FROM AUTHOR]

• The feasibility of adding multi-temporal radar Sentinel-1A data to SOC prediction was explored. • Climate variables contributed the most to explaining SOC content variation. • In this ecosystem, RF and BRT models showed similar performance and produced similar SOC spatial distribution patterns. Soil organic carbon (SOC) has a large impact on soil quality and global climate change. It is therefore important to be able to predict SOC accurately to promote sustainable soil management. Although the synthetic aperture radar (SAR) has many advantages and has been widely used in soil science research, it has rarely been used in previous SOC mapping studies based on remote sensing images. The purpose of this study was to investigate the ability of multi-temporal Sentinel-1A data in SOC prediction, by comparing the predictive performance of random forest (RF) and boosted regression tree (BRT) models in the Heihe River Basin in northwestern China. A set of 162 topsoil (0–20 cm) samples were taken and 15 environmental variables were obtained including land use, topography, climate, and remote sensing images (optical and SAR data). Using a cross-validation procedure to evaluate the performance of the models, three statistical indices were calculated. Overall, both RF and BRT models effectively predicted SOC content, exhibiting similar performance and producing similar spatial distribution patterns of SOC. The results showed that the addition of multi-temporal Sentinel-1A images improved prediction accuracy, with the root mean squared error (RMSE), the mean absolute error (MAE) and the coefficient of determination (R2) improving by 9.0%, 8.3% and 13.5%, respectively. Furthermore, the combination of all environmental variables had the best prediction performance explaining 75% of SOC variation. The most important environmental variables explaining SOC variation were precipitation, elevation, and temperature. The multi-temporal Sentinel-1A data in RF and BRT models explained 9% and 7%, respectively. The results from our case study highlight the usefulness of multi-temporal Sentinel-1 data in SOC mapping. [ABSTRACT FROM AUTHOR]

Soil total nitrogen (STN) is an important indicator of soil quality and plays a key role in global nitrogen cycling. Accurate prediction of STN content is essential for the sustainable use of soil resources. Synthetic aperture radar (SAR) provides a promising source of data for soil monitoring because of its all-weather, all-day monitoring, but it has rarely been used for STN mapping. In this study, we explored the potential of multi-temporal Sentinel-1 data to predict STN by evaluating and comparing the performance of boosted regression trees (BRTs), random forest (RF), and support vector machine (SVM) models in STN mapping in the middle reaches of the Heihe River Basin in northwestern China. Fifteen predictor variables were used to construct models, including land use/land cover, multi-source remote sensing-derived variables, and topographic and climatic variables. We evaluated the prediction accuracy of the models based on a cross-validation procedure. Results showed that tree-based models (RF and BRT) outperformed SVM. Compared to the model that only used optical data, the addition of multi-temporal Sentinel-1A data using the BRT method improved the root mean square error (RMSE) and the mean absolute error (MAE) by 17.2% and 17.4%, respectively. Furthermore, the combination of all predictor variables using the BRT model had the best predictive performance, explaining 57% of the variation in STN, with the highest R2 (0.57) value and the lowest RMSE (0.24) and MAE (0.18) values. Remote sensing variables were the most important environmental variables for STN mapping, with 59% and 50% relative importance in the RF and BRT models, respectively. Our results show the potential of using multi-temporal Sentinel-1 data to predict STN, broadening the data source for future digital soil mapping. In addition, we propose that the SVM, RF, and BRT models should be calibrated and evaluated to obtain the best results for STN content mapping in similar landscapes. [ABSTRACT FROM AUTHOR]

• Sentinel-1 utilization strategies greatly affected model outputs. • The models built by Sentinel-1 and Sentinel-2 performed similarly. • Models based on the fusion of Sentinel-1 and Sentinel-2 imagery improved mapping accuracy. • Models involving more years of satellite observations yielded more accurate predictions. • The GEE platform and the Sentinel satellites offer new opportunities for soil mapping. Modeling accurate and detailed soil spatial information is essential for environmental modeling, precision soil management and decision-making. In this study, we integrated long-term optical (Sentinel-2) and radar (Sentinel-1) satellite observations via the Google Earth Engine (GEE) platform for high-resolution national-scale digital mapping of soil organic carbon (SOC) and total soil nitrogen (TSN) in Austria. Our soil predictive models based on boosted regression tree (BRT) and regression kriging (RK) methods were constructed from 449 soil samples (0–20 cm) covering the study area in the LUCAS soil database and Sentinel observations synthesized with different time intervals. The different input predictors of these soil predictive models resulted in seven modeling scenarios, and their prediction performance was evaluated by a cross-validation technique. Comparative analysis indicated that satellite sensors, modeling techniques, and SAR data acquisition configurations greatly affected the model outputs. Cross-polarization and co-polarization had similar performance in TSN and SOC predictions, and their combination improved the prediction accuracy. Predictive models based on Sentinel-1 with the "ASCENDING" orbits outperformed the models involving the "DESCENDING" orbits; the prediction accuracy of the former was comparable to models involving two orbital data. The models built by Sentinel-1 and Sentinel-2 performed similarly in predicting SOC (R2 = 0.51 vs. R2 = 0.52, respectively) and TSN (their R2 were both 0.42); their synergistic utilization improved the prediction results. Models involving more years of Sentinel observations on the GEE platform provided more accurate modeling results. The best soil predictive models explained 55% and 45% of soil variability for SOC and TSN, respectively, both constructed from long-term Sentinel-1/2 observations using the RK method. The overall trends of the mapping results of the models constructed by Sentinel-1 and Sentinel-2 and their combinations were consistent. The predicted digital soil maps displayed high spatial heterogeneity: SOC and TSN—shared similar spatial patterns—were greater in high-altitude central and western regions than other regions. This study provides valuable information for revealing the effects of satellite sensors, modeling techniques and SAR configurations on mapping SOC and TSN. [ABSTRACT FROM AUTHOR]