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• Soil reflectance composites were generated with Sentinel-2 L2A data. • A multi-channel CNN learning framework for soil organic carbon prediction is proposed. • A dual input deep learning architecture improves the predictions. • The framework produced results that can be interpreted. The Copernicus Sentinel-2 multispectral imagery data may be aggregated to extract large-scale, bare soil, reflectance composites, which enable soil mapping applications. In this paper, this approach was tested in the German federal state of Bavaria, to provide estimations for soil organic carbon (SOC). Different temporal ranges were considered for the generation of the composites, including multi-annual and seasonal ranges. A novel multi-channel convolutional neural network (CNN) is proposed. By leveraging the advantages of deep learning techniques, it utilizes complementary information from different spectral pre-treatment techniques. The SOC predictions indicated little dissimilarity amongst the different composites, with the best performance attained for the six-year composite containing only spring months (RMSE = 12.03 g C · kg−1, R2 = 0.64, RPIQ = 0.89). It has been demonstrated that these outcomes outperform other well-known machine learning techniques. An ablation analysis was accordingly performed to evaluate the interplay of the CNN's different components to disentangle the advantages of each aspect of the proposed framework. Finally, a DUal inPut deep LearnIng architecture, named DUPLICITE, is proposed, which concatenates deep spectral features derived from the CNN mentioned earlier, as well as topographical and environmental covariates through an artificial neural network (ANN) to exploit their complementarity. The proposed approach was demonstrated to provide an improvement in the overall prediction performance (RMSE = 11.60 gC · kg−1, R2 = 0.67, RPIQ = 0.92). [ABSTRACT FROM AUTHOR]

The Copernicus Sentinel-2 multispectral imagery data may be aggregated to extract large-scale, bare soil, reflectance composites, which enable soil mapping applications. In this paper, this approach was tested in the German federal state of Bavaria, to provide estimations for soil organic carbon (SOC). Different temporal ranges were considered for the generation of the composites, including multi-annual and seasonal ranges. A novel multi-channel convolutional neural network (CNN) is proposed. By leveraging the advantages of deep learning techniques, it utilizes complementary information from different spectral pre-treatment techniques. The SOC predictions indicated little dissimilarity amongst the different composites, with the best performance attained for the six-year composite containing only spring months (RMSE = 12.03 g C · kg−1, R2 = 0.64, RPIQ = 0.89). It has been demonstrated that these outcomes outperform other well-known machine learning techniques. An ablation analysis was accordingly performed to evaluate the interplay of the CNN’s different components to disentangle the advantages of each aspect of the proposed framework. Finally, a DUal inPut deep LearnIng architecture, named DUPLICITE, is proposed, which concatenates deep spectral features derived from the CNN mentioned earlier, as well as topographical and environmental covariates through an artificial neural network (ANN) to exploit their complementarity. The proposed approach was demonstrated to provide an improvement in the overall prediction performance (RMSE = 11.60 gC · kg−1, R2 = 0.67, RPIQ = 0.92).

Digital soil mapping (DSM) techniques are widely employed to generate soil maps. Soil properties are typically predicted individually, while ignoring the interrelation between them. Models for predicting multiple properties exist, but they are computationally demanding and often fail to provide accurate description of the associated uncertainty. In this paper a convolutional neural network (CNN) model is described to predict several soil properties with quantified uncertainty. CNN has the advantage that it incorporates spatial contextual information of environmental covariates surrounding an observation. A single CNN model can be trained to predict multiple soil properties simultaneously. I further propose a two-step approach to estimate the uncertainty of the prediction for mapping using a neural network model. The methodology is tested mapping six soil properties on the French metropolitan territory using measurements from the LUCAS dataset and a large set of environmental covariates portraying the factors of soil formation. Results indicate that the multivariate CNN model produces accurate maps as shown by the coefficient of determination and concordance correlation coefficient, compared to a conventional machine learning technique. For this country extent mapping, the maps predicted by CNN have a detailed pattern with significant spatial variation. Evaluation of the uncertainty maps using the median of the standardized squared prediction error and accuracy plots suggests that the uncertainty was accurately quantified, albeit slightly underestimated. The tests conducted using different window size of input covariates to predict the soil properties indicate that CNN benefits from using local contextual information in a radius of 4.5 km. I conclude that CNN is an effective model to predict several soil properties and that the associated uncertainty can be accurately quantified with the proposed approach. • A convolutional neural network is used for soil mapping. • Soil texture, organic carbon, pH and nitrogen are predicted using a single model. • The model is trained on topsoil LUCAS data over France. • A two-step method to quantify the uncertainty of the prediction is tested. • The method for uncertainty quantification is applicable to any neural network model. [ABSTRACT FROM AUTHOR]

Mapping the extent and locations of peatland at landscape scale has implications for carbon inventories, conservation and ecosystem services assessments. The main aim of this paper was to model and map the extent of northern peat soils while taking into account its uncertainty, and in particular exploring: 1. the use of radar Sentinel 1 as alternative to optical sensors to reduce problems due to clouds while taking advantage of the seasonality changes and 2. the use of deep learning for peat classification and as application of Digital Soil Mapping. The data sets defining presence or absence of peat in the soil were obtained from different sources and different sampling schemes, densities and distributions. Scotland was used as test case, because of its cloudy weather and fragmented distribution of different types of peat. An extension of the scorpan-kriging approach was used. The trend was estimated with different approaches: Generalized Additive Models, RandomForest and deep learning (convolutional neural networks). Each approach produced the probability of belonging to a class and the predicted class for each pixel. The results were assessed using out-of-sample measures. In this study 108 combinations of data sets and models (including trend approaches, sets of covariates and modelling of the spatial structure) were assessed. Overall, spatially explicit models performed better. The choice of the statistical method can have a significant impact on the predictive performances, while the sets of environmental covariates had a lower impact. Sentinel-1 with morphological features proved to be a good alternative to optical data for peat mapping. It is important to have balanced data sets representing the distribution of the data, because merging heterogeneous sources of data from different populations does not necessarily improve predictions. The use of deep learning and convolutional neural network provided initial promising results. There were large differences in the modelling approaches. These differences and uncertainties need to be taken into account for further modelling such as earth surface modelling or carbon accounting. • The model type can have a significant impact on the predictive performances. • Sentinel-1 proved to be a good candidate covariate for peat mapping. • Spatially explicit models performed better. • Deep learning provided initial promising results. [ABSTRACT FROM AUTHOR]

• Soil spectral library in urban and peri -urban areas was built with 3492 samples. • CNN can estimate urban SOC from large SSL with high accuracy. • LULC environmental classification is the best way to stratify the urban SSL. • After LULC stratification, Cubist model achieved the optimal prediction for SOC. • Areas with high SOC values were primarily distributed in constructed land. Accurate quantification of urban soil organic carbon (SOC) is essential for understanding anthropogenic changes and further guiding effective city managements. Visible and near infrared (vis–NIR) spectroscopy can monitor the SOC content in a time- and cost-effective manner. However, processes and mechanisms dominating the relationships between SOC and spectral data in urban soils remain unknown. The main objective of this paper was to evaluate whether multiple stratification strategies (i.e., based on land-use/land-cover [LULC], pH, and spectral clustering) resulted in better predicted performance for SOC compared to the non-stratified (global) models. Results showed that regarding the non-stratified models, the convolutional neural network (CNN) model exhibited the best performance (validation R 2 = 0.73), followed by Cubist (validation R 2 = 0.66) and memory-based learning (validation R 2 = 0.65). After LULC stratification, Cubist model achieved the best prediction (validation R 2 = 0.76), improving the value of ratio of performance to interquartile distance by 0.11 compared to the global CNN model. Areas with high SOC values were mainly located in the city center. Stratification by LULC class is a promising strategy for addressing the impact of the soil-landscape diversity and complexity on vis–NIR spectral estimation of SOC in urban soil spectral library. [ABSTRACT FROM AUTHOR]

[Display omitted] • The autoencoder framework is applied in VNIR-SWIR soil spectroscopy. • It maps the input data into a new compressed latent space in an unsupervised way. • This compressed information leads to enhanced accuracy of prediction. • A multi-input autoencoder utilizing multiple spectral pre-treatments is also presented. • The proposed methodologies outperform their counterparts. In the past two decades, research efforts have focused on using near infrared diffuse reflectance spectroscopy (350–2500 nm) as a rapid and cost-effective method for soil analysis. Given the multi-faceted importance of the soil ecosystem, and considering the increasing pressures exerted upon it due to climate change, degradation and urbanization, the advantages of soil spectroscopy are significant. Large soil spectral libraries have been developed to this end throughout the world. The soil matrix is however complex, posing a challenge in the determination of key soil properties from the spectra. To tackle this challenge, two methodologies are generally used: a) the use of spectral pre-processing techniques to transfer the spectra into a new space in which the association between spectrum and soil property is supposed to be more clear, and b) the use of more sophisticated machine learning models (e.g. deep learning). In this paper, we propose a novel methodology using stacked autoencoders to transform the initially recorded spectra in a new compressed (i.e. latent) space which can help the chemometric models enhance the accuracy of prediction. This is an unsupervised learning approach which only depends on the input data (i.e. the spectra). Following the significant results obtained in the literature using combinations of different spectra pre-processing techniques and the simultaneous prediction of multiple soil properties, the proposed methodology is extended to facilitate these approaches. We demonstrate this capacity by applying it in the mineral samples of the LUCAS 2009 topsoil database, and simultaneously predicting eight properties (the particle size distribution, pH, CEC, organic carbon, calcium carbonate, and total nitrogen) using an artificial neural network. Compared to standard pre-processing techniques and other transformations such as the principal components space, the proposed methodology using only one spectral source as input decreases the RMSE on average by 8.4% and by 3.5%, respectively. With respect to the current state-of-the-art and in particular a multi-input convolutional neural network which was recently proposed and outperformed the compared methodologies, the results of our multi-input methodology exhibit an average RMSE decrease of 9.9%. The interpretability aspect of the transformed feature space and the compressed spectra was also examined to identify how the compressed information encodes the input data and enables better associations between input and output. [ABSTRACT FROM AUTHOR]

• A novel 1-D CNN learning framework for soil spectral analysis is proposed. • Multiple spectral sources are combined to maximize the spectral information. • All soil properties are simultaneously predicted to utilize the cross-correlations. • A local adaptive error-correction mechanism enhances the prediction performance. • The framework produced interpretable and accurate results in the LUCAS SSL. The use of visible near-infrared and shortwave-infrared (VNIR-SWIR) diffuse reflectance spectroscopy for the estimation of soil properties is increasingly maturing with large-scale soil spectral libraries (SSLs) of laboratory spectra developed across the globe. Such an SSL is the publicly available LUCAS topsoil database with approximately 20,000 soil samples encompassing 23 countries of the European Union. A wide variety of machine learning tools have been applied to the LUCAS SSL to predict some of the soil samples' physicochemical properties with different degrees of accuracy. In this paper, we developed and examined the use of a novel one-dimensional convolutional neural network (CNN) to simultaneously predict ten physicochemical properties of the LUCAS SSL. Leveraging on the use of multiple-input channels it uses as model inputs the absorbance spectra along with some pre-processed spectra developed using standard techniques. Moreover, it exploits the use of local spectral neighborhoods to perform an adaptive error-correction mechanism. This novel localized multi-channel 1-D CNN was applied to all the available physicochemical properties of the LUCAS SSL and was statistically compared with the current state-of-the-art where it was shown to statistically outperform its counterparts, as well as with other CNNs where it exhibited the best performance. In particular, for the mineral soil samples, the RMSE for the Clay content was 4.80% ( R 2 0.86), for soil organic carbon the RMSE was 10.96 g kg−1 ( R 2 0.86), while for total nitrogen the RMSE was 0.66 g kg−1 ( R 2 0.83). [ABSTRACT FROM AUTHOR]