Improving the remote estimation of soil organic carbon in complex ecosystems with Sentinel-2 and GIS using Gaussian processes regression

A new digital soil mapping approach based on the adjacency effect

Accurate soil mapping is crucial for agriculture, land, ecosystem and environmental management. Digital Soil Mapping (DSM) is one of the most conventional and widely used methods for mapping soil. This study introduces a novel strategy for DSM by incorporating the neighborhood effect of environmental covariates (ECs), aiming to enhance mapping accuracy of soil properties. The research focused on modeling organic carbon (OC), cation exchange capacity (CEC), bulk density (BD), and pH in southern Canada using 18 ECs derived from the Soil Landscapes of Canada dataset and satellite imagery. Two strategies were compared: a conventional approach using standard ECs, and a proposed method incorporating neighboring ECs through Inverse Distance Weighting (IDW). Both strategies employed Gaussian Process Regression (GPR) for modeling. Results demonstrated significant improvements in accuracy using the proposed strategy. Mean absolute errors were reduced by 32 %, 36 %, 28 %, and 14 % for OC, CEC, BD, and pH, respectively. The proposed method also decreased the coverage of high-error areas and improved R2 values across all soil properties. Moreover, mean uncertainty in soil property modeling decreased by 3.4 % to 3.9 % using the proposed strategy. This study highlights the importance of considering spatial context in DSM through neighborhood effects. The proposed strategy offers a more nuanced and accurate approach to soil property modeling, with potential applications extending beyond soil science to other environmental mapping domains. These improvements in soil mapping accuracy have significant implications for sustainable land management, precision agriculture, and environmental conservation.

Soil organic carbon stock retrieval from Sentinel-2A using a hybrid approach

Digital soil maps find application in numerous fields, making their accuracy a crucial factor. Mapping soil properties in homogeneous landscapes where the soil surface is concealed, as in forests, presents a complex challenge. In this study, we evaluated the spatial distribution of soil organic carbon stocks (SOCstock) under forest vegetation using three methods: regression kriging (RK), random forest (RF), and RF combined with ordinary kriging of residuals (RFOK) in combination with Sentinel-2A satellite data. We also compared their accuracies and identified key influencing factors. We determined that SOCstock ranged from 0.6 to 10.9 kg/m2 with an average value of 4.9 kg/m2. Among the modelling approaches, we found that the RFOK exhibited the highest accuracy (RMSE = 1.58 kg/m2, NSE = 0.33), while the RK demonstrated a lack of spatial correlation of residuals, rendering this method inapplicable. An analysis of variable importance revealed that the SWIR B12 band of the Sentinel-2A satellite contributed the most to RFOK predictions. We concluded that the RFOK hybrid approach outperformed the others, potentially serving as a foundation for digital soil mapping under similar environmental conditions. Therefore, it is essential to consider spatial correlations when mapping soil properties in ecosystems that are inaccessible for capturing the spectral response of the soil surface.

Spatial and Temporal Analysis of Water Quality in High Andean Lakes with Sentinel-2 Satellite Automatic Water Products

The water of high Andean lakes is strongly affected by anthropic activities. However, due to its complexity this ecosystem is poorly researched. This study analyzes water quality using Sentinel-2 (S2) images in high Andean lakes with apparent different eutrophication states. Spatial and temporal patterns are assessed for biophysical water variables from automatic products as obtained from versions of C2RCC (Case 2 Regional Coast Color) processor (i.e., C2RCC, C2X, and C2X-COMPLEX) to observe water characteristics and eutrophication states in detail. These results were validated using in situ water sampling. C2X-COMPLEX appeared to be an appropriate option to study bodies of water with a complex dynamic of water composition. C2RCC was adequate for lakes with high transparency, typical for lakes of highlands with excellent water quality. The Yambo lake, with chlorophyll-a concentration (CHL) values of 79.6 ± 5 mg/m3, was in the eutrophic to hyper-eutrophic state. The Colta lake, with variable values of CHL, was between the oligotrophic to mesotrophic state, and the Atillo lakes, with values of 0.16 ± 0.1 mg/m3, were oligotrophic and even ultra-oligotrophic, which remained stable in the last few years. Automatic S2 water products give information about water quality, which in turn makes it possible to analyze its causes.

Introducing ARTMO's Machine-Learning Classification Algorithms Toolbox: Application to Plant-Type Detection in a Semi-Steppe Iranian Landscape

Accurate plant-type (PT) detection forms an important basis for sustainable land management maintaining biodiversity and ecosystem services. In this sense, Sentinel-2 satellite images of the Copernicus program offer spatial, spectral, temporal, and radiometric characteristics with great potential for mapping and monitoring PTs. In addition, the selection of a best-performing algorithm needs to be considered for obtaining PT classification as accurate as possible. To date, no freely downloadable toolbox exists that brings the diversity of the latest supervised machine-learning classification algorithms (MLCAs) together into a single intuitive user-friendly graphical user interface (GUI). To fill this gap and to facilitate and automate the usage of MLCAs, here we present a novel GUI software package that allows systematically training, validating, and applying pixel-based MLCA models to remote sensing imagery. The so-called MLCA toolbox has been integrated within ARTMO's software framework developed in Matlab which implements most of the state-of-the-art methods in the machine learning community. To demonstrate its utility, we chose a heterogeneous case study scene, a landscape in Southwest Iran to map PTs. In this area, four main PTs were identified, consisting of shrub land, grass land, semi-shrub land, and shrub land-grass land vegetation. Having developed 21 MLCAs using the same training and validation, datasets led to varying accuracy results. Gaussian process classifier (GPC) was validated as the top-performing classifier, with an overall accuracy (OA) of 90%. GPC follows a Laplace approximation to the Gaussian likelihood under the supervised classification framework, emerging as a very competitive alternative to common MLCAs. Random forests resulted in the second-best performance with an OA of 86%. Two other types of ensemble-learning algorithms, i.e., tree-ensemble learning (bagging) and decision tree (with error-correcting output codes), yielded an OA of 83% and 82%, respectively. Following, thirteen classifiers reported OA between 70% and 80%, and the remaining four classifiers reported an OA below 70%. We conclude that GPC substantially outperformed all classifiers, and thus, provides enormous potential for the classification of a diversity of land-cover types. In addition, its probabilistic formulation provides valuable band ranking information, as well as associated predictive variance at a pixel level. Nevertheless, as these are supervised (data-driven) classifiers, performances depend on the entered training data, meaning that an assessment of all MLCAs is crucial for any application. Our analysis demonstrated the efficacy of ARTMO's MLCA toolbox for an automated evaluation of the classifiers and subsequent thematic mapping.