A Parsimonious Approach to Estimate Soil Organic Carbon Applying Unmanned Aerial System (UAS) Multispectral Imagery and the Topographic Position Index in a Heterogeneous Soil Landscape

Abstract

Remote sensing plays an increasingly key role in the determination of soil organic carbon (SOC) stored in agriculturally managed topsoils at the regional and field scales. Contemporary Unmanned Aerial Systems (UAS) carrying low-cost and lightweight multispectral sensors provide high spatial resolution imagery (<10 cm). These capabilities allow integrate of UAS-derived soil data and maps into digitalized workflows for sustainable agriculture. However, the common situation of scarce soil data at field scale might be an obstacle for accurate digital soil mapping. In our case study we tested a fixed-wing UAS equipped with visible and near infrared (VIS-NIR) sensors to estimate topsoil SOC distribution at two fields under the constraint of limited sampling points, which were selected by pedological knowledge. They represent all releva nt soil types along an erosion-deposition gradient; hence, the full feature space in terms of topsoils’ SOC status. We included the Topographic Position Index (TPI) as a co-variate for SOC prediction. Our study was performed in a soil landscape of hummocky ground moraines, which represent a significant of global arable land. Herein, small scale soil variability is mainly driven by tillage erosion which, in turn, is strongly dependent on topography. Relationships between SOC, TPI and spectral information were tested by Multiple Linear Regression (MLR) using: (i) single field data (local approach) and (ii) data from both fields (pooled approach). The highest prediction performance determined by a leave-one-out-cross-validation (LOOCV) was obtained for the models using the reflectance at 570 nm in conjunction with the TPI as explanatory variables for the local approach (coefficient of determination (R²) = 0.91; root mean square error (RMSE) = 0.11% and R² = 0.48; RMSE = 0.33, respectively). The local MLR models developed with both reflectance and TPI using values from all points showed high correlations and low prediction errors for SOC content (R² = 0.88, RMSE = 0.07%; R² = 0.79, RMSE = 0.06%, respectively). The comparison with an enlarged dataset consisting of all points from both fields (pooled approach) showed no improvement of the prediction accuracy but yielded decreased prediction errors. Lastly, the local MLR models were applied to the data of the respective other field to evaluate the cross-field prediction ability. The spatial SOC pattern generally remains unaffected on both fields; differences, however, occur concerning the predicted SOC level. Our results indicate a high potential of the combination of UAS-based remote sensing and environmental covariates, such as terrain attributes, for the prediction of topsoil SOC content at the field scale. The temporal flexibility of UAS offer the opportunity to optimize flight conditions including weather and soil surface status (plant cover or residuals, moisture and roughness) which, otherwise, might obscure the relationship between spectral data and SOC content. Pedologically targeted selection of soil samples for model development appears to be the key for an efficient and effective prediction even with a small dataset.

Study on the Estimation of Forest Volume Based on Multi-Source Data

“…The closer is to 1, the smaller the and are, indicating that the model predicts more effectively. The leave-one-out method is used for evaluating learners in machine learning and is a special type of cross-validation [ 70 ]. The expressions of each evaluation index are as follows: where is the fold of cross-validation, and here, ; and represent the observation and the predicted value of the model for the time, respectively.…”

The biogeography of microbial N cycle guilds of the rye rhizosphere along a tillage erosion catena

“…All samples were stored at − 80°C until further processing. Images were pre-and post-processed according to a previous published procedure using Agisoft Metashape Professional software [30].…”

Re-Examining Field-Surveyed Variations in Elevation and Soil Properties with a 1-m Resolution LiDAR-Generated DEM

“…Among these, the increase with Silt % and the decrease with NO 3 -N are more significantly related to TOC (Equation ( 3)). Generally, TOC increases downhill in silt-accumulating depressions across hummocky fields [30] [31] [32] [33], but -in this survey -uphill to downhill TOC did not vary significantly (Figure 10), i.e., TOC % = 2.20 − 0.018 log 10 (DTW > 0.25 ha); R 2 = 0.002 and TOC % = 2.00 + 0.0039 DEM; R 2 = 0.0018.…”