Soil Organic Carbon Monitoring Research Articles

Defying the flat-Earth myth: Soil organic carbon monitoring in topographically complex temperate pasture landscapes

Soil sampling design to capture the spatiotemporal variability of soil organic carbon (SOC) stocks in complex landscapes is challenged by the influence of topographical features on primary production, animal behaviour, and carbon and nutrient return to grasslands. Here we explore a range of approaches for streamlining the sampling process using measures of SOC stock data (2003, 2020, 2021) from a long-term hill-country fertilizer experiment. The stepwise procedure included a) evaluation of sampling efficiency between stratified and random soil sampling designs to capture spatial variability of SOC stocks, b) semi-parametric regression analysis to evaluate the representativeness of certain strata (e.g. medium slopes), c) exploring the appropriateness of using shallow SOC stock estimates to represent the full sampling depth, and d) the use of a process-based model to determine expected SOC stock changes and temporal variability to estimate the number of representative soil samples required to achieve a significant minimum change. Our analyses suggest that a) the stratified sampling design was more efficient reflected in the number of samples required to identify 5 % deviation in SOC stocks, b) estimates obtained on the medium slope were a reasonable approximation of the SOC stocks and its spatial variation across the farmlets, c) shallow (0–75 mm) SOC stock estimates from an adjusted regression model offered an accurate inference of its vertical distribution (0–300 mm), and d) the Grass-NEXT model provided insights on where more intensive sampling is required to detect a significant minimum change in SOC stocks over time. Tailored, stratified soil sampling and context-specific strategies, such as using representative strata and shallow sampling as effective steps for estimating SOC stocks and addressing data gaps, are crucial for simplifying monitoring and minimizing the cost of on-farm soil sampling designs in topographically complex landscapes covered by long-term pastures.

Soil Organic Carbon Estimation via Remote Sensing and Machine Learning Techniques: Global Topic Modeling and Research Trend Exploration

Understanding and monitoring soil organic carbon (SOC) stocks is crucial for ecosystem carbon cycling, services, and addressing global environmental challenges. This study employs the BERTopic model and bibliometric trend analysis exploration to comprehensively analyze global SOC estimates. BERTopic, a topic modeling technique based on BERT (bidirectional encoder representatives from transformers), integrates recent advances in natural language processing. The research analyzed 1761 papers on SOC and remote sensing (RS), in addition to 490 related papers on machine learning (ML) techniques. BERTopic modeling identified nine research themes for SOC estimation using RS, emphasizing spectral prediction models, carbon cycle dynamics, and agricultural impacts on SOC. In contrast, for the literature on RS and ML it identified five thematic clusters: spatial forestry analysis, hyperspectral soil analysis, agricultural deep learning, the multitemporal imaging of farmland SOC, and RS platforms (Sentinel-2 and synthetic aperture radar, SAR). From 1991 to 2023, research on SOC estimation using RS and ML has evolved from basic mapping to topics like carbon sequestration and modeling with Sentinel-2A and big data. In summary, this study traces the historical growth and thematic evolution of SOC research, identifying synergies between RS and ML and focusing on SOC estimation with advanced ML techniques. These findings are critical to global ecosystem SOC assessments and environmental policy formulation.

Advancements in Soil Organic Carbon Mapping and Interpolation Techniques: A Case Study from Lithuania’s Moraine Plains

Monitoring soil organic carbon (SOC) is essential for assessing the sustainability of soil usage. This study explores the spatial variability and mapping of SOC in Lithuania’s Nevėžis Plain using various interpolation methods, with an emphasis on understanding the impacts of soil typological units, moisture regimes, and erosion on SOC distribution. A total of 275 soil samples were collected from agricultural croplands at depths of 0–10 cm, supplemented by 38 samples from previous studies. The SOC map was created based on the contours of the Lithuanian soil geodatabase. Statistical analysis revealed that the distribution of SOC in the studied area was significantly influenced by soil moisture and the degree of erosion. Based on these findings, SOC mapping was conducted according to the contours of Lithuanian soils. Comparing the interpolation methods that were analyzed, it was found that the kriging, RBF, and EBK methods fail to adequately capture the minimum and maximum values of SOC, while the IDW fails to adequately capture only the minimum values. In summary, the integrated geographical approach is complex but applicable to SOC mapping. This method facilitates the creation of adaptable SOC maps that are both geographically and pedologically informed. Key principles to apply this approach for future research and practical application should include establishing a statistically reliable data foundation, categorizing samples based on contrasting soil moisture regime, degrees of erosion, and land use patterns, and developing contouring principles along with a criteria algorithm that enables accurate spatial interpolation of average SOC values.

Advanced Modelling of Soil Organic Carbon Content in Coal Mining Areas Using Integrated Spectral Analysis: A Dengcao Coal Mine Case Study

Effective modelling and integrated spectral analysis approaches can advance modelling precision. To develop an integrated spectral forecast modelling of soil organic carbon (SOC), this research investigated a mining coal in Dengcao Coal Mine Area, Zhengzhou. The study utilizes the Lasso and Ranger algorithms were utilized in spectral band analysis. Four primary models employed during this process include Artificial Neural Network (ANN), Support Vector Machine, Random Forest (RF), and Partial Least Squares Regression (PLSR). The ideal model was chosen. The results showed that, in contrast to when band collection was based on Lasso algorithm modelling, model precision was higher when it was based on the Ranger algorithm. ANN model had an ideal goodness acceptance, and the modelling developed by RF showed the steadiest modelling consequences. Based on the results, a distinct method is proposed in this study for band assortment at the earlier stage of integrated spectral modelling of SOC. The Ranger method can be used to check the spectral particles, and RF or ANN can be chosen to develop the prediction modelling based on different statistics sets, which is appropriate to create the prediction modelling of SOC content in Dengcao Coal Mine Area. This research avails a position for the integrated spectral of Analysis for Advanced Modelling of Soil Organic Carbon Content in Coal Sources alongside a theoretical foundation for innovating portable device for the integrated spectral assessment of SOC content in coal mining habitats. This study might be significant for the changing modelling and monitoring of SOC in mining and environmental areas.

Predictive monitoring of soil organic carbon using multispectral UAV imagery: a case study on a long-term experimental field

Effective monitoring of the soil organic carbon (SOC) content at the field scale is crucial for supporting sustainable agricultural practices. This study evaluates the utility of multispectral data acquired by an unmanned aerial vehicles (UAV) during bare soil conditions for predicting the SOC content of a long-term experimental field site (LTE) in Saxony-Anhalt, Germany. Our methodology involves constructing predictive models using multiple algorithms (CUBIST, MARS, linear regression) and applying image correction techniques to enhance prediction accuracy by mitigating the influence of confounding factors such as crop residuals. Among the tested models, the CUBIST algorithm, combined with a pixel selection strategy employing a 2 m radius and stratified image correction, demonstrates the most promising results, achieving an R-squared value of 0.54 and an RMSE of 1.9 g kg−1. Spatial distribution maps generated by this optimized model effectively depict the impact of organic fertilization on the SOC content, although the clarity of these patterns varies depending on the image processing method and algorithm used. Our findings highlight the potential of utilizing UAV-derived multispectral data for SOC monitoring at the LTE scale. However, further research is warranted to assess the generalizability of this approach to agricultural fields with lower SOC variability.

Prediction of soil organic carbon in black soil based on a synergistic scheme from hyperspectral data: Combining fractional-order derivatives and three-dimensional spectral indices

Monitoring soil organic carbon (SOC) content is crucial for climate change mitigation and sustaining ecological balance. Despite the unparalleled advantages of hyperspectral data in capturing nuanced variations in soil properties through its high spectral resolution, effectively extracting useful features from numerous bands via spectral processing techniques remains a formidable challenge. This study proposes an integrated approach combining fractional-order derivative (FOD) technique and optimal band combination algorithm using ZY1-02D satellite hyperspectral data to estimate SOC in Northeast China’s Black soil region. Three modeling strategies were compared: (1) FOD-transformed reflectance (FOD spectra), (2) FOD spectra with traditional 2D spectral indices (FOD + 2D SI), and (3) FOD spectra with new 3D spectral indices (FOD + 3D SI). These strategies were implemented using the random forest model with the aim of the optimal SOC prediction. Results showed that the application of FOD technique for spectral transformation effectively addressed the challenges posed by overlapping peaks and baseline drift inherent in the original spectral reflectance. Additionally, FOD transformation enhanced subtle soil spectral features and yielded more pronounced spectral variations with increasing fractional order, as compared to the original spectral data and conventional integer-order derivatives (i.e., first and second-order derivatives). However, as the FOD order continued to increase beyond 1.4, the spectral curve exhibited amplified noise and distortion, thereby adversely impacting subsequent model development. The 3D spectral indices correlate more robustly with SOC than 2D indices. The model that combines 0.6-order FOD and 3D spectral indices achieved the best accuracy (R2 = 0.66, RMSE = 2.99 g/kg and MAE = 2.42 g/kg), significantly outperforming the models built by 0.6-order FOD spectra (R2 = 0.48, RMSE = 3.65 g kg−1, and MAE = 2.93 g kg−1) and 0.8-order FOD + 2D SI modeling strategy (R2 = 0.55, RMSE = 3.54 g kg−1, and MAE = 2.85 g kg−1). These findings indicated that FOD and 3D spectral indices exhibit superior synergistic performance in SOC prediction, demonstrating their feasibility and providing valuable insights for large-scale soil property prediction and mapping using satellite hyperspectral data.

Prediction, mapping, and implication for better soil organic carbon management in Ethiopia

AbstractA precise soil organic carbon (SOC) content estimate is crucial soil quality parameter for agricultural produce and ecological safety. Moreover, geospatial modeling of SOC is critical when there are limited laboratory equipment and chemical reagents for soil analysis. This study used geostatistics—ordinary kriging (OK) and inverse distance weighting (IDW)—to map SOC in Libokemkem area, Northwest Ethiopia, for improved SOC management. About 107 soil samples were obtained from the plow layer at a 20‐cm depth and SOC was determined. Statistical Package for Social Sciences version 24.0 was used to generate descriptive statistics, and geostatistical analysis was also performed on the data using ArcGIS platform. The coefficient of determination (R2) and root mean square error (RMSE) derived from the validation of the predicted maps were used to assess the models. The results revealed homogeneity (coefficient of variation < 10%), low (0.12%–1.74%), and optimal (1.74%–4.06%) mean levels of SOC in study area. The OK showed an R2 of 0.74 and an RMSE of 13%, and the IDW revealed an R2 of 0.69 and an RMSE of 14%. The semivariogram results indicate a moderate dependence for SOC with stable, circular, spherical, exponential, and Gaussian models. We conclude that the sustainable monitoring of SOC is significant in enhancing soil quality. However, further study considering all drivers of spatial variability for SOC in the study and other soil sampling approaches improving performance of the prediction models is needed.

Monitoring soil organic carbon under coastal restoration using time series Sentinel‐1

AbstractSalt marsh soils are key reservoirs of organic carbon. Although the potential for using Sentinel‐1 data to estimate organic carbon in marsh soils has been recognized, there is still a need to find suitable proxies and effective algorithms that enhance predictions. In this study, we assessed the effectiveness of Sentinel‐1 data for predicting soil organic carbon (SOC) stocks in salt marshes located on the coast of eastern‐central China. We defined the relations between SOC stocks and remotely sensed data using a knowledge‐based approach (i.e., structural equation modeling (SEM)), and the results were compared with those obtained using a common data‐driven approach (i.e., random forest (RF)). Predictive models were developed using (1) refined images associated with phenological stages (phenological variables), (2) refined images that were classified as statistically important using Monte Carlo feature selection (MCFS‐based variables), and (3) images from the combination of these refined datasets. The predictions were validated using a leave‐one‐out cross‐validation approach. The results showed that SEM was more accurate than RF when predicting SOC stocks using the same type of predictor variables. Models using phenological variables alone yielded the least accurate predictions. Adding phenological variables to the structural equation model with MCFS‐based variables increased the prediction accuracy by 36% in terms of the R2. The results of the case study suggest that images related to phenological stages are good predictors for mapping SOC stocks in marshes. This study highlights the superiority of SEM over RF for developing effective remote sensing‐based models to quantify and map SOC stocks in salt marshes.

Predicting soil organic carbon with different approaches and spatial resolutions for the southern Iberian Peninsula, Spain

Quantification and monitoring of soil organic carbon (SOC) stocks across local-to-global scales are needed to assess soil resource management practices and adapt environmental policies. Multiple SOC estimates are available worldwide; however, verification and validation are required to quantify the discrepancies and provide improved estimates. Here, we evaluated four different digital soil mapping (DSM) approaches (i.e., linear models, support vector machine, random forest, and quantile regression forest) to estimate SOC concentration (SOCc) and SOC stocks (SOCs) in the Region of Murcia (11,313 km2), a complex topographic and climatic area in the southern Iberian Peninsula, at three spatial resolutions (100, 250, and 1000 m). We estimated SOC spatially using a local harmonized database of 255 soil profiles for modeling and 1100 topsoils for external validation. We found that the quantile regression forest (QRF) approach had the best data-model agreement at 100 m spatial resolution, with the highest accuracy percentage (79%), external validation (correlation coefficient of 52%), and spatial interpretability of patterns, especially for SOCc. The QRF model showed a mean SOCc of 12.18 g/kg with an overall uncertainty of 10.54 g/kg and an accuracy percentage of 79%, whereas the total SOCs was 27,572 GgC with an uncertainty of 0.016 GgC. Our results showed that using local environmental covariates and local soil information to predict SOC within this region resulted in a relative improvement in the prediction accuracy of ∼40% for SOCc and ∼ 65% for SOCs compared to the SOC products derived from national and global databases. Our results showed a large discrepancy between the national and global estimates for reporting SOC locally. Consequently, local-to-regional efforts are needed to describe SOC spatial variability better to reduce uncertainty and improve the assessment of soil resources. We provide the resulting SOC maps with associated spatial uncertainty on the public Environmental Data Initiative Repository at https://portal.edirepository.org/nis/mapbrowse?packageid=edi.1238.2.

How does national SOC monitoring on agricultural soils align with the EU strategies? An example using five case studies

AbstractSoil functioning contributes to the delivery of a vast range of ecosystem goods and services, and ecosystem health is therefore reflected by the capacity of the soil to perform underlying functions. Soil organic carbon (SOC) is a key indicator for soil quality as it is an integral driver of many soil functions and associated ecosystem services. Across the globe, SOC stocks are declining due to expanding agriculture and unsustainable practices. Awareness of the fact that soil is a non‐renewable resource and its functioning important for all life on Earth is increasing, especially among policymakers. As such, goals for the preservation and restoration of SOC are formulated in policies under the European Green Deal. However, the evaluation of these goals at the European level is hampered by a non‐harmonized diversity in national SOC monitoring strategies. While some SOC indicators can be useful for the evaluation of most policy goals (i.e., baseline and potential SOC stocks), additional and contrasting SOC data are often required for the evaluation of the goals formulated by the different EU directives. This study provides an overview of five ongoing SOC monitoring programmes across Europe and discusses how national programmes may be aligned to evaluate goals at the EU level. Five countries with very different soil monitoring programmes were included in a case study to illustrate the potential for harmonization and standardization of SOC assessment. Based on this study, we conclude that SOC monitoring strategies can be harmonized, but not standardized. We further suggest five sampling strategies that have potential for harmonization under the proposed Directive on Soil Monitoring and Resilience.

Significant Improvement in Soil Organic Carbon Estimation Using Data-Driven Machine Learning Based on Habitat Patches

Soil organic carbon (SOC) is generally thought to act as a carbon sink; however, in areas with high spatial heterogeneity, using a single model to estimate the SOC of the whole study area will greatly reduce the simulation accuracy. The earth surface unit division is important to consider in building different models. Here, we divided the research area into different habitat patches using partitioning around a medoids clustering (PAM) algorithm; then, we built an SOC simulation model using machine learning algorithms. The results showed that three habitat patches were created. The simulation accuracy for Habitat Patch 1 (R2 = 0.55; RMSE = 2.89) and Habitat Patch 3 (R2 = 0.47; RMSE = 3.94) using the XGBoost model was higher than that for the whole study area (R2 = 0.44; RMSE = 4.35); although the R2 increased by 25% and 6.8%, the RMSE decreased by 33.6% and 9.4%, and the field sample points significantly declined by 70% and 74%. The R2 of Habitat Patch 2 using the RF model increased by 17.1%, and the RMSE also decreased by 10.5%; however, the sample points significantly declined by 58%. Therefore, using different models for corresponding patches will significantly increase the SOC simulation accuracy over using one model for the whole study area. This will provide scientific guidance for SOC or soil property monitoring with low field survey costs and high simulation accuracy.

A Deep Learning Approach to Estimate Soil Organic Carbon from Remote Sensing

Monitoring soil organic carbon (SOC) typically assumes conducting a labor-intensive soil sampling campaign, followed by laboratory testing, which is both expensive and impractical for generating useful, spatially continuous data products. The present study leverages the power of machine learning (ML) and, in particular, deep neural networks (DNNs) for segmentation, as well as satellite imagery, to estimate the SOC remotely. We propose a new two-stage pipeline for remote SOC estimation, which relies on using a DNN trained to classify land cover to perform feature extraction, while the SOC estimation is performed by a different ML model. The first stage is an image segmentation DNN with the U-Net architecture, which is trained to estimate the land cover for an observed geographical region, based on multi-spectral images taken by the Sentinel-2 satellite constellation. This estimator is subsequently used to extract the latent feature vector for each of the output pixels, by rolling back from the output (dense) layer of the U-Net and accessing the last available convolutional layer of the same dimension as our desired output. The second stage is trained on a set of feature vectors extracted at the coordinates for which manual SOC measurements exist. We tested a variety of ML models and report on their performance. Using the best extremely randomized trees model, we generated a spatially continuous map of SOC estimations for the region of Tuscany, in Italy, with a resolution of 10 m, to share with the researchers as a means of validating the results and to demonstrate the efficiency of the proposed approach, which can can easily be scaled to create a global continuous SOC map.

Spectral Data Processing for Field-Scale Soil Organic Carbon Monitoring

Carbon sequestration in soils under agricultural use can contribute to climate change mitigation. Spatial–temporal soil organic carbon (SOC) monitoring requires more efficient data acquisition. This study aims to evaluate the potential of spectral on-the-go proximal measurements to serve these needs. The study was conducted as a long-term field experiment. SOC values ranged between 14 and 25 g kg−1 due to different fertilization treatments. Partial least squares regression models were built based on the spectral laboratory and field data collected with two spectrometers (site-specific and on-the-go). Correction of the field data based on the laboratory data was done by testing linear transformation, piecewise direct standardization, and external parameter orthogonalization (EPO). Different preprocessing methods were applied to extract the best possible information content from the sensor signal. The models were then thoroughly interpreted concerning spectral wavelength importance using regression coefficients and variable importance in projection scores. The detailed wavelength importance analysis disclosed the challenge of using soil spectroscopy for SOC monitoring. The use of different spectrometers under varying soil conditions revealed shifts in wavelength importance. Still, our findings on the use of on-the-go spectroscopy for spatial–temporal SOC monitoring are promising.

Guatemala soil organic carbon database (GTMSOC)

AbstractStudies on soil organic carbon stocks (SOCS) are increasingly relevant to developing efficient mitigation and adaptation strategies for climate change. Reliable information on soil organic carbon (SOC) content, bulk density (BD), and coarse fragments (CF) are required for precise SOCS calculation. The data quality of SOCS‐related variables is important to represent SOCS realistically across different levels of disturbance in the terrestrial ecosystems of Guatemala. The main objective is to develop Guatemala's first national SOC database to support studies of SOCS magnitudes and spatial trends. We identified and collected national sources of variables related to SOCS (SOC, BD, CF) across the country, mainly distributed in the central zone dominated by disturbed ecosystems and agricultural territories. We integrated 910 observations (soil samples and soil profiles) of SOC content (range 1.45–162 g·kg−1), 704 of BD (range 0.42–1.69 g·cm−3), and 8 of CFs (0%–21% weight). This new database represents the edaphic, climatic, and land use variability of Guatemalan territory. The database contains soil observations collected from 1965 to 2010. The year 2010 has the majority of soil observations (41%). SOCS‐related variables are standardized at 0–30 centimetres of depth using mass conservative splines, which are used to represent SOCS and soil depth relationships. We provide new information on SOCS across various ecological and environmental conditions to enable SOC monitoring systems to report reliable and accurate estimates. The new database is appealing for scientific and commercial purposes, such as representing Guatemalan soils in Earth system models or using soil information in the ecosystem services market (e.g., carbon markets). The new database is accessible to all users through the platform of the Environmental Data Initiative https://doi.org/10.6073/pasta/8dd15238c604c3ac75daf985548bd05c.

Potential of globally distributed topsoil mid-infrared spectral library for organic carbon estimation

Accurate monitoring of soil organic carbon (SOC) is critical for sustainable management of soil for improving its quality, function, and carbon sequestration. As a nondestructive, efficient, and low-cost technique, mid-infrared (MIR) spectroscopy has shown a great potential in rapid estimation of SOC, despite limited studies of the global scale. The objective of this work was to use a globally distributed topsoil MIR spectral library with 33,039 samples to predict SOC using different modeling methods. Effects of nine fractional-order derivatives (FODs) on the predicted accuracy of SOC were evaluated using four regression algorithms (i.e., ratio index-based linear regression, RI-LR; partial least squares regression, PLSR; Cubist; convolutional neural network, CNN). Square-root transformation to SOC data was performed to minimize the skewness and non-linearity. Results indicated FOD to capture the subtle spectral details related to SOC, leading to improved predictions that may not be possible by the raw absorbance and common integer-order derivatives. Concerning the RI-LR models, the optimal validation result for SOC was obtained by 0.75-order derivative, with the ratio of performance to inter-quartile distance (RPIQ) of 1.85. Regarding the full-spectrum modeling for SOC, the CNN outperformed PLSR and Cubist models, irrespective of raw absorbance or eight FODs; the best-performing CNN model was achieved by 1.25-order derivative (validation RPIQ = 6.33). It can be concluded that accurate estimation of SOC using large and diverse MIR spectral library at the global scale combined with deep-learning CNN model is feasible. This global-scale database is extremely valuable for us to deal with the shortage of soil data and to monitor the soils at different geographical scales.

Satellite-based estimation of soil organic carbon in Portuguese grasslands

Introduction:Soil organic carbon (SOC) sequestration is one of the main ecosystem services provided by well-managed grasslands. In the Mediterranean region, sown biodiverse pastures (SBP) rich in legumes are a nature-based, innovative, and economically competitive livestock production system. As a co-benefit of increased yield, they also contribute to carbon sequestration through SOC accumulation. However, SOC monitoring in SBP require time-consuming and costly field work.Methods:In this study, we propose an expedited and cost-effective indirect method to estimate SOC content. In this study, we developed models for estimating SOC concentration by combining remote sensing (RS) and machine learning (ML) approaches. We used field-measured data collected from nine different farms during four production years (between 2017 and 2021). We utilized RS data from both Sentinel-1 and Sentinel-2, including reflectance bands and vegetation indices. We also used other covariates such as climatic, soil, and terrain variables, for a total of 49 inputs. To reduce multicollinearity problems between the different variables, we performed feature selection using the sequential feature selection approach. We then estimated SOC content using both the complete dataset and the selected features. Multiple ML methods were tested and compared, including multiple linear regression (MLR), random forests (RF), extreme gradient boosting (XGB), and artificial neural networks (ANN). We used a random cross-validation approach (with 10 folds). To find the hyperparameters that led to the best performance, we used a Bayesian optimization approach.Results:Results showed that the XGB method led to higher estimation accuracy than the other methods, and the estimation performance was not significantly influenced by the feature selection approach. For XGB, the average root mean square error (RMSE), measured on the test set among all folds, was 2.78 g kg−1(r2equal to 0.68) without feature selection, and 2.77 g kg−1(r2equal to 0.68) with feature selection (average SOC content is 13 g kg−1). The models were applied to obtain SOC content maps for all farms.Discussion:This work demonstrated that combining RS and ML can help obtain quick estimations of SOC content to assist with SBP management.

Carbon Storage in Cropland Soils: Insights from Iowa, United States

The restoration of soil organic matter (SOM, as measured by soil organic carbon (SOC)) within the world’s agricultural soils is imperative to sustaining crop production and restoring other ecosystem services. We compiled long-term studies on the effect of management practices on SOC from Iowa, USA—an agricultural region with relatively high-quality soil data—to highlight constraints on detecting changes in SOC and inform research needed to improve SOC measurement and management. We found that strip-tillage and no-tillage increased SOC by 0.25–0.43 Mg C ha−1 yr−1 compared to losses of 0.24 to 0.46 Mg C ha−1 yr−1 with more intensive tillage methods. The conversion of cropland to perennial grassland increased SOC by 0.21–0.74 Mg C ha−1 yr−1. However, diversifying crop rotations with extended rotations, and supplementing synthetic fertilizer with animal manure, had highly variable and inconsistent effects on SOC. The improved prediction of changes in SOC requires: the use of methods that can identify and disentangle multiple sources of variability; looking beyond total SOC and toward systematic collection of data on more responsive and functionally relevant fractions; whole-profile SOC monitoring; monitoring SOC in long-term studies on the effect of multiple conservation practices used in combination; and deeper collaboration between field soil scientists and modelers.

On-the-Go Vis-NIR Spectroscopy for Field-Scale Spatial-Temporal Monitoring of Soil Organic Carbon

Agricultural soils serve as crucial storage sites for soil organic carbon (SOC). Their appropriate management is pivotal for mitigating climate change. Continuous monitoring is imperative to evaluate spatial and temporal changes in SOC within agricultural fields. In-field datasets of Vis-NIR soil spectra were collected on a long-term experimental site using an on-the-go spectrophotometer. Data processing for continuous SOC prediction involves a two-step modeling approach. In Step 1, a partial least square (PLSR) regression model is trained to establish a relationship between the SOC content and spectral information, including spectral preprocessing. In Step 2, the predicted SOC content obtained from the PLSR models is interpolated using ordinary kriging. Among the tested spectral preprocessing techniques and semivariogram models, Savitzky–Golay and the Gap-Segment derivative preprocessing along with a Gaussian semivariogram model, yielded the best performance resulting in a root mean square error of 1.24 and 1.26 g kg−1. A striping effect due to the transect-based data collection was addressed by testing the effectiveness of extending the spatial separation distance, employing data aggregation, and defining the distribution based on treatment plots using block kriging. Overall, the results highlight the high potential of on-the-go spectral Vis-NIR data for field-scale spatial-temporal monitoring of SOC.

Multi-site evaluation of stratified and balanced sampling of soil organic carbon stocks in agricultural fields

Estimating soil organic carbon (SOC) stocks in agricultural fields is essential for environmental and agronomic research, management, and policy. Stratified sampling is a classic strategy for estimating mean soil properties, and has recently been codified in SOC monitoring protocols. However, for the specific task of estimating the SOC stock of an agricultural field, concrete guidance is needed for which covariates to stratify on and how much stratification can improve estimation efficiency. It is also unknown how stratified sampling of SOC stocks compares to modern alternatives, notably doubly balanced sampling. To address these gaps, we collected high-density (average of 7 samples ha−1) and deep (average of 75 cm) measurements of SOC stocks at eight commercial fields under maize-soybean production in two US Midwestern states. We combined these measurements with a Bayesian geostatistical model to evaluate stratified and balanced sampling strategies that use a set of readily-available geographic, topographic, spectroscopic, and soil survey data. We examined the number of samples needed to achieve a given level of SOC stock estimation accuracy. While stratified sampling using these variables enables an average sample size reduction of 17% (95% CI, 11% to 23%) compared to simple random sampling, doubly balanced sampling is consistently more efficient, reducing sample sizes by 32% (95% CI, 25% to 37%). The data most important to these efficiency gains are a remotely-sensed SOC index, SSURGO estimates of SOC stocks, and the topographic wetness index. We conclude that in order to meet the urgent challenge of climate change, SOC stocks in agricultural fields could be more efficiently estimated by taking advantage of this readily-available data, especially with doubly balanced sampling.

Combining machine learning and environmental covariates for mapping of organic carbon in soils of Russia

Robust and detailed quantitative prediction of soil organic carbon (SOC) is of great significance to studying the carbon budget, soil management and decision-making. Spatial variations of SOC content were modelled using 863 soil profiles and a set of 22 environmental covariates representing relief, bioclimate variables and remote sensing data. The article provided the results of 3D modeling of SOC content in several soil layers (0–5, 5–15, 15–30, 30–60 and 60–100 cm) for the territory of the Russian Federation with 500 m spatial resolution. Machine learning framework was used, with random forest and spatial cross-validation techniques (150 km blocks) to handle the spatial autocorrelation of the training points. Compared with randomized cross-validation (R2 0.66, Concordance Correlation Coefficient (CCC) 0.79, RMSE 0.99 g/kg), using spatial cross-validation to predict the SOC content yielded less accurate results — R2 0.45, CCC 0.63, RMSE 1.41 g/kg. Regarding the importance of the variables, soil depth and temperature seasonality were major contributors to the SOC content prediction, followed by the EVI, 7 (MIR) MODIS band, and the topographic wetness index. The model was next evaluated with procedure so-called „area of applicability“ (AOA) of prediction model — the areas for which we cannot estimate prediction quality. AOA spatial distribution showed that the feature space not represented by training data is located in the mountain provinces. The proposed framework can be used for SOC modeling with a limited soil profile number, and it is provides a reproducible approach for long-term SOC monitoring.