Estimating Soil Organic Carbon Research Articles

Mapping Topsoil Carbon Storage Dynamics of Croplands Based on Temporal Mosaicking Images of Landsat and Machine Learning Approach

Understanding changes of soil organic carbon (SOC) in top layers of croplands and their driving factors is a vital prerequisite in decision-making for maintaining sustainable agriculture. However, high-precision estimation of SOC of croplands at regional scale is still an issue to be solved. Based on soil samples, synthetic image of bare soil and geographical data, this paper predicted SOC density of croplands using Random Forest model in the Black Soil Region of Jilin Province, China in 2005 and 2020, and analyzed its influencing factors. Results showed that random forest model that integrates bare soil composite images improve the accuracy and robustness of SOC density prediction. From 2005 to 2020, the total SOC storage in croplands decreased from 89.96 to 82.79 Tg C with an annual decrease of 0.48 Tg C yr−1. The mean value of SOC density of croplands decreased from 3.42 to 3.32 kg/m2, and high values are distributed in middle parts. Changes of SOC represented significant heterogeneity spatially. 62.14% of croplands with SOC density greater than 4.0 kg/m2 decreased significantly, and 38.60% of croplands with SOC density between 2.5 and 3.0 kg/m2 significantly increased. Climatic factors made great contributions to SOC density, however, their relative importance (RI) to SOC density decreased from 44.65% to 37.26% during the study period. Synthetic images of bare soil constituted 23.54% and 26.29% of RI in the SOC density prediction, respectively, and the contribution of each band was quite different. The RIs of topographic and vegetation factors were low but increased significantly from 2005 to 2020. This study can aid local land managers and governmental agencies in assessing carbon sequestration potential and carbon credits, thus contributing to the protection and sustainable use of black soils.

Evaluating performance of miniaturized spectrometers in predicting soil properties using multivariate statistical analysis

Despite the increasing use of portable, low-cost spectrometers in estimating soil properties, there is lack of documentation regarding the factors contributing to the lower performance of these spectrometers when compared to conventional ones. This study investigates potential factors influencing performance of the Nanoquest, a low-cost spectrometer, in estimating soil organic carbon (SOC) and total nitrogen (TN). To conduct the study, five different models (cubist, partial least squares regression, support vector machines, random forest, and generalised boosted models) were tested for the estimation SOC and TN and a fivefold cross-validation analysis was conducted for model hyperparameter optimization. The Nanoquest achieved a Lin’s concordance correlation coefficient (CCC) value of 0.84 and an R2 value of 0.74 for SOC. For TN, CCC values of 0.86 and an R2 value of 0.78 were obtained. To understand the impact of the spectral range and spectral resolution on SOC and TN estimation, the ASD spectra were digitally resampled to match the Nanoquest spectral range and resolution. This resampling resulted in a slight decrease in model performance for the spectral range and a more pronounced decrease for the spectral resolution.

Estimating soil organic carbon and total nitrogen across inhabited and uninhabited islands based on dual simulations in the entire- and sub-areas

Soil total organic carbon (TOC) and total nitrogen (TN) mapping methods have been developed for different ecosystems; however, little attention has been paid to archipelagos comprising inhabited and uninhabited islands. In this study, we proposed a method of dual simulations in the entire- and sub-areas to adequately improve the simulation accuracy of soil TOC and TN in the archipelago. A typical archipelago, Miaodao Archipelago, was selected to demonstrate the method. The dual simulations comprehensively considered the common characteristics of the entire-area and the unique conditions of each sub-area in the archipelago. Through the dual simulations, the accuracy can be viewed in three hierarchies: the entire area (the first hierarchy), the inhabited and uninhabited islands (the second hierarchy), and the single island (the third hierarchy). The simulation accuracies for TOC were increased by 12.03%, 12.03%, and 16.94% in the first, second, and third hierarchies, respectively, compared with the baseline, and those for TN were increased by 7.13%, 9.21%, and 14.72%, respectively. The lowest normalized root mean squared errors for TOC and TN were 0.114 and 0.105, respectively, which were similar to those reported in previous studies that achieved high simulation accuracy in other areas. Considerable differences in soil TOC and TN existed across the inhabited (39.95 g/kg TOC and 3.61 g/kg TN) and uninhabited (63.42 g/kg TOC and 7.08 g/kg TN) islands, which were driven by the great differences in natural conditions and human activities across the two sub-areas. The dual simulations contributed to the further improvement of simulation accuracy in soil mapping, providing a practical method for soil TOC and TN estimation in the archipelago, and revealing the spatial differences in soil TOC and TN across the inhabited and uninhabited islands.

Estimating soil organic carbon sequestration potential in the Chinese Mollisols region

AbstractMollisols region of China is a ballast stone of Chinese food security. In this study, 484 soil organic carbon (SOC) data points from the documented soil surface (0–20 cm) samples in the typical Chinese Mollisols region were selected. The regional soil fertility was divided into high, medium, and low levels based on the SOC content from the long‐term positioning fertilization stations in Shenyang, Harbin, and Hailun of Northeast China. Furthermore, the regional SOC balance point (SOCb) model was built driven by the factors of annual mean temperature, annual precipitation, annual effective accumulative temperature, the ratio of precipitation and temperature, soil clay content, and pH. Using geographic information systems (GIS) interpolation method, the regional mean SOCb and SOC sequestration potential (SOCsp) at the SOCb's status were simulated to be 40.10 g kg−1 and 10.71 kg m−2, respectively. Moreover, the SOC content increased (SOCc) and the SOC sequestration capacity increased of medium‐ or low‐fertility soil were evaluated by GIS subtraction. Consequently, the means of SOCc in medium‐ and low‐fertility soils could be 16.88 g kg−1 and 26.79 g kg−1, respectively. Furthermore, the means of SOCsp in medium‐ and low‐fertility soils could be 4.30 kg m−2 and 7.43 kg m−2, respectively. The sensitivity analysis of this model showed that the high SOCb area was distributed in study region with low temperature, dry, high clay content, and low pH. The results provide an actual perspective on estimating regional SOCsp and soil fertility promotion target induced by the different soil fertility levels.

Estimation of soil organic carbon by combining hyperspectral and radar remote sensing to reduce coupling effects of soil surface moisture and roughness

Soil organic carbon (SOC) is important in the global carbon cycle. Accurate estimation of SOC content in cultivated land is a prerequisite for evaluating the carbon sequestration potential and quality of soils. However, existing SOC prediction studies based on hyperspectral remote sensing neglect the spectral response of the physical properties of surface soil, leading to inadequate model generalization. With the exponential growth of remote sensing data, the development of pixel-level soil spectral correction methods based on multi-source remote sensing data has become an interesting and challenging topic. This method aims to minimize the effect of soil physical properties on spectra, thus addressing the poor spatiotemporal transferability of SOC prediction models due to uncertain variations in surface soil physical properties. In this study, a soil spectral correction strategy is constructed using satellite hyperspectral image (HSI) and synthetic aperture radar (SAR) images through multi-order polynomial regression and convolutional neural networks. This strategy considers soil physical variables such as soil moisture (SM) content and root mean square height (RMSH) of soil surface roughness. The soil spectral correction model and SOC content prediction model were established using 80 soil samples collected from Site 1. Afterward, the performance and transferability of both models were verified using the remaining 25 samples from Site 1 and 50 samples from Site 2. The results showed that: 1) The effect of SM and RMSH on the soil pixel spectrum can be significantly reduced after correcting HSI using soil spectral correction strategy. The correlation coefficients between the corrected pixel spectrum and the ground-based spectrum increase by over 60 % compared with those between the original spectrum and the ground-based spectrum. 2) Soil spectral correction improves the prediction accuracy and mapping capability of HSI for SOC content, with the highest RP2 of 0.743 and RMSEP of 3.455 g/kg at Site 1. 3) Compared with the original HSI-based SOC prediction model, the soil spectral correction strategy based on multi-order polynomial and convolutional neural network reduced the RMSEP of SOC prediction results at Site 2 by 5.082 g/kg and 5.454 g/kg, and the RP2 increased by 0.390 and 0.409, respectively. 4) When predicting SOC content from raw HIS, SM and RMSH contribute to more than 60 % of the bias, with SM having a larger bias than RMSH. The findings of this study emphasize the influence of soil physical properties on SOC prediction and contribute to the existing research on SOC mapping using HSI and SAR data.

Temporal variability is a major source of uncertainty in soil carbon measurements

AbstractMonitoring changes in soil organic carbon over time is important to many agricultural and environmental goals. Despite decades of measurements, temporal variability in soil carbon measurements has not been studied extensively. In this report, we examine five sets of monthly samples extending up to 3 years each that were collected from field experiments at four locations representative of dryland farming in the Pacific Northwest. The variance from month‐to‐month was 15%–32% of the random error, averaging 20%. This was often greater than the variance between replicate experimental units (2%–42%, averaging 17%). At certain sites, sequential samples were found to be temporally autocorrelated, but no consistent trend patterned on seasonal factors like precipitation was found. This suggests that a single point‐in‐time sample can deviate substantially from the long‐term average soil carbon at the site. We illustrate this problem with the results of repeated soil samples taken from 12 commercial farm fields. We recommend that confidence intervals for soil organic carbon estimates should include variance based on a large population of samples rather than from a single sample set at one timepoint.

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.

Machine learning-driven modeling for soil organic carbon estimation from multispectral drone imaging: a case study in Corvera, Murcia (Spain)

Machine learning-driven modeling for soil organic carbon estimation from multispectral drone imaging: a case study in Corvera, Murcia (Spain)

A random forest model to predict soil organic carbon storage in mangroves from Southern Colombian Pacific coast

Mangrove ecosystems serve as significant reservoirs of carbon stocks, particularly within the soil. However, these ecosystems face widespread degradation globally, underscoring the necessity for comprehensive technical insights to facilitate effective management. Enhancing the precision of soil organic carbon estimates within mangroves necessitates access to precise field data. Hence, this study aimed to assess the soil organic carbon stored in mangrove ecosystems along the southern Colombian Pacific coast. Employing a Random Forest model, constructed with data from 28 soil profiles spanning a depth of 2 m and 16 predictor variables, we evaluated the model's performance using repeated 10-fold cross-validation (with 5 random repeats). Uncertainty in carbon storage predictions was quantified through 95% confidence intervals. Our model successfully accounted for 88% of the variability in soil organic carbon storage. Depth, cation exchange capacity, pH, and sand content emerged as the most influential predictors of soil organic carbon storage variation. The study estimated a mean of 270.32 ± 52.75 t C ha−1, with a total carbon reserve of 25458088 t in the study area. Notably, this value falls below global estimates, suggesting a potential overestimation of carbon stocks in mangrove soils by existing global models.

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.

Upscaling Soil Organic Carbon Measurements at the Continental Scale Using Multivariate Clustering Analysis and Machine Learning

AbstractEstimates of soil organic carbon (SOC) stocks are essential for many environmental applications. However, significant inconsistencies exist in SOC stock estimates for the U.S. across current SOC maps. We propose a framework that combines unsupervised multivariate geographic clustering (MGC) and supervised Random Forests regression, improving SOC maps by capturing heterogeneous relationships with SOC drivers. We first used MGC to divide the U.S. into 20 SOC regions based on the similarity of covariates (soil biogeochemical, bioclimatic, biological, and physiographic variables). Subsequently, separate Random Forests models were trained for each SOC region, utilizing environmental covariates and SOC observations. Our estimated SOC stocks for the U.S. (52.6 ± 3.2 Pg for 0–30 cm and 108.3 ± 8.2 Pg for 0–100 cm depth) were within the range estimated by existing products like Harmonized World Soil Database, HWSD (46.7 Pg for 0–30 cm and 90.7 Pg for 0–100 cm depth) and SoilGrids 2.0 (45.7 Pg for 0–30 cm and 133.0 Pg for 0–100 cm depth). However, independent validation with soil profile data from the National Ecological Observatory Network showed that our approach (R2 = 0.51) outperformed the estimates obtained from Harmonized World Soil Database (R2 = 0.23) and SoilGrids 2.0 (R2 = 0.39) for the topsoil (0–30 cm). Uncertainty analysis (e.g., low representativeness and high coefficients of variation) identified regions requiring more measurements, such as Alaska and the deserts of the U.S. Southwest. Our approach effectively captures the heterogeneous relationships between widely available predictors and the current SOC baseline across regions, offering reliable SOC estimates at 1 km resolution for benchmarking Earth system models.

Relative and absolute difference in soil organic carbon stocks in grassland soils in Ireland: Impact of rock fragments, bulk density and calculation methods

On-farm management measures can sequester carbon by removing carbon dioxide from the atmosphere and storing it securely for long periods, in our soils. Estimates of soil organic carbon (SOC) stocks differ by calculation methods in terms of how they account for the coarse >2 mm fraction (i.e. rock fragments) of the soil. Irish grassland soils have variable volumetric stone contents and the extent of overestimation is unknown. Without best practice guidance, calculations of SOC in these soils are vulnerable to overestimation. The objectives of the present study for grassland soils in Ireland were to: 1) calculate mean SOC stocks using different methods by soil horizon and volumetric stone content class; 2) highlight relative differences in SOC stocks between these calculation methods and 3) compare normalised layer (Ly, fixed depth) versus horizon (Hz) type derived sampling data in terms of SOC stocks, to establish a best practice standardised sampling protocol. Five methods (M, 1–3 overestimate SOC stocks, whereas 4–5 are equivalent and are closest to reality) to calculate SOC stocks were used in combination with three soil databases i.e. Irish Soil Information System (I-SIS), the Heavy Soils Programme (HSP) and the Soil Quality Assessment Research Project (SQUARE). Objectives 1 and 2 were assessed using all three databases across methods, whereas objective 3 used the SQUARE database and M4. These data were sorted into volumetric stone content (%) classes (< 5%, 5–10%, 10–20%, 20–30% and > 30%) and sampling horizon (O, A, E, A/B, B, B/C, and C) with SOC stocks calculated using M1–5 equations. Results confirmed the high volumetric stone content assumption of grassland soils in Ireland (exception O horizons). Volumetric stone contents ranged from 0% to 36% for all horizons. When M1–5 are considered the average relative difference in SOC stocks between M1 and M5 was 18.9% (max: 388%), M2 and M5 was 7.7% (max: 60.7%) and M3 and M5 was 8.9 (max: 310%). In terms of Ly versus Hz sampling, no significant differences were found for the A horizons sampled. However, in Ireland, best practice should still utilise elements from both Ly (using an auger to get fixed depth soil samples; analysed for organic C) and Hz (using a soil profile pit to extract representative BD cores and to gather bulk samples for volumetric stone content analysis) approaches in the field. Laboratory methods used must fit with the needs and assumptions of equations M4/5 to calculate SOC stocks (thereby minimising overestimation and indeed underestimation).

Uncertainty Quantification of Soil Organic Carbon Estimation from Remote Sensing Data with Conformal Prediction

Soil organic carbon (SOC) contents and stocks provide valuable insights into soil health, nutrient cycling, greenhouse gas emissions, and overall ecosystem productivity. Given this, remote sensing data coupled with advanced machine learning (ML) techniques have eased SOC level estimation while revealing its patterns across different ecosystems. However, despite these advances, the intricacies of training reliable and yet certain SOC models for specific end-users remain a great challenge. To address this, we need robust SOC uncertainty quantification techniques. Here, we introduce a methodology that leverages conformal prediction to address the uncertainty in estimating SOC contents while using remote sensing data. Conformal prediction generates statistically reliable uncertainty intervals for predictions made by ML models. Our analysis, performed on the LUCAS dataset in Europe and incorporating a suite of relevant environmental covariates, underscores the efficacy of integrating conformal prediction with another ML model, specifically random forest. In addition, we conducted a comparative assessment of our results against prevalent uncertainty quantification methods for SOC prediction, employing different evaluation metrics to assess both model uncertainty and accuracy. Our methodology showcases the utility of the generated prediction sets as informative indicators of uncertainty. These sets accurately identify samples that pose prediction challenges, providing valuable insights for end-users seeking reliable predictions in the complexities of SOC estimation.

Soil organic carbon stocks in native forest of Argentina: a useful surrogate for mitigation and conservation planning under climate variability

BackgroundThe nationally determined contribution (NDC) presented by Argentina within the framework of the Paris Agreement is aligned with the decisions made in the context of the United Nations Framework Convention on Climate Change (UNFCCC) on the reduction of emissions derived from deforestation and forest degradation, as well as forest carbon conservation (REDD+). In addition, climate change constitutes one of the greatest threats to forest biodiversity and ecosystem services. However, the soil organic carbon (SOC) stocks of native forests have not been incorporated into the Forest Reference Emission Levels calculations and for conservation planning under climate variability due to a lack of information. The objectives of this study were: (i) to model SOC stocks to 30 cm of native forests at a national scale using climatic, topographic and vegetation as predictor variables, and (ii) to relate SOC stocks with spatial–temporal remotely sensed indices to determine biodiversity conservation concerns due to threats from high inter-annual climate variability.MethodsWe used 1040 forest soil samples (0–30 cm) to generate spatially explicit estimates of SOC native forests in Argentina at a spatial resolution of approximately 200 m. We selected 52 potential predictive environmental covariates, which represent key factors for the spatial distribution of SOC. All covariate maps were uploaded to the Google Earth Engine cloud-based computing platform for subsequent modelling. To determine the biodiversity threats from high inter-annual climate variability, we employed the spatial–temporal satellite-derived indices based on Enhanced Vegetation Index (EVI) and land surface temperature (LST) images from Landsat imagery.ResultsSOC model (0–30 cm depth) prediction accounted for 69% of the variation of this soil property across the whole native forest coverage in Argentina. Total mean SOC stock reached 2.81 Pg C (2.71–2.84 Pg C with a probability of 90%) for a total area of 460,790 km2, where Chaco forests represented 58.4% of total SOC stored, followed by Andean Patagonian forests (16.7%) and Espinal forests (10.0%). SOC stock model was fitted as a function of regional climate, which greatly influenced forest ecosystems, including precipitation (annual mean precipitation and precipitation of warmest quarter) and temperature (day land surface temperature, seasonality, maximum temperature of warmest month, month of maximum temperature, night land surface temperature, and monthly minimum temperature). Biodiversity was influenced by the SOC levels and the forest regions.ConclusionsIn the framework of the Kyoto Protocol and REDD+, information derived in the present work from the estimate of SOC in native forests can be incorporated into the annual National Inventory Report of Argentina to assist forest management proposals. It also gives insight into how native forests can be more resilient to reduce the impact of biodiversity loss.

Factors controlling the spatial heterogeneity of soil organic carbon concentrations and stocks in a boreal forest

Boreal soils play a crucial role in the global terrestrial carbon cycle, serving as significant carbon reservoirs. However, this region is experiencing rapid climate change which poses a threat to the stability of the soil carbon pool. To understand the carbon balance in the boreal region, it is essential to obtain accurate estimates of soil organic carbon (SOC) stocks. The objective of this study was to investigate factors controlling the spatial heterogeneity of SOC concentrations and stocks in a boreal forested catchment and to quantify spatial variability of SOC concentrations and stocks using a random forest-based regression kriging approach. Soil carbon content, gravel content and bulk density were obtained from the surface soil layer (0–30 cm) of 106 samples across a 390 ha catchment in northwestern Ontario, Canada. Spatial attributes including elevation, wetness index, distance to nearest ridge, relative position index, plan curvature, profile curvature and normalized difference vegetation index were used in the random forest-based regression kriging to estimate spatial heterogeneity of SOC stocks and concentrations across the catchment. Our results showed that shallow soils in well-drained upland areas contained low to moderate SOC concentrations (1.2–16.2%) while depressional wetlands had high concentrations (19.2–54.0%). Topographic variables, including wetness index were the strongest predictors of SOC concentrations, but SOC stocks were very heterogeneous across the study site (12.9–139.0 Mg ha−1) and were not well explained by topographic or vegetation variables. The gravel content was highly variable (0–29.0%), and without accounting for this fraction, SOC stocks were overestimated by 52.5% in this landscape. These results emphasize the substantial catchment scale variability in SOC stocks in boreal landscapes and underscore the significance of considering both landscape-scale drivers and intrinsic soil properties when estimating soil carbon in this region. By understanding these factors, we can better manage and protect the important carbon stores in boreal soils.

Changes in Soil Organic Carbon Stocks Due to Land Use Changes in the Extended São Francisco River Basin

As the largest carbon reservoir in terrestrial ecosystems, soils play a critical role in food production, mitigation and adaption to climate change, and sustainability of agroecosystems. In this context, the aim of this study is to estimate variations in soil carbon stocks resulting from land use and management changes in different biomes that compose the Bacia Estendida do Rio São Francisco (BESF - Extended São Francisco River Basin), between 1985 and 2017. For this, remote sensing data and information from the IBGE agriculture and livestock census were used, in addition to emission factors to estimate soil organic carbon (SOC) changes. The results indicate that BESF had about 5.70 million ha degraded in the analyzed period, in addition to an increase of 0.72 Tg C year-1 in SOC stocks. The sub-medium São Francisco River basin recorded the highest SOC gain, with an increase of 0.54 Tg C ha-1; on the other hand, the sub-medium São Francisco River sub-basin had the greatest SOC losses, with an estimated reduction of 0.07 Tg C year-1. In short, this study provides important evidence on changes in SOC stocks in the region, emphasizing the importance of native vegetation conversion to agriculture and livestock systems under sustainable soil management for mitigating greenhouse gas emissions and maintaining soil quality.

Regional topsoil organic carbon content in the agricultural soils of Slovakia and its drivers, as revealed by the most recent national soil monitoring data

Soil organic carbon (SOC) is a primary constituent of soil organic matter and plays an important role in the regulation of many soil processes, including greenhouse gas emissions. Recently, SOC also became an indicator for monitoring climate change mitigation policies in the agricultural sector. The availability of up-to-date SOC inventories is thus crucial in terms of supporting SOC–related actions at country or sub-country scales. Currently, the National Monitoring System of the Agricultural Soils of Slovakia (CMS-P), whose network of 318 monitoring sites was last surveyed in 2018, is the only available source of up-to-date topsoil SOC data for agricultural land in Slovakia. Although very useful at the national scale, the number of CMS-P observations it contains is too limited for much needed sub-national SOC inventories. We hypothesized that with the aid of well-chosen macro-scale drivers of topsoil SOC accumulation in agricultural land in Slovakia, and by mapping those drivers geographically, we could upscale the CMS-P observations and produce a regional estimate of topsoil SOC. Altitude, land cover, topsoil texture, and soil type were assumed to be the key factors controlling topsoil SOC accumulation in Slovakia, and based on these, the country was classified into 14 macro-scale geographical regions. Typical ranges and mid-class values of 0–30cm topsoil SOC concentrations (%) and stocks (t ha−1) were calculated for each macro-scale region from CMS-P data. The average topsoil SOC content in agricultural land was estimated to be 2.13% (72.9 t ha−1). The highest topsoil SOC stock (> 90 t ha−1) was estimated for the lowlands of Slovakia, and the lowest (< 50 t ha−1) for the shallow and stony soils of mountain regions. When aggregated to 78 administrative regions at LAU1 level, the area-weighted averages ranged between 39.20 t ha−1 and 80.0 t ha−1, with the highest values (> 65 t ha−1) being in LAU1 regions in the south-west, south-east, and north of Slovakia where arable land is most prevalent. Total SOC storage in 0–30cm topsoil of agricultural land in Slovakia was estimated at 118.39 Mt, with two-thirds of this amount stored in arable soils in 33 south-west, south-east, and south LAU1 administrative regions. As there is no alternative and up-to-date dataset on topsoil SOC content in Slovakia, the upscaling algorithm presented in this study is an important step toward utilizing CMS-P data for sub-national SOC inventories. It may also offer a new way of providing inputs to help predict future or alternative regional topsoil SOC accumulation trajectories in Slovakian agricultural land using process-based or statistical models.

Preliminary Results in Innovative Solutions for Soil Carbon Estimation: Integrating Remote Sensing, Machine Learning, and Proximal Sensing Spectroscopy

This paper explores the application and advantages of remote sensing, machine learning, and mid-infrared spectroscopy (MIR) as a popular proximal sensing spectroscopy tool in the estimation of soil organic carbon (SOC). It underscores the practical implications and benefits of the integrated approach combining machine learning, remote sensing, and proximal sensing for SOC estimation and prediction across a range of applications, including comprehensive soil health mapping and carbon credit assessment. These advanced technologies offer a promising pathway, reducing costs and resource utilization while improving the precision of SOC estimation. We conducted a comparative analysis between MIR-predicted SOC values and laboratory-measured SOC values using 36 soil samples. The results demonstrate a strong fit (R² = 0.83), underscoring the potential of this integrated approach. While acknowledging that our analysis is based on a limited sample size, these initial findings offer promise and serve as a foundation for future research. We will be providing updates when we obtain more data. Furthermore, this paper explores the potential for commercialising these technologies in Australia, with the aim of helping farmers harness the advantages of carbon markets. Based on our study’s findings, coupled with insights from the existing literature, we suggest that adopting this integrated SOC measurement approach could significantly benefit local economies, enhance farmers’ ability to monitor changes in soil health, and promote sustainable agricultural practices. These outcomes align with global climate change mitigation efforts. Furthermore, our study’s approach, supported by other research, offers a potential template for regions worldwide seeking similar solutions.

Sentinel-1 Imagery Used for Estimation of Soil Organic Carbon by Dual-Polarization SAR Vegetation Indices

Despite optical remote sensing (and the spectral vegetation indices) contributions to digital soil-mapping studies of soil organic carbon (SOC), few studies have used active radar remote sensing mission data like that from synthetic aperture radar (SAR) sensors to predict SOC. Bearing in mind the importance of SOC mapping for agricultural, ecological, and climate interests and also the recently developed methods for vegetation monitoring using Sentinel-1 SAR data, in this work, we aimed to take advantage of the high operationality of Sentinel-1 imaging to test the accuracy of SOC prediction at different soil depths using machine learning systems. Using linear, nonlinear, and tree regression-based methods, it was possible to predict the SOC content of soils from western Bahia, Brazil, a region with predominantly sandy soils, using as explanatory variables the SAR vegetation indices. The models fed with SAR sensor polarizations and vegetation indices produced more accurate results for the topsoil layers (0–5 cm and 5–10 cm in depth). In these superficial layers, the models achieved an RMSE in the order of 5.0 g kg−1 and an R2 ranging from 0.16 to 0.24, therefore explaining about 20% of SOC variability using only Sentinel-1 predictors.

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.