Estimating Soil Organic Carbon Research Articles

Accuracy Assessment of Kriging, artificial neural network, and a hybrid approach integrating spatial and terrain data in estimating and mapping of soil organic carbon.

This study aimed to produce a soil organic carbon (SOC) content map with high accuracy and spatial resolution using the most effective factors in the model. The spatial SOC estimation success of Inverse Distance Weighting (IDW), Ordinary Kriging (OK), Empirical Bayesian Kriging (EBK), Multi-Layered Perception Network (MLP) and MLP-OK Hybrid models were compared to obtain the most reliable model in estimating the SOC content. The study area was located in Besni district in the Southeastern Anatolia Region of Turkey. Total of 132 surface (0–30 cm) soil samples were collected from the covers 1330 km2 land and analyzed for SOC, lime, clay and sand content and soil reaction included in the estimation models. Mean annual precipitation and temperature, elevation, compound topographic index, enhanced vegetation and normalized difference vegetation index, were also used as the inputs in the modelling. The spatial distribution of SOC was determined using a MLP and a two-stage ensemble model (MLP-OK) combining the estimation of OK residuals. Soil surveys and covariates were used to train and validate the MLP-OK hybrid model. The MLP-OK model provided a more accurate estimation of SOC content with minimal estimation errors (ME: -0.028, 45 MAE: 0.042, RMSE: 0.066) for validation points compared to the other models. The MLP-OK model outperformed other models by 75.09 to 77.92%. The MLP-OK model estimated the lower and upper limits of the estimated and the measured values in a consistent manner compared to the other models. The spatial distribution map of SOC content obtained by ANN-kriging approach was significantly affected by ancillary variables, and revealed more detail than other interpolation methods in the northern, central, southwestern and southeastern parts of the study area. The results revealed that the assembling of MLP with OK model can contribute to obtain more reliable regional, national and global spatial soil information.

Estimating soil organic carbon levels in cultivated soils from satellite image using parametric and data-driven methods

ABSTRACT Soil organic carbon (SOC) is one of the key soil components for cultivated soils. SOC is regularly monitored and mapped to improve the quality, health, and productivity of the soil. However, traditional SOC-level monitoring is expensive for land managers and farmers. Estimating SOC using satellite imagery provides an easy, efficient, and cost-effective way to monitor surface SOC levels. The objective of this study was to estimate the surface SOC distribution in selected soils of Major Land Resource Areas (MLRA), 102A (Rolling Till Plain, Brookings County, SD), and 103 (Central Iowa and Minnesota Till Prairies, Lac qui Parle County, MN), using satellite imagery with different resolutions (Landsat 8 and PlanetScope). The dominant soils in the study area are Haplustolls, Calciustolls, and Endoaquolls, which are formed in silty sediments, local silty alluvium, and till. Landsat 8 and PlanetScope spectral bands were used to develop SOC prediction models. Parametric and data-driven methods were employed to predict the SOC. Multiple linear regression and Linear Spatial Mixed Model (LSMM) were used on the Landsat 8 and PlanetScope data. In addition to the parametric models, Regression Trees and Random Forest were also employed on both satellite data. The results showed that reduced LSMM provided the lowest RMSE, which are 0.401 and 0.367 for Landsat 8 and PlanetScope, respectively. Furthermore, the random forest has the highest RPD and RPIQ for Landsat 8 (RPD of 2.67 and RPIQ of 2.49) and PlanetScope (RPD of 2.85 and RPIQ of 3.7). In all cases, models obtained from PlanetScope are better than those obtained from Landsat 8.

Deep transfer learning of global spectra for local soil carbon monitoring

There is global interest in spectroscopy and the development of large and diverse soil spectral libraries (SSL) to model soil organic carbon (SOC) and monitor, report, and verify (MRV) its changes. The reason is that increasing SOC can improve food production and mitigate climate change. However, ‘global’ modelling of SOC with such diverse and hyperdimensional SSLs do not generalise well locally, e.g. at a field scale. To address this challenge, we propose deep transfer learning (DTL) to leverage useful information from large-scale SSLs to assist local modelling. We used one global, three country-specific SSLs and data from three local sites with DTL to improve the modelling and localise the SOC estimates in individual fields or farms in each country. With DTL, we transferred instances from the SSLs, representations from one-dimensional convolutional neural networks (1D-CNNs) trained on the SSLs, and both instances and representations to improve local modelling. Transferring instances effectively used information from the global SSL to most accurately estimate SOC in each site, reducing the root mean square error (RMSE) by 25.8% on average compared with local modelling. Our results highlight the effectiveness of DTL and the value of diverse, global SSLs for accurate local SOC predictions. Applying DTL with a global SSL one could estimate SOC anywhere in the world more accurately, rapidly, and cost-effectively, enabling MRV protocols to monitor SOC changes.

Can Low-Cost, Handheld Spectroscopy Tools Coupled with Remote Sensing Accurately Estimate Soil Organic Carbon in Semi-Arid Grazing Lands?

Soil organic carbon influences several landscape ecological processes, and soils are becoming recognized as a mechanism to mitigate the negative impacts of climate change. There is a need to define methods and technologies for addressing soils’ spatial variability as well as the time and cost of sampling soil organic carbon (SOC). Visible and near-infrared spectroscopy have been suggested as a sampling tool to reduce inventory cost. We sampled nineteen ranch properties totaling 17,347 ha across Oklahoma and Texas in 2019 to evaluate the effectiveness and accuracy of a handheld reflectometer (Our Sci, Ann Arbor, MI, USA) (370–940 nm) and existing remote sensing approaches to estimate SOC in semi-arid grazing lands. Our data suggest that the Our Sci Reflectometer estimated soil organic carbon with a precision of approximately (±0.3% SOC); however, it was least accurate at higher carbon concentrations. The Our Sci reflectometer, although consistently accurate at lower SOC concentrations, was still less accurate than a model built using only remote sensing and digital soil map data as predictors. Combining the two data sources was the most accurate means of determining SOC. Our results indicated that the Our Sci handheld Vis-NIR reflectometer tested may have only limited applications for reducing inventory costs at scale.

Soil organic carbon: Associating the Walkley-Black Wet Oxidation Method to Loss on Ignition (Gravimetric method) and Clay Content for Selected Samoan Inceptisols

Precise estimation of soil organic carbon (SOC) is vital in many scientific fields including natural and applied sciences, as well as climate science researches. Ordinary methods of measuring soil organic carbon (SOC) aren’t sufficient for accurate estimation of C sequestration nor to meet environmental safety standards. Methods that allow for accurate assessment of SOC, which are eco-friendly, and yet practicable are more desirable for use in developing countries. Loss on ignition method (LOI) of appraising SOC offers an alternative method to the traditional Walkley-Black (W-B) method of organic carbon determination. A quantitative relation between LOI and Walkley-Black methods was studied for selected Samoan inceptisols. For this study, 36 soil samples from 0-15 cm depth were collected from 12 sites to determine the levels of SOC (%) by both the methods. Clay contents were also determined and used to develop simple and multiple linear regression equations. Highly significant positive associations (P<0.001) were observed between %OM-WB and %OM-LOI (r = 0.92); %OM-WB and %Clay content (r = 0.91); and, %OM-LOI and %Clay content (r = 0.76). Multiple rectilinear regression equation using clay content of soil and LOI as independent factors and W-B as dependent factor provided reliable estimates of SOC since significant multiple relationship was established (R2 = 0.69, p < 0.001).

Estimation of Soil Organic Carbon Content in the Ebinur Lake Wetland, Xinjiang, China, Based on Multisource Remote Sensing Data and Ensemble Learning Algorithms.

Soil organic carbon (SOC), as the largest carbon pool on the land surface, plays an important role in soil quality, ecological security and the global carbon cycle. Multisource remote sensing data-driven modeling strategies are not well understood for accurately mapping soil organic carbon. Here, we hypothesized that the Sentinel-2 Multispectral Sensor Instrument (MSI) data-driven modeling strategy produced superior outcomes compared to modeling based on Landsat 8 Operational Land Imager (OLI) data due to the finer spatial and spectral resolutions of the Sentinel-2A MSI data. To test this hypothesis, the Ebinur Lake wetland in Xinjiang was selected as the study area. In this study, SOC estimation was carried out using Sentinel-2A and Landsat 8 data, combining climatic variables, topographic factors, index variables and Sentinel-1A data to construct a common variable model for Sentinel-2A data and Landsat 8 data, and a full variable model for Sentinel-2A data, respectively. We utilized ensemble learning algorithms to assess the prediction performance of modeling strategies, including random forest (RF), gradient boosted decision tree (GBDT) and extreme gradient boosting (XGBoost) algorithms. The results show that: (1) The Sentinel-2A model outperformed the Landsat 8 model in the prediction of SOC contents, and the Sentinel-2A full variable model under the XGBoost algorithm achieved the best results R2 = 0.804, RMSE = 1.771, RPIQ = 2.687). (2) The full variable model of Sentinel-2A with the addition of the red-edge band and red-edge index improved R2 by 6% and 3.2% over the common variable Landsat 8 and Sentinel-2A models, respectively. (3) In the SOC mapping of the Ebinur Lake wetland, the areas with higher SOC content were mainly concentrated in the oasis, while the mountainous and lakeside areas had lower SOC contents. Our results provide a program to monitor the sustainability of terrestrial ecosystems through a satellite perspective.

Prediction of topsoil organic carbon content with Sentinel-2 imagery and spectroscopic measurements under different conditions using an ensemble model approach with multiple pre-treatment combinations

Estimating soil organic carbon (SOC) using visible near infrared (Vis-NIR) spectroscopy has proven to be a rapid and reliable approach. However, when working across large geographical scales, remote sensing may be more suitable. Acquiring these spectra data normally under different measurement conditions could introduce artefacts that reduce SOC prediction accuracy. A common procedure has been using calibration or multivariate techniques in conjunction with one or more pre-treatment algorithms. The results of several comparative studies based on these predictive calibration techniques used alone were inconsistent. Moreover, protocols to select the most appropriate pre-treatment algorithms rarely exist. This study combines predictions from different techniques into a single model based on an ensemble learning approach. The main objective is to improve the accuracy of SOC prediction by assessing the effectiveness of using different calibration techniques individually against an ensemble model consisting of one statistical method, which includes partial least squares regression (PLSR), and three machine learning (ML) algorithms, including random forest (RF), support vector machine regression (SVMR), and Cubist. Several pre-treatment algorithms were also employed to improve the spectral data before prediction. Spectra data were collected from three different agricultural fields (with different soil types), under different spectral measurement conditions (field, wet and dry). Additionally, Sentinel-2 (S2) data was collected from one of these fields. Furthermore, to ascertain the effectiveness of the developed model on regional scale dataset, two options were employed: (1) merged data from all fields, and (2) merged data from fields measured under the same spectral measurement conditions. The models were evaluated using root mean square error of prediction (RMSEPCV), the coefficient of determination (R²CV), the ratio of performance to interquartile range (RPIQ), the ratio of performance to deviation (RPD) and BIAS. The results show that, across the three agricultural fields, the ensemble model predicted SOC more accurately than each of the individual calibration techniques (R2CV = 0.92, RMSEPCV (g/kg) = 1.00, RPD = 3.06, RPIQ = 3.74, BIAS (g/kg) = 0.067). The models derived from merged data (regional dataset) show that the ensemble approach predicted SOC more accurately with option 2 than option 1. Finally, while the ensemble model improves SOC accuracy with S2 data, the final output was poor. Further research to determine the underlying problem is strongly recommended. Nonetheless, these results indicate that the ensemble model is advantageous because it improved the prediction accuracy of SOC and reduced the error margin.

Spatio-temporal characterization of microbial heat production on undisturbed soil samples combining infrared thermography and zymography

Passive infrared thermography (IRT) has already been applied in several approaches for high-resolution and non-contact imaging of microbial hot spots and hot moments on soil sample surfaces. The technique has only been used on homogenized disturbed samples to characterize the in-situ heterogeneity of heat development. In this study, undisturbed top- and subsoils samples from two forest sites were used in a substrate-induced approach to capture surface heat production using passive IRT with homogeneously applied glucose and water. The soil sample surface temperature was measured at 10-minute intervals and a spatial resolution of 0.17 mm per pixel. The soil samples were incubated for five days during passive IRT measurements under controlled ambient conditions with a relative air humidity >95% and constant ambient air temperature of 20 °C. Soil sample surface characterization was done by using active IRT for soil moisture approximation and surface structure, digital photography to estimate soil organic carbon (SOC) contents from soil color parameters, and zymography to get an indicator of initial microbial activity. In a first step, surface temperature dynamics were characterized using mathematical and geostatistical methods concerning microbial hot spots and hot moments. The characterization of hot moments was performed using a Gaussian curve fit. The spatial information of the hot spots was described using geostatistical semivariance. In a second step, a stepwise forward regression using sample surface properties was performed to find explanatory variables for surface heat production. Finally, a hierarchical k-means clustering was applied to the sequential thermal images and transferred to the other spatial datasets for deeper insights into small-scale variations in heat production and to also consider the temporal perspective. With an ANOVA combined with Tukey’s HSD post hoc test, significant differences between the cluster groups were calculated. This study showed that the temperature difference between averaged glucose- and water-treated sample surface temperature increased up to 0.2 K with a maximum hot moment duration of nearly 46 h. The variance in the samples could not be mapped, since the maximum spatial variability of the sample surface temperature could not be represented in the sample sections. The regression analysis revealed significant influences by soil moisture content, approximated SOC content, as well as initial β-glucosidase activity in the subsoil, which could be confirmed by the k-means cluster analysis. There was an indication that soil brightness (SOC contents) was influencing heat production due to the varying soil-borne substrate availability. The initial β-glucosidase activity showed that the initial biotic state of the soil contributed to the temperature increase concerning the provision of energy for microbial activity. In conclusion, passive IRT is a helpful mapping tool for the spatio-temporal characterization of hot spots and hot moments. In this context, additional mapping techniques such as zymography help to gain insights into the process level of heat generation by microbial activity.

Simulation of Soil Organic Carbon Content Based on Laboratory Spectrum in the Three-Rivers Source Region of China

Soil organic carbon (SOC) changes affect the land carbon cycle and are also closely related to climate change. Visible-near infrared spectroscopy (Vis-NIRS) has proven to be an effective tool in predicting soil properties. Spectral transformations are necessary to reduce noise and ensemble learning methods can improve the estimation accuracy of SOC. Yet, it is still unclear which is the optimal ensemble learning method exploiting the results of spectral transformations to accurately simulate SOC content changes in the Three-Rivers Source Region of China. In this study, 272 soil samples were collected and used to build the Vis-NIRS simulation models for SOC content. The ensemble learning was conducted by the building of stack models. Sixteen combinations were produced by eight spectral transformations (S-G, LR, MSC, CR, FD, LRFD, MSCFD and CRFD) and two machine learning models of RF and XGBoost. Then, the prediction results of these 16 combinations were used to build the first-step stack models (Stack1, Stack2, Stack3). The next-step stack models (Stack4, Stack5, Stack6) were then made after the input variables were optimized based on the threshold of the feature importance of the first-step stack models (importance > 0.05). The results in this study showed that the stack models method obtained higher accuracy than the single model and transformations method. Among the six stack models, Stack 6 (5 selected combinations + XGBoost) showed the best simulation performance (RMSE = 7.3511, R2 = 0.8963, and RPD = 3.0139, RPIQ = 3.339), and obtained higher accuracy than Stack3 (16 combinations + XGBoost). Overall, our results suggested that the ensemble learning of spectral transformations and simulation models can improve the estimation accuracy of the SOC content. This study can provide useful suggestions for the high-precision estimation of SOC in the alpine ecosystem.

Factors controlling soil organic carbon content in wetlands at multiple scales and assessment of the universality of estimation equations: A mega-data study

The factors controlling soil organic carbon (SOC) content in wetlands need to be identified to estimate the global stores of SOC. Although there have been a large number of small-scale studies of the local patterns of SOC content, global studies are still required. We used a random forest algorithm and other statistical approaches to determine the controls on the SOC content in wetlands at global, continental, and national scales based on the Harmonized World Soil Database and field data. The results showed that, at the three scales explored, the soil cation exchange capacity and bulk density were the main controls on the SOC content in wetlands. Moreover, equations for estimating global SOC content were established. To assess the universality of SOC content estimation equations, the soil properties were considered as a “community” and the normalized stochasticity ratio (NST) was used to assess the stochasticity in the assembly of soil “communities”. The results showed that, globally, the interaction of these factors was stochastic in the “community” composed of the controllers and SOC. The reason for this result might be that microbes were not considered in the equation. Therefore, the weighted abundance of related microbes (WARM) was therefore recommended in the estimation of SOC. With NST and WARM factors, we found that microbes play a key role in increasing the determinacy of SOC estimation equations in wetlands with less anthropogenic contamination. Our findings show that when microbial impacts are taken into account, the patterns of SOC content in pristine wetlands are more universal. Our newly established equations for estimating global SOC content are crucial in projecting changes in wetland SOC, and the two factors indicated in this study favor the universality for SOC content estimation.

Comparing Two Different Development Methods of External Parameter Orthogonalization for Estimating Organic Carbon from Field-Moist Intact Soils by Reflectance Spectroscopy

Visible and near-infrared (Vis–NIR) spectroscopy can provide a rapid and inexpensive estimation for soil organic carbon (SOC). However, with respect to field in situ spectroscopy, external environmental factors likely degrade the model accuracy. Among these factors, moisture has the greatest effect on soil spectra. The external parameter orthogonalization (EPO) algorithm in combination with the Chinese soil spectroscopic database (Dataset A, 1566 samples) was investigated to eliminate the interference of the external parameters for SOC estimation. Two different methods of EPO development, namely, laboratory-rewetting archive soil samples and field-collecting actual moist samples, were compared to balance model performance and analytical cost. Memory-based learning (MBL), a local modeling technique, was introduced to compare with partial least square (PLS), a global modeling method. A total of 250 soil samples from Central China were collected. Of these samples, 120 dry ground samples (Dataset B) were rewetted to different moisture levels to develop EPO P1 matrix. Seventy samples (Dataset C) containing field-moist intact and laboratory dry ground soils were used to establish EPO P2 matrix. The remaining 60 samples (Dataset D) also containing field-moist intact and laboratory dry ground soils were employed to validate the spectral models developed based on Dataset A. Results showed that EPO could correct the effect of external factors on soil spectra. For PLS, the validation statistics were as follows: no correction, validation R2 = 0.02; P1 correction, validation R2 = 0.56; and P2 correction, validation R2 = 0.57. For MBL, the validation results were as follows: no correction, validation R2 = 0.06; P1 correction, validation R2 = 0.65; and P2 correction, validation R2 = 0.69. The P2 consistently yielded better results than P1 did but simultaneously increased the sampling time and economic cost. The use of the P1 matrix and the MBL algorithm was recommended because it could reduce the cost of establishing in situ models for SOC.

The silent carbon pool: Cryoturbic enriched organic matter in Canadian High Arctic semi-deserts

High Arctic soil organic carbon (SOC) is an important component in the global C cycle, yet there is considerable uncertainty in the estimates for the polar deserts and semi-deserts that dominate these regions. Some of this uncertainty in SOC estimates arises from the cryoturbic processes including diapirism that structure polar desert soils. Diapirism occurs when the top, viscous layer of permafrost is deformed during freezing and ejected upwards into the soil profile forming a distinct diapiric soil patch or diapir. The diapiric is often nutrient rich relative to the surrounding soil; plants seek out and forage for nutrients in these patches creating a localized mixture of old carbon ejected from the permafrost and new carbon. Here we investigate how the subsurface SOC-rich patches in frost boils resulting from diapirism contribute to overall SOC storage in these environments. We quantify the rates of diapirism and fine-scale distribution of SOC in 560 frost boils at two Canadian high Arctic polar desert sites differing in parent material (dolomite versus granitic) with strikingly different plant surface communities. Though total soil organic carbon content did not differ between the dolomite and granite polar semi-deserts, SOC was being stored differently. The dolomitic site had greater SOC content below 10 cm reflecting the more common occurrence of subsurface SOC patches (46% of frost boils) compared to the granitic site (30%). When a subsurface patch of SOCC (SOC expressed on a m−2 basis) was present in a frost boil the boil contained nearly double the SOCC compared to frost boils without subsurface patches (11 ± 6.3 kg SOC m−2 compared to 6.4 ± 3.6 kg SOC m−2). Diapirism occurs in only 35% of all frost boils, but these diapiric patches represent an important, yet heterogenous, pool of SOC in polar semi-deserts. We upscale from these data to generate an improved estimate of SOC stored in the active layer of High Arctic polar semi-deserts of 8.14 ± 0.45 Pg SOC.

Synergetic use of multi-temporal Sentinel-1, Sentinel-2, NDVI, and topographic factors for estimating soil organic carbon

Soil organic carbon (SOC) is a reliable indicator of soil productivity and land management. Despite the widespread use of remote sensing data in estimating SOC variation, the predictive power of single-date and multi-temporal as well as the individual and combined effects of radar and optical images have rarely been investigated. This study created six two-month interval composite layers including VH and VV polarizations of Sentinel-1 (S1), spectral bands of Sentinel-2 (S2), normalized difference vegetation index (NDVI), slope and altitude for the target year of 2019–2020 to estimate the SOC variation. Based on 80 soil samples (0–20 cm) taken in a field survey from northern Iran, random forest (RF) and support vector regression (SVR) were calibrated to estimate the SOC content. All models were fine-tuned using grid search and evaluated using spatial cross-validation. The results showed that multi-temporal data were more predictive than single-date data, and SVR outperformed the RF algorithm. SVR model using the multi-temporal S2 achieved the highest R2 and RMSE values of 57.59% and 0.94%, respectively. Feature selection has shown no benefit of adding S1 data and identified band-2 (green), band-3 (red), and band-10 (shortwave infrared) of S2 imagery as the most explanatory variables of SOC variation. The results of this study highlight the potential of freely available high-resolution S2 for mapping SOC variation using the SVR model. SVR is particularly suggested in small sample size studies, an issue which is dominant in soil studies.

Utilisation of spaceborne C-band dual pol Sentinel-1 SAR data for simplified regression-based soil organic carbon estimation in Rupnagar, Punjab, India

Soil Organic Carbon (SOC) is a measure of the total carbon content of the soil and is a vital soil health indicator. Over the decades, SOC has been estimated using sampling followed by rigorous laboratory-based testing methods. Spaceborne Microwave/Synthetic Aperture RADAR (SAR) remote sensing has proven to be a versatile tool for various soil study applications. However, there have been very few studies conducted for SOC estimation using SAR remote sensing. This study utilises time-series, C-band remotely sensed SAR data from Sentinel-1 A satellite for SOC estimation and compared the performances of Random Forest (RF) and Ordinary Least Squares (OLS) Regression models over agricultural areas of Rupnagar district of Punjab in India. A set of 96 soil samples were collected from 32 different agricultural field locations in Rupnagar district between November 2019 to January 2020. SAR backscatter of Vertically emitted and Vertically received (VV) and Vertically emitted and Horizontally received (VH) polarisation channels, from Sentinel-1, soil moisture, electrical conductivity, pH, temperature and SOC from the laboratory-based testing methods were used as regression parameters. The RF regression gave a Root Mean Square Error (RMSE) of 0.78 and R2 statistics of 0.887, while the OLS method performed better with an RMSE of 0.53 and an R2 value of 0.907. It was also observed that the backscatter from VV and VH polarisation channels, when used synergistically with field data, have the highest Feature Importance (FI) score in both RF and OLS regression models for SOC estimation.

Estimating soil organic carbon and nitrogen stock based on high-resolution soil databases in a subtropical agricultural area of China

Quantifying soil organic carbon stock (SOCS) and total nitrogen stock (STNS) of the agriculture soils in China is crucial for predicting the future of global climate change and planning agricultural management practices. However, most of existing studies were based on relatively coarse soil databases, leading to large discrepancies in the SOC and STN estimates. The present study was conducted in Changting County of Fujian Province in China, covering an area of 29,375ha. A high-resolution soil database at a county-scale of 1:10,000 was employed to analyze the SOC density (SOCD) and STN density (STND) under different soil groups and land use types. Results indicated that the mean SOCD and SOCS for the surface layer (0–15 cm) were 2.93 ± 0.38 kg C m−2 and 861 Gg, respectively, and those for STND and STNS were 0.37 ± 0.06 kg N m−2 and 108 Gg, respectively. The SOCD and STND showed significant disparities among soil groups and land use types. We also compared the SOCD, SOCS, STND and STNS estimated from a coarser 1:1000,000 soil database, which was the most detailed soil database at the national scale, with the estimates in this study. The relative deviations in estimating SOCD and STND by soil groups from the coarser database were 8.1% and 60.6%, respectively, which could be primarily attributed to the assignment errors of soil attributes and polygon area variations in the coarse-scale soil database. Moreover, we found that the dominant factors determining SOCD were topographic and climatic factors (e.g. elevation and mean annual precipitation), while farming practices (e.g. N fertilizer application and irrigation conditions) as well as elevation were more important for STN estimates. Further, the deviations of STN estimates from the coarse- and fine-scale soil database were higher than those of the SOC estimates because: (1) The topographic and climatic factors had slight effects on STND from coarse- and fine-scale soil database while significantly affected SOCD; and (2) STN estimates tended to be more easily influenced by anthropic farming practices (e.g. significant irrigation conditions and N fertilization application) in the coarse-scale database than that in the fine-scale database. Therefore, a high-resolution soil database may improve the accuracy of SOC and STN assessment, especially for STN assessment, and the application of finer soil databases is a critical step for estimating SOCS and STNS at national and even global levels, and thus for accurately assessing regional C budget.

Soil carbon sequestration potential of planting hedgerows in agricultural landscapes

Realising the carbon (C) sequestration capacity of agricultural soils is needed to reach Paris Climate Agreement goals; thus, quantifying hedgerow planting potential to offset anthropogenic CO2 emissions is crucial for accurate climate mitigation modelling. Although being a widespread habitat in England and throughout Europe, the potential of hedgerows to contribute to net-zero targets is unclear. This is the first study to quantify the soil organic carbon (SOC) sequestration rate associated with planting hedgerows. We derived SOC stocks beneath hedgerows based on two estimation methods to assess differences from adjacent intensively managed grassland fields and how these may be affected by sampling depth and hedgerow age, as well as the SOC estimation method used. Twenty-six hedgerows on five dairy farms in Cumbria, England, were classified based on the time since their planting. We measured SOC stocks in 10 cm depth intervals in the top 50 cm of soil beneath hedgerows and in adjacent grassland fields. SOC beneath hedgerows was on average 31.3% higher than in the fields, 3.3% for 2–4 year old hedgerows, 14.4% for 10 year old, 45.2% for 37 year old, and 57.2% for older ones. We show that SOC sequestration rate beneath 37 year old hedgerows was 1.48 Mg C ha−1 yr−1 in the top 50 cm of soil. If England reaches its goal of a 40% increase in hedgerow length, 6.3 Tg CO2 will be stored in the soil over 40 years, annually offsetting 4.7%–6.4% of present-day agricultural CO2 emissions. However, the current rate of planting funded by agri-environment schemes, which today reaches only 0.02% of emissions, is too slow. Private-sector payments for ecosystem services initiatives (e.g., ‘Milk Plan’) show much higher rates of planting and are needed alongside agri-environment schemes to ensure hedgerow planting contributes to net-zero targets.

How to estimate soil organic carbon stocks of agricultural fields? Perspectives using ex-ante evaluation

Estimating soil organic carbon (SOC) stocks of agricultural fields has a range of important applications from development of sustainable management practices to monitoring carbon stocks. There are many estimation strategies with the potential for more reliable estimates of SOC stock and more efficient use of soil sampling and analysis resources, especially by leveraging readily available auxiliary information such as remote sensing. However, concrete guidance for strategy selection is lacking. This study narrows this gap with a comparison of strategies for estimating deep SOC stock (0–60 cm) in a prototypical field. Using high density SOC stock measurements and simulation, we built on past studies by 1) ex-ante evaluating a large number of strategy options, 2) using a Bayesian approach to quantify the uncertainty of the comparison, and 3) considering multiple Bayesian models to assess sensitivity to this modeling choice. We found that, using readily available auxiliary information, both balanced and stratified sampling offer substantial improvements over simple random sampling. The auxiliary information most important for this improvement is a Sentinel-2 SOC index = blue / (green × red), followed by the topographic wetness index. We found that these results are robust to the choice of mapping method, but that there is uncertainty in the magnitude of improvement. We recommend future studies implement this Bayesian approach for simulated ex-ante evaluation of SOC stock estimation strategies across more fields to investigate the generalizability of these findings.

Deep learning-based national scale soil organic carbon mapping with Sentinel-3 data

Mapping of soil organic carbon (SOC) at the regional level is critical for climate change policy and the mitigation of its adverse effects. However, reliable SOC estimates particularly over a large extent remains a major challenge due to among others limited sample points, quality of simulation data and the algorithm adopted. Remote sensing (RS) strategies have emerged as a suitable alternative to field and laboratory SOC determination, especially at large spatial extent. The use of Sentinel-3 sensor, the latest of the Sentinel series is minimal and has not been fully developed, despite its impressive attributes that include high spectral-temporal resolution and large coverage. Compared to linear and classical ML models, deep learning (DL) models offer a considerable improvement in data analysis due to their ability to extract more representative features and identify complex spatial patterns associated with big data. Yet, there is paucity in literature on the application of dl-based remote sensing strategies for SOC prediction. Consequently, this study adopted a deep neural network (DNN) to predict SOC at a national scale, using Sentinel-3 image, and compared the results with random forest (RF), support vector machine (SVM) and artificial neural network (ANN) models. The models were trained based on 10-fold cross-validation with 1936 soil samples and 31 predictors. The DNN model generated the best result with a root mean square error (RMSE) of 10.35 t/ha (26 % of the mean), followed by RF (RMSE = 11.2 t/ha), ANN (RMSE = 11.6 t/ha) and SVM (RMSE = 13.6 t/ha). The analytical prowess of the DNN, together with its ability to handle big data by learning patterns through a series of hidden layers (10) to draw conclusions, gives it an edge over other classical ML models. The study concluded that the DNN model with Sentinel-3 data is promising and provides an effective framework for continuous national level SOC modelling.

Estimation of soil organic carbon in agricultural fields: A remote sensing approach

Aim: In view of the importance of Soil Organic Carbon (SOC) in agricultural management, a study was conducted to develop a digital SOC map using remotely sensed spectral indices. The present study was conducted on the Tawdeehiya Farms, located in the central region of Saudi Arabia between Al-Kharj and Haradh cities. Methodology: Landsat-8 (L8) and Sentinel-2 (S2A) satellite images were used for the characterization of SOC stocks in the topsoil layer (0-10 cm) of the experimental fields. Soil samples were randomly collected from six (50 ha each) agricultural fields and analyzed in the laboratory for SOC (SOCA) following Walkley and Black method. While, vegetation indices (VI), such as the Normalized Difference Vegetation Index (NDVI), NDVIRedEdge, Enhanced Vegetation Index (EVI), Bare Soil Index (BSI), and Reduced Simple Ratio (RSR) were computed and subsequently used for the development of SOC prediction models. Results: Univariate linear regression technique was employed for the recognition of a suitable band/VI for SOC (SOCP) mapping. The SWIR-1 band of both L8 (R2 = 0.86) and S2A (R2 = 0.77) data was promising for predicting SOC with 16% (S2A) and 18% (L8) of BIAS. Interpretation: The NDVI and BSI (for L8 data) and BSI and RSR (for S2A data) were found most suitable VI in the prediction of SOC. The R2 values of linear regression models were 0.68 (BSI) and 0.78 (RSR), indicating that nearly 68% and 78% of the SOC could be predicted through L8 and S2A datasets, respectively.

Estimation of soil organic carbon and its uncertainty in Qinghai Province

Estimation of soil organic carbon and its uncertainty in Qinghai Province