Soil Organic Carbon Monitoring Research Articles

Tropical altitudinal gradient soil organic carbon and nitrogen estimation using Specim IQ portable imaging spectrometer

The largest actively cycling terrestrial carbon pool, soil, has been disturbed during latest centuries by human actions through reduction of woody land cover. Soil organic carbon (SOC) content can reliably be estimated in laboratory conditions, but more cost-efficient and mobile techniques are needed for large-scale monitoring of SOC e.g. in remote areas. We demonstrate the capability of a mobile hyperspectral camera operating in the visible-near infrared wavelength range for practical estimation of soil organic carbon (SOC) and nitrogen content, to support efficient monitoring of soil properties. The 191 soil samples were collected in Taita Taveta County, Kenya representing an altitudinal gradient comprising five typical land use types: agroforestry, cropland, forest, shrubland and sisal estate. The soil samples were imaged using a Specim IQ hyperspectral camera under controlled laboratory conditions, and their carbon and nitrogen content was determined with a combustion analyzer. We use machine learning for estimating SOC and N content based on the spectral images, studying also automatic selection of informative wavelengths and quantification of prediction uncertainty. Five alternative methods were all found to perform well with a cross-validated R2 of approximately 0.8 and an RMSE of one percentage point, demonstrating feasibility of the proposed imaging setup and computational pipeline.

Use of remote sensing data to predict soil organic carbon in some agricultural soils of Iran

Quantifying and monitoring of SOC (soil organic carbon) content in an agriculture soil is essential for understanding the ecological environment, influencing the climate change and implementing the best agriculture management. Present study tried to derive some machine learning-based models for predicting SOC content by employing remote sensing data in some agriculture soils enriched by calcareous materials, in central part of Iran. Soil samples were taken from 336 points at arable and permanent agriculture soils. Soil features such as SOC, sand, silt, clay, bulk density and calcium carbonate equivalent contents were measured at each point. After that, machine learning models including artificial neural networks (ANN), support vector regression (SVR) and genetic expression programming (GEP) were employed to estimate SOC content. The input variables were spectral indices extracted from Landsat-8, Sentinel-2, Sentinel-3 and MODIS images (Scenario I), integrating of the spectral indices extracted from Landsat-8—MODIS and Sentinel-2—Sentinel-3 images (Scenario II) and the spectral indices extracted from the fusing Sentinel-2 and Sentinel-3 images (Scenario IIII). The results of present study showed that the SVR model was the best performance (MAE = 0.0056%, RMSE = 0.620% and R2 = 0.627 at test data sets) for all sampled data by using the extracted remote sensing information at scenario II. Moreover, SVR model yielded the better result in the arable and permanent agriculture soils by using the extracted remote sensing information at scenario II (MAE = 0.0042%, RMSE = 0.672% and R2 = 0.574) and scenario III (MAE = 0.0032%, RMSE = 0.342% and R2 = 0.381), respectively. It was concluded that the combining of remote sensing data extracted from different types of satellites in a various method could improve the accuracy of SOC prediction.

Hyperspectral Estimation of Soil Organic Carbon Content Based on Continuous Wavelet Transform and Successive Projection Algorithm in Arid Area of Xinjiang, China

Soil organic carbon (SOC), an important indicator to evaluate soil fertility, is essential in agricultural production. The traditional methods of measuring SOC are time-consuming and expensive, and it is difficult for these methods to achieve large area measurements in a short time. Hyperspectral technology has obvious advantages in soil information analysis because of its high efficiency, convenience and non-polluting characteristics, which provides a new way to achieve large-scale and rapid SOC monitoring. The traditional mathematical transformation of spectral data in previous studies does not sufficiently reveal the correlation between the spectral data and SOC. To improve this issue, we combine the traditional method with the continuous wavelet transform (CWT) for spectral data processing. In addition, the feature bands are screened with the successive projection algorithm (SPA), and four machine learning algorithms are used to construct the SOC content estimation model. After the spectral data is processed by CWT, the sensitivity of the spectrum to the SOC content and the correlation between the spectrum and the SOC content can be significantly improved (p < 0.001). SPA was used to compress the spectral data at multiple decomposition scales, greatly reducing the number of bands containing covariance and enabling faster screening of the characteristic bands. The support vector machine regression (SVMR) model of CWT-R′ gave the best prediction, with the coefficients of determination (R2) and the root mean square error (RMSE) being 0.684 and 1.059 g∙kg−1, respectively, and relative analysis error (RPD) value of 1.797 for its validation set. The combination of CWT and SPA can uncover weak signals in the spectral data and remove redundant bands with covariance in the spectral data, thus realizing the screening of characteristic bands and the fast and stable estimation of the SOC content.

First soil organic carbon report of Paraguay

Assessing soil organic carbon (SOC) stocks at a national scale provides relevant information on soil fertility and soil functions, such as soil water retention capacity, nutrient availability, and soil structure. Our overall goal is to map the SOC stock at the topsoil layer (0–30 cm) in Paraguay, using spatial information of key environmental factors (Environmental Factors - EF; 119 variables) related to SOC and by performing a Regression Kriging (RK) model. We fitted a RK model with 954 sample points, evaluating and computing the model uncertainty of SOC predictions with 10% of independent sample points. Our results show that on average, the SOC stock across Paraguay was 4,46 ± 2,66 kg m−2 where the highest SOC is found in humid regions covered by broad-leaf forests (8.66 × 10−6); while the lowest SOC is found within water-limited ecosystems (1.97 × 10−6). We determined that soil type, climate, and vegetation land cover were the strongest SOC predictors. Since this study represents the first Paraguayan effort to develop a SOC monitoring framework, it also provides the first national SOC map with its associated uncertainty. Enabling a unique opportunity to fulfill relevant information gaps and the extended opportunity to be used to validate global and regional SOC products.

Carbon farming: Are soil carbon certificates a suitable tool for climate change mitigation?

Increasing soil organic carbon (SOC) stocks in agricultural soils removes carbon dioxide from the atmosphere and contributes towards achieving carbon neutrality. For farmers, higher SOC levels have multiple benefits, including increased soil fertility and resilience against drought-related yield losses. However, increasing SOC levels requires agricultural management changes that are associated with costs. Private soil carbon certificates could compensate for these costs. In these schemes, farmers register their fields with commercial certificate providers who certify SOC increases. Certificates are then sold as voluntary emission offsets on the carbon market.In this paper, we assess the suitability of these certificates as an instrument for climate change mitigation. From a soils' perspective, we address processes of SOC enrichment, their potentials and limits, and options for cost-effective measurement and monitoring. From a farmers’ perspective, we assess management options likely to increase SOC, and discuss their synergies and trade-offs with economic, environmental and social targets. From a governance perspective, we address requirements to guarantee additionality and permanence while preventing leakage effects. Furthermore, we address questions of legitimacy and accountability.While increasing SOC is a cornerstone for more sustainable cropping systems, private carbon certificates fall short of expectations for climate change mitigation as permanence of SOC sequestration cannot be guaranteed. Governance challenges include lack of long-term monitoring, problems to ensure additionality, problems to safeguard against leakage effects, and lack of long-term accountability if stored SOC is re-emitted. We conclude that soil-based private carbon certificates are unlikely to deliver the emission offset attributed to them and that their benefit for climate change mitigation is uncertain. Additional research is needed to develop standards for SOC change metrics and monitoring, and to better understand the impact of short term, non-permanent carbon removals on peaks in atmospheric greenhouse gas concentrations and on the probability of exceeding climatic tipping points.

Insights into the Effects of Study Area Size and Soil Sampling Density in the Prediction of Soil Organic Carbon by Vis-NIR Diffuse Reflectance Spectroscopy in Two Forest Areas

Sustainable forest land management requires measuring and monitoring soil organic carbon. Visible and near-infrared diffuse reflectance spectroscopy (Vis-NIR, 350–2500 nm), although it has become an important method for predicting soil organic carbon (SOC), requires further studies and methods of analysis to realize its full potential. This study aimed to determine if the size of the study area and soil sampling density may affect the performance of Vis-NIR diffuse reflectance spectroscopy in the prediction of soil organic carbon. Two forest sites in the Calabria region (southern Italy), which differ in terms of area and soil sampling density, were used. The first one was Bonis catchment area (139 ha) with a cover consisting mainly of Calabrian pine, while the second was Mongiana forest area (33.2 ha) within the “Marchesale” Biogenetic Nature Reserve, which is covered by beech. The two study areas are relatively homogeneous regarding parent material and soil type, while they have very different soil sampling density. In particular, Bonis catchment has a lower sampling density (135 samples out of 139 ha) than Mongiana area (231 samples out of 33.2 ha). Three multivariate calibration methods (principal component regression (PCR), partial least square regression (PLSR), and support vector machine regression (SVMR)) were combined with different pretreatment techniques of diffuse reflectance spectra (absorbance, ABS, standard normal variate, SNV, and Savitzky–Golay filtering with first derivative (SG 1st D). All soil samples (0–20 cm) were analyzed in the laboratory for SOC concentration and for measurements of diffuse reflectance spectra in the Vis-NIR region. The set of samples from each study area was randomly divided into a calibration set (70%) and a validation set (30%). The assessment of the goodness for the different calibration models and the following SOC predictions using the validation sets was based on three parameters: the coefficient of determination (R2), the root mean square error (RMSE), and the interquartile range (RPIQ). The results showed that for the two study areas, different levels of goodness of the prediction models depended both on the type of pretreatment and the multivariate method used. Overall, the prediction models obtained with PLSR and SVMR performed better than those of PCR. The best performance was obtained with the SVMR method combined with ABS + SNV + SG 1st D pretreatment (R2 ≥ 0.77 and RPIQ > 2.30). However, there is no result that can absolutely provide definitive indications of either the effects of the study area size and soil sampling density in the prediction of SOC by vis-NIR spectroscopy, but this study fostered the need for future investigations in areas and datasets of different sizes from those in this study and including also different soil landscapes.

Spatial prediction of organic carbon in German agricultural topsoil using machine learning algorithms

Abstract. As the largest terrestrial carbon pool, soil organic carbon (SOC) has the potential to influence and mitigate climate change; thus, SOC monitoring is of high importance in the frameworks of various international treaties. Therefore, high-resolution SOC maps are required. Machine learning (ML) offers new opportunities to develop these maps due to its ability to data mine large datasets. The aim of this study was to apply three algorithms commonly used in digital soil mapping – random forest (RF), boosted regression trees (BRT), and support vector machine for regression (SVR) – on the first German agricultural soil inventory to model the agricultural topsoil (0–30 cm) SOC content and develop a two-model approach to address the high variability in SOC in German agricultural soils. Model performance is often limited by the size and quality of the soil dataset available for calibration and validation. Therefore, the impact of enlarging the training dataset was tested by including data from the European Land Use/Cover Area frame Survey for agricultural sites in Germany. Nested cross-validation was implemented for model evaluation and parameter tuning. Grid search and the differential evolution algorithm were also applied to ensure that each algorithm was appropriately tuned . The SOC content of the German agricultural soil inventory was highly variable, ranging from 4 to 480 g kg−1. However, only 4 % of all soils contained more than 87 g kg−1 SOC and were considered organic or degraded organic soils. The results showed that SVR produced the best performance, with a root-mean-square error (RMSE) of 32 g kg−1 when the algorithms were trained on the full dataset. However, the average RMSE of all algorithms decreased by 34 % when mineral and organic soils were modelled separately, with the best result from SVR presenting an RMSE of 21 g kg−1. The model performance was enhanced by up to 1 % for mineral soils and by up to 2 % for organic soils. Despite the ability of machine learning algorithms, in general, and SVR, in particular, to model SOC on a national scale, the study showed that the most important aspect for improving the model performance was to separate the modelling of mineral and organic soils.

Monitoring changes in global soil organic carbon stocks from space

Soils are under threat globally, with declining soil productivity and soil health in many places. As a key indicator of soil functioning, soil organic carbon (SOC) is crucial for ensuring food, soil, water and energy security, together with biodiversity protection. While there is a global effort to map SOC stock and status, SOC is a dynamic soil property and can change rapidly as a function of land management and land use. Here, we introduce a semi-mechanistic model to monitor SOC stocks at a global scale, underpinned by one of the largest worldwide soil database to date. Our model generates a SOC stock baseline for the year 2001, which is then propagated through time by keeping track of annual landcover changes obtained from remote sensing products with loss and gain dynamics dependent on temperature and precipitation, which finally define the magnitude, rate and direction of the SOC changes. We estimated a global SOC stock in the top 30 cm of around 793 Pg with annual losses due to landcover change of 1.9 Pg SOC yr−1 from 2001 to 2020, 20% larger than the annual production-based emissions of the United States in 2018. The biggest losses were found in the tropic and sub-tropical regions, accounting for almost 50% of the total global loss. This is a considerable contribution to greenhouse gas emissions but it also has a direct impact on agricultural production with more than 16 million hectares yr−1 falling below critical SOC limits. The proposed modelling framework is flexible, allowing it to be updated as more remote sensing and soil data becomes available, offering a first-of-its-kind global spatio-temporal SOC stock assessment and monitoring system.

Portable mid-infrared spectroscopy to predict parameters related to carbon storage in vineyard soils: Model calibrations under varying geopedological conditions

Portable mid-infrared spectroscopy (pMIRS) combined with machine learning was used to predict selected parameters for soil organic carbon (SOC) storage. In particular, SOC, soil inorganic C (SIC), hot-water extractable C (hwC), clay and sand content were predicted for ten vineyards with varying geopedological settings. As a pre-test, spectra were collected from sieved and pressed tablets with 30 and 90 kPa respectively and compared to powdery samples in order to optimise sample preparation. Further, spectra from 30 kPa tablets were used to calibrate prediction models for a sample set (n = 540) of 10 vineyards with pronounced geopedological variation using Support Vector Machines (SVM). The calibrated SVM models performed well with R 2 = 0.81–0.98 and RPIQ = 5.20–13.0 for all investigated parameters. Third, two years after the calibration samples, follow-up samples were collected from four of the vineyards. While the models performed excellent for hwC (R 2 = 0.93), prediction accuracy for SOC was lower. Segmentation of the total dataset into SIC-free and SIC-containing samples resulted in better predictions of SOC of the first sampling period. For the prediction of the follow-up sampling dates, model performance could not be maintained. We conclude that pMIRS-SVM calibrations are suited for the prediction of parameters related to soil C storage under varying geopedological conditions and may provide potential for future C monitoring. Extending the database with additional samples from geopedological scenarios not included in this dataset may strengthen model robustness and help to evaluate effects of SIC content on model performance. • Compressed soil tablets reduce sample preparation labour for portable mid-infrared spectroscopy. • SVM model calibrations provide robust predictions for parameters related SOC storage under varying geopedological conditions. • Prediction for follow-up sampling via calibrated SVM models highlights the potential for future SOC monitoring purposes. • Arbitrarily occurring SIC influences MIR SVM model performance.

Soil and land use factors control organic carbon status and accumulation in agricultural soils of Lower Austria

Policies for restoring soil health and mitigating climate change require information on soil organic carbon (SOC) stocks and their spatial and temporal variation, and related sequestration potentials. Using the province of Lower Austria as environmentally diverse model region, we present a detailed analysis of SOC stocks, saturation potentials and deficits along with SOC monitoring data for the past three decades.Using the provincés soil database (sampling 1991) we estimated the saturation potential for mineral-protected C (Csat) by boundary line regression to the particle fraction <20 µm (f<20µm). Compared to published work, parameterization by Lower Austrian data yielded considerably larger Csat values which agree well with independent maximum organic C load estimates based on specific surface area. Csat is particularly small in topsoils of regions with non-calcareous igneous rock, but large on fine-textured quaternary and tertiary sediments, and weathering residuals of carbonate rock. During the past three decades, the medians of SOC in grassland topsoils (0–20 cm) increased significantly (p < 0.05) by 29.7% from 39.4 to 51.1 g kg−1, corresponding to annual accumulation rates of 0.87 Mg C ha−1. SOC in cultivated soils increased by 17.1% from 12.7 to 14.8 g kg−1 (0.20 Mg C ha−1 y−1). Because of the large initial C gap, the observed SOC accumulation is not related to Cdef but likely reflects improved soil management.

Measuring Soil Colour to Estimate Soil Organic Carbon Using a Large-Scale Citizen Science-Based Approach

Rapid, low-cost methods for large-scale assessments of soil organic carbon (SOC) are essential for climate change mitigation. Our work explores the potential for citizen scientists to gather soil colour data as a cost-effective proxy of SOC instead of conventional lab analyses. The research took place during a 2-year period using topsoil data gathered by citizen scientists and scientists from urban parks in the UK and France. We evaluated the accuracy and consistency of colour identification by comparing “observed” Munsell soil colour estimates to “measured” colour derived from reflectance spectroscopy, and calibrated colour observations to ensure data robustness. Statistical relationships between carbon content obtained by loss on ignition (LOI) and (i) observed and (ii) measured soil colour were derived for SOC prediction using three colour components: hue, lightness, and chroma. Results demonstrate that although the spectrophotometer offers higher precision, there was a correlation between observed and measured colour for both scientists (R2 = 0.42; R2 = 0.26) and citizen scientists (R2 = 0.39; R2 = 0.19) for lightness and chroma, respectively. Foremost, a slightly stronger relationship was found for predicted SOC using the spectrophotometer (R2 = 0.69), and citizen scientists produced comparable results (R2 = 0.58), highlighting the potential of a large-scale citizen-based approach for SOC monitoring.

Countries’ commitments to soil organic carbon in Nationally Determined Contributions

ABSTRACT Soil carbon is the major active pool of terrestrial carbon, and as such, soil organic carbon (SOC) targets, policies and measures will be pivotal to achieving global climate targets. SOC sequestration may reduce the net annual greenhouse gas emissions from Agriculture, Forestry and Other Land Use by between 3% and 71%, while simultaneously supporting various ecosystem services. Accurate SOC accounting and monitoring, however, is constrained by various technical challenges related to indicators, rates of SOC change, measuring the impact of management practices on SOC, and the long-term persistence of sequestered SOC. We assessed countries’ pledges to the Paris Agreement for SOC in agriculture to better understand the level, transparency, and specificity of commitments. Reviewing 184 countries’ initial Nationally Determined Contributions (NDCs), we considered whether SOC was included, what was pledged, the level of ambition promised and the specificity of mitigation targets. Twenty-eight countries referred to SOC in their NDCs, citing quantified or unquantified mitigation targets, national policies or programs, and actions and measures to be implemented in agricultural lands (14), peatlands (6) or wetlands (14). Countries’ reasons for not including SOC in NDCs included the need to prioritize goals of sustainable development and food security above climate mitigation, a lack of incentives for farmers to improve management practices, and the difficulty of accurately monitoring changes in SOC. Including SOC targets in NDCs can improve NDCs’ comprehensiveness and transparency to track and compare policy progress across NDCs; it can also leverage SOC-related climate finance, technical support, and capacity building. Key policy insights Many NDCs specify practices known to have the potential to achieve SOC sequestration or protection without explicitly mentioning SOC. The SOC-related mitigation potential of these practices can be quantified in future NDCs. NDCs are not presently a good indicator of countries’ interest or commitment to SOC action at national level. To improve this, countries with existing SOC policies, programs, and actions can specify their SOC-related commitments in future NDCs. Increased collaboration between countries with experience managing SOC and countries needing support to develop SOC-related targets, policies, measures and incentives for land users and farmers would facilitate the provision of such needed support. To increase country commitments and attention to managing SOC, there is a need for improved SOC measurement and monitoring, for better evidence on the impacts of management practices on SOC, and for incentives for farmers to change practices and overcome barriers.

Sentinel-2 and Landsat-8 Multi-Temporal Series to Estimate Topsoil Properties on Croplands

The spatial and temporal monitoring of soil organic carbon (SOC), and other soil properties related to soil erosion, is extremely important, both from the environmental and economic perspectives. Sentinel-2 (S2) and Landsat-8 (L8) time series increase the probability to observe bare soil fields in croplands, and thus, monitor soil properties over large regions. In this regard, this work suggests an automated pixel-based approach to select only pure soil pixels in S2 and L8 time series, and to make a synthetic bare soil image (SBSI). The SBSIs and the soil properties measured in the framework of the European LUCAS survey were used to calibrate SOC, clay, and CaCO3 prediction models. The results highlight a high correlation between laboratory soil spectra and the SBSIs median spectra, especially for the SBSI obtained by a three-year S2 collection, which provides satisfactory results in terms of SOC prediction accuracy (RPD: 1.74). The comparison between S2 and L8 results demonstrated the higher capability of the S2 sensor in terms of SOC prediction accuracy, mainly due to the greater spatial resolution of the bands in the visible region. Whereas, neither S2 nor L8 could accurately predict the clay and CaCO3 content. This is because of the low spectral and spatial resolution of their SWIR bands that prevent the exploitation of the narrow spectral features related to these two soil attributes. The results of this study prove that large S2 time series can estimate and monitor SOC in croplands using an automated pixel-based approach that selects pure soil pixels and retrieves reliable synthetic soil spectra.

Managing Soil Organic Carbon for Mitigating Climate Change and Increasing Food Security

This Special Issue contains articles presenting advances in soil organic carbon (SOC) sequestration practices, considering their benefits, trade-offs and monitoring. The studies deal with (1) agricultural practices and climate change, (2) the effect of organic matter amendments, and (3) the development of monitoring, reporting and verification (MRV) strategies. It is concluded that region-specific approaches are required for the implementation and monitoring of SOC sequestering practices.

Marginal more than mesic sites benefit from groundnut diversification of maize: Increased yield, protein, stability, and profits

Sustainable Intensification (SI) interventions are urgently required, particularly those suited to resource poor farms in Africa. Legume crops have been promoted as a key ingredient for SI of rainfed grain production on smallholder farms, with variable results. There is a need to explore the extent to which legume diversification of maize production impacts stability, nutrition, and income. This is particularly so for marginal environments. On-farm experimentation was conducted in Central Malawi over two to seven years on 29 farm sites (120 year-site combinations). The farms were located within four areas that included lakeshore, dissected hills (two locations) and upland plain. Maize diversification included a long-duration legume pigeonpea and a medium duration legume groundnut, grown in rotation or intercropped with maize, and as a doubled-up legume rotation (DLR). To quantify the performance of systems under low, medium, and high yield environments, we used long-term average maize yield to categorize each farm site. All legume diversified systems supported stable grain production in the low yield environment, as shown by 37–41% coefficient of variation for yield, in comparison to 62% for sole maize. The groundnut systems consistently produced the highest grain yield, protein, stable yields, and economic returns, and this performance held up in marginal, low yield environments. In this multi-site, multi-year, on-farm replicated study, the performance of groundnut systems (GnRot and DLR) stood out for high protein (0.529 T ha−1 2 yr−1 and 0.615 T ha −1 2 yr−1, respectively over two years) versus unfertilized maize (0.169 T ha−1 2 yr−1). These two groundnut-based systems were produced with half-fertilizer rates compared to sole maize and were economically high performers. However, there was a barrier to adoption of GnRot and DLR in that improved groundnut seed was expensive (USD 157 ha−1 2 yr−1), this initial investment being beyond the means of many farmers, despite the cost largely offset by the generation of high income (USD 1636–1993 ha−1 2 yr−1). Long-term sustainability was assessed by monitoring soil organic carbon (SOC), which was found to be markedly influenced by soil texture (sites with SOC >1.5% had sand content <50%). Legume diversification effects on SOC were not discerned, possibly due to high sand content on the oldest trial sites. This study highlights the value of longitudinal data and including a wide range of soil texture sites in on-farm experimentation to identify overall legume diversification effects within maize systems.

ORGANIC CARBON STOCKS IN ARABLE LAND OF REPUBLIC OF SRPSKA - BOSNIA AND HERZEGOVINA

On the territory of Republic of Srpska (RS – Entity of Bosnia and Herzegovina), in the period 2014 - 2017, the fertility control of arable land was performed in 4125 average samples (taken from top soil, 0 - 30 cm) representing the surface area of 5776 ha. All samples were geo-positioned and linked to the SOTER database (soil and terrain databases). RS is divided into 262 SOTER units. In each soil sample humus was analysed (colorimetric method, wet burning with K2Cr2O7 and conc H2SO4). Soil organic carbon (SOC) was calculated from humus (% humus x factor 0.58). SOC stock (t ha-1) for each plot were calculated on the basis of the volume mass (mg m-3) of the soil type on which the plot was located, the soil weights up to 30 cm (kg ha-1) and the area of the plot (ha). SOC stock on 5776 ha of agricultural land was 225168 t ha-1. The analyzed area was represented by 24 types of soil (FAO class). The highest average SOC stocks of 130 t ha-1 (based on 31 samples) was found in Calacaric Cambisol and the lowest in Stagnic Luvisol 38 t ha-1 (based on 464 samples). In 84% of the tested samples, representing 89% of researched area, the SOC stocks were less than 57 t ha-1. Estimation of the SOC stocks on the total arable land was prepared by GIS analysis interpolation of the SOC results for 4125 samples on the agricultural land area (arable land, gardens, orchards, vineyards and meadows). Estimated SOC stocks on 578894 ha of arable land were 32833549 t. The result of this research is the first step towards the establishment of SOC monitoring system in RS.

Predictive soil mapping using historic bare soil composite imagery and legacy soil survey data

There is an increasing need for detailed soil property maps to support land use planning, soil carbon accounting, and precision agriculture. While soil maps exist in Saskatchewan, they are at coarse scales (1:100,000), which are not always suitable for detailed soil management. One emerging technique for predictive soil mapping is the use of bare soil composite imagery derived from multi-temporal satellite imagery. This study focused on using bare soil composite imagery along with legacy soil data (1958–1998) with high location uncertainty to predict soil organic carbon, clay, and cation exchange capacity. The bare soil composite images were created from Landsat 5 imagery (1985 to 1995) using Google Earth Engine. Predictive models were built using a Random Forest model for each parameter and evaluated using a 75–25 train-test split. The soil organic carbon model had an R2 value of 0.55 with a root mean square error (RMSE) of 0.67 percent, with the near infrared and visible light bands being the most important features in the model. The clay predictive model has an R2 of 0.44 and a RMSE of 5.0 percent, with the shortwave infrared bands being most important. The cation exchange capacity model had an R2 of 0.50 with a RMSE of 5.7 meq 100 g−1, with the shortwave and near infrared bands as the most important predictors. Based on these results, bare soil composite imagery represents a valuable covariate for predictive soil mapping in the Canadian Prairies. This work also illustrates that for regions with extensive adoption of conservation farming practices, satellite imagery should be obtained for time periods before these practices were adopted from the months of the year where crop residues have decomposed. By combining historical soil survey data with historical imagery, maps of legacy soil properties can be generated to make comparisons against with modern data for applications such as monitoring soil organic carbon change over time.

A regional-scale hyperspectral prediction model of soil organic carbon considering geomorphic features

The prediction of soil organic carbon (SOC) from hyperspectral data often lacks geographic and environmental information related to soil genesis, which would improve the accuracy of the predicted SOC. The main purpose of this study was to improve the accuracy of SOC prediction and the mapping of SOC spatial distributions. We employed satellite hyperspectral image (HSI) data combined with ancillary variables (spectral indexes (SIs), terrain attributes (TAs) and spectral texture features (TFs)) by first stratifying the soil at the great group level. The central part of the Songnen Plain in Northeast China was selected as a region for a case study, because the region attracts considerable research interest as major grain production area in China. In different prediction models, recursive feature elimination (RFE) was applied to optimize input variables to reflect the soil-landscape relationships of different soil classes. The results showed that when the soil stratification strategy and ancillary variables were comprehensively considered, the accuracy of the model was significantly improved (with a coefficient of determination (R2) of 0.76, root mean square error (RMSE) of 3.16 g kg−1, and ratio of performance to interquartile distance (RPIQ) of 2.28). The introduction of SIs, TAs and TFs improved the R2 values by 6.15%, 6.15%, and 13.85%, respectively, compared to those achieved with the original reflectance (OR) bands alone. Moreover, the introduction of ancillary variables improved the accuracies of the SOC models, yielding R2 values of Phaeozems, Chernozems, Arenosols and Cambisols of 0.79, 0.53, 0.76, and 0.81, respectively. Compared with the prediction model, which is based on only the OR, the proposed model can better explain SOC spatial variations. The performance comparison highlights the advantage of the considering geomorphic features when utilized for SOC prediction in regional-scale; this model covers the elimination and expression of optimal ancillary variables for different soil classes, which are closely related to the formation of various soil types and the geomorphic evolution of the region. The SOC map that we obtained shows detailed soil information and effectively expresses the soil factors associated with the environment. The map can support planners in establishing efficient SOC monitoring methods and assessments and prioritizing inputs for future exploitation and research.

Mapping soil organic carbon stocks and trends with satellite-driven high resolution maps over South Africa

Estimation and monitoring of soil organic carbon (SOC) stocks is important for maintaining soil productivity and meeting climate change mitigation targets. Current global SOC maps do not provide enough detail for landscape-scale decision making, and do not allow for tracking carbon sequestration or loss over time. Using an optical satellite-driven machine learning workflow, we mapped SOC stocks (topsoil; 0 to 30 cm) under natural vegetation (86% of land area) over South Africa at 30 m spatial resolution between 1984 and 2019. We estimate a total topsoil SOC stock of 5.6 Pg C with a median SOC density of 6 kg C m−2 (IQR: interquartile range 2.9 kg C m−2). Over 35 years, predicted SOC underwent a net increase of 0.3% (relative to long-term mean) with the greatest net increases (1.7%) and decreases (−0.6%) occurring in the Grassland and Nama Karoo biomes, respectively. At the landscape scale, SOC changes of up to 25% were evident in some locations, as evidenced from fence-line contrasts, and were likely due to local management effects (e.g. woody encroachment associated with increased SOC and overgrazing associated with decreased SOC). Our SOC mapping approach exhibited lower uncertainty (R2 = 0.64; RMSE = 2.5 kg C m−2) and less bias compared to previous low-resolution (250–1000 m) national SOC mapping efforts (average R2 = 0.24; RMSE = 3.7 kg C m−2). Our trend map remains an estimate, pending repeated measures of soil samples in the same location (time-series); a global priority for tracking SOC changes. While high resolution SOC maps can inform land management decisions aimed at climate mitigation (natural climate solutions), potential increases in SOC are likely limited by local climate and soils. It is also important that climate mitigation efforts such as planting trees balance trade-offs between carbon, biodiversity and overall ecosystem function.

Exploring the Suitability of UAS-Based Multispectral Images for Estimating Soil Organic Carbon: Comparison with Proximal Soil Sensing and Spaceborne Imagery

Soil organic carbon (SOC) is a variable of vital environmental significance in terms of soil quality and function, global food security, and climate change mitigation. Estimation of its content and prediction accuracy on a broader scale remain crucial. Although, spectroscopy under proximal sensing remains one of the best approaches to accurately predict SOC, however, spectroscopy limitation to estimate SOC on a larger spatial scale remains a concern. Therefore, for an efficient quantification of SOC content, faster and less costly techniques are needed, recent studies have suggested the use of remote sensing approaches. The primary aim of this research was to evaluate and compare the capabilities of small Unmanned Aircraft Systems (UAS) for monitoring and estimation of SOC with those obtained from spaceborne (Sentinel-2) and proximal soil sensing (field spectroscopy measurements) on an agricultural field low in SOC content. Nine calculated spectral indices were added to the remote sensing approaches (UAS and Sentinel-2) to enhance their predictive accuracy. Modeling was carried out using various bands/wavelength (UAS (6), Sentinel-2 (9)) and the calculated spectral indices were used as independent variables to generate soil prediction models using five-fold cross-validation built using random forest (RF) and support vector machine regression (SVMR). The correlation regarding SOC and the selected indices and bands/wavelengths was determined prior to the prediction. Our results revealed that the selected spectral indices slightly influenced the output of UAS compared to Sentinel-2 dataset as the latter had only one index correlated with SOC. For prediction, the models built on UAS data had a better accuracy with RF than the two other data used. However, using SVMR, the field spectral prediction models achieved a better overall result for the entire study (log(1/R), RPD = 1.40; R2CV = 0.48; RPIQ = 1.65; RMSEPCV = 0.24), followed by UAS and then Sentinel-2, respectively. This study has shown that UAS imagery can be exploited efficiently using spectral indices.