Scale Soil Organic Carbon Research Articles

Multiple soil map comparison highlights challenges for predicting topsoil organic carbon concentration at national scale

Soil organic carbon (SOC) concentration is the fundamental indicator of soil health, underpinning food production and climate change mitigation. SOC storage is highly sensitive to several dynamic environmental drivers, with approximately one third of soils degraded and losing carbon worldwide. Digital soil mapping illuminates where hotspots of SOC storage occur and where losses to the atmosphere are most likely. Yet, attempts to map SOC often disagree. Here we compare national scale SOC concentration map products to reveal agreement of data in mineral soils, with progressively poorer agreement in organo-mineral and organic soils. Divergences in map predictions from each other and survey data widen in the high SOC content land types we stratified. Given the disparities are highest in carbon rich soils, efforts are required to reduce these uncertainties to increase confidence in mapping SOC storage and predicting where change may be important at national to global scales. Our map comparison results could be used to identify SOC risk where concentrations are high and should be conserved, and where uncertainty is high and further monitoring should be targeted. Reducing inter-map uncertainty will rely on addressing statistical limitations and including covariates that capture convergence of physical factors that produce high SOC contents.

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.

Methods of evaluating soil bulk density: Impact on estimating large scale soil organic carbon storage

Bulk density (BD) is one of the most important parameters used to calculate soil organic carbon (SOC) storage. Differences in the methods available to substitute missing BD data, including mean, median, and pedo-transfer functions (PTFs), are considered the main reason for the high uncertainty in SOC storage estimations at large scales. In this study, we used the measured BD and SOC contents of 1007 soil profiles to evaluate the accuracy of six BD substitution methods (two mean methods, a median method, and three PTFs). The results showed that PTFs underestimated SOC storage by 8% in the 0–20cm soil layer, while mean and median methods overestimated SOC storage by 45% and 51%, respectively. Furthermore, the accuracy of PTFs estimates decreased with increasing soil depth. Relative to the measured values, and based on mean errors, root mean square errors, and method efficiency, the PFTs estimates were more accurate than those of the mean and median methods. Estimation bias increased with increasing SOC content for all methods, but differed among different soil types. The results of this study demonstrate that PTFs provide more accurate estimates of SOC storage when the extent of missing BD is substantial. Furthermore, the results provide new insights into establishing optimized PTFs, which contain more soil properties and piecewise functions of SOC content, in order to improve SOC storage estimates at large scales.

Soil carbon stock in relation to soil properties and landscape position in a forest ecosystem of southern Italy (Calabria region)

Carbon (C) storage in forest soils is of great importance both to forest ecosystems and to reduce the CO2 in atmosphere. Knowledge of spatial pattern of soil organic carbon (SOC) and the factors influencing it in various soil-landscapes are essential for understanding global C cycle.The objective of this study was to investigate at profile scale SOC stock in a forested area of southern Italy (Calabria) in relation to soil properties and landscape position. Twenty-eight soil profiles were sampled to cover all soil types and physiographic units of the study area and each horizon was sampled and characterized for its physical-chemical properties (bulk density, texture, pH, SOC, and nitrogen). Moreover, the organic horizon (O) was sampled and SOC concentration was determined. Then, the SOC stock for the organic layer and mineral horizons was calculated.Soils developed in the study area belong to the Inceptisol and Entisol orders varying from shallow to moderately deep, with chemical and physical characteristics mainly controlled by granitic parent rock. SOC stock for the organic layer varied from 3Mgha−1 to 6Mgha−1 whereas for mineral horizons ranged between 20.2Mgha−1 and 310.9Mgha−1.The results showed a different behaviour of physical-chemical properties and carbon storage for coarse-textured soils and more fine-textured soils. In addition, soil types and topographic features such as slope gradient, slope curvature and landscape position controlled SOC stored in mineral horizons through changes in both in-depth variability of SOC concentration and profile thickness. In particular, higher SOC stocks were recorded in flat areas than on steep slopes, and Inceptisols developed on slopes with concave morphology exhibited higher values than Entisols located on convex slope.

Do we really need large spectral libraries for local scale SOC assessment with NIR spectroscopy?

Near infrared (NIR) spectroscopy was used to predict the soil organic carbon (SOC) contents at local scale in eleven target sites. For that, eight spectral libraries of different sizes (ranging from 3482 to 36 samples) were used to construct national, provincial and local scale models. Inaccurate predictions were obtained except when the largest national library was used to construct the model. We also obtained SOC predictions once the models were adapted to target sites characteristics. For the models’ adaptation, we used a two-step approach consisting on spiking (as first step) and extra-weighting (as second step). The effect of spiking was small in larger-sized models and high in smaller-sized models, whereas the effect of extra-weighting was small in smaller-sized models and large in larger-sized models. The very high accuracy obtained after models’ adaptation (R2>0.95; RPIQ>5.48), regardless of the size of the spectral library, suggests that large spectral libraries are not needed for local scale SOC assessment. These results have important implications regarding the way that NIR spectroscopy can result highly effective for land management and how users can focus and organize the analytical efforts.

Effects of soil types and land use - land cover on soil organic carbon density at Madendere watershed

Identifying the primary factors influencing watershed scale soil organic carbon (SOC) spatial distribution is critical for improving the accuracy of SOC stock estimates. The primary objective of the current study is to determine the effects of soil type and land use-land cover on SOC in Maden Dere Watershed. To determine land use and land cover of the study area, Geoeye satellite image was used. Four main land use and land cover that are forest, pasture, orchard and cultivated land were determined. Results indicate soil types and land use-land cover were two influencing factors of SOC density spatial variation. SOC density of soil profiles, Haplustept (37.58 kg/m 2 ) was significantly higher than other soil great groups. Main reasons of this result are indicated as profile depth and pedological development. In addition, it was determined land use and land cover affect on SOC by taking soil samples. For surface soils SOC density, the lowest average carbon storage (5.05 kg/m 2 ) was found in cultivated soils. In conclusion, it should be developed proper land use policy and sustainable soil management and cropping practices to combat the ongoing soil degradation and improve soil fertility in the study area.

Soil properties and environmental tracers: A DEM based assessment in an Australian Mediterranean environment

Terrain properties vary at the hillslope and catchment scale and play a significant role in the distribution of water and sediment. Of particular interest in recent years has been the role of hillslope and catchment properties in the spatial and temporal distribution of soil organic carbon (SOC) and the ability to predict SOC from DEM terrain analysis. SOC plays a significant role in soil health and productivity as well as providing a significant store of terrestrial carbon. This study examined SOC concentration along representative pasture transects in a catchment located in southern Western Australia with a Mediterranean climate. Results demonstrate that the majority of SOC (%) is located in the near-surface (300mm) and is concentrated in the top 0.2m. There was no relationship found between SOC (or microbial biomass) and topography or topographic derivatives such as wetness and terrain indices from DEMs. Significant relationships were however found between SOC and environmental tracers (137Cs and 210Pbex) down the soil profile. Weak, yet significant, relationships were found between SOC and the environmental tracers along the hillslope transects, suggesting that organic carbon moves along the same pathways as clay particles in soil. An erosion assessment using 137Cs and also a numerical soil erosion and landscape evolution model found low and comparable erosion rates at the site. The results demonstrate that SOC concentration is relatively uniform across the study site and that a transect scale assessment can provide a measure of hillslope and catchment scale SOC in this environment.

Estimation of soil organic carbon stock and its spatial distribution in the Republic of Ireland

AbstractData scarcity often prevents the estimate of regional (or national) scale soil organic carbon (SOC) stock and its spatial distribution. This study attempts to overcome the data limitations by combining two existing Irish soil databases [SoilC and national soil database (NSD)] at the national scale, to create an improved estimate of the national SOC stock. Representative regression models between the near‐surface SOC concentration and those of deeper depths, and between SOC concentration and bulk density (BD) were developed based on the SoilC database. These regression models were then applied to the NSD derived SOC concentration map, resulting in an improved SOC stock and spatial distribution map for the top 10 cm, 30 cm and 50 cm depths. Western Ireland, particularly coastal areas, was found to have higher SOC densities than eastern Ireland, corresponding to the spatial distribution of peatland. We estimated the national SOC stock at 383 ± 38 Tg for the near‐surface of 0–10 cm depth; 1016 ± 118 Tg for 0–30 cm depth; and 1474 ± 181 Tg for 0–50 cm depth.

Hillslope and catchment scale soil organic carbon concentration: An assessment of the role of geomorphology and soil erosion in an undisturbed environment

The role of geomorphology in relation to the spatial and temporal distribution of soil carbon is of considerable interest in terms of landscape management and carbon sequestration. Soil carbon plays an important role in soil water holding capacity, soil structure and overall soil health. Soil is also a significant store of terrestrial carbon. This study examines total soil carbon (SC) concentration at the hillslope and catchment scale in the Tin Camp Creek catchment, Arnhem Land, Northern Territory, Australia. The catchment is largely undisturbed by European agriculture or management practices and is located in the monsoonal tropics. Results show that SC concentration along hillslope transects has remained consistent over a number of years and it is strongly related to hillslope position and topographic factors derived from precision surveying and provides a baseline assessment. Poor relationships were found when using a good quality medium resolution digital elevation model to derive topographic factors. This finding demonstrates the need for high resolution survey data for the prediction of total C at the hillslope and catchment scale. There was little difference in SC concentration between years and overall, SC down the hillslope profile varies little temporally suggesting that concentrations are relatively stable in this environment. An assessment of the relationship between SC and soil erosion using 137Cs and erosion pins demonstrates that sediment transport and deposition play little role in the distribution of SC in this environment. Vegetative biomass appears to be the major contributor to SC concentration with vegetative biomass being strongly controlled by topographic factors. While the SC concentration is constant over the study period further sampling is required to assess decadal trends.

Vegetation controls on soil organic carbon dynamics in an arid, hyperthermic ecosystem

The large land area occupied by arid lands, roughly 36% to 40% globally, underscores the importance for understanding how these ecosystems function in the global carbon cycle. Few studies have directly examined soil organic carbon (SOC) dynamics and the effect of vegetation on SOC and microbial community structure in arid ecosystems. The objective of this study was to determine the effect of vegetation type on SOC dynamics in an arid, hyperthermic Sonoran Desert ecosystem. We specifically examined the influence of Prosopis velutina (mesquite), Larrea tridentata (creosote), and a combination of Bouteloua barbata, Bouteloua aristidoides, Aristida adscensionis, and Cynodon dactylon (mixed grass) vegetation types on SOC dynamics by quantifying: (i) local scale SOC stocks; (ii) soil aggregate stability; (iii) SOC turnover; and (iv) soil microbial community composition. There was significantly greater SOC in mesquite A-horizons relative to creosote and grass sites with values of 46.7, 30.4, and 24.4 g m − 2 , respectively. Subsurface SOC content did not vary significantly between vegetation types. Aggregate stability determined using an ultrasonic dispersion technique was found to be similar among vegetation types. The only significant difference noted was greater energy required to disperse stable aggregates in mesquite relative to grass soils, 1500 and 735 J g − 1 soil, respectively. Laboratory incubations were performed to determine SOC dynamics, pool sizes, and active pool mean residence times (MRT) for each vegetation type. Incubation results indicated significant variation in the cumulative respired CO 2 under mesquite, creosote, and grasses with 151, 186, and 207 mg C g − 1 soil C respired from each respective vegetation type. The incubation data indicated that 7–11% of total SOC was highly labile across all vegetation types with modeled active pool SOC MRT averaging 17 days. Bacterial community analysis by Terminal Restriction Fragment Length Polymorphism (TRFLP) indicated significant differences in microbial community structure among vegetation types. Microbial composition was highly correlated with soil pH and electrical conductivity. Furthermore, community composition was correlated with cumulative respired CO 2, suggesting an interaction among vegetation type, soil properties and microbial community control SOC dynamics in this ecosystem. The combined results indicated significant variation in SOC dynamics within a specific ecosystem by vegetation type. Understanding local-scale vegetation controls of soil carbon cycling may improve efforts to model regional carbon dynamics in arid environments.

Regional simulation of long‐term organic carbon stock changes in cropland soils using the DNDC model: 1. Large‐scale model validation against a spatially explicit data set

AbstractBecause of the large spatial and temporal variability of soil organic carbon (SOC) dynamics, a modelling approach is crucial in detailed regional analyses. Several estimates of regional scale SOC sequestration potential have been made using dynamic soil organic matter (SOM) models which have been linked to spatial databases contained within a Geographic Information System. In all these previous studies, a large‐scale model validation, which provides information on the general model performance for the study area under concern, was impossible because of lack of data. A data set of over 190 000 SOC measurements, grouped as means per community and covering the period 1989–2000 was available for Flanders in northern Belgium. In order to validate the DNDC model at a large spatial scale, we used this data set along with detailed pH, soil texture and crop areas which were all available at the municipality scale to simulate SOC stocks for the entire study area during the period 1990–2000. A minor adjustment of the initial distribution of SOC in the model's SOC pool was necessary to fit the simulated SOC stock changes to the measured decrease of −475 kg OC ha−1 year−1 (0–30 cm). Although DNDC was able to simulate the SOC stock changes well for the whole study area, the simulated decrease in the SOC stocks was overestimated for communities predominantly having sandy textures and underestimated for communities with silt loam to silt textures. This study also urges caution with the application of SOM models at regional scales after limited validation or calibration at the field scale as these do not guarantee good simulation of spatial variation in SOC changes.