Estimation of Soil Organic Carbon Contents in Croplands of Bavaria from SCMaP Soil Reflectance Composites

After effects of historical grassland on soil organic carbon content and plant growth in croplands in southern Germany determined using satellite data

Numerous studies have reported that grasslands harbor higher soil organic carbon (SOC) stocks compared with arable land; however, the relevant carbon dynamics and sink persistence remain unclear. Herein, arable fields characterized by historical grassland zones (h_GL; grassland use decades ago) and permanent arable land zones (h_CL) were examined. The h_GL zones were determined using historical maps. The change in land use from grassland to cropland occurred 30-50 years ago. In eight arable fields, SOC and total nitrogen (TN) stocks in the topsoil were analyzed at a high spatial resolution. Additionally, remote sensing via satellites was employed to determine the biomass yield at a high spatial resolution using the normalized difference vegetation index (NDVI). In all the fields, the mean SOC content of the h_GL zones (1.81 %, n = 97 measuring points) was higher than the mean SOC content of the h_CL zones (1.52 %, n = 220). Furthermore, the mean relative NDVI was higher in the h_GL zones than in the h_CL zones. SOC and NDVI were positively correlated (up to r = 0.79), as well as TN and NDVI (up to r = 0.72). To evaluate the first dataset, zonal soil samples were collected from the h_GL and h_CL zones from 14 arable fields to determine the SOC and TN content. The mean SOC content of the h_GL zones was 1.92 % and that of the h_CL zones was 1.39 %-a difference of absolute SOC stocks in the topsoil of 23.8 t ha-1 (bulk density: 1.5 g cm-3). The work combines the knowledge of historical soil maps, remote sensing applications and georeferenced soil sampling and shows that SOC stocks in grassland have a high persistence and can have positive impact on yields even decades after a land use change. Historical land use proved to be a major factor for spatial SOC variability at the study site.

A novel remote sensing-based approach to determine loss of agricultural soils due to soil sealing - a case study in Germany

Soils provide habitat, regulation and utilization functions. Therefore, Germany aims to reduce soil sealing to 30 ha day- 1 by 2030 and to eliminate it by 2050. About 55 ha day- 1 of soil are damaged (average 2018-2021), but detailed information on its soil quality is lacking. This study proposes a new approach using geo-information and remote sensing data to assess agricultural soil loss in Lower Saxony and Brandenburg. Soil quality is assessed based on erosion resistance, runoff regulation, filter functions, yield potential and the Müncheberg Soil Quality Rating from 2006 to 2015. Data from the German Soil Map at a scale of 1:200,000 (BÜK 200), climate, topography, CORINE Land Cover (CLC) and Imperviousness Layer (IMCC), both provided by the Copernicus Land Monitoring Service (CLMS), are used to generate information on soil functions, potentials and agricultural soil loss due to sealing. For the first time, soil losses under arable land are assessed spatially, quantitatively and qualitatively. An estimate of the qualitative loss of agricultural soil in Germany between 2006 and 2015 is obtained by intersecting the soil evaluation results with the quantitative soil loss according to IMCC. Between 2006 and 2015, about 73,300 ha of land were sealed in Germany, affecting about 37,000 ha of agricultural soils. This corresponds to a sealing rate of 11 ha per day for Germany. In Lower Saxony and Brandenburg, agricultural soils were sealed at a rate of 1.9 ha day- 1 and 0.8 ha day- 1 respectively, removing these soils from primary land use. In Lower Saxony, 75% of soils with moderate or better biotic yield potential have been removed from primary land use, while in Brandenburg this figure is as high as 88%. Implementing this approach can help decision-makers reassess sealed land and support Germany's sustainable development strategy.

Remote sensing-based prediction of organic carbon in agricultural and natural soils influenced by salt and sand mining using machine learning

It is important to keep soil organic carbon (SOC) in balance to ensure soil health and quality. In this manner, mining activities have crucial impacts on SOC stocks, especially in semi-arid and arid regions such as Iran. For this purpose, SOC was measured at 180 randomly selected points in both natural and agricultural soils in the central part of Iran. Machine learning methods, such as GEP (Genetic Expression Programming), SVR (Support Vector Regression), and ANNs (Artificial Neural Networks), were developed and employed to estimate SOC for all sampled points, including both natural and agricultural soils. Following that, topography and remotely sensed data were employed as input variables to improve SOC prediction influenced by mining. The remotely sensed data and topography factors were extracted from Landsat 9 images and Digital Elevation Models (DEMs), respectively. Input variables were considered in three scenarios, including the use of topography factors (scenario I), the use of remote sensing data (scenario II), and the use of both topography factors and remote sensing data (scenario III). The results of this study showed that the most effective model for predicting SOC across all sampled data was SVR (ME = -0.1539%, R2 = 0.642 and RMSE = 0.620%) when employing scenario III. Furthermore, the results indicated that the optimal method for both natural and agricultural soils was the SVR method when employing scenario III. Further analysis through mapping SOC contents showed that mining activities influenced the distribution of SOC in the studied region. Overall, the predicted maps of SOC contents indicated that lower SOC contents were predominantly distributed in the vicinity of salt and sand mines, particularly in salt-rich areas, for both natural and agricultural soils.

Digital mapping of the soil available water capacity: tool for the resilience of agricultural systems to climate change

Soil available water capacity (AWC) is a key function for human survival and well-being. However, its direct measurement is laborious and spatial interpretation is complex. Digital soil mapping (DSM) techniques emerge as an alternative to spatial modeling of soil properties. DSM techniques commonly apply machine learning (ML) models, with a high level of complexity. In this context, we aimed to perform a digital mapping of soil AWC and interpret the results of the Random Forest (RF) algorithm and, in a case study, to show that digital AWC maps can support agricultural planning in response to the local effects of climate change. To do so, we divided this research into two approaches: In the first approach, we showed a DSM using 1857 sample points in a southeastern region of Brazil with laboratory-determined soil attributes, together with a pedotransfer function (PTF), remote sensing and DSM techniques. In the second approach, the constructed AWC digital soil map and weather station data were used to calculate climatological soil water balances for the periods between 1917-1946 and 1991-2020. The result showed the selection of covariates using Shapley values as a criterion contributed to the parsimony of the model, obtaining goodness-of-fit metrics of R² 0.72, RMSE 16.72 mm m^-1, CCC 0.83, and Bias of 0.53 over the validation set. The highest contributing covariates for soil AWC prediction were the Landsat multitemporal images with bare soil pixels, mean diurnal, and annual temperature range. Under the current climate conditions, soil available water content (AW) increased during the dry period (April to August). May had the highest increase in AW (∼17 mm m^-1) and decrease in September (∼14 mm m^-1). The used methodology provides support for AWC modeling at 30 m resolution, as well as insight into the adaptation of crop growth periods to the effects of climate change.

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.

The Role of Soil Salinization in Shaping the Spatio-Temporal Patterns of Soil Organic Carbon Stock

Soil salinization is closely related to land degradation, and it is supposed to exert a significant negative effect on soil organic carbon (SOC) stock dynamics. This effect and its mechanism have been examined at site and transect scales in previous studies while over a large spatial extent, the salinity-induced changes in SOC stock over space and time have been less quantified, especially by machine learning and remote sensing techniques. The main focus of this study is to answer the following question: to what extent can soil salinity exert an additional effect on SOC stock over time at a larger spatial scale? Thus, we employed the extreme gradient boosting models (XGBoost) combined with field site-level measurements from 433 sites and 41 static and time-varying environmental covariates to construct methods capable of quantifying the salinity-induced SOC changes in a typical inland river basin of China between the 1990s and 2020s. Results showed that the XGBoost models performed well in predicting the soil electrical conductivity (EC) and SOC stock at 0–20 cm, with the R2 value reaching 0.85 and 0.81, respectively. SOC stock was found to vary significantly with increasing soil salinity following an exponential decay function (R2 = 0.27), and salinity sensitivity analysis showed that soils in oasis were expected to experience the largest carbon loss (−137.78 g m−2), which was about 4.84, 14.37, and 25.95 times higher than that in the saline, bare, and sandy land, respectively, if the soil salinity increased by 100%. In addition, the decrease in the soil salinity (−0.32 dS m−1) from the 1990s to the 2020s was estimated to enhance the SOC stock by 0.015 kg m−2, which contributed an additional 10% increase to the total SOC stock enhancement. Overall, the proposed methods can be applied for quantification of the direction and size of the salinity effect on SOC stock changes in other salt-affected regions. Our results also suggest that the role of soil salinization should not be neglected in SOC changes projection, and soil salinization control measures should be further taken into practice to enhance soil carbon sequestration in arid inland river basins.

Satellite Imagery to Map Topsoil Organic Carbon Content over Cultivated Areas: An Overview

There is a need to update soil maps and monitor soil organic carbon (SOC) in the upper horizons or plough layer for enabling decision support and land management, while complying with several policies, especially those favoring soil carbon storage. This review paper is dedicated to the satellite-based spectral approaches for SOC assessment that have been achieved from several satellite sensors, study scales and geographical contexts in the past decade. Most approaches relying on pure spectral models have been carried out since 2019 and have dealt with temperate croplands in Europe, China and North America at the scale of small regions, of some hundreds of km2: dry combustion and wet oxidation were the analytical determination methods used for 50% and 35% of the satellite-derived SOC studies, for which measured topsoil SOC contents mainly referred to mineral soils, typically cambisols and luvisols and to a lesser extent, regosols, leptosols, stagnosols and chernozems, with annual cropping systems with a SOC value of ~15 g·kg−1 and a range of 30 g·kg−1 in median. Most satellite-derived SOC spectral prediction models used limited preprocessing and were based on bare soil pixel retrieval after Normalized Difference Vegetation Index (NDVI) thresholding. About one third of these models used partial least squares regression (PLSR), while another third used random forest (RF), and the remaining included machine learning methods such as support vector machine (SVM). We did not find any studies either on deep learning methods or on all-performance evaluations and uncertainty analysis of spatial model predictions. Nevertheless, the literature examined here identifies satellite-based spectral information, especially derived under bare soil conditions, as an interesting approach that deserves further investigations. Future research includes considering the simultaneous analysis of imagery acquired at several dates i.e., temporal mosaicking, testing the influence of possible disturbing factors and mitigating their effects fusing mixed models incorporating non-spectral ancillary information.

Scale-Specific Prediction of Topsoil Organic Carbon Contents Using Terrain Attributes and SCMaP Soil Reflectance Composites

There is a growing need for an area-wide knowledge of SOC contents in agricultural soils at the field scale for food security and monitoring long-term changes related to soil health and climate change. In Germany, SOC maps are mostly available with a spatial resolution of 250 m to 1 km2. The nationwide availability of both digital elevation models at various spatial resolutions and multi-temporal satellite imagery enables the derivation of multi-scale terrain attributes and (here: Landsat-based) multi-temporal soil reflectance composites (SRC) as explanatory variables. In the example of a Bavarian test of about 8000 km2, relations between 220 SOC content samples as well as different aggregation levels of the explanatory variables were analyzed for their scale-specific predictive power. The aggregation levels were generated by applying a region-growing segmentation procedure, and the SOC content prediction was realized by the Random Forest algorithm. In doing so, established approaches of (geographic) object-based image analysis (GEOBIA) and machine learning were combined. The modeling results revealed scale-specific differences. Compared to terrain attributes, the use of SRC parameters leads to a significant model improvement at field-related scale levels. The joint use of both terrain attributes and SRC parameters resulted in further model improvements. The best modeling variant is characterized by an accuracy of R2 = 0.84 and RMSE = 1.99.

Earth Observation Data-Driven Cropland Soil Monitoring: A Review

We conducted a systematic review and inventory of recent research achievements related to spaceborne and aerial Earth Observation (EO) data-driven monitoring in support of soil-related strategic goals for a three-year period (2019–2021). Scaling, resolution, data characteristics, and modelling approaches were summarized, after reviewing 46 peer-reviewed articles in international journals. Inherent limitations associated with an EO-based soil mapping approach that hinder its wider adoption were recognized and divided into four categories: (i) area covered and data to be shared; (ii) thresholds for bare soil detection; (iii) soil surface conditions; and (iv) infrastructure capabilities. Accordingly, we tried to redefine the meaning of what is expected in the next years for EO data-driven topsoil monitoring by performing a thorough analysis driven by the upcoming technological waves. The review concludes that the best practices for the advancement of an EO data-driven soil mapping include: (i) a further leverage of recent artificial intelligence techniques to achieve the desired representativeness and reliability; (ii) a continued effort to share harmonized labelled datasets; (iii) data fusion with in situ sensing systems; (iv) a continued effort to overcome the current limitations in terms of sensor resolution and processing limitations of this wealth of EO data; and (v) political and administrative issues (e.g., funding, sustainability). This paper may help to pave the way for further interdisciplinary research and multi-actor coordination activities and to generate EO-based benefits for policy and economy.