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Oxyhydroxides, soil texture and soil organic carbon (SOC) fractions are key parameters determining organic carbon cycling in soils. Standard laboratory methods to determine these soil properties are, however, time --consuming and expensive. Visible near infrared (Vis-NIR) and mid infrared (MIR) spectroscopy have been recognized as a promising alternative, but previous studies have not explicitly considered the above-mentioned soil properties as compositional data. The fractional components in compositional data are interrelated but their sum should be unity - these features should be represented in the spectral modeling process to minimize the prediction bias. In this study, two unique datasets were used to test these premises. The first one consisted of 655 samples collected from agricultural terraces and lynchets across Europe, which were scanned to acquire MIR spectra, while in the second one 4516 samples from private gardens across Flanders, Belgium were used to acquire Vis-NIR spectra. Memory-based learning models were optimized using both raw data (conventional method) and transformed data of soil properties by additive log-ratio (alr), centered log-ratio (clr), and isometric log-ratio (ilr) transformation methods. Results showed that the log-ratio transformation methods produced predictions as accurate as the conventional method, whilst also added two significant benefits: (1) they ensured the predicted fractions added up to 100% and (2) they reduced the number of samples with extreme prediction errors. We found that for 11 out of 18 investigated soil properties, the three log-ratio transformation methods provided similar model performance, whilst ilr outperformed clr for the prediction of silt and sand content of garden soils, for coarse particulate SOC (>250 mu m) and microaggregate-associated SOC (250-53 mu m) of terrace soils. For the remaining three properties (Al oxyhydroxides) alr outperformed ilr. Fair to excellent predictive models (RPD from 1.4 to 4.3; R2 from 0.50 to 0.95) were achieved for soil oxyhydroxides (Fe, Al, Mn) and soil texture from MIR spectra. Our approach also enabled accurate predictions of silt and sand content of garden soils using Vis-NIR spectra (RPD = 1.9; R2 = 0.72), although accuracy for clay was lower (RPD = 1.3; R2 = 0.49). This study demonstrates that combining soil infrared spectroscopy with a compositional data analysis is a promising technique that enables cost-effective and reliable quantification of soil properties relevant to SOC stability, thus offering a practical opportunity to assess the role of SOC in global C cycling.

Yearly, per-area carbon sequestration rates are used to estimate mitigation potentials by comparing types and areas of land management in 1990 and 2000 and projected to 2010, for the European Union (EU)-15 and for four country-level case studies for which data are available: UK, Sweden, Belgium and Finland. Because cropland area is decreasing in these countries (except for Belgium), and in most European countries there are no incentives in place to encourage soil carbon sequestration, carbon sequestration between 1990 and 2000 was small or negative in the EU-15 and all case study countries. Belgium has a slightly higher estimate for carbon sequestration than the other countries examined. This is at odds with previous reports of decreasing soil organic carbon stocks in Flanders. For all countries except Belgium, carbon sequestration is predicted to be negligible or negative by 2010, based on extrapolated trends, and is small even in Belgium. The only trend in agriculture that may be enhancing carbon stocks on croplands at present is organic farming, and the magnitude of this effect is highly uncertain. Previous studies have focused on the potential for carbon sequestration and have shown quite significant potential. This study, which examines the sequestration likely to occur by 2010, suggests that the potential will not be realized. Without incentives for carbon sequestration in the future, cropland carbon sequestration under Article 3.4 of the Kyoto Protocol will not be an option in EU-15.

Soil organic carbon (SOC) prediction from remote sensing is often hindered by disturbing factors at the soil surface, such as photosynthetic active and non–photosynthetic active vegetation, variation in soil moisture or surface roughness. With the increasing amount of freely available satellite data, recent studies have focused on stabilizing the soil reflectance by building reflectance composites using time series of images. Although composite imagery has demonstrated its potential in SOC prediction, it is still not well established if the resulting composite spectra mirror the reflectance fingerprint of the optimal conditions to predict topsoil properties (i.e. a smooth, dry and bare soil). We have collected 303 photos of soil surfaces in the Belgian loam belt where five main classes of surface conditions were distinguished: smooth seeded soils, soil crusts, partial cover by a growing crop, moist soils and crop residue cover. Reflectance spectra were then extracted from the Sentinel–2 images coinciding with the date of the photos. After the growing crop was removed by an NDVI < 0.25, the Normalized Burn Ratio (NBR2) was calculated to characterize the soil surface, and a threshold of NBR2 < 0.05 was found to be able to separate dry bare soils from soils in unfavorable conditions i.e. wet soils and soils covered by crop residues. Additionally, we found that normalizing the spectra (i.e. dividing the reflectance of each band by the mean reflectance of all spectral bands) allows for cancelling the albedo shift between soil crusts and smooth soils in seed–bed conditions. We then built the exposed soil composite from Sentinel–2 imagery for southern Belgium and part of Noord-Holland and Flevoland in the Netherlands (covering the spring periods of 2016–2021). We used the mean spectra per pixel to predict SOC content by means of a Partial Least Squares Regression Model (PLSR) with 10–fold cross–validation. The uncertainty of the models was assessed via the prediction interval ratio (PIR). The cross validation of the model gave satisfactory results (mean of 100 bootstraps: model efficiency coefficient (MEC) = 0.48 ± 0.07, RMSE = 3.5 ± 0.3 g C kg–1, RPD = 1.4 ± 0.1 and RPIQ = 1.9 ± 0.3). The resulting SOC prediction maps show that the uncertainty of prediction decreases when the number of scenes per pixel increases, and reaches a minimum when at least six scenes per pixel are used (mean PIR of all pixels is 12.4 g C kg–1, while mean SOC predicted is 14.1 g C kg–1). The results of a validation against an independent data set showed a median difference of 0.5 g C kg–1 ± 2.8 g C kg–1 SOC between the measured (average SOC content 13.5 g C kg–1) and predicted SOC contents at field scale. Overall, this compositing method shows both realistic within field and regional SOC patterns.

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.

Soil erosion by water is a major cause of land degradation in the highlands of Ethiopia and anywhere else in the world, but its magnitude and variability are rarely documented across land uses and climatological conditions. The purpose of this study was to examine runoff and soil loss responses under cropland (CL) and grazing land (GL) management practices in three climatic regions of the Ethiopian highlands: semi-arid (Mayleba), dry sub-humid (Gumara), and humid (Guder). We measured runoff and soil loss using runoff plots with and without soil and water conservation (SWC) measures (trenches, stone/soil bunds [embankments] with trenches on the upslope side, and exclosure) during the rainy season (July-September). The results revealed significant variation in runoff and soil loss amounts across land uses, SWC measures, and climatic regions. At Mayleba, seasonal runoff and soil loss in control plot were far higher from GL (280 mm, 26.5 t ha-1) than from CL (108 mm, 7.0 t ha-1) largely due to lack of protective vegetation cover and soil disruption because of intense grazing. In contrast, at Gumara and Guder, seasonal soil loss values were much higher from CL (21.4-71.2 t ha-1) than from GL (0.6-24.2 t ha-1) irrespective of runoff values. This was attributed to the excessive tillage/weeding operations involved in cultivation of teff (cereal crop) at Gumara and potato at Guder. Although SWC measures (practices) substantially reduced runoff and soil loss (decreased by 23%-86%) relative to control plot, seasonal soil loss under GL uses with trenches at Mayleba (12.6 t ha-1), CL with soil bunds and trenches at Gumara (22.1 t ha-1), and Guder (21.4 t ha-1) remained higher than the average tolerable soil loss rate (10 t ha-1 year-1) proposed for the Ethiopian highlands. This suggests that SWC measures should be carefully designed and evaluated specific to land use and climatic conditions. Overall, the results of this study can help improve SWC planning in regions where land use and climate impact on soil erosion vary across geographical areas, as they do in Ethiopia and anywhere else. However, further investigation is crucial with replication of measurements over years and locations to provide more accurate information on land use, management and climate controls on hydrological and soil erosion processes. ispartof: JOURNAL OF ENVIRONMENTAL MANAGEMENT vol:326 issue:Pt A ispartof: location:England status: published

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.

The heterogeneity of soil organic matter (SOM) and the small changes in soil organic carbon (SOC) compared to large total SOC stocks hinder a robust estimation of SOC turnover, in particular for more stable SOC. We developed a simple fractionation protocol for agricultural topsoils and tested it extensively on a range of soils in southern Belgium, including farmed soils, soils from long-term field trials, and paired sites after recent conversion to conservation farming. Our simple fractionation involves shaking the soil, wet sieving over 20 μm and analysing the SOC concentration in the soil as well as in the fine fraction (< 20 μm). Eight biological indicators measured in an earlier study across the same monitoring network for the 0–10 cm topsoil were analysed in a conditional inference forest model in order to investigate the factors influencing the SOC fractions. Soil microbial biomass N explained the largest proportion of variation in both fractions. The fine fraction was also associated with factors explaining the regional trend in SOC distribution such as farmyard manure input, precipitation, land use and flow length. The variation in SOC content between treatments both in long-term trials and in farmers’ fields converted to conservation management was mainly attributed to changes within the coarse fraction. Thus, this fraction proves to be sensitive to management changes, although care should be taken to sample deep enough to represent the former plough layer inherited from the conventional tillage practice. Furthermore, the ratio between the coarse and the fine fraction showed a linear relationship (r²=0.66) with the relative changes in SOC concentration over the last ten years. These fractions derived from a simple analytical approach are thus useful as an indicator for changes in SOC concentration. In analogy to biological indicators such as the soil microbial biomass C, the relationship between the fractions and relative changes in SOC concentration are likely to depend on climate conditions. Our methodology provides an indicator for use in routine analysis of agricultural topsoils, which is capable of predicting the effects of management practices on SOC concentrations in the short to mid-term (5–10 years)

The capacity of soils to store organic carbon represents a key function of soils that is not only decisive for climate regulation but also affects other soil functions. Recent efforts to assess the impact of land management on soil functionality proposed that an indicator- or proxy-based approach is a promising alternative to quantify soil functions compared to time- and cost-intensive measurements, particularly when larger regions are targeted. The objective of this review is to identify measurable biotic or abiotic properties that control soil organic carbon (SOC) storage at different spatial scales and could serve as indicators for an efficient quantification of SOC. These indicators should enable both an estimation of actual SOC storage as well as a prediction of the SOC storage potential, which is an important aspect in land use and management planning. There are many environmental conditions that affect SOC storage at different spatial scales. We provide a thorough overview of factors from micro-scales (particles to pedons) to the global scale and discuss their suitability as indicators for SOC storage: clay mineralogy, specific surface area, metal oxides, Ca and Mg cations, microorganisms, soil fauna, aggregation, texture, soil type, natural vegetation, land use and management, topography, parent material and climate. As a result, we propose a set of indicators that allow for time- and cost-efficient estimates of actual and potential SOC storage from the local to the regional and subcontinental scale. As a key element, the fine mineral fraction was identified to determine SOC stabilization in most soils. The quantification of SOC can be further refined by including climatic proxies, particularly elevation, as well as information on land use, soil management and vegetation characteristics. To enhance its indicative power towards land management effects, further “functional soil characteristics”, particularly soil structural properties and changes in the soil microbial biomass pool should be included in this indicator system. The proposed system offers the potential to efficiently estimate the SOC storage capacity by means of simplified measures, such as soil fractionation procedures or infrared spectroscopic approaches.

Accurate soil organic carbon content estimation is critical as a proxy for carbon sequestration, and as one of the indicators for soil health. Here, we collected 497 soil samples during 2015 and 2019, as well as five environ- mental covariates (organic carbon (OC) input from the crops, normalized difference vegetation index (NDVI), elevation, clay content and precipitation) at a resolution of 30 m. We then aggregated these to represent agri- cultural fields and compiled a soil organic carbon (SOC) content map for the agricultural soils of Wallonia using Gradient Boosting Machine. We calculated OC input from both main crops and cover crops for each individual field. As the cover crops do not occur in the agricultural census, we identified cover crops based on long time- series of NDVI values obtained from the Google Earth Engine platform. The quality of the SOC predictions was assessed by validation data and we obtained an R2 of 0.77. The Empirical Mode Decomposition indicated that OC input and NDVI were the dominant factors at field scale, whereas the remaining covariates determined the distribution of SOC at the scale of the entire Walloon region. The SOC map showed an overall northwest to southeast trend i.e. an increase in SOC contents up to the Ourthe river followed by a decrease further to the South. The map shows both regional trends in SOC and effects of differences in land use and/or management (including crop rotation and frequency of cover crops) between individual fields. The field-scale map can be used as a benchmark and reference to farmers and agencies in maintaining SOC contents at an appropriate level and optimizing decisions for sustainable land use

This chapter first reviews recent pilot studies covering limited areas often with exposed bare soils. Then we focus on the challenges for large-scale application of spectral mapping when the soil and parent material are heterogeneous and surface conditions are unknown. In order to deal with these constraints we discuss i) calibration of spectral models based on large spectral libraries, ii) surface conditions that disturb the soil signal, and iii) time series of images in order to delimit cropland fields and increase the extent of bare soil that can be mapped. Finally, a case study deals with a SOC prediction map derived from the spectra of a Sentinel-2 image and calibrated using the LUCAS spectral library.

Pilot studies have demonstrated the potential of remote sensing for soil organic carbon (SOC) mapping in exposed croplands. However, the use of remote sensing for SOC prediction is often hindered by disturbing factors at the soil surface, such as photosynthetic active and non-photosynthetic active vegetation, variation in soil moisture or surface roughness. With the increasing amount of freely available satellite data, recent studies have focused on stabilizing the soil reflectance by building image composites. These composites tend to minimize the disturbing effects by applying sets of criteria. Here, we aim to develop a robust method that allows selecting Sentinel-2 (S-2) pixels with minimal influence of the following disturbing factors: crop residues, surface roughness and soil moisture. We selected all S-2 cloud-free images covering the Belgian Loam Belt from January 2019 to December 2020 (in total 36 images). We then built nine exposed soil composites based on four sets of criteria: (1) lowest Normalized Burn Ratio (NBR2), (2) Normalized Difference Vegetation Index (NDVI) < 0.25, (3–5) NDVI < 0.25 and NBR2 < threshold, (6) the ‘greening-up’ period of a crop and (7–9) the ‘greening-up’ period of a crop and NBR2 < threshold. The ‘greening-up’ period was selected based on the NDVI timeline, where ‘greening-up’ is considered as the last date of acquisition where the soil is exposed (NDVI < 0.25) before the crop develops (NDVI > 0.25). We then built a partial least square regression (PLSR) model with 10-fold cross-validation to estimate the SOC content based on 137 georeferenced calibration samples on the nine composites. We obtained non-satisfactory results (R2 < 0.30, RMSE > 2.50 g C kg–1, and RPD < 1.4, n > 68) for all composites except for the composite in the ‘greening-up’ stage with a NBR2 < 0.07 (R2 = 0.54 ± 0.12, RPD = 1.68 ± 0.45 and RMSE = 2.09 ± 0.39 g C kg–1, n = 49). Hence, the ‘greening-up’ method combined with a strict NBR2 threshold allows selecting the purest exposed soil pixels suitable for SOC prediction. The limit of this method might be its coverage of the total cropland area, which in a two-year period reached 62%, compared to 95% coverage if only the NDVI threshold is applied.

Sustainable management of agricultural systems is a major challenge for ensuring food security of the growing world population. Organic farming and reduced tillage are assumed to be sustainable agricultural practices improving soil quality relative to conventional management strategies. However, assessment of soil quality is often restrained to either physical, chemical or biological parameters. Soil organic carbon (SOC) is the most widely used indicator of soil quality, but it is not necessarily reactive to change, nor representative of the functioning of soil systems, in particular in relation to the realization of soil microbial processes, as it is composed of fractions with different availability for microbial activity and decomposition.The objective of this study was to assess the influence of three major cropping systems: organic agriculture and conservation agriculture (no/reduced-tillage) vs. conventional agriculture on SOC fractions and microbial processes related to C and N cycling and to establish relationships between carbon fractions and microbial processes in order to identify the most relevant indicator of soil functioning. We hypothesized that 1) organic farming and conservation agriculture would improve soil functioning, 2) labile organic fractions would be better indicators of soil functioning.We measured C and N in physical and chemical fractions (bulk, 2SO4 extractable C, microbial biomass C) and microbial processes (respiration potential, net N mineralization, metabolic diversity of soil bacteria) in 16 cropland sites in Wallonia, south Belgium (CARBIOSOL project).Preliminary data analyses indicate improved soil functioning in organic managed sites relative to sites under conventional and conservation management and reveal hot water extractable carbon as a promising proxy for monitoring changes in soil functioning in response to agricultural practices. Final detailed data analyses will be presented.

High spatial and temporal soil information is crucial to analyze soil developments and for monitoring long term changes to avoid soil degradation. A sufficient soil organic carbon (SOC) content is one of the key soil properties to achieve sustainable high productivity of soils, soil health and increased agroecosystem resiliency. For the usage of remote sensing approaches, naturally exposed soils in Germany occur rarely. Mainly agricultural regions can provide areas of exposed soils for short periods of time during a year. The Soil Composite Mapping Processor (SCMaP) is a fully automated approach to make use of per-pixel based bare-soil compositing to overcome the issue of limited soil exposure based on multispectral Landsat (TM 4, ETM 5, ETM+ 7 and OLI 8) imagery for individually determined time periods between 1984 and 2019.Due to the high spatial and temporal resolution the SCMaP soil reflectance composites contain a considerable potential to derive detailed soil parameters as the SOC contents of exposed soils to add information to existing soil maps on field scale for areawide applications. Besides the soil reflectance composites several field soil samples provided by different federal authorities build the data base for the SOC modeling. Machine learning (ML) algorithms incl. Partial Least Squares and Random Forest regression with various inputs and set-ups are used and applied for several test areas in Germany. Furthermore, the capabilities of different compositing lengths (5-, 10- and 30-years) to derive spatial SOC contents are tested. The results and the validation of the different ML approaches and compositing lengths will be shown, providing insight into the benefits of this approach.

Optical remote sensing and in particular hyperspectral or imaging spectroscopy remote sensing has been long proved to be an adequate method to predict topsoil organic carbon (Corg) content with good accuracy when the soils are well exposed and undisturbed. Several recent studies demonstrated further in science cases the potential of multispectral Copernicus Sentinel-2 data for bare soils Corg prediction, although challenges were reported related to the impact of disturbing factors. Disturbing factors that can affect the prediction and performances of soil surface properties from optical remote sensing are several and can be e.g. due to mixing in the field-of-view with partial vegetation cover depending on the landscape fragmentation. Most pixels at the remote sensing level are composites and in croplands, mixtures of soils with trees or green plants, or mixture with crop residues after harvest are likely. Another factor might be the presence of residual soil moisture or standing water after rain events. Soil reflectance decreases with increasing soil moisture and increasing soil roughness. Soil Surface roughness changes are observed due to variations in soil texture and to variable microtopography. Possible angular and solar illumination changes may affect the soil reflectance as well.In the frame of the ESA WORLDSOILS Project (https://www.world-soils.com) aiming at developing a pre-operational Soil Monitoring System to provide yearly estimations of soil organic carbon at global scale based on space-based EO data, we are working on the development of a spatially upscaled soil spectral library (SUSSL). The SUSSL is based on a sub-selection of the European LUCAS soil database, and includes simulation of realistic scenarios of ‘landscape-like’ cropland reflectance data with effect of mixture with green and dry vegetation, effect of varying soil moisture content, and effect of variable soil roughness. This database is further convoluted to the different spectral response functions of several EO sensors to simulate EO view of surface reflectances in croplands. In a next step, the SUSSL shall be used for the test and validation of different correction, disaggregation and unmixing techniques to assess the capabilities of the retrieval of undisturbed surface reflectance, to which soil prediction models can be applied with increased accuracy. In this talk, we will present the database developed, including methodological choices and parameter selections for the simulation of the different disturbing effects. Further, preliminary assessments will be shown on the uncertainties of the undisturbed vs. disturbed signal and impact on soil properties prediction.

Pilot studies have demonstrated the potential for remote sensing techniques for soil organic carbon (SOC) mapping in exposed croplands. However, the use of remote sensing for SOC prediction is often hindered by disturbing factors at the soil surface such as photosynthetic active and nonphotosynthetic active vegetation, variation in soil moisture or surface roughness. With the increasing amount of freely available satellite data, many studies have focused on stabilizing the soil reflectance by building image composites that are generated using a set of criteria. These composites tend to minimize and cancel out the disturbing effects. Here we aim to develop a robust method that allows selecting Sentinel-2 (S-2) pixels that are not affected by the following disturbing factors: crop residues, surface roughness and soil moisture. We selected all S-2 cloud-free images covering the Loam Belt of Belgium from January 2019 to December 2020 (in total 38 images). We then built four exposed soil composites based on four sets of criteria: (1) NDVI < 0.25, (2) NDVI < 0.25 & Normalized Burn Ratio (NBR2) < 0.07, (3) the ‘greening-up’ period of a crop and (4) the ‘greening-up’ period of a crop & NBR2 < 0.07. The ‘greening-up’ period was selected based on the NDVI timeline, where ‘greening-up’ is considered as the last date of acquisition where the soil is bare (NDVI < 0.25) before the crop develops (NDVI > 0.6).,We then built a partial least square regression (PLSR) model with 10-fold cross-validation to estimate the SOC content based on 137 georeferenced calibration samples on the four above described composites. We obtained a non-satisfactory result for composites (1) to (3): R² = 0.22, RMSE = 3.46 g C kg-1 and RPD 1.12 for (1), R² = 0.19, RMSE = 3.43 g C kg-1 and RPD 1.10 for (2) and R² = 0.15, RMSE = 2.74 g C kg-1 and RPD 1.06 for (3). We, however, obtained a satisfactory result for composite (4): R² = 0.54, RMSE = 2.09 g C kg-1 and RPD 1.68. Hence, the ‘greening-up’ method combined with a strict NBR2 threshold allows selecting the purest exposed soil pixels suitable for SOC prediction. The limit of this method might be the surface coverage, which for a two-year period reached 47% of croplands, compared to 89% exposure if only the NDVI threshold is applied.

Visible–near infrared–shortwave infrared (VNIR–SWIR) spectroscopy is being increasingly used for soil organic carbon (SOC) assessment. Common practice consists of scanning soil samples using a single spectrometer. Considerations have rarely been documented of the effects of using multiple instruments and scanning conditions on SOC model calibration that occur when merging soil spectral libraries (SSLs), particularly in soils with low SOC concentration and using both field spectroradiometers and laboratory fixed spectrometers. To address this gap, we scanned 143 low-SOC-content soil samples using three spectrometers (ASD FieldSpec 3, ASD FieldSpec 4, and FOSS XDS) and four setup features - FOSS, contact probe (CP), dark-box (DB), and open laboratory (LAB) - at three laboratories. The application of an internal soil standard (ISS) to align one laboratory spectrum with another for spectral correction and spectral merging of various SSLs was examined. SOC models were developed using i) data from each single spectrometer – single laboratory separately and ii) merged data from multiple spectrometers – different laboratories, applying the 1st derivatives of spectra and random forest (RF) regression. The results indicate that the spectral shape and wavelength position of key features obtained from all spectrometers and setups did not show any noticeable differences, though spectra based on FOSS setup, particularly on low-SOC samples, demonstrated greater range in absolute derivative values regardless of ISS application. The derivative ISS-corrected spectra showed less variation among different spectrometers compared to their uncorrected raw reflectance spectra. All single spectrometer models predicted SOC reasonably well. However, the spectra acquired by the FOSS setup predicted SOC more accurately (R2 = 0.77, RPIQ = 3.30, RMSE = 0.22 %, and SD = 0.04) than the spectra acquired by the other setups. The models derived from merged uncorrected raw reflectance spectra yielded poor results (R2 = 0.48, RPIQ = 2.33, RMSE = 0.33 %, and SD = 0.10); nevertheless, assessment of SOC using the 1st derivative ISS-corrected merged SSLs considerably improved the prediction accuracy (R2 = 0.70, RPIQ = 3.10, RMSE = 0.25 %, and SD = 0.06). Hence, the derivative spectra coupled with the ISS correction improved the accuracy of SOC prediction models obtained from the merged soil spectra collected in different environments using different instruments. We therefore recommend application of the ISS spectral alignment method linked to the 1st derivative approach to enhance the compilation of SSLs at the regional and global scales for SOC assessment.

The German Agricultural Soil Inventory (BZE-LW) is running a general project evaluation as this network is currently in the phase between the first and second sampling campaign, the latter starting in 2023. A one-day meeting was held in Braunschweig to discuss the general results of the first campaign and the organisation planned for the next campaign. The following scientists were present:●Bas van Wesemael, Earth and Life Institute, UCLouvain, Belgium●Nicolas Saby, unité Infosol, INRAE, Orléans, France●Claudy Jolivet, unité Infosol, INRAE, Orléans, France●Heikkinen Jaakko, Natural Resources Institute, Luke, Finland●Lars Elsgaard, Department of Agroecology, Aarhus University, Denmark●Axel Don and Christopher Poeplau, Thünen Institute This report gathers some general comments and ideas organised according to the list of questions raised before and during the workshop.

High-resolution, field-scale soil organic carbon (SOC) mapping in croplands is crucial for effective and precise agricultural management. Recent developments in unmanned aerial vehicles (UAVs) combined with miniaturized visible–near infrared spectrometers have enabled the rapid and low-cost field-scale SOC mapping. However, a field-specific spectrotransfer model is often needed for such UAV-based hyperspectral measurements, implying local sampling and model development are still required, and this hampers the widespread application of UAV-based methods. In this study, we aim to test to what extent SOC prediction models derived from an existing regional soil spectral library (SSL) can be applied to UAV-based hyperspectral data, without the need for additional field sampling. To this end, an UAV survey was conducted over a bare cropland within the Belgian Loam Belt for field-scale SOC mapping. We evaluated two calibration approaches, one based on local sampling and model development, and one where we capitalized on an existing (laboratory-based) regional SSL. For the local calibration approach, we obtained a good prediction performance with RMSE of 0.57 g kg−1 and RPIQ of 2.35. For the regional model, a spectral alignment procedure was needed to resolve the discrepancy between UAV- and laboratory-based measurements. This resulted in a fair SOC prediction accuracy with RMSE of 0.93 g kg−1 and RPIQ of 1.45. The comparison of SOC maps derived from the two approaches, along with an external validation showed a high consistency, indicating that UAV-based spectral measurements, in combination with SSLs have the potential to improve the efficiency of high-resolution SOC mapping.

Visible–near infrared–shortwave infrared (VIS–NIR–SWIR) spectroscopy is being increasingly used for soil organic carbon (SOC) assessment. Common practice consists of scanning soil samples using a single spectrometer. Considerations have rarely been documented of the effects of using multiple instruments and scanning conditions on SOC model calibration that occur when merging soil spectral libraries (SSLs), particularly in soils with low SOC concentration and using both field spectroradiometers and laboratory fixed spectrometers. To address this gap, we scanned 143 low-SOC-content soil samples using three spectrometers (ASD FieldSpec 3, ASD FieldSpec 4, and FOSS XDS) and four setup features - FOSS, contact probe (CP), dark-box (DB), and open laboratory (LAB) - at three laboratories. The application of an internal soil standard (ISS) to align one laboratory spectrum with another for spectral correction and spectral merging of various SSLs was examined. SOC models were developed using i) data from each single spectrometer – single laboratory separately and ii) merged data from multiple spectrometers – different laboratories, applying the 1st derivatives of spectra and random forest (RF) regression. The results indicate that the spectral shape and position obtained from all spectrometers and setups did not show any noticeable differences, though spectra based on FOSS setup, particularly on low-SOC samples, demonstrated the highest absolute derivative values regardless of ISS application. The derivative ISS-corrected spectra showed less variation among different spectrometers compared to their uncorrected reflectance spectra. All single spectrometer models predicted SOC reasonably well. However, the spectra acquired by the FOSS setup predicted SOC more accurately (R2 = 0.77 and SD = 0.04) than the spectra acquired by the other setups. The models derived from merged uncorrected reflectance spectra yielded poor results (R2 = 0.48 and SD = 0.10); nevertheless, assessment of SOC using the 1st derivative ISS-corrected merged SSLs considerably improved the prediction accuracy (R2 = 0.70 and SD = 0.06). Hence, the derivative spectra coupled with the ISS correction improved the accuracy of SOC prediction models obtained from merged soil spectra collected in different environments using different instruments. We therefore recommend application of the ISS spectral alignment method linked to the 1st derivative approach to enhance the compilation of SSLs at the regional and global scales for SOC assessment.

Intra-field heterogeneity of soil properties is function of complex interaction between biological, physical factors and historic agricultural management. Quantifying the spatial patterns of soil properties such as soil organic carbon (SOC), nitrogen (N), phosphorous, exchangeable cations, pH, soil texture will contribute to an optimization of fertilizer application and crop yields. We tested the capacity of the multispectral Micasense rededge camera mounted on a UAV in order to map the development of winter wheat and related the Red-Edge NDVI (RENDVI) from the sensor to the Plant Area Index (PAI) measured in the field. The geo-referenced grain yield of the winter wheat was measured by a combine harvester and the top soil characteristics analysed by a grid based sampling. The spatial patterns in RENDVI at three phenological stages were mapped together with the yields. For each of these images conditional inference trees were used to derive the soil properties that significantly influenced these spatial patterns. Within-field variation in PAI (cv 41 % in March, 27% in April and 9 % in May) and yield (cv 4%) can be observed. The spatial patterns of RENDVI are rather constant and their correlation with yields is highest in March and April (r=0.64). Soil properties explain between 67 to 79 % of the variance in vegetation index throughout the growing season as well as 67 % of the variance in yield. Legacy effects of land consolidation two years earlier reflected in the field lay-out, pH and exchangeable K are significant factors explaining around 12-18 % of the variance in crop yield each. The SOC contents were overall low (8-15 g kg-1). Hence, the N supply resulting from SOC mineralization throughout the growing season covered less than 10% of the crop needs and the yield patterns did not reflect the variation in SOC contents.