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Soils are at the crossroads of many existential issues that humanity is currently facing. Soils are a finite resource that is under threat, mainly due to human pressure. There is an urgent need to map and monitor them at field, regional, and global scales in order to improve their management and prevent their degradation. This remains a challenge due to the high and often complex spatial variability inherent to soils. Over the last four decades, major research efforts in the field of pedometrics have led to the development of methods allowing to capture the complex nature of soils. As a result, digital soil mapping (DSM) approaches have been developed for quantifying soils in space and time. DSM and monitoring have become operational thanks to the harmonization of soil databases, advances in spatial modeling and machine learning, and the increasing availability of spatiotemporal covariates, including the exponential increase in freely available remote sensing (RS) data. The latter boosted research in DSM, allowing the mapping of soils at high resolution and assessing the changes through time. We present a review of the main contributions and developments of French (inter)national research, which has a long history in both RS and DSM. Thanks to the French SPOT satellite constellation that started in the early 1980s, the French RS and soil research communities have pioneered DSM using remote sensing. This review describes the data, tools, and methods using RS imagery to support the spatial predictions of a wide range of soil properties and discusses their pros and cons. The review demonstrates that RS data are frequently used in soil mapping (i) by considering them as a substitute for analytical measurements, or (ii) by considering them as covariates related to the controlling factors of soil formation and evolution. It further highlights the great potential of RS imagery to improve DSM, and provides an overview of the main challenges and prospects related to digital soil mapping and future sensors. This opens up broad prospects for the use of RS for DSM and natural resource monitoring.

Satellite-based soil organic carbon content (SOC) mapping over wide regions is generally hampered by the low soil sampling density and the diversity of soil sampling periods. Some unfavorable topsoil conditions, such as high moisture, rugosity, the presence of crop residues, the limited amplitude of SOC values and the limited area of bare soil when a single image is used, are also among the influencing factors. To generate a reliable SOC map, this study addresses the use of Sentinel-2 (S2) temporal mosaics of bare soil (S2Bsoil) over 6 years jointly with soil moisture products (SMPs) derived from Sentinel 1 and 2 images, SOC measurement data and other environmental covariates derived from digital elevation models, lithology maps and airborne gamma-ray data. In this study, we explore (i) the dates and periods that are preferable to construct temporal mosaics of bare soils while accounting for soil moisture and soil management; (ii) which set of covariates is more relevant to explain the SOC variability. From four sets of covariates, the best contributing set was selected, and the median SOC content along with uncertainty at 90% prediction intervals were mapped at a 25-m resolution from quantile regression forest models. The accuracy of predictions was assessed by 10-fold cross-validation, repeated five times. The models using all the covariates had the best model performance. Airborne gamma-ray thorium, slope and S2 bands (e.g., bands 6, 7, 8, 8a) and indices (e.g., calcareous sedimentary rocks, “calcl”) from the “late winter–spring” time series were the most important covariates in this model. Our results also indicated the important role of neighboring topographic distances and oblique geographic coordinates between remote sensing data and parent material. These data contributed not only to optimizing SOC mapping performance but also provided information related to long-range gradients of SOC spatial variability, which makes sense from a pedological point of view.

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.

In agronomy, soil organic carbon (SOC) content is important for the development and growth of crops. From an environmental monitoring viewpoint, SOC sequestration is essential for mitigating the emission of greenhouse gases into the atmosphere. SOC dynamics in cropland soils should be further studied through various approaches including remote sensing. In order to predict SOC content over croplands in southwestern France (area of 22,177 km²), this study addresses (i) the influence of the dates on which Sentinel-2 (S2) images were acquired in the springs of 2017–2018 as well as the influence of the soil sampling period of a set of samples collected between 2005 and 2018, (ii) the use of soil moisture products (SMPs) derived from Sentinel-1/2 satellites to analyze the influence of surface soil moisture on model performance when included as a covariate, and (iii) whether the spatial distribution of SOC as mapped using S2 is related to terrain-derived attributes. The influences of S2 image dates and soil sampling periods were analyzed for bare topsoil. The dates of the S2 images with the best performance (RPD ≥ 1.7) were 6 April and 26 May 2017, using soil samples collected between 2016 and 2018. The soil sampling dates were also analyzed using SMP values. Soil moisture values were extracted for each sample and integrated into partial least squares regression (PLSR) models. The use of soil moisture as a covariate had no effect on the prediction performance of the models; however, SMP values were used to select the driest dates, effectively mapping topsoil organic carbon. S2 was able to predict high SOC contents in the specific soil types located on the old terraces (mesas) shaped by rivers flowing from the southwestern Pyrénées.

The spatial assessment of soil organic carbon (SOC) is a major environmental challenge, notably for evaluating soil carbon stocks. Recent works have shown the capability of Sentinel-2 optical data to predict SOC content over temperate agroecosystems characterized by annual crops, using a single acquisition date. Considering a Sentinel-2 time series, this work intends to analyze the impact of acquisition date, and related weather and soil surface conditions on the prediction performance of topsoil SOC content (plough layer). A Sentinel-2 time-series was gathered, comprised of the dates corresponding to both the maximum of bare soil coverage and minimum of cloud coverage. Cross-validated partial least squares regression (PLSR) models were constructed between soil reflectance image spectra, and SOC content analyzed from 329 top soil samples collected over the study area. Cross-validation R2 ranged from 0.005 to 0.58, root mean square error from 5.86 to 3.02 g·kg−1 and residual prediction deviation values from 1.0 to 1.5 (without unit), according to date. The main factors influencing these differences were soil roughness, in conjunction with soil moisture, and the cloud and cloud shadow cover of the entire tile. The best performing dates were spring dates characterized by both lowest soil surface roughness and moisture content. Normalized difference vegetation index (NDVI) values below 0.35 did not influence prediction performance. This consolidates the previous results obtained during single date acquisitions and offers wider perspectives for the further use of Sentinel-2 into multidate mosaics for digital soil mapping.

Soils are at the crossroads of many existential issues that humanity is currently facing. Soils are a finite resource that is under threat, mainly due to human pressure. There is an urgent need to map and monitor them at field, regional, and global scales in order to improve their management and prevent their degradation. This remains a challenge due to the high and often complex spatial variability inherent to soils. Over the last four decades, major research efforts in the field of pedometrics have led to the development of methods allowing to capture the complex nature of soils. As a result, digital soil mapping (DSM) approaches have been developed for quantifying soils in space and time. DSM and monitoring have become operational thanks to the harmonization of soil databases, advances in spatial modeling and machine learning, and the increasing availability of spatiotemporal covariates, including the exponential increase in freely available remote sensing (RS) data. The latter boosted research in DSM, allowing the mapping of soils at high resolution and assessing the changes through time. We present a review of the main contributions and developments of French (inter)national research, which has a long history in both RS and DSM. Thanks to the French SPOT satellite constellation that started in the early 1980s, the French RS and soil research communities have pioneered DSM using remote sensing. This review describes the data, tools, and methods using RS imagery to support the spatial predictions of a wide range of soil properties and discusses their pros and cons. The review demonstrates that RS data are frequently used in soil mapping (i) by considering them as a substitute for analytical measurements, or (ii) by considering them as covariates related to the controlling factors of soil formation and evolution. It further highlights the great potential of RS imagery to improve DSM, and provides an overview of the main challenges and prospects related to digital soil mapping and future sensors. This opens up broad prospects for the use of RS for DSM and natural resource monitoring. [ABSTRACT FROM AUTHOR]

Satellite-based soil organic carbon content (SOC) mapping over wide regions is generally hampered by the low soil sampling density and the diversity of soil sampling periods. Some unfavorable topsoil conditions, such as high moisture, rugosity, the presence of crop residues, the limited amplitude of SOC values and the limited area of bare soil when a single image is used, are also among the influencing factors. To generate a reliable SOC map, this study addresses the use of Sentinel-2 (S2) temporal mosaics of bare soil (S2Bsoil) over 6 years jointly with soil moisture products (SMPs) derived from Sentinel 1 and 2 images, SOC measurement data and other environmental covariates derived from digital elevation models, lithology maps and airborne gamma-ray data. In this study, we explore (i) the dates and periods that are preferable to construct temporal mosaics of bare soils while accounting for soil moisture and soil management; (ii) which set of covariates is more relevant to explain the SOC variability. From four sets of covariates, the best contributing set was selected, and the median SOC content along with uncertainty at 90% prediction intervals were mapped at a 25-m resolution from quantile regression forest models. The accuracy of predictions was assessed by 10-fold cross-validation, repeated five times. The models using all the covariates had the best model performance. Airborne gamma-ray thorium, slope and S2 bands (e.g., bands 6, 7, 8, 8a) and indices (e.g., calcareous sedimentary rocks, "calcl") from the "late winter–spring" time series were the most important covariates in this model. Our results also indicated the important role of neighboring topographic distances and oblique geographic coordinates between remote sensing data and parent material. These data contributed not only to optimizing SOC mapping performance but also provided information related to long-range gradients of SOC spatial variability, which makes sense from a pedological point of view. [ABSTRACT FROM AUTHOR]

I Remote Sensing i was able to uphold its high standards for published papers due to the outstanding efforts of our reviewers. Thanks to the efforts of our reviewers in 2022, the median time to first decision was 20 days and the median time to publication was 44 days. Regardless of whether the articles they examined were ultimately published, the editors would like to express their appreciation and thank the following reviewers for the time and dedication that they have shown I Remo Footnotes 1 B Disclaimer/Publisher's Note: b The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). [Extracted from the article]

Knowledge of physical and mechanical properties of geomaterials is fundamental to characterise their response to external forcings (mechanical, climatic) at various scales. This is true, for instance, in slope stability assessments, civil engineering works, and agriculture. The direct evaluation of these properties in situ can be difficult, especially in inaccessible or vast areas, and so can be the sampling and subsequent testing in the laboratory—where ensuring the representativeness of the acquired data at the scale of analysis poses an additional challenge. Thus, empirical correlations with more readily determinable quantities remain a powerful and practical tool. Recently, several sensors, able to inform on various geomaterial properties, have been developed. However, applications have typically targeted rocks, while studies on uncemented geomaterials (soils, geotechnically speaking) are lacking. Here, we propose a simple method to evaluate the porosity and critical state friction angle of soils via infrared thermography, consisting of periodic acquisitions of images in infrared wavelengths. To demonstrate the method's capability, we analysed the cooling behaviour of samples of bentonite, kaolin, and sand (for which an extensive characterisation exists in the literature), after compaction to different porosities and pre-heating in an oven. We interpreted the results by seeking the optimal time interval for which a cooling rate index (CRI) could be defined, which is best linked with the target property. We found that the CRI correlates very well with the critical state friction angle (R2 > 0.85) and that different materials show unique and strong (R2 = 0.86–0.99) relationships between their porosity and the CRI, which also varies in a material-specific fashion according to the explored time interval. Although a systematic investigation on a wide range of natural soils is warranted, we argue that our method can be highly informative and could be used to calibrate remote sensing-based full-scale implementations in situ for various purposes. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In agronomy, soil organic carbon (SOC) content is important for the development and growth of crops. From an environmental monitoring viewpoint, SOC sequestration is essential for mitigating the emission of greenhouse gases into the atmosphere. SOC dynamics in cropland soils should be further studied through various approaches including remote sensing. In order to predict SOC content over croplands in southwestern France (area of 22,177 km²), this study addresses (i) the influence of the dates on which Sentinel-2 (S2) images were acquired in the springs of 2017–2018 as well as the influence of the soil sampling period of a set of samples collected between 2005 and 2018, (ii) the use of soil moisture products (SMPs) derived from Sentinel-1/2 satellites to analyze the influence of surface soil moisture on model performance when included as a covariate, and (iii) whether the spatial distribution of SOC as mapped using S2 is related to terrain-derived attributes. The influences of S2 image dates and soil sampling periods were analyzed for bare topsoil. The dates of the S2 images with the best performance (RPD ≥ 1.7) were 6 April and 26 May 2017, using soil samples collected between 2016 and 2018. The soil sampling dates were also analyzed using SMP values. Soil moisture values were extracted for each sample and integrated into partial least squares regression (PLSR) models. The use of soil moisture as a covariate had no effect on the prediction performance of the models; however, SMP values were used to select the driest dates, effectively mapping topsoil organic carbon. S2 was able to predict high SOC contents in the specific soil types located on the old terraces (mesas) shaped by rivers flowing from the southwestern Pyrénées. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

For food security issues or global climate change, there is a growing need for large-scale knowledge of soil organic carbon (SOC) contents in agricultural soils. To capture and quantify SOC contents at a field scale, Earth Observation (EO) can be a valuable data source for area-wide mapping. The extraction of exposed soils from EO data is challenging due to temporal or permanent vegetation cover, the influence of soil moisture or the condition of the soil surface. Compositing techniques of multitemporal satellite images provide an alternative to retrieve exposed soils and to produce a data source. The repeatable soil composites, containing averaged exposed soil areas over several years, are relatively independent from seasonal soil moisture and surface conditions and provide a new EO-based data source that can be used to estimate SOC contents over large geographical areas with a high spatial resolution. Here, we applied the Soil Composite Mapping Processor (SCMaP) to the Landsat archive between 1984 and 2014 of images covering Bavaria, Germany. Compared to existing SOC modeling approaches based on single scenes, the 30-year SCMaP soil reflectance composite (SRC) with a spatial resolution of 30 m is used. The SRC spectral information is correlated with point soil data using different machine learning algorithms to estimate the SOC contents in cropland topsoils of Bavaria. We developed a pre-processing technique to address the issue of combining point information with EO pixels for the purpose of modeling. We applied different modeling methods often used in EO soil studies to choose the best SOC prediction model. Based on the model accuracies and performances, the Random Forest (RF) showed the best capabilities to predict the SOC contents in Bavaria (R² = 0.67, RMSE = 1.24%, RPD = 1.77, CCC = 0.78). We further validated the model results with an independent dataset. The comparison between the measured and predicted SOC contents showed a mean difference of 0.11% SOC using the best RF model. The SCMaP SRC is a promising approach to predict the spatial SOC distribution over large geographical extents with a high spatial resolution (30 m). [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Assessing the spatial dynamics of soil organic carbon (SOC) is essential for carbon monitoring. Since variability of SOC is mainly attributed to biophysical land surface variables, integrating a compressive set of such indices may support the pursuit of an optimum set of predictor variables. Therefore, this study was aimed at predicting the spatial distribution of SOC in relation to remotely sensed variables and other covariates. Hence, the land surface variables were combined from remote sensing, topographic, and soil spectral sources. Moreover, the most influential variables for prediction were selected using the random forest (RF) and classification and regression tree (CART). The results indicated that the RF model has good prediction performance with corresponding R2 and root-mean-square error (RMSE) values of 0.96 and 0.91 mg·g−1, respectively. The distribution of SOC content showed variability across landforms (CV = 78.67%), land use (CV = 93%), and lithology (CV = 64.67%). Forestland had the highest SOC (13.60 mg·g−1) followed by agriculture (10.43 mg·g−1), urban (9.74 mg·g−1), and water body (4.55 mg·g−1) land uses. Furthermore, soils developed in bauxite and laterite lithology had the highest SOC content (14.69 mg·g−1). The SOC content was remarkably lower in soils developed in sandstones; however, the values obtained in soils from the rest of the lithologies could not be significantly differentiated. The mean SOC concentration was 11.70 mg·g−1, where the majority of soils in the study area were classified as highly humus and extremely humus. The soils with the highest SOC content (extremely humus) were distributed in the mountainous regions of the study area. The biophysical land surface indices, brightness removed vegetation indices, topographic indices, and soil spectral bands were the most influential predictors of SOC in the study area. The spatial variability of SOC may be influenced by landform, land use, and lithology of the study area. Remotely sensed predictors including land moisture, land surface temperature, and built-up indices added valuable information for the prediction of SOC. Hence, the land surface indices may provide new insights into SOC modeling in complex landscapes of warm subtropical urban regions. [ABSTRACT FROM AUTHOR]

As an important evaluation index of soil quality, soil organic carbon (SOC) plays an important role in soil health, ecological security, soil material cycle and global climate cycle. The use of multi-source remote sensing on soil organic carbon distribution has a certain auxiliary effect on the study of soil organic carbon storage and the regional ecological cycle. However, the study on SOC distribution in Ebinur Lake Basin in arid and semi-arid regions is limited to the mapping of measured data, and the soil mapping of SOC using remote sensing data needs to be studied. Whether different machine learning methods can improve prediction accuracy in mapping process is less studied in arid areas. Based on that, combined with the proposed problems, this study selected the typical area of the Ebinur Lake Basin in the arid region as the study area, took the sentinel data as the main data source, and used the Sentinel-1A (radar data), the Sentinel-2A and the Sentinel-3A (multispectral data), combined with 16 kinds of DEM derivatives and climate data (annual average temperature MAT, annual average precipitation MAP) as analysis. The five different types of data are reconstructed by spatial data and divided into four spatial resolutions (10, 100, 300, and 500 m). Seven models are constructed and predicted by machine learning methods RF and Cubist. The results show that the prediction accuracy of RF model is better than that of Cubist model, indicating that RF model is more suitable for small areas in arid areas. Among the three data sources, Sentinel-1A has the highest SOC prediction accuracy of 0.391 at 10 m resolution under the RF model. The results of the importance of environmental variables show that the importance of Flow Accumulation is higher in the RF model and the importance of SLOP in the DEM derivative is higher in the Cubist model. In the prediction results, SOC is mainly distributed in oasis and regions with more human activities, while SOC is less distributed in other regions. This study provides a certain reference value for the prediction of small-scale soil organic carbon spatial distribution by means of remote sensing and environmental factors. [ABSTRACT FROM AUTHOR]

The spatial assessment of soil organic carbon (SOC) is a major environmental challenge, notably for evaluating soil carbon stocks. Recent works have shown the capability of Sentinel-2 optical data to predict SOC content over temperate agroecosystems characterized by annual crops, using a single acquisition date. Considering a Sentinel-2 time series, this work intends to analyze the impact of acquisition date, and related weather and soil surface conditions on the prediction performance of topsoil SOC content (plough layer). A Sentinel-2 time-series was gathered, comprised of the dates corresponding to both the maximum of bare soil coverage and minimum of cloud coverage. Cross-validated partial least squares regression (PLSR) models were constructed between soil reflectance image spectra, and SOC content analyzed from 329 top soil samples collected over the study area. Cross-validation R2 ranged from 0.005 to 0.58, root mean square error from 5.86 to 3.02 g·kg−1 and residual prediction deviation values from 1.0 to 1.5 (without unit), according to date. The main factors influencing these differences were soil roughness, in conjunction with soil moisture, and the cloud and cloud shadow cover of the entire tile. The best performing dates were spring dates characterized by both lowest soil surface roughness and moisture content. Normalized difference vegetation index (NDVI) values below 0.35 did not influence prediction performance. This consolidates the previous results obtained during single date acquisitions and offers wider perspectives for the further use of Sentinel-2 into multidate mosaics for digital soil mapping. [ABSTRACT FROM AUTHOR]