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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]

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]

Increasing attention has been placed on the agroecological impact of applying exogenous organic matter (EOM) amendments, such as green waste compost (GWC) and livestock manure, to agricultural landscapes. However, monitoring the frequency and locality of this practice poses a major challenge, as these events are typically unreported. The purpose of this study is to evaluate the utility of Sentinel-2 imagery for the detection of EOM amendments. Specifically, we investigated the spectral shift resulting from GWC and manure application at two spatial scales, satellite and proximal. At the satellite scale, multispectral Sentinel-2 image pairs were analyzed before and after EOM application to six cultivated fields in the Versailles Plain, France. At the proximal scale, multi-temporal spectral field measurements were taken of experimental plots consisting of 14 total treatments: EOM variety, amendment quantity (15, 30 and 60 t.ha−1) and tillage. The Sentinel-2 images showed significant spectral differences before and after EOM application. Exogenous Organic Matter Indices (EOMI) were developed and analyzed for separative performance. The best performing index was EOMI2, using the B4 and B12 Sentinel-2 spectral bands. At the proximal scale, simulated Sentinel-2 reflectance spectra, which were created using field measurements, successfully monitored all EOM treatments for three days, except for the buried green waste compost at a rate of 15 t.ha−1. [ABSTRACT FROM AUTHOR]

Introduction of temporary grasslands into cropping cycles could be a sustainable management practice leading to increased soil organic carbon (SOC) to contribute to climate change adaption and mitigation. To investigate the impact of temporary grassland management practices on SOC storage of croplands, we used a spatially resolved sampling approach combined with geostatistical analyses across an agricultural experiment. The experiment included blocks (0.4- to 3-ha blocks) of continuous grassland, continuous cropping and temporary grasslands with different durations and N-fertilizations on a 23-ha site in western France. We measured changes in SOC storage over this 9-year experiment on loamy soil and investigated physicochemical soil parameters. In the soil profiles (0–90 cm), SOC stocks ranged from 82.7 to 98.5 t ha−1 in 2005 and from 81.3 to 103.9 t ha−1 in 2014. On 0.4-ha blocks, the continuous grassland increased SOC in the soil profile with highest gains in the first 30 cm, while losses were recorded under continuous cropping. Where temporary grasslands were introduced into cropping cycles, SOC stocks were maintained. These observations were only partly confirmed when changing the scale of observation to 3-ha blocks. At the 3-ha scale, most grassland treatments exhibited both gains and losses of SOC, which could be partly related to soil physicochemical properties. Overall, our data suggest that both management practices and soil characteristics determine if carbon will accumulate in SOC pools. For detailed understanding of SOC changes, a combination of measurements at different scales is necessary. [ABSTRACT FROM AUTHOR]

Remotely-sensed vegetation phenology is used here to identify key stages of annual cropdevelopment and, in this basis, as an indicator of annual crop type. Crop monitoring requires both high spatial resolution data (HSR) for observing withinsub-parcel scale, and high temporal resolution (HTR) to monitor vegetation changes duringalong the crop cycle. However, these simultaneous requirements are difficult to fulfill by thesame satellite. In temperate areas such as the Versailles Plain, near Paris, France, HRS datahave at best a dozen images exploitable per year, even with Sentinel-2 because of cloud cover,while those with medium spatial resolution (MRS) provide daily images, but at the generallymixed pixel scale. In France, the Land Parcel Identification System (LPIS) is an information system of thecrop types declared by farmers, providing reference information about the annual cropscultivated within each agricultural parcel. In this work, the objective was to monitor thephenology of annual crops recorded in the LPIS of 2016, using satellite image time seriesfrom HRS Sentinel-2 (10m) and MRS Proba-V (100m) acquired from early to end of 2016over the Versailles Plain, a small agricultural region (221 km2) cultivated with annualcrops. From the two types of time series, the temporal variations of vegetation indices (NDVI/ EVI2) of crops were extracted in order to analyze the crop seasonal variationsof winter wheat, winter oilseed rape and maize over 2857 parcels with averagesize of 6.88 ha. The linear method of spatial disaggregation was applied on theMRS data, using fractions of each crop type in the mixed pixels calculated from the2016-LPIS. The temporal responses from HRS data were compared with those of the MRS sub-pixels.Comparisons between both time series revealed significant correlations for thethree studied crops (winter wheat = 0.94, winter oilseed rape = 0.74 and maize =0.79). By improving the temporal frequency of the monitoring, from13 imagesfor HRS to 25 images for MRS, the disaggregated MRS time series enabled todistinguish the phenological stages of the three studied crops better than the HRS timeseries. In conclusion, our method of spatial disaggregation can be used to improve theexploitation of satellite data at MRS in seasonal crop monitoring, especially during thetransition periods when the spectral indices of crops are likely to change quickly. [ABSTRACT FROM AUTHOR]