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.

Various stakeholders, such as modelers, policy makers, farmers, and environmental regulators need reliable soil bulk density and coarse fragment content data. These two soil parameters are necessary to calculate soil carbon and nutrients stocks, to estimate water availability for plants, or to assess soil compaction. However, measuring these two parameters is labor intensive and time consuming. Therefore, many agricultural and environmental studies often miss these two soil parameters. Here, we provide four datasets, one with bulk density and coarse fragment contents of topsoil and subsoil, measured in two campaigns of the French Soil Quality Monitoring Network (RMQS for its acronym in French), a second one with the average values for bulk density and coarse fragments of the two campaigns at 0–30 cm and 30–50 cm. The third and the fourth ones are the raw data needed to calculate the two first datasets divided by campaign. In addition, the R script for calculating the depth-weighted values per soil layer is provided