Abstract
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.
Highlights
Soils contain the largest terrestrial pool of organic carbon [1,2,3]
The performance of soil organic carbon (SOC) content predictions were first analyzed according to acquisition date, in relation to possible disturbing factors: acquisition condition (§3.2); the presence of vegetation not sufficiently discarded, according to the Normalized difference vegetation index (NDVI) cut-off (§3.3); surface roughness (§3.4); and topsoil moisture (§3.5)
Performance of SOC content predictions varied according to date, being intermediate (RPD ≥ 1.4) for three spring dates only, and for none of the autumn-winter dates (Table 4)
Summary
Soils contain the largest terrestrial pool of organic carbon [1,2,3]. A relatively small increase in the soil organic carbon (SOC) stocks could, play an important role in limiting the net flux of greenhouse gases towards the atmosphere and mitigating climate change. Because soil reflectance in visible near infrared and short wave infrared (Vis-NIR-SWIR, 0.4–2.5 μm) is strongly influenced by SOC and some other soil compounds, such as clay minerals, calcium carbonate and iron oxides, empirical spectral models relating soil reflectance spectra to spectrally-influent properties have been successfully built; e.g., [6,7]. This was mostly done under controlled lab conditions (e.g., [6]) and over limited spatial coverages or soil sampling densities. Hyperspectral satellite HYPERION has a low signal-to-noise ratio, while multispectral SPOT satellite sensors have low spectral diversity and resolution, limiting prediction performances
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