Improving soil organic carbon predictions from a Sentinel-2 soil composite by assessing surface conditions and uncertainties
Improving soil organic carbon predictions from a Sentinel-2 soil composite by assessing surface conditions and uncertainties
Samenvatting
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).
Organisatie | Aeres Hogeschool |
Lectoraat | Duurzaam Bodembeheer (Aeres) |
Gepubliceerd in | Geoderma Vol. 429, Pagina: 1166128 |
Jaar | 2023 |
Type | Artikel |
ISSN | 1872-6259 |
DOI | 10.1016/j.geoderma.2022.116128 |
Taal | Engels |