"Uncertainty propagation in VNIR reflectance spectroscopy soil organic carbon mapping"@en . "SI" . . "Jak\u0161\u00EDk, Ond\u0159ej" . . . "199" . "41210" . "Visible and near infrared (VNIR) diffuse reflectance spectroscopy (DRS) offers high potential as a fast and accurate proximal soil sensing technique for soil carbon estimation. The objective of this study is to evaluate the use of VNIR soil spectroscopy for mapping soil organic carbon (SOC) spatial distribution on a 100 ha arable field strongly affected by erosion. The analysis was performed in two main steps: firstly, we focused on the uncertainty in the VNIR spectroscopy regression model (PLSR) under varying number and locations of training samples from which an optimal number of input samples were selected; secondly, we analysed uncertainty propagation in the coupled PLSR and spatial prediction for the selected optimal number of training samples. The PLSR quality parameters are changing exponentially with increasing number of input training samples. The PLSR model constructed using only 37 samples provided a good predictive capability with R2 over 0.7 and RPD over 1.5. The uncertainty of"@en . "Klement, Ale\u0161" . . . . . . "NL - Nizozemsko" . . . "000317882900008" . . "Uncertainty propagation in VNIR reflectance spectroscopy soil organic carbon mapping" . . . "10"^^ . . . . . . . "Soil organic carbon, VNIR spectroscopy, Partial least squares regression, Digital soil mapping, Uncertainty"@en . "Va\u0161\u00E1t, Radim" . . "5"^^ . "0016-7061" . "RIV/60460709:41210/13:60047" . . "5"^^ . "P(GA526/08/0434), P(GA526/09/1762), Z(MSM6046070901)" . "Geoderma" . . "Brodsk\u00FD, Luk\u00E1\u0161" . . "Uncertainty propagation in VNIR reflectance spectroscopy soil organic carbon mapping" . "Z\u00E1dorov\u00E1, Tereza" . "RIV/60460709:41210/13:60047!RIV14-MSM-41210___" . . "Uncertainty propagation in VNIR reflectance spectroscopy soil organic carbon mapping"@en . . "Visible and near infrared (VNIR) diffuse reflectance spectroscopy (DRS) offers high potential as a fast and accurate proximal soil sensing technique for soil carbon estimation. The objective of this study is to evaluate the use of VNIR soil spectroscopy for mapping soil organic carbon (SOC) spatial distribution on a 100 ha arable field strongly affected by erosion. The analysis was performed in two main steps: firstly, we focused on the uncertainty in the VNIR spectroscopy regression model (PLSR) under varying number and locations of training samples from which an optimal number of input samples were selected; secondly, we analysed uncertainty propagation in the coupled PLSR and spatial prediction for the selected optimal number of training samples. The PLSR quality parameters are changing exponentially with increasing number of input training samples. The PLSR model constructed using only 37 samples provided a good predictive capability with R2 over 0.7 and RPD over 1.5. The uncertainty of" . "[9482BB69C0A8]" . "112556" .