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- W4382866892 abstract "This work explores the use of multitemporal hyperspectral PRISMA data to estimate clay, sand, organic carbon (SOC) and calcium carbonates. A pixel-wise approach was carried out obtaining composite bare soil data from: (1) median spectra; (2) spectra at the minimum value of the soil surface moisture index S2WI; (3) at the maximum value of the bare soil index; (4) the 90th quantile; and (5) the 10th quantile. These data were used for the calibration of machine learning regression algorithms. The minimum of the S2WI was the extraction method, and support vector regression was the algorithm, in the best performing calibrated models. Clay was estimated with the best performance, with a RMSE of 5.25% and an R2 of 0.89. SOC provided good results, with a RMSE of 0.84 g/kg and R2 of 0.86." @default.
- W4382866892 created "2023-07-02" @default.
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- W4382866892 date "2023-07-02" @default.
- W4382866892 modified "2023-10-14" @default.
- W4382866892 title "105. Estimation of agronomic soil properties from multitemporal PRISMA satellite imaging spectroscopy" @default.
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- W4382866892 doi "https://doi.org/10.3920/978-90-8686-947-3_105" @default.
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