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Estimating soil organic carbon of sown biodiverse permanent pastures in Portugal using near infrared spectral data and artificial neural networks
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Orientador(es)
Resumo(s)
Grasslands in Portugal are key managed ecosystems, supporting and providing a diverse number of ecosystem
services. Here, we developed a procedure for rapid estimation of soil organic carbon (SOC) in soil samples of
sown biodiverse permanent pastures rich in legumes (SBP) in Portugal. We combined laboratory NIR spectral
data analysis with artificial neural networks (ANN) to estimate the SOC content of SBP soil samples. To train and
test the ANN, we used more than 340 soil samples collected in the 0–20 cm topsoil layer from three farms in 2018
and 2019 and two other farms in 2019 only. The number of bands of the spectra (800–2778 nm) was reduced
using two different approaches: (a) aggregation to Sentinel-2 (S2) bands using the average reflectance within
each bandwidths; and (b) principal component analysis (PCA). For the S2 approach, we considered the six S2
bands that overlap with the spectral range of the instrument used. For the PCA approach, we considered the five
first principal components. Additional covariates were used for prediction, including weather and terrain attributes,
e.g. accumulated precipitation, average temperature, elevation, and slope. To test for transferability of
the models to different farms, we used an eight-fold leave-one-out cross-validation approach to calculate estimation
errors. Each fold is a unique combination of farm and year and is used to assess the model’s performance
calibrated from the seven other folds. The ANN was able to estimate both low and high SOC contents without
systematic errors and with similar estimation errors for both full and reduced spectral data approaches. The
average root mean squared error (RMSE) for the S2 approach was 1.95 g kg 1 (0.45 – 2.33 g kg 1 depending on
the hold-out fold) and for the PCA approach was 1.81 g kg 1 (0.74 – 2.42 g kg 1) (compared to the average SOC
content of 12 g kg 1). These RMSE values were similar to the RMSE obtained using the full spectra, suggesting
that the original spectral resolution could be reduced without losing information. These results suggest the
potential for using remotely sensed data to estimate the variation of SOC content for SBP. They are a first step
towards developing algorithms that can alleviate the cost and time of soil sampling and chemical SOC laboratory
analysis through indirect estimation
Descrição
Palavras-chave
soil organic matter grassland spectroscopy machine learning Sentinel-2
Contexto Educativo
Citação
Geoderma 404 (2021) 115387
Editora
Elsevier