Development and Update Process of VNIR-Based Models Built to Predict Soil Organic Carbon

The large number of samples, time, and cost to assess soil organic C (SOC) with standard procedures has led to the interest in proximal sensing with visible and near-infrared (VNIR) diffuse reflectance spectroscopy. The objectives of the present study were to (i) evaluate the effect of multivariate techniques and spectra preprocessing methods on the performance of VNIR-based models, (ii) evaluate the effect of subsetting datasets to improve the prediction accuracy of models, and (iii) present a systematic iterative model development and update process. There were three datasets: Dataset-1 was used to the initial model development; Dataset-2 was used to revalidate models developed with Dataset-1; Dataset-3 was used to update promising models identified with Dataset-1 and -2. During initial model development with Dataset-1, the dataset was subset in clusters to try to improve model performance. Subsetting datasets did not improve model performance. Revalidating models with Dataset-2 helped to identify the lack of robustness in the initial models. This is related to the increased sample diversity in Dataset-2 compared to Dataset-1 and highlights the importance of continuously updating models to cover more variability. Based on Dataset-1 and 2, promising models were updated with the larger and more diverse Dataset-3. Following this update, the best model had a coefficient of multiple determination (R²), root mean squared prediction error (RMSPE), and residual prediction deviation (RPD) of 0.95, 2.062, and 4.39%, respectively. Collecting and evaluating data in separate sets allowed models to be revalidated and updated with new independent samples. This continuous process provides robust models to end users.