soil-spectroscopy

R package: Misc. Functions for Processing and Sample Selection of Spectroscopic Data

resemble is an R package for similarity-based modelling and local learning in spectroscopy. It provides tools for dissimilarity computation, nearest-neighbour search, memory-based learning, and spectral library optimisation (methods designed for large, heterogeneous spectral datasets where global models underperform)

R scripts for predicting soil organic carbon using soil spectral library from visible, near-infrared and shortwave-infrared (VNIR) and middle-infrared (MIR) using LASSO and PLS regression methods and the target-oriented cross-validation strategy.

A Python package for handling soil spectroscopy data, with a focus on the Open Soil Spectral Library (OSSL).

Prediction of Exchangeable Potassium in Soil through Mid-Infrared Spectroscopy and Deep Learning: from Prediction to Explainability, Albinet et al., 2022

Soil VIS-NIR reflectance spectra simulation based on generative model. PROSAIL model is integrated..

Functions to analyse mid infrared spectra of peat samples

Provides Scikit-Learn compatible transforms for spectroscopic data preprocessing.

Comparing different data preprocessing methods to predict soil organic carbon content on soil spectra features

R implementation of a Vis-NIR soil spectroscopy workflow for predicting soil properties using Principal Component Regression (PCR), Partial Least Squares Regression (PLSR), Cubist, Random Forest (RF), Support Vector Regression (SVM), Memory-Based Learning (MBL), Artificial Neural Networks (ANN) and Extreme Gradient Boosting (XGBoost) algorithms

Code and precomputed results for "Unconditional Flow Matching With Classifier-Based Pruning for Distribution-Aligned Soil Spectral Synthesis" (IEEE GRSL 2026). Generates synthetic LUCAS 2015 topsoil data with flow matching, pruned by a classifier to align distributions.

Generative deep learning (cWGAN-GP) for vis-NIR soil spectral library augmentation. MSc thesis code.