Collection of articles (ordered by date) which address the issue of spatial autocorrelation in environmental modeling, possibly by making use of spatial paritioning methods.

2021

• Wadoux et al. 2021 - Spatial cross-validation is not the right way to evaluate map accuracy
• Karasiak et al. 2021 - Spatial dependence between training and test sets: another pitfall of classification accuracy assessment in remote sensing

2020

• Ploton et al. 2020 - Spatial validation reveals poor predictive performance of large-scale ecological mapping models
• Rocha et al. 2019 - Spatially-explicit modelling with support of hyperspectral data can improve prediction of plant traits

2019

• Meyer et al. 2019 - Importance of spatial predictor variable selection in machine learning applications – Moving from data reproduction to spatial prediction
• Rocha et al. 2019 - Spatially-explicit modelling with support of hyperspectral data can improve prediction of plant traits
• Schratz et al. 2019 - Hyperparameter tuning and performance assessment of statistical and machine-learning algorithms using spatial data
• Valavi et al. 2019 - blockCV: An r package for generating spatially or environmentally separated folds for kfold cross-validation of species distribution models
• Karasiak et al. 2019 - Statistical Stability and Spatial Instability in Mapping Forest Tree Species by Comparing 9 Years of Satellite Image Time Series

2018

• Meyer et al. 2018 - Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation
• Lyons et al. 2018 - A comparison of resampling methods for remote sensing classification and accuracy assessment

2017 and older

• Geiß et al. 2017 - On the Effect of Spatially Non-Disjoint Training and Test Samples on Estimated Model Generalization Capabilities in Supervised Classification With Spatial Features