Thomas Scholten
Orcid: 0000-0002-4875-2602
According to our database1,
Thomas Scholten
authored at least 20 papers
between 2020 and 2024.
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Bibliography
2024
Assessment of Land Suitability Potential Using Ensemble Approaches of Advanced Multi-Criteria Decision Models and Machine Learning for Wheat Cultivation.
Remote. Sens., July, 2024
Random Forest-Based Soil Moisture Estimation Using Sentinel-2, Landsat-8/9, and UAV-Based Hyperspectral Data.
Remote. Sens., June, 2024
Uncertainty Quantification of Soil Organic Carbon Estimation from Remote Sensing Data with Conformal Prediction.
Remote. Sens., February, 2024
SSL-SoilNet: A Hybrid Transformer-Based Framework With Self-Supervised Learning for Large-Scale Soil Organic Carbon Prediction.
IEEE Trans. Geosci. Remote. Sens., 2024
Advanced Prediction of Soil Organic Carbon: A Hybrid Transformer Network with Cost-Sensitive Learning Using Remote Sensing and Climate Data.
Proceedings of the IGARSS 2024, 2024
2023
Remote. Sens., February, 2023
SoilNet: An Attention-based Spatio-temporal Deep Learning Framework for Soil Organic Carbon Prediction with Digital Soil Mapping in Europe.
CoRR, 2023
2022
A Comparison of Model Averaging Techniques to Predict the Spatial Distribution of Soil Properties.
Remote. Sens., 2022
Monitoring and Integrating the Changes in Vegetated Areas with the Rate of Groundwater Use in Arid Regions.
Remote. Sens., 2022
2021
Spatio-Temporal Analysis of Heavy Metals in Arid Soils at the Catchment Scale Using Digital Soil Assessment and a Random Forest Model.
Remote. Sens., 2021
Bio-Inspired Hybridization of Artificial Neural Networks: An Application for Mapping the Spatial Distribution of Soil Texture Fractions.
Remote. Sens., 2021
Remote. Sens., 2021
Comparative Analysis of TMPA and IMERG Precipitation Datasets in the Arid Environment of El-Qaa Plain, Sinai.
Remote. Sens., 2021
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2021, 2021
2020
Improving the Spatial Prediction of Soil Organic Carbon Content in Two Contrasting Climatic Regions by Stacking Machine Learning Models and Rescanning Covariate Space.
Remote. Sens., 2020
Predicting and Mapping of Soil Organic Carbon Using Machine Learning Algorithms in Northern Iran.
Remote. Sens., 2020
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2020, 2020
A Distributed Neural Network Architecture for Robust Non-Linear Spatio-Temporal Prediction.
Proceedings of the 28th European Symposium on Artificial Neural Networks, 2020