Soil organic carbon mapping utilizing convolutional neural networks and Earth observation data, a case study in Bavaria state Germany-Reference-Cited by-同舟云学术

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Soil organic carbon mapping utilizing convolutional neural networks and Earth observation data, a case study in Bavaria state Germany

Published:2024-04 Issue: Volume:444 Page:116867
ISSN:0016-7061
Container-title:Geoderma
language:en
Short-container-title:Geoderma

Author:

Tziolas Nikolaos^ORCID,Tsakiridis Nikolaos^ORCID,Heiden Uta^ORCID,van Wesemael Bas^ORCID

Funder

European Space Agency

Publisher

Elsevier BV

Reference66 articles.

1. Incorporating environmental variables, remote and proximal sensing data for digital soil mapping of USDA soil great groups;Asgari;Int. J. Remote Sens.,2020

2. Integration of Sentinel-1/2 and topographic attributes to predict the spatial distribution of soil texture fractions in some agricultural soils of western Iran;Azizi;Soil Tillage Res.,2023

3. A physically based, variable contributing area model of basin hydrology / Un modèle à base physique de zone d’appel variable de l’hydrologie du bassin versant;Beven;Hydrol. Sci. Bull.,1979

4. Random forests;Breiman;Mach. Learn.,2001

5. logD7.4 modeling using Bayesian regularized neural networks. Assessment and correction of the errors of prediction;Bruneau;J. Chem. Inf. Model.,2006

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. SSL-SoilNet: A Hybrid Transformer-Based Framework With Self-Supervised Learning for Large-Scale Soil Organic Carbon Prediction;IEEE Transactions on Geoscience and Remote Sensing;2024

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