Zobrazeno 1 - 10
of 76
pro vyhledávání: '"Thorsten Behrens"'
Publikováno v:
Remote Sensing, Vol 16, Iss 15, p 2712 (2024)
Soils play a central role in ecosystem functioning, and thus, mapped soil property information is indispensable to supporting sustainable land management. Digital Soil Mapping (DSM) provides a framework to spatially estimate soil properties. However,
Externí odkaz:
https://doaj.org/article/54565562246a45cf8318e2dfe85f4ac9
Autor:
Tobias Rentschler, Martin Bartelheim, Thorsten Behrens, Marta Díaz-Zorita Bonilla, Sandra Teuber, Thomas Scholten, Karsten Schmidt
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-11 (2022)
Abstract Multi-scale contextual modelling is an important toolset for environmental mapping. It accounts for spatial dependence by using covariates on multiple spatial scales and incorporates spatial context and structural dependence to environmental
Externí odkaz:
https://doaj.org/article/3e263a605d0d4a44bb018dab7a21e66a
Autor:
Tobias Rentschler, Ulrike Werban, Mario Ahner, Thorsten Behrens, Philipp Gries, Thomas Scholten, Sandra Teuber, Karsten Schmidt
Publikováno v:
Vadose Zone Journal, Vol 19, Iss 1, Pp n/a-n/a (2020)
Abstract Soil organic C (SOC) and soil moisture (SM) affect the agricultural productivity of soils. For sustainable food production, knowledge of the horizontal as well as vertical variability of SOC and SM at field scale is crucial. Machine learning
Externí odkaz:
https://doaj.org/article/1710d72746c6419f89d9a855386e3545
Autor:
Tobias Rentschler, Philipp Gries, Thorsten Behrens, Helge Bruelheide, Peter Kühn, Steffen Seitz, Xuezheng Shi, Stefan Trogisch, Thomas Scholten, Karsten Schmidt
Publikováno v:
PLoS ONE, Vol 14, Iss 8, p e0220881 (2019)
As limited resources, soils are the largest terrestrial sinks of organic carbon. In this respect, 3D modelling of soil organic carbon (SOC) offers substantial improvements in the understanding and assessment of the spatial distribution of SOC stocks.
Externí odkaz:
https://doaj.org/article/56dfd7b21e134b87ac38f43bf539e9c6
Autor:
Ruhollah Taghizadeh-Mehrjardi, Karsten Schmidt, Alireza Amirian-Chakan, Tobias Rentschler, Mojtaba Zeraatpisheh, Fereydoon Sarmadian, Roozbeh Valavi, Naser Davatgar, Thorsten Behrens, Thomas Scholten
Publikováno v:
Remote Sensing, Vol 12, Iss 7, p 1095 (2020)
Understanding the spatial distribution of soil organic carbon (SOC) content over different climatic regions will enhance our knowledge of carbon gains and losses due to climatic change. However, little is known about the SOC content in the contrastin
Externí odkaz:
https://doaj.org/article/85115f9d0d1e46b2afcc312307bd6cec
Autor:
Zefang Shen, Leonardo Ramirez-Lopez, Thorsten Behrens, Lei Cui, Mingxi Zhang, Lewis Walden, Johanna Wetterlind, Zhou Shi, Kenneth A Sudduth, Philipp Baumann, Yongze Song, Kevin Catambay, Raphael A. Viscarra Rossel
Publikováno v:
ISPRS Journal of Photogrammetry and Remote Sensing. 188:190-200
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7e7d99b3d7f1e0969b72ed94aebce382
https://doi.org/10.1016/j.isprsjprs.2022.04.009
https://doi.org/10.1016/j.isprsjprs.2022.04.009
Publikováno v:
Waldböden – intakt und funktional.
Flächenhafte Bodeninformationen sind sowohl für die Bestimmung und Beurteilung der Multifunktionalität der Böden als auch für die vielfältigen Bedürfnisse verschiedener Nutzergruppen unabdingbar. In der Schweiz liegen diese bislang nur für we
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ba9026bea4428291006a8811ae50cc80
https://doi.org/10.55419/wsl:32071
https://doi.org/10.55419/wsl:32071
Autor:
Raphael A. Viscarra Rossel, Thorsten Behrens, Eyal Ben‐Dor, Sabine Chabrillat, José Alexandre Melo Demattê, Yufeng Ge, Cecile Gomez, César Guerrero, Yi Peng, Leonardo Ramirez‐Lopez, Zhou Shi, Bo Stenberg, Richard Webster, Leigh Winowiecki, Zefang Shen
Publikováno v:
European Journal of Soil Science
European Journal of Soil Science, 2022, 73 (4), ⟨10.1111/ejss.13271⟩
Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual)
Universidade de São Paulo (USP)
instacron:USP
European Journal of Soil Science, 2022, 73 (4), ⟨10.1111/ejss.13271⟩
Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual)
Universidade de São Paulo (USP)
instacron:USP
International audience; Spectroscopic measurements of soil samples are reliable because they are highly repeatable and reproducible. They characterise the samples' mineral-organic composition. Estimates of concentrations of soil constituents are inev
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::121ae6d79e6b533467e21ae21f3ad030
https://doi.org/10.1111/ejss.13271
https://doi.org/10.1111/ejss.13271
Autor:
Philipp Baumann, Juhwan Lee, Thorsten Behrens, Asim Biswas, Johan Six, Gordon McLachlan, Raphael A. Viscarra Rossel
Publikováno v:
European Journal of Soil Science, 73 (2)
We need measurements of soil water retention (SWR) and available water capacity (AWC) to assess and model soil functions, but methods are time-consuming and expensive. Our aim here was to investigate the modelling of AWC and SWR with visible–near-i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8442360e060a7ce302a76abdfc7b246a
https://doi.org/10.1111/ejss.13220
https://doi.org/10.1111/ejss.13220
Autor:
Thomas Scholten, Mohammad Jamshidi, Kamran Eftekhari, Ruhollah Taghizadeh-Mehrjardi, Thorsten Behrens, Norair Toomanian, Naser Davatgar, Karsten Schmidt
Publikováno v:
European Journal of Soil Science. 71:352-368
Most common machine learning (ML) algorithms usually work well on balanced training sets, that is, datasets in which all classes are approximately represented equally. Otherwise, the accuracy estimates may be unreliable and classes with only a few va
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::42701a23877206403c3117c8b2aeb82a
https://doi.org/10.1111/ejss.12893
https://doi.org/10.1111/ejss.12893