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ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume X-4/W1-2022
https://doi.org/10.5194/isprs-annals-X-4-W1-2022-691-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/isprs-annals-X-4-W1-2022-691-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
14 Jan 2023
| 14 Jan 2023
DECISION-BASED FUSION OF PANSHARPENED VHR SATELLITE IMAGES USING TWO-LEVEL ROLLING SELF-GUIDANCE FILTERING AND EDGE INFORMATION
F. Samadzadegan
School of Surveying and Geospatial Engineering, University College of Engineering, University of Tehran, Tehran, Iran
A. Toosi
School of Surveying and Geospatial Engineering, University College of Engineering, University of Tehran, Tehran, Iran
F. Dadrass Javan
School of Surveying and Geospatial Engineering, University College of Engineering, University of Tehran, Tehran, Iran
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, The Netherlands
A. Stein
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, The Netherlands
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Manuscript not accepted for further review
Short summary
Short summary
Weather stations are often sparse and systematic under/overestimation of a global weather forecast system leads to bias. Most of the available bias correction methods do not consider higher order moments of a probability distribution and they use same distributions families to estimate both marginal and multivariate distributions. We propose three new copula-based bias correction methods, which describe the dependence structure between air temperature and covariates.
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H. Fazeli, F. Samadzadegan, and F. Dadrasjavan
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Rahul Raj, Nicholas Alexander Samuel Hamm, Christiaan van der Tol, and Alfred Stein
Biogeosciences, 13, 1409–1422, https://doi.org/10.5194/bg-13-1409-2016, https://doi.org/10.5194/bg-13-1409-2016, 2016
Short summary
Short summary
We present a Bayesian estimation of uncertainty in half-hourly GPP partitioned from flux tower measurements of NEE. The results show that it is possible to do this at any desirable time step. This, in turn, can be used to quantify the propagated uncertainty when validating process-based simulators. We further show the importance of using non-informative priors compared to informative priors of the parameters of flux partitioning model as they speed up calculation without loss of precision.
The International Society for Photogrammetry and Remote Sensing is a non-governmental organization devoted to the development of international cooperation for the advancement of photogrammetry and remote sensing and their applications. The Society operates without any discrimination on grounds of race, religion, nationality, or political philosophy.
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