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| Home > Publications database > Spatio-Temporal Estimation and Validation of Remotely Sensed Vegetation and Hydrological Fluxes in the Rur Catchment, Germany > print |
| 001 | 843656 | ||
| 005 | 20220930130140.0 | ||
| 020 | _ | _ | |a 978-3-95806-287-0 |
| 024 | 7 | _ | |2 Handle |a 2128/17571 |
| 024 | 7 | _ | |2 URN |a urn:nbn:de:0001-2018030704 |
| 024 | 7 | _ | |2 ISSN |a 1866-1793 |
| 037 | _ | _ | |a FZJ-2018-01225 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |0 P:(DE-HGF)0 |a Ali, Muhammad |b 0 |e Corresponding author |g male |u fzj |
| 245 | _ | _ | |a Spatio-Temporal Estimation and Validation of Remotely Sensed Vegetation and Hydrological Fluxes in the Rur Catchment, Germany |f - 2017-12-04 |
| 260 | _ | _ | |a Jülich |b Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag |c 2018 |
| 300 | _ | _ | |a 116 S. |
| 336 | 7 | _ | |2 DataCite |a Output Types/Dissertation |
| 336 | 7 | _ | |0 PUB:(DE-HGF)3 |2 PUB:(DE-HGF) |a Book |m book |
| 336 | 7 | _ | |2 ORCID |a DISSERTATION |
| 336 | 7 | _ | |2 BibTeX |a PHDTHESIS |
| 336 | 7 | _ | |0 2 |2 EndNote |a Thesis |
| 336 | 7 | _ | |0 PUB:(DE-HGF)11 |2 PUB:(DE-HGF) |a Dissertation / PhD Thesis |b phd |m phd |s 1520329802_21023 |
| 336 | 7 | _ | |2 DRIVER |a doctoralThesis |
| 490 | 0 | _ | |a Schriften des Forschungszentrums Jülich Reihe Energie & Umwelt / Energy & Environment |v 403 |
| 502 | _ | _ | |a Universität Bonn, Diss., 2017 |b Dissertation |c Universität Bonn |d 2017 |
| 520 | _ | _ | |a Operational availability of spatio-temporal vegetation and hydrological estimates are becoming increasingly attractive for hydrologic studies from local through regional and global scales, especially in remote areas and ungauged basins. More advancement and versatility in satellite-based remotely sensed methods towards consistent and timely information for monitoring regional scale vegetation and hydrological fluxes may lead to efficient and unprecedented planning and management of agricultural practices and water resources. This thesis develops and analyses remote sensing methods for regional scale vegetation and land surface water fluxes estimation. Results from this study are validated at various test sites in the Rur catchment, Germany. These sites are equipped with sophisticated and state-of-the-art instruments for monitoring vegetation and hydrological fluxes. Second chapter in this thesis explains a direct retrieval method and validation of the Leaf Area Index (LAI) from time-series of multispectral RapidEye images. LAI, quantifying the amount of leaf material, considered as an important variable for numerous processes in hydrological studies that link vegetation to climate. $\textit{In situ}$ LAI measuring methods have the limitation of being labor intensive and site specific. Remote sensing LAI (LAI$_{rapideye}$) were derived using different vegetation indices, namely SAVI (Soil Adjusted Vegetation Index) and NDVI (Normalized Difference Vegetation Index). Additionally, applicability of the newly available red-edge band (RE) was also analyzed through Normalized Difference Red-Edge index (NDRE) and Soil Adjusted Red-Edge index (SARE). The LAI$_{rapideye}$ obtained from vegetation indices with red-edge band showed better correlation with destructive LAI$_{destr}$ (r = 0.88 and Root Mean Square Deviation, RMSD = 1.01 & 0.92) than LAI from vegetation indices without red-edge band. This study also investigated the need to apply relative and absolute atmospheric correction methods to the time-series of RapidEye Level 3A data prior to LAI estimation. Analysis of the RapidEye data set showed that application of the atmospheric corrections did not improve correlation of the estimated LAI with in situ LAI, because RapidEye Level 3A data are provided with simplified atmospheric corrections and the vegetation indices used for LAI retrieval ware already normalized. Third chapter investigates estimation of spatio-temporal latent heat using an energy balance approach and simplified regression between calculated latent heat (from energy balance) and downward shortwave radiation data from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard Meteosat Second Generation (MSG) Satellites. Mapping the spatio-temporal [...] |
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