Advanced methods for earth observation data synergy for geophysical parameter retrieval
The first part of the thesis focuses on the analysis of relevant factors to estimate the response time between satellite-based and in-situ soil moisture (SM) using a Dynamic Time Warping (DTW). DTW was applied to the SMOS L4 SM, and was compared to in-situ root-zone SM in the REMEDHUS network in Wes...
Main Author: | |
---|---|
Other Authors: | , , |
Format: | Doctoral or Postdoctoral Thesis |
Language: | English |
Published: |
Universitat Politècnica de Catalunya
2022
|
Subjects: | |
Online Access: | http://hdl.handle.net/2117/376085 http://hdl.handle.net/10803/675942 https://doi.org/10.5821/dissertation-2117-376085 |
id |
ftupcatalunyair:oai:upcommons.upc.edu:2117/376085 |
---|---|
record_format |
openpolar |
institution |
Open Polar |
collection |
Universitat Politècnica de Catalunya, BarcelonaTech: UPCommons - Global access to UPC knowledge |
op_collection_id |
ftupcatalunyair |
language |
English |
topic |
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació |
spellingShingle |
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació Herbert, Christoph Josef Advanced methods for earth observation data synergy for geophysical parameter retrieval |
topic_facet |
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació |
description |
The first part of the thesis focuses on the analysis of relevant factors to estimate the response time between satellite-based and in-situ soil moisture (SM) using a Dynamic Time Warping (DTW). DTW was applied to the SMOS L4 SM, and was compared to in-situ root-zone SM in the REMEDHUS network in Western Spain. The method was customized to control the evolution of time lag during wetting and drying conditions. Climate factors in combination with crop growing seasons were studied to reveal SM-related processes. The heterogeneity of land use was analyzed using high-resolution images of NDVI from Sentinel-2 to provide information about the level of spatial representativity of SMOS data to each in-situ station. The comparison of long-term precipitation records and potential evapotranspiration allowed estimation of SM seasons describing different SM conditions depending on climate and soil properties. The second part of the thesis focuses on data-driven methods for sea ice segmentation and parameter retrieval. A Bayesian framework is employed to segment sets of multi-source satellite data. The Bayesian unsupervised learning algorithm allows to investigate the ‘hidden link’ between multiple data. The statistical properties are accounted for by a Gaussian Mixture Model, and the spatial interactions are reflected using Hidden Markov Random Fields. The algorithm segments spatial data into a number of classes, which are represented as a latent field in physical space and as clusters in feature space. In a first application, a two-step probabilistic approach based on Expectation-Maximization and the Bayesian segmentation algorithm was used to segment SAR images to discriminate surface water from sea ice types. Information on surface roughness is contained in the radar backscattering images which can be - in principle - used to detect melt ponds and to estimate high-resolution sea ice concentration (SIC). In a second study, the algorithm was applied to multi-incidence angle TB data from the SMOS L1C product to harness the ... |
author2 |
Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions Vall-Llossera Ferran, Mercedes Magdalena Camps Carmona, Adriano José |
format |
Doctoral or Postdoctoral Thesis |
author |
Herbert, Christoph Josef |
author_facet |
Herbert, Christoph Josef |
author_sort |
Herbert, Christoph Josef |
title |
Advanced methods for earth observation data synergy for geophysical parameter retrieval |
title_short |
Advanced methods for earth observation data synergy for geophysical parameter retrieval |
title_full |
Advanced methods for earth observation data synergy for geophysical parameter retrieval |
title_fullStr |
Advanced methods for earth observation data synergy for geophysical parameter retrieval |
title_full_unstemmed |
Advanced methods for earth observation data synergy for geophysical parameter retrieval |
title_sort |
advanced methods for earth observation data synergy for geophysical parameter retrieval |
publisher |
Universitat Politècnica de Catalunya |
publishDate |
2022 |
url |
http://hdl.handle.net/2117/376085 http://hdl.handle.net/10803/675942 https://doi.org/10.5821/dissertation-2117-376085 |
genre |
Sea ice |
genre_facet |
Sea ice |
op_source |
TDX (Tesis Doctorals en Xarxa) |
op_relation |
Herbert, C.J. Advanced methods for earth observation data synergy for geophysical parameter retrieval. Tesi doctoral, UPC, Departament de Teoria del Senyal i Comunicacions, 2022. DOI 10.5821/dissertation-2117-376085 . http://hdl.handle.net/2117/376085 doi:10.5821/dissertation-2117-376085 http://hdl.handle.net/10803/675942 |
op_rights |
ADVERTIMENT. Tots els drets reservats. L'accés als continguts d'aquesta tesi doctoral i la seva utilització ha de respectar els drets de la persona autora. Pot ser utilitzada per a consulta o estudi personal, així com en activitats o materials d'investigació i docència en els termes establerts a l'art. 32 del Text Refós de la Llei de Propietat Intel·lectual (RDL 1/1996). Per altres utilitzacions es requereix l'autorització prèvia i expressa de la persona autora. En qualsevol cas, en la utilització dels seus continguts caldrà indicar de forma clara el nom i cognoms de la persona autora i el títol de la tesi doctoral. No s'autoritza la seva reproducció o altres formes d'explotació efectuades amb finalitats de lucre ni la seva comunicació pública des d'un lloc aliè al servei TDX. Tampoc s'autoritza la presentació del seu contingut en una finestra o marc aliè a TDX (framing). Aquesta reserva de drets afecta tant als continguts de la tesi com als seus resums i índexs. Open Access |
op_doi |
https://doi.org/10.5821/dissertation-2117-376085 |
_version_ |
1810478132599193600 |
spelling |
ftupcatalunyair:oai:upcommons.upc.edu:2117/376085 2024-09-15T18:35:11+00:00 Advanced methods for earth observation data synergy for geophysical parameter retrieval Herbert, Christoph Josef Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions Vall-Llossera Ferran, Mercedes Magdalena Camps Carmona, Adriano José 2022-07-15 241 p. application/pdf http://hdl.handle.net/2117/376085 http://hdl.handle.net/10803/675942 https://doi.org/10.5821/dissertation-2117-376085 eng eng Universitat Politècnica de Catalunya Herbert, C.J. Advanced methods for earth observation data synergy for geophysical parameter retrieval. Tesi doctoral, UPC, Departament de Teoria del Senyal i Comunicacions, 2022. DOI 10.5821/dissertation-2117-376085 . http://hdl.handle.net/2117/376085 doi:10.5821/dissertation-2117-376085 http://hdl.handle.net/10803/675942 ADVERTIMENT. Tots els drets reservats. L'accés als continguts d'aquesta tesi doctoral i la seva utilització ha de respectar els drets de la persona autora. Pot ser utilitzada per a consulta o estudi personal, així com en activitats o materials d'investigació i docència en els termes establerts a l'art. 32 del Text Refós de la Llei de Propietat Intel·lectual (RDL 1/1996). Per altres utilitzacions es requereix l'autorització prèvia i expressa de la persona autora. En qualsevol cas, en la utilització dels seus continguts caldrà indicar de forma clara el nom i cognoms de la persona autora i el títol de la tesi doctoral. No s'autoritza la seva reproducció o altres formes d'explotació efectuades amb finalitats de lucre ni la seva comunicació pública des d'un lloc aliè al servei TDX. Tampoc s'autoritza la presentació del seu contingut en una finestra o marc aliè a TDX (framing). Aquesta reserva de drets afecta tant als continguts de la tesi com als seus resums i índexs. Open Access TDX (Tesis Doctorals en Xarxa) Àrees temàtiques de la UPC::Enginyeria de la telecomunicació Doctoral thesis 2022 ftupcatalunyair https://doi.org/10.5821/dissertation-2117-376085 2024-07-25T11:16:38Z The first part of the thesis focuses on the analysis of relevant factors to estimate the response time between satellite-based and in-situ soil moisture (SM) using a Dynamic Time Warping (DTW). DTW was applied to the SMOS L4 SM, and was compared to in-situ root-zone SM in the REMEDHUS network in Western Spain. The method was customized to control the evolution of time lag during wetting and drying conditions. Climate factors in combination with crop growing seasons were studied to reveal SM-related processes. The heterogeneity of land use was analyzed using high-resolution images of NDVI from Sentinel-2 to provide information about the level of spatial representativity of SMOS data to each in-situ station. The comparison of long-term precipitation records and potential evapotranspiration allowed estimation of SM seasons describing different SM conditions depending on climate and soil properties. The second part of the thesis focuses on data-driven methods for sea ice segmentation and parameter retrieval. A Bayesian framework is employed to segment sets of multi-source satellite data. The Bayesian unsupervised learning algorithm allows to investigate the ‘hidden link’ between multiple data. The statistical properties are accounted for by a Gaussian Mixture Model, and the spatial interactions are reflected using Hidden Markov Random Fields. The algorithm segments spatial data into a number of classes, which are represented as a latent field in physical space and as clusters in feature space. In a first application, a two-step probabilistic approach based on Expectation-Maximization and the Bayesian segmentation algorithm was used to segment SAR images to discriminate surface water from sea ice types. Information on surface roughness is contained in the radar backscattering images which can be - in principle - used to detect melt ponds and to estimate high-resolution sea ice concentration (SIC). In a second study, the algorithm was applied to multi-incidence angle TB data from the SMOS L1C product to harness the ... Doctoral or Postdoctoral Thesis Sea ice Universitat Politècnica de Catalunya, BarcelonaTech: UPCommons - Global access to UPC knowledge |