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Table of contents
1. Introduction
1.1 Background and Motivation
1.2 Dissertation objectives
1.3 Dissertation outline
2. Soil moisture and its importance/measurements
2.1 Soil moisture and its importance
2.1.1 Soil moisture
2.1.2 General importance of soil moisture for the environment and our climate system
2.2 Measurements of soil moisture
2.2.1 In-situ measurements
2.2.2 Remote sensing of soil moisture
2.2.2.1 Optical remote sensing (Visible and near-infrared)
2.2.2.2 Thermal Infrared remote sensing
2.2.2.3 Microwave remote sensing
2.2.3 Soil moisture modelling
2.2.3.1 MERRA-Land
2.2.4 Soil moisture data assimilation
2.2.4.1 SM-DAS-2
3. SMOS/ASCAT/AMSR-E Mission overview
3.1 SMOS
3.1.1 SMOS mission overview
3.1.2 SMOS products overview
3.1.3 SMOS SSM algorithm
3.1.3.1 Input datasets
3.1.3.2 The SMOSL2 algorithm
3.1.3.3 The SMOSL3 SSM algorithm
3.1.4 SMOS RFI issues
3.2 ASCAT
3.2.1 ASCAT mission overview
3.2.2 ASCAT SSM algorithm
3.2.3 ASCAT Products
3.3 AMSR-E
3.3.1 AMSR-E mission overview
3.3.2 AMSR-E VU-NASA algorithm:
3.4 Pre-Processing
4. Global-scale evaluation of two satellite-based passive microwave soil moisture datasets (SMOS and AMSR-E) with respect to Land Data Assimilation System estimates
4.1 Introduction
4.2 Materials and methods
4.2.1 Global-scale soil moisture datasets
4.2.1.1 SMOSL3
4.2.1.2 AMSRM
4.2.1.3 ECMWF soil moisture analysis
4.2.2 Pre-processing
4.2.3 Comparison metrics
4.2.4 Regional-scale analyses
4.2.5 SSM seasonal anomalies
4.2.6 Global-scale analyses
4.3 Results
4.3.1 Comparison of SMOSL3 ascending and descending overpasses
4.3.2 Comparison of the SSM time series over eight selected sites
4.3.3 Spatial analysis of SSM retrievals at global scale
4.3.4 Biome influence
4.3.5 Influence of leaf area index (LAI)
4.4 Discussion and conclusions
5. Global-scale comparison of passive (SMOS) and active (ASCAT) satellite based microwave soil moisture retrievals with soil moisture simulations (MERRA-Land)
5.1 Introduction
5.2 Materials and methods
5.2.1 Surface soil moisture datasets
5.2.1.1 SMOSL3
5.2.1.2 ASCAT
5.2.1.3 MERRA-Land
5.2.2 Pre-processing
5.2.3 Comparison using classical metrics
5.2.4 Comparison using Hovmöller diagrams (space–time distribution)
5.2.5 Comparison using triple collocation error model
5.3 Results
5.3.1 Spatial Analysis of SSM retrievals at the global scale
5.3.2 Influence of leaf area index (LAI)
5.3.3 Hovmöller diagrams
5.3.4 Triple collocation error model
5.4 Discussion and conclusions
5.4.1 Summary of the results
5.4.2 Discussion
6. Testing simple regression equations to derive long-term global soil moisture datasets from satellite-based brightness temperature observations
6.1 Introduction
6.2 Materials and methods
6.2.1 Datasets
6.2.1.1 AMSR-E Level 3 brightness temperatures
6.2.1.2 SMOS level 3 soil moisture products
6.2.1.3 ECMWF Soil temperature
6.2.1.4 MODIS NDVI
6.2.2 Methods
6.2.2.1 Regression calibration
6.2.2.2 Producing SSM data
6.3 Results and discussion
6.3.1 Regression calibration
6.3.2 Regression’s quality and new AMSR-E SSM products
6.3.3 Product comparison with original AMSR-E SSM product
6.3.4 Product evaluation against a reference (MERRA-Land)
6.4 Summary and conclusions
7. Conclusions and perspectives
7.1 Summary
7.2 Main conclusions
7.3 Limitations
7.4 Perspectives
References
