This is the online version of my PhD thesis. A PDF version is here (292 pages, 43 MB). Suggested citation:
Heberger, Matthew. 2024. Improved observation of the global water cycle with satellite remote sensing and neural network modeling. PhD thesis. Sorbonne University, Paris, France.
1.2 Remote Sensing of the Water Cycle
1.4 Research Questions and Objectives
1.5 Organization of this Thesis
Chapter 2 Earth Observation Datasets
2.1 Earth Observations of Water Cycle Components
2.4 Total Water Storage Change
2.5 Runoff and River Discharge
Chapter 3 Balancing the Water Budget with Earth Observations
3.2 Pre-Processing of Total Water Storage Data
3.3 Upscaling of Gridded EO data
3.4 Calculating Basin Means for EO variables
3.5 Preliminary Analysis of the Water Cycle Imbalance
3.6 Combining Multiple Estimates of Water Cycle Components
3.9 Optimal Interpolation Results
3.10 Chapter 3 Conclusions and Discussion
Chapter 4 Modeling Approaches to Close the Water Budget
4.2 Model Selection and the Bias-Variance Tradeoff
4.3 Regression Modeling Methods
Chapter 5 Results of Modeling to Balance the Water Budget
5.1 Regression Model Development
5.2 Neural Network Model Development
5.3 Calibration of EO Variables at the Pixel Scale
5.4 Comparison of the Two Modeling Methods
5.5 Chapter 5 Conclusions and Discussion
Chapter 6 Evaluation and Exploitation of the Calibrated EO Database
6.1 Indirect Estimation of Evapotranspiration
6.2 Indirect Estimation of Total Water Storage Change
6.3 Indirect Estimation of Runoff
6.4 Chapter 6 Conclusions and Discussion