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.

Table of Contents

Front Matter



List of Tables

List of Figures

Chapter 1 Introduction

1.1 The Water Cycle

1.2 Remote Sensing of the Water Cycle

1.3 Literature Review

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

2.2 Precipitation

2.3 Evapotranspiration

2.4 Total Water Storage Change

2.5 Runoff and River Discharge

2.6 Environmental Indices

2.7 Preliminary Analysis

Chapter 3 Balancing the Water Budget with Earth Observations

3.1 River Basin Delineation

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

3.7 Simple Weighting

3.8 Post Filtering

3.9 Optimal Interpolation Results

3.10 Chapter 3 Conclusions and Discussion

Chapter 4 Modeling Approaches to Close the Water Budget

4.1 Assessing Model Fit

4.2 Model Selection and the Bias-Variance Tradeoff

4.3 Regression Modeling Methods

4.4 Neural Network Modeling

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

Chapter 7 Conclusion

7.1 Summary and Significance of Findings

7.2 Perspectives


Appendix Earth Observation Datasets of the Water Cycle