In this chapter will focus on surface water – notably the water that is in lakes and reservoirs, rather than rivers and groundwater. This is the water that remains directly on land and represents a significant reservoir for the water cycle. The storage of such water drives many water management applications, as we shall see later, such as reservoir and flood management (chapter 8), irrigation (chapter 9). Here, we will overview the various remote sensing techniques that can be used to detect if a land is covered with water and if so, what is the extent. Later in the chapter we will learn how two successive satellite overpasses can help us estimate storage change a water body may have experience. This storage change can be a crucial component for various water management applications as it helps us understand how much water lakes or reservoirs are storing, losing (to diversion or evaporation) or releasing.

Chapter 11 highlights the need for ground control, such as GNSS survey points, to bring InSAR deformation measurements into a geodetic reference frame. It also explains the theory for projecting vector GNSS displacement into scalar line-of-sight (LOS) InSAR displacement and the computation of strain rate from InSAR.

In the previous chapters, we built the basic foundation of satellite remote sensing. In this chapter we will explore a relatively recent innovation in information technology called cloud computing that has dramatically improved data accessibility and the practicality of applying large satellite remote sensing datasets for water management. Future chapters on specific targets and water management themes will have hands-on examples and assignments based on actual satellite data. Most of these chapters will assume prior knowledge of cloud computing for understanding and completing assignments. Since cloud computing is gradually proliferating in all walks of water management practice, the aim of this chapter is to introduce readers to cloud computing concepts and specific tools currently available for dealing with the very large satellite data sets on water.

In the previous chapter, we introduced ourselves to the importance of satellite remote sensing for water management and why the technique is going to take greater importance in years to come as challenges mount from climate change, competing needs and lack of ground data. In this chapter, we will overview the basics of remote sensing, define key concepts and terms. Using these concepts and terms, we will develop an understanding of the fundamental principle required for the success of remote sensing.

Chapter 7 introduces the basic concepts and fundamental limitations (i.e., residues) of phase unwrapping. It presents three common unwrapping methods: the global Fourier transform method, the path-following branch-cut method, and the minimum cost flow method. Additionally, it covers methods for correcting integer ambiguities using phase closure within stacks of interferograms.

Earlier in Chapter 2, we had formalized our approach to remote sensing in the form of ‘target’, background and foreground. Now the first thing we need to focus on is the electromagnetic behavior of the target. This is best captured by the term ‘Black Body’. From here on, we will try to think of the target in water management relative to a black body and understand how much a black body it is under certain circumstances.

Chapter 1 discusses six types of remote sensing methods possible from Earth’s orbit and introduces radar interferometry as the optimal approach for measuring small surface deformation.

This is the first chapter of the book. The goal of this chapter is to introduce ourselves to the growing importance of using satellite remote sensing to manage our water. We will try to understand this in the context of the underlying challenges and new global forces shaping up this century that are expected to make traditional ways of managing water using in-situ data more challenging.

Chapter 2 explains the basic physics of radar imaging from orbital altitude, including the limits on accuracy, spatial resolution in the range and azimuth directions, and the fundamental limitation on swath width.

Chapter 6 discusses the main methods for filtering complex interferograms, computing coherence, and computing phase gradient.

Chapter 10 presents complementary approaches to measuring surface deformation by radar, including pixel offset tracking, multiple aperture interferometry, and burst overlap interferometry. The second part of the chapter discusses methods for extracting surface velocity and time series from a large set of interferograms, as well as identifying pixels that remain stable over long periods.

The total 2pN net shifts per orbit and the orbital precessions are calculated as the sum of two contributions: the direct ones due to the 2pN acceleration and the mixed, or indirect, ones caused by the 1pN instantaneous shifts during the orbital revolution. A comparison with other approaches existing in the literature is made.