1 Introduction

Gert A. Schultz l and Edwin T. Engman2

IRuhr University Bochum, 44780 Bochum, Germany 2Hydrological Science Branch, Code 974, Laboratory for Hydrospheric Processes­NASA/Goddard Space Flight Center, Greenbelt, MD 29771, USA

1.1 Introduction

The Nilometer in Cairo may be considered as an ancient device of remote sensing, since it measures - for millennia - the water level of the river Nile not in the river itself, but rather in a historic tower building of very fine architecture near the river Nile. The type of remote sensing discussed in this book is, however, of a rather different type: It deals with techniques and methodologies the electronic age can offer to hydrologists and water managers. At present a rather small international community of hydrological scientists have developed - and are still developing -methods for application of remote sensing information to the solution of hydro­logical and water management problems. Although many of these techniques are already far advanced - several of them are operational - unfortunately many prac­titioners responsible for hydrological networks a..'ld water resources development are still reluctant to use these methods. This situation is due to several reasons, e.g. unavailability of relevant hardware and software, lack of knowledge in the appli­cation of remote sensing techniques, reluctance to change conventional and well established methods etc. It is the aim of this book to overcome these barriers and show to practitioners the potential of remote sensing and, hopefully convince them of the advantages of these techniques for their future work. They will soon see, that several of their problems which could not be treated at all so far will become tractable and other problems dealt with at present with difficulty may be solved in a much more elegant way.

1.2 Remote Sensing Defined

The literature provides many definitions of remote sensing and it may suffice here to cite only one (Ritchie and Rango, 1996), "Remote sensing has been defined as the science and art of obtaining information about an object, area, or phenomenon through the analyses of data acquired by a sensor that is not in direct contact with the target of investigation". It must be recognized at the outset that remote sensing data are different from traditional hydrologic data. Compared to conventional hydrological measurements remote sensing has certain significant advantages, but also some disadvantages. One of the disadvantages of remote sensing is the fact,

G. A. Schultz et al. (eds.), Remote Sensing in Hydrology and Water Management© Springer-Verlag Berlin Heidelberg 2000

4 G.A. Schultz and E.T. Engman

that hydrological parameters are almost never measured directly. RS means al­ways the acquisition of data from the electromagnetic spectrum. This implies the necessity, that, in order to use RS data for hydrology or water management, the RS data have to be transformed into hydrologically relevant information. This requires the development and application of certain methodology and algorithms suitable for the purpose. In many cases we are limited to inferring the hydrologic information.

Remote sensing uses measurements of the electromagnetic spectrum to charac­terize the landscape, or infer properties of it, or in some cases, actually measure hydrologic state variables. Aerial photography in the visible wavelengths is the remote sensing technique that most hydrologists are familiar with; however, mod­em remote sensing is centered around satellite systems and most of the discussions will emphasize satellite data. Over the years remote sensing techniques have ex­panded to the point that they now include most of the electromagnetic spectrum. Different sensors can provide unique information about properties of the surface or shallow layers of the Earth. For example, measurements of the reflected solar radiation give information on albedo, thermal sensors measure surface tempera­ture, and microwave sensors measure the dielectric properties and hence, the moisture content, of surface soil or of snow. Remote sensing and its continued development has added new techniques that hydrologist can use in a large number of applications.

Because remote sensing data are different from traditional hydrologic data, the hydrologist must recognize what these differences are and take advantage of their strengths and not be discouraged by their weeknesses. For example, we usually deal with a finite resolution element known as a PIXEL whose basic dimensions may vary from a few meters to kilometers. Obviously one looses some detail compared to point samples. This can perhaps best be explained by considering a very typical remote sensing application: measuring various classes of land use (i.e., pasture, forests, urban, etc.). In many cases one will have a pixel (say 100 m square) that will not be pure forest or pure pasture if it straddles the boundary. One has to classify it as either forest or pasture, in either case it will be technically incorrect. In remote sensing applications, one seldom duplicates detailed land use statistics exactly. For example, a study by the Corps of Engineers (Rango et aI., 1983) estimated that an individual pixel may be incorrectly classified about one­third of the time. However, by aggregating land use over a significant area, the misclassification of land use can be reduced to about two percent which is too small to affect a hydrologic application such as computing the runoff coefficient and the resulting flood statistics.

1.3 The Nature of Remote Sensing Data

When considering how remote sensing data may be used in hydrology and water management, it is necessary to consider the characteristics of remote sensing data and how these may be used to improve our understanding and operational tech­niques. There are four characteristics of remote sensing data that make it a poten-

1 Introduction 5

tially very powerful tool for advancing hydrologic sciences. Each of these charac­teristics are discussed below:

Measuring System States. Thermal infrared and microwave remote sensing, because of their unique responses to surface properties important to hydrology, such as surface temperature, soil moisture and snow water content, have the capa­bility to measure these system states directly. However, using system-state data will require new models to incorporate the new data types. Such models would structurally resemble contemporary simulation models but would be more capable of accounting for spatial variability and changes. Also, the subprocess algorithms (infiltration, evapotranspiration, etc.) would be designed to use remote sensing data as well as the more traditional inputs.

Area versus Point Data. The use of data representing an area in which the spatial variability of specific parameters of the area have been integrated may help pro­vide one of the keys to understanding scaling and scale interdependence in hy­drologic systems. The capability to aggregate up in scale or dis aggregate down in scale by electronic means may provide a perspective of scaling that may instill new insight to answering the scale questions that dominate scientific hydrology.

Temporal Data. Remote sensing data from a satellite platform can provide unique time series data for hydrologic use. The actual frequency of observation can vary from continuous to once every two weeks or so, depending upon the sensors and type of orbit. This approach is appealing because it may be a very cost-effective method to monitor various hydrologic states over very large areas as well as monitor the dynamic properties in hydrology. Temporal data may provide a means for imparting a hydrologic interpretation to certain observations. For example, observing the time changes in soil moisture may provide information on soil types and even hydraulic properties such as hydraulic conductivity. In fact the interpre­tation of soil properties as a remote sensing signature could be extremely useful for hydrology because it would represent an areal value rather than a point value determined in a laboratory or with a field measurement.

New Data Forms. Entirely new data types may be formed by merging several data sets of different wavelengths, polarizations, look angles, etc. to provide en­tirely new hydrologic parameters that are developed from the unique characteris­tics of remote sensing. New data forms could also be considered to be combina­tions of remote sensing data combined with other spatial data (such as soil maps) and even point data through a data assimilation scheme or sophisticated GIS (Geographical Information System).

Weather radar is a good example of a new remote sensing data form that com­bines an areal signature and a temporal signature. The weather radars produce a nearly continuous picture of the space-time changes of rainfall rates over the ra­dar's operational area.

These and other ideas need to be explored through research that combines re­mote sensing and hydrologic modeling. Each presents a unique opportunity for hydrologists and water managers to apply remote sensing in ways other than sim-

6 G.A. Schultz and E.T. Engman

pIe extensions of photogrammetry. Remote sensing can produce an integrated measurement that is simultaneously observing several factors. It is also giving us a view that is uncommon to our past thinking in that it looks at a relatively large area and somehow integrates information from the entire scene. A great deal of research is needed to learn how to properly interpret the complex response ob­tained from the various remote sensing instruments. To use these data effectively, we also must develop new concepts and change our historical way of conceptual­izing hydrologic processes.

1.4 Satellite Systems

This book addresses the subject mostly from the perspective of satellite sensors because these observations are almost universally available worldwide. Although the choice of satellite orbit and design are beyond the scope of this book, a prac­ticing hydrologist should understand the basics of sensors and orbits of the major satellite systems because they can influence the choice of data. The existing satel­lite systems provide very good coverage of the Earth and give the hydrologist a number of options for satisfying data needs. The choice of which satellite system to use depends upon the requirements for the data, which translate into the need for specific spectral bands, spatial requirements, temporal coverage, and the pos­sible need for stereo coverage, all of which are related to the satellite platform. Each of these is discussed more thoroughly below:

1.4.1 Remote Sensing Platforms

The choice of the remote sensing platform is also important to the hydrologist. Platforms include ground based (usually truck or tower mounted), aircraft and the space shuttle, in addition to the satellite systems.

Generally truck mounted and ground based systems are used for sensor devel­opment, investigating sensor-target interactions, and algorithm development. These systems enable one to control very precisely what the sensor is "seeing". Figure 1.1 is an example of truck mounted instruments being used for soil mois­ture experiments over a controlled target.

The aircraft and space shuttle provide an intermediate step before going to a sat­ellite for further instrument and algorithm validation. Aircraft, however, also pro­vide a very useful platform for coverage of relatively small areas and nonrepetitive missions such as aerial photography, multispectral and thermal imaging missions and side-looking airborne radar surveys. Figure 1.2 is the NASA C-130 that has been used for many aircraft campaigns. This aircraft is essentially a flying labo­ratory and is designed to collect data from multiple instruments at any time.

The Space Shuttle (Fig. 1.3) is frequently used as a space borne platform for proof of concept and testing of new instruments. The Shuttle Imaging Radar (SIR­C) is a good example of this. SIR-C was a 1994 experiment with a two frequency Synthetic Aperture Radar (SAR) for measuring a number of Earth science char­acteristics, including snow (see Chap. 11) and soil moisture (see Chap. 9).

1 Introduction 7

Fig. 1.1. A cherry picker boom truck being used to make controlled measurements over a known target. There are two passive microwave radiometers (L and C band) mounted on the instrument package at the end of the boom. (Courtesy of Peggy O'Neill, NASA-GSFC)

Fig. 1.2. The NASA C-130 is a typical aircraft platform that has been used in many hydrologic remote sensing campaigns

8 G.A. Schultz and E.T. Engman

Fig. 1.3. The Space Shuttle Discovery during a launch of a multi-day science mission

Satellites are an ideal platform (Fig. 1.4) for remote sensing because they can provide essentially global coverage if they are polar-orbiting or continuous cover­age if they are geostationary (Fig. 1.5). Polar-orbiting satellites generally fly in a low Earth orbit (hundreds of km) and provide relatively high resolution measure­ments with repeat times of days to tens of days. Typical polar orbiting satellites are the NOAA-A VHRR (Advanced Very High Resolution Radiometer), the French SPOT (Systeme probatoire d'Observation de la Terre), and the u.S Land­sat and TM (Thematic Mapper) series.

Geostationary satellites orbit the earth with the earth 's rotation so that they ob­serve the same point on the Earth continuously, but from a much higher altitude approaching 36,000 km. Geostationary satellites are the primary meteorological observation platforms and provide continuous but somewhat coarser spatial data. The European community'S Meteosat, the U.S. GOES series, and the Japanese GMS are typical of the geostationary satellites.

There are also satellites that are neither polar orbiting nor geosynchronous. In these cases the orbit path has been chosen to meet a specific science requirememt. The Tropical Rainfall Mapping Mission (TRMM) is a good example of this. TRMM's orbital path takes it to + and - 35 degrees above and below the equator to improve the sampling frequency of tropical rainfall.

10 G.A. Schultz and E.T. Engman

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Fig. 1.6. A schematic diagram of the electromagnetic spectrum illustrating the various regions of the spectrum (i.e., visible, near lR, etc.), the wavelength and frequency. The figure also illus­trates the relative transmission of regions of the spectrum through the atmosphere

Earth targets for the visible and near infrared portion of the spectrum. Notice how one can separate a chosen land feature from other land features by choice of the wavelength. For example, water has a very low reflectance at a wavelength of 1.1 )lm (less than 10%) compared to that of vegetation (about 50%).

In many other cases data from the thermal infrared bands are of high interest, particularly since the thermal infrared data is a measure of the surface temperature and can also be obtained during the night. Microwave data (active and passive) are of particular relevance for certain hydrological variables such as soil moisture and precipitation, and because they can be obtained during the night and are not re­stricted to cloud free conditions. Table 1.1 lists some representative applications for different spectral bands available from existing satellites.

The application that the hydrologist or water resource manager is addressing will dictate the region of the spectrum that will provide the proper information and thus guide the selection of the sensor(s).

1.4.3 Spatial Resolution

A hydrologist also has to define the spatial resolution needed. This choice depends to a great deal on the nature of his problem and the details needed in his model. In some cases for very large basins, one would not need or want high resolution data. The spatial resolution varies very much from sensor to sensor. In general, the satellites in higher orbits are not able to provide high spatial resolution data, if all other aspects are equal. However, we will see that this is very much dependent upon what parts of the spectrum are used. There are satellites providing data with

1 Introduction 9

Fig. 1.4. An artists rendition of TERRA, the EOS-AM satellite scheduled to be launched in the second half of 1999

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Geostationary Orbil

Fig. 1.5. A schematic illustrating common orbits used for Earth remote sensing

1.4.2 Remote Sensing Sensors

Just about all regions of the electromagnetic spectrum (Fig. 1.6) are used in re­mote sensing. RS data are acquired in predetermined spectral bands (wave lengths). Visible and near infrared spectral bands (which can be displayed as col­ors) are chosen to amplify or separate specific earth features such as vegetation and water. Figure 1.7 illustrates the relative response (reflectance) of common

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1 Introduction 11

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a high resolution of e.g. 10 m (SPOT satellite) and others having a rather coarse resolution of 5 kIn (e.g. geostationary satellites like Meteosat, GOES, GMS) or even up to 25 kIn (e.g. satellite sensors in the passive microwave channel).

Table 1.1. Remote sensing Applications for Different spectral bands

S eelral band m -------B lue (0.45 - 0.50) Green (0.50 - 0.60) Red (0.60 - 0.70

Panch romatic (0.50 -0.75) Reflective Infrared (0.75 - 0.90)

Mid-infrared (1.5 -. 1.75) Mid-infrared (2.0 - 2.35) Thermal infrared (10 -12.5) Microwave - Short wave (0.1 - 5 cm) Microwave-Long wave (5 - 24 cm)

A lications Water penetrat ion, land use, vegetation characterist ics, sediment Green reflectance of heal thy vegetation Vegetation di scrimination because of red chl orophyll absorption

Mapping. land use, stereo pairs Biomass, crop identification. soil-crop, land-water boundaries

Plant turgid ity, droughts, clouds, snow-i ce discriminat ion Geology, rock formations Relative temperature, thennal discharges, vegetation classifica­tion, moi sture stud ies, thennal inert ia Snow cover, depth, vegetation water content

Melti ng snOI , soil moisture, water- land boundaries, penetrate vegetation

12 G.A. Schultz and E.T. Engman

1.4.4 Temporal Resolution

RS data are acquired with a given resolution in time. Also here the resolution varies very much from sensor to sensor and satellite system. Ground-based weather radar data can be acquired every 5 minutes, geostationary satellites pro­vide data every half hour and some polar orbiting satellites provide data as seldom as every 16 days (e.g. Landsat) or longer with some of the narrow swath SARs. The hydrologist considering use of remote sensing data has to choose data that match the needs of his analysis. In some cases of dynamic processes and small basins, the data may be needed daily of more often. In other cases for less dy­namic processes and large basins, data on two week or longer may be satisfactory. An example of this might be snow melt runoff prediction in a large drainage basin. Then again, there are some needs that have little or no temporal criteria. Examples of this would be delineating stream channels where maps do not exist or land use which changes very slowly.

Appendix 20.1 lists the currently available and future satellite systems that are of interest to hydrologists and water managers. Appendix 20.2 is a similar table that lists the specific sensor and orbit characteristics that are of great interest to hydrologists and water managers.

1.5 Remote Sensing and Hydrology

It should be kept in mind, that RS data are not only used for monitoring of hydro­logical state variables, but also as the basis for parameter estimation of hydrologi­cal models. Remote sensing, particularly from various satellites in various spectral bands, can provide information on catchment characteristics (e.g. landcover, lan­duse, slope, vegetation), from which the parameters of hydrological models can be gathered. Particularly in combination with other spatial information, such as digi­tal elevation models, digital terrain models, digital soil maps. RS will allow the spatial estimation of hydrological model parameters, e.g. the maximum soil water storage capacity in a river basin.

Another important facet of remote sensing is the fact, that such data can be ac­quired in remote areas, where no measurements are feasible or can be carried out only under very difficult circumstances which cause high costs. For these meas­urements particularly airplanes and satellites are suitable. Furthermore satellite remote sensing allows coverage of the whole globe, which is highly relevant in the development of global coupled atmospheric-hydrological models for weather and flood forecasting as well as for long-term analysis and forecast of climate condi­tions. This property makes RS data particularly valuable for all activities within the framework of the world climate research program (e;g. GEWEX, CLIV AR, ACSYS etc.).

Looking at the historical development of hydrological modeling one comes eas­ily to the conclusion, that all hydrological models are data limited. Models are generally not built in the way which would be scientifically most sound, but rather according to data availability. A significant example of this deficiency of existing models is the fact, that rainfall-runoff models as well as water balance models are

1 Introduction 13

all calibrated with the aid of observed data, e.g. rainfall, runoff, evaporation etc. The hydrological variable having the most significant influence on hydrological processes is, however, the state of soil moisture in space and time. Soil moisture determines, how much water goes up from an area element (evaporation, transpi­ration, interception), how much goes down (infiltration, percolation) and how much water moves laterally (surface runoff, interflow, groundwater flow). Due to the limited data situation in hydrology, this most important parameter soil mois­ture is almost never measured in the field. Therefore it occurs in hydrological models in the form of a residual. This fact is certainly one of the reasons, why the performance of so many hydrological models is unsatisfactory. It can be hoped, that in the near future remote sensing will allow to measure soil moisture with an acceptable resolution in time and space and with an appropriate accuracy. If this becomes operational, the time has arrived for the development of completely new - and hopefully better - hydrological models, in which soil moisture becomes the central parameter instead of a residual and the information required for model calibration and validation will come from remote sensing sources. It can also be expected, that in many other fields of hydrological modeling the existing or ex­pected availability of remote sensing information will lead to the development of much more efficient hydrological models.

1.6 Structure of the Book

It is surprising that there are very few books or compendiums that cover the com­plete subject of remote sensing in hydrology and water management, where as there are many books on hydrology and water resources management. The number dealing with hydrology can be easily counted on one hand, Wiesnet et aI, 1979, Schultz and Barrett, 1989, Engman and Gurney, 1991, Rango, 1994, and a soon to be published compendium by Rango and Shalaby. All except the Engman and Gurney book have been sponsored by International organizations such as the UN and WMO. Only the Schultz and Barrett publication addressed water resources management. There have been many advances both in instrumentation and appli­cations since the last of these have been published and there is an urgent need to provide an updated source to the hydrology and water management communities.

Since this book is not meant to be a textbook with problem and examples, but rather a reference book in which information on any aspect of interest can easily be found, the book is sub-divided into four general sections, each of which is again sub-divided into various chapters. The sections are as follows:

Section I: Overview and Basis Principles Section II: RS Application to Hydrological Monitoring and Modeling Section III: Water Management with the Aid ofRS Data Section IV: Future Perspective.

The first section provides general principles and techniques which should be un­derstood in order to recognize what remote sensing can - and cannot - deliver. Section II is devoted to hydrology, while Section III deals with water management problems. Section IV gives some information on potential future developments in

14 G.A. Schultz and E.T. Engman

the field of remote sensing and the applications of those new data in the fields of hydrology and water management.

Section II on hydrology deals with the various fields of hydrology, in which re­mote sensing can be applied. These fields are discussed in the chapters on pre­cipitation, landuse and catchment characteristics, evapotranspiration, soil mois­ture, surface water, snow and ice, soil erosion, water quality, groundwater. In Section III on water management the following major problems are discussed in different chapters: flood forecasting and control, irrigation and drainage, hydro­logical data in ungauged river basins and detection of landuse changes and their effect on water management.

References

Engman, E.T., and R.1. Gurney, 1991, Remote Sensing in Hydrology, Chapman and Hall, Lon­don, 225pp.

Rango, A., A. Feldman, T.S. George, III, and R.M. Ragan, 1983. Effective use of Landsat data in hydrologic models. Water Resour. Bull. 19, 165-174

Rango, A. 1994. Applications of remote sensing by satellite, radar, and other methods to hydrol­ogy. World Meteorological Organization, WMO-No. 804, Operational Hydrology Report No. 39, Geneva Switzerland, 33pp.

Ritchie, J.C. and Rango, A., 1996 Remote sensing applications to hydrology: Introduction. Hy­drological Sciences Journal 41 (4):429-431

Schultz, G.A., and E.C. Barrett, 1989. Advances in remote sensing for hydrology and water resources management. UNESCO, International Hydrological Programme, Paris, \o2pp.

Wiesnet, D.R., V.G. Konovalov and S.I. Solomon, 1979. Applications of Remote Sensing to Hydrology. World Meteorological Organization, WMO-No. 513, Operational Hydrology Re­port No. 12, 52pp.