application of remote sensing methods to hydrology and...

Hydrological Sciences -Journal- des Sciences Hydrologiques,39,4, August 1994 309

Application of remote sensing methods to hydrology and water resources*

A. RANGO USDA Hydrology Laboratory, Agricultural Research Service, Beltsville, Maryland 20705, USA

Abstract A brief review of research in remote sensing of water resources indicates that there are many positive results, and some techniques have been applied operationally. Currently, remote sensing data are being used operationally in precipitation estimates, soil moisture measurements for irrigation scheduling, snow water equivalent and snow cover extent assessments, seasonal and short term snowmelt runoff forecasts, and surface water inventories. In the next decade other operational applications are likely using remote measurements of land cover, sediment loads, erosion, groundwater, and areal inputs to hydrological models. Many research challenges remain, and significant progress is expected in areas like albedo measurements, energy budgets, and évapotranspiration estimation. The research in remote sensing and water resources also has much relevance for related studies of climate change and global habitability.

Application des méthodes de la télédétection à l'hydrologie et aux ressources en eau Résumé Une brève revue des recherches dans le domaine de la télédétection des ressources en eau montre qu'il existe de nombreux résultats positifs et que certaines techniques sont utilisées au niveau opérationnel. A l'heure actuelle on utilise les données télédétectées pour estimer les précipitations, pour mesurer l'humidité des sols afin de programmer l'irrigation, pour évaluer l'étendue et l'équivalent en eau des manteaux neigeux, pour prévoir à court et à long terme l'écoulement résultant de la fonte des neiges et pour réaliser des inventaires des eaux de surface. Au cours des dix prochaines années d'autres applications opérationnelles sont probables, utilisant la télédétection pour des mesures concernant l'occupation des sols, le transport des sédiments, l'érosion, les eaux souterraines et les apports surfaciques aux modèles hydrologiques La recherche demeure confrontée à de nombreux défis, et on s'attend à des avancées significatives dans des domaines tels que la mesure de 1'albedo, les bilans d'énergie et l'estimation de l'évapotranspiration. Les recherches en télédétection et ressources en eau sont également pertinentes en ce qui concerne les études connexes des modifications climatiques et de l'habitabilité de la terre.

*Keynote Paper from Symposium J1.1 : Remote Sensing Applications to Large-scale Hydrological Modelling (Yokohama, July 1993)

Open for discussion until 1 February 1995

310 A. Rango

INTRODUCTION

This paper is not meant to be a comprehensive review of all that remote sensing and water resources scientists have achieved in research and operational applications. Time and space limitations make such a review impossible. Rather, examples of research accomplishments and operational applications are highlighted.

In addition to the proven aspects of remote sensing of water resources, a portion of the paper is devoted to those areas where remote sensing has not yet been successful in extracting relevant hydrological information. These gaps in the knowledge base can be traced to a variety of problems. Whatever the reason for the lack of success, these gaps serve as challenges for future research and development. Among these challenges are areas where remote sensing has produced promising results, but much pertinent research still needs to be done. In some cases remote sensing research has firmly established the potential for operational use, but widespread operational application has not yet been achieved.

Certain goals for future research will be discussed. Simply stated, they include the need to make better use of the areal measurement capability of remote sensing for inputting data to hydrological models. Associated with this is the need to develop remote sensing techniques far enough so that operational application is the logical result.

Advances in remote sensing measurements of hydrological variables have provided an additional dimension to measurement capabilities, namely, the ability to measure hydrological variables areally. This development has made the remote sensing of water resources a useful tool for other related problems. Good examples of this are contributions to studies of global change and global habitability.

SELECTED RESEARCH ACCOMPLISHMENTS AND OPERATIONAL APPLICATIONS

Rainfall

Being one of the driving forces in the hydrological cycle, rainfall is a prime candidate variable for remote sensing measurement. There are many remote sensing possibilities including ground-based radar, visible and thermal infrared satellite imagery, and microwave satellite data. The use of ground-based radars has been successful especially when used with an integrated raingauge network and in areas with low relief. It is estimated that radar rainfall measurements are accurate to within about 15% of actual raingauge network totals (Goldhirsh et al., 1987). More difficulties are experienced in basins with mountainous terrain. Visible and thermal infrared techniques using measurements of cloud

Application of remote sensing methods 311

top reflectance and temperatures have been used in a variety of ways by meteorologists and other scientists to estimate monthly, daily, and storm precipitation totals (Barrett, 1970; Follansbee, 1973; Griffith et al, 1978; Griffith, 1987; Scofield & Oliver, 1977; Scofield, 1987; Shih, 1989). The best success so far has been found in the tropics and in countries not possessing detailed ground raingauge networks where cloud indexing methods for estimating precipitation have been used operationally.

The utilization of microwave data from space for rainfall estimation is still very much in the research stage, but the idea that the microwave signal is attenuated by the precipitation particles seems to have an advantage over the more indirect visible and thermal infrared techniques. In addition to actually measuring the rain falling, the microwave approach can be used to monitor radiation emitted by the soil to infer rainfall totals (Camillo & Schmugge, 1984). Microwave radiometers were used in the Monsoon 90 experiment in Arizona to monitor the daily changes in brightness temperature. Decreases in brightness temperature were shown to be directly correlated to cumulative rainfall amounts (Schmugge et al., 1992). In addition, the strong gradients of rainfall associated with the convective processes active in this area were readily apparent in the microwave data.

Land cover

Multispectral classification of land cover types was one of the first well established remote sensing applications for water resources. Numerous investigators have used classifications of land cover from Landsat and other remote sensing sources as input to various water resources studies. Rango et al. (1983) have shown that Landsat MSS data can be used as input to produce essentially the same flood flow frequency curves for planning in urbanizing areas as those derived from detailed conventional methods. It was also shown (Rango et al., 1983) that classification accuracy was about 95 % on a watershed basis, and that Landsat MSS costs of obtaining the land-cover data are about one-third the cost of conventional techniques for basins larger than 25 km2. The use of higher resolution multispectral data like Landsat TM and SPOT reduces the effective basin size to at least 10 km2 and possibly smaller.

Most studies that have used multispectral land cover information have employed it as just one facet of a larger study. Ragan & Jackson (1980) probably focused on the land cover categories the most. They actually modified the land cover categories of the Soil Conservation Service (SCS) curve number model to be compatible with Landsat MSS data. SCS curve numbers obtained with these alternate land cover categories for use in the model compared closely to those obtained in published examples using conventional techniques. Since the early studies were made, the use of remotely sensed land cover has been a valuable part of just a few studies. As a result, it has had low visibility when compared to other applications. Because of this, it may be overlooked by

312 A. Rango

operational users of land cover data who continue to utilize the more expensive conventional techniques. This is one area where there is a gap that needs to be bridged before true operational application will result. The solution may merely involve better publication of the existing results. A recent project in the United Kingdom used Landsat TM data to produce a country-wide classification of land cover which includes 25 different land classes with a minimum mappable area of 1 ha (Natural Environment Research Council, 1993). This operational product may go a long way towards gaining acceptance of this type of data for hydrological studies.

Snow cover extent

The measurement of the area of a basin covered by snow is similar to land cover measurement. Various types of snow cover can be classified (Rango & Itten, 1976). The various categories relate to snow wetness, vegetation, shadows, surface impurities, or transitional snowpacks (partially snowfree areas). In general these various categories can be combined to determine the

1 6 0

1 2 0

1 COEFFICIENT OF DETERMINATION (R 2 ) « 0 .83

VOLUMETRIC DIFFERENCE ( D v ) - - 5 . 3 6 %

A P R ' M A Y 1 9 7 9

J U N

Fig. 1 Discharge simulations for the Okutadami basin (422 km2), in central Japan using the snowmelt-runoff model (SRM).


snow covered and snow free basin areas, although vegetation obscuration of the snow cover can be a problem in densely vegetated areas.

Resolution of the sensor has been used to determine the minimum basin sizes for various satellites. Generally, NOAA-AVHRR data can be used on basins as small as 200 km2, Landsat MSS data on basins as small as 10 km2, and Landsat TM data on basins as small as 2.5 km2 (Rango et al., 1985). More important than the resolution is the frequency of coverage which is adequate only on NOAA-AVHRR (one visible overpass per day). Cloud cover is always a problem, and the 1.55-1.75 /um band on Landsat TM allows automatic discrimination between snow and clouds. Where clouds are present over a basin, a method has been developed to estimate the snow cover under the cloud cover by extrapolation from the cloud free portion of the basin (Lichtenegger etal, 1981; Baumgartner et al, 1986).

The snow cover data extracted by satellite remote sensing is immediately usable in a snowmelt-runoff model (SRM) (Martinec et al., 1983) for both simulation (Martinec & Rango, 1986) and forecasting (Rango & van Katwijk, 1990) of snowmelt-runoff. It has been shown that both snow cover mapping and SRM calculations can be handled on the same microcomputer, thus improving the chances of operational application of the data by users such as power companies (Baumgartner & Rango, 1991). Figure 1 shows a simulated hydrograph on the Okutadami basin (422 km2) in Japan using Landsat snow cover input to SRM. This is another good example of areal input to hydrological models.

Surface water extent

This research area is similar to the remote determination of land cover and snow cover areas. Because water is easily distinguishable in the near infrared spectral region, the potential for using Landsat MSS to map water bodies larger than 0.02 km2 was demonstrated many years ago by McKim et al. (1972). The availability of higher resolution Landsat TM and SPOT data enhances this long established capability, and mapping of smaller water bodies is possible. This use of remote sensing for surface water inventories is another application that has not been fully exploited. Similar techniques can be used to map the extent of flooding as has been done for the Mississippi River floods of 1973 (Deutsch & Ruggles, 1974) and 1993; however, the presence of clouds becomes an additional complicating factor. Surface water inventories can wait until clear sky images are acquired. Flood mapping needs real-time coverage, and unfortunately clouds frequently are present when floods occur. Some excellent near infrared flood mapping has been performed, but only when cloud-free images can be acquired. As a result, a prime research area is the use of satellite-based radar to provide high resolution, all-weather coverage at the time of flooding.

314 A. Rango

Soil moisture

This research accomplishment demonstrates the ability of remote sensing to measure more than just an areal surface value. The soil moisture beneath the surface can be measured because of microwave capability to penetrate the surface. The passive microwave region (particularly around 21 cm wavelength) has been exploited the most so far. Substantial research has been accomplished in the last 10-15 years. This research has established that moisture content can be measured to a depth of about 50 mm (perhaps as much as 100 mm) to a relative accuracy of 10-15 %. This measurement can be made under all-weather conditions and through light and moderate vegetation cover with the limiting case being a mature corn crop (Schmugge, 1990).

Several experiments have begun to demonstrate the potential for operational application (Kondratyev et al., 1977; Jackson et al., 1987; Kustas et al., 1991). Using algorithms developed in large area field experiments, microwave radiometer data can be converted to areal surface layer soil moisture maps (Jackson et al., 1993). Additional research needs to be conducted to correct for uncertainties introduced by surface roughness, soil texture and vegetation, and to develop soil profile linkages to the surface layer measurements, especially in non-irrigated areas. Gamma ray aircraft flights have also shown the potential for measuring soil moisture over linear transects (Jones & Carroll, 1983). Both active microwave and thermal infrared applications need much additional research before they can be used to extract soil moisture information.

Operational applications

For remote sensing techniques to be termed operational, several qualifications must be met. The application must produce an output on a regular basis or the remote sensing approach must be used regularly and on a continuing basis as part of a procedure to solve a problem. There are several good examples of operational remote sensing applications for water resources.

Scientists in Russia have taken the advances in the passive microwave remote sensing of soil moisture and developed a system for irrigation scheduling. Three radiometers at wavelengths of 2.25, 18, and 30 cm are mounted on a small biplane (Antonov-2 type) and flown over agricultural fields (Shutko, 1986). The multi-wavelength capability not only provides surface layer soil moisture measurements but also allows some indication of soil moisture through a depth of one metre in conjunction with a priori information on local soil properties (Reutov & Shutko, 1986). The soil moisture data are supplied to the farm operators 5-10 hours after the flights (Shutko, 1986). On some of the agricultural areas, similar radiometers mounted on tractors provide supplemental data. The soil moisture data thus obtained are then used as a direct input to irrigation scheduling decisions.


In the field of snow hydrology, the US National Weather Service has set up a National Remote Sensing Hydrology Center in Minneapolis, Minnesota. Two operational products are provided by the Center. Gamma radiation remote sensing from low altitude aircraft is used to measure snow water equivalent over a network of more than 1500 flight lines covering portions of 25 US States and seven Canadian Provinces. NOAA-AVHRR satellite data are used to map the areal extent of snow cover digitally over regions covering two-thirds of the US and southern Canada where snow cover is an important hydrological variable. The snow cover was mapped for about 2000 basins in 1990 and about 300 of these basins were mapped by elevation zone. The airborne and satellite alphanumerical and digital image snow cover data sets are available electronically to users in near realtime for hydrological forecasting purposes (Carroll, 1990).

A somewhat related operational application takes place in the Himalayan region. Ramamoorthi (1983, 1987) has developed a regression model relating April basin snow cover to the April-June seasonal runoff on the Sutlej River basin (43 230 km2) of India. The snow cover data are obtained from NOAA-AVHRR. Four years of forecasts produced an average forecast error of 10% on the Sutlej basin with similar results also being found on other major river basins in India. These forecasts are being used operationally in India and being extended to additional basins. In addition, Kumar et al. (1991) have started inputting satellite snow cover data to SRM for use in operational forecasts of daily snowmelt-runoff on the Beas and Parbati Rivers in India.

Passive microwave determination of snow water equivalent in relatively flat areas has shown much promise (Rango et al., 1979; Hall et al., 1984, 1987; Goodison étf a/., 1986; Foster etal., 1980; Chang etal., 1982). The new SSM/I data are being used to produce snow water equivalent maps of the Canadian prairies (Goodison et al., 1990) that are now supplied operationally to Canadian users (Goodison & Walker, 1993).

Regions for optimum remote sensing application

Because most techniques are attempting to measure surface features, remote sensing will work best in areas with little to obscure ;he surface. Because vegetation is the most common obscurant, remote sensing tends to work best in arid and semiarid regions. In the area of snow hydrology, snow mapping applications also tend to work best where there is little vegetation to obscure the snowpack.

GAPS IN KNOWLEDGE BASE

Soil erosion is a large problem in water resources and agriculture. Recent work has shown that airborne lasers can be used to measure the occurrence and

316 A. Rango

dimensions of ephemeral gullies (Ritchie & Jackson, 1989). Such work could be used to estimate soil loss, but research is needed to determine how such measurements can be used in a sampling scheme to get estimates of soil loss over large areas.

Many good examples of how multispectral remote sensing data can be used to determine sediment load in reservoirs and rivers also exist (Ritchie & Cooper, 1988; Ritchie etal., 1986, 1987; Verdin, 1985). Because of relatively easy application, these multispectral techniques should be promoted for operational monitoring of sediment loads in water bodies.

The use of passive microwave data for determining snow water equivalent information over flat homogeneous terrain is operational in Canada. Additional research needs to be performed to correct for variations in vegetation cover and snow grain size. Additional complications are present in mountainous areas where resolution of the passive microwave measurements may not be adequate. However, some promising results were derived in the Rio Grande basin of Colorado (3419 km2). Nimbus SMMR data were used to estimate the average basin snow water equivalent to within 15% of the actual value for both 1986 and 1987 (Rango et al., 1989). Improvements in resolution are necessary for obtaining snow water equivalent distribution in a basin. Such improvement will occur in the future. Another possibility would be active microwave data becoming available in the K-band.

Sporadic success has been obtained in using remote sensing data to locate groundwater. Most approaches use surficial indicators of the underlying groundwater reservoir and require considerable skill and knowledge on the part of the interpreter. More documentation of the techniques used is needed, perhaps in handbook form. One such area, among many, that would benefit by such specific guidance would be the use of fracture intersections to locate new groundwater wells.

A number of early studies on the remote measurement of physiographic and basin characteristics indicated that adequate measurements could be made at certain map scales (Rango et al., 1975). At that time the need was for better resolution data. Only a few studies have been made with new high resolution data (Gardner et al., 1989). More research with high resolution (e.g. from SPOT and Landsat TM) and digital topographic data needs to be conducted to determine the current capabilities regarding detectable features and applicable map scales.

Albedo investigations have been conducted, but as yet there is not an effective way to measure albedo remotely. Remote sensors collect only a small portion of light reflected by the Earth's surface, i.e. light reflected toward the sensor in the specific electromagnetic band of the sensor. The albedo is made up of light reflected in all directions over all portions of the electromagnetic spectrum. Much research, like that of Dozier et al. (1988), needs to be done on the bidirectional reflectance-distribution function so that one remote sensing reflectance measurement can be used to deduce the albedo.

Evapotranspiration is the most elusive component of the hydrological


cycle to measure. Research is progressing slowly on this topic, but various parts of the problem are falling into place. Remote determinations of surface temperature, vegetation cover, and upper layer soil moisture are essential for estimation of évapotranspiration (Johnson & Rango, 1985). Evaluations of how remote sensing can be used in energy balance computations, e.g. the work by Kustas & Daughtry (1989), are needed before évapotranspiration can be calculated operationally. Large-scale field experiments like Monsoon 90 are providing valuable data for évapotranspiration studies. In Monsoon 90 techniques have been developed to estimate the fraction of available energy used for the évapotranspiration process (Kustas et al., 1993). It has additionally been shown that laser data can be used to estimate the surface roughness parameter useful for input to aerodynamic approaches for évapotranspiration calculations (Menenti & Ritchie, 1992).

In addition to these areas, there are several aspects of water resources that have not yet been found to be amenable to remote sensing measurement. Some of these may never be measured with remote sensing. So far it has been impossible to measure streamflow occurring in river basins. No remote sensing method for measuring infiltration of precipitation into the soil has been found. Snow grain size affects the microwave measurement of snow water equivalent, but only limited information on grain sizes on the surface of the snowpack has been available using visible and near infrared data (Dozier, 1987, 1989). These visible and infrared measurements may or may not be related to the grain size influencing microwave measurements. Microwave penetration into the soil is limited to the surface layer. As yet, no methods for measuring deep soil moisture or groundwater are available. Although remote sensing of sediment pollution in water bodies is possible, no techniques have been found to measure the levels of chemical pollutants.

FUTURE GOALS

The only reason for hydrologists to be involved with remote sensing is for improvement in the application of a specific hydrological method. Particularly, hydrologists need to be extracting remote sensing data for input to hydrological models. As such the characteristics of these models need to be considered when developing pertinent remote sensing techniques. Remote sensing information is areal in nature so that some effort needs to be given to designing or modifying hydrological models that will accept this areal input. Kite (1993) has made some progress in this direction using satellite-derived land cover classifications as the prime input to grouped response units of a new hydrological model.

Researchers have made outstanding progress in developing remote sensing techniques. However, as has been pointed out in areas such as utilization of land cover and surface water extent, full operational application has not been realized. This may be at least partially due to a lack of technology

318 A. Rango

transfer efforts, i.e., the research approaches have not been taken far enough to be adopted by operational hydrologists. As research continues, there is a need to make sure that researchers take the extra steps necessary to assure operational utilization.

CONCLUSIONS

The following application areas now use remote sensing methods in an operational way: • Rainfall estimates using cloud indexing • Soil moisture for irrigation scheduling • Snow water equivalent (from gamma rays and passive microwaves over

flat areas) • Snow cover mapping • Seasonal and short term snowmelt runoff forecasts • Surface water inventories

In some cases these operational remote sensing techniques are used in only limited areas, but their use is expected to expand in the future. In addition, many very promising application areas exist that probably only require additional research and development before operational applications can be made. The applications requiring additional emphasis are:

Land cover Sediment loads & erosion Snow water equivalent (complex terrain) Soil moisture Groundwater Physiography Albedo Evapotranspiration Areal inputs to models Certain hydrological variables may never be amenable to remote sensing,

although this may be proved wrong by future research. The following variables or hydrological processes currently are not adequately addressed with remote sensing: • Streamflow • Infiltration » Snow grain size • Deep soil moisture or groundwater • Chemical pollutants

Each new remote sensing scientific conference produces one or two new examples of a remote sensing method being used operationally. Rapid advances in this field are expected in the next several years, especially if adequate effort is devoted by technique developers to technology transfer of the remote sensing methods.


REFERENCES

Barrett, E. C. (1970) The estimation of monthly rainfall from satellite data. Mon. Weath. Rev. 98, 322-327.

Baumgartner, M. R, Seidel, K., Haefner, H., Itten, K. I. & Martinec, J. (1986) Snow cover mapping for runoff simulations based on Landsat-MSS data in an alpine basin. In: Hydrologie Applications of Space Technology (Proc. Cocoa Beach Workshop, August 1985), IAHS Publ. no. 160, 191-199.

Baumgartner, M. F. & Rango, A. (1991) Snow cover mapping using microcomputer image svstems. Nordic Hydrol. 22(4), 193-210.

Camillo, P. J. & Schmugge, T. J. (1984) Correlating rainfall with remotely sensed microwave radiation using physically based models. In: IEEE Trans. Geosci. Remote Sens. GE-22, 415-423.

Carroll, T. R. (1990) Operational remote sensing of snow cover in the US and Canada. Proc. 1990 National Conference on Hydraulic Engineering, ASCE, San Diego, California, USA.

Chang, A. T. C, Foster, J. L., Hall, D. K., Rango, A. & Hartline, B. K. (1982) Snow water equivalent estimation by microwave radiometry. Cold Regions Res. & Technol. 5, 259-267.

Deutsch, M. & Ruggles, F. (1974) Optical data processing and projected applications of the ERTS-1 imagery covering the 1973 Mississippi River valley floods. Wat. Resour. Bull. 10(5), 1023-1039.

Dozier, J. (1987) Recent research in snow hydrology. Rev. Geophys. 25(2), 153-161. Dozier, J., Davis, R. E., Chang, A. T. C. & Brown, K. (1988) The spectral bidirectional reflectance

of snow. In: Spectral Signatures of Objects in Remote Sensing ESA SP-287, 4th Int. Colloquium, European Space Agency, Paris, France. 87-92.

Dozier, J. (1989) Spectral signature of alpine snow cover from the Landsat Thematic Mapper. Remote Sens. Environ. 28, 9-22.

Follansbee, W. A. (1973) Estimation of average daily rainfall from satellite cloud photographs. NOAA Technical Memo. NESS 44, Washington, DC, USA.

Foster, J. L., Rango, A., Hall, D. K., Chang, A. T. C, Allison, L. J. & Diesen, B. C. (1980) Snowpack monitoring in North America and Eurasia using passive microwave satellite data. Remote Sens. Environ. 10, 285-298.

Gardner, T. W., Connors, K. F. & Hu, H. (1989) Extraction of fluvial networks from SPOT panchromatic data in a low relief, arid basin. Int. J. Remote Sens. 10(11), 1789-1801.

Goldhirsn, J., Rowland I. & Musiani, B. (1987) Rain measurement results derived from a two-polarization frequency-diversity S-band radar at Wallops Island, Virginia. IEEE Trans. Geosci. Remote Sens. GE-25(6), 654-661.

Goodison, B., Walker, A. E. & Thirkettle, F. (1990) Determination of snow water equivalent on the Canadian prairies using near real-time passive microwave data. In: Proc. Workshop on Application of Remote Sensing in Hydrology (National Hydrology Research Institute, Saskatoon, Saskatchewan, Canada) 297-316.

Goodison, B. & Walker, A. E. (1993) Canadian development and use of snow cover information from passive microwave satellite data. In: Proc. ESA/NASA Int. Workshop on Passive Microwave Remote Sensing Research Related to Land-Atmosphere Interactions (St. Lary, France).

Goodison, B. E., Rubinstein, I., Thirkettle, F. W. & Langham, E. J. (1986) Determination of snow water equivalent on the Canadian prairies using microwave radiometry. In: Modelling Snowmelt-InducedProcesses (Proc. Budapest Symposium, My 1986), IAHS Publ. no. 155, 163-173.

Griffith, C. G., Woodley, W. E. & Grubb, P. G. (1978) Rain estimation from geosynchronous satellite imagery-visible and infrared study. Mon. Weath. Rev. 106, 1153-1171.

Griffith, C. G. (1987) Comparison of gauge and satellite rain estimates for the central United States during August 1979. /. Geophys. Res. 92(D8), 9551-9566.

Hall, D. K., Foster, J. L. & Chang, A. T. C. (1984) Nimbus-7 SMMR polarization responses to snow depth in the mid-western US Nordic Hydrol. 15, 1-8.

Hall, D. K., Chang, A. T. C. & Foster, J. L. (1987) Seasonal and interannual observations and modeling of the snowpack on the arctic coastal plain of Alaska using satellite data. In: Proc. Cold Regions Hydrology Symposium (American Water Resources Association, Fairbanks, Alaska, USA) 521-529.

Jackson, T. J., Hawley, M. E. & O'Neill, P. E. (1987) Preplanting soil moisture using passive microwave sensors. Wat. Resour. Bull. 23(1), 11-19.

Jackson, T., Le Vine, D., Griffis, A., Goodrich, D., Schmmuge, T , Swift, C. & O'Neill, P. (1993) Soil moisture and rainfall estimation over a semiarid environment with the ESTAR microwave radiometer. IEEE Trans. Geosci. Remote Sens. 31(4).

Johnson, A. I. & Rango, A. (ed.) (1985) Remote Sensing Applications for Consumptive Use (Evapotranspiration), AWRA Monograph Series, no. 6. American Water Resources Association, Bethesda, MD, USA.

Jones, W. K. & Carroll, T. R. (1983) Error analysis of airborne gamma radiation soil moisture measurements. Agric. Met. 28, 19-30.

Kite, G. W. (1993) Application of a land class hydrological model to climatic change. Wat. Resour. Res. 29(7), 2377-2384.

Kondratyev, K. Ya., Melentyev, V V., Rabinovich, Yu. I. & Shulginc, E. M. (1977) Passive microwave remote sensing of soil moisture. In: Proc. 11th Symp. of Remote Sensing of Environment, 1641-1661.(Environmental Res. Inst, of Michigan, Ann Arbor, USA).

Kumar, V. S., Haefner, H. & Seidel, K. (1991) Satellite snow cover mapping and snowmelt-runoff modelling in Beas Basin. In: Snow, Hydrology and Forests in High Alpine Areas (Proc. Vienna Symposium August 1991), IAHS Publ. no. 205, 101-109.

320 A. Rango

Kustas, W. P. & Daughtry, C. S. T. (1989) Estimation of the soil heat flux/net radiation ratio from spectral data. Agric. For. Met. 49, 205-233.

Kustas, W. P. (& 36 co-authors) (1991) An interdisciplinary field study of the energy and water fluxes in the atmosphere-biosphere system over semiarid rangelands: descriptions and some preliminary results. Bull. Am. Met. Soc. 72(11), 1683-1705.

Kustas, W. P., Schmugge, T. J., Humes, K. S., Weltz, M. A., Moran, M. S., Jackson, T. J. & Parry, R. (1993) Relationships between evaporative fraction and remotely sensed vegetation index and microwave brightness temperature for semiarid rangelands. J. Appl. Met. 32, 1781-1790.

Lichtenegger, J., Seidel, K., Keller, M. & Haefner, H. (1981) Snow surface measurements from digital Landsat MSS date. Nordic Hydrol. 12, 275-288.

Martinec, J., Rango, A. & Major, E. (1983) The Snowmelt-Runqff Model (SRM) User's Manual. NASA Reference Publ. 1100, Washington, DC, USA.

Martinec, J. & Rango, A (1986) Parameter values for snowmelt runoff modelling. J. Hydrol. 84, 197-219.

McKim, H. L., Marlar, T. L. & Anderson, D. M. (1972) The Use ofERTS-1 Imagery in the National Program for Inspection of Dams. Special Report 183, US Army Corps of Engineers, Cold Regions Research and Engineering Laboratory, Hanover, New Hampshire, USA.

Menenti, M. & Ritchie, J. C. (1992) Estimation of effective aerodynamic roughness with altimeter measurements. Proc. IGARSS '92 Symposium, (Houston, Texas, USA) 1508-1510.

Natural Environment Research Council (1993) Launch of a new land cover map. Earth Space Review, 2(3), 4-5.

Ragan, R. M. & Jackson, T. J. (1980) Runoff synthesis using Landsat and SCS model. /. Hydraul. Div. ASCE, 106(HY5), 667-678.

Ramamoorthi, A. S. (1983) Snow-melt runoff studies using remote sensing data. Proc. Indian Acad. Sci. 6(3), 279-286.

Ramamoorthi, A. S. (1987) Snow cover area (SCA) is the main factor in forecasting snowmelt runoff from major river basins. In: Large Scale Effects of Seasonal Snow Cover (Proc. Vancouver Syma August 1987), 187-198, IAHS Publ. no. 166.

Rango, A., Foster, J. & Salomonson, V. V. (1975) Extraction and utilization of space acquired physiographic data for water resources development. Wat. Resour. Bull. 11(6), 1245-1255.

Rango, A. & Itten, K. (1976) Satellite potentials in snowcover monitoring and runoff prediction. Nordic Hydrol. 7, 209-230.

Rango, A., Chang, A. T. C. & Foster, J. L. (1979) The utilization of spaceborne microwave radiometers for monitoring snowpack properties. Nordic Hydrol. 10, 25-40.

Rango, A., Feldman, A., George, T. & Ragan R. (1983) Effective use of Landsat data in hydrologie models. Wat. Resour. Bull. 19(2), 165-174.

Rango, A., Martinec, J., Foster, J. & Marks, D. (1985) Resolution in operational remote sensing of snow cover. In: Hydrological Applications of Remote Sensing and Remote Data Transmission (Proc. Hamburg Symp. August 1983) IAHS Publ. no. 145, 371-382.

Rango, A., Martinec, J., Chang, A. T. C. & Foster, J. L. (1989) Average areal water equivalent of snow in a mountain basin using microwave and visible satellite data. IEEE Trans, on Geosci. Remote Sens. 2(6), 740-745.

Rango, A. & van Katwijk, V. (1990) Development and testing of a snowmelt-runoff forecasting technique. Wat. Resour. Bull. 26(1), 135-144.

Reutov, E. A. & Shutko, A. M. (1986) Prior knowledge based soil moisture determination by microwave radiometry. Sov. J. Remote Sens. 5(1), 100-125.

Ritchie, J. C , Schiebe, F. R. & Cooper, C. M. (1986) Surface water quality measurements of Lake Chicot, Arkansas using data from Landsat satellites. /. Freshwater Ecol. 3(3), 391-397.

Ritchie, J. C , Cooper, C. M. & Jiang, Y. (1987) Using Landsat multispectral scanner data to estimate suspended sediments in Moon Lake, Mississippi. Remote Sensing of Environment, 23, 65-81.

Ritchie, J. C. & Cooper, C. M. (1988) Comparison of measured suspended sediment concentrations with suspended sediment concentrations estimated from Landsat MSS data. Int. J. Remote Sens. 9(3), 379-387.

Ritchie, J. C. & Jackson, T. J. (1989) Airborne laser measurements of the surface topography of simulated concentrated flow gullies. Trans. Am. Soc. Agric. Engrs. 32(2), 645-648.

Schmugge, T. (1990) Measurements of surface soil moisture and temperature. In: Remote Sensing of Biosphere Functioning, ed. R. J. Hobbs & H. A. Mooney, 31-62. Springer-Verlag, Germany. Schmugge, T., Jackson, T., Kustas, W. P. & Wang, J. R. (1992) Passive microwave remote

sensing of soil moisture: results from HAPEX, FIFE and Monsoon 90. ISPRS J. Photogrammetry & Remote Sensing, 47, 127-143.

Scofield, R. A. & Oliver, V. J. (1977)^4 Scheme for Estimating Connective Rain From Satellite Imagery. NOAA Technical Memo. NESS 86, Washington, DC, USA.

Scofield, R. A. (1987) The NESDIS operational convective precipitation estimation technique. Mon. Weath. Rev. 115, 1773-1792.

Shih, S. F. (1989) Potential application of satellite data for rainfall estimation. In: Remote Sensing and Large-Scale Global Processes (Proc. IAHS 3rd Int. Assembly, Baltimore, USA, May 1989), IAHS Publ. no. 186, 97-104.

Shutko, A. M. (1986) Remote sensing of the waters and lands via microwave radiometry. In: Remote Sensing and its Impact on Developing Countries, ed. C. Chagas & V. Canute, Pontificiae Academiae Scientiarum Scripta Vana-68, 413-441.

Verdin, J. P. (1985) Monitoring water quality conditions in a large western reservoir with Landsat imagery, Photogramm. Engng Remote Sens. 51, 343-353.

Received 17 November 1993; accepted 8 March 1994

application of remote sensing methods to hydrology and...

Documents