3--Hydrology

The cryosphere is a major component of the hydrosphere and interacts significantly with the global climate system, the geosphere, and the biosphere. Over 30 percent of the Earth's land surface is covered seasonally by snow, and about 10 percent is covered permanently with snow or ice.

Snow and ice play important interactive roles in regional climates, because snow has a higher albedo than any other natural surface. The earliest signs of global climate change are likely in the polar regions and in the seasonal snow cover and alpine glaciers. For investigations in hydrology and land-surface climatology, seasonal snow cover and alpine glaciers are critical to the radiation and water balances. Over major portions of the middle and high latitudes, and at high elevations in the tropical latitudes, snow and alpine glaciers are the largest contributors to runoff in rivers and to ground-water recharge. The dual problems of estimating both the quantity of water held by seasonal snow packs and timing of snowmelt confront snow hydrologists. Understanding the processes in the seasonal snow cover is also important for studies of the chemical balance of alpine drainage basins, because of translocation of anions and cations within the snow pack and possible concentrated release in the first phases of the melt season.

Soil moisture is an environmental descriptor that integrates much of the land surface hydrology and is the interface for interaction between the solid Earth surface and life. As central as this seems to man's existence and biogeochemical cycles, it is a descriptor that has not had wide spread application as a variable in any land process models. There are two primary reasons for this. It is a difficult variable to measure, not at one point in time, but in a consistent and spatially comprehensive basis. Also, it exhibits very large spatial and temporal variability; thus point measurements have had very little meaning. The practical result of this is that soil moisture has not been used as measurable variable in any of our current hydrologic, climatic, agricultural, or biogeochemical models.

Both passive microwave and active microwave (SAR) techniques have provided solid theoretical and experimental results that the top five cm of soil moisture can be measured from aircraft and space platforms under a variety of environmental conditions and through a moderate vegetation cover (Figures 3-1, 3-2). This section will define the science issues and application opportunities that can be addressed by microwave remote sensing of soil moisture and snow water equivalence, describe the capabilities and limitations of the passive and SAR systems, describe the progress to date and document the research still needed to take this technology to an operational status.

The scientific objectives related to snow and ice identified by the National Research Council are:

* To study the global hydrologic cycle to determine the distributions, dimensions, properties, and relevant dynamics of snow, sea and lake ice, ice sheets and shelves.

* To determine and understand the mechanisms for the transfers of water between the major global reservoirs (glacier flow, melting, and freezing).

* To predict on appropriate time scales the distributions, volumes, and fluxes associated with snow cover, sea ice, ice sheets and shelves, ground ice, and permafrost.

Although snow and ice phenomena play important roles in global processes, difficult access hampers data collection, and conventional studies of snow and ice cannot address the correct scales of most phenomena. In situ sampling methods have limited utility for capturing the spatial and temporal variability of snow and ice processes. Operational hydrology and climatology require timely measurement of snow-pack parameters.

The redistribution of solar energy over the globe is central to studies in climate. Water serves a fundamental role in this redistribution through the energy associated with evapotranspiration, the transport of atmospheric water vapor, and precipitation. Residence time for atmospheric water is on the order of a week, and for soil moisture, from a couple of days to months, which emphasize the active nature of the hydrologic cycle. Understanding the importance of the land-surface hydrology to climate has emerged as an important research area since the mid 1960s when researchers at the Geophysical Fluid Dynamics Laboratory placed a land hydrology component into their general circulation model (GCM) (see Manabe et al., 1965; Manabe, 1969).

As the sophistication of GCMs has increased, the role of the land surface component has become more and more important. However, a major weakness in the current GCMs is, according to the CES-89 report, "the adequate parameterization of variables representing the terrestrial phase of the hydrologic cycle..., which is primarily the result of totally inadequate information concerning the degree of spatial variability of precipitation, evapotranspiration, and other components of hydrology." This report goes further to say "the lack of regional-scale measurements introduces a severe shortcoming in the testing of GCM output."

Perhaps the most important role that the land surface component of a GCM performs is the partitioning of incoming radiative energy into sensible and latent heat fluxes. The major factor involved in determining the relative proportions of the two heat fluxes is the availability of water, generally in the form of soil moisture. A number of modeling studies have demonstrated the sensitivity of soil moisture anomalies to climate (Walker and Rowntree, 1977, Rowntree and Bolton, 1983, Rind, 1982, Shukla and Mintz, 1982, Delworth and Manabe, 1989). Researchers have reported, for instance, that soil moisture is the second most important forcing function, second only to the sea surface temperature in the mid latitudes, and it becomes the most important forcing function in the summer months.

The role of soil moisture is equally important at smaller scales. Recent studies with mesoscale atmospheric models have similarly demonstrated a sensitivity to spatial gradients of soil moisture. For example, Fast and McCorcle (1991) have shown that soil moisture gradients can induce thermally induced circulations similar to sea breezes. Chang and Wetzel (1991) have concluded that the spatial variations of vegetation and soil moisture affect the surface baroclinic structures through differential heating, which in turn indicate the location and intensity of surface dynamic and thermodynamic discontinuities necessary to develop severe storms. In yet another study, Lanicci et al., (1987) have shown that dry soil conditions over northern Mexico and variable soil moisture conditions over the southern Great Plains can dynamically interact to alter prestorm conditions and subsequent convective rainfall patterns.

It is still unclear whether the spatial distribution of soil moisture collected at regional scales is useful for GCM and mesoscale modeling. One indication in favor is the recent study by Betts et al., (1994) showing that initialization of ECMWF weather predictions on current soil moisture (during the summer of 1993 in the US Great Plains region) can lead to improved rainfall predictions. The extreme wetness conditions, in comparison to climatological average soil moisture, clearly was a factor in the effect. For more normal conditions, soil moisture anomalies will vary with the spatial and temporal scales of rain events--scales that may be meaningful to 4-dimensional data assimilations (4DDA) and mesoscale modeling.

Based on these studies and scales ranging from GCM to mesoscale, it appears that soil moisture will be an important hydrologic variable for hydrometeorological modeling and validation studies. Because of the ubiquitous nature of soil moisture in many disciplines, there are numerous potential science applications for frequent and spatially comprehensive measurements of soil moisture. However, most of these will fit under the umbrella of the following four science issues which are the highest priority:

* To understand the role of surface soil moisture in the partitioning of incoming radiant energy into latent and sensible heat fluxes at a variety of scales from mesoscale to GCM scale.

* To understand the relationship between the surface 5 cm of soil moisture observable by microwave techniques and the total profile (1 m or more) soil moisture that is accessible to plants and transpiration to the atmosphere.

* To understand how spatial and temporal patterns of soil moisture are related to the physical and hydrologic properties of soils.

* To understand how the spatial and temporal patterns of soil moisture can be used to improve our ability to model runoff at a variety of scales and adapt hydrologic models to areas of differing climate, biomes and soils, and geology.

With the potential for measuring soil moisture having been demonstrated, the obvious question to ask is how might society use such soil moisture measurements? As in the science issues, there are four general areas in which routine measurements of soil moisture could have major impacts on our day-to-day lives:

* Improvements in medium range weather forecasting by incorporating measured soil moisture on a 30-km grid on a daily basis.

* Agricultural applications would include on-farm uses for improving irrigation scheduling and efficiencies, to improving crop yield modeling for both domestic and foreign areas. The scales of interest here would be 100 m to 1 km and three to seven days.

* Water management applications require better quantification of water uses, storages and runoff to monitor existing resources and to assist decision makers in allocation of limited resources or coordination of relief efforts in times of flooding. The scales of interest here would also be 100 m to 1 km and three to seven days.

* Climate models, particularly for annual and inter annual variability, need to be able to represent the land surface hydrologic processes accurately. Measured soil moisture can be used as a state variable and as a validity measure for GCMs. The scales of interest here are 1 to 10 km and 7 to 30 days.

Satellite remote sensing has become increasingly important to hydrologists and climatologists because the data provide information on the spatial and temporal distributions of parameters of climatic and hydrologic importance: snow covered area, surface albedo, snow water equivalence, and snow wetness (liquid water content). For the seasonal snow cover, remote sensing improves the monitoring of existing conditions and has been incorporated into several runoff-forecasting and management systems. Visible and near-infrared sensors have been used extensively to measure snow-covered area. While the signal in this portion of the electromagnetic spectrum is sensitive to snow grain size and impurities, it is not sensitive to wetness and is sensitive to water equivalence only for shallow snow packs. In addition, cloud cover hampers data collection from these sensors, so the opportunities for obtaining suitable data can be infrequent. In addition to accurate measurements of snow extent and albedo, forecasting of melt (at both continental and drainage-basin scales) requires information about spatial and temporal distributions of snow water equivalence and free liquid-water content.

Passive microwave signals are sensitive to snow properties of hydrologic interest. However, the spatial resolution of spaceborne passive microwave sensors is much coarser than the natural scale of variation in mountainous areas. Active microwave remote sensing has long promised the advantages of: (1) all-weather day or night imaging capabilities; (2) high resolution suitable for alpine regions; (3) sensitivity to most snow properties of interest to snow hydrologists and climatologists. Over the last decade, major improvements in technology from data acquisition and processing to quantitative interpretation have put capabilities for advanced snow and ice measurements at our doorstep.

For snow, recent studies (using SIR-C/X-SAR, AIRSAR, and ERS-1 data) have shown a significant improvement in understanding and modeling the backscattering and polarization properties as a function of snow-pack parameters. An accurate algorithm to retrieve snow wetness, which indicates where and at what rate snow is melting, has been developed and tested using C-band SIR C and JPL AIRSAR data (Figures 3-3a, 3-3b). Thus, accurate information about the spatial and temporal distributions and melting status of snow cover can be provided for hydrological and climatic investigations and operations. Maps of snow-covered area derived from SIR-C/X-SAR and AIRSAR now compare reasonably well (85-90% accuracy) with those derived from visible imagery, which require clear weather and daylight.

For soil moisture, recent advances in remote sensing technology have demonstrated that soil moisture can be measured by a variety of techniques. However, only microwave technology has the ability to quantitatively measure soil moisture under a variety of topographic and vegetation cover conditions, so potentially it could be extended to routine measurements from a satellite system. A number of experiments using sensors mounted in trucks, aircraft, and spacecraft have shown that the moisture within a thin layer of soil, on the order of 5 cm, can be accurately measured for bare soil and thinly vegetated surfaces.

Soil Moisture Remote Sensing

There are two basic microwave approaches that are typically used to measure soil moisture. One is passive (which is based on radiometry) and the other is active and uses radar. Both approaches utilize the large contrast between the dielectric constant of dry soil and water. At L-band, the dielectric constant can vary from about 3 for dry soil to about 20 for wet soil, which can result in a change in emissivity for passive systems from about 0.95 to 0.6 or lower and an increase in the radar backscatter approaching 10 dB. There are also major differences between the two systems in spatial resolution, swath width, data rate, and power requirements. However, almost without exception, the two systems are complementary; that

is, strengths in one are matched by weaknesses in the other, and vice versa. The advantages of passive microwave systems include frequent coverage, low data rates, and (relative to active microwave) simpler data processing. The disadvantages include poor resolution. In the case of the active microwave systems, the advantages include high resolution, but this comes at the expense of higher data rates and more complex processing.

Measurements show that the estimated soil moisture for L-band frequencies correlate best with soil moistures in the top 5 cm of the soil. The sensitivity of active microwave sensors to soil moisture was demonstrated with many ground, airborne, and even some spaceborne experiments ( Ulaby and Batlivala, 1976; Ulaby, et al., 1978; Chang et al., 1980; Jackson et al., 1981; Wang et al., 1986; Dobson and Ulaby, 1986; Lin et al., 1994a, b). Even though these experiments have documented the sensitivity of the radar signal to soil moisture, algorithms to invert radar measurements to infer soil moisture (Oh et al., 1992; Dubois et al., 1994) must still be further evaluated over a broader range of conditions.

Difficulties in Soil Moisture Remote Sensing

A significant difficulty in remotely sensing soil moisture, either with active and passive systems, is the effect of target characteristics other than the soil moisture; for example surface roughness, vegetation, and topography. The two most important target properties are surface roughness and those related to the vegetation canopy. Applying a backscattering model, Lin et al., (1994) have studied the sensitivity of radar signals to various land surface parameters; specifically for L-band (1.25 GHz) over a short grass canopy they found that the most important parameter appears to be the surface roughness. In most natural settings, the effect of roughness may be equal or greater than the effect of soil moisture on the radar backscatter. Thus, the soil moisture problem becomes one of determining the roughness effect independently so that a model can be inverted to yield a soil moisture estimate.

Surface Roughness Effects: Improved understanding of surface scattering processes is needed to further our understanding of the role of surface roughness in soil moisture estimation. Thus, theoretical surface backscattering models have been developed for this purpose; namely, the small perturbation model (SPM), the physical optics model (POM) and

geometrical optics model (GOM). In a broad sense, the geometrical optics model is best suited for very rough surface, the physical optics model is suitable for surfaces with intermediate scales of roughness, and the small perturbation model is suitable for smooth surfaces. In general, these models have not provided good predictive values in field-scale observed soil moisture, and more research is needed into surface scattering models. Examples of recent work are Fung et al. (1992) and Oh et al. (1992).

The approach adopted by Oh et al. (1992) is based on scattering behavior in limiting cases and on experimental data. They have developed an empirical model in terms of the root mean square (rms) roughness height, the wave number, and the relative dielectric constant. By using this model with multipolarized radar data, the soil moisture content and the surface roughness can be determined. The key to this approach is that the co-polarization ratios (HH/VV) and cross-polarization ratios (HV/VV) which are given explicitly in terms of the roughness and the soil dielectric constant. In more recent work (Dubois et al., 1995), an algorithm was derived that uses L-band HH and VV radar cross sections only to estimate surface roughness and soil moisture. In this case, the algorithm was tested with both airborne and spaceborne SAR data and accuracies of 3-4% absolute was found for surfaces with vegetation that has a normalized difference vegetation index (NDVI) < 0.4. Thus, initial results look promising when applied to bare soil and sparse vegetation. But, current SAR satellite sensors (ERS-1/2 and RADARSAT) are both single-polarization instruments (C-VV and C-HH, respectively), and even when used together cannot provide cross-polarization ratios.

Vegetation Effects: The effect of vegetation is to attenuate the microwave emission from the soil; it also adds to the total radiative flux with its own emission. The degree to which vegetation affects the determination of soil moisture depends upon the wavelength, the mass and water content of vegetation, and the vegetation's structural characteristics as it influences its scattering properties. Thus, with radar the effect of the vegetation canopy adds more complexity to the problem. To infer soil moisture, one must determine the soil roughness effects and the effects of the vegetation canopy, which is a complex inference problem and may not be unique without high temporal resolution data in which only soil moisture is changing. As with the roughness case, the effect of vegetation on the active microwave sensing of soil moisture is greatly dependent upon the instrument incidence angle, frequency, and polarization. The vegetation contains water along and has plant structure. Radar backscatter is sensitive to both of these characteristics (Lin et al., 1994a). Therefore, the radar backscatter from a vegetated surface will have the integrated effect of the vegetation and underlying soil.

Two general approaches have been used to model the volume scattering, the wave approach and the intensity (or radiative transfer) approach. Both approaches have their constraints, especially in dealing with complex vegetation structures. Several groups are involved in research in the area of estimating soil moisture from active microwave data in the presence of vegetation. Few results have been presented or published, however. Saatchi et al. (1994) showed that by using C-band and L-band data, the canopy water content of grasses in the Konza Prairie could be estimated with an accuracy of about 20%, while Lin et al. (1994) reported an accuracy of about 6% when estimating soil moisture over grass covered areas in

England.

Assuming that the effects of vegetation could be accounted for in the case of active microwave sensors, and given the fact that the best resolution than could be expected from a spaceborne low frequency radiometer is on the order of tens of kilometers, it is clear that SAR can play an important role in providing remotely sensed surface soil moisture maps. The optimum radar parameters based on the demonstrated algorithms would be a polarimetric L- band radar system with a resolution on the order of 100 m. There is evidence that suggests that inferring soil moisture in vegetated areas would require some higher frequency SAR data, either C-band or X-band. Continued research is needed to help resolve the effectiveness of radar data for soil moisture under different vegetation types and densities. One approach is using multi- temporal data from a satellite platform, which may provide the long term data sets necessary to isolate the fast-changing soil moisture effects from the slowly changing vegetation and roughness effects. Another promising approach is combining the SAR radar measurements with a distributed hydrologic models to infer soil moisture (for example Lin et al., 1994b).

Field Measurements of Soil Moisture

When deciding the usefulness of the remote sensing instruments and the inferred soil moisture estimates, one has to take into account the natural spatial variability of soil moisture. Thus, when estimating the accuracy of a technique, estimated and in situ measured values are typically compared. In this way, areal averages from the remotely sensed data are compared to point measurements, and the natural variation in the measured quantity becomes a very important factor in judging the performance of a given technique.

In most countries, there are a number of in-situ soil moisture measurements taken in support of operational activities related to agriculture. Two examples of such measurements programs are the Illinois State (U.S.) Soil Moisture Program, an 18-site measurement network in operation since 1981; and the long term soil moisture measurements in the former Soviet Union and reported by Vinnikov and Yeserkepova (1991). Typically these measurements are taken using a neutron probe, Time Domain Reflectivity (TD) instrument, or through labor-intensive techniques such as gravimetric measurements. Also, typically these are made on a three-day, weekly, or longer basis and in some cases (for agricultural experiments) only during the growing season. For example the current Illinois network makes biweekly samples during the growing season and monthly samples at other times; the data reported by Vinnikov and Yeserkepova (1991) were sampled at a monthly time interval.

The in-situ measurements generally provide good definition with depth, usually consisting of measurements every 5 or 10 cm throughout the root zone to depths of one to two meters. Thus, the profile is well defined but there is usually not an extensive spatial sampling to detect soil moisture gradients in the horizontal plane. A number of investigators have looked into the spatial variability of soil properties. Rogowski (1972) found that over a range of soils that the coefficient of variation in the distribution of water content at 15 bars was in the range of 15 to 35%; and for experimental hydraulic conductivity was 5 to 68%. Hawley et al., (1982) investigated the effect of sample volume on estimated soil moisture content. Using a 2m square plot and 10 samples, the estimated CV was in the range of 5% for the three different soil moisture conditions investigated; implying a CV of 15-20% for a single sample, which is consistent with the other similar studies. In addition, they concluded that samples greater than 200g (50 cc) are needed for accurate estimates.

Field scale variability (16 ha, > 40 acres) was evaluated by Bell et al., (1980) using data collected in 57 fields in Arizona, Kansas, and South Dakota. The nominal standard deviation of all the data analyzed was between 2 and 3%, soil moisture. For remote sensing studies, this means that perfect correlations to ground observations are unlikely and that the inherent error due to field-scale variability is of the same order of magnitude as the demonstrated accuracies of the inversion results for both active and passive microwave instruments.

While there are direct uses for the surface soil moistures, a much wider range of research and applications would benefit from an estimate of the profile soil moisture. Although there is still much work to be done in this area, some basic concepts have been proven. Kostov and Jackson (Kostov, 1993) have identified four general approaches: statistical, knowledge-based (a priori information), radiometric inversion, and soil water modeling. Regional profile mapping using a straightforward regression approach was demonstrated in Jackson et al., (1987). Knowledge-based approaches have been used extensively by Reutov and Shutko (1986 and 1990) and also by Jackson (1980). Recent research has focused on a combination of the inversion and modeling approaches (Entekhabi et al., 1994). Each of these approaches needs to be considered in the development of large scale mapping programs because any one in particular may not be compatible due to limitations caused by ancillary data requirements or the temporal and spatial sampling.

Radar Observations for Soil Moisture

SAR satellites offer perhaps the best opportunity to measure soil moisture routinely at regional scales. Currently, the ERS-1 C-band (VV) and JERS-1 L-band SARs are operating, and the Canadian RADARSAT, also C-band (HH), will be launched in mid-1995. Although it is believed that an L-band polarimetric system would be optimal for soil moisture, the preliminary results from the ERS-1 demonstrate its capability as a soil moisture instrument under ideal conditions: bare soil, near saturation, constant roughness. The main drawback to the SAR systems is the lack of existing algorithms for the routine determination of soil moisture. However, through airborne and SIR-C remote sensing experiments, field observations are accumulating for a diverse set of sites. It is hoped that these data are sufficient for developing suitable algorithms. It is critical that these diverse data sets be brought together in a SAR soil moisture data base.

Experimental data sets can be grouped into three classes, of which only one is discussed in this report. These classes are: (i) In-situ operational soil moisture data sets, taken by water resources and agricultural agencies for a variety of reasons. Even though these data sets are important for hydrological research, the lack of simultaneous remote sensing data make these soil moisture measurements of limited value for the development of soil moisture remote sensing algorithms. (ii) Remote sensing experiments with passive microwave sensors. Examples of these include HAPEX-MOBILHY (Hydrologic Atmospheric Pilot Experiment and Modelisation du Bilan Hydrique) program in southern France in 1985; HAPEX-SAHEL in Niger (West Africa) in 1992; and the First ISLSCP Field Experiment (FIFE) in Kansas in 1987. (iii) Remote sensing experiments with SAR and in some cases passive microwave sensors. These experiments include:

Mac-Hydro-90 in the Mahantango catchment (central Pennsylvania) in 1990 which was a multi-sensor campaign (using the DC-8 with the three frequency polarimetric Synthetic Aperture Radar (SAR) ,and the C-130 carrying the PBMR L-band radiometer and the NS001 thematic mapper simulator.

Washita-92, conducted over the Little Washita catchment in Oklahoma in 1992, was another multi-sensor campaign (using the DC-8 with the three frequency polarimetric Synthetic Aperture Radar (SAR), and the C-130 carrying the NS001 thematic mapper simulator, the Thermal Imaging Mapper, the Electronically Steered Thinned Array Radiometer (ESTAR), a 37-GHz radiometer, and a USDA laser profiler.)

Mac-Europe-91, a multi-aircraft campaign DC-8 and EU-2 for remote sensing in Europe during 1991. Soil moisture data were collected at a number of sites including Slapton Wood, Devon, England; Montespertoli, Tuscony, Italy; EFEDA experiment in Spain.

Shuttle Imaging Radar, SIR-C, flights in April and October 1994. There were a number of sites for which radar data were collected for soil moisture studies. These sites included the Little Washita (OK, USA), Mahantango catchment (PA, USA), Alcona (Manitoba, Canada), Zwalm catchment (Belgium), and Montespertoli (Italy).

It is critical that these data be made available to provide algorithm developers a broader range of soil moisture data.

Several methods for mapping melting snow-covered regions have been developed and tested using AIRSAR and ERS-1 imagery. Mapping wet snow and glaciers in remote alpine regions using a conventional single-pass, single-polarization SAR imagery requires an accurate Digital Elevation Model (DEM), but analysis of time-series ERS-1 data sets showed significant improvement in mapping accuracy and corresponding decrease in the need for a DEM. However, the capability of a single-polarization SAR to study alpine snow is limited to map wet snow cover only. Furthermore, multi-frequency, multi-polarization SAR can effectively map the extent of wet-snow regions without requiring any topographic information. Numerical simulations show that a similar technique could map dry snow cover; verification is ongoing. Thus, a multi-frequency, polarized SAR provides snow mapping capability from small to large scales. SIR-C/X-SAR also can map firn regions, which indicates the regions of annual snow accumulation or of glacier growth. This capability provides an important tool for mass-balance studies of glaciers.

The most fundamental snow cover property, in terms of water supply forecasting, is the snow-water equivalence: the total amount of water the snow would yield at a point if it melted. This variable has been traditionally measured at several hundred snow courses throughout the mountainous regions of the western U.S. How to extrapolate these widely dispersed measurements remains a fundamental problem in estimating the total water volume in the mountains. Numerical simulations with multi-frequency, dual-polarization SAR have shown it can monitor the spatial and temporal distributions of snow-water equivalence. Data analysis with SIR-C/X-SAR is ongoing. With accurate estimates of snow-covered area, detection of melting snow, and the measurement of the spatial distribution of snow water equivalence, we will better understand the most fundamental problems in snow hydrology: the spatial and temporal distributions of snow properties in alpine regions.

To derive the maximum benefit in terms of research or application out of remotely sensed soil moisture, the remote sensing data must be integrated into a geographic information system and analyzed taking into account the soil properties and land characteristics. Therefore, the required resolution of the remotely sensed soil moisture data is related to the intended usage and the ancillary data bases available for interpretation. It makes little sense to collect data globally at a resolution much higher than that of the ancillary data. It therefore appears that based on the range of projected applications and the available ancillary data bases that a spatial resolution on the order of 100 m to 200 m may be advantageous for processing and interpretation. It is true that the detail offered in higher resolution SAR data is interesting, however, it does not seem to be really needed except for mapping and other less time critical analyses.

The timing and frequency of observation are very important in the valuation of the remote sensing data for application and analysis. Surface soil moisture is diurnal in nature, generally decreasing during the day and rising slightly at night (in the absence of precipitation). The time of the observation will most certainly be considered in its use. Certain times of the day may offer certain advantages. For instance, to extrapolate a surface observation to the profile using a physically based approach, Jackson (1980) has shown that a predawn observation is advantageous. In fact, late afternoon observations of surface soil moisture using shorter wavelengths may provide little to no information. An improvement in this single observation could be made by utilizing diurnal changes, an a.m. and a p.m. observation. For bare soils this could provide a good estimate of the total surface flux during the day. Frequency of coverage is a much more difficult subject. Of course, this too is dependent on the application. It seems that daily coverage would be of greatest value for all regions. There is a significant sacrifice in increasing this, however, the next plateau would be three days. Observations at lower frequencies might only be of selected value.

Given this sampling requirement and the previous experimental results, it is clear that the currently planned international SARs can provide little beyond change detection for changing moisture conditions and mapping of wet alpine snow cover only.

The recent results indicating that soil moisture and snow water equivalence could indeed be measured accurately with spaceborne multiparameter SARs bring the remote sensing one step closer to providing some important variables to help understand and routinely monitor the hydrologic cycle. However, even though the status of SAR for measuring these quantities is very promising, it is recognized that there are a number of logical steps to be taken in order for the general scientific and public communities to benefit from this research.

Given the previous experimental results, and the recent algorithms derived to infer soil moisture and snow water equivalence, it is clear that single frequency SARs can provide little beyond change detection for changing moisture conditions. It is highly doubtful whether even the magnitude of the change in the soil moisture could be quantified from these types of single parameter SAR measurements without a significant number of risky assumptions. Therefore, the logical conclusion is that the ultimate goal should certainly be an operational spaceborne multiparameter SAR for routine soil moisture and snow water equivalence mapping. Recognizing that this is not likely to happen soon, we therefore urge the continued support of the NASA multiparameter airborne SAR program as a vehicle to continue the development and validation of soil moisture, snow wetness, and snow water equivalence algorithms in the short term with the long term goal the definition and launch of an operational SAR to monitor these important variables of the hydrologic cycle. Below we outline a few steps we consider necessary to achieve this goal:

(1) Combine all data collected during AIRSAR and SIR-C/X-SAR campaigns and analyze their ability to determine soil moisture empirically (sigma 0 vs. measured soil moisture) and using all available algorithms. Develop statistics to define how large the errors are, what type of errors, and what conditions the analysis or algorithms work or do not work well. Using both US and foreign campaigns should provide data from more than twenty sites and numerous conditions.

(2) Continue a vigorous research program to extend current algorithms to infer soil moisture from vegetated surfaces and to refine the snow water equivalence algorithms. The emphasis should be on well coordinated campaigns involving both active and passive microwave instruments over a variety of different terrain types.

(3) Examine the relationship between surface measurements and profile measurements of soil moisture to evaluate the severity of the problem, if it exists. Data exist to address the following question: How well can the profile moisture be modeled if the surface 5 cm of soil moisture can be measured every 3 days at a precision of +4% by volume? What are the performances of various models including simple regression models?

(4) Encourage land process modelers (mesoscale, GCM and runoff) to attempt to use measured soil moisture and snow water equivalence from SIR-C/X and AIRSAR campaigns in their modeling.