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An interpretation of methodologies for indirect measurement of soil water content
AGRICULTURAL AND FOREST METEOROLOGY ELSEVIER
Agricultural
and Forest Meteorology
77 (1995) 191-205
An interpretation of methodologies for indirect measurement of soil water content Toby N. Carlson ‘7*, Robert R. Gillies b, Thomas J. Schmugge ’ ’Department of Meteorology, Penn State University, University Park, PA 16802, USA h Earth System Science Center, Penn State University, Uniuersity Park, PA 16802, USA ’ USDA / ARS, Hydrology Laboratory, Belts&e, MD 20705, USA Received 24 June 1994; accepted 24 March 1995
Abstract Using a new technique referred to as the triangle method, surface soil water content and fractional vegetation cover were derived from surface radiant temperature measurements and normalized difference vegetation index (NDVI). Application of the technique is made with reference to NSOOl multispectral scanner measurements made by a C-130 aircraft over the Mahantango Watershed in Pennsylvania. The derived surface soil water content values were
compared with those obtained from the Push Broom Microwave Radiometer (PBMR) aboard the same aircraft and with in-situ ground measurements. A large disparity was found to exist between all three measurements, suggesting that the surface becomes decoupled from the deeper substrate in regions of rapid drying, where large vertical gradients in soil water content may exist near the surface.
1. Introduction
Current methods for remotely estimating soil water status are based on measurements of (1) surface radiant temperature (T,) (Carlson et al., 1981; Price, 1982; Carlson, 1986; Taconet et al., 1986), (2) a combination of To and vegetation index (Price, 1990; Carlson et al., 1994; Moran et al., 1994; Gillies and Carlson, 1995), or (3) microwave brightness temperatures (Schmugge et al., 1988; Wang et al., 1990; O’Neill et al., 1993). Parameters (e.g., evapotranspiration, canopy resistance) derived by any of these three methods require additional information and a reduction algorithm, such as a surface energy balance model.
* Corresponding
author
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A current and relevant issue in the operational use of these methods is verification of the derived values. Indeed, some studies show unacceptably large errors in the thermal infrared method (Hall et al., 1992). This has led to a greater emphasis on the use of microwave measurements, which tend to show good agreement with in-situ soil water content measurements when the microwave data are spatially smoothed. The problem in trying to calculate a surface energy budget, even if the soil water content profile with depth is correct is a complex one. This is because transpiration is dependent upon the distribution of vegetation, the depth of the root zone and its water content, whereas evaporation depends more on the surface soil water content. Surface radiant temperatures are highly sensitive to this surface soil water content (the top millimeter or so) and the temperature of the leaves, whereas the microwave brightness temperature measurements are more a function of the soil water content averaged over the top few centimeters. Even if the radiant temperature of the soil surface could be separated from that of the surrounding leaves, the microwave brightness and radiant surface temperatures are likely to indicate two very different values of soil water content. The implication here is that a single soil water content alone, regardless of its accuracy, may be insufficient to evaluate the correct evapotranspiration. This is particularly so in the presence of vegetation where both of these remote methods have difficulty in resolving the soil water content. It is not obvious, however, how to assign a representative soil water content that will permit accurate determination of the surface energy balance in a land surface model. An assessment of soil moisture parameters in current land surface models (Mahfouf, 199 1; Mahfouf and Noilhan, 1991) suggests that soil water content in these models is as much symbolic as real, in that parameters representing a particular value of soil water content may pertain to a variety of situations in which the surface soil water content may differ significantly from the mean over a layer from the surface to several centimeters below the surface. It is not uncommon in situations of rapid soil drying that a very large vertical gradient of soil water content develops close to the surface in bare soil patches, as opposed to a slowly varying profile at greater depths. In such instances, the recharge of water from below to the surface may be reduced due to low hydraulic conductivity in the dry surface layer (Capehart and Carlson, 1994). The result of rapid drying over a shallow layer is not only that the deep layer soil water content becomes decoupled from that at the surface, but that the spatial distribution of surface drying becomes inhomogeneous because of spatial variations in soil surface properties. Consequently, although T, over bare soil may be highly correlated with average soil water content over a layer several centimeters deep (Idso et al., 19751, relatively high To can sometimes occur when the deeper substrate (root zone) is moist (Carlson et al., 1990). For example, Wang et al. (1989) (their Fig. 41, using values of soil water content derived from microwave brightness temperatures obtained from a passive microwave radiometer, show that large spatial variations (implying large vertical variations) in soil water content occur within a few days after a heavy rain. It is true that the correlation between derived soil water content obtained from spatially averaged microwave brightness temperatures and measured soil water content is generally good (Schmugge et al., 1988; O’Neill et al., 1993; Jackson et al., 1994).
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Nevertheless, it is not surprising that absolute values of soil water content derived from microwave measurements may differ substantially from estimates derived from surface radiant temperatures (Wuthrich, 1994) or from in-situ methods (Schmugge et al., 1988; their Fig. 5) even when the correlation between thermal and microwave measurements is good. Such a case has been reported by Perry and Carlson (1988). Remote measurements of To, supported by simulations of the surface energy balance, indicate that To is very insensitive to soil water content in the root zone, except in conditions of extreme soil water deficit (Carlson et al., 1994; Arrett and Clark, 1994). Indeed, we find from inspection of a dozen images over differing types of terrain that the surface radiant temperature of dense vegetation is invariably less than a few degrees Celsius (“C) above the ambient air temperature. This insensitivity of To over vegetation is due to the fact that plants are able to maintain homeostasis by various means. Conversely, it is not unusual to encounter To well above air temperature over bare soil during conditions of strong sunlight. It follows that the spatial variation of surface radiant temperature must be highly dependent on the fraction of bare soil viewed by a radiometer and the surface soil water content (Carlson et al., 1994; Arrett and Clark, 1994; Friedel and Davis, 1994). As suggested by Gillies and Carlson (1995) an approximate solution for these two land surface parameters may be achieved using two independent parameters-T, and NDVI. This paper addresses various issues: (1) the range of derived surface soil water content and fractional vegetation cover over a watershed, (2) a comparison between these derived soil water content values, in-situ measurements and those from microwave (the PBMR), and (3) a brief reflection upon the need for a synergistic, multispectral approach to remotely sensing the soil water content.
2. Data analyses 2.1.
Measurements
Surface radiant temperature was derived from the NSOOI multispectral scanner onboard a NASA C-130 aircraft flying at approximately 2 km altitude over the Mahantango Watershed in Pennsylvania at about 11:45 am local time on July 18, 1990. The NSOOI multispectral scanner is similar to that aboard Landsat-TM, with channels in the solar and thermal bands. The thermal data (10.4-12.5 km) were first calibrated to blackbody temperature using the algorithms described by Markham and Barker (1986) and then corrected for atmospheric attenuation (principally due to water vapor and carbon dioxide absorption) to obtain at-surface values. This was done using MODTRAN (a high resolution version of LOWTRAN, LOWTRAN-7 users guide (Kneizys et al., 1988)). In addition, corrections were applied to the solar channels (3; 0.63-0.69 km and 4; 0.76-0.9 km) to obtain at-surface reflectances. The horizontal resolution of the surface pixels was therefore about 5 m at nadir (IFOV of the NSOOI sensor is 2.5 mrad) and resampled to 5 m after georeferencing the image to the pertinent UTM map projection.
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Both sets of corrections required local sounding data at the time of the aircraft overflight. As none were available for the Mahantango area, it was necessary to composite temperature and humidity soundings based on an interpolation of the nearest rawindsonde measurements and local surface observations. Both rawindsonde stations (Albany, NY, and Huntington, WV) were situated about 200-300 km from Mahantango, while the surface stations (Williamsport, Scranton and Allentown) were located between 50 and 100 km from the site. A standard vertical distribution of aerosols, adjusted for the reported visibility observations, was used. These corrections did not differ very much from those made with the standard summertime temperature and humidity sounding for mid-latitudes which is contained as a default sounding in MODTRAN. The correction to at-surface radiant temperatures varied with surface temperature, but was typically a few degrees Celsius or less. The normalized difference vegetation index (NDVI) was calculated using the following formula: NDvl =
(%ir
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( ‘nir
+
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where unrr is the surface reflectance (albedo) in the near infrared band (channel 4) and a, is the surface reflectance in the red band (channel 3). Correcting NDVI to at-surface values results in a somewhat larger range of the vegetation index (zero to 0.8) than in the uncorrected index (zero to 0.6). Mounted on board the C-130 was a PBMR (L-band; 21 cm wavelength) radiometer, which has been well described in the literature (Wang et al., 1989). Resolution for the PBMR was about 80 m at nadir. Brightness temperatures measured by this instrument are customarily calibrated to soil water content with the aid of in-situ measurements. This was done for the non-forested part of the watershed shown in Fig. 1. The theory, as described by Jackson et al. (1984), leads to a linear relationship between microwave brightness temperature and volumetric soil water content. 2.2. Reduction cover
of NSOOI measurements
to soil wuter content and fractional
vegetation
Initial inspection of the NSOOl image showed noticeable differences in temperatures and in visible reflectances between the northern and southern sides of the flight track due to greater illumination by the sun on southward-facing surfaces. To minimize this and the effect of terrain, only data within 20” of nadir over relatively flat, agricultural and forested land were retained, while those for steeply sloping surfaces were not included. Fig. 2 represents the data (NDVI; 7”) in the form of a scatterplot of T, versus fractional vegetation cover (Fr); NDVI has been transformed to Fr by a procedure described later in the text. The most significant aspect of this figure is the triangular cloud of points representing several million pixels. The pixel density is plotted logarithmically according to the density of points. This is done to aid visual inspection by maintaining contrast between regions of high and low pixel density without losing contrast in regions with a small number of pixels. The heaviest concentration of points
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Surface Radiant Temperature (Centigrade) Fig. 2. Scatterplot of surface radiant temperature PC) versus derived fractional vegetation cover (Fr; a transform of NDVI) for the area shown in Fig. I. Sloping lines marked by symbols (see legend) are isopleths of surface moisture availability CM,); the isopleth of M, = 0 conforms to the objectively determined warm edge (see Fig. 5). The vertical dashed line represents the approximateair temperature(28.X); the dewpoint temperature was 20°C.
corresponds to dense vegetation cover in the left hand side and in the vertex of the triangle; the majority of these pixels are forested (Fig. 3). Gillies and Carlson (1995) argue that a triangular distribution of pixels, such as that shown in Fig. 2, emerges given a full range of vegetation cover and surface soil water content. Moran et al. (1994) describe a similar feature, referred to as a trapezoid, which they obtain for measurements made over alfalfa. Vidal and Devaux-Ros (1995) apply the concept to forests, relating the location of the pixel within the trapezoid to a water deficit index (WDI). That the triangle has a narrow vertex implies a relative insensitivity of leaf temperature to soil water content, as compared to that along its base (the limit of bare soil). Indeed, we find that the temperatures in the triangle’s vertex are consistently close to air temperature (Fig. 2), whereas it is also evident from this figure that pixels near the base of the triangle can attain quite elevated values above air temperature. With increasing vegetation fraction, the bare soil signal becomes increasingly masked by the vegetation, resulting in a decrease in temperature. The locus of highest temperatures at differing amounts of bare soil and vegetation delineate a sharply defined boundary, which we call the ‘warm edge’ and which is assumed to represent a lower limiting (e.g., zero-valued) surface soil water content.
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Mahantango Watershed: Surface Temperature vs. NDVI (Classification signatures overlaid)
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Surface Radiant Temperature (Centigrade) Fig. 3. Same as Fig. 2 but with the ordinate expressed in terms of NDVI corrected to at-surface values and the moisture availability isopleths are omitted from the interior of the triangle. Land use categories as defined by the classification scheme (see Fig. 1) are overlaid. Note that the ellipses represent the probability of a pixel belonging to a particular class. For example, a pixel centered within the ellipse defined as class ‘vegetation I ’ may also be included within the forest class, but will be assigned to the vegetation I class since the probability that it belongs to this class is greater than that for forest.
Given this interpretation of the triangular pattern, a solution can be obtained for the surface soil moisture availability (M,) and the fractional vegetation cover (Fr) by the triangle method, as described by Gillies and Carlson (1995). kl, is here defined as the ratio of soil water content to that at field capacity (about 0.34 by volume for Mahantango). Solutions for M, and Fr are obtained with the aid of a soil-vegetationatmosphere-transfer (SVAT) model. The model is described in detail by Carlson (1986) Taconet et al. (1986), Carlson et al. (1990) and Lynn and Carlson (1990). Briefly stated, the SVAT model is essentially one-dimensional, time dependent, and operates from an initial set of conditions. It proceeds to simulate atmospheric, soil and plant variables up to a 24 h period starting near sunrise. The procedure for using the SVAT model is briefly described as follows: Given values of NDVI and T,, solutions for Fr as a function of NDVI and for M,, as a function of Fr and To are achieved by executing repeated simulations with the SVAT model within the theoretical range of these parameters, and matching the simulated and measured radiant temperatures at the time of satellite or aircraft overflight. The solution, however, depends on the physical interpretation of the triangle. Specifically, (1) the warm edge corresponds to an isopleth of zero extractable soil water content, and (2)
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limits for bare soil and 100% vegetation cover are identifiable with the base of the triangle and a point near the upper vertex, respectively. These constraints minimize the solution to the exact choice of vegetation and soil parameters in the SVAT model. A crucial element in the analysis is the determination of the warm edge. The selection of edge points is based upon the assumption that a certain small number of points at the edge is noise. We do not know what this number is, but it is chosen on the basis that the result be physically realistic. The important point to be made here is that we apply the same rules across cases, such that we maintain objectivity in defining the warm edge. Given this argument, the procedure was to sum the number of pixels from high to low values of NDVI over temperature bins 1°C wide until 0.15% of the total number of pixels in the envelope (about 1500) is reached. This definition allows the warm edge to correspond to a locus of points slightly inside the warm edge as determined by eye on the scatterplot. Fig. 4(a) and (b) illustrate the procedure. Thus, in choosing the warm edge objectively, the isopleth of zero moisture availability (M, = 0) is fixed and the solution for the interior points can be determined from the SVAT simulations. Simulations with the SVAT model show that the highest temperatures near the base of the triangle indeed correspond to a surface soil moisture availability very close to zero. Although it is not conclusive that the zero moisture availability isopleth coincides precisely with the warm side of the triangle, it can be shown that the temperature variation along the warm edge is consistent with varying Fr between values of 0 and 100% where M, is set equal to zero. A solution for the cold side of the triangle is not imposed and is not necessary, as this additional constraint would over specify the problem. Note that some pixels bordering the cold side of the triangle have temperatures near or below that of the air (285°C). These pixels constitute only a few percent of the total and from previous analyses of both high- and low-resolution data it can be shown that these are often a result of standing water or shading. Specifically, the method of solution is first to determine the relationship between Fr and NDVI by matching simulated and measured surface radiant temperatures along the warm edge. Once this relationship is determined, the SVAT model is executed over a full range of input M, and Fr. These simulations lead to a solution for M, as a function of Fr and T,. Both Fr, and M, vary between their limits of zero to 1.0 or 100% within the triangle (Fig. 2).
3. Implications
of the triangle
3. I. Soil moisture
variability
Vertical averages of soil water content were calculated from measurements made at about 20 sites at two depths (0.15 and 0.35 m> using a system of sampling rods with soil moisture access tubes for inserting neutron probes (A. Rogowski, personal communication, 1994). Additional surface (O-0.05 m) samples were taken gravimetrically at sites shown in Fig. 1. Soil water content values for 0.15 and 0.35 m depths indicate a generally moist soil, with volumetric water content values between 20 and 30%. This relatively moist substrate is consistent with the fact that about 0.9 inches of rain fell 3 days prior to July 18. In contrast, however, Fig. 2 implies a full range of surface soil
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water content over the area. Although further drying of the soil surface would undoubtedly shift the overall pixel distribution somewhat further toward the warm side of the triangle, it is evident from inspection of Fig. 4(a) and (b) that a substantial number of pixels had already migrated close to the dry limit (i.e., the warm edge), while many other points remained near the wet limit. 3.2. Issues
qf scale
A recurring issue for the application of derived satellite data (Carlson et al., 1995) is whether techniques for one scale are appropriate to another. In this case, we were interested to see whether the triangle and warm edge remain identifiable at all scales. To investigate, pixels were first agglomerated by a simple linear average of neighboring pixels from 5 m resolution (Fig. 5(A)) to 20 m resolution (Fig. 5(B)) and then to 80 m resolution (Fig. 5(C)) and finally to 320 m resolution (Fig. 5(D)). Note that the objectively determined warm edge remains fixed, whereas the visible edge moves successively toward colder temperatures with increasing pixel size to merge with the former. Thus, in Fig. 5(C) and (D), the triangular distribution of pixels almost coincides with the domain of M, isopleths shown in Fig. 2. These larger-scale composites resemble those found from AVHRR satellite images (1.1 km resolution) shown by Gillies and Carlson (1995). It is also evident from Fig. 5(A)-(D) that increasing pixel size does not destroy the triangle nor eliminate the warm edge. However, agglomeration not only tends to eliminate spurious warm and cold pixel values but erodes parts of the triangle where the initial pixel density is low. 3.3. Comparisons
of derived MO with microwave
and sur$ace measurements
Six areas identified in Fig. 1 (Tl-T6) were chosen to represent differing land use types within the triangle (see the caption of Fig. 6). The distribution within the triangle of these land cover categories is indicated in Fig. 3. The PBMR derived values of soil water content were directly compared to M, derived from the triangle method, but using resampled values of NDVI and TO at the same resolution of the PBMR (80 m). Although it appears that some correlation may exist at high values of soil water content, as measured by the PBMR, there appears to be little correlation when the entire sample is considered. Other scientists (Kustas et al., 1993) however, have found stronger correlations for other surfaces, e.g., semiarid rangeland. Overall though, a point by point correlation for this area in the Mahantango watershed was rather poor. It is possible that a weak correlation exists between surface soil water content measurements (B5, B6, Pl, P2 and P3 in Fig. 1) for the O-5 cm layer and those obtained from the triangle method (Fig. 7). Note that the latter values are much lower than the former; a similar result was found by Perry and Carlson (1988). Agreement of the derived surface soil water content with soil water content measurements made at deeper levels was virtually nil. Poor agreement was also found between soil water content values inferred from microwave brightness temperatures and those measured at any depth in the ground.
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4. Conclusions Although the seeming lack of agreement between soil water content values obtained by microwave, by the triangle method and by in-situ measurement may seem puzzling, if not distressing, we can offer a physical explanation for the lack of agreement. This leads to some interesting implications. Simply stated, the microwave and triangle methods derive soil water content appropriate to different depths, the values of which may be uncorrelated during conditions of rapid soil drying. The triangular pattern shown in Fig. 2 implies that a full range in surface soil moisture availability, from zero to one, was present over a full range of vegetation fraction. As the deeper-layer water content was greater than 20% by volume over most of the area, it can be surmised that there were present not only a large spatial variability in surface soil water content but, in certain areas, large vertical differences in soil water content. The rapid formation of such large vertical soil water variations is an indication that the drier portions of the soil surface become decoupled from the deeper layer, insofar as there would be little correlation between the water content at the surface and that in the root zone in these regions. Several aspects are especially clear from this study: (1) a simple linear correlation
Triangle-derived Moisture Availability vs. PBMR Volumetric Water Content 1.2
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between T, and NDVI is not representative of the soil water content given a full range of surface moisture conditions; (2) the vertical variation in soil water content cannot be captured by either microwave or thermal methods at times of rapid drying; (3) the spatial variations in surface soil water content implied by T, tend to be appreciably higher than those determined by other methods such as microwave, and (4) drying at the soil surface proceeds rapidly and unevenly. This leads to a decoupling of the root zone soil water content from that at the surface. Our recent (unpublished) simulations with a hydrological model (Capehart and Carlson, 1994) indicate that the rate of drying in this shallow surface layer is highly sensitive to the choice of hydraulic conductivity when the soil water content is below about 50% of field capacity. The emergence of the triangle seems to depend more on the number of pixels rather than just pixel resolution. This is significant because it means that the triangle can be found in lower resolution images, such as AVHRR (Gillies and Carlson, 1995) and even GOES (Diak et al., 1995). There are, therefore, implications for extension of the triangle concept to regional-scale analyses and operational uses. It also appears that a combination of microwave and thermal methods is necessary to obtain a more complete picture of the soil water content than either method is capable of resolving by itself. Surface radiant temperature, coupled with NDVI, may yield a soil water content more suitable to determining the surface energy budget, whereas microwave brightness temperatures are capable of resolving an integrated soil water content. Interestingly, Kustas et al. (1993) found that the O-5 cm layer appears to be important to the relative contribution of soil evaporation. This relationship is probably
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strongest for sparse cover areas; nevertheless, it has implications as to whether the surface soil water content will best describe the surface energy balance over all surfaces. Still, together, the two types of measurements yield a more complete picture of the vertical gradient of soil water content, from which a recent history of soil drying and possibly some information on surface properties (e.g., soil hydraulic conductivity) may be inferred. Given this conclusion, it would be appropriate to re-examine existing sources of microwave and corresponding surface radiant temperatures and NDVI, along with field measurements from major programs (e.g., PIPE, MONSOON-90 and Washita, OK), in order to address this issue further.
Acknowledgements We would like to thank David Ripley for his assistance in the preparation of the diagrams. The research could not have been conducted without the financial assistance of the following funding agencies: the Hydrology Laboratory of the USDA/ARS in Beltsville, MD (as part of a cooperative research agreement, contract 58- 1270 3030) and the National Aeronautics and Space Administration (NASA, contract NAGW-2647). We are also indebted to Andrew Rogowski of the Northeast Watershed Research Center, USDA-ARS, University Park, PA, for use of his soil water content measurements. We would also like to thank Doug Miller of Earth System Science Center (ESSC) for his assistance. This research constitutes part of the Susquehanna River Basin Experiment (SRBEX) within the EOS program of ESSC.
References Arrett, R.W. and Clark, C.A., 1994. Functional relationship among soil moisture, vegetation cover and surface fluxes. Proceedings 21st Conference in Agricultural and Forest Meteorology, American Meteorological Society, March 7-l 1, 1994, San Diego, CA 537-538. Capehart, W.J. and Carlson, T.N., 1994. Estimating near-surface soil moisture availability using a meteorologically driven soil water profile model. J. Hydrol.. 160: I-20. Carlson, T.N., 1986. Regional-scale estimates of surface moisture availability and thermal inertia using remote thermal measurements. Remote Sens. Rev., 1: 197-247. Carlson, T.N., Dodd, J.K., Benjamin, S.G. and Cooper, J.N., 1981. Remote estimation of surface energy balance, moisture availability and thermal inertia. J. Appl. Meteorol., 20: 67-87. Carlson, T.N., Perry, E.M. and Schmugge, T.J., 1990. Remote estimation of soil moisture availability and fractional vegetation cover for agricultural fields. Agric. For. Meteorol., 52: 45-69. Carlson, T.N., Gillies, R.R. and Perry, E.M., 1994. A method to make use of thermal infrared temperature and NDVI measurements to infer soil water content and fractional vegetation cover. Remote Sens. Rev., 52: 45-s9. Carlson, T.N., Taconet, 0.. Vidal, A., Gillies, R.R., Olioso, A. and Humes, K., 1995. An overview of the workshop on thermal remote sensing held at La Londe les Maures, France, 20-24, 1993. Agric. For. Meteorol., in press and Remote Sens. Rev., in press. Diak, G.R., Rabin, R.M., Gallo, K.P. and Neale, C.M., 1995. Regional-scale comparisons of NDVI, soil moisture indices from surface and microwave data and surface energy budgets evaluated from satellite and in-situ data. Remote Sens. Rev., submitted for publication,
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Friedel, M.A. and Davis, F.W., 1994. Sources of variation in radioactive surface temperature over a tall grass prairie. Remote Sens. Environ., 48: l-17. Gillies, R.R. and Carlson, T.N., 1995. Thermal remote sensing of surface soil water content with partial vegetation cover for incorporation into mesoscale prediction models. J. Appl. Meteorol., 34: 745-756. Hall, F.G., Huemmrich, K.F., Geotz, S.J., Sellers, P.J. and Nickerson, J.E., 1992. Satellite remote sensing of surface energy balance: success, failures and unresolved issues in FIFE. J. Geophys. Res., FIFE Special Issue, 19061-19090. Idso, S.B., Schmugge, T.J., Jackson, R.D. and Reginato, R.J., 1975. The utility of surface temperature measurements for remote sensing of soil water studies. J. Geophys. Res., 80: 3044-3049. Jackson, T.J., Schmugge, T.J. and O’Neill, P.E., 1984. Passive microwave remote sensing of soil moisture from an aircraft platform. Remote Sens. Environ., 14: 135-151. Jackson, T.J., Engman, E.T., Le Vine, D., Schmugge, T.J., Lang, R., Wood, E. and Teng, W., 1994. Multitemporal passive microwave in Machydro’90. IEEE Trans. Geosci. Remote Sens., 31(l): 201-206. Kneizys, F.X., Shettle, E.P., Abreu, L.W., Chetwynd, J.H., Anderson, G.P., Gallery, W.O., Selby, J.E. and Clough, S.A., 1988. Users Guide to Lowtran-7, Air Force Geophysics Laboratory Research Paper 401010, Project 7670, 34 pp. Kustas, W.P., Schmugge, T.J., Humes, KS., Jackson, T.J., Parry, R.. Weltz, M.A. and Moran, M.S., 1993. Relationships between evaporative fraction and remotely sensed vegetation index and microwave brightness temperature for semiarid rangelands. J. Appl. Meteorol., 32: 1781-1790. Lynn, B. and Carlson, T.N., 1990. A model illustrating plant versus external control of transpiration. Agric. For. Meteorol., 52: 5-43. Mahfouf, J.F., 1991. Analysis of soil moisture from near surface parameters: a feasibility study. J. Appl. Meteorol., 30: 1534- 1547. Mahfouf, J.F. and Noilhan, I., 1991. Comparative study of various formulations of evaporation from bare soil using in-situ data. J. Appl. Meteorol., 30: 1354-1363. Markham, B.L. and Barker, J.L., 1986. Landsat MSS and TM post-calibration dynamic ranges, exoatmospheric reflectances and at-satellite temperatures. Landsat Technical Notes (EOSAT), August 1986, No. 1, pp. 3-8. Moran, M.S., Clarke, T.R., moue, Y. and Vidal, A., 1994. Estimating crop water deficit using the relation between surface-air temperature and spectral vegetation index. Remote Sens. Environ., submitted for publication. O’Neill, P.E., Chauhan, N.S. and Jackson, T.J., 1993. Synergistic use of active and passive microwave in soil moisture estimation. IEEE Trans. Geosci. Remote Sens., submitted for publication. Perry, E.M. and Carlson, T.N., 1988. Comparison of active microwave soil water content with infrared surface temperatures and surface moisture availability. Water Resour. Res., 24: 18 l8- 1824. Price, J.C., 1982. Estimation of regional scale evapotranspiration through analysis of satellite thermal-infrared data. IEEE Trans. Geosci. Remote Sens., GE-20: 286-292. Price, J.C., 1990. Using spatial context in satellite data to infer regional scale evapotranspiration. IEEE Trans. Geosci. Remote Sens., 28: 940-948. Schmugge, T.J., Wang, J.R. and Asrar, G., 1988. Results from the push broom microwave radiometer flights near the Konza Prairie in 1985. IEEE Trans. Geosci. Remote Sens., 26: 590-596. Taconet, 0.. Carlson, T.N., Bernard, R. and Vidal-Madjar, D., 1986. Evaluation of a surface/vegetation model using satellite infrared surface temperatures. J. Clim. Appl. Meteorol., 25: 1752-1767. Vidal, A. and Devaux-Ros, C., 1995. Forest water stress monitoring for tire risk management using Landsat TM data. Agric. For. Meteorol., in press. Wang, J.R., Shiue, J.C., Schmugge, T.J. and Engman, E.T., 1989. Mapping surface soil moisture with L-band radiometric measurements. Remote Sens. Environ., 27: 305-3 12. Wang, J.R., Shiue, J.C., Schmugge, T.J. and Engman, E.T., 1990. The L-band PBMR measurements of surface soil moisture in FIFE. IEEE Trans. Geosci. Remote Sens., 28: 906-914. Wuthrich, M., 1994. ERS-I SAR compared to thermal infrared to estimate surface soil moisture. 2lst Conference on Agricultural and Forest Meteorology, AMS, March 7-10, 1994, San Diego, CA, pp. 197-200.
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An interpretation of methodologies for indirect measurement of soil water content Toby N. Carlson ‘7*, Robert R. Gillies b, Thomas J. Schmugge ’ ’Department of Meteorology, Penn State University, University Park, PA 16802, USA h Earth System Science Center, Penn State University, Uniuersity Park, PA 16802, USA ’ USDA / ARS, Hydrology Laboratory, Belts&e, MD 20705, USA Received 24 June 1994; accepted 24 March 1995
Abstract Using a new technique referred to as the triangle method, surface soil water content and fractional vegetation cover were derived from surface radiant temperature measurements and normalized difference vegetation index (NDVI). Application of the technique is made with reference to NSOOl multispectral scanner measurements made by a C-130 aircraft over the Mahantango Watershed in Pennsylvania. The derived surface soil water content values were
compared with those obtained from the Push Broom Microwave Radiometer (PBMR) aboard the same aircraft and with in-situ ground measurements. A large disparity was found to exist between all three measurements, suggesting that the surface becomes decoupled from the deeper substrate in regions of rapid drying, where large vertical gradients in soil water content may exist near the surface.
1. Introduction
Current methods for remotely estimating soil water status are based on measurements of (1) surface radiant temperature (T,) (Carlson et al., 1981; Price, 1982; Carlson, 1986; Taconet et al., 1986), (2) a combination of To and vegetation index (Price, 1990; Carlson et al., 1994; Moran et al., 1994; Gillies and Carlson, 1995), or (3) microwave brightness temperatures (Schmugge et al., 1988; Wang et al., 1990; O’Neill et al., 1993). Parameters (e.g., evapotranspiration, canopy resistance) derived by any of these three methods require additional information and a reduction algorithm, such as a surface energy balance model.
* Corresponding
author
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A current and relevant issue in the operational use of these methods is verification of the derived values. Indeed, some studies show unacceptably large errors in the thermal infrared method (Hall et al., 1992). This has led to a greater emphasis on the use of microwave measurements, which tend to show good agreement with in-situ soil water content measurements when the microwave data are spatially smoothed. The problem in trying to calculate a surface energy budget, even if the soil water content profile with depth is correct is a complex one. This is because transpiration is dependent upon the distribution of vegetation, the depth of the root zone and its water content, whereas evaporation depends more on the surface soil water content. Surface radiant temperatures are highly sensitive to this surface soil water content (the top millimeter or so) and the temperature of the leaves, whereas the microwave brightness temperature measurements are more a function of the soil water content averaged over the top few centimeters. Even if the radiant temperature of the soil surface could be separated from that of the surrounding leaves, the microwave brightness and radiant surface temperatures are likely to indicate two very different values of soil water content. The implication here is that a single soil water content alone, regardless of its accuracy, may be insufficient to evaluate the correct evapotranspiration. This is particularly so in the presence of vegetation where both of these remote methods have difficulty in resolving the soil water content. It is not obvious, however, how to assign a representative soil water content that will permit accurate determination of the surface energy balance in a land surface model. An assessment of soil moisture parameters in current land surface models (Mahfouf, 199 1; Mahfouf and Noilhan, 1991) suggests that soil water content in these models is as much symbolic as real, in that parameters representing a particular value of soil water content may pertain to a variety of situations in which the surface soil water content may differ significantly from the mean over a layer from the surface to several centimeters below the surface. It is not uncommon in situations of rapid soil drying that a very large vertical gradient of soil water content develops close to the surface in bare soil patches, as opposed to a slowly varying profile at greater depths. In such instances, the recharge of water from below to the surface may be reduced due to low hydraulic conductivity in the dry surface layer (Capehart and Carlson, 1994). The result of rapid drying over a shallow layer is not only that the deep layer soil water content becomes decoupled from that at the surface, but that the spatial distribution of surface drying becomes inhomogeneous because of spatial variations in soil surface properties. Consequently, although T, over bare soil may be highly correlated with average soil water content over a layer several centimeters deep (Idso et al., 19751, relatively high To can sometimes occur when the deeper substrate (root zone) is moist (Carlson et al., 1990). For example, Wang et al. (1989) (their Fig. 41, using values of soil water content derived from microwave brightness temperatures obtained from a passive microwave radiometer, show that large spatial variations (implying large vertical variations) in soil water content occur within a few days after a heavy rain. It is true that the correlation between derived soil water content obtained from spatially averaged microwave brightness temperatures and measured soil water content is generally good (Schmugge et al., 1988; O’Neill et al., 1993; Jackson et al., 1994).
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Nevertheless, it is not surprising that absolute values of soil water content derived from microwave measurements may differ substantially from estimates derived from surface radiant temperatures (Wuthrich, 1994) or from in-situ methods (Schmugge et al., 1988; their Fig. 5) even when the correlation between thermal and microwave measurements is good. Such a case has been reported by Perry and Carlson (1988). Remote measurements of To, supported by simulations of the surface energy balance, indicate that To is very insensitive to soil water content in the root zone, except in conditions of extreme soil water deficit (Carlson et al., 1994; Arrett and Clark, 1994). Indeed, we find from inspection of a dozen images over differing types of terrain that the surface radiant temperature of dense vegetation is invariably less than a few degrees Celsius (“C) above the ambient air temperature. This insensitivity of To over vegetation is due to the fact that plants are able to maintain homeostasis by various means. Conversely, it is not unusual to encounter To well above air temperature over bare soil during conditions of strong sunlight. It follows that the spatial variation of surface radiant temperature must be highly dependent on the fraction of bare soil viewed by a radiometer and the surface soil water content (Carlson et al., 1994; Arrett and Clark, 1994; Friedel and Davis, 1994). As suggested by Gillies and Carlson (1995) an approximate solution for these two land surface parameters may be achieved using two independent parameters-T, and NDVI. This paper addresses various issues: (1) the range of derived surface soil water content and fractional vegetation cover over a watershed, (2) a comparison between these derived soil water content values, in-situ measurements and those from microwave (the PBMR), and (3) a brief reflection upon the need for a synergistic, multispectral approach to remotely sensing the soil water content.
2. Data analyses 2.1.
Measurements
Surface radiant temperature was derived from the NSOOI multispectral scanner onboard a NASA C-130 aircraft flying at approximately 2 km altitude over the Mahantango Watershed in Pennsylvania at about 11:45 am local time on July 18, 1990. The NSOOI multispectral scanner is similar to that aboard Landsat-TM, with channels in the solar and thermal bands. The thermal data (10.4-12.5 km) were first calibrated to blackbody temperature using the algorithms described by Markham and Barker (1986) and then corrected for atmospheric attenuation (principally due to water vapor and carbon dioxide absorption) to obtain at-surface values. This was done using MODTRAN (a high resolution version of LOWTRAN, LOWTRAN-7 users guide (Kneizys et al., 1988)). In addition, corrections were applied to the solar channels (3; 0.63-0.69 km and 4; 0.76-0.9 km) to obtain at-surface reflectances. The horizontal resolution of the surface pixels was therefore about 5 m at nadir (IFOV of the NSOOI sensor is 2.5 mrad) and resampled to 5 m after georeferencing the image to the pertinent UTM map projection.
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Both sets of corrections required local sounding data at the time of the aircraft overflight. As none were available for the Mahantango area, it was necessary to composite temperature and humidity soundings based on an interpolation of the nearest rawindsonde measurements and local surface observations. Both rawindsonde stations (Albany, NY, and Huntington, WV) were situated about 200-300 km from Mahantango, while the surface stations (Williamsport, Scranton and Allentown) were located between 50 and 100 km from the site. A standard vertical distribution of aerosols, adjusted for the reported visibility observations, was used. These corrections did not differ very much from those made with the standard summertime temperature and humidity sounding for mid-latitudes which is contained as a default sounding in MODTRAN. The correction to at-surface radiant temperatures varied with surface temperature, but was typically a few degrees Celsius or less. The normalized difference vegetation index (NDVI) was calculated using the following formula: NDvl =
(%ir
-
( ‘nir
+
‘r> ‘1.1
(1)
where unrr is the surface reflectance (albedo) in the near infrared band (channel 4) and a, is the surface reflectance in the red band (channel 3). Correcting NDVI to at-surface values results in a somewhat larger range of the vegetation index (zero to 0.8) than in the uncorrected index (zero to 0.6). Mounted on board the C-130 was a PBMR (L-band; 21 cm wavelength) radiometer, which has been well described in the literature (Wang et al., 1989). Resolution for the PBMR was about 80 m at nadir. Brightness temperatures measured by this instrument are customarily calibrated to soil water content with the aid of in-situ measurements. This was done for the non-forested part of the watershed shown in Fig. 1. The theory, as described by Jackson et al. (1984), leads to a linear relationship between microwave brightness temperature and volumetric soil water content. 2.2. Reduction cover
of NSOOI measurements
to soil wuter content and fractional
vegetation
Initial inspection of the NSOOl image showed noticeable differences in temperatures and in visible reflectances between the northern and southern sides of the flight track due to greater illumination by the sun on southward-facing surfaces. To minimize this and the effect of terrain, only data within 20” of nadir over relatively flat, agricultural and forested land were retained, while those for steeply sloping surfaces were not included. Fig. 2 represents the data (NDVI; 7”) in the form of a scatterplot of T, versus fractional vegetation cover (Fr); NDVI has been transformed to Fr by a procedure described later in the text. The most significant aspect of this figure is the triangular cloud of points representing several million pixels. The pixel density is plotted logarithmically according to the density of points. This is done to aid visual inspection by maintaining contrast between regions of high and low pixel density without losing contrast in regions with a small number of pixels. The heaviest concentration of points
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Surface Radiant Temperature (Centigrade) Fig. 2. Scatterplot of surface radiant temperature PC) versus derived fractional vegetation cover (Fr; a transform of NDVI) for the area shown in Fig. I. Sloping lines marked by symbols (see legend) are isopleths of surface moisture availability CM,); the isopleth of M, = 0 conforms to the objectively determined warm edge (see Fig. 5). The vertical dashed line represents the approximateair temperature(28.X); the dewpoint temperature was 20°C.
corresponds to dense vegetation cover in the left hand side and in the vertex of the triangle; the majority of these pixels are forested (Fig. 3). Gillies and Carlson (1995) argue that a triangular distribution of pixels, such as that shown in Fig. 2, emerges given a full range of vegetation cover and surface soil water content. Moran et al. (1994) describe a similar feature, referred to as a trapezoid, which they obtain for measurements made over alfalfa. Vidal and Devaux-Ros (1995) apply the concept to forests, relating the location of the pixel within the trapezoid to a water deficit index (WDI). That the triangle has a narrow vertex implies a relative insensitivity of leaf temperature to soil water content, as compared to that along its base (the limit of bare soil). Indeed, we find that the temperatures in the triangle’s vertex are consistently close to air temperature (Fig. 2), whereas it is also evident from this figure that pixels near the base of the triangle can attain quite elevated values above air temperature. With increasing vegetation fraction, the bare soil signal becomes increasingly masked by the vegetation, resulting in a decrease in temperature. The locus of highest temperatures at differing amounts of bare soil and vegetation delineate a sharply defined boundary, which we call the ‘warm edge’ and which is assumed to represent a lower limiting (e.g., zero-valued) surface soil water content.
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Mahantango Watershed: Surface Temperature vs. NDVI (Classification signatures overlaid)
0.8
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Surface Radiant Temperature (Centigrade) Fig. 3. Same as Fig. 2 but with the ordinate expressed in terms of NDVI corrected to at-surface values and the moisture availability isopleths are omitted from the interior of the triangle. Land use categories as defined by the classification scheme (see Fig. 1) are overlaid. Note that the ellipses represent the probability of a pixel belonging to a particular class. For example, a pixel centered within the ellipse defined as class ‘vegetation I ’ may also be included within the forest class, but will be assigned to the vegetation I class since the probability that it belongs to this class is greater than that for forest.
Given this interpretation of the triangular pattern, a solution can be obtained for the surface soil moisture availability (M,) and the fractional vegetation cover (Fr) by the triangle method, as described by Gillies and Carlson (1995). kl, is here defined as the ratio of soil water content to that at field capacity (about 0.34 by volume for Mahantango). Solutions for M, and Fr are obtained with the aid of a soil-vegetationatmosphere-transfer (SVAT) model. The model is described in detail by Carlson (1986) Taconet et al. (1986), Carlson et al. (1990) and Lynn and Carlson (1990). Briefly stated, the SVAT model is essentially one-dimensional, time dependent, and operates from an initial set of conditions. It proceeds to simulate atmospheric, soil and plant variables up to a 24 h period starting near sunrise. The procedure for using the SVAT model is briefly described as follows: Given values of NDVI and T,, solutions for Fr as a function of NDVI and for M,, as a function of Fr and To are achieved by executing repeated simulations with the SVAT model within the theoretical range of these parameters, and matching the simulated and measured radiant temperatures at the time of satellite or aircraft overflight. The solution, however, depends on the physical interpretation of the triangle. Specifically, (1) the warm edge corresponds to an isopleth of zero extractable soil water content, and (2)
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limits for bare soil and 100% vegetation cover are identifiable with the base of the triangle and a point near the upper vertex, respectively. These constraints minimize the solution to the exact choice of vegetation and soil parameters in the SVAT model. A crucial element in the analysis is the determination of the warm edge. The selection of edge points is based upon the assumption that a certain small number of points at the edge is noise. We do not know what this number is, but it is chosen on the basis that the result be physically realistic. The important point to be made here is that we apply the same rules across cases, such that we maintain objectivity in defining the warm edge. Given this argument, the procedure was to sum the number of pixels from high to low values of NDVI over temperature bins 1°C wide until 0.15% of the total number of pixels in the envelope (about 1500) is reached. This definition allows the warm edge to correspond to a locus of points slightly inside the warm edge as determined by eye on the scatterplot. Fig. 4(a) and (b) illustrate the procedure. Thus, in choosing the warm edge objectively, the isopleth of zero moisture availability (M, = 0) is fixed and the solution for the interior points can be determined from the SVAT simulations. Simulations with the SVAT model show that the highest temperatures near the base of the triangle indeed correspond to a surface soil moisture availability very close to zero. Although it is not conclusive that the zero moisture availability isopleth coincides precisely with the warm side of the triangle, it can be shown that the temperature variation along the warm edge is consistent with varying Fr between values of 0 and 100% where M, is set equal to zero. A solution for the cold side of the triangle is not imposed and is not necessary, as this additional constraint would over specify the problem. Note that some pixels bordering the cold side of the triangle have temperatures near or below that of the air (285°C). These pixels constitute only a few percent of the total and from previous analyses of both high- and low-resolution data it can be shown that these are often a result of standing water or shading. Specifically, the method of solution is first to determine the relationship between Fr and NDVI by matching simulated and measured surface radiant temperatures along the warm edge. Once this relationship is determined, the SVAT model is executed over a full range of input M, and Fr. These simulations lead to a solution for M, as a function of Fr and T,. Both Fr, and M, vary between their limits of zero to 1.0 or 100% within the triangle (Fig. 2).
3. Implications
of the triangle
3. I. Soil moisture
variability
Vertical averages of soil water content were calculated from measurements made at about 20 sites at two depths (0.15 and 0.35 m> using a system of sampling rods with soil moisture access tubes for inserting neutron probes (A. Rogowski, personal communication, 1994). Additional surface (O-0.05 m) samples were taken gravimetrically at sites shown in Fig. 1. Soil water content values for 0.15 and 0.35 m depths indicate a generally moist soil, with volumetric water content values between 20 and 30%. This relatively moist substrate is consistent with the fact that about 0.9 inches of rain fell 3 days prior to July 18. In contrast, however, Fig. 2 implies a full range of surface soil
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water content over the area. Although further drying of the soil surface would undoubtedly shift the overall pixel distribution somewhat further toward the warm side of the triangle, it is evident from inspection of Fig. 4(a) and (b) that a substantial number of pixels had already migrated close to the dry limit (i.e., the warm edge), while many other points remained near the wet limit. 3.2. Issues
qf scale
A recurring issue for the application of derived satellite data (Carlson et al., 1995) is whether techniques for one scale are appropriate to another. In this case, we were interested to see whether the triangle and warm edge remain identifiable at all scales. To investigate, pixels were first agglomerated by a simple linear average of neighboring pixels from 5 m resolution (Fig. 5(A)) to 20 m resolution (Fig. 5(B)) and then to 80 m resolution (Fig. 5(C)) and finally to 320 m resolution (Fig. 5(D)). Note that the objectively determined warm edge remains fixed, whereas the visible edge moves successively toward colder temperatures with increasing pixel size to merge with the former. Thus, in Fig. 5(C) and (D), the triangular distribution of pixels almost coincides with the domain of M, isopleths shown in Fig. 2. These larger-scale composites resemble those found from AVHRR satellite images (1.1 km resolution) shown by Gillies and Carlson (1995). It is also evident from Fig. 5(A)-(D) that increasing pixel size does not destroy the triangle nor eliminate the warm edge. However, agglomeration not only tends to eliminate spurious warm and cold pixel values but erodes parts of the triangle where the initial pixel density is low. 3.3. Comparisons
of derived MO with microwave
and sur$ace measurements
Six areas identified in Fig. 1 (Tl-T6) were chosen to represent differing land use types within the triangle (see the caption of Fig. 6). The distribution within the triangle of these land cover categories is indicated in Fig. 3. The PBMR derived values of soil water content were directly compared to M, derived from the triangle method, but using resampled values of NDVI and TO at the same resolution of the PBMR (80 m). Although it appears that some correlation may exist at high values of soil water content, as measured by the PBMR, there appears to be little correlation when the entire sample is considered. Other scientists (Kustas et al., 1993) however, have found stronger correlations for other surfaces, e.g., semiarid rangeland. Overall though, a point by point correlation for this area in the Mahantango watershed was rather poor. It is possible that a weak correlation exists between surface soil water content measurements (B5, B6, Pl, P2 and P3 in Fig. 1) for the O-5 cm layer and those obtained from the triangle method (Fig. 7). Note that the latter values are much lower than the former; a similar result was found by Perry and Carlson (1988). Agreement of the derived surface soil water content with soil water content measurements made at deeper levels was virtually nil. Poor agreement was also found between soil water content values inferred from microwave brightness temperatures and those measured at any depth in the ground.
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4. Conclusions Although the seeming lack of agreement between soil water content values obtained by microwave, by the triangle method and by in-situ measurement may seem puzzling, if not distressing, we can offer a physical explanation for the lack of agreement. This leads to some interesting implications. Simply stated, the microwave and triangle methods derive soil water content appropriate to different depths, the values of which may be uncorrelated during conditions of rapid soil drying. The triangular pattern shown in Fig. 2 implies that a full range in surface soil moisture availability, from zero to one, was present over a full range of vegetation fraction. As the deeper-layer water content was greater than 20% by volume over most of the area, it can be surmised that there were present not only a large spatial variability in surface soil water content but, in certain areas, large vertical differences in soil water content. The rapid formation of such large vertical soil water variations is an indication that the drier portions of the soil surface become decoupled from the deeper layer, insofar as there would be little correlation between the water content at the surface and that in the root zone in these regions. Several aspects are especially clear from this study: (1) a simple linear correlation
Triangle-derived Moisture Availability vs. PBMR Volumetric Water Content 1.2
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Triangle-derived volumetric water content vs. field measurements. 20 , 8 I
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between T, and NDVI is not representative of the soil water content given a full range of surface moisture conditions; (2) the vertical variation in soil water content cannot be captured by either microwave or thermal methods at times of rapid drying; (3) the spatial variations in surface soil water content implied by T, tend to be appreciably higher than those determined by other methods such as microwave, and (4) drying at the soil surface proceeds rapidly and unevenly. This leads to a decoupling of the root zone soil water content from that at the surface. Our recent (unpublished) simulations with a hydrological model (Capehart and Carlson, 1994) indicate that the rate of drying in this shallow surface layer is highly sensitive to the choice of hydraulic conductivity when the soil water content is below about 50% of field capacity. The emergence of the triangle seems to depend more on the number of pixels rather than just pixel resolution. This is significant because it means that the triangle can be found in lower resolution images, such as AVHRR (Gillies and Carlson, 1995) and even GOES (Diak et al., 1995). There are, therefore, implications for extension of the triangle concept to regional-scale analyses and operational uses. It also appears that a combination of microwave and thermal methods is necessary to obtain a more complete picture of the soil water content than either method is capable of resolving by itself. Surface radiant temperature, coupled with NDVI, may yield a soil water content more suitable to determining the surface energy budget, whereas microwave brightness temperatures are capable of resolving an integrated soil water content. Interestingly, Kustas et al. (1993) found that the O-5 cm layer appears to be important to the relative contribution of soil evaporation. This relationship is probably
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strongest for sparse cover areas; nevertheless, it has implications as to whether the surface soil water content will best describe the surface energy balance over all surfaces. Still, together, the two types of measurements yield a more complete picture of the vertical gradient of soil water content, from which a recent history of soil drying and possibly some information on surface properties (e.g., soil hydraulic conductivity) may be inferred. Given this conclusion, it would be appropriate to re-examine existing sources of microwave and corresponding surface radiant temperatures and NDVI, along with field measurements from major programs (e.g., PIPE, MONSOON-90 and Washita, OK), in order to address this issue further.
Acknowledgements We would like to thank David Ripley for his assistance in the preparation of the diagrams. The research could not have been conducted without the financial assistance of the following funding agencies: the Hydrology Laboratory of the USDA/ARS in Beltsville, MD (as part of a cooperative research agreement, contract 58- 1270 3030) and the National Aeronautics and Space Administration (NASA, contract NAGW-2647). We are also indebted to Andrew Rogowski of the Northeast Watershed Research Center, USDA-ARS, University Park, PA, for use of his soil water content measurements. We would also like to thank Doug Miller of Earth System Science Center (ESSC) for his assistance. This research constitutes part of the Susquehanna River Basin Experiment (SRBEX) within the EOS program of ESSC.
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