Current research being conducted in remote sensing techniques for measuring hydrologic parameters and variables deals with runoff curve numbers (CN), evapotranspiration (ET), and soil moisture. The CN and ET research utilizes visible and infrared measurements. Soil moisture investigations focus on the microwave region of the electromagnetic spectrum

Jackson, T. J.

English

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USDA-ARS Hydrology Laboratory
T. J. Jackson
MISWG Hydrology Workshop
April 269 1982
The USDA-ARS Hydrology Laboratory is investigating remote sensing techniques
for measuring hydrologic parameters and variables. Current research deals
with runoff Curve Numbers (CN), evapotranspiratlon (ET), and soll moisture.
CN and ET research utilizes visible and infrared measurements and soll
moisture investigations focus on the microwave region of the electromagnetic
spectrum. Previous studies have involved visible and near infrared data in
estimating the percent of impervious area.
CN research is a_med at developing and evaluating methods for using remote
sensing for the USDA-SCS. The role of remote sensing in the SCS procedures is
shown in Figure i. It is essentially an alternative source of land cover
information. However, Sources such as Landsat also have the advantage of a
computer compatible format.
Whether or not remote sensing will eventually play an operational role will
depend upon its cost-effectiveness. Improved spectral and spatial measurements
will influence the effectiveness side of these tradeoffs.
Curve number accuracy is limited by the number of land cover categories that
can be reliably defined, which in turn depends upon the available spectral
bands and the spatial resolution. In this application, the accuracy can be
evaluated by the expected error in estimating the CN of the basic modeling
unit, a subwatershed. Conventional methods are assumed to have a small
constant error as shown in Figure 2. Investigations have shown that when
using Landsat data the error depends upon the subwatershed size and decays as
it increases. Figure 2 shows that if the decision criterion was error the
user would not choose Landsat for areas less than S in size.
Improved spectral and spatial measurements should shift the error-subwatershed
size relationship down as illustrated in Figure 2. Under these conditions,
remote sensing is advantageous at a smaller subwatershed size S'.
Our research has not considered the questions of what spectral information
could improve the procedure and how improved spatial resolution would affect
accuracy. The reason for this is that we arefocuslng on making the best use
of existing data sources. However, these problems are important and could be
studied using laboratory and/or small scale experiments. Research on these
subjects would be similar to the problems involved in crop discrimination and
urban land cover studies. At this state it is not possible to say whether
spectral or spatial measurement improvement is more valuable.
Aside from the question of the best spectral hands, studies are needed to
evaluate the temporal features of remotely sensed data. Classification and
analysis for CN depends upon the time of year. However_ studies to identify
optimal conditions have not been conducted. Also, the use of multl-date data
has not been adequately investigated.
33
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Figure i. - Comparison of Conventional and Remote Sensing Approaches
to Curve Number Estimation.
34
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Figure 2. - General Relationship Between the Expected Error in
Estimsting the CN and Subwatershed Size.
35
Other problems that should be considered which significantly affect the
utility of remotely sensed data in quasl-operatlonal applications, such as CN
estimation, are data calibration and registration. Information extraction
will always be limited by calibration. It seems that procedures could be
developed to make this more quantitative. CN estimation, as illubtrated in
Figure I, involves the combination of several data bases, remotely sensed land
cover being ore. Accuracy'and reliability depend upon registering these.
Reliable and easy to implement procedures for ground registration would be
useful. Also, since all data bases are ultimately referenced to the USGS
topographic maps, why not obtain the data at a matching 1"24,000 spatial scale?
Another topic of interest is the estimation of the percent of impervious area
directly from remotely sensed data without intermediate land cover
classification. Based upon .available Landsat bands, procedures for doing this
have been developed. One example is shown in Figure 3. Studies to determine
the optimal spectral bands have not been conducted and would be useful.
Relationships between the expected error and the size of the study area have
been developed based upon Landsat data bases, however, the effect of spatial
resolution has not been investigated.
Current research in ET estimation employs an energy and moisture budget
approach for the interpretation of thermal infrared data as acquired by the
NOAA operational satellites. The I.I kilometer spatial resolution of the
Advanced Very High Resolution Radiometer prevents field by field evaluation of
moisture characteristics and thus has potential utility only for regional
scale ET assessment. Aircraft data should be acquired and analyzed to
ascertain the significance of improved spatial resolution in the analysis of
thermal infrared data.
REFERENCES
Bondelld, T. R., Jackson, T. J., and McCuen, R. H., 1980, Comparison of
Conventional and Remotely Sensed Estlmatesof Runoff Curve Numbers in
Southeastern Pennsylvania, Proceedings of the ACSM-ASP Annual Meeting,
St. Louis, Mo.
Bondelld, T. R., Jackson, T. J., and McCuen, R. H., 1981, A Computer Based
Approach to Estimating Runoff Curve Numbers Using Landsat Data, AgRISTARS
Technical Report, CR-RI-04040.
Carlson, T. N., Dodd, J. K., Benjamin, S. G., and Cooper, J. N., 1982,
Satellite Estimation of Surface Energy Balance, Moisture Availability and
Thermal Inertia, Journal of Applied Meteorology 20, pp. 67-87.
Jackson, T. J., 1975, Computer Aided Techniques for Estimating the Percent of
Impervious Area from Landsat Data, Proceedings of the Workshop on
Environmental Applications of Multlspectral Imagery, Ft. Belvolr, Va.
Jackson, T. J., and McCuen, R. H., 1979, Accuracy of Impervious Area Values
Estimated Using Remotely Sensed Data, Water Resources Bulletin, Vol. 15,
No. 2, pp. 436-446.
36
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0 10 20 30 40 50 60 70 80 90 .100
PERCENT IMPERVIOUSNESS
Figure 3. - Relationship Between the Percent of Impervious Area and
the Ratio of Two Landsat Bands+(McKeon, et al., 1979).
37


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