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Wiegand, C. L.

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E7.4- 1 0.334
TITLE: "Reflectance of vegetation, soil, and water" r
NUMBER OF INVESTIGATION: ERTS-1 GSFC ID AG 339 W o
CONTRACT NUMBER: S-70251-AG, Task 3 (431-641-14-01-07) o
PERIOD COVERED: Dec. 19, 1973 - Feb. 19, 1974 5. -
TYPE OF REPORT: TYPE I REPORT #6 0 0 E
TECHNICAL MONITOR: G. Richard Stonesifer, Mail Code 430
NASA, Goddard Space Flight Center m
Greenbelt, MD 20771 2,
PRINCIPAL INVESTIGATOR: Craig L. Wiegand
SPONSORING INSTITUTION: USDA, Agricultural Research Service
P. O. Box 267
Weslaco, TX 78596
DATE PREPARED: February 20, 1974 NUMBER OF PAGES: 5
Obiective of the Contract:
The seasonal changes in eflectance of various soils and of various
crops grown in Hidalgo County, Texas, are being studied using ERTS-1,
ground, and low to medium altitude (3000-10,000 ft AGL) aircraft spectral
data. Discrimination of specific crop and soil conditions is being
attempted; chlorophyll content of plant leaves is being correlated with
reflectance in the visible channels, and comparisons are being made be-
tween ERTS data and predictions from analytical models describing inter-
action of light with plant canopies.
Cloud-free ERTS Scenes:
The useable MSS CCT data furnished by NASA to date in support of th2
contract include:
MSS CCT CCT
Pace Orbit No. Scene I.D. Received Studied?
Dec. 16, '72 2035 1146-16323 Mar. 9, '73 Yes
Jan. 21, '73 2537 1182-16322 Mar. 14, '73 Yes
May 27, '73 4294 1308-16323 Sept. 14, '71 Partly
Sept. 12, '73 5800 1416-16310 Feb. 19, '74 NO
Oct. 18, '73 6302 1452-16300 Ordered 1/21/74 No
Dec. 11, '73 7055 1506-16290 Ordered 2/4/74 No
1506-16293
Dec. 29, '73 Photoproducts not yet rec'd.
(E74-103 3 4 ) REFLECTANCE OF VEGETATION, N74-18010
SOIL, AND WATEi Proqress Report, 19
Dec. 1973 - 19 Feb. 1974 (Aqricultural
Research Service) 5 p HC $4.00 CSCL 08M Unclas
G3/13 06334
https://ntrs.nasa.gov/search.jsp?R=19740009897 2020-03-23T10:25:03+00:00Z
-2-
Emphasis is being placed on analyzing the Jan. 21 and May 27, 1973,
scenes. The January scene has a maximum of bare soil but also represents
the winter vegetable season. The May 27 scene represents the warm season
crons dominated by cotton and grain sorghum. The Dec. 16, 1972, scene is
less desireable than the Jan. 1973 scene because of high thin cirrus and a
few low cumulus clouds.
The September 12, 1973, scene is of interest because it was preceded
by heavy rainfall that caused surface ponding. The scene offers the possi-
bility of assessing flooding that existed at the time of the overpass. Both
the September and Oct. 18, 1973, scenes would be difficult to interpret in
terms of crops because of antecedent wet conditions that promoted growth of.
weeds and prevented the usual harvest of crops and the tillage of fields.
The December 11 and December 29, 1973, scenes are again back to our
winter vegetable season. A hard freeze occurred on the night cf December 20.
Therefore, it is of interest to study the change in spectra of certain crops,
such as sugarcane that was severely damaged by the freeze.
It is unlikely that all these various conditions and events can be
studied under the contract, but the CCT are being requested in case the
opportunities present themselves.
The tabulation of number of sample segments and individual fields that
have been located from the county-wide sample in each of the data sets
received to date, along with causes of nonidentification are:
Satellite data sets
Data processing 12-16-73 1-21-73 5-27-73
status Fields Segments Fields Segments Fields Segpnnts
Total locacel 1262 159 1292 193 778 103
Total missii.g 181 38 L49 4 556 72
-Under clouds 75 9 - - - -
-Not on CCT 3 3 - - - -
-Split between CCT 4 2 - -
-Cannot ljcate on
gray n.ps 99 24 149 4 - -
-Processing not
complete - - - - 556 72
Total 1443 197 1441 197 1334 175
Sof to al located
in CCT 87.4 80.7 89.6 98.0 Not yet completed
-3-
For example, in the Dec. 16, 1972, scene a total of 9 sample segments
comprising 75 individual fields were obscured by clouds or cloud shadows.
Difficulty in locating segments is experienced in those parts of the county
where soil patterns are complex; mosaics of high altitude aircraft photo-
graphs often have a mottled appearance in such areas. Segment and field
boundaries are obscured by the variability in soil patterns in such cases.
Field and segment boundaries are also undefined when adjacent segments and
fields are spectrally noncontrasting.
For the Jan. 21, 1973, scene 193 out of 197 segments (1292 out of 1441
fields) have been identified and the spectra extracted from the CCT. Four
segments were unlocatable because the segment boundaries could not be distin-
guished from the surroundings.
The Iifference in number of segments between January and May is explained
by the elimination of ground truth collection of winter vegeti~)te segments
during summer months. The final count for May should be better than for
January because better photographic aids were available to help locate seg-
ments and because CCT were merged into one tape, eliminating segments split
between CCT. The photography from the SKYLAB pass of May 29, 1973, was used
to help locate segments; it gave a truer indication of current ground condi-
tions (ground cover, field divisions, urban development, etc) than the
March 4, 1971, high altitude aircraft photography used in conjunction with
the December and January data sets. The SKYLAB photographs corresponded
closely with ERTS-1 band 5 images and computer gray maps using band 5.
Training Field Selection:
One of the problems of automatic land use mapping of agricultural scenes
is the difficulty in matching ground truth to spectrally distinctive a.id
meeningful agricultural discrimination categories.
The s :,onrvised training field selection technique used at Weslaco to
date employ.s two-dimensional (2D) scatter diagrams of the MSS digital counts
along with ground truth. The mean MSS ERTS-1 digital values for 1400 randomly
located fields in Hidalgo County, Texas, are displayed in a 2D scatter diagram
in which bands 7 and 7 are the two axes of the diagram. An alphanumeric label,
corresponding to crop identification, geographical location in the County, crop
ground cover, and crop condition, is used to indicate the identity of each
field (as a 4 character point in the scatter diagram). Spectrally similar
points form clusters in the diagrams. Since the fields of the scatter diagrams
are rep,'esentative of the County, training fields from the clusters are repre-
sentative of crop and soil conditions in the County.
-4-
Currently, an unsupervised training field selection technique
(clustering) is being adapted to determine the distinguishable spectral
signatures present and where they are located geographically. (Such tech-
niques have been successfully employed elsewhere, e.g. Ellefsen, R.,
Swain, R., and Wray, J. 1973, "Urban Land-Use Mapping by Machine Processing
of ERTS-1 Multispectral Data: A San Francisco Bay Example," Proc. Conf.
Machine Processing of Remotely Sensed Data, Purdue Univ., p. 2A-7.) Samples
of the natural spectra, identified in the unsupervised mode, can be compared
with known spectra displayed on 2D scatter diagrams to determine what known
land use categories the natural spectra most closely resemble. It may be
that some land use categories can be represented by more than one of the
natural spectra discovered in the unsupervised mode. The computer program
to be used at Weslaco for unsupervised training field selection is a modifi-
cation of a one pass clustering program called CLUSTD developed by Haskell
of the Earth Observations Division, Johnson Space Center, Houston,
Statement of Problems in the Report Period:
We are getting classification accuracies of about 60% for a typical set
of agricultural land use categories, using either 10,000-foot altitude air-
craft MSS or ERTS-1 MSS data. This low classification accuracy results,
we believe,from the spectral similarity of various categories, and from
the natural variability in the soil background. Thus we are looking into
improving the selection of the training sample signatures, the stratifica-
tion of the test county into areas that appear on ERTS-L MSS imagery to be
similar in soil background and land uses, and into the proportion of the
MSS digital counts that can be explained by ground truth.
Utilization of Aircraft Data:
A paper has been accepted for presentation at the 9th Remote Sensing of
the Enviro.nmnt Symposium at Ann Arbor in April in which MSS data simultaneously
collected u:;ing the 24-channel aircraft MSS (Mission 226) and the ERTS-1 MSS
are compared for crop recognition and acreage estimation using the two data
sources; recognition maps and acreage estimates were very similar for one
aircraft flight line (61.6 square kilometers of land area). In this case,
the spacecraft MSS was as useful as the 10,000-foot altitude aircraft MSS
data for discriminating agricultural land use categories.
-5-
Significant Results and Practical Applications:
To be cited in next Type IT report.
Publications:
None.
Reco=..endations Concerning Changes in Operations, Additional Investiga-
tions, Efforts and Effort/Results as Related to the ERTS System:
See the section "Cloud-free ERTS Scenes" for worthwhile investiga-
tions suggested using ERTS data that are not covered by this contract.
Changes in Standard Order Forms:
None.
ERTS Image DescriDtor Form:
None,
Changes in Retrospective Data Requests:
Will probably not order digital tapes of any scenes later than the
December 29, 1973, scene.
Planned Work for the Next Reporting Period:
Statistical estimates of the acreages devoted to winter vegetables
will continue to be produced as the winter vegetable season continues.
The zpectra for the May 27, 1973, overpass (scene 1308-16323) will
continue to be located in the CCT.
Improve training sample signature selection through the use of
clustering, scee stratification, and other procedures and incorporate
them into data p.ocessing procedures.
Produce county-wide estimates of acreages devoted to various major
crops aPi land uses from January 21 and May 27, 1973, overpasses. Compare
acreages with those determined from the ground truth statistical estimate
of the County. Also, compare estimates obtained using a single training
set for the whole County with composite estimates obtained using training
signatures representative of southern, central, and northern subsections
of the County.


Reflectance of vegetation, soil, and water

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