In foreign crop condition monitoring, satellite acquired imagery is routinely used. To facilitate interpretation of this imagery, it is advantageous to have estimates of the crop types and their extent for small area units, i.e., grid cells on a map represent, at 60 deg latitude, an area nominally 25 by 25 nautical miles in size. The feasibility of imputing historical crop statistics, soils information, and other ancillary data to crop area for a province in Argentina is studied

Lautenschlager, L.

Perry, C. R., Jr.

Willis, R. W.

English

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for any use made thereat,"
Early Warning and Crop
Condition Assessment
E83-10 13s
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EW-J2-04358
JSC-18572
A Joint Program for
Agriculture and
Resources Inventory
Surveys Through
Aerospace
Remote Sensing
September 1982
IMPUTING HISTORICAL STATISTICS, SOILS INFORMATION,
AND OTHER LANDSAT-USE DATA TO CROP AREA
(E 83 —
 10138) IMPUTING HISTCRICAL STATISTICS,
SOILS INFORMATION, AND OTHER LAND
— USE DATA
TO CEOP AREA (Department of Agriculture)
5 p HC A02 ,1 MF A01
	 CSCL 02C
PREPRINT FOR THE ANNUAL MEETING OF THE AMERICAN STATI.TICAL ASSOCIATION
- SURVEY RESEARCH METHODS SECTION
CINCINNATI, OHIO, AUGUST 1 6. 19, 1982
Lyndon 8. Johnson Specs Center
Hou^•on, Texas 77058
QRIGINAR QUALiT'
OF P00
IMPUTING HISTORICAL STATISTICS, SOILS INFORMATION, AND OTHER LAND-USE DATA TO CROP AREA
Charles R. Perry, Jr., Ronald W. Willis, and Lyle Lautenschlager
U.S. Department of Agriculture
ABSTRACT
Today, in foreign crop condition monitoring,
use is routinely made of satellite acquired image-
ry. To facilitate interpretation of this imagery,
it is advantageous to have estimates of the crop
types and their extent for small area units, i.e.,
grid cells on a map represent, at 60 0 latitude, an
area nominally 25 by 25 nautical miles in size.
The feasibility of imputing historical crop sta-
tistics, soils information, and other ancillary
data to crop area for a province in Argentina is
studied.
1. INTRODUCTION
The Foreign Agricultural Service (FAS) of the
U. S. Department of Agriculture (USDA) estimates
crop acreages and production for foreign coun-
tr:es
'
Input into these estimates is provided by
the Foreign Crop Condition Assessment Division
(FCCAD) of FAS, currently located in Houston near
the Johnson Space Center. The FCCAD is responsi-
ble for assessing the change in area plant ,sd to
various crops in important foreign agriculture ar-
eas. The primary data source used by FCCAD ana-
lysts to assess the areal change is Landsat Multi-
spectral Scanner (MSS) data (Wenderoth and Yost,
1972). Four of these satellites have been put
into orbit, the latest being in July of this year.
The design and orbital pattern of Landsats 1, 2,
and 3 enabled each to collect data over the same
spot every 18 days. At times, two Landsats were
operating concurrently, but staggered, so that
datat could be collected every 9 days (providing
cloud cover did not interfere). Landsat-4 is
designed to collect data every 16 days ever the
same *put. Each Landsat MSS irrse covers an area
of 100 by 100 nautical miles.
The FCCAD analysts monitor the majority of the
crop region within a country, and a great deal of
time is required to analyze each Landsat MSS
image. Thus, only certain high density or highly
variable areas are closely monitored to determine
if a shift in cropping patterns has occurred. A
grid mesh system, which is rectangular to a polar
stereographic projection of the 4orthern Hemis-
phere [Earth Satellite Corporation (1976)j, was
selected as the basis for choosing the highly var-
iable areas. The spacing between successive grid
points at 600 I r.titude is nominally 25 nautical
miles. The grid .ells were further divided into
four quadrants	 sampling purposes.
A data set -ontainin% soils, agricultural den-
sity, and meteorological data for each quadrant is
part of the FCCAD geo-gridded data base. However,
historical agricultural area statistics are avail-
able only at the state or country level for most
countries. Therefore, to estimate the crop mix in
a particular grid cell quadrant, the state or
country level historical statistics must be appor-
tioned to the grid cell quadrant.
The purpose of this study was to determine if
data elements stored in the geo-gridded data base
could be used to apportion historical agricultural
area statistics to the 12.5- by 12.5-nautical-mile
grid cell quadrant.
The scope of this study was limited to the use
of historical agricultural area statistics for one
year for four crops (corn, wheat, soybeans, grain
sorghum) from the Province of Cordoba, Argentina;
all grid cell quadrants which lie within the
boundaries of that province; and the associated
data available from the geo-gridded data base.
Although there are 444 grid cell quadrants in the
province, only 434 were used, as 10 quadrants were
cloud covered on the imagery used to determine the
agricultural density.
2. DATA SET
HISTORICAL AGRICULTURAL AREA STATISTICS
Historical agricultural area statistics at the
province (state) and partido (county) level were
available from the Province of Cordoba, Argentina,
for four crops for one year. The crops were corn,
wheat, soybeans and grain sorghum with the crop
year being 1978/79 for corn, soybeans, and grain
sorghum and 1977/79 for wheat. The partido-level
area statistics for each crop in each partido were
initially apportioned equally to each quadrant in
the partido. This procedure provided the initial
figure for each crop in each quadrant for use in
the correlation analysis.
AGRICULTURAL DENSITY
Using historical Landsat data, Dr. Fred Westin,
South Dakota State University, determined the
quadrant-level agricultural density and divided it
into five categories based on the percentage of
cultivated crop land. The categories and percent
ranges were: 1 - 80 to 100 percent; 2 - 60 to 80
percent; 3 - 40 to 50 percent; 4 - 5 to 40 per-
cent; and 5 - less than 5 percent. Neither the
categories nor the percentage ranges provided a
usable value with which to work.
	 Consequently,
the midpoint of each range was selected to replace
the category. The categories were replaced as
follows: I with 0.90; 2 with 0.70; 3 with 0.50; 4
-ith 0.225; and 5 with 0.025. Also stored in the
data base with the categories were the reasons for
the different rankings for each quadrant; e.g.,
"hve" indicates hilly with vegetation. Approxi-
mately 60 different reasons were available. The
reasons w^re used only as an aide throughout the
analysis.
ORIGINAL PAGE 19
Of POOR OUAL17Y
SOILS DATA
The soil type chosen to represent the soils in
the quadrant was the predominant soil within the
quadrant. Associated with the soil types were
five categories: texture, depth, drainage, salin-
ity and slope. Each soil type received a rating
from one to four in each of the five categories
with one being the best and four the worst. The
values of each -ategory were summed for each soil
type and then ranked, with the lowest total
receiving a ranking of one. Dummy values were
also added for the five categories. The category
or categories with the highest value were deemed
to be the limiting factor(s) for that soil type.
For example, a soil type might have these ratings:
texture 1, depth 2, drainage 3, salinity 1, and
slope 3. The limiting categories would be drain-
age and slope. Therefore, the dummy values would
be: texture 0, depth 0, drainage 1, salinity 0,
and slope 1.
AGRO-PHYSICAL UNITS
Agro-physicAl units (APU's) for Argentina were
delineated by H. Edward Bulloch, of the FCC.AD. An
APU is a relatively homogeneouA area similar in
climate, soils, and crops. Each quadrant lies
within only one APU dependin g, on which APU was
predominant in the quadrant. All APU's were
grouped in similar climatic regimes and then
numbered within the regime for analysis purposes.
3. ANALYSTS
To evaluate the apportioning procedure, accur-
acy was selected as the primary criterion for
determining its general performance. Secondary
criteria considered were efficiency and repeat-
ability.
The accuracy criterion was selected as the pri-
mary criterion because if the. apportioning proce-
dure did not accurately determine the area to be
apportioned to each grid cell quadrant, then the
efficiency and repeatability criteria were moot
questions to be considered. The apportioning pro-
cedure should Accurately apportion the crop area
to Bich grid cell quadrant, since the analyst will
use this as a basis for evaluating subsequent crop
mixes in that grid cell quadrant.
GRAPHS
Scatter plots were created to help determine
visually if any relationship existed between the
crops and any of the other variables described in
Part 2.	 No clear relationship was indicated in
any of the graphs. Therefore, no variables were
deleted from consideration for use in the appor-
tioning procedure.
APPORTIONMENT AHD CORRELATION
Partido-level Statistics Equally Apportioned
The initial apportioning was to Allocate
equally the partido-level statistics of each of
the four crops to each grid c=11 quadrant. TS is
was accomplished by dividing the partido-level
area statistics b y the number of grid cell
quadrants in the partido. A Spearman correlation
was computed between the apportioned amount and
the different soil variables. No variable showed
a high correlat.on for any of the four crops.
Partido-level Statistics Apportioned Using Ag
Density
The next apportioning procedure utilized the
quadrant level ag density to apportion the
partido-level statistics. A Spearman correlation
was also computed in this apportionment (Willis,
1992). Ag density had the highest correlation
with all four of the crops. This was expected
because the ag density was used in the apportion-
ing procedure. No other variables showed a high
correlation for any of the crops.
Province-level Statistics Apportioned Using Ag
Density
The next apportioning procedure analyzed was to
use province-level statistics and apportion them
to the quadrant using ag density. A Spearman cor-
relation was computed between the apportioned
amounts and the soil variables. No variable other
than ag density indicated a very high correlation
with all of the crops - an expected result since
ag density was used in the apportionment.
Province-level Statistics Using Equal Apportion-
ment
The last apportionment procedure analyzed was
to apportion equally each crop to the partido
based on partido size. A Spearman correlation was
computed. None of the soil variables showed a
very high correlation with any of the the four
crops.
Partido Totals for Two Province-Level Procedures
Tables 1-4 list the actual partido statistics
and the values for each partido for each of the
province-level apportionment procedures for each
crop. The partido total for each procedure was
determined by sunning the values apportioned to
each .quadrant in the partido. The totals at the
bottom of each column indicate that no difference
existed between the two procedures. The major
problem lies at the partido level. Partidos with
no area for a crop were apportioned some area of
that crop, and partidos with a large actual area
for A crop were invariably underapportioned. The
soybean crop is a prime example (Table 2).
Approximately one-half of the partidos had soy-
bean area; yet the apportioning procedure using
province-level statistics allocated so-ne soybean
area to every partido. Similarly, partidos with
large actual areas were allocated much less than
the actual area.
Table 5 shows the Spearman correlation coeffi-
cients computed at the partido level between the
actual statistics and the two estimates using
province-level data. The coefficients ranged from
.13 !. to .915. The correlation coefficients ap-
peared quite -high for sorghum, but when comparing
the estimates there were some large differences.
OR1GINoR QUALITY
Of
4. CONCLUSIONS AND RECOMMENDATIONS
CONCLUSIONS
Apportioning of province-level statiatics was
very poor. Although the province total vas appor-
tioned to the quadrants, the individual partido
totals varied greatly from the actual partido
areas. This procedure would lead to inaccurate
analyst conclusions concerning cropping practices.
An analyst could conclude that a major shift from
one crop to another has occurred within a partido
while in actustity no such shift occurred. There-
fore, because very few countries publish partido-
level statistics and the apportioning procedure
employing province-level statistics was very poor,
some other method or procedure must be devised to
apportion province, or some higher hierarchial le-
vel, statistics to the quadrant.
RRCOMMF.NDATTONS
Because of the low correlation bets en the
soils information and the crops, the first recom-
mendation is thst more detailed information about
the soils be obtained. These variables could then
be studied to determine their value in an appor-
tioning procedure.
Quadrant level ag density is also required in
any procedure, and the ranges used in the categor-
ies in this study may be too broad, especially in
TABLE 1. Actual partido statistics for sorghum vs
the two province-level apportionment
procedures based on ag density and par-
tido size.
AG
ACTUAL DENSITY	 SIZE
the fourth category (5 to 40 percent). The second
recommendation is that categories should be broken
down into five-percent ranges. Although the small
ranges are highly desirable, they may be unfeasib-
le due to limitations of present technology.
5. REFERENCES
Earth Satellite Corporation, 1976, "Earthsat
Spring Wheat Yield System Test 1975," NASA/JSC
NAS9-14655.
Wenderoth, Sondra and Edward Yost, 1972, "Multi-
spectral Photography for Earth Resources,"
NASA/JSC NA99-11188.
Willis, Ron, 1982, "Apportioning Historical Agri-
cultural Statistics Using Geo-gridded Data Base
Elements," FCCAD TM 15.
*Charles R. Perry, Jr. and Lyle Lautenschlager are
with the Statistical Reporting Service (SRS) as-
signed to the Early Warning/Crop Condition Assess-
ment (EW/CCA) project within the Agriculture Re-
sources Inventory Surveys Through Aerospace Remote
Sensing (AgRISTARS) program. Ron Willis is with
the Foreign Agriculture Service (FAS) assigned to
the Foreign Crop Condition Assessment Division
(FCCAD). All are located at 1050 Bay Area Blvd.,
Houston, Texas 77058.
TABLE 2. Actual Partido statistics for sovbeans
vs the two province-level apportionment
procedures based on ag density and par-
tido size.
AG
ACTUAL	 DENSITY	 SIZE
Calamuch.ta 3,600 3,001 18,680 Calamuchita 2,400 1,059 6,590
Capital 1,000 2,348 1,698 Capital 0 829 599
Colon 5,700 12,003 10,189 Colon 400 4,234 3,594
Cruz Del Eje 1,000 14,938 30,567 Cruz Del Eje 0 5,270 10,793
General Roca 68,000 67,451 61,134 General Roca 0 23,795 21,567
General San Martin 21,800 30,529 22,076 General San Margin 4,100 10,770 7,788
Ishilin 1,500 5,153 20,378 Ischilin 0 1,818 7,189
Juarez Gelman 47,000 41,749 30,567 Juarez Celman 16,000 14,728 10,783
Marcos Juarez 32,000 64,711 47,548 Marcos Juarez 82,000 22,829 16,774
Minas 0 5,153 15,293 Minas 0 1,819 5,392
Pocho 1,700 5,349 15,283 Pocho 0 1,887 5,392
Pres. Roque Saenz Pena 60,000 49,642 39,059 Pres. Roque Saenz Pena 0 17,513 13,779
Punilla 1,000 11,546 11,987 Punilla 0 4,073 4,194
Ric Cuarto 100,000 91,522 73,021 Rio Cuarto 6,000 32,287 25,760
Rio Primero 23,000 32,682 32,265 Rio Primero 3,000 11,530 11,382
Rio Seco 1,000 29,290 49,247 Rio Seco 0 10,333 17,373
Rio Segundo 57,000 33,465 25,472 Rio Segundo 29,000 11,806 8,986
San Alberto 1,000 11,938 13,585 San Alberto 0 4,211 4,793
San Javier 0 5,349 6,793 San Javier 0 1,887 2,396
San Justo 172,000 107,067 76,417 San Justo 2,000 36,360 26,959
Santa Maria 8,000 18,787 13,595 Santa Maria 22,000 6,528 4,793
Sobremonte 300 391 10,189 Sobremonte 0 138 3,594
Tercero Arriba 39,000 21,721 16,982 Tercero Arriba 61,000 7,563 5,991
Totoral 4,100 9,067 11,887 Totoral 0 3,199 4,194
Tulumba 3,300 10,763 37,359 Tulumba 0 3,797 13,180
Union 84,000 55,393 45,850 Union 32,000 19,538 16,175
TOTAL 737,000 737,000 737,000 TOTAL 260,000 260,000 260,000
3
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t
ORIGINAL ri-u! t
OF POOR QUAL[TY
TABLE 3.	 Actual	 partido	 statistics	 fot corn vs. TABLE 4.	 Actual partido statistics	 for wheat vs.
the	 two province-level apportionment the two	 province-level	 apportionment
procedures	 based	 on ag	 density	 and procedures based on ag density and par-
partido size. tido size.
AG AG
ACTUAL DENSITY SIZE ACTUAL DENSITY SIZE
Calamuchita 8,500 2,687 16,728 Calamuchita 3,600 2,036 12,673
Capital 2,000 2,103 1,521 Capital 0 1,593 1,152
Colon 6,500 10,749 9,124 Colon 500 8,143 6,912
Cruz Del Eje 1,700 13,378 27,373 Cruz Del Eje 0 10,135 20,737
General Roca 11,500 60,404 54,747 General Roca 22,000 45,760 41,475
General San Martin 5,000 27,340 19,770 General San Martin 10,500 20,712 14,977
Ischilin 2,000 4,615 18,249 Ischilin 0 3,496 13,825
Juarez Celman 25,000 37,387 27,373 Juarez Celmen 15,000 28,324 20,737
Marcos Juarez 174,000 57,951 42,581 Marcos Juarez 180,000 43,902 32,258
Minas 2,000 4,615 13,687 Minas 0 3,496 10,369
Pocho 50,000 4,790 13,697 Pocho 0 3,629 10,369
Pres. Roque Saenz Pena
	 7,070 44,455 34,977 Pres. Roque Saenz Pena	 27,000 33,679 26,498
Punilla 2,800 10,340 10,645 Punilla G 7,833 8,065
Rio Cuarto 226,000 81,960 65,392 Rio Cuarto 10,000 62,091 49,539
Rio Primero 4,000 29,267 28,894 Rio Primero 6,500 22,172 21,889
Rio Seco 4,000 26,229 44,101 Rio Seo 0 19,871 33,410
Rio Segundo 11,000 29,969 22,811 Rio Segundo 16,000 22,703 17,281
San Alberto 14,000 10,690 12,166 San Alberto 0 8,099 9,217
San Javier 2,000 4,790 6,083 San Javie • 0 3,629 4,15'08
San Justo 4,000 92,299 68,433 San Justo 22,000 69,924 51,843
Santa Maria 9,000 16,824 12,166 Santa Maria 2,900 12,746 9,217
Sobremonte 1,000 351 9,124 Sobremonte 0 266 6,912
Tercero Arriba 17,000 19,453 15,207 Tercero Arriba 21,000 14,737 11,521
Totoral 7,000 9,120 10,645 Totoral 0 6,152 8,064
Tulumba 3,000 9,619 33,456 Tulumba 0 7,302 25,346
Union 60,000 49,596 41,060 Union 163,000 37,573 31,106
TOTAL 660,000 660,000 660,000 TOTAL 500,000 500,000 500,000
TABLE 5. Correlation coefficients computed at the
partido level between actual data and
two apportionment estimates.
Sorghum Soybeans Corn Wheat
Ag Density	 .915
	 .282	 .558	 .457
Size	 .800	 .134	 .498	 .365


Imputing historical statistics, soils information, and other land-use data to crop area

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