The Effect of the LANDSAT Cloud Cover Domain on Winter Wheat Acreage Estimation in Kansas during 1976

A preliminary analysis was done to determine the effect of a cloud cover domain on winter wheat acreage estimation utilizing LANDSAT data in Kansas during 1976. The objective of the analysis was to see the effect of domain estimation in arriving at unbiased estimates of wheat acreage. Thus, the extent and location of cloud cover on LANDSAT imagery in relation to wheat acreage recorded on SRS ground surveys was examined. For the state of Kansas in 1976, two mosaics were constructed. Both mosaics were constructed using the best quality and most cloud free imagery from the entire months of April and May in 1976. The first mosaic uses only LANDSAT-I imagery and the second mosaic uses only LANDSAT-II imagery. SRS sample segments were manually located on the imagery. Each segment was classified as cloud covered or cloud free. Segments that were partially cloud covered or in cloud shadows were classified into the cloud covered domain. A composite of the best imagery from both satellites was constructed algebraically. The intersection of the segments that were cloud covered on LANDSAT-I and LANDSAT-II was used to define the cloud cover domain for a composite of LANDSATS I and II. Significant differences were found for average wheat acres per segment using ground survey data in the two domains (cloud covered vs. cloud free) for LANDSAT I, LANDSAT-11, and the composite of LANDSATS I and II. Two types of estimates were made for each of LANDSAT-I, LANDSAT-II and the composite of LANDSATS I and II The first estimate uses only cloud free segment data and the second estimate uses all segment data. The ratio of the cloud free segment estimate to the all segment estimate for LANDSAT-I, LANDSAT-II, and the LANDSATS I and II are respectively 0.845, 0.927, and 0.929. The conclusion of the author, based upon a random sample of area segments for which wheat acreage was acquired by ground enumeration for the cloud covered and cloud free domains, is that biased estimates would have resulted if data for both were not available. This analysis would indicate that effects of cloud cover must be adjusted for, using auxiliary data, if unbiased estimates are to be obtained when clouds are present

LANDSAT Estimation with Cloud Cover

The problem addressed in this paper is crop acreage estimation techniques which utilize LANDSAT I imagery that is not cloud free. Several statistical techniques are proposed that would allow inferences about the population even if cloud cover is an extensive problem. These techniques are entirely dependent upon a random sample of ground data; from the total population of interest

Visualization of a Crop Season: The Integration of Remotely Sensed Data and Survey Data

A visual map display of the complete 1999 growing season will be created for internal use on NASS’s Intranet using GIS to display crop development and condition as supplementary information to the standard NASS collected survey data. The crop information comes mainly from expert opinion of USDA agricultural extension agents across the country. They submit weekly reports to NASS on crop development and condition which is summarized at the state level and published for external use in the “Crop Progress Report” and in the “Weekly Weather and Crop Bulletin.” Using this information for a selected group of states at the county level with the purpose to expand the Agency GIS capabilities for internal analysis, crop progress of the specific stages of crop development for corn and soybeans, along with vegetative index maps, farmer reported yield data at the county level, weekly growing degree data, and NASS county estimate data provides a good visual overview of a growing season

Cost and Benefit Analysis of a Cropland Data Layer

In the last several years, the National Agricultural Statistics Service (NASS) has developed a Cropland Data Layer (CDL) in geographic information system (GIS) format (See Figure 1) for public use. It is being used by over 1000 GIS proficient users for watershed monitoring, agribusiness planning, prairie water pothole monitoring, crop rotation pattern analysis and animal habitat monitoring and value added analysis by private sector remote sensing/GIS industry. The CDL can be viewed and ordered on CD at the http: //www.nass.usda.gov/research/Cropland/SARS1a.htm address. The benefits to GIS proficient data users seem to easily outweigh the program costs. State governments especially are saving considerable resources by partnering with NASS in the Cropland Data Layer Program. As the number of States continue to expand, the costs per State will continue to decline and the benefits will be expanding with more data users and uses

An Exploratory Study on the Effects of Field Size and Field Boundary Pixels on Crop Spectral Signatures

The LANDSAT data for the sample segments in a LANDSAT scene in each of California and Illinois was analyzed for the purpose of determining whether or not size of field and/or boundary pixels significantly influences the spectral signature of certain ground cover types. The data was divided into 4 field size classes: (1) less than 20 acres, (2) greater than or equal to 20 but less than 80 acres, (3) greater than or equal to 80, but less than 200 acres and (4) greater than or equal to 200 acres. The pixels for each field size class were combined and mean vectors and variance-covariance matrices were derived. Then concentration ellipses were plotted. Apparently the larger field sizes produce somewhat different signatures than the smaller ones. The smallest field size class (less than 20 acres) was subdivided into field size classes of (a) less than 10 acres and (b) greater than or equal to 10 acres, but less than 20 acres. The smaller field sizes tend to produce a more compact ellipse in California; not so in Illinois. These two smaller field size classes were also used to study the difference in signature, if any, produced by the inclusion and exclusion of boundary pixels. Not much difference was observed. Tables of descriptive statistics on the MSS bands by field size as well as concentration (90%) ellipses are presented

Stratified Acreage Estimates in the Illinois Crop-Acreage Experiment

The approach of the Statistical Reporting Service (SRS) for using LANDSAT remote sensor data is to use it as an auxiliary variable with existing operational ground surveys. SRS objectives have been to investigate the use of LANDSAT data to improve crop-acreage estimates at several levels for which acreage statistics are needed; such as counties, groups of counties such as Crop Reporting Districts (CRD1s), and entire states. To determine the feasibility of these objectives, the Illinois crop-acreage experiment was established in 1975. The experiment employs LANDSAT data for the state of Illinois and data from SRS\u27s June Enumerative Survey (JES) for Illinois. The JES data was collected and edited by the Illinois Cooperative Crop Reporting Service. In addition the JES data was supplemented by monthly-updates conducted throughout the growing season and by low-altitude color-infrared photography for 202 of the 300 JES segments in Illinois. This paper describes: 1. The statistical methodology for the auxiliary use of LANDSAT data to estimate crop acreages, 2. The procedure for designing the pixel classifier which is required by this methodology, and 3. Results obtained by applying this methodology for three LANDSAT frames in western Illinois. Software systems have been developed jointly by SRS and the Center for Advanced Computation of the University of Illinois which implement the estimation methodology. The use of LANDSAT data as an auxiliary variable developed from a realization that using LANDSAT data as a survey variable produces biased estimates. The two major types of bias in using LANDSAT data as a survey variable are: 1. Mensuration biases due to the large pixel size of the LANDSAT data (57m x 79m), and 2. Classifier-related procedural biases due to different discrimination functions (linear or quadratic), training sets, prior probabilities, and classification categories used in the design of the classifier