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USDA National Agricultural Statistics Service, 2023 Wyoming Cropland Data Layer
STATEWIDE AGRICULTURAL ACCURACY REPORT
Crop-specific covers only *Correct Accuracy Error Kappa
------------------------- ------- -------- ------ -----
FSA Crops 258,226 76.1% 23.9% 0.705
Cover Attribute *Correct Producer's Omission User's Commission Cond'l
Type Code Pixels Accuracy Error Kappa Accuracy Error Kappa
---- ---- ------ -------- ----- ----- -------- ----- -----
Corn 1 15,142 84.8% 15.2% 0.845 84.2% 15.8% 0.839
Sorghum 4 623 37.6% 62.4% 0.375 58.6% 41.4% 0.585
Sunflower 6 1,888 79.2% 20.8% 0.791 78.2% 21.8% 0.782
Sweet Corn 12 0 0.0% 100.0% 0.000 n/a n/a n/a
Barley 21 15,025 76.6% 23.4% 0.762 86.9% 13.1% 0.866
Spring Wheat 23 126 38.3% 61.7% 0.383 50.2% 49.8% 0.502
Winter Wheat 24 26,644 86.0% 14.0% 0.855 89.0% 11.0% 0.886
Rye 27 302 44.9% 55.1% 0.449 79.1% 20.9% 0.790
Oats 28 2,746 45.6% 54.4% 0.454 71.7% 28.3% 0.715
Millet 29 3,801 60.9% 39.1% 0.607 73.1% 26.9% 0.730
Safflower 33 41 53.9% 46.1% 0.539 83.7% 16.3% 0.837
Alfalfa 36 64,175 82.0% 18.0% 0.806 80.8% 19.2% 0.792
Other Hay/Non Alfalfa 37 89,170 78.6% 21.4% 0.760 78.0% 22.0% 0.754
Sugarbeets 41 7,288 90.2% 9.8% 0.901 96.1% 3.9% 0.960
Dry Beans 42 2,307 82.7% 17.3% 0.827 85.1% 14.9% 0.851
Potatoes 43 118 88.7% 11.3% 0.887 100.0% 0.0% 1.000
Other Crops 44 29 80.6% 19.4% 0.806 35.4% 64.6% 0.354
Chick Peas 51 13 5.5% 94.5% 0.055 37.1% 62.9% 0.371
Sod/Grass Seed 59 903 67.9% 32.1% 0.679 89.7% 10.3% 0.897
Fallow/Idle Cropland 61 26,575 84.8% 15.2% 0.844 87.4% 12.6% 0.870
Open Water 111 4,987 90.2% 9.8% 0.902 92.9% 7.1% 0.928
Perennial Ice/Snow 112 83 57.2% 42.8% 0.572 71.6% 28.4% 0.715
Developed/Open Space 121 3,172 74.6% 25.4% 0.744 55.1% 44.9% 0.549
Developed/Low Intensity 122 1,847 93.9% 6.1% 0.938 71.6% 28.4% 0.716
Developed/Med Intensity 123 1,127 97.9% 2.1% 0.979 90.1% 9.9% 0.901
Developed/High Intensity 124 278 96.2% 3.8% 0.962 93.9% 6.1% 0.939
Barren 131 7,443 85.0% 15.0% 0.849 84.2% 15.8% 0.841
Deciduous Forest 141 1,912 50.7% 49.3% 0.505 62.0% 38.0% 0.619
Evergreen Forest 142 79,946 91.6% 8.4% 0.909 89.6% 10.4% 0.886
Mixed Forest 143 104 15.1% 84.9% 0.150 37.4% 62.6% 0.374
Shrubland 152 406,064 95.7% 4.3% 0.927 93.4% 6.6% 0.889
Grassland/Pasture 176 146,439 87.8% 12.2% 0.855 88.2% 11.8% 0.860
Woody Wetlands 190 1,650 28.5% 71.5% 0.283 44.1% 55.9% 0.437
Herbaceous Wetlands 195 3,690 37.4% 62.6% 0.369 45.8% 54.2% 0.453
Triticale 205 1,290 37.7% 62.3% 0.376 68.7% 31.3% 0.686
Dbl Crop Triticale/Corn 228 20 15.4% 84.6% 0.154 21.5% 78.5% 0.215
*Correct Pixels represents the total number of independent validation pixels correctly identified in the error matrix.
**The Overall Accuracy represents only the FSA row crops and annual fruit and vegetables (codes 1-61, 66-80, 92 and 200-255).
FSA-sampled grass and pasture. Non-agricultural and NLCD-sampled categories (codes 62-65, 81-91 and 93-199) are not included in the Overall Accuracy.
The accuracy of the non-agricultural land cover classes within the Cropland Data Layer is entirely dependent upon the USGS, National Land Cover Database. Thus, the USDA NASS recommends that users consider the NLCD for studies involving non-agricultural land cover. For more information on the accuracy of the NLCD please reference <https://www.mrlc.gov/>.
Attribute_Accuracy_Value:
Classification accuracy is generally 85% to 95% correct for the major crop-specific land cover categories. See the 'Attribute Accuracy Report' section of this metadata file for the detailed accuracy report.
Attribute_Accuracy_Explanation:
The strength and emphasis of the CDL is crop-specific land cover categories. The accuracy of the CDL non-agricultural land cover classes is entirely dependent upon the USGS, National Land Cover Database. Thus, the USDA NASS recommends that users consider the NLCD for studies involving non-agricultural land cover.
These definitions of accuracy statistics were derived from the following book: Congalton, Russell G. and Kass Green. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. Boca Raton, Florida: CRC Press, Inc. 1999. The 'Producer's Accuracy' is calculated for each cover type in the ground truth and indicates the probability that a ground truth pixel will be correctly mapped (across all cover types) and measures 'errors of omission'. An 'Omission Error' occurs when a pixel is excluded from the category to which it belongs in the validation dataset. The 'User's Accuracy' indicates the probability that a pixel from the CDL classification actually matches the ground truth data and measures 'errors of commission'. The 'Commission Error' represent when a pixel is included in an incorrect category according to the validation data. It is important to take into consideration errors of omission and commission. For example, if you classify every pixel in a scene to 'wheat', then you have 100% Producer's Accuracy for the wheat category and 0% Omission Error. However, you would also have a very high error of commission as all other crop types would be included in the incorrect category. The 'Kappa' is a measure of agreement based on the difference between the actual agreement in the error matrix (i.e., the agreement between the remotely sensed classification and the reference data as indicated by the major diagonal) and the chance agreement which is indicated by the row and column totals. The 'Conditional Kappa Coefficient' is the agreement for an individual category within the entire error matrix.