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M. Tapela & R. Tsheko Botswana College of Agriculture

M. Tapela & R. Tsheko Botswana College of Agriculture Validation of the SADC THEMA Agriculture Service Products Practice Example. Validation Example. The agriculture mask from JRC outlines those areas that are dedicated to cultivation in each country

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M. Tapela & R. Tsheko Botswana College of Agriculture

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  1. M. Tapela & R. Tsheko Botswana College of Agriculture Validation of the SADC THEMA Agriculture Service Products Practice Example

  2. Validation Example • The agriculture mask from JRC outlines those areas that are dedicated to cultivation in each country • This crop mask has a spatial resolution of 300m • It has not been validated

  3. Validation Example Sampling grid This grid is 100kmx100km clusters within Make 10kmx10km grid Sampling unit is 1kmx1km

  4. Validation Example GPS coordinates 12 samples per 1 GPS location

  5. Validation Example

  6. Validation Example • Confusion matrix

  7. Validation Example • Overall accuracy (proportion of pixels correctly classified) is obtained dividing the sum of the values in the main diagonal by the total number of pixels:

  8. Validation Example • Producer’s accuracy is calculated dividing the diagonal element by the row total: • It gives the probability of being correctly classified and its complement, the off-diagonal elements’ weight, is the error of omission (portion of that class not mapped)

  9. Validation Example • User’s accuracy (reliability) calculated dividing the diagonal element by the column total: • It is indicative of the probability that a pixel in the map actually represents that category on the ground. Its complement is the error of commission (points wrongly classified in that class).

  10. Validation Example Kappa coefficient is an overall accuracy index that takes into account the off-diagonal elements as well. It ranges from –1 (maximum disagreement) to 1 (best agreement), while 0 means that the agreement between reference and classification data is the same that can be obtained by chance.

  11. Error or confusion matrix - Example Where N = Grand Total = 200 A = 9+8+28+55 = 100 B= 12x31 + 43x22 + 64x66 +81x 81

  12. Validation Exercise • Crop Mask Matrix for accuracy assessment

  13. Rainfall Estimates - Statistical Inference

  14. Rainfall Estimates - Statistical Inference Probability of detection (POD) Probability of false detection (POFD) Correlation (r) Root mean square error (RMSE) Multiplicative bias (BIAS)).

  15. DMP - Statistical Inference • Calculate Percentage Dry Matter: • Satellite cumulative DMP and in-situ DM Correlation (r) Root mean square error (RMSE)

  16. The END Thank You.

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