1The remote sensing science and applications communities have developed increasingly reliable, 2 consistent, and robust approaches for capturing land dynamics to meet a range of information 3 needs. Statistically robust and transparent approaches for assessing accuracy and estimating area 4 of change are critical to ensure the integrity of land change information. We provide 5 practitioners with a set of "good practice" recommendations for designing and implementing an 6 accuracy assessment of a change map and estimating area based on the reference sample data. 7The good practice recommendations address the three major components: of the process 8 including the sampling design, response design and analysis. The primary good practice 9 recommendations for assessing accuracy and estimating area are: (i) implement a probability 10 sampling design that is chosen to achieve the priority objectives of accuracy and area estimation 11 while also satisfying practical constraints such as cost and available sources of reference data; 12(ii) implement a response design protocol that is based on reference data sources that provide 13 sufficient spatial and temporal representation to accurately label each unit in the sample (i.e., the 14 "reference classification" will be considerably more accurate than the map classification being 15 evaluated); (iii) implement an analysis that is consistent with the sampling design and response 16 design protocols; (iv) summarize the accuracy assessment by reporting the estimated error matrix 17 in terms of proportion of area and estimates of overall accuracy, user's accuracy (or commission 18 error), and producer's accuracy (or omission error); (v) estimate area of classes (e.g., types of 19 change such as wetland loss or types of no changepersistence such as stable forest) based on the 20 reference classification of the sample units; (vi) quantify uncertainty by reporting confidence 21 intervals for accuracy and area parameters; (vii) evaluate variability and potential error in the 22 3 reference classification; and (viii) document deviations from good practice that may substantially 23 affect the results. An example application is provided to illustrate the recommended process. 24 4

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