tokens: over the whole solar disc. The umbra area is the area of the umbra of the sunspot, and pores are excluded. The total umbra area is the area of the umbrae summed over the whole solar disc. Projected areas are measured in millionths of the solar disc ( md ) and corrected areas in millionths of the solar hemisphere ( mh ). The relationship between the areas of a solar feature in two catalogues (C 1 and C 2 ) is modelled by linear regression in the form A 2 aA 1 , where A 1 and A 2 are the areas in C 1 and in C 2 , respectively. We provide three types of plots to show details of the regression: the scatter plot of the regression, the moving root mean square residual (MRMR) and the moving relative root mean square residual (MRRMR). MRMR measures how the root mean square residual (RMR) relative to the regression line depends on the area. For a given area A , the MRMR is determined from the n -nearest areas to A . Similarly, MRRMR measures how the relative root mean square residual (RRMR) relative to the regression line depends on the area. For a given area A , the MRRMR is determined from the n -nearest areas to A . We choose the value 20 for n . To compile the various sunspot catalogues compared in this paper, we used the software package SAM (Sunspot Automatic Measurement). SAM is a set of cooperative computer programs that embraces every aspect of compiling a sunspot catalogue, from (1) setting up the necessary data base for the observational data of the solar images and the telescopes and (2) automatically detecting sunspots (umbra and penumbra) on solar images and determining their heliographic coordinates and area through (3) to making the catalogue ready for publishing (Gyri 1998 ). To identify sunspots on solar images, we used the method published by Gyri ( 2015 ). A solar image may contain not only features belonging to the Sun but also various artefacts too, such as emulsion deficiency, emulsion abrasion, scratches, dust grains, clouds, inscriptions, patches, orientation markers, and much more. The properties of some of these artefacts are like sunspots (especially when the image quality is not too good), so it is hard to filter them out automatically, but a trained human eye/brain can do this in many cases (not always). Even when the average seeing of the image is good, there are regions with poorer seeing and small sunspots in these regions cannot be surely perceived. However, if, in a close-in-time image, these spots are situated in regions with good seeing, then they will show up clearly and could provide conformation. Near the solar limb, especially when the image quality is not too good, the penumbra and the umbra borders are not too definitive. In this case, the human intervention could increase the accuracy of finding these borders. To cope with these situations and with others not mentioned here, the automated sunspot catalogue created by SAM can be humanly revised by using special software, SAMm (Sunspot Automatic Measurement
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