## 3 Results
### Accuracy Assessment  
Accuracy assessment for time-series analysis is notoriously difficult [77] (see also [PERSON] [PERSON], 2019 [78], p. 233), and in this case also complicated by the lack of field recording, the less-than-optimal coverage, and the still-prohibitive costs and political restrictions for obtaining high-resolution images from commercial satellites. For these reasons, accuracy assessment was carried out on pansharpened true-colour composites from the same satellites (a similar approach due to the same limitations was adopted by [PERSON] et al., 2020 [53]). While this is  
Figure 32: Water retraction cycles around Tell Yelkhi, in the Hamrin Dam, during each month of 2018, as seen on Planetscope images. (**a**) January 2018; (**b**) February 2018; (**c**) March 2018; (**d**) April 2018; (**e**) May 2018; (**f**) June 2018; (**g**) July 2018; (**h**) August 2018; (**i**) September 2018; (**j**) October 2018; (**k**) November 2018; (**l**) December 2018. Images: Planetscope ©TPMO (2020). not always optimal, because it mostly assesses the accuracy of the reclassification for that single area [79], it was the best option especially for less recent years [78] (p.235) (see also [PERSON] et al., 2014 [77], p. 14) and it also provides consistency between reclassified and reference data. Pansharpening is not possible on Landsat 5; therefore, validation has been carried out on 30 m true-colour composites (thus, validation results should be taken with care). For more recent years, when possible, high-resolution satellite images from Bing Maps or Google Earth were used. For the monthly composites, pansharpened composites and Planetscope images were used as reference data [53]. Given the number of images and the different sensors used in this research, validation was carried out for two images per sensor, one with a lower water level, and one with a higher water level. Sampling was carried out at the per-pixel level using a stratified random sampling with two classes: water, and non-water pixels [77; 78]. This method allows for allocating enough samples for each class depending on their area proportion, thus accounting for the lake area variation between images. For the annual images, validation was carried out on the entire research area. When mixed pixels were detected, the reference class was assigned to either water or non-water depending on the most prevalent class inside the sample. For monthly composites, validation was limited in areas covered by the Planetscope data, around 20 km maximum around some archaeological sites (Glai'a in the Haditha Dam, Khirbet Kharasan in the Mosul Dam, and Tell Yelkhi in the Hamrin Dam, see Figures 30-32). All the validations were carried out in QGIS, using the Semi-Automatic Classification plugin [80]. Overall, the NDWI showed promising results for all the areas and satellites used (Table 5), as usually expected by a simple water/non-water classification in areas with a clear separation between a lake and its surroundings [81]. Errors of omission were generally rare and were mostly tied to the medium resolution of the images used for the NDWI or to the surroundings of each lake. In fact, agricultural fields around the Hamrin Dam lake increased in number since the last decade, and more recent Landsat or Sentinel-2 images did not identify smaller canals. However, in the context of highlighting emerged areas from the lakes, these errors had a minimum impact on the results. This is especially evident when assessing the accuracy at a smaller scale for the monthly images, in which the omission errors of the Hamrin Dam were significantly lowered.