Abstract: Abstract: In order to cope with the deteriorating ecological environment, the Chinese government has carried out a series of major forestry ecological construction projects. The selection of remote sensing products is the basis of ecological benefit monitoring and resource asset accounting for major forestry projects. This study took the Taihang Mountain Greening Project in Beijing as an example to assess the applicability of different scale remote sensing products in major forestry ecological construction projects. The study compared the area and special accuracy of remote sensing products extracted from MCD12Q1、NLCD-China and FROMGLC10 of different spatial resolutions in 2017 and tried to propose the recommended strategies of 3 kinds of products at different spatial scales and application levels in the scope of the project. The difference analysis of forest area was carried out by area deviation analysis and root-mean-square error based on the statistical area. The accuracy of remote sensing products was evaluated by the overall accuracy and Kappa coefficient based on the high-resolution image test samples, and the spatial consistency analysis was performed by the confusion matrix. The study analyzed the priority recommendation strategy of 3 remote sensing products by priority index based on the results of area and accuracy difference analysis in the Taihang Mountain Afforestation Project was calculated. The results showed that: 1) From the perspective of the Taihang mountain afforestation project as a whole, FROMGLC10 was more adaptable than NLCD-China and MCD12Q1. NLCD-China had good consistency of forest area and statistical area by area deviation, followed by MCD12Q1 and FROMGLC10. FROMGLC10 had the highest spatial accuracy, followed by NLCD-China and MCD12Q1. FROMGLC10 had the highest priority index, followed by NLCD-China and MCD12Q1. The difference in priorities between FROMGLC10 and NLCD-China was smaller. FROMGLC10 had a large advantage in spatial position monitoring, and NLCD-China had a greater advantage in forest area estimation. Therefore, remote sensing products should be selected for research purposes. MCD12Q1 and FROMGLC10 had the best forest area consistency, followed by NLCD-China and FROMGLC10. NLCD-China and MCD12Q1 had the most spatial confusion. Among the 3 remote sensing products, grassland and shrubbery were seriously confused with forest. They were difficult to distinguish in land classification. 2) Due to differences in data sources, classification methods, terrain, vegetation cover landscape features, etc., there were some differences in forest area, accuracy evaluation, spatial consistency and priority index of 3 different scales, and their applicability was not consistent at the district levels in the Taihang mountain afforestation project. In Shijingshan, Mentougou, Changping, and Haidian, FROMGLC10 had the best applicability. In Fengtai District, NLCD-China was the preferred data. In Fangshan District, FROMGLC10 and NLCD-China had a relatively close priority. This was mainly due to the high spatial accuracy of FROMGLC10 but the larger area deviation in Fangshan District. NLCD-China's accuracy of area and space was more balanced in Fangshan District. 3) The definition of forest classification, data sources and timely selection, spatial resolution and classification methods were constraints on the selection of suitability of remote sensing products. The applicability of remote sensing products was comprehensively evaluated by the priority index. If there was a small gap between the priority indexes of different remote sensing products, it was necessary to further study the threshold rules and criteria for determining the applicability of data in combination with the characteristics of data and the purpose of research. In practical application, it was necessary to select the data of the most suitable remote sensing products according to the different monitoring and research purposes of forestry ecological construction projects. This study provided theoretical support for the real-time monitoring, quantitative evaluation, and resource accounting of the Taihang mountain afforestation project in Beijing.