Evaluating the Performance of Global Land Cover Maps in Agricultural Land Delineation (Case Study: Lake Urmia Basin)

Continuous monitoring of agricultural lands is imperative for managing water and soil resources in a watershed, due to its impact on ecosystem health and food security. Global Land Cover (GLC) maps can be used as a proxy for local and regional land use maps because of their availability, variety, and ease of use without complex processing. This study investigates the performance of three GLC products including MCD12Q1 LC, CGLS LC, and CCI LC against a reference land use/ land cover map of the year 2015 in the LUB. First, identical classes between the reference map and the GLC maps were determined based on the main land use/ land cover classes of the reference map of 2015 (rangeland, agricultural land, water, built-up areas, and bare land). To do so, different classes were merged accordingly to match the classes of the reference map. Subsequently, performance (Area and spatial consistency, and classification accuracy) of the GLC products was evaluated based on ground truth points. Results showed that MCD12Q1 LC and CGLS LC outperformed CCI LC in providing an overview of the surface cover of the LUB with 74% and 86% overall accuracy, respectively. Moreover, MCD12Q1 LC and CGLS LC had an acceptable performance in classifying rangeland and agriculture land as the dominant land cover types in the LUB with 81% and 92% classification accuracy, respectively. The CGLS LC can also be used to continuously monitor agriculture areas in practical applications to examine the overall trend of urbanization and agricultural development. Another important finding is that the GLC product with higher spatial resolution does not necessarily provide better classification accuracy for all types of covers. This study can also be used as a methodological reference in the performance evaluation of the GLC products at different scales and other parts of the country.