Denmark's croplands are relatively flat, with gently rolling hills shaped by past glacial activity and fluvial processes, and are dominated by intensive farming. These topography features allow surface or near-surface water to flow towards depressions within individual fields, forming pond-like landscapes. Recently, these temporary flooded depressions have been identified as critical biogeochemical hotspots, especially for Greenhouse Gass (GHG) emissions. For example, Elberling et al. found that temporary flooded depressions represent only less than 1% of the total cultivated area in Denmark can release 80 times more N2O, one of the most potent GHGs, compared to the rest of the fields.

The drivers of such disproportionately high GHG emissions can be primarily due to hydrological fluctuation, which profoundly influenced biogeochemical processes and GHG production. By mapping the hydrological fluctuation, it can thereby identify the GHG “hotspots”- flooded depressions within cropland, as well as their size in “hot moment”.

However, until now, there is limited research about this topic in Denmark. For example, Elberling et al. suggested an urgent need to study this completely new topic and pointed out the extreme importance of temporal flooded depressions within field for N2O emissions. In addition, the relevant literature is only based on “one-region” and “one-period” research about N2O emissions, without the insight of the other GHGs and both within a year, and year-to-year variation. To bridge the current knowledge gap, this project is structured into three phases. In phase 1, a systematic literature review is conducted to assess the current state of research on GHG emission from flooded depressions in cropland. In phase 2, the RS approach is implemented to map the spatial and temporal variability of temporary flooded depressions in Denmark. In Phase 3, the integrated model and RS-based framework is utilized to assess the GHG emissions from the temporary flooded depressions in Denmark. The aim of this project is to identify the seasonal hydrological dynamics of flooded depressions and assess the corresponding GHG emissions with different farming management scenarios in Denmark. These results can be used to improve the future GHG budgets in Denmark and serve as the basis to study the corresponding best management practices.