Rewetting peatlands is an important measure to reduce greenhouse gas (GHG) emissions. However, after rewetting, the areas are highly heterogeneous in terms of GHG exchange, which depends on water level and source, vegetation, previous use, and duration of rewetting. These challenging conditions require new technologies that go beyond discrete sampling. Here we present data from two autonomous lander platforms deployed at the sediment-water interface (bottom lander) of a shallow coastal peatland (approx. 1 m water depth) that was rewetted by brackish water from the Baltic Sea, thus becoming part of the coastal water through a permanent connection. These landers were equipped with six commercially available state-of-the-art sensors, and temporal high-resolution measurements of physico-chemical variables, including partial pressures of carbon dioxide (CO2) and methane (CH4), were made. The resolution of the field data ranged from 10 seconds to 120 minutes and was obtained for partial pressure of CO2 (Contros HydroC-CO2) and CH4 (Contros HydroC-CH4), temperature, salinity, pressure (water depth), oxygen (O2) (CTD-O2 with SBE-37SMP-ODO), the concentrations of phosphate (SBE HydroCycle PO4), nitrate (SBE SUNA V2), chlorophyll a and the turbidity (both with SBE-FLNTUSB ECO) as stationary measurements at two different locations in close proximity. The CTD and oxygen measurements provide exact water depth data for the respective lander locations. In the other data sets (e.g., CO2 measurements) rounded data are inserted instead of the exact depth data, which is 0.6 m for lander_1 and 0.9 m for lander_2. SUNA raw data are provided for completeness. However, we found them of insufficient quality to estimate nitrate concentrations due to interferences and biofouling. The deployment and recovery of the landers, and thus the measurements, took place between 02 June 2021 and 09 August 2021, and the sensors were operated under permanent wired power supply and a centralized timestamp. The sensors were maintained and cleaned bi-weekly. Results show considerable temporal fluctuations expressed as multi-day, diurnal, and event-based variability, with spatial differences caused by biologically-dominated variables.

Data post-processing and calculated variables based on the descriptions of Bittig and Körtzinger, 2015