Rewetting is the most effective way to reduce greenhouse gas (GHG) emissions from drained peatlands and must significantly contribute to the implementation of the Paris Agreement on Climate within the land sector. In 2010–2013, more than 73 thousand hectares of fire-prone peatlands were rewetted in the Moscow Region (the hitherto largest rewetting program in the Northern Hemisphere). As the Russian Federation has no national accounting of rewetted areas yet, this paper presents an approach to detect them based on multispectral satellite data verified by ground truthing. We propose that effectively rewetted areas should minimally include areas with wet grasslands and those covered with water (cf. the IPCC categories “rewetted organic soils” and “flooded lands”). In 2020, these lands amounted in Moscow Region to more than 5.3 and 3.6 thousand hectares, respectively. Assuming that most rewetted areas were former peat extraction sites and using IPCC default GHG emission factors, an overall GHG emission reduction of over 36,000 tCO₂-eq year⁻¹ was calculated. We furthermore considered the uncertainty of calculations. With the example of a 1535 ha large rewetted peatland, we illustrate the estimation of GHG emission reductions for the period up to 2050. The approach presented can be used to estimate GHG emission reductions by peatland rewetting on the national, regional, and object level.

A new method of identifying the validity of pressure sensor readings has been developed. The method uses duplication of measurements which allows an estimation of the magnitude of an error, although it does not make it possible to establish which sensor is responsible for the error. The method helps to evaluate the systematic error magnitude and to test whether the error exists within a permission range accounting for a correlation structure of measurement series. An autoregressive model with a drift coefficient is applied to investigate a time series of the differences in readings. To test the significance of this coefficient, a modified Student’s test is used. Unlike the standard Student’s test, this new method tests an interval hypothesis. The null hypothesis assumes the systematic error is within the range and the alternative is out of the range. Error probabilities of type I and type II are calculated. An example of 2nd order autoregressive model was considered and the sensitivity of the proposed method was investigated.