Peatlands play an important role in the global carbon cycle because of their huge carbon stocks. Northern peatlands—those located in mid to high latitudes—have been accumulating organic matter for thousands of years. Estimates of their carbon stock range from 250 to more than 450 Gt [1
], which represents a major share of the world’s soil carbon. Shallow water tables and associated anoxia slow down mineralization so that vegetation production exceeds decomposition in peatlands [3
]. The storage of partially decomposed vegetation (known as peat) has caused a net long-term biogeochemical-driven cooling effect of peatlands on Earth’s climate [4
]. One of the key parameters affecting the capacity of peatlands to accumulate organic matter is the water table depth (WTD), i.e., the position of the water table in the peat layer relative to the ground surface [7
]. Unlike most mineral soils, the upper layer of peat soil has a high hydraulic conductivity, which leads to the strong linkage between shallow WTD and surface soil moisture [9
Peatlands’ surface soil moisture can be estimated remotely using microwave and optical data. During the last decades, various satellites were launched that allow monitoring of near-surface soil moisture. Among them were the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions equipped with L-band radiometers [13
]. Though L-band frequency enables accurate monitoring of soil moisture changes when accounting for the vegetation and surface roughness effects, passive microwave sensing suffers from a coarse spatial resolution (40–50 km), which is unfavorable for monitoring a large fraction of global peatlands that are smaller in size. Active microwave missions, such as Sentinel-1 [15
], yield data at a much higher spatial resolution, but they suffer from confounding effects caused by scattering in the vegetation layer and the upper peat surface [16
Optical remote sensing with missions such as Landsat and Sentinel-2, also allows monitoring of soil moisture dynamics for permanently vegetated soils. With these sensors, soil moisture monitoring occurs indirectly through the monitoring of the spectral properties of vegetation layer [21
]. One of the widely applied approaches to estimate soil moisture from optical data is the so-called “trapezoid” model. This model utilizes a moisture-sensitive signal and vegetation signal to construct the trapezoid space [22
]. The modeled soil moisture is based on the pixels’ distribution within the constructed space. One of the varieties of the trapezoid concept is the OPtical TRApezoid Model (OPTRAM) [22
]. OPTRAM uses a normalized difference vegetation index (NDVI) and short-wave infrared transformed reflectance (STR) from a short-wave infrared (SWIR) band. In previous studies, the reflectance at the SWIR from band 12 of Sentinel 2 (2.19 μm), band 7 of Landsat 5, 7 (2.08–2.35 μm), and 8 (2.107–2.294 μm) was utilized for OPTRAM estimation [22
]. In OPTRAM, STR is assumed to be correlated with root-zone soil moisture due to the response of the vegetation water status to root-zone soil moisture deficit [22
]. The depth of the root zone varies between the different vegetation types and thus, NDVI is used to account for a broad range of vegetation diversity and activity.
Recently, Burdun et al. [27
] showed in a case study that OPTRAM has a promising potential for monitoring temporal changes of WTD in peatlands. OPTRAM outperformed other trapezoid approaches for soil moisture monitoring based on thermal imagery. However, this study also indicated that vegetation cover may have a strong impact on the applicability of OPTRAM for WTD monitoring. It was observed that for pixels with trees, OPTRAM possessed a low temporal sensitivity to changes in WTD, possibly because of the deeper rooting depth of trees and, therefore, fewer changes of vegetation water status due to water deficits. In contrast, some treeless pixels were shown to have a high temporal correlation between the OPTRAM index and in situ WTD, most probably because of the stronger response in SWIR band to the temporal changes in WTD reported, e.g., for Sphagnum
The high dependency of OPTRAM on vegetation poses a major challenge for its applicability in peatlands. To overcome the uncertainty related to spatial variability of the quality of OPTRAM index for monitoring temporal variation in WTD, there is the critical need to localize the pixel within a peatland that presents a strong SWIR response to changes in WTD (further called ‘best pixel’). Additionally, the vegetation dependency does further raise the question of the appropriate spatial resolution needed for the use of OPTRAM in peatlands. For homogenous agricultural areas, it was shown that OPTRAM from MODerate-Resolution Imaging Spectroradiometer (MODIS) data was capable of capturing the changes in soil moisture [24
]. However, for the highly variable vegetation patches in peatlands, OPTRAM from coarse- and moderate-resolution data might be less informative than from data of higher resolution, such as Landsat.
Another concern for OPTRAM applicability in peatlands is associated with the limited number of peatlands where this approach has been tested. There are two main types of peatlands: bogs, which are rain-fed, and fens, which are additionally fed with groundwater and, sometimes, surface runoff. Bogs and fens differ in hydrological regimes and vegetation cover. Even within those types of peatlands, there is a wide variety of vegetation types. So far, OPTRAM was tested only in Sphagnum
bogs, while its usefulness for WTD monitoring in fens or treed/low-shrub bogs has not yet been studied [27
]. Nevertheless, OPTRAM has the potential to be used as an indicator of WTD fluctuations in different types of peatlands because peatland vegetation is generally adapted to a shallow WTD [32
]. As the deeper WTD causes a decrease of soil moisture in the rooting zone and, sometimes, vegetation moisture stress [28
] that could be detected by OPTRAM.
Peatlands have at least 30 cm thickness of peat layer [35
], which have a high water storage capacity and high hydraulic conductivity near the surface. In addition to that, the upper layer of peat soil does not typically have an impermeable horizon and inclusions (e.g., tills) that strongly constrain the flow of water [36
]. These hydraulic properties result in low resistance to horizontal water movements through the upper part of the peat within a peatland and quickly redistributes water inputs such as rainfall, snowmelt, or run-on [37
]. If WTD has deepened into peat layers with low hydraulic conductivity, its variation is dominated by precipitation and evaporation [37
]. Therefore, water table in peat acts as a synchronized system, and WTD in peatland fluctuates rather coherently in space over several kilometers. The remaining spatial variability is mainly in the mean value of WTD and the amplitude of fluctuations, both not affecting the temporal correlation of fluctuations across a peatland. For example, Burdun et al. [27
] revealed a very high temporal correlation between WTD measured in eight wells located along transect in two Estonian peatlands (including EE_LIN peatland used in this study). In Mer Bleue peatland (CA_MER in this study), two 20 × 20 m plots with a dense grid of wells (2 m spacing) revealed strongly synchronized WTD variations despite absolute differences related to fine-scale variations in surface topography [38
]. These findings confirm that although the local spatial variation of the distance between the ground surface and water table, i.e., WTD, can differ due to the microtopography variation, but the absolute height of water table (relative to a common reference) is spatially very uniform in peatlands, apart from the large-scale gradients along major topographic features such as the typical dome structure of bogs. Larger scale topographical differences within peatlands can lead to differences in the amplitude of WTD fluctuations while remaining fluctuations well correlated. For example, a treeless central area may have a shallower WTD and lower amplitude of WTD fluctuations than the tree margins, where the deepest WTD and highest amplitudes are observed [27
The coherence in WTD fluctuations within a peatland can be exploited to address the aforementioned challenges with the OPTRAM sensitivity to temporal changes in WTD for different types of peatlands, i.e., to localize OPTRAM pixels that are representative of the overall peatland WTD dynamics, further called ‘best pixels’. We hypothesize that even a small number of ‘best pixels’ is enough to reveal the general dynamics of WTD, bearing in mind the synchronized WTD fluctuation in peatlands. We suggest that the ‘best pixels’ for the application of OPTRAM can be localized by the use of WTD measured in situ (if available) or modeled by a land surface model (if no in situ records are available) (Figure 1
). Given the assumption that WTD uniformly varies within a peatland, in situ measurements from one monitoring well are sufficient to determine the overall temporal variation of WTD in a whole peatland. Localizing the ‘best pixels’ for the OPTRAM monitoring based on in situ WTD allows the estimation of the temporal changes in WTD beyond the time period for which in situ records are available. Using the Landsat archive, WTD could then be estimated back to 1982, when the first Landsat satellite with the SWIR band was launched.
However, the usage of in situ data limits the OPTRAM applicability to only a small number of peatlands. To provide the basis for a future global application of OPTRAM over northern peatlands, we propose localizing the ‘best pixels’ using WTD data modeled by a land surface model with a peatland-specific modeling scheme. The catchment land surface model (CLSM) is a state-of-the-art land surface water and energy budget model that has a peatland-specific adaptation—PEATCLSM [42
This paper aims at localizing the ‘best pixels’ to apply OPTRAM in peatlands for monitoring WTD by using either historical in situ or modeled WTD data. In this study, we analyzed five northern peatlands (fens and bogs) where long-term records of in situ WTD were available. The specific objectives of this research were to:
evaluate the applicability of OPTRAM based on long-term remotely sensed data of different spatial resolutions (namely, Landsat, MODIS, and Landsat spatially rescaled to the MODIS resolution) for monitoring temporal changes in WTD;
analyze the effects of the vegetation type on the OPTRAM sensitivity to temporal changes in WTD based on available vegetation maps and literature;
compare the performance of applying in situ and PEATCLSM WTD data for selecting the ‘best pixels’, i.e., OPTRAM pixels with the highest sensitivity to the in situ WTD fluctuations in peatlands;
assess the quality of ‘best pixels’ selected based on in situ WTD in comparison to PEATCLSM WTD data for WTD monitoring.