Hidden becomes clear: Optical remote sensing of vegetation reveals water table dynamics in northern peatlands

Burdun, Iuliia; Bechtold, Michel; Aurela, Mika; Lannoy, Gabriëlle De; Desai, Ankur R.; Humphreys, Elyn; Kareksela, Santtu; Komisarenko, Viacheslav; Liimatainen, Maarit; Marttila, Hannu; Minkkinen, Kari; Nilsson, Mats; Ojanen, Paavo; Salko, Sini-Selina; Tuittila, Eeva‐Stiina; Uuemaa, Evelyn; Rautiainen, Miina

doi:10.1016/j.rse.2023.113736

Cited by 25 publications

(14 citation statements)

References 82 publications

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“…The use of land cover masks to exclude water bodies and forests demonstrates that STR values associated with forests are higher than those over grassland with the same EVI (Figure 4). A previous study on peatlands [66] concluded that OPTRAM is insensitive to water table depth, especially for high (>50%) tree-cover areal density. This result also implies insensitivity to soil moisture in tree-covered areas and justifies the use of land masks to focus the analysis on grassland.…”

Section: Variable Vegetation Cover and Soil Moisturementioning

confidence: 97%

“…It is relevant for rewetting efforts in the European Union [86] where member states are committed to restoring 20% of degraded ecosystems, including grassland and drained peatlands under agricultural use, after centuries of wetland loss due to artificial drainage [87]. Soil moisture information at ~10 m scale can also help target the re-wetting of peatlands for carbon sequestration [66,88,89] which require assessments of soil moisture and drainage status prior to, during and after re-wetting efforts.…”

Section: Satellite-derived Surface Moisture Ts For Land-use Managementmentioning

confidence: 99%

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Temporal Stability of Grassland Soil Moisture Utilising Sentinel-2 Satellites and Sparse Ground-Based Sensor Networks

Basu,

Daly,

Brown

et al. 2024

Remote Sensing

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Soil moisture is important for understanding climate, water resources, water storage, and land use management. This study used Sentinel-2 (S-2) satellite optical data to retrieve surface soil moisture at a 10 m scale on grassland sites with low hydraulic conductivity soil in a climate dominated by heavy rainfall. Soil moisture was estimated after modifying the Optical Trapezoidal Model to account for mixed land cover in such conditions. The method uses data from a short-wave infra-red band, which is sensitive to soil moisture, and four vegetation indices from optical bands, which are sensitive to overlying vegetation. Scatter plots of these data from multiple, infrequent satellite passes are used to define the range of surface moisture conditions. The saturated and dry edges are clearly non-linear, regardless of the choice of vegetation index. Land cover masks are used to generate scatter plots from data only over grassland sites. The Enhanced Vegetation Index demonstrated advantages over other vegetation indices for surface moisture estimation over the entire range of grassland conditions. In poorly drained soils, the time lag between satellite surface moisture retrievals and in situ sensor soil moisture at depth must be part of the validation process. This was achieved by combining an approximate solution to the Richards’ Equation, along with measurements of saturated and residual moisture from soil samples, to optimise the correlations between measurements from satellites and sensors at a 15 cm depth. Time lags of 2–4 days resulted in a reduction of the root mean square errors between volumetric soil moisture predicted from S-2 data and that measured by in situ sensors, from ~0.1 m3/m3 to <0.06 m3/m3. The surface moisture results for two grassland sites were analysed using statistical concepts based upon the temporal stability of soil water content, an ideal framework for the intermittent Sentinel-2 data in conditions of persistent cloud cover. The analysis could discriminate between different natural drainages and surface soil textures in grassland areas and could identify sub-surface artificial drainage channels. The techniques are transferable for land-use and agricultural management in diverse environmental conditions without the need for extensive and expensive in situ sensor networks.

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Section: Variable Vegetation Cover and Soil Moisturementioning

confidence: 97%

Section: Satellite-derived Surface Moisture Ts For Land-use Managementmentioning

confidence: 99%

Temporal Stability of Grassland Soil Moisture Utilising Sentinel-2 Satellites and Sparse Ground-Based Sensor Networks

Basu,

Daly,

Brown

et al. 2024

Remote Sensing

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“…However, the grid-level input could not discern the soil conditions at both sites due to coarser spatial resolution, resulting in low model predictive performance at the RU-CHE site. US-Los is a small wetland where previous work has shown that water-table depth and shortwave radiation covary and are good predictors of methane fluxes (Burdun et al, 2023). Half-hourly air temperature is also correlated with diurnal methane fluxes, with some component of methanogenesis likely transported via lateral aquatic fluxes (Reed et al, 2018).…”

Section: Text 5 Model Predictive Performance At Sitesmentioning

confidence: 99%

Supplementary material to "WetCH₄: A Machine Learning-based Upscaling of Methane Fluxes of Northern Wetlands during 2016–2022"

Ying,

Poulter,

Watts

et al. 2024

Preprint

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Text 1. Existing ML-based wetland CH 4 upscaling products (Peltola et al., 2019) upscaled monthly CH4 fluxes for the Arctic-boreal freshwater wetland in 2013-3014 at 0.25°-0.5° spatial resolution with three temporal variables including MODIS LST at night, snow cover, and potential radiation, as well as a static binary permafrost map. The training data composed 488 monthly data records from 25 EC tower sites, spanning 2005 -2016 upscaled monthly CH4 fluxes for global freshwater wetland in 2001-2018 at 0.25° with air temperature (with and without a 2-week lag), MODIS EVI with a 3-week lag, mean temperature of the driest quarter, precipitation of the wettest month, and vegetation canopy height (Simard et al., 2011). Meteorological data was from WorldClim (Fick & Hijmans, 2017). The training data consisted of 6,210 weekly observations between 2006 and 2018 acquired from 43 EC sites. Text 2. Tower EC flux dataThe base of our EC data collection stems from a publicly available global synthesis coordination of FLUXNET-CH4, which includes 79 EC tower sites (42 are freshwater wetland sites) and 293 site-years of data. We collected both daily and half-hourly data from 44 sites in the Arctic-boreal region (>45° N), accounting for 167 site years as our base dataset, to which we added data from 6 new sites (31 site-years) and added additional data to 9 existing sites (21 site-years) contributed by site PIs (Table S2). In total, we assembled data from 50 EC tower sites in northern latitudes (219 site-years), of which 33 are from wetlands (155 site-years), with 13 wet tundra sites, 11 fens, and 9 bogs. Data entries with missing data in gridded predictors were excluded, including 5 wetland sites (FI-LOM, DE-SFN, RU-SAM, RU-VRK, SE-ST1) where data was collected before SMAP data was available. Another 2 sites (CA-BOU, RU-COK) were excluded after quality control. After quality filtering, data from 26 wetland sites were used for analysis (Table S2). Half-hourly data obtained from FLUXNET-CH4 were gap-filled following the FLUXNET protocols (Pastorello et al., 2020). Specifically, for CH4 fluxes (FCH4), the FLUXNET-CH4 gap-filling procedure includes filling gaps in meteorological variables with ERA-Interim reanalysis data and then gap-filling FCH4 using artificial neural networks (ANN) (Knox et al., 2019). Variables used to gap-fill FCH4 included air temperature (TA), downward-incoming shortwave radiation (SWin), wind speed (WS), air pressure (PA), and sine and cosine functions to represent seasonality. For the sites with additional half-hourly data that we assembled in this study, we used the same predictors to fill gaps in FCH4 except for gap-filling meteorological variables with ERA5 data. We used RF algorithm as it can fill gaps within 12 days with low normalized MAE for fens and bogs (Irvin et al., 2021). The R 2 of gap-filling models across sites ranged 0.35-0.89 (mean R 2 = 0.68). *GIEMS2 represents the minimum extents of northern wetlands. **GLWD provides a representation of the maximum extent of northern wetlands. ***These num...

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“…These data sets were combined with spatial estimations of wetland extent from remote sensing (Bohn et al, 2015;Gerlein-Safdi et al, 2021;Kuhn et al, 2021;Melton et al, 2013) to generate regional and global button-up estimates of methane fluxes from wetlands (Bloom et al, 2017;Poulter et al, 2017;Saunois et al, 2016;Saunois et al, 2020). Advancements in remote sensing continuously improve the estimate of wetland extent (Jensen & Mcdonald, 2019), and have recently started estimating water table depth, water elevation, and vegetation type characterization to improve wetland detection and characterization (Burdun et al, 2023;Domeneghetti et al, 2018;Hess et al, 2015;Ju & Bohrer, 2022;Melton et al, 2022;Yazbeck et al, 2024). Recently, the development of fast methane gas analyzers, which can be used as a part of eddy covariance systems, has led to a significant increase in surface flux research related to wetlands.…”

Section: Overview Of Regional/global Methane Flux Observation Data Setsmentioning

confidence: 99%

Three Decades of Wetland Methane Surface Flux Modeling by Earth System Models‐Advances, Applications, and Challenges

Forbrich,

Yazbeck,

Sulman

et al. 2024

JGR Biogeosciences

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Earth System Models (ESMs) simulate the exchange of mass and energy between the land surface and the atmosphere, with a key focus on modeling natural greenhouse gas feedbacks. Methane is the second most important greenhouse gas after carbon dioxide. There are growing concerns over the rapidly increasing methane concentration in the atmosphere, underscoring the need for accurate global modeling of its emissions using ESMs. Of the multitude of sources of methane globally, wetlands are the largest natural emitters for methane, leading to significant efforts targeting their representation in ESMs with a special focus on their methane emissions. In this review, we first provide a historical overview of including wetland‐methane components in ESMs and how methane modeling approaches have evolved over time. Second, we discuss recent modeling advancements that show promise for improvements in methane emissions predictions, namely the coupling of surface and atmospheric modules of ESMs, the representation of microtopography and transport mechanisms, the resolution of microbial processes at different spatial‐temporal scales, and the improved mapping of wetland area extent across the different wetland types. Third, we shed light on the different challenges hindering accurate estimations of wetland‐methane emissions, as shown by the consistent discrepancy between bottom‐up and top‐down models' predictions. Finally, we emphasize that more detailed representation of biogeochemistry and dynamic hydrology while resolving the within‐wetland vegetation heterogeneity should improve model predictions, especially when coupled with expanding ground‐based measurement networks and high‐resolution remote sensing mapping of methane‐relevant variables, such as water elevation, water table depth, and methane concentration.

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Hidden becomes clear: Optical remote sensing of vegetation reveals water table dynamics in northern peatlands

Cited by 25 publications

References 82 publications

Temporal Stability of Grassland Soil Moisture Utilising Sentinel-2 Satellites and Sparse Ground-Based Sensor Networks

Temporal Stability of Grassland Soil Moisture Utilising Sentinel-2 Satellites and Sparse Ground-Based Sensor Networks

Supplementary material to "WetCH₄: A Machine Learning-based Upscaling of Methane Fluxes of Northern Wetlands during 2016–2022"

Three Decades of Wetland Methane Surface Flux Modeling by Earth System Models‐Advances, Applications, and Challenges

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Hidden becomes clear: Optical remote sensing of vegetation reveals water table dynamics in northern peatlands

Cited by 25 publications

References 82 publications

Temporal Stability of Grassland Soil Moisture Utilising Sentinel-2 Satellites and Sparse Ground-Based Sensor Networks

Temporal Stability of Grassland Soil Moisture Utilising Sentinel-2 Satellites and Sparse Ground-Based Sensor Networks

Supplementary material to "WetCH4: A Machine Learning-based Upscaling of Methane Fluxes of Northern Wetlands during 2016–2022"

Three Decades of Wetland Methane Surface Flux Modeling by Earth System Models‐Advances, Applications, and Challenges

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Supplementary material to "WetCH₄: A Machine Learning-based Upscaling of Methane Fluxes of Northern Wetlands during 2016–2022"