Abdel-Hamid, A., Dubovyk, O., & Greve, K. (2021). The potential of Sentinel-1 InSAR coherence for grasslands monitoring in Eastern Cape, South Africa. International Journal of Applied Earth Observation and Geoinformation, 98, 102306. https://doi.org/10.1016/j.jag.2021.102306 (Crossref)

Adinugroho, W. C., Imanuddin, R., Krisnawati, H., Syaugi, A., Santosa, P. B., Qirom, M. A., & Prasetyo, L. B. (2021). Exploring the potential of soil moisture maps using Sentinel Imagery as a Proxy for groundwater levels in peat. IOP Conference Series: Earth and Environmental Science, 874 (1), 012011. https://doi.org/10.1088/1755-1315/874/1/012011 (Crossref)

Asmuß, T., Bechtold, M., & Tiemeyer, B. (2018). Towards Monitoring Groundwater Table Depth in Peatlands from Sentinel-1 Radar Data. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018, 7793–7796. https://doi.org/10.1109/IGARSS.2018.8518838 (Crossref)

Bechtold, M., Schlaffer, S., Tiemeyer, B., & De Lannoy, G. (2018). Inferring Water Table Depth Dynamics from ENVISAT-ASAR C-Band Backscatter over a Range of Peatlands from Deeply-Drained to Natural Conditions. Remote Sensing, 10 (4), 536. https://doi.org/10.3390/rs10040536 (Crossref)

Bechtold, M., Tiemeyer, B., Laggner, A., Leppelt, T., Frahm, E., & Belting, S. (2014). Large-scale regionalization of water table depth in peatlands optimized for greenhouse gas emission upscaling. Hydrology and Earth System Sciences, 18 (9), 3319–3339. https://doi.org/10.5194/hess-18-3319-2014 (Crossref)

Bring, A., Thorslund, J., Rosén, L., Tonderski, K., Åberg, C., Envall, I., & Laudon, H. (2022). Effects on groundwater storage of restoring, constructing or draining wetlands in temperate and boreal climates: a systematic review. Environmental Evidence, 11 (1), 38. https://doi.org/10.1186/s13750-022-00289-5 (Crossref)

Chen, S. H., & Pollino, C. A. (2012). Good practice in Bayesian network modelling. Environmental Modelling & Software, 37, 134–145. https://doi.org/10.1016/j.envsoft.2012.03.012 (Crossref)

Cieśliński, R. (2024). The use of the GEST method to estimate greenhouse gases uptake or emissions in the absence of data for a raised bog. Journal of Water and Land Development, 60, 59–64. https://doi.org/10.24425/jwld.2023.148460 (Crossref)

Cobb, B. R., Rumí, R., & Salmerón, A. (2007). Bayesian network models with discrete and continuous variables. In P. Lucas, J. A. Gámez, A. Salmerón (Eds.), Advances in probabilistic graphical models (pp. 81–102). Springer. https://doi.org/10.1007/978-3-540-68996-6_4 (Crossref)

Daly, R., Shen, Q., & Aitken, S. (2011). Learning Bayesian networks: approaches and issues. The Knowledge Engineering Review, 26 (2), 99–157. https://doi.org/10.1017/S0269888910000251 (Crossref)

European Commission [EC]. (2022). Nature restoration law: for people, climate, and planet. Publications Office of the European Union. https://data.europa.eu/doi/10.2779/86148

Evans, C. D., Peacock, M., Baird, A. J., Artz, R. R. E., Burden, A., Callaghan, N., Chapman, P. J., Cooper, H. M., Coyle, M., Craig, E., Cumming, A., Dixon, S., Gauci, V., Grayson, R. P., Helfter, C., Heppell, C. M., Holden, J., Jones, D. L., Kaduk, J., Levy, P., Matthews, R., McNamara, N. P., Misselbrook, T., Oakley, S., Page, S. E., Rayment, M., Ridley, L. M., Stanley, K. M., Williamson, J. L., Worrall, F., & Morrison, R. (2021). Overriding water table control on managed peatland greenhouse gas emissions. Nature, 593 (7860), 548–552. https://doi.org/10.1038/s41586-021-03523-1 (Crossref)

Food and Agriculture Organization of the United Nations [FAO]. (2021). Practical guidance for peatland restoration monitoring in Indonesia – A remote sensing approach using FAO-SEPAL platform. Technical working paper. Food and Agriculture Organization of the United Nations.

Georgiou, S., Mitchard, E. T. A., Crezee, B., Dargie, G. C., Young, D. M., Jovani-Sancho, A. J., Kitambo, B., Papa, F., Bocko, Y. E., Bola, P., Crabtree, D. E., Emba, O. B., Ewango, C. E. N., Girkin, N. T., Ifo, S. A., Kanyama, J. T., Mampouya, Y. E. W., Mbemba, M., Ndjango, J-B. N., Palmer, P. I., Sjӧgersten, S., & Lewis, S. L. (2023). Mapping water levels across a Region of the Cuvette Centrale Peatland Complex. Remote Sensing, 15 (12), 3099. https://doi.org/10.3390/rs15123099 (Crossref)

Ghazaryan, G., Krupp, L., Seyfried, S., Landgraf, N., & Nendel, C. (2024). Enhancing peatland monitoring through multisource remote sensing: optical and radar data applications. International Journal of Remote Sensing, 45 (18), 6372–6394. https://doi.org/10.1080/01431161.2024.2387133 (Crossref)

Ghezelayagh, P., Oleszczuk, R., Stachowicz, M., Eini, M. R., Kamocki, A., Banaszuk, P., & Grygoruk, M. (2024). Developing a remote-sensing-based indicator for peat soil vertical displacement. A case study in the Biebrza Valley, Poland. Ecological Indicators, 166, 112305. https://doi.org/10.1016/j.ecolind.2024.112305 (Crossref)

Grand-Clement, E., Anderson, K., Smith, D., Angus, M., Luscombe, D. J., Gatis, N., Bray, L. S., & Brazier, R. E. (2015). New approaches to the restoration of shallow marginal peatlands. Journal of Environmental Management, 161, 417–430. https://doi.org/10.1016/j.jenvman.2015.06.023 (Crossref)

Gutierrez Pacheco, S., Lagacé, R., Hugron, S., Godbout, S., & Rochefort, L. (2021). Estimation of Daily Water Table Level with Bimonthly Measurements in Restored Ombrotrophic Peatland. Sustainability, 13 (10), 5474. https://doi.org/10.3390/su13105474 (Crossref)

Habib, W., & Connolly, J. (2023). A national-scale assessment of land use change in peatlands between 1989 and 2020 using Landsat data and Google Earth Engine—a case study of Ireland. Regional Environmental Change, 23 (4), 124. https://doi.org/10.1007/s10113-023-02116-0 (Crossref)

Hamner, B., & Frasco, M. (2018). Metrics: Evaluation metrics for machine learning. R package version 0.1.4. Retrieved from: https://CRAN.R-project.org/package=Metrics

Harris, A., & Bryant, R. G. (2009). A multi-scale remote sensing approach for monitoring northern peatland hydrology: Present possibilities and future challenges. Journal of Environmental Management, 90 (7), 2178–2188. https://doi.org/10.1016/j.jenvman.2007.06.025 (Crossref)

Henriksen, H. J., Rasmusssen, P., Brandt, G., Bulow, D. von, & Jensen, F. V. (2007). Bayesian networks as a participatory modelling tool for groundwater protection. In A. Castellati & R. Soncini-Sessa (Eds.), Topics on System Analysis and Integrated Water Resources Management (pp. 49–72). Elsevier. https://doi.org/10.1016/B978-008044967-8/50003-8 (Crossref)

Hikouei, I. S., Eshleman, K. N., Saharjo, B. H., Graham, L. L. B., Applegate, G., & Cochrane, M. A. (2023). Using machine learning algorithms to predict groundwater levels in Indonesian tropical peatlands. Science of The Total Environment, 857 (Part 3), 159701. https://doi.org/10.1016/j.scitotenv.2022.159701 (Crossref)

Hoyt, A. M., Chaussard, E., Seppalainen, S. S., & Harvey, C. F. (2020). Widespread subsidence and carbon emissions across Southeast Asian peatlands. Nature Geoscience, 13 (6), 435–440. https://doi.org/10.1038/s41561-020-0575-4 (Crossref)

Hrysiewicz, A., Williamson, J., Evans, C. D., Jovani-Sancho, A. J., Callaghan, N., Lyons, J., White, J., Kowalska, J., Menichino, N., & Holohan, E. P. (2024). Estimation and validation of InSAR-derived surface displacements at temperate raised peatlands. Remote Sensing of Environment, 311, 114232. https://doi.org/10.1016/j.rse.2024.114232 (Crossref)

Irfan, M., Kurniawati, N., Ariani, M., Sulaiman, A., & Iskandar, I. (2020). Study of groundwater level and its correlation to soil moisture on peatlands in South Sumatra. Journal of Physics: Conference Series, 1568 (1), 012028. https://doi.org/10.1088/1742-6596/1568/1/012028 (Crossref)

Jones, C. N., Evenson, G. R., McLaughlin, D. L., Vanderhoof, M. K., Lang, M. W., McCarty, G. W., Golden, H. E., Lane, C. R., & Alexander, L. C. (2018). Estimating restorable wetland water storage at landscape scales. Hydrological Processes, 32 (2), 305–313. https://doi.org/10.1002/hyp.11405 (Crossref)

Joosten, H., & Clarke, D. (2002). Wise use of mires and peatlands - background and principles including a framework for decision-making. International Mire Conservation Group and International Peat Society.

Kameoka, T., Kozan, O., Hadi, S., Asnawi, & Hasrullah. (2021). Monitoring the groundwater level in tropical peatland through UAV mapping of soil surface temperature: a pilot study in Tanjung Leban, Indonesia. Remote Sensing Letters, 12 (6), 542–552. https://doi.org/10.1080/2150704X.2021.1906974 (Crossref)

Kardel, I., Chormański, J., Mirosław-Świątek, D., Okruszko, T., Grygoruk, M., & Wassen, M. J. (2009). Decision support system for Biebrza National Park. In Ch. Jao (Eds.), Hydroinformatics in Hydrology, Hydrogeology and Water Resources (pp. 441–458). IAHS Publications.

Karimi, S., Hasselquist, E. M., Salimi, S., Järveoja, J., & Laudon, H. (2024). Rewetting impact on the hydrological function of a drained peatland in the boreal landscape. Journal of Hydrology, 641, 131729. https://doi.org/10.1016/j.jhydrol.2024.131729 (Crossref)

Kim, J. W., Lu, Z., Gutenberg, L., & Zhu, Z. (2017). Characterizing hydrologic changes of the Great Dismal Swamp using SAR/InSAR. Remote Sensing of Environment, 198, 187–202. https://doi.org/10.1016/j.rse.2017.06.009 (Crossref)

Koch, J., Elsgaard, L., Greve, M. H., Gyldenkærne, S., Hermansen, C., Levin, G., Wu, S., & Stisen, S. (2023). Water-table-driven greenhouse gas emission estimates guide peatland restoration at national scale. Biogeosciences, 20 (12), 2387–2403. https://doi.org/10.5194/bg-20-2387-2023 (Crossref)

Kuhn, M. (2008). Building predictive models in R using the caret Package. Journal of Statistical Software, 28 (5). https://doi.org/10.18637/jss.v028.i05 (Crossref)

Lees, K. J., Artz, R. R. E., Chandler, D., Aspinall, T., Boulton, C. A., Buxton, J., Cowie, N. R., & Lenton, T. M. (2021). Using remote sensing to assess peatland resilience by estimating soil surface moisture and drought recovery. Science of The Total Environment, 761, 143312. https://doi.org/10.1016/j.scitotenv.2020.143312 (Crossref)

Lees, K. J., Quaife, T., Artz, R. R. E., Khomik, M., & Clark, J. M. (2018). Potential for using remote sensing to estimate carbon fluxes across northern peatlands – A review. Science of The Total Environment, 615, 857–874. https://doi.org/10.1016/j.scitotenv.2017.09.103 (Crossref)

Liu, S., McGree, J., Ge, Z., & Xie, Y. (2016). Classification methods. In Computational and Statistical Methods for Analysing Big Data with Applications (pp. 7–28). Elsevier. https://doi.org/10.1016/B978-0-12-803732-4.00002-7 (Crossref)

Liu, W., Fritz, C., van Belle, J., & Nonhebel, S. (2023). Production in peatlands: Comparing ecosystem services of different land use options following conventional farming. Science of The Total Environment, 875, 162534. https://doi.org/10.1016/j.scitotenv.2023.162534 (Crossref)

Ma, L., Zhu, G., Chen, B., Zhang, K., Niu, S., Wang, J., Ciais, P., & Zuo, H. (2022). A globally robust relationship between water table decline, subsidence rate, and carbon release from peatlands. Communications Earth & Environment, 3 (1), 254. https://doi.org/10.1038/s43247-022-00590-8 (Crossref)

Marcot, B. G. (2012). Metrics for evaluating performance and uncertainty of Bayesian network models. Ecological Modelling, 230, 50–62. https://doi.org/10.1016/j.ecolmodel.2012.01.013 (Crossref)

Marcot, B. G., & Penman, T. D. (2019). Advances in Bayesian network modelling: Integration of modelling technologies. Environmental Modelling & Software, 111, 386–393. https://doi.org/10.1016/j.envsoft.2018.09.016 (Crossref)

Masegosa, A. R., Feelders, A. J., & Gaag, L. C. van der (2016). Learning from incomplete data in Bayesian networks with qualitative influences. International Journal of Approximate Reasoning, 69, 18–34. https://doi.org/10.1016/j.ijar.2015.11.004 (Crossref)

Millard, K., Thompson, D. K., Parisien, M.-A., & Richardson, M. (2018). Soil moisture monitoring in a temperate peatland using multi-sensor remote sensing and linear mixed effects. Remote Sensing, 10 (6), 903. https://doi.org/10.3390/rs10060903 (Crossref)

Neapolitan, R. E. (2007). Learning Bayesian networks. Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2007, 1–1. https://doi.org/10.1145/1327942.1327961 (Crossref)

Nielsen, C. K., Elsgaard, L., Jørgensen, U., & Lærke, P. E. (2023). Soil greenhouse gas emissions from drained and rewetted agricultural bare peat mesocosms are linked to geochemistry. Science of The Total Environment, 896, 165083. https://doi.org/10.1016/j.scitotenv.2023.165083 (Crossref)

Okruszko, H., & Byczkowski, A. (1996). Osuszanie mokradeł w Basenie Środkowym Biebrzy w ujęciu historycznym. Zeszyty Problemowe Postępów Nauk Rolniczych, 432, 33–43.

Rao, M. B., & Rao, C. R. (2014). In M. B. Rao & C. R. Rao (Eds.), Bayesian networks (pp. 357–385). https://doi.org/10.1016/B978-0-444-63431-3.00010-3 (Crossref)

R Core Team (2023). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/

Räsänen, A., Tolvanen, A., & Kareksela, S. (2022). Monitoring peatland water table depth with optical and radar satellite imagery. International Journal of Applied Earth Observation and Geoinformation, 112, 102866. https://doi.org/10.1016/j.jag.2022.102866 (Crossref)

Rohmer, J. (2020). Uncertainties in conditional probability tables of discrete Bayesian Belief Networks: A comprehensive review. Engineering Applications of Artificial Intelligence, 88, 103384. https://doi.org/10.1016/j.engappai.2019.103384 (Crossref)

Rositano, F., Piñeiro, G., Bert, F. E., & Ferraro, D. O. (2017). A comparison of two sensitivity analysis techniques based on four bayesian models representing ecosystem services provision in the Argentine Pampas. Ecological Informatics, 41, 33–39. https://doi.org/10.1016/j.ecoinf.2017.07.005 (Crossref)

Stachowicz, M., Venegas-Cordero, N., & Ghezelayagh, P. (in press). Two centuries of changes – revision of the hydrography of the Biebrza Valley, its transformation and probable ecohydrological challenges. Ecohydrology & Hydrobiology. https://doi.org/10.1016/j.ecohyd.2023.08.008 (Crossref)

Tanneberger, F., Berghöfer, A., Brust, K., Hammerich, J., Holsten, B., Joosten, H., Michaelis, D., Moritz, F., Reichelt, F., Schäfer, A., Scheid, A., Trepel, M., Wahren, A., & Couwenberg, J. (2024). Quantifying ecosystem services of rewetted peatlands − the MoorFutures methodologies. Ecological Indicators, 163, 112048. https://doi.org/10.1016/j.ecolind.2024.112048 (Crossref)

Tiemeyer, B., Freibauer, A., Borraz, E. A., Augustin, J., Bechtold, M., Beetz, S., Beyer, C., Ebli, M., Eickenscheidt, T., Fiedler, S., Förster, C., Gensior, A., Giebels, M., Glatzel, S., Heinichen, J., Hoffmann, M., Höper, H., Jurasinski, G., Laggner, A., Leiber-Sauheitl, K., & Drösler, M. (2020). A new methodology for organic soils in national greenhouse gas inventories: Data synthesis, derivation and application. Ecological Indicators, 109, 105838. https://doi.org/10.1016/j.ecolind.2019.105838 (Crossref)

United Nations Environment Programme [UNEP]. (2022). Global Peatlands Assessment – The State of the World’s Peatlands: Evidence for action toward the conservation, restoration, and sustainable management of peatlands. Main Report. Global Peatlands Initiative. United Nations Environment Programme.

Veci, L., Prats-Iraola, P., Scheiber, R., Collard, F., Fomferra, N., & Engdahl, M. (2014). The sentinel-1 toolbox. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2014, 1–3.

Venegas-Cordero, N., Marcinkowski, P., Stachowicz, M., & Grygoruk, M. (in press). On the role of water balance as a prerequisite for aquatic and wetland ecosystems management: A case study of the Biebrza catchment, Poland. Ecohydrology & Hydrobiology. https://doi.org/10.1016/j.ecohyd.2024.08.001 (Crossref)

Villaverde, A. F., Ross, J., Morán, F., & Banga, J. R. (2014). MIDER: Network Inference with Mutual Information Distance and Entropy Reduction. PLoS ONE, 9 (5), e96732. https://doi.org/10.1371/journal.pone.0096732 (Crossref)

Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. Springer International Publishing. (Crossref)