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The cloud-penetrating Sentinel-1 radar constellation provides frequent and high-resolution observations with global coverage, but few studies have assessed its potential for mapping disturbances in temperate forests. This study investigated the sensitivity of temporally dense C-band backscatter data from Sentinel-1 to varying management-related disturbance intensities in temperate forests, and the influence of confounding factors such as radar backscatter signal seasonality, shadow, and layover on the radar backscatter signal at a pixel level. A unique network of 14 experimental sites in the Netherlands was used in which trees were removed to simulate different levels of management-related forest disturbances across a range of representative temperate forest species. Results from six years (2016\u20132022) of Sentinel-1 observations indicated that backscatter seasonality is dependent on species phenology and degree of canopy cover. The backscatter change magnitude was sensitive to medium- and high-severity disturbances, with radar layover having a stronger impact on the backscatter disturbance signal than radar shadow. Combining ascending and descending orbits and complementing polarizations compared to a single orbit or polarization was found to result in a 34% mean increase in disturbance detection sensitivity across all disturbance severities. This study underlines the importance of linking high-quality experimental ground-based data to dense satellite time series to improve future forest disturbance mapping. It suggests a key role for C-band backscatter time series in the rapid and accurate large-area monitoring of temperate forests and, in particular, the disturbances imposed by logging practices or tree mortality driven by climate change factors.<\/jats:p>","DOI":"10.3390\/rs16091553","type":"journal-article","created":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T04:26:16Z","timestamp":1714364776000},"page":"1553","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Sensitivity of Sentinel-1 Backscatter to Management-Related Disturbances in Temperate Forests"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-1427-2312","authenticated-orcid":false,"given":"Sietse","family":"van der Woude","sequence":"first","affiliation":[{"name":"Laboratory of Geo-Information Science and Remote Sensing, Wageningen University, Droevendaalsesteeg 3, 6708 PB Wageningen, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4327-4349","authenticated-orcid":false,"given":"Johannes","family":"Reiche","sequence":"additional","affiliation":[{"name":"Laboratory of Geo-Information Science and Remote Sensing, Wageningen University, Droevendaalsesteeg 3, 6708 PB Wageningen, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7559-6572","authenticated-orcid":false,"given":"Frank","family":"Sterck","sequence":"additional","affiliation":[{"name":"Forest Ecology and Forest Management Group, Wageningen University and Research, P.O. Box 47, 6700 AA Wageningen, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9761-074X","authenticated-orcid":false,"given":"Gert-Jan","family":"Nabuurs","sequence":"additional","affiliation":[{"name":"Forest Ecology and Forest Management Group, Wageningen University and Research, P.O. Box 47, 6700 AA Wageningen, The Netherlands"},{"name":"Wageningen Environmental Research, Droevendaalsesteeg 3, 6708 PB Wageningen, The Netherlands"}]},{"given":"Marleen","family":"Vos","sequence":"additional","affiliation":[{"name":"Forest Ecology and Forest Management Group, Wageningen University and Research, P.O. Box 47, 6700 AA Wageningen, The Netherlands"}]},{"given":"Martin","family":"Herold","sequence":"additional","affiliation":[{"name":"Laboratory of Geo-Information Science and Remote Sensing, Wageningen University, Droevendaalsesteeg 3, 6708 PB Wageningen, The Netherlands"},{"name":"GFZ German Research Centre for Geosciences, Remote Sensing and Geoinformatics Section, Telegrafenberg, 14473 Potsdam, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,27]]},"reference":[{"key":"ref_1","unstructured":"Nabuurs, G.-J., Hatab, A.A., Bustamante, M., Clark, H., Havl\u00edk, P., Ninan, K.N., Popp, A., Roe, S., Aoki, L., and Angers, D. (2022). IPCC, 2022: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press."},{"key":"ref_2","unstructured":"European Commission (2021). New EU Forest Strategy for 2030, European Commission."},{"key":"ref_3","unstructured":"Canadian Council of Forest Ministers (2023, February 15). Renewed Forest Bioeconomy Framework. Available online: https:\/\/www.ccfm.org\/releases\/renewed-forest-bioeconomy-framework\/."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"eaba2724","DOI":"10.1126\/sciadv.aba2724","article-title":"Direct and Seasonal Legacy Effects of the 2018 Heat Wave and Drought on European Ecosystem Productivity","volume":"6","author":"Bastos","year":"2020","journal-title":"Sci. Adv."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/j.foreco.2015.10.042","article-title":"Forest Disturbance across the Conterminous United States from 1985\u20132012: The Emerging Dominance of Forest Decline","volume":"360","author":"Cohen","year":"2016","journal-title":"For. Ecol. Manag."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"eaaz9463","DOI":"10.1126\/science.aaz9463","article-title":"Pervasive Shifts in Forest Dynamics in a Changing World","volume":"368","author":"McDowell","year":"2020","journal-title":"Science"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1359","DOI":"10.1111\/gcb.16531","article-title":"Significant Increase in Natural Disturbance Impacts on European Forests since 1950","volume":"29","author":"Patacca","year":"2023","journal-title":"Glob. Chang. Biol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1038\/nclimate3303","article-title":"Forest Disturbances under Climate Change","volume":"7","author":"Seidl","year":"2017","journal-title":"Nat. Clim. Chang."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1038\/s41586-020-2438-y","article-title":"Abrupt Increase in Harvested Forest Area over Europe after 2015","volume":"583","author":"Ceccherini","year":"2020","journal-title":"Nature"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"E15","DOI":"10.1038\/s41586-021-03292-x","article-title":"Concerns about Reported Harvests in European Forests","volume":"592","author":"Valbuena","year":"2021","journal-title":"Nature"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"112502","DOI":"10.1016\/j.rse.2021.112502","article-title":"Human or Natural? Landscape Context Improves the Attribution of Forest Disturbances Mapped from Landsat in Central Europe","volume":"262","author":"Sebald","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1007\/s40725-017-0047-2","article-title":"Methods for Mapping Forest Disturbance and Degradation from Optical Earth Observation Data: A Review","volume":"3","author":"Hirschmugl","year":"2017","journal-title":"Curr. For. Rep."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1038\/s41893-020-00609-y","article-title":"Mapping the Forest Disturbance Regimes of Europe","volume":"4","author":"Senf","year":"2021","journal-title":"Nat. Sustain."},{"key":"ref_14","first-page":"102663","article-title":"An Open Science and Open Data Approach for the Statistically Robust Estimation of Forest Disturbance Areas","volume":"106","author":"Francini","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Giannetti, F., Pecchi, M., Travaglini, D., Francini, S., D\u2019Amico, G., Vangi, E., Cocozza, C., and Chirici, G. (2021). Estimating VAIA Windstorm Damaged Forest Area in Italy Using Time Series Sentinel-2 Imagery and Continuous Change Detection Algorithms. Forests, 12.","DOI":"10.3390\/f12060680"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Lastovicka, J., Svec, P., Paluba, D., Kobliuk, N., Svoboda, J., Hladky, R., and Stych, P. (2020). Sentinel-2 Data in an Evaluation of the Impact of the Disturbances on Forest Vegetation. Remote Sens., 12.","DOI":"10.3390\/rs12121914"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1016\/j.isprsjprs.2017.07.004","article-title":"Using Landsat Time Series for Characterizing Forest Disturbance Dynamics in the Coupled Human and Natural Systems of Central Europe","volume":"130","author":"Senf","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Thonfeld, F., Gessner, U., Holzwarth, S., Kriese, J., da Ponte, E., Huth, J., and Kuenzer, C. (2022). A First Assessment of Canopy Cover Loss in Germany\u2019s Forests after the 2018\u20132020 Drought Years. Remote Sens., 14.","DOI":"10.3390\/rs14030562"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"103001","DOI":"10.1088\/1748-9326\/abaad7","article-title":"Remote Sensing of Forest Degradation: A Review","volume":"15","author":"Gao","year":"2020","journal-title":"Environ. Res. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.rse.2013.08.050","article-title":"Change Detection of Boreal Forest Using Bi-Temporal ALOS PALSAR Backscatter Data","volume":"155","author":"Pantze","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.rse.2017.10.034","article-title":"Improving Near-Real Time Deforestation Monitoring in Tropical Dry Forests by Combining Dense Sentinel-1 Time Series with Landsat and ALOS-2 PALSAR-2","volume":"204","author":"Reiche","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.rse.2014.04.014","article-title":"New Global Forest\/Non-Forest Maps from ALOS PALSAR Data (2007\u20132010)","volume":"155","author":"Shimada","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"112643","DOI":"10.1016\/j.rse.2021.112643","article-title":"Refined Algorithm for Forest Early Warning System with ALOS-2\/PALSAR-2 ScanSAR Data in Tropical Forest Regions","volume":"265","author":"Watanabe","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1104","DOI":"10.1016\/j.scitotenv.2019.06.494","article-title":"Synthetic Aperture Radar Sensitivity to Forest Changes: A Simulations-Based Study for the Romanian Forests","volume":"689","author":"Tanase","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"112411","DOI":"10.1016\/j.rse.2021.112411","article-title":"Detecting Tropical Selective Logging with C-Band SAR Data May Require a Time Series Approach","volume":"259","author":"Hethcoat","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"024005","DOI":"10.1088\/1748-9326\/abd0a8","article-title":"Forest Disturbance Alerts for the Congo Basin Using Sentinel-1","volume":"16","author":"Reiche","year":"2021","journal-title":"Environ. Res. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1080\/01431161.2022.2157684","article-title":"Inter-Comparison of Optical and SAR-Based Forest Disturbance Warning Systems in the Amazon Shows the Potential of Combined SAR-Optical Monitoring","volume":"44","author":"Lima","year":"2023","journal-title":"Int. J. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Hoekman, D., Kooij, B., Qui\u00f1ones, M., Vellekoop, S., Carolita, I., Budhiman, S., Arief, R., and Roswintiarti, O. (2020). Wide-Area Near-Real-Time Monitoring of Tropical Forest Degradation and Deforestation Using Sentinel-1. Remote Sens., 12.","DOI":"10.3390\/rs12193263"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"R\u00fcetschi, M., Small, D., and Waser, L.T. (2019). Rapid Detection of Windthrows Using Sentinel-1 C-Band SAR Data. Remote Sens., 11.","DOI":"10.3390\/rs11020115"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Dasc\u0103lu, A., Catal\u00e3o, J., and Navarro, A. (2023). Detecting Deforestation Using Logistic Analysis and Sentinel-1 Multitemporal Backscatter Data. Remote Sens., 15.","DOI":"10.3390\/rs15020290"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Olesk, A., Voormansik, K., Pohjala, M., and Noorma, M. (2015, January 1\u20134). Forest Change Detection from Sentinel-1 and ALOS-2 Satellite Images. Proceedings of the 2015 IEEE 5th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR), Singapore.","DOI":"10.1109\/APSAR.2015.7306263"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"111345","DOI":"10.1016\/j.rse.2019.111345","article-title":"Burned Area Detection and Mapping Using Sentinel-1 Backscatter Coefficient and Thermal Anomalies","volume":"233","author":"Tanase","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1080\/15481603.2021.1907896","article-title":"A Workflow Based on Sentinel-1 SAR Data and Open-Source Algorithms for Unsupervised Burned Area Detection in Mediterranean Ecosystems","volume":"58","author":"Silva","year":"2021","journal-title":"GISci. Remote Sens."},{"key":"ref_34","unstructured":"JRC (2013). Forest Landscape in Europe: Pattern, Fragmentation and Connectivity, Publications Office."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"112159","DOI":"10.1016\/j.rse.2020.112159","article-title":"SAR Data for Tropical Forest Disturbance Alerts in French Guiana: Benefit over Optical Imagery","volume":"252","author":"Bouvet","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Bouvet, A., Mermoz, S., Ball\u00e8re, M., Koleck, T., and Le Toan, T. (2018). Use of the SAR Shadowing Effect for Deforestation Detection with Sentinel-1 Time Series. Remote Sens., 10.","DOI":"10.3390\/rs10081250"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Carstairs, H., Mitchard, E.T.A., McNicol, I., Aquino, C., Chezeaux, E., Ebanega, M.O., Dikongo, A.M., and Disney, M. (2022). Sentinel-1 Shadows Used to Quantify Canopy Loss from Selective Logging in Gabon. Remote Sens., 14.","DOI":"10.3390\/rs14174233"},{"key":"ref_38","first-page":"337","article-title":"The Development of Pine Plantation Silviculture in the Southern United States","volume":"105","author":"Fox","year":"2007","journal-title":"J. For."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1111\/nph.14255","article-title":"Forest Ecosystems of Temperate Climatic Regions: From Ancient Use to Climate Change","volume":"212","author":"Gilliam","year":"2016","journal-title":"New Phytol."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Kaiser, P., Buddenbaum, H., Nink, S., and Hill, J. (2022). Potential of Sentinel-1 Data for Spatially and Temporally High-Resolution Detection of Drought Affected Forest Stands. Forests, 13.","DOI":"10.3390\/f13122148"},{"key":"ref_41","first-page":"1013","article-title":"Monitoring of Soil Moisture and Vegetation Water Content Variations in Boreal Forest from C-Band SAR Data","volume":"Volume 2","author":"Pulliainen","year":"2004","journal-title":"Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, IGARSS \u201904"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"7738","DOI":"10.1080\/01431161.2018.1479788","article-title":"Annual Seasonality in Sentinel-1 Signal for Forest Mapping and Forest Type Classification","volume":"39","author":"Wagner","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Dost\u00e1lov\u00e1, A., Lang, M., Ivanovs, J., Waser, L.T., and Wagner, W. (2021). European Wide Forest Classification Based on Sentinel-1 Data. Remote Sens., 13.","DOI":"10.3390\/rs13030337"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.rse.2015.11.006","article-title":"Using Spatial Context to Improve Early Detection of Deforestation from Landsat Time Series","volume":"172","author":"Hamunyela","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"668","DOI":"10.1109\/36.225533","article-title":"Seasonal Changes in Relative C-Band Backscatter of Northern Forest Cover Types","volume":"31","author":"Ahern","year":"1993","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"R\u00fcetschi, M., Schaepman, M., and Small, D. (2017). Using Multitemporal Sentinel-1 C-Band Backscatter to Monitor Phenology and Classify Deciduous and Coniferous Forests in Northern Switzerland. Remote Sens., 10.","DOI":"10.3390\/rs10010055"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Udali, A., Lingua, E., and Persson, H.J. (2021). Assessing Forest Type and Tree Species Classification Using Sentinel-1 C-Band SAR Data in Southern Sweden. Remote Sens., 13.","DOI":"10.3390\/rs13163237"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"113728","DOI":"10.1016\/j.rse.2023.113728","article-title":"Seasonality and Directionality Effects on Radar Backscatter Are Key to Identify Mountain Forest Types with Sentinel-1 Data","volume":"296","author":"Santoro","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"97","DOI":"10.5194\/isprs-annals-V-3-2020-97-2020","article-title":"Characterization of land cover seasonality in sentinel-1 time series data","volume":"V-3-2020","author":"Dubois","year":"2020","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_50","unstructured":"Woodhouse, I.H. (2006). Introduction to Microwave Remote Sensing, Taylor&Francis."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1109\/TGRS.1995.8746018","article-title":"A Three-Dimensional Radar Backscatter Model of Forest Canopies","volume":"33","author":"Sun","year":"1995","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"120731","DOI":"10.1016\/j.foreco.2022.120731","article-title":"Aboveground Carbon and Nutrient Distributions Are Hardly Associated with Canopy Position for Trees in Temperate Forests on Poor and Acidified Sandy Soils","volume":"529","author":"Vos","year":"2023","journal-title":"For. Ecol. Manag."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"120791","DOI":"10.1016\/j.foreco.2023.120791","article-title":"The Sustainability of Timber and Biomass Harvest in Perspective of Forest Nutrient Uptake and Nutrient Stocks","volume":"530","author":"Vos","year":"2023","journal-title":"For. Ecol. Manag."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"795","DOI":"10.1038\/s41598-017-19048-5","article-title":"A Framework for Quantifying the Relationship between Intensity and Severity of Impact of Disturbance across Types of Events and Species","volume":"8","author":"Iwasaki","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Schelhaas, M.-J., Clerkx, S., and Lerink, B. (2022). Zevende Nederlandse Bosinventarisatie: 2017\u20132021, Wettelijke Onderzoekstaken Natuur & Milieu.","DOI":"10.18174\/575334"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-Scale Geospatial Analysis for Everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_57","unstructured":"Fletcher, K. (2012). Sentinel-1: ESA\u2019s Radar Observatory Mission for GMES Operational Services, ESA Communications. ESA SP-1322\/1."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"26","DOI":"10.18637\/jss.v082.i13","article-title":"LmerTest Package: Tests in Linear Mixed Effects Models","volume":"82","author":"Kuznetsova","year":"2017","journal-title":"J. Stat. Soft."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"2349","DOI":"10.1002\/(SICI)1097-0258(19971030)16:20<2349::AID-SIM667>3.0.CO;2-E","article-title":"Using the General Linear Mixed Model to Analyse Unbalanced Repeated Measures and Longitudinal Data","volume":"16","author":"Cnaan","year":"1997","journal-title":"Statist. Med."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Millard, K., Thompson, D., Parisien, M.-A., and Richardson, M. (2018). Soil Moisture Monitoring in a Temperate Peatland Using Multi-Sensor Remote Sensing and Linear Mixed Effects. Remote Sens., 10.","DOI":"10.3390\/rs10060903"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"2815","DOI":"10.21105\/joss.02815","article-title":"Effectsize: Estimation of Effect Size Indices and Standardized Parameters","volume":"5","author":"Makowski","year":"2020","journal-title":"JOSS"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1002\/bimj.200810425","article-title":"Simultaneous Inference in General Parametric Models","volume":"50","author":"Hothorn","year":"2008","journal-title":"Biom. J."},{"key":"ref_63","unstructured":"Lenth, R. (2023, February 15). emmeans: Estimated Marginal Means, aka Least-Squares Means. Available online: https:\/\/CRAN.R-project.org\/package=emmeans."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1080\/00031305.1980.10483031","article-title":"Population Marginal Means in the Linear Model: An Alternative to Least Squares Means","volume":"34","author":"Searle","year":"1980","journal-title":"Am. Stat."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1214\/14-AOAS788","article-title":"Inferring Causal Impact Using Bayesian Structural Time-Series Models","volume":"9","author":"Brodersen","year":"2015","journal-title":"Ann. Appl. Stat."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"4349","DOI":"10.5194\/essd-13-4349-2021","article-title":"ERA5-Land: A State-of-the-Art Global Reanalysis Dataset for Land Applications","volume":"13","author":"Dutra","year":"2021","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Benninga, H.-J., van der Velde, R., and Su, Z. (2019). Impacts of Radiometric Uncertainty and Weather-Related Surface Conditions on Soil Moisture Retrievals with Sentinel-1. Remote Sens., 11.","DOI":"10.3390\/rs11172025"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"112267","DOI":"10.1016\/j.rse.2020.112267","article-title":"Sentinel-1 Based Soil Freeze\/Thaw Estimation in Boreal Forest Environments","volume":"254","author":"Cohen","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1080\/00031305.2016.1256839","article-title":"A Coefficient of Determination for Generalized Linear Models","volume":"71","author":"Zhang","year":"2017","journal-title":"Am. Stat."},{"key":"ref_70","unstructured":"Zhang, D. (2023, February 15). Package \u2018Rsq\u2019 2022. Available online: https:\/\/cran.r-project.org\/web\/packages\/rsq\/rsq.pdf."},{"key":"ref_71","unstructured":"Kassambara, A. (2019). Practical Statistics in R II\u2014Comparing Groups: Numerical Variables, Datanovia. [1st ed.]."},{"key":"ref_72","unstructured":"Dost\u00e1lov\u00e1, A., Milenkovi\u0107, M., Hollaus, M., and Wagner, W. (2016, January 9\u201313). Influence of Forest Structure on the Sentinel-1 Backscatter Variation\u2014Analysis with Full-Waveform LIDAR Data. Proceedings of the Living Planet Symposium, Prague, Czech Republic."},{"key":"ref_73","first-page":"102505","article-title":"Potential of C-Band Synthetic Aperture Radar Sentinel-1 Time-Series for the Monitoring of Phenological Cycles in a Deciduous Forest","volume":"104","author":"Soudani","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LGRS.2022.3187295","article-title":"Attenuation of Radar Signal by a Boreal Forest Canopy in Winter","volume":"19","author":"Lemmetyinen","year":"2022","journal-title":"IEEE Geosci. Remote Sensing Lett."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Philpot, C., and Mutch, R. (1971). The Seasonal Trends in Moisture Content, Ether Extractives, and Energy of Ponderosa Pine and Douglas-Fir Needles.","DOI":"10.5962\/bhl.title.68984"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Puhm, M., Deutscher, J., Hirschmugl, M., Wimmer, A., Schmitt, U., and Schardt, M. (2020). A Near Real-Time Method for Forest Change Detection Based on a Structural Time Series Model and the Kalman Filter. Remote Sens., 12.","DOI":"10.3390\/rs12193135"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Ruiz-Ramos, J., Marino, A., Boardman, C., and Suarez, J. (2020). Continuous Forest Monitoring Using Cumulative Sums of Sentinel-1 Timeseries. Remote Sens., 12.","DOI":"10.3390\/rs12183061"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.1109\/TGRS.2002.800235","article-title":"Quantitative Analysis of RADARSAT SAR data over a Sparse Forest Canopy","volume":"40","author":"Magagi","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"758","DOI":"10.1109\/36.499781","article-title":"Seasonal Dynamics of C-Band Backscatter of Boreal Forests with Applications to Biomass and Soil Moisture Estimation","volume":"34","author":"Pulliainen","year":"1996","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Frison, P.-L., Fruneau, B., Kmiha, S., Soudani, K., Dufr\u00eane, E., Le Toan, T., Koleck, T., Villard, L., Mougin, E., and Rudant, J.-P. (2018). Potential of Sentinel-1 Data for Monitoring Temperate Mixed Forest Phenology. Remote Sens., 10.","DOI":"10.3390\/rs10122049"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Ulaby, F., and Long, D. (2014). Microwave Radar and Radiometric Remote Sensing, University of Michigan Press.","DOI":"10.3998\/0472119356"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"538","DOI":"10.1017\/S1759078718000016","article-title":"Radiometric Accuracy and Stability of Sentinel-1A Determined Using Point Targets","volume":"10","author":"Schmidt","year":"2018","journal-title":"Int. J. Microw. Wirel. Technol."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Eriksson, L.E.B., Fransson, J.E.S., Soja, M.J., and Santoro, M. (2012, January 22\u201327). Backscatter Signatures of Wind-Thrown Forest in Satellite SAR Images. Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany.","DOI":"10.1109\/IGARSS.2012.6352732"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"1018762","DOI":"10.3389\/ffgc.2022.1018762","article-title":"Reliably Mapping Low-Intensity Forest Disturbance Using Satellite Radar Data","volume":"5","author":"Aquino","year":"2022","journal-title":"Front. For. Glob. Chang."},{"key":"ref_85","unstructured":"(2023, February 15). Google Developers Sentinel-1 Algorithms. Available online: https:\/\/developers.google.com\/earth-engine\/guides\/sentinel1."},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Mullissa, A., Vollrath, A., Odongo-Braun, C., Slagter, B., Balling, J., Gou, Y., Gorelick, N., and Reiche, J. (2021). Sentinel-1 SAR Backscatter Analysis Ready Data Preparation in Google Earth Engine. Remote Sens., 13.","DOI":"10.3390\/rs13101954"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1038\/s41597-021-01059-7","article-title":"The Normalised Sentinel-1 Global Backscatter Model, Mapping Earth\u2019s Land Surface with C-Band Microwaves","volume":"8","author":"Cao","year":"2021","journal-title":"Sci Data"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"3552","DOI":"10.1111\/pce.14177","article-title":"Drone-based Physiological Index Reveals Long-term Acclimation and Drought Stress Responses in Trees","volume":"44","author":"Vitali","year":"2021","journal-title":"Plant Cell Environ."},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Penner, J., and Long, D. (2017). Ground-Based 3D Radar Imaging of Trees Using a 2D Synthetic Aperture. Electronics, 6.","DOI":"10.3390\/electronics6010011"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"287","DOI":"10.32614\/RJ-2019-024","article-title":"Shadow: R Package for Geometric Shadow Calculations in an Urban Environment","volume":"11","author":"Dorman","year":"2019","journal-title":"R J."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/9\/1553\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:34:38Z","timestamp":1760106878000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/9\/1553"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,27]]},"references-count":90,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["rs16091553"],"URL":"https:\/\/doi.org\/10.3390\/rs16091553","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,27]]}}}