{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T05:17:51Z","timestamp":1770527871179,"version":"3.49.0"},"reference-count":76,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2018,5,25]],"date-time":"2018-05-25T00:00:00Z","timestamp":1527206400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Kalimantan poses one of the highest carbon emissions worldwide since its landscape is strongly endangered by deforestation and degradation and, thus, carbon release. The goal of this study is to conduct large-scale monitoring of above-ground biomass (AGB) from space and create more accurate biomass maps of Kalimantan than currently available. AGB was estimated for 2007, 2009, and 2016 in order to give an overview of ongoing forest loss and to estimate changes between the three time steps in a more precise manner. Extensive field inventory and LiDAR data were used as reference AGB. A multivariate linear regression model (MLR) based on backscatter values, ratios, and Haralick textures derived from Sentinel-1 (C-band), ALOS PALSAR (Advanced Land Observing Satellite\u2019s Phased Array-type L-band Synthetic Aperture Radar), and ALOS-2 PALSAR-2 polarizations was used to estimate AGB across the country. The selection of the most suitable model parameters was accomplished considering VIF (variable inflation factor), p-value, R2, and RMSE (root mean square error). The final AGB maps were validated by calculating bias, RMSE, R2, and NSE (Nash-Sutcliffe efficiency). The results show a correlation (R2) between the reference biomass and the estimated biomass varying from 0.69 in 2016 to 0.77 in 2007, and a model performance (NSE) in a range of 0.70 in 2016 to 0.76 in 2007. Modelling three different years with a consistent method allows a more accurate estimation of the change than using available biomass maps based on different models. All final biomass products have a resolution of 100 m, which is much finer than other existing maps of this region (&gt;500 m). These high-resolution maps enable identification of even small-scaled biomass variability and changes and can be used for more precise carbon modelling, as well as forest monitoring or risk managing systems under REDD+ (Reducing Emissions from Deforestation, forest Degradation, and the role of conservation, sustainable management of forests, and enhancement of forest carbon stocks) and other programs, protecting forests and analyzing carbon release.<\/jats:p>","DOI":"10.3390\/rs10060831","type":"journal-article","created":{"date-parts":[[2018,5,28]],"date-time":"2018-05-28T03:54:21Z","timestamp":1527479661000},"page":"831","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":78,"title":["SAR-Based Estimation of Above-Ground Biomass and Its Changes in Tropical Forests of Kalimantan Using L- and C-Band"],"prefix":"10.3390","volume":"10","author":[{"given":"Anna","family":"Berninger","sequence":"first","affiliation":[{"name":"Remote Sensing Solutions GmbH, Isarstr. 3, 82065 Baierbrunn, Germany"},{"name":"Department of Biology, Ludwig-Maximilians-University Munich, Gro\u00dfhaderner Str. 2, 82152 Planegg-Martinsried, Germany"}]},{"given":"Sandra","family":"Lohberger","sequence":"additional","affiliation":[{"name":"Remote Sensing Solutions GmbH, Isarstr. 3, 82065 Baierbrunn, Germany"}]},{"given":"Matthias","family":"St\u00e4ngel","sequence":"additional","affiliation":[{"name":"Remote Sensing Solutions GmbH, Isarstr. 3, 82065 Baierbrunn, Germany"}]},{"given":"Florian","family":"Siegert","sequence":"additional","affiliation":[{"name":"Remote Sensing Solutions GmbH, Isarstr. 3, 82065 Baierbrunn, Germany"},{"name":"Department of Biology, Ludwig-Maximilians-University Munich, Gro\u00dfhaderner Str. 2, 82152 Planegg-Martinsried, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2018,5,25]]},"reference":[{"key":"ref_1","unstructured":"World Bank Group (2018, February 02). Forest Area (% of Land Area): Indonesia. Available online: https:\/\/data.worldbank.org\/indicator\/AG.LND.FRST.ZS?end=2015&locations=IDtart=2015&type=shaded&view=map&year=2010."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Page, S.E., Hoscilo, A., Langner, A., Tansey, K., Siegert, F., Limin, S., and Rieley, J.O. (2009). Tropical peatland fires in Southeast Asia. Tropical Fire Ecology, Springer Praxis Books.","DOI":"10.1007\/978-3-540-77381-8_9"},{"key":"ref_3","unstructured":"Tyrrell, M.L., Ashton, M.S., Spalding, D., and Gentry, B. (2009). Forests and Carbon: A Synthesis of Science, Management, and Policy for Carbon Sequestration in Forests, Yale School Forestry & Environmental Studies. Available online: http:\/\/environment.yale.edu\/publication-series\/5947.html."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"737","DOI":"10.1038\/ngeo671","article-title":"CO2 emissions from forest loss","volume":"2","author":"Morton","year":"2009","journal-title":"Nat. Geosci."},{"key":"ref_5","unstructured":"Core Writing Team, Pachauri, R.K., and Meyer, L.A. (2015). Climate Change 2014. Synthesis Report, Intergovernmental Panel on Climate Change. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change."},{"key":"ref_6","unstructured":"MacKinnon, K., Hatta, G., Halim, H., and Mangalik, A. (2013). The Ecology of Kalimantan: Indonesian Borneo, Tuttle Publishing."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1038\/nclimate1354","article-title":"Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps","volume":"2","author":"Baccini","year":"2012","journal-title":"Nat. Clim. Chang."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"798","DOI":"10.1111\/j.1365-2486.2010.02279.x","article-title":"Global and regional importance of the tropical peatland carbon pool","volume":"17","author":"Page","year":"2011","journal-title":"Glob. Chang. Biol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.geoderma.2008.08.008","article-title":"Determination of the amount of carbon stored in Indonesian peatlands","volume":"147","author":"Jaenicke","year":"2008","journal-title":"Geoderma"},{"key":"ref_10","unstructured":"Olivier, J.G.J., Janssens-Maenhout, G., Muntean, M., and Peters, J.A.H.W. (2015). Trends in Global CO2 Emissions: 2016 Report, PBL Netherlands Environmental Assessment Agency."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.biocon.2011.10.028","article-title":"Indonesia\u2019s REDD+ pact: Saving imperilled forests or business as usual?","volume":"151","author":"Edwards","year":"2012","journal-title":"Biol. Conserv."},{"key":"ref_12","unstructured":"Global Canopy Foundation (2018, February 07). The REDD Desk. Available online: https:\/\/theredddesk.org\/countries\/search-countries-database?f%5B0%5D=type%3Aactivity&f%5B1%5D=field_project%3A1."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"231","DOI":"10.4155\/cmt.11.18","article-title":"Advances in remote sensing technology and implications for measuring and monitoring forest carbon stocks and change","volume":"2","author":"Goetz","year":"2011","journal-title":"Carbon Manag."},{"key":"ref_14","unstructured":"FAO (2009). Assessment of the Status of the Development of the Standards for the Terrestrial Essential Climate Variables, FAO. Biomass (T12)."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1431","DOI":"10.1175\/BAMS-D-13-00047.1","article-title":"The Concept of Essential Climate Variables in Support of Climate Research, Applications, and Policy","volume":"95","author":"Bojinski","year":"2014","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1016\/j.foreco.2017.06.042","article-title":"Tree size thresholds produce biased estimates of forest biomass dynamics","volume":"400","author":"Searle","year":"2017","journal-title":"For. Ecol. Manag."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4442","DOI":"10.3390\/rs70404442","article-title":"L-Band SAR Backscatter Related to Forest Cover, Height and Aboveground Biomass at Multiple Spatial Scales across Denmark","volume":"7","author":"Joshi","year":"2015","journal-title":"Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Olesk, A., Praks, J., Antropov, O., Zalite, K., Arum\u00e4e, T., and Voormansik, K. (2016). Interferometric SAR Coherence Models for Characterization of Hemiboreal Forests Using TanDEM-X Data. Remote Sens., 8.","DOI":"10.3390\/rs8090700"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1016\/j.isprsjprs.2010.09.001","article-title":"Status and future of laser scanning, synthetic aperture radar and hyperspectral remote sensing data for forest biomass assessment","volume":"65","author":"Koch","year":"2010","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_20","first-page":"3505","article-title":"Understanding \u2018saturation\u2019 of radar signals over forests","volume":"7","author":"Joshi","year":"2017","journal-title":"Nat. Sci. Rep."},{"key":"ref_21","first-page":"776","article-title":"A review on biomass estimation methods using synthetic aperture radar data","volume":"1","author":"Ghasemi","year":"2011","journal-title":"Int. J. Geomat. Geosci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.rse.2014.01.029","article-title":"Biomass assessment in the Cameroon savanna using ALOS PALSAR data","volume":"155","author":"Mermoz","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_23","unstructured":"Hamdan, O. (2015). Assessment of Alos Palsar L-Band SAR for Estimation of above Ground Biomass in Tropical Forests. [Ph.D. Thesis, Univeriti Putra Malaysia]."},{"key":"ref_24","first-page":"14","article-title":"Evaluation of ALOS Palsar mosaic data for estimating stem volume and biomass: A case study from tropical rainforest of Central Indonesia","volume":"2","author":"Wijaya","year":"2009","journal-title":"J. Geogr."},{"key":"ref_25","first-page":"318","article-title":"Remotely sensed L-band SAR data for tropical forest biomass estimation","volume":"23","author":"Hamdan","year":"2011","journal-title":"J. Trop. For. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2861","DOI":"10.1016\/j.rse.2010.02.022","article-title":"Measuring biomass changes due to woody encroachment and deforestation\/degradation in a forest\u2013savanna boundary region of central Africa using multi-temporal L-band radar backscatter","volume":"115","author":"Mitchard","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1111\/j.1365-2486.2011.02551.x","article-title":"Quantifying small-scale deforestation and forest degradation in African woodlands using radar imagery","volume":"18","author":"Ryan","year":"2012","journal-title":"Glob. Chang. Biol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"551","DOI":"10.5194\/isprsarchives-XL-7-W3-551-2015","article-title":"Estimation of Biomass Carbon Stocks over Peat Swamp Forests using Multi-Temporal and Multi-Polratizations SAR Data","volume":"XL-7\/W3","author":"Wijaya","year":"2015","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1260","DOI":"10.1016\/j.rse.2011.01.008","article-title":"Aboveground biomass retrieval in tropical forests\u2014The potential of combined X- and L-band SAR data use","volume":"115","author":"Englhart","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"9899","DOI":"10.1073\/pnas.1019576108","article-title":"Benchmark map of forest carbon stocks in tropical regions across three continents","volume":"108","author":"Saatchi","year":"2011","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Watanabe, M., Motohka, T., Shiraishi, T., Thapa, R.B., Kawano, N., and Shimada, M. (2013, January 21\u201326). Dependency of forest biomass on full Polarimetric parameters obtained from L-band SAR data for a natural forest in Indonesia. Proceedings of the 2013 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Melbourne, Australia.","DOI":"10.1109\/IGARSS.2013.6723689"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"571","DOI":"10.1016\/j.asr.2017.04.018","article-title":"Polarimetric SAR Interferometry based modeling for tree height and aboveground biomass retrieval in a tropical deciduous forest","volume":"60","author":"Kumar","year":"2017","journal-title":"Adv. Space Res."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Hoekman, D.H., and Quinones, M.J. (1997). Land Cover Type and Forest Biomass Assessment in the Colombian Amazon. 1997 International Geoscience and Remote Sensing Symposium 3\u20138 August 1997, Singapore International Convention & Exhibition Centre, Singapore Remote Sensing\u2014A Scientific Vision for Sustainable Development, IEEE Service Center Distributor.","DOI":"10.1109\/IGARSS.1997.609045"},{"key":"ref_34","first-page":"117","article-title":"Potential of Envisat ASAR data for woody biomass assessement","volume":"51","author":"Pandey","year":"2010","journal-title":"Trop. Ecol."},{"key":"ref_35","first-page":"1","article-title":"Uncertainty in the spatial distribution of tropical forest biomass: A comparison of pan-tropical maps","volume":"8","author":"Mitchard","year":"2013","journal-title":"Carbon Balance Manag. J."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1406","DOI":"10.1111\/gcb.13139","article-title":"An integrated pan-tropical biomass map using multiple reference datasets","volume":"22","author":"Avitabile","year":"2016","journal-title":"Glob. Chang. Biol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1525\/bio.2011.61.1.10","article-title":"Biodiversity and Conservation of Tropical Peat Swamp Forests","volume":"61","author":"Posa","year":"2011","journal-title":"BioScience"},{"key":"ref_38","unstructured":"Pearson, T., Walker, S., and Brown, S. (2017, December 01). Sourcebook for Land Use, Land-use Change and Forestry Projects. Available online: https:\/\/theredddesk.org\/resources\/sourcebook-land-use-land-use-change-and-frestry-projects."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/s00442-005-0100-x","article-title":"Tree allometry and improved estimation of carbon stocks and balance in tropical forests","volume":"145","author":"Chave","year":"2005","journal-title":"Oecologia"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2368","DOI":"10.3390\/rs5052368","article-title":"Quantifying Dynamics in Tropical Peat Swamp Forest Biomass with Multi-Temporal LiDAR Datasets","volume":"5","author":"Englhart","year":"2013","journal-title":"Remote Sens."},{"key":"ref_41","unstructured":"NASA, and JAXA (2018, April 27). Tropical Rainfall Measuring Mission, Available online: https:\/\/pmm.nasa.gov\/trmm."},{"key":"ref_42","unstructured":"NASA (2018, April 27). Near Real-Time and MCD14DL MODIS Active Fire Detections (SHP Format): Data Set, Available online: https:\/\/earthdata.nasa.gov\/earth-observation-data\/near-real-time\/firms\/c6-mcd14dl."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/S0034-4257(03)00184-6","article-title":"An Enhanced Contextual Fire Detection Algorithm for MODIS","volume":"87","author":"Giglio","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_44","unstructured":"Esri, Garmin International, Inc (2018, April 27). World Water Bodies Layer. Available online: https:\/\/www.arcgis.com\/home\/item.html?id=e750071279bf450cbd510454a80f2e63."},{"key":"ref_45","unstructured":"European Space Agency (2018, April 27). CCI Land Cover. Available online: http:\/\/maps.elie.ucl.ac.be\/CCI\/viewer\/download.php."},{"key":"ref_46","first-page":"1892","article-title":"Biomass, Carbon, and Nutrient Dynamics of Secondary Forests in a Humid Tropical Region of Mexico","volume":"80","author":"Hughes","year":"1999","journal-title":"Ecology"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"3917","DOI":"10.5194\/bg-10-3917-2013","article-title":"Detection of large above-ground biomass variability in lowland forest ecosystems by airborne LiDAR","volume":"10","author":"Jubanski","year":"2013","journal-title":"Biogeosciences"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2353","DOI":"10.1016\/j.rse.2010.05.011","article-title":"Estimating spruce and pine biomass with interferometric X-band SAR","volume":"114","author":"Solberg","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1957","DOI":"10.3390\/rs3091957","article-title":"ICESat\/GLAS Data as a Measurement Tool for Peatland Topography and Peat Swamp Forest Biomass in Kalimantan, Indonesia","volume":"3","author":"Ballhorn","year":"2011","journal-title":"Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1109\/36.842003","article-title":"Multitemporal ERS SAR analysis applied to forest mapping","volume":"38","author":"Quegan","year":"2000","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.rse.2015.01.007","article-title":"Potential of high-resolution ALOS\u2013PALSAR mosaic texture for aboveground forest carbon tracking in tropical region","volume":"160","author":"Thapa","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"786","DOI":"10.1109\/PROC.1979.11328","article-title":"Statistical and structural approaches to texture","volume":"67","author":"Haralick","year":"1979","journal-title":"Proc. IEEE"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1109\/JSTARS.2011.2176720","article-title":"Modeling Aboveground Biomass in Tropical Forests Using Multi-Frequency SAR Data\u2014A Comparison of Methods","volume":"5","author":"Englhart","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"16738","DOI":"10.1073\/pnas.1004875107","article-title":"High-resolution forest carbon stocks and emissions in the Amazon","volume":"107","author":"Asner","year":"2010","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/0022-1694(70)90255-6","article-title":"River Flow Forecasting Through Conceptual Models Part I- A Discussion of Principles","volume":"10","author":"Nash","year":"1970","journal-title":"J. Hydrol."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.rse.2017.04.004","article-title":"Hybrid three-phase estimators for large-area forest inventory using ground plots, airborne lidar, and space lidar","volume":"197","author":"Holm","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"895","DOI":"10.1007\/s13595-016-0590-1","article-title":"Hierarchical model-based inference for forest inventory utilizing three sources of information","volume":"73","author":"Saarela","year":"2016","journal-title":"Ann. For. Sci."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1046\/j.1365-2745.2003.00757.x","article-title":"Spatial and temporal variation of biomass in a tropical foret: Resutls from a large cencus plot in Panama","volume":"91","author":"Chave","year":"2003","journal-title":"J. Ecol."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"636","DOI":"10.1016\/j.rse.2010.10.008","article-title":"Simulated impact of sample plot size and co-registration error on the accuracy and uncertainty of LiDAR-derived estimates of forest stand biomass","volume":"115","author":"Frazer","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1186\/s13021-016-0048-7","article-title":"Scaling wood volume estimates from inventory plots to landscapes with airborne LiDAR in temperate deciduous forest","volume":"11","author":"Levick","year":"2016","journal-title":"Carbon Balance Manag."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.rse.2012.10.017","article-title":"A meta-analysis of terrestrial aboveground biomass estimation using lidar remote sensing","volume":"128","author":"Zolkos","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Kachamba, D., \u00d8rka, H., N\u00e6sset, E., Eid, T., and Gobakken, T. (2017). Influence of Plot Size on Efficiency of Biomass Estimates in Inventories of Dry Tropical Forests Assisted by Photogrammetric Data from an Unmanned Aircraft System. Remote Sens., 9.","DOI":"10.3390\/rs9060610"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"936","DOI":"10.3390\/f5050936","article-title":"Analysis of the Influence of Plot Size and LiDAR Density on Forest Structure Attribute Estimates","volume":"5","author":"Ruiz","year":"2014","journal-title":"Forests"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"88","DOI":"10.17221\/86\/2016-JFS","article-title":"Impact of plot size and model selection on forest biomass estimation using airborne LiDAR: A case study of pine plantations in southern Spain","volume":"63","author":"Rafael","year":"2017","journal-title":"J. For. Sci."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1186\/s13021-015-0021-x","article-title":"Effects of field plot size on prediction accuracy of aboveground biomass in airborne laser scanning-assisted inventories in tropical rain forests of Tanzania","volume":"10","author":"Mauya","year":"2015","journal-title":"Carbon Balance Manag."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1186\/s13021-018-0093-5","article-title":"Estimation of forest aboveground biomass and uncertainties by integration of field measurements, airborne LiDAR, and SAR and optical satellite data in Mexico","volume":"13","author":"Urbazaev","year":"2018","journal-title":"Carbon Balance Manag."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Hong, W., and Yirong, W. (2008, January 7\u201311). Analysis of Temporal Decorrelation in Dual-Baseline Polinsar Vegetation Parameter Estimation. Proceedings of the IGARSS 2008 IEEE International Geoscience and Remote Sensing Symposium, Boston, MA, USA.","DOI":"10.1109\/IGARSS.2008.4779031"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.rse.2014.04.029","article-title":"L-band ALOS PALSAR for biomass estimation of Matang Mangroves, Malaysia","volume":"155","author":"Hamdan","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"651","DOI":"10.5194\/isprsarchives-XL-8-651-2014","article-title":"Forest above ground biomass estimation and forest\/non-forest classification for Odisha, India, using L-band Synthetic Aperture Radar (SAR) data","volume":"XL-8","author":"Suresh","year":"2014","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"L23401","DOI":"10.1029\/2009GL040692","article-title":"Using satellite radar backscatter to predict above-ground woody biomass: A consistent relationship across four different African landscapes","volume":"36","author":"Mitchard","year":"2009","journal-title":"Geophys. Res. Lett."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Antropov, O., Rauste, Y., H\u00e4me, T., and Praks, J. (2017). Polarimetric ALOS PALSAR Time Series in Mapping Biomass of Boreal Forests. Remote Sens., 9.","DOI":"10.3390\/rs9100999"},{"key":"ref_72","first-page":"388","article-title":"L-Band saturation level for above-ground Biomass of Dipterocarp forests in Peninsula Malaysia","volume":"27","author":"Hamdan","year":"2015","journal-title":"J. Trop. For. Sci."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"4325","DOI":"10.1029\/2004WR003905","article-title":"Comparison of four models to determine surface soil moisture from C-band radar imagery in a sparsely vegetated semiarid landscape","volume":"42","author":"Thoma","year":"2006","journal-title":"Water Resour. Res."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"576","DOI":"10.1109\/JSTARS.2010.2086436","article-title":"An Evaluation of the ALOS PALSAR L-Band Backscatter\u2014Above Ground Biomass Relationship Queensland, Australia: Impacts of Surface Moisture Condition and Vegetation Structure","volume":"3","author":"Lucas","year":"2010","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1186\/s13021-016-0062-9","article-title":"Performance of non-parametric algorithms for spatial mapping of tropical forest structure","volume":"11","author":"Xu","year":"2016","journal-title":"Carbon Balance Manag."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.isprsjprs.2015.04.007","article-title":"Savannah woody structure modelling and mapping using multi-frequency (X-, C- and L-band) Synthetic Aperture Radar data","volume":"105","author":"Naidoo","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/6\/831\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:05:59Z","timestamp":1760195159000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/6\/831"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,5,25]]},"references-count":76,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2018,6]]}},"alternative-id":["rs10060831"],"URL":"https:\/\/doi.org\/10.3390\/rs10060831","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,5,25]]}}}