Evaluating SAR-optical sensor fusion for aboveground biomass estimation in a Brazilian tropical forest


  • Aline Bernarda Debastiani University of State of Parana, Avenida Rio Grande do Norte, 1525 87701-020 - Centro - Paranavaí - PR, Brazil
  • Carlos Roberto Sanquetta University of State of Parana, Avenida Rio Grande do Norte, 1525 87701-020 - Centro - Paranavaí - PR, Brazil
  • Ana Paula Dalla Corte University of State of Parana, Avenida Rio Grande do Norte, 1525 87701-020 - Centro - Paranavaí - PR, Brazil
  • Naiara Sardinha Pinto University of State of Parana, Avenida Rio Grande do Norte, 1525 87701-020 - Centro - Paranavaí - PR, Brazil
  • Franciel Eduardo Rex University of State of Parana, Avenida Rio Grande do Norte, 1525 87701-020 - Centro - Paranavaí - PR, Brazil




Amazon Forest, artificial intelligence, Sentinel 1, Sentinel 2, AGB, carbon


The aim of the present study is to evaluate the potential of C-band SAR data from the Sentinel-1/2 instruments and machine learning algorithms for the estimation of forest above ground forest biomass (AGB) in a high-biomass tropical ecosystem. This study was carried out in Jamari National Forest, located in the Brazilian Amazon. The response variable was AGB (Mg/ha) estimated from airborne laser surveys. The following treatments were considered as model predictors: 1) Sentinel-1 Sigma 0 at VV and VH polarizations; 2) (1) plus Sentinel-1 textural metrics; 3) (2) plus Sentinel-2 bands and derived vegetation indices (LAI, RVI, SAVI, NDVI).Our modeling design estimated the relative importance of SAR vs. optical variables in explaining AGB. The modeling was performed with twelve machine-learning algorithms including, neural network and regression tree. The addition of texture and optical data provided a noticeable improvement (3%) over models with SAR backscatter only. The best model performance was achieved with the Random Tree algorithm. Our results demonstrate the potential of freely-available SAR data and machine learning for mapping AGB in tropical ecosystems.


Allen RG., Tasumi M., Trezza R. 2002. SEBAL (Surface Energy balance Algorithms for land). Advanced Training and users´ manual. Idaho: Implementation. Andersen HE., McGaughey RJ., Reutebuch SE. 2005. Estimating forest canopy fuel parameters using LiDAR data. Remote Sensing of Environment 94: 441-449. DOI: 10.1016/j.rse.2004.10.013 Berninger A.,Lohberger S., Stangel M., Siegert F. 2018. SAR-Based estimation of above-ground biomass and its changes in Tropical Forests of Kalimantan using L- and C-band. Remote Sensing 10: 1-22. DOI: 10.3390/rs10060831 Carreiras J., Melo JB., Vasconcelos MJ. 2013. Estimating the above-ground biomass in miombo savanna woodlands (Mozambique, East Africa) using l-band synthetic aperture radar data. Remote Sensing 5: 1524-1548. DOI: 10.3390/rs5041524 Cartus O., Santoro M., Kellndorfer J. 2012. Mapping forest aboveground biomass in the northeastern united states with AlosPalsar dual polarization l-band. Remote Sensing of Environment 466-478. DOI: 10.1016/j.rse.2012.05.029 Castillo JAA., Apan AA., Maraseni TN., Salmo SGS. 2017. Estimation and mapping of above-ground biomass of mangrove forests and their replacement land uses in the Philippines using Sentinel imagery. ISPRS Journal of Photogrammetry and Remote Sensing 134: 70-85. DOI: 10.1016/j.isprsjprs.2017.10.016 Chave J., Condit R., Aguilar S., Hernandez A., Lao S., Perez R. 2004. Error propagation and scaling for tropical forest biomass estimates. Philosophical Transactions of the Royal Society B: Biological Sciences 359: 409-420. DOI: 10.1098/rstb.2003.1425 Chave J., Réjou‐Méchain M., Búrquez A., Chidumayo E., Colgan MS., Delitti WBC., Duque A., Eid T., Fearnside PM., Goodman RC., Henry M., Martínez‐Yrízar A., Mugasha WA., Muller‐Landau HC., Mencuccini M., Nelson BW., Ngomanda A., Nogueira EM., Ortiz‐Malavassi E., Pélissier R., Ploton P., Ryan CM., Saldarriaga JG., Vieilledent G. 2014. Improved allometric models to estimate the aboveground biomass of tropical trees. Global Change Biological 20: 3177–3190. DOI: 10.1111/gcb.12629 Cutler MEJ., Boyd DS., Foofy GM., Vetrivel A. 2012. Estimating tropical forest biomass with a combination of SAR image texture and Landsat TM data: An assessment of predictions between regions. ISPRS Journal of Photogrammetry and Remote Sensing 70: 66-77. DOI: 10.1016/j.isprsjprs.2012.03.011 DeGrandi G., Lucas RM., Kropacek J. 2009. Analysis by wavelet frames of spatial statistics in SAR data for characterising structural properties of forests. IEEE Transactions on Geoscience and Remote Sensing 47: 494-507. DOI: 10.1109/TGRS.2008.2006183 Deo R., Russell M., Domke G. 2017. Using Landsat Time-series and LiDAR to Inform Aboveground Forest Biomass Baselines in Northern Minnesota, USA. Canadian Journal of Remote Sensing 43: 28–47. DOI: 10.1080/07038992.2017.1259556 D’Oliveira MVN., Reutebuch SE., McGaughey RJ., Andersen HE. 2012. Estimating forest biomass and identifying low-intensity logging areas using airborne scanning lidar in Antimary State Forest, Acre State, Western Brazilian Amazon. Remote Sensing of Environment 124: 479-491. DOI: 10.1016/j.rse.2012.05.014 Englhart V., Keuck V., Siegert F. 2011. Aboveground biomass retrieval in tropical forest – The potential of combined X and L band Sar data use. Remote Sensing of Environment 115: 1260-1271. DOI: 10.1016/j.rse.2011.01.008 ESA. 2018. Sentinels Scientific Data Hub, European Space Agency. Available online: https://scihub.copernicus.eu/. Accessed 01.02.2018European Space Agency (ESA) 2015. Sentinel-2 User Handbook, 64 p. FAO. 2015. Global Forest Resources Assessment. FAO Forestry Paper n. 1. Rome. Garcia M., Saatchi S., Ustin S., Balzter H. 2018. Modelling forest canopy height by integrating airborne LiDAR samples with satellite Radar and multispectral imagery. International Journal of Applied Earth Observation and Geoinformation 66: 159-173. DOI: 10.1016/j.jag.2017.11.017 Ghasemi N., Reza Sahebi M., Mohammadzadeh A. 2011. A review on biomass estimation methods using synthetic aperture radar data. International Journal of Geomatics and Geosciences 1: 776–788. Hall MA. 1999. Correlation-based Feature Selection for Machine Learning. Department of Computer Science University of Waikato, Hamilton, New Zealand.Haralick RM. 1979. Statistical and structural approaches to texture. Proceedings of the IEEE 67: 786-804. Houghton RA., Hall F., Goetz SJ. 2009. Importance of biomass in the global carbon cycle. Journal of Geophysical Research 114: G00E03. DOI: 10.1029/2009JG000935 Huete AR. 1988. A soil-adjusted vegetation index (SAVI). Remote Sensing Environment 25: 295-309. DOI: 10.1016/0034-4257(88)90106-XIBGE. 2012. Manual Técnico da Vegetação Brasileira. 2ª ed. revisada e ampliada. [Technical manual of the Brazilian vegetation], Rio de janeiro: IBGE- Diretoria de Geociências, 271 p. Joshi N., Mitchard ETA., Brolly M., Schumacher J., Fernández-Landa A., Johannsen VK., Marchamalo M., Fensholt R. 2017. Understanding ‘saturation’ of radar signals over forests. Scientific Reports 7: 3505. Kirimi F., Kuria DN., Thonfeld F., Amler E., Mubea K., Misana S., Menz G. 2016. Influence of vegetation cover on the oh soil moisture retrieval model: a case study of the Malinda Wetland, Tanzania. Advances in Remote Sensing 5: 28-42. DOI: 10.4236/ars.2016.51003 Kuplich TM., Curran PJ., Atkinson PM. 2005. Relating SAR image texture to the biomass of regenerating tropical forests. International Journal of Remote Sensing 6: 4829-4854. DOI: 10.1080/01431160500239107 Li G. 1985. Robust regression. In: Hoaglin D.C., Mosteller F., Tukey J.W., Exploring data tables, trends, and shapes. Wiley, 527 p. Le Toan T., Quegan S., Davidson MWJ., Balzter H., Paillou P., Plummer S., Papathanassiou K., Rocca F., Saatchi S., Shugart H., Ulander L. 2011. The BIOMASS mission: Mapping global forest biomass to better understand the terrestrial carbon cycle. Remote Sensing Environment 115: 2850-2860. DOI: 10.1016/j.rse.2011.03.020 Lu DS. 2006. The potential and challenge of remote sensing-based biomass estimation. International Journal of Remote Sensing 27: 1297–1328. DOI: 10.1080/01431160500486732 Lucas RM., Cronin N., Moghaddam M., Lee A., Armston J., Bunting P., Witte C. 2006. Integration of radar and Landsat-derived foliage projected cover for woody regrowth mapping, Queensland, Australia. Remote Sensing Environment 100: 388-406. DOI: 10.1016/j.rse.2005.09.020 Mahdianpari M., Motagh M. 2017. Random Forest Wetland classification using ALOS-2 L-band, RADARSAT-2 C-band, and TerraSar-X imagery. ISPRS Journal of Photogrammetry and Remote Sensing 130: 13-31. DOI: 10.1016/j.isprsjprs.2017.05.010 Mcgaughey RJ. 2016. FUSION / LDV: software para análise e visualização de dados LIDAR. Seattle, WA, USFS, pp. 11. Minh DHT., Le Toan T., Rocca F., Tebaldini S., Villard L., Réjou-Méchain M., Phillips OL., Feldpausch TR., Dubois-Fernandez P., Scipal K., Chave J. 2016. SAR tomography for the retrieval of forest biomass and height: Cross-validation at two tropical forest sites in French Guiana. Remote Sensing Environment 175: 138-147. DOI: 10.1016/j.rse.2015.12.037 Morel AC., Saatchi SS., Malhi Y., Berry NJ., Banin L., Burslem D., Nilus R., Ong RC. 2011. Estimating aboveground biomass in forest and oil palm plantation in Sabah, Malaysian Borneio using ALOS PALSAR data. Forest Ecology and Management 262: 1786-1798. DOI: 10.1016/j.foreco.2011.07.008 Neuenschwander A., Pitts K. 2019. The ATL08 land and vegetation product for the ICESat-2 Mission. Remote Sensing of Environment 221: 247-259. DOI: 10.1016/j.rse.2018.11.005 Ningthoujam RK., Joshi PK., Roy PS. 2018. Retrieval of forest biomass for tropical deciduous mixed forest using ALOS PALSAR mosaic imagery and field plot data. International Journal of Applied Earth Observation and Geoinformation 69: 206-2016. DOI: 10.1016/j.jag.2018.03.007 Oon A., Azhar B. 2019. Assessment of ALOS-2 PALSAR-2 L-band and Sentinel-1 C-band SAR backscatter for discriminating between large-scale oil palm plantations and smallholdings on tropical peatlands. Remote Sensing Applications: Society and Environment 13: 183-190. DOI: 10.1016/j.rsase.2018.11.002 Podest E., Saatchi S. 2002. Application of multiscale texture in classifying JERS-1 radar data over tropical vegetation. International Journalof Remote Sensing 23: 1487-1506. R Core Team. 2017. R: Uma Linguagem e Ambiente para Computação Estatística. Web: https://www.-project.org. Accessed: 01.02.2018Ruiz LA., Hermosilla T., Mauro F., Godino M. 2014. Analysis of the Influence of Plot Size and LiDAR Density on Forest Structure Attribute Estimates. Forests 5: 936-951. DOI: 10.3390/f5050936 Saatchi S., Houghton RA., Dos Santos Alvala RC., Soares JV., Yu Y. 2017. Distribution of aboveground live biomass in the Amazon basin. Global Change Biology 13: 816-837. DOI: 10.1111/j.1365-2486.2007.01323.x Saatchi S., Marlier M., Chazdon R., Clark D., Russell A. 2011a. Impact of spatial variability of tropical forest structure on radar estimation of aboveground biomass. Remote Sensing Environment 115: 2836-2849. DOI: 10.1016/j.rse.2010.07.015 Saatchi SS., Harris NL., Brown S., Lefsky M., Mitchard ETA., Salas W., Zutta BR., Buermann W., Lewis SL., Hagen S., Petrova S., White L., Silman M., Morel A. 2011b. Supporting Information. In: Benchmark map of forest carbon stocks in tropical regions across three continents. Proceedings of the National Academy of Sciences of the United States of America 108: 1-9. DOI: 10.1073/pnas.1019576108 Santi E., Paloscia S., Pettinato S., Fontanelli G., Mura M., Zolli C., Maselli F., Chiesi M., Bottai L., Chirici G. 2017. The potential of multifrequency SAR images for estimating forest biomass in Mediterranean areas. Remote Sensing Environment 200: 63-73. DOI: 10.1016/j.rse.2017.07.038Santos J., Lacruz M., Araujo L., Keil M. 2002. Savanna and tropical rainforest biomass estimation and spatialization using jers-1 data. International Journal of Remote Sensing 23: 1217-1229. DOI: 10.1080/01431160110092867 Sanquetta CR., Wojciechowski J., Corte APD., Behling A., Péllico S No., Rodrigues AL., Sanquetta MNI. 2015. Comparison of data mining and allometric model in estimation of tree biomass. BMC Bioinformatics 16: 247. DOI: 10.1186/s12859-015-0662-5 Silva CA., Hudak AT., Klauberg C., Vierling AA., Gonzale-Benecke C., Carvalho SOC. 2017. Rodriguez LCE, Cardil A. Combined effect of pulse density and grid cell size on predicting and mapping aboveground carbon in fast-growing Eucalyptus forest plantation using airborne LiDAR data. Carbon Balance and Management 12: 1-16. DOI: 10.1186/s13021-017-0081-1Singh M., Evans D., Friess DA., Tan BS., Nin CS. 2015. Mapping Above-Ground Biomass in a Tropical Forest in Cambodia Using Canopy Textures Derived From Google Earth. Remote Sensing 7: 5057-5076. DOI: 10.3390/rs70505057SNAP. 2018. Sentinels Application Platform software ver. 5.0. European Space Agency.Stickler CM., Nepstad DC., Coe MT., McGrath DG., Rodrigues HO., Walker WS., Soares-Filho BS., Davidson EA. 2009. The potential ecological costs and cobenefits of REDD: a critical review and case study from the Amazon region Global. Change Biological 15: 2803-2824. DOI: 10.1111/j.1365-2486.2009.02109.xThapa RB., Watanabe M., Motohka T., Shimada M. 2015. Potential of high-resolution ALOS-PALSAR mosaic texture for aboveground forest carbon tracking in tropical region. Remote Sensing Environment 160: 122-133. DOI: 10.1016/j.rse.2015.01.007Urbazaev M., Thiel C., Cremer F., Dubayah R., Migliavacca M., Reichstein M., Schmullius C. 2018. Estimation of forest aboveground biomass and uncertainties by integration of field measurements, airborne LiDAR, and SAR and optical satellite data in Mexico. Carbon Balance Management 13: 1-20. DOI: 10.1186/s13021-018-0093-5Waring HR., Running SW. 2007. Forest Ecosystems: Analysis at Multiples Scales. 3rd edition, Academic Press, San Diego, USA, pp. 440






Research article