Integrating SAR and Optical Data for Aboveground Biomass Estimation of Coastal Wetlands Using Machine Learning: Multi-Scale Approach
<p>An overview of the study area.</p> "> Figure 2
<p>Different earth observation data are used for AGB estimation.</p> "> Figure 3
<p>Flowchart of the proposed method. Boxes are color-coded with orange for spaceborne, blue for airborne, and red for ground information. Yellow boxes denote RF models, green indicates validation, and purple signifies results.</p> "> Figure 4
<p>Scatter plot of the predicted and actual AGB (Mg ha<sup>−1</sup>) values for: (<b>A</b>) spring, (<b>B</b>) fall, and (<b>C</b>) multi-temporal models. Fitted lines with 95% interval indicators are shown in blue lines.</p> "> Figure 5
<p>The importance of different features from the RF models.</p> "> Figure 6
<p>AGB map of the spring season (March and April 2021) and zoomed maps of (<b>A</b>–<b>C</b>) regions. The first row shows results from AVIRIS-NG (small-scale model), and the second row shows results from Sentinel-1 and -2 (large-scale model).</p> "> Figure 7
<p>AGB map of the fall season (August 2021) and zoomed maps of (<b>A</b>–<b>C</b>) regions. The first row shows results from AVIRIS-NG (small-scale model), and the second row shows results from Sentinel-1 and -2 (large-scale model).</p> "> Figure 8
<p>AGB monthly maps produced using the multi-temporal model.</p> "> Figure 9
<p>Average AGB (lines) and estimated total AGB (bars) for each wetland class in the region.</p> ">
Abstract
:1. Introduction
2. Method
2.1. Study Area
2.2. Satellite Datasets
2.3. Field and Aerial Dataset
2.4. AGB Regression Models
3. Results
3.1. Model Assessment
3.2. Seasonal Models
3.3. Multi-Temporal Model
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Mcleod, E.; Chmura, G.L.; Bouillon, S.; Salm, R.; Björk, M.; Duarte, C.M.; Lovelock, C.E.; Schlesinger, W.H.; Silliman, B.R. A blueprint for blue carbon: Toward an improved understanding of the role of vegetated coastal habitats in sequestering CO2. Front. Ecol. Environ. 2011, 9, 552–560. [Google Scholar] [CrossRef] [PubMed]
- Pendleton, L.; Donato, D.C.; Murray, B.C.; Crooks, S.; Jenkins, W.A.; Sifleet, S.; Craft, C.; Fourqurean, J.W.; Kauffman, J.B.; Marbà, N.; et al. Estimating Global “Blue Carbon” Emissions from Conversion and Degradation of Vegetated Coastal Ecosystems. PLoS ONE 2012, 7, e43542. [Google Scholar] [CrossRef] [PubMed]
- Intergovernmental Panel on Climate Change. Climate Change 2014: Mitigation of Climate Change: Working Group III Contribution to the IPCC Fifth Assessment Report, 1st ed.; Cambridge University Press: Cambridge, UK, 2015; ISBN 978-1-107-05821-7. [Google Scholar]
- Bridgham, S.D.; Megonigal, J.P.; Keller, J.K.; Bliss, N.B.; Trettin, C. The carbon balance of North American wetlands. Wetlands 2006, 26, 889–916. [Google Scholar] [CrossRef]
- Wylie, L.; Sutton-Grier, A.E.; Moore, A. Keys to successful blue carbon projects: Lessons learned from global case studies. Mar. Policy 2016, 65, 76–84. [Google Scholar] [CrossRef]
- Howard, J.; Sutton-Grier, A.; Herr, D.; Kleypas, J.; Landis, E.; Mcleod, E.; Pidgeon, E.; Simpson, S. Clarifying the role of coastal and marine systems in climate mitigation. Front. Ecol. Environ. 2017, 15, 42–50. [Google Scholar] [CrossRef]
- Duarte, C.M.; Losada, I.J.; Hendriks, I.E.; Mazarrasa, I.; Marbà, N. The role of coastal plant communities for climate change mitigation and adaptation. Nat. Clim. Chang. 2013, 3, 961–968. [Google Scholar] [CrossRef]
- Kouhgardi, E.; Hemati, M.; Shakerdargah, E.; Shiri, H.; Mahdianpari, M. Monitoring Shoreline and Land Use/Land Cover Changes in Sandbanks Provincial Park Using Remote Sensing and Climate Data. Water 2022, 14, 3593. [Google Scholar] [CrossRef]
- Kelleway, J.J.; Serrano, O.; Baldock, J.A.; Burgess, R.; Cannard, T.; Lavery, P.S.; Lovelock, C.E.; Macreadie, P.I.; Masqué, P.; Newnham, M.; et al. A national approach to greenhouse gas abatement through blue carbon management. Glob. Environ. Chang. 2020, 63, 102083. [Google Scholar] [CrossRef]
- Needelman, B.A.; Emmer, I.M.; Emmett-Mattox, S.; Crooks, S.; Megonigal, J.P.; Myers, D.; Oreska, M.P.J.; McGlathery, K. The Science and Policy of the Verified Carbon Standard Methodology for Tidal Wetland and Seagrass Restoration. Estuaries Coasts 2018, 41, 2159–2171. [Google Scholar] [CrossRef]
- Holmquist, J.R.; Windham-Myers, L.; Bliss, N.; Crooks, S.; Morris, J.T.; Megonigal, J.P.; Troxler, T.; Weller, D.; Callaway, J.; Drexler, J.; et al. Accuracy and Precision of Tidal Wetland Soil Carbon Mapping in the Conterminous United States. Sci. Rep. 2018, 8, 9478. [Google Scholar] [CrossRef] [PubMed]
- Wilen, B.O.; Bates, M.K. The US Fish and Wildlife Service’s National Wetlands Inventory Project. In Classification and Inventory of the World’s Wetlands; Finlayson, C.M., van der Valk, A.G., Eds.; Springer: Dordrecht, The Netherlands, 1995; pp. 153–169. ISBN 978-94-010-4190-4. [Google Scholar]
- IPCC. Good Practice Guidance for Land Use, Land-Use Change and Forestry/The Intergovernmental Panel on Climate Change; Penman, J., Ed.; IPCC: Hayama, Japan, 2003; ISBN 978-4-88788-003-0. [Google Scholar]
- Gonzalez, P.; Asner, G.P.; Battles, J.J.; Lefsky, M.A.; Waring, K.M.; Palace, M. Forest carbon densities and uncertainties from Lidar, QuickBird, and field measurements in California. Remote Sens. Environ. 2010, 114, 1561–1575. [Google Scholar] [CrossRef]
- Pettorelli, N.; Laurance, W.F.; O’Brien, T.G.; Wegmann, M.; Nagendra, H.; Turner, W. Satellite remote sensing for applied ecologists: Opportunities and challenges. J. Appl. Ecol. 2014, 51, 839–848. [Google Scholar] [CrossRef]
- NOAA Office for Coastal Management. NOAA Coastal Change Analysis Program (C-CAP) Regional Land Cover Database 2015; NOAA Office for Coastal Management: Charleston, SC, USA, 2015. [Google Scholar]
- Hemati, M.; Mahdianpari, M.; Shiri, H.; Mohammadimanesh, F. Comprehensive Landsat-Based Analysis of Long-Term Surface Water Dynamics over Wetlands and Waterbodies in North America. Can. J. Remote Sens. 2023, 50, 2293058. [Google Scholar] [CrossRef]
- Hemati, M.; Hasanlou, M.; Mahdianpari, M.; Mohammadimanesh, F. A Systematic Review of Landsat Data for Change Detection Applications: 50 Years of Monitoring the Earth. Remote Sens. 2021, 13, 2869. [Google Scholar] [CrossRef]
- Berger, M.; Moreno, J.; Johannessen, J.A.; Levelt, P.F.; Hanssen, R.F. ESA’s sentinel missions in support of Earth system science. Remote Sens. Environ. 2012, 120, 84–90. [Google Scholar] [CrossRef]
- Hemati, M.; Hasanlou, M.; Mahdianpari, M.; Mohammadimanesh, F. Iranian wetland inventory map at a spatial resolution of 10 m using Sentinel-1 and Sentinel-2 data on the Google Earth Engine cloud computing platform. Environ. Monit. Assess. 2023, 195, 558. [Google Scholar] [CrossRef]
- Mahdianpari, M.; Brisco, B.; Granger, J.; Mohammadimanesh, F.; Salehi, B.; Homayouni, S.; Bourgeau-Chavez, L. The Third Generation of Pan-Canadian Wetland Map at 10 m Resolution Using Multisource Earth Observation Data on Cloud Computing Platform. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2021, 14, 8789–8803. [Google Scholar] [CrossRef]
- Gorelick, N.; Hancher, M.; Dixon, M.; Ilyushchenko, S.; Thau, D.; Moore, R. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 2017, 202, 18–27. [Google Scholar] [CrossRef]
- Tamiminia, H.; Salehi, B.; Mahdianpari, M.; Quackenbush, L.; Adeli, S.; Brisco, B. Google Earth Engine for geo-big data applications: A meta-analysis and systematic review. ISPRS J. Photogramm. Remote Sens. 2020, 164, 152–170. [Google Scholar] [CrossRef]
- Bai, T.; Wang, L.; Yin, D.; Sun, K.; Chen, Y.; Li, W.; Li, D. Deep learning for change detection in remote sensing: A review. Geo-Spat. Inf. Sci. 2023, 26, 262–288. [Google Scholar] [CrossRef]
- Hemati, M.; Mahdianpari, M.; Hasanlou, M.; Mohammadimanesh, F. Iranian Wetland Hydroperiod Change Detection Using an Unsupervised Method on 20 Years of Landsat Data Within the Google Earth Engine. In Proceedings of the IGARSS 2022—2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, 17–22 July 2022; pp. 6209–6212. [Google Scholar]
- Pasquarella, V.J.; Arévalo, P.; Bratley, K.H.; Bullock, E.L.; Gorelick, N.; Yang, Z.; Kennedy, R.E. Demystifying LandTrendr and CCDC temporal segmentation. Int. J. Appl. Earth Obs. Geoinf. 2022, 110, 102806. [Google Scholar] [CrossRef]
- Mutanga, O.; Adam, E.; Cho, M.A. High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm. Int. J. Appl. Earth Obs. Geoinf. 2012, 18, 399–406. [Google Scholar] [CrossRef]
- González Trilla, G.; Pratolongo, P.; Beget, M.E.; Kandus, P.; Marcovecchio, J.; Di Bella, C. Relating Biophysical Parameters of Coastal Marshes to Hyperspectral Reflectance Data in the Bahia Blanca Estuary, Argentina. J. Coast. Res. 2013, 286, 231–238. [Google Scholar] [CrossRef]
- Byrd, K.B.; Windham-Myers, L.; Leeuw, T.; Downing, B.; Morris, J.T.; Ferner, M.C. Forecasting Tidal Marsh Elevation and Habitat Change through Fusion of Earth Observations and a Process Model. Ecosphere 2016, 7, e01582. [Google Scholar] [CrossRef]
- Ghosh, S.; Mishra, D.R.; Gitelson, A.A. Long-Term Monitoring of Biophysical Characteristics of Tidal Wetlands in the Northern Gulf of Mexico—A Methodological Approach Using MODIS. Remote Sens. Environ. 2016, 173, 39–58. [Google Scholar] [CrossRef]
- Schalles, J.; Hladik, C.; Lynes, A.; Pennings, S. Landscape Estimates of Habitat Types, Plant Biomass, and Invertebrate Densities in a Georgia Salt Marsh. Oceanog 2013, 26, 88–97. [Google Scholar] [CrossRef]
- Lobell, D.B.; Thau, D.; Seifert, C.; Engle, E.; Little, B. A scalable satellite-based crop yield mapper. Remote Sens. Environ. 2015, 164, 324–333. [Google Scholar] [CrossRef]
- Glenn, E.; Huete, A.; Nagler, P.; Nelson, S. Relationship Between Remotely-sensed Vegetation Indices, Canopy Attributes and Plant Physiological Processes: What Vegetation Indices Can and Cannot Tell Us About the Landscape. Sensors 2008, 8, 2136–2160. [Google Scholar] [CrossRef]
- Liu, C.; Tao, R.; Li, W.; Zhang, M.; Sun, W.; Du, Q. Joint Classification of Hyperspectral and Multispectral Images for Mapping Coastal Wetlands. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2021, 14, 982–996. [Google Scholar] [CrossRef]
- Nagler, P. Leaf area index and normalized difference vegetation index as predictors of canopy characteristics and light interception by riparian species on the Lower Colorado River. Agric. For. Meteorol. 2004, 125, 1–17. [Google Scholar] [CrossRef]
- Mutanga, O.; Skidmore, A.K. Narrow band vegetation indices overcome the saturation problem in biomass estimation. Int. J. Remote Sens. 2004, 25, 3999–4014. [Google Scholar] [CrossRef]
- Ollinger, S.V. Sources of variability in canopy reflectance and the convergent properties of plants. New Phytol. 2011, 189, 375–394. [Google Scholar] [CrossRef]
- Langley, J.A.; Megonigal, J.P. Field-Based Radiometry to Estimate Tidal Marsh Plant Growth in Response to Elevated CO2 and Nitrogen Addition. Wetlands 2012, 32, 571–578. [Google Scholar] [CrossRef]
- Wang, Y.; Shen, X.; Tong, S.; Zhang, M.; Jiang, M.; Lu, X. Aboveground Biomass of Wetland Vegetation Under Climate Change in the Western Songnen Plain. Front. Plant Sci. 2022, 13, 941689. [Google Scholar] [CrossRef]
- Woltz, V.L.; Stagg, C.L.; Byrd, K.B.; Windham-Myers, L.; Rovai, A.S.; Zhu, Z. Above- and Belowground Biomass Carbon Stock and Net Primary Productivity Maps for Tidal Herbaceous Marshes of the United States. Remote Sens. 2023, 15, 1697. [Google Scholar] [CrossRef]
- Hemati, M.; Hasanlou, M.; Mahdianpari, M.; Mohammadimanesh, F. Wetland Mapping of Northern Provinces of Iran Using Sentinel-1 and Sentinel-2 in Google Earth Engine. In Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, 11–16 July 2021; pp. 96–99. [Google Scholar]
- Aslan, A.; Rahman, A.F.; Warren, M.W.; Robeson, S.M. Mapping spatial distribution and biomass of coastal wetland vegetation in Indonesian Papua by combining active and passive remotely sensed data. Remote Sens. Environ. 2016, 183, 65–81. [Google Scholar] [CrossRef]
- Jensen, D.; Cavanaugh, K.C.; Simard, M.; Okin, G.S.; Castañeda-Moya, E.; McCall, A.; Twilley, R.R. Integrating imaging spectrometer and synthetic aperture radar data for estimating wetland vegetation aboveground biomass in coastal Louisiana. Remote Sens. 2019, 11, 2533. [Google Scholar] [CrossRef]
- Du, Y.; Wang, J.; Liu, Z.; Yu, H.; Li, Z.; Cheng, H. Evaluation on Spaceborne Multispectral Images, Airborne Hyperspectral, and LiDAR Data for Extracting Spatial Distribution and Estimating Aboveground Biomass of Wetland Vegetation Suaeda Salsa. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2019, 12, 200–209. [Google Scholar] [CrossRef]
- Sun, S.; Wang, Y.; Song, Z.; Chen, C.; Zhang, Y.; Chen, X.; Chen, W.; Yuan, W.; Wu, X.; Ran, X.; et al. Modelling aboveground biomass carbon stock of the bohai rim coastal wetlands by integrating remote sensing, terrain, and climate data. Remote Sens. 2021, 13, 4321. [Google Scholar] [CrossRef]
- Wang, Z.H.; Dai, H.Y.; Liu, J.B.; Ren, J.T. Aboveground biomass estimation of caohai wetland vegetation based on optical and radar remote sensing. UPB Sci. Bull. Ser. C Electr. Eng. Comput. Sci. 2023, 85, 339–350. [Google Scholar]
- Chen, C.; Ma, Y.; Ren, G.; Wang, J. Aboveground Biomass of Salt-Marsh Vegetation in Coastal Wetlands: Sample Expansion of in Situ Hyperspectral and Sentinel-2 Data Using a Generative Adversarial Network. Remote Sens. Environ. 2022, 270, 112885. [Google Scholar] [CrossRef]
- Blount, T.; Silvestri, S.; Marani, M.; D’Alpaos, A. Lidar Derived Salt Marsh Topography and Biomass: Defining Accuracy and Spatial Patterns of Uncertainty. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 2023, 57–62. [Google Scholar] [CrossRef]
- Eon, R.S.; Goldsmith, S.; Bachmann, C.M.; Tyler, A.C.; Lapszynski, C.S.; Badura, G.P.; Osgood, D.T.; Brett, R. Retrieval of salt marsh above-ground biomass from high-spatial resolution hyperspectral imagery using PROSAIL. Remote Sens. 2019, 11, 1385. [Google Scholar] [CrossRef]
- Navarro, A.; Young, M.; Allan, B.; Carnell, P.; Macreadie, P.; Ierodiaconou, D. The Application of Unmanned Aerial Vehicles (UAVs) to Estimate above-Ground Biomass of Mangrove Ecosystems. Remote Sens. Environ. 2020, 242, 111747. [Google Scholar] [CrossRef]
- Morgan, G.R.; Wang, C.; Morris, J.T. Rgb indices and canopy height modelling for mapping tidal marsh biomass from a small unmanned aerial system. Remote Sens. 2021, 13, 3406. [Google Scholar] [CrossRef]
- Shlemon, R.J. Subaqueous Delta Formation—Atchafalaya Bay, Louisiana; Broussand, M.L., Ed.; Deltas-models for exploration, Houston Geological Society; Houston Geological Society: Houston, TX, USA, 1975; pp. 209–221. [Google Scholar]
- Louis, J.; Debaecker, V.; Pflug, B.; Main-Knorn, M.; Bieniarz, J.; Mueller-Wilm, U.; Cadau, E.; Gascon, F. SENTINEL-2 SEN2COR: L2A Processor for Users. In Proceedings of the Living Planet Symposium 2016, Prague, Czech Republic, 9–13 May 2016; Ouwehand, L., Ed.; pp. 1–8. [Google Scholar]
- Torres, R.; Snoeij, P.; Geudtner, D.; Bibby, D.; Davidson, M.; Attema, E.; Potin, P.; Rommen, B.; Floury, N.; Brown, M.; et al. GMES Sentinel-1 mission. Remote Sens. Environ. 2012, 120, 9–24. [Google Scholar] [CrossRef]
- Jensen, D.; Cavanaugh, K.C.; Simard, M.; Christensen, A.; Rovai, A.; Twilley, R. Aboveground biomass distributions and vegetation composition changes in Louisiana’s Wax Lake Delta. Estuar. Coast. Shelf Sci. 2021, 250, 107139. [Google Scholar] [CrossRef]
- Castañeda-Moya, E.; Solohin, E. Delta-X: Aboveground Biomass and Necromass across Wetlands, MRD, Louisiana, 2021; ORNL DAAC: Oak Ridge, TN, USA, 2022. [Google Scholar] [CrossRef]
- Green, R.O.; Eastwood, M.L.; Sarture, C.M.; Chrien, T.G.; Aronsson, M.; Chippendale, B.J.; Faust, J.A.; Pavri, B.E.; Chovit, C.J.; Solis, M.; et al. Imaging Spectroscopy and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). Remote Sens. Environ. 1998, 65, 227–248. [Google Scholar] [CrossRef]
- Thompson, D.R.; Cawse-Nicholson, K.; Erickson, Z.; Fichot, C.G.; Frankenberg, C.; Gao, B.-C.; Gierach, M.M.; Green, R.O.; Jensen, D.; Natraj, V.; et al. A unified approach to estimate land and water reflectances with uncertainties for coastal imaging spectroscopy. Remote Sens. Environ. 2019, 231, 111198. [Google Scholar] [CrossRef]
- Greenberg, E.; Thompson, D.R.; Jensen, D.; Townsend, P.A.; Queally, N.; Chlus, A.; Fichot, C.G.; Harringmeyer, J.P.; Simard, M. An Improved Scheme for Correcting Remote Spectral Surface Reflectance Simultaneously for Terrestrial BRDF and Water-Surface Sunglint in Coastal Environments. JGR Biogeosci. 2022, 127, e2021JG006712. [Google Scholar] [CrossRef]
- Queally, N.; Ye, Z.; Zheng, T.; Chlus, A.; Schneider, F.; Pavlick, R.; Townsend, P.A. FlexBRDF: A Flexible BRDF Correction for Grouped Processing of Airborne Imaging Spectroscopy Flightlines. J. Geophys. Res. Biogeosci. 2021, 127, e2021JG006622. [Google Scholar] [CrossRef]
- Arasumani, M.; Singh, A.; Bunyan, M.; Robin, V.V. Testing the efficacy of hyperspectral (AVIRIS-NG), multispectral (Sentinel-2) and radar (Sentinel-1) remote sensing images to detect native and invasive non-native trees. Biol. Invasions 2021, 23, 2863–2879. [Google Scholar] [CrossRef]
- Badola, A.; Panda, S.K.; Roberts, D.A.; Waigl, C.F.; Jandt, R.R.; Bhatt, U.S. A novel method to simulate AVIRIS-NG hyperspectral image from Sentinel-2 image for improved vegetation/wildfire fuel mapping, boreal Alaska. Int. J. Appl. Earth Obs. Geoinf. 2022, 112, 102891. [Google Scholar] [CrossRef]
- Behera, M.D.; Barnwal, S.; Paramanik, S.; Das, P.; Bhattyacharya, B.K.; Jagadish, B.; Roy, P.S.; Ghosh, S.M.; Behera, S.K. Species-Level Classification and Mapping of a Mangrove Forest Using Random Forest—Utilisation of AVIRIS-NG and Sentinel Data. Remote Sens. 2021, 13, 2027. [Google Scholar] [CrossRef]
- Mahdianpari, M.; Salehi, B.; Mohammadimanesh, F.; Motagh, M. Random forest wetland classification using ALOS-2 L-band, RADARSAT-2 C-band, and TerraSAR-X imagery. ISPRS J. Photogramm. Remote Sens. 2017, 130, 13–31. [Google Scholar] [CrossRef]
- Breiman, L. Random forests. Mach. Learn. 2001, 45, 5–32. [Google Scholar] [CrossRef]
- Jensen, D.J.; Castañeda-Moya, E.; Solohin, E.; Rovai, A.; Thompson, D.R.; Simard, M. Delta-X: AVIRIS-NG L3 Derived Aboveground Biomass, MRD, Louisiana, USA, 2021, V2; ORNL DAAC: Oak Ridge, TN, USA, 2023. [Google Scholar] [CrossRef]
- Jensen, D.; Simard, M.; Thompson, D.R.; Solohin, E.; Castañeda-Moya, E. Towards consistent imaging spectroscopy-based global aboveground biomass retrievals in coastal wetlands across atmospheric states. In AGU Fall Meeting Abstracts; 2022; Volume 2022, Available online: https://ui.adsabs.harvard.edu/abs/2022AGUFMGC42D0745J/abstract (accessed on 1 December 2023).
- Wan, R.; Wang, P.; Wang, X.; Yao, X.; Dai, X. Mapping Aboveground Biomass of Four Typical Vegetation Types in the Poyang Lake Wetlands Based on Random Forest Modelling and Landsat Images. Front. Plant Sci. 2019, 10, 1281. [Google Scholar] [CrossRef] [PubMed]
- Byrd, K.B.; Ballanti, L.; Thomas, N.; Nguyen, D.; Holmquist, J.R.; Simard, M.; Windham-Myers, L. A remote sensing-based model of tidal marsh aboveground carbon stocks for the conterminous United States. ISPRS J. Photogramm. Remote Sens. 2018, 139, 255–271. [Google Scholar] [CrossRef]
- Hosseiny, B.; Mahdianpari, M.; Hemati, M.; Radman, A.; Mohammadimanesh, F.; Chanussot, J. Beyond supervised learning in remote sensing: A systematic review of deep learning approaches. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2023, 17, 1035–1052. [Google Scholar] [CrossRef]
Feature | Formula | |
---|---|---|
Sentinel-2 | Normalized Difference Water Index | |
Normalized Difference Vegetation Index | ||
Green Normalized Difference Vegetation Index | ||
Ratio Vegetation Index | ||
Normalized Difference Built-Up Index | ||
Normalized Burn Ratio | ||
Bare Soil Index | ||
Soil-Adjusted Vegetation Index (L = 0.5) | ||
Enhanced Vegetation Index (g = 2.5, C1 = 6, C2 = 7.5) | ||
Normalized Difference Snow Index | ||
Red Edge Normalized Difference Vegetation Index | ||
Sentinel-1 | Span or Total Scattering Power | |
Ratio |
Spring | Fall | Multi-Temporal | |
---|---|---|---|
Training Samples | 2610 | 2619 | 1269 |
Test Samples | 1105 | 1082 | 921 |
OOB Error (Mg ha−1) | 0.97 | 1.01 | 1.73 |
Training RMSE (Mg ha−1) | 0.55 | 0.57 | 0.99 |
Training R-squared | 0.85 | 0.85 | 0.91 |
Test RMSE (Mg ha−1) | 0.97 | 0.98 | 1.61 |
Test R-squared | 0.45 | 0.36 | 0.65 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Hemati, M.; Mahdianpari, M.; Shiri, H.; Mohammadimanesh, F. Integrating SAR and Optical Data for Aboveground Biomass Estimation of Coastal Wetlands Using Machine Learning: Multi-Scale Approach. Remote Sens. 2024, 16, 831. https://doi.org/10.3390/rs16050831
Hemati M, Mahdianpari M, Shiri H, Mohammadimanesh F. Integrating SAR and Optical Data for Aboveground Biomass Estimation of Coastal Wetlands Using Machine Learning: Multi-Scale Approach. Remote Sensing. 2024; 16(5):831. https://doi.org/10.3390/rs16050831
Chicago/Turabian StyleHemati, Mohammadali, Masoud Mahdianpari, Hodjat Shiri, and Fariba Mohammadimanesh. 2024. "Integrating SAR and Optical Data for Aboveground Biomass Estimation of Coastal Wetlands Using Machine Learning: Multi-Scale Approach" Remote Sensing 16, no. 5: 831. https://doi.org/10.3390/rs16050831