environmental gradients in Sonoma County, California. Remote Sensing of Environment, 242:111779, June 2020. MAG ID: 3014417423.
[14] Vivien Sainte Fare Garnot and Loic Landrieu. Panoptic segmentation of satellite image time series with convolutional temporal attention networks. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4872-4881, 2021.
[15] Sujit Madhab Ghosh, Mukunda Dev Behera, Somnath Paramanik, and Somnath Paramanik. Canopy Height Estimation Using Sentinel Series Images through Machine Learning Models in a Mangrove Forest. Remote Sensing, 12(9):1519, May 2020. MAG ID: 3025784245.
[16] Noel Gorelick, Matt Hancher, Mike Dixon, Simon Ilyushchenko, David Thau, and Rebecca Moore. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202:18-27, Dec. 2017.
[17] Sebastian Hafner, Yifang Ban, and Andrea Nascetti. Unsupervised domain adaptation for global urban extraction using Sentinel-1 SAR and Sentinel-2 MSI data. Remote Sensing of Environment, 280:113192, Oct. 2022.
[18] M. C. Hansen, P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. High-Resolution Global Maps of 21st-Century Forest Cover Change. Science, 342(6160):850–853, Nov. 2013. Publisher: American Association for the Advancement of Science.
[19] Ali Hatamizadeh, Vishwesh Nath, Yucheng Tang, Dong Yang, Holger R Roth, and Daguang Xu. Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In *Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 7th International Workshop*, *BrainLes* 2021, Held in Conjunction with MICCAI 2021, Virtual Event, September 27, 2021, Revised Selected Papers, Part I, pages 272–284. Springer, 2022.
[20] Barbara Koch. Status and future of laser scanning, synthetic aperture radar and hyperspectral remote sensing data for forest biomass assessment. IsPRS Journal of Photogrammetry and Remote Sensing, 65(6):581-590, Nov. 2010. MAG ID: 1969245801.
[21] Florian Kugler, Daniel Schulze, Irena Hajnsek, Hans Pretzsch, and Konstantinos Papathanassiou. TanDEM-X Pol-InSAR Performance for Forest Height Estimation. IEEE Transactions on Geoscience and Remote Sensing, 52(10):6404-6422, Jan. 2014. MAG ID: 2001470990.
[22] Manoj Kumar, Subrata Nandy, Ritika Srinet, and Hitendra Padalia. Mapping Forest Height and Aboveground Biomass by Integrating ICESat-2, Sentinel-1 and Sentinel-2 Data Using Random Forest Algorithm in Northwest Himalayan Foothills of India. Geophysical Research Letters, 48(14), July 2021. MAG ID: 3179206166 S2ID: 768e49dc70ff5788f67382fa7c8b61d79c126ad7.
[23] Xiaoping Liu, Guohua Hu, Yimin Chen, Xia Li, Xiaocong Xu, Shaoying Li, Fengsong Pei, and Shaojian Wang. High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform. Remote Sensing of Environment, 209:227-239, May 2018.
[24] Yanan Liu, Weishu Gong, Yanqiu Xing, Xiangyun Hu, and Jianya Gong. Estimation of the forest stand mean height and aboveground biomass in Northeast China using SAR Sentinel-1B, multispectral Sentinel2A, and DEM imagery. IsPRS Journal of Photogrammetry and Remote Sensing, 151:277–289, May 2019. MAG ID: 2928790886.
[25] Magdalena Main-Knorn, Warren B. Cohen, Robert E. Kennedy, Wojciech Grodzki, Dirk Pflugmacher, Patrick Griffiths, and Patrick Hostert. Monitoring coniferous forest biomass change using a Landsat trajectory-based approach. Remote Sensing of Environment, 139:277-290, Dec. 2013.
[26] Giona Matasci, Txomin Hermosilla, Michael A. Wulder, Joanne C. White, Nicholas C. Coops, Georgie W. Hobart, Douglas K. Bolton, Piotr Tompalski, and Christopher W. Bater. Three decades of forest structural dynamics over Canada's forested ecosystems using Landsat time-series and lidar plots. Remote Sensing of Environment, 216:697-714, Oct. 2018.
[27] Jean-Matthieu Monnet, Jocelyn Chanussot, and Frédéric Berger. Support Vector Regression for the Estimation of Forest Stand Parameters Using Airborne Laser Scanning. IEEE Geoscience and Remote Sensing Letters, 8(3):580-584, May 2011. Conference Name: IEEE Geoscience and Remote Sensing Letters.
[28] R. Nelson, K. J. Ranson, G. Sun, D. S. Kimes, V. Kharuk, and P. Montesano. Estimating Siberian timber volume using MODIS and ICESat/GLAS. Remote Sensing of Environment, 113(3):691-701, Mar. 2009.
[29] Amy L. Neuenschwander and Katherine L. Pitts. The ATL08 land and vegetation product for the ICESat-2 Mission. Remote Sensing of Environment, 221:247–259, Feb. 2019. MAG ID: 2901674962.
[30] Trung H. Nguyen, Simon D. Jones, Mariela Soto-Berelov, Andrew Haywood, and Samuel Hislop. Monitoring aboveground forest biomass dynamics over three decades using Landsat time-series and single-date inventory data. International Journal of Applied Earth Observation and Geoinformation, 84:101952, Feb. 2020.
[31] Wenjian Ni, Zhiyu Zhang, and Guoqing Sun. Assessment of Slope-Adaptive Metrics of GEDI Waveforms for Estimations of Forest Aboveground Biomass over Mountainous Areas. Journal of remote sensing, 2021:1-17, Aug. 2021. MAG ID: 3198478239 S2ID: dd4626543ba423368e87c76fb5a679f6208a39e4.
[32] Scott L. Powell, Warren B. Cohen, Sean P. Healey, Robert E. Kennedy, Gretchen G. Moisen, Kenneth B. Pierce, and Janet L. Ohmann. Quantification of live aboveground forest biomass dynamics with Landsat time-series and field inventory data: A comparison of empirical modeling approaches. Remote Sensing of Environment, 114(5):1053-1068, May 2010.