The FCM project ended long since, but the journal articles still keep coming out! One article describing the European wide maps (Santoro et al. 2026) was published earlier this year and an article on the deep learning models used in the Norway demonstration (Koma et al. 2026) was just recently accepted. And there is still at least one manuscript under finalization…
Altogether, already eight articles from the FCM project (some of them in cooperation with other projects) have been published. Many of them have been highlighted in the blog posts during the project. But to make it easier to find them, we have now compiled a reference library for the FCM project.
Below, you can find a list of all the articles published in connection with the project. All of them are openly available and can be accessed by clicking the link in the DOI number. In addition to the journal articles, we would also like to highlight the FCM Algorithm Theoretical Basis Document (ATBD), which provides full descriptions of the algorithms underlying the tools and the datasets used in the demonstrations. We hope that this list makes it easier for you all to find the appropriate reference and information on the FCM tools.
Wishing you enlightening reading moments!
Scientific journal articles connected to the FCM project:
Koma, Z., Antropov, O., Cartus, O. Miettinen, J. and Breidenbach, J. (2026) Deep learning models for estimating volume and Lorey’s height across Nordic countries using optical and SAR satellite images. International Journal of Applied Earth Observation and Geoinformation 153: 105527. DOI: 10.1016/j.jag.2026.105527
Santoro, M., Cartus, O., Araza, A., Herold, M., Miettinen, J., Rosenqvist, A., Kobayashi, K., Tadono, T. and Seifert, F.M. (2026) Europe-wide maps of biomass density based on satellite remote sensing data for 2017, 2020, 2021 and 2023. Data in Brief 65: 112536. DOI: 10.1016/j.dib.2026.112536
Teijido-Murias, I., Antropov, O., López-Sánchez, C.A., Barrio-Anta, M. and Miettinen, J. (2025) Forest Height and Volume Mapping in Northern Spain with Multi-Source Earth Observation Data: Method and Data Comparison. Forests 16: 563. DOI: 10.3390/f16040563
Minunno, F., Miettinen, J., Tian, X., Häme, T., Holder, J., Koivu, K. and Mäkelä, A. (2025) Data assimilation of forest status using Sentinel-2 data and a process-based model. Agricultural and Forest Meteorology 363: 110436. DOI: 10.1016/j.agrformet.2025.110436
Santoro, M., Cartus, O., Antropov, O. and Miettinen, J. (2024) Estimation of Forest Growing Stock Volume with Synthetic Aperture Radar: A Comparison of Model-Fitting Methods. Remote Sensing 16: 4079. DOI: 10.3390/rs16214079
Ge, S., Antropov, O., Häme, T., McRoberts, R.E. and Miettinen, J. (2023) Deep Learning Model Transfer in Forest Mapping Using Multi-Source Satellite SAR and Optical Images. Remote Sensing 15: 5152. DOI: 10.3390/rs15215152
Málaga, N., de Bruin, S., McRoberts, R.E., Arana Olivos, A., de la Cruz Paiva, R., Durán Montesinos, P., Requena Suarez, D. and Herold, M. (2022) Precision of subnational forest AGB estimates within the Peruvian Amazonia using a global biomass map. International Journal of Applied Earth Observation and Geoinformation 115: 103102. DOI: 10.1016/j.jag.2022.103102
Ge, S., Gu, H., Su, W., Praks, J. and Antropov, O. (2022) Improved Semisupervised UNet Deep Learning Model for Forest Height Mapping With Satellite SAR and Optical Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 15: 5776-5787. DOI: 10.1109/JSTARS.2022.3188201