{"id":604592,"date":"2026-08-25T23:08:14","date_gmt":"2026-08-25T23:08:14","guid":{"rendered":"https:\/\/www.newsbeep.com\/ie\/604592\/"},"modified":"2026-08-25T23:08:14","modified_gmt":"2026-08-25T23:08:14","slug":"disturbance-characteristics-and-forest-properties-regulate-surface-warming-and-recovery-across-europe","status":"publish","type":"post","link":"https:\/\/www.newsbeep.com\/ie\/604592\/","title":{"rendered":"Disturbance characteristics and forest properties regulate surface warming and recovery across Europe"},"content":{"rendered":"

Bonan, G. B. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320, 1444\u20131449 (2008).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Bright, R. M. et al. Local temperature response to land cover and management change driven by non-radiative processes. Nat. Clim. Change 7, 296\u2013302 (2017).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

De Frenne, P. et al. Global buffering of temperatures under forest canopies. Nat. Ecol. Evol. 3, 744\u2013749 (2019).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Seidl, R. et al. Forest disturbances under climate change. Nat. Clim. Change 7, 395\u2013402 (2017).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Forzieri, G. et al. Emergent vulnerability to climate-driven disturbances in European forests. Nat. Commun. 12, 1081 (2021).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Bednar-Friedl, B. et al. in Climate Change 2022: Impacts, Adaptation and Vulnerability (eds P\u00f6rtner, H.-O. et al.) 1817\u20131928 (Cambridge Univ. Press, 2023); https:\/\/doi.org\/10.1017\/9781009325844.015<\/a><\/p>\n

Li, Y. et al. Local cooling and warming effects of forests based on satellite observations. Nat. Commun. 6, 6603 (2015).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Vargas Zeppetello, L. R. et al. Large scale tropical deforestation drives extreme warming. Environ. Res. Lett. 15, 084012 (2020).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Boysen, L. R. et al. Global climate response to idealized deforestation in CMIP6 models. Biogeosciences 17, 5615\u20135638 (2020).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Lawrence, D., Coe, M., Walker, W., Verchot, L. & Vandecar, K. The unseen effects of deforestation: biophysical effects on climate. Front. For. Glob. Change 5, 756115 (2022).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Viana-Soto, A. & Senf, C. The European Forest Disturbance Atlas: a forest disturbance monitoring system using the Landsat archive. Earth Syst. Sci. Data 17, 2373\u20132404 (2025).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zhao, J. et al. Forest fire size amplifies postfire land surface warming. Nature 633, 828\u2013834 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Forzieri, G. et al. Increased control of vegetation on global terrestrial energy fluxes. Nat. Clim. Change 10, 356\u2013362 (2020).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Liu, Z., Ballantyne, A. P. & Cooper, L. A. Biophysical feedback of global forest fires on surface temperature. Nat. Commun. 10, 214 (2019).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Randerson, J. T. et al. The impact of boreal forest fire on climate warming. Science 314, 1130\u20131132 (2006).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Schwaab, J. et al. Increasing the broad-leaved tree fraction in European forests mitigates hot temperature extremes. Sci. Rep. 10, 14153 (2020).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Yao, Y. et al. Conversion from coniferous to broadleaved trees can make European forests more climate-effective. Nat. Commun. 16, 9536 (2025).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Kim, J. E., Lagu\u00eb, M. M., Pennypacker, S., Dawson, E. & Swann, A. L. S. Evaporative resistance is of equal importance as surface albedo in high-latitude surface temperatures due to cloud feedbacks. Geophys. Res. Lett. 47, e2019GL085663 (2020).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Seidl, R., Potterf, M., M\u00fcller, J., Turner, M. G. & Rammer, W. Patterns of early post-disturbance reorganization in Central European forests. Proc. R. Soc. B 291, 20240625 (2024).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Senf, C. & Seidl, R. Mapping the forest disturbance regimes of Europe. Nat. Sustain. 4, 63\u201370 (2020).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Li, J. et al. Contrasting temporal dynamics of land surface temperature responses to different types of forest loss. Innovation 6, 100875 (2025).<\/p>\n


\n Google Scholar<\/a>\u00a0\n <\/p>\n

Li, Y. et al. Amplified local cooling effect of forestation in warming Europe. Nat. Commun. 16, 8412 (2025).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Grieger, S. et al. Impact of forest disturbance derived from Sentinel-2 time series on Landsat 8\/9 land surface temperature: the case of Norway spruce in Central Germany. ISPRS J. Photogramm. Remote Sens. 228, 388\u2013407 (2025).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Cooper, L. A., Ballantyne, A. P., Holden, Z. A. & Landguth, E. L. Disturbance impacts on land surface temperature and gross primary productivity in the western United States. J. Geophys. Res. Biogeosci. 122, 930\u2013946 (2017).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zheng, W., Zhang, Y. & Chen, X. The offset of the ecological benefits of decreasing forest disturbance severity in Europe caused by climate change. Forests 16, 852 (2025).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Nabuurs, G.-J. et al. Climate-Smart Forestry: Mitigation Impacts in Three European Regions (European Forest Institute, 2018); https:\/\/doi.org\/10.36333\/fs06<\/a><\/p>\n

Bowditch, E. et al. What is climate-smart forestry? A definition from a multinational collaborative process focused on mountain regions of Europe. Ecosyst. Serv. 43, 101113 (2020).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Roy, D. P. et al. Landsat-8: science and product vision for terrestrial global change research. Remote Sens. Environ. 145, 154\u2013172 (2014).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Crawford, C. J. et al. The 50-year Landsat collection 2 archive. Sci. Remote Sens. 8, 100103 (2023).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Wulder, M. A. et al. Fifty years of Landsat science and impacts. Remote Sens. Environ. 280, 113195 (2022).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Yu, X., Guo, X. & Wu, Z. Land surface temperature retrieval from Landsat 8 TIRS\u2014comparison between radiative transfer equation-based method, split window algorithm and single channel method. Remote Sens. 6, 9829\u20139852 (2014).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Avdan, U. & Jovanovska, G. Algorithm for automated mapping of land surface temperature using LANDSAT 8 satellite data. J. Sens. 2016, 1\u20138 (2016).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Su, Y. et al. A remote sensing-based assessment of the cooling effects of urban trees in European cities. npj Urban Sustain 6, 96 (2026).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Breiman, L. Random forests. Mach. Learn. 45, 5\u201332 (2001).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Ho, T. K. Random decision forests. In Proc. 3rd International Conference on Document Analysis and Recognition 1, 278\u2013282 (IEEE Computer Society Press, 1995).<\/p>\n

Su, Y., Zhang, H., Gabrielle, B. & Makowski, D. Performances of machine learning algorithms in predicting the productivity of conservation agriculture at a global scale. Front. Environ. Sci. 10, 812648 (2022).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Reek, J. E. et al. Forest edges are globally warmer than interiors and exceed optimal temperatures for vegetation productivity. Commun. Earth Environ. 6, 635 (2025).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Hesslerov\u00e1, P., Huryna, H., Pokorn\u00fd, J. & Proch\u00e1zka, J. The effect of forest disturbance on landscape temperature. Ecol. Eng. 120, 345\u2013354 (2018).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Banbury Morgan, R. & Jucker, T. A unifying framework for understanding how edge effects reshape the structure, composition and function of forests. New Phytol. 248, 529\u2013541 (2025).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Watts, A. C. & Kobziar, L. N. Hydrology and fire regulate edge influence on microclimate in wetland forest patches. Freshwater Sci. 34, 1383\u20131393 (2015).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Francini, S. et al. An open science and open data approach for the statistically robust estimation of forest disturbance areas. Int. J. Appl. Earth Obs. Geoinf. 106, 102663 (2022).<\/p>\n


\n Google Scholar<\/a>\u00a0\n <\/p>\n

Huete, A. et al. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 83, 195\u2013213 (2002).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zeng, Y. et al. Optical vegetation indices for monitoring terrestrial ecosystems globally. Nat. Rev. Earth Environ. 3, 477\u2013493 (2022).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Balling, J., Herold, M. & Reiche, J. How textural features can improve SAR-based tropical forest disturbance mapping. Int. J. Appl. Earth Obs. Geoinf. 124, 103492 (2023).<\/p>\n


\n Google Scholar<\/a>\u00a0\n <\/p>\n

Kuplich, T. M., Curran, P. J. & Atkinson, P. M. Relating SAR image texture to the biomass of regenerating tropical forests. Int. J. Remote Sens. 26, 4829\u20134854 (2005).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Bourgoin, C., Verhegghen, A., Carboni, S., Degr\u00e8ve, L. & Ameztoy Aramendi, I. Global Forest Maps for the Year 2020 to Support the EU Regulation on Deforestation-free Supply Chains \u2013 Improved Map of Global Forest Cover (GFC2020) and Preliminary Map of Global Forest Types (GFT2020) (Publications Office of the European Union, 2025); https:\/\/doi.org\/10.2760\/1975879<\/a><\/p>\n

Bourgoin, C. et al. Global map of forest types 2020 – version 0. European Commission JRC http:\/\/data.europa.eu\/89h\/037ca376-ba92-49db-a8f7-0c277c1e5436<\/a> (2024).<\/p>\n

Carnicer, J. et al. Forest resilience to global warming is strongly modulated by local-scale topographic, microclimatic and biotic conditions. J. Ecol. 109, 3322\u20133339 (2021).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Alibakhshi, S. et al. Natural forest regeneration is projected to reduce local temperatures. Commun. Earth Environ. 5, 577 (2024).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Wang, Z. et al. Early spring post-fire snow albedo dynamics in high latitude boreal forests using Landsat-8 OLI data. Remote Sens. Environ. 185, 71\u201383 (2016).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Seidl, R. & Rammer, W. Climate change amplifies the interactions between wind and bark beetle disturbances in forest landscapes. Landsc. Ecol. 32, 1485\u20131498 (2017).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zhang, H. & Makowski, D. Assessment of the climatic suitability for invasive agricultural insect pests in a warming Europe based on K\u00f6ppen-Geiger classification. Entomol. Gen. 45, 1029\u20131035 (2025).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Tang, R., Huang, X., Zhou, D. & Ding, A. Biomass-burning-induced surface darkening and its impact on regional meteorology in eastern China. Atmos. Chem. Phys. 20, 6177\u20136191 (2020).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Vargas Zeppetello, L. R., Battisti, D. S. & Baker, M. B. The origin of soil moisture evaporation \u2018regimes\u2019. J. Clim. 32, 6939\u20136960 (2019).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Alessi, M. J., Herrera, D. A., Evans, C. P., DeGaetano, A. T. & Ault, T. R. Soil moisture conditions determine land\u2013atmosphere coupling and drought risk in the northeastern United States. J. Geophys. Res.: Atmos. 127, e2021JD034740 (2022).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Kelly, L. T. et al. Fire and biodiversity in the Anthropocene. Science 370, eabb0355 (2020).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Perry, K. I. & Herms, D. A. Dynamic responses of ground-dwelling invertebrate communities to disturbance in forest ecosystems. Insects 10, 61 (2019).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Thom, D. & Seidl, R. Natural disturbance impacts on ecosystem services and biodiversity in temperate and boreal forests. Biol. Rev. 91, 760\u2013781 (2016).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Scherrer, D. et al. Canopy disturbances catalyse tree species shifts in Swiss forests. Ecosystems 25, 199\u2013214 (2022).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Thom, D., Rammer, W. & Seidl, R. Disturbances catalyze the adaptation of forest ecosystems to changing climate conditions. Glob. Change Biol. 23, 269\u2013282 (2017).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Fenberg, P. B. & Roy, K. Ecological and evolutionary consequences of size-selective harvesting: how much do we know? Mol. Ecol. 17, 209\u2013220 (2008).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Andriuzzi, W. Thinning benefits small trees. Nat. Ecol. Evol. 8, 1384\u20131384 (2024).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Piazza, N., Malanchini, L., Nevola, E. & Vacchiano, G. Where to start with climate-smart forest management? Climatic risk for forest-based mitigation. Nat. Hazards Earth Syst. Sci. 24, 3579\u20133595 (2024).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Hl\u00e1sny, T., Augustynczik, A. L. D. & Dobor, L. Time matters: resilience of a post-disturbance forest landscape. Sci. Total Environ. 799, 149377 (2021).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Cerioni, M. et al. Recovery and resilience of European temperate forests after large and severe disturbances. Glob. Change Biol. 30, e17159 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Senf, C. & Seidl, R. Post-disturbance canopy recovery and the resilience of Europe\u2019s forests. Glob. Ecol. Biogeogr. 31, 25\u201336 (2022).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Cerioni, M., Klop\u010di\u010d, M., Ro\u017eenbergar, D. & Nagel, T. A. Multiple dimensions of forest resilience to compound disturbances in a mixed sub-montane forest landscape. For. Ecol. Manage. 577, 122400 (2025).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

M\u00e1li\u0161, F. et al. Microclimate variation and recovery time in managed and old-growth temperate forests. Agric. For. Meteorol. 342, 109722 (2023).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Starck, I. et al. Slow recovery of microclimate temperature buffering capacity after clear-cuts in boreal forests. Agric. For. Meteorol. 363, 110434 (2025).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Landsat 8 Data Users Handbook (USGS, 2019); https:\/\/www.usgs.gov\/media\/files\/landsat-8-data-users-handbook<\/a><\/p>\n

Landsat normalized difference vegetation index. USGS (accessed 01 October 2025). https:\/\/www.usgs.gov\/landsat-missions\/landsat-normalized-difference-vegetation-index<\/a><\/p>\n

Momeni, M. & Saradjian, M. Evaluating NDVI-based emissivities of MODIS bands 31 and 32 using emissivities derived by Day\/Night LST algorithm. Remote Sens. Environ. 106, 190\u2013198 (2007).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Sobrino, J. A. & Romaguera, M. Land surface temperature retrieval from MSG1-SEVIRI data. Remote Sens. Environ. 92, 247\u2013254 (2004).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Sobrino, J. A comparative study of land surface emissivity retrieval from NOAA data. Remote Sens. Environ. 75, 256\u2013266 (2001).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Sobrino, J. A. et al. Land surface emissivity retrieval from different VNIR and TIR sensors. IEEE Trans. Geosci. Remote Sens. 46, 316\u2013327 (2008).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

USGS Landsat 8 level 2, collection 2, tier 1. USGS (accessed 01 October 2025). https:\/\/developers.google.com\/earth-engine\/datasets\/catalog\/LANDSAT_LC08_C02_T1_L2#description<\/a><\/p>\n

Dominant Leaf Type 2018-present (raster 10 m), Europe, yearly. European Environment Agency (accessed 01 October 2025). https:\/\/land.copernicus.eu\/en\/products\/high-resolution-layer-forests-and-tree-cover\/dominant-leaf-type-2018-present-raster-10-m-europe-yearly<\/a><\/p>\n

Mu\u00f1oz Sabater, J. ERA5-land monthly averaged data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store https:\/\/doi.org\/10.24381\/cds.68d2bb30<\/a> (2019).<\/p>\n

Copernicus DEM GLO-30: global 30\u2009m digital elevation model. Copernicus (accessed 01 October 2025). https:\/\/developers.google.com\/earth-engine\/datasets\/catalog\/COPERNICUS_DEM_GLO30<\/a><\/p>\n

Hengl, T. Clay content in % (kg \/ kg) at 6 standard depths (0, 10, 30, 60, 100 and 200\u2009cm) at 250\u2009m resolution (version v02). Zenodo https:\/\/doi.org\/10.5281\/zenodo.1476854<\/a> (2018).<\/p>\n

Hengl, T. Sand content in % (kg \/ kg) at 6 standard depths (0, 10, 30, 60, 100 and 200\u2009cm) at 250\u2009m resolution (version v02). Zenodo https:\/\/doi.org\/10.5281\/zenodo.1476851<\/a> (2018).<\/p>\n

Kruskal, W. H. & Wallis, W. A. Use of ranks in one-criterion variance analysis. J. Am. Stat. Assoc. 47, 583\u2013621 (1952).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Lundberg, S. M. et al. From local explanations to global understanding with explainable AI for trees. Nat. Mach. Intell. 2, 56\u201367 (2020).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zhu, L. et al. Comparable biophysical and biogeochemical feedbacks on warming from tropical moist forest degradation. Nat. Geosci. 16, 244\u2013249 (2023).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Wani, J. M., Thayyen, R. J., Ojha, C. S. P. & Gruber, S. The surface energy balance in a cold and arid permafrost environment, Ladakh, Himalayas, India. Cryosphere 15, 2273\u20132293 (2021).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Hartmann, D. L. Chapter 4 the energy balance of the surface. Int. Geophys. 56, 81\u2013114 (1994).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Murray, R. S., Nagler, P. L., Morino, K. & Glenn, E. P. An empirical algorithm for estimating agricultural and riparian evapotranspiration using MODIS enhanced vegetation index and ground measurements of ET. II. application to the Lower Colorado River, U.S. Remote Sens. 1, 1125\u20131138 (2009).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Nagler, P. L., Morino, K., Murray, R. S., Osterberg, J. & Glenn, E. P. An empirical algorithm for estimating agricultural and riparian evapotranspiration using MODIS enhanced vegetation index and ground measurements of ET. I. description of method. Remote Sens. 1, 1273\u20131297 (2009).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Wang, K., Wang, P., Li, Z., Cribb, M. & Sparrow, M. A simple method to estimate actual evapotranspiration from a combination of net radiation, vegetation index, and temperature. J. Geophys. Res.: Atmos. 112, D15107 (2007).<\/p>\n


\n Google Scholar<\/a>\u00a0\n <\/p>\n

Gao, X. Optical\u2013biophysical relationships of vegetation spectra without background contamination. Remote Sens. Environ. 74, 609\u2013620 (2000).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zhou, S., Yu, B., Zhang, Y., Huang, Y. & Wang, G. Partitioning evapotranspiration based on the concept of underlying water use efficiency. Water Resour. Res. 52, 1160\u20131175 (2016).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Bonan, G. B. et al. Modeling canopy-induced turbulence in the Earth system: a unified parameterization of turbulent exchange within plant canopies and the roughness sublayer (CLM-ml v0). Geosci. Model Dev. 11, 1467\u20131496 (2018).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Maurer, K. D., Hardiman, B. S., Vogel, C. S. & Bohrer, G. Canopy-structure effects on surface roughness parameters: observations in a Great Lakes mixed-deciduous forest. Agric. For. Meteorol. 177, 24\u201334 (2013).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Kohavi, R. A study of cross-validation and bootstrap for accuracy estimation and model selection. In Proc. 14th International Joint Conference on Artificial Intelligence 2, 1137\u20131143 (1995).<\/p>\n

Han, J., Kamber, M. & Pei, J. in Data Mining 327\u2013391 (Elsevier, 2012); https:\/\/doi.org\/10.1016\/B978-0-12-381479-1.00008-3<\/a><\/p>\n

Su, Y. et al. Canopy height and biomass distribution across the forests of Iberian Peninsula. Sci. Data 12, 678 (2025).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Brereton, R. G. in Comprehensive Chemometrics 577\u2013590 (Elsevier, 2009); https:\/\/doi.org\/10.1016\/B978-044452701-1.00037-5<\/a><\/p>\n

Su, Y. et al. Code and data used in \u2018disturbance characteristics and forest properties regulate surface warming and recovery across Europe\u2019. Figshare https:\/\/doi.org\/10.6084\/m9.figshare.31147237<\/a> (2026).<\/p>\n","protected":false},"excerpt":{"rendered":"Bonan, G. B. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320, 1444\u20131449…\n","protected":false},"author":2,"featured_media":604593,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[22],"tags":[12775,72787,10837,246,9705,54536,1437,7692,21746,72786,61,60,82],"class_list":["post-604592","post","type-post","status-publish","format-standard","has-post-thumbnail","category-environment","tag-earth-sciences","tag-earth-system-sciences","tag-ecosystem-ecology","tag-environment","tag-environmental-impact","tag-forest-ecology","tag-general","tag-geochemistry","tag-geology","tag-geophysics-geodesy","tag-ie","tag-ireland","tag-science"],"_links":{"self":[{"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/posts\/604592","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/comments?post=604592"}],"version-history":[{"count":0,"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/posts\/604592\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/media\/604593"}],"wp:attachment":[{"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/media?parent=604592"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/categories?post=604592"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/tags?post=604592"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}