Turner, B. L., Lambin, E. F. & Reenberg, A. The emergence of land change science for global environmental change and sustainability. Proc. Natl Acad. Sci. USA 104, 20666–20671 (2007).
Pongratz, J., Reick, C. H., Raddatz, T. & Claussen, M. Biogeophysical versus biogeochemical climate response to historical anthropogenic land cover change. Geophys. Res. Lett. 37, L08702 (2010).
Forzieri, G., Alkama, R., Miralles, D. G. & Cescatti, A. Satellites reveal contrasting responses of regional climate to the widespread greening of Earth. Science 356, 1180–1184 (2017).
Ouyang, Z. et al. Albedo changes caused by future urbanization contribute to global warming. Nat. Commun. 13, 3800 (2022).
Sherwood, S. C., Dixit, V. & Salomez, C. The global warming potential of near-surface emitted water vapour. Environ. Res. Lett. 13, 104006 (2018).
Andrews, T., Betts, R. A., Booth, B. B. B., Jones, C. D. & Jones, G. S. Effective radiative forcing from historical land use change. Clim. Dyn. 48, 3489–3505 (2017).
Lejeune, Q. et al. Biases in the albedo sensitivity to deforestation in CMIP5 models and their impacts on the associated historical radiative forcing. Earth Syst. Dyn. 11, 1209–1232 (2020).
Ghimire, B. et al. Global albedo change and radiative cooling from anthropogenic land cover change, 1700 to 2005 based on MODIS, land use harmonization, radiative kernels, and reanalysis. Geophys. Res. Lett. 41, 9087–9096 (2014).
Hasler, N. et al. Accounting for albedo change to identify climate-positive tree cover restoration. Nat. Commun. 15, 2275 (2024).
IPCC Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) (Cambridge Univ. Press, 2021).
Bala, G. et al. Combined climate and carbon-cycle effects of large-scale deforestation. Proc. Natl Acad. Sci. USA 104, 6550–6555 (2007).
Betts, R. A., Falloon, P. D., Goldewijk, K. K. & Ramankutty, N. Biogeophysical effects of land use on climate: model simulations of radiative forcing and large-scale temperature change. Agric. For. Meteorol. 142, 216–233 (2007).
Bonan, G. B. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320, 1444–1449 (2008).
Bright, R. M., Zhao, K., Jackson, R. B. & Cherubini, F. Quantifying surface albedo and other direct biogeophysical climate forcings of forestry activities. Glob. Change Biol. 21, 3246–3266 (2015).
Weber, J. et al. Chemistry-albedo feedbacks offset up to a third of forestation’s CO2 removal benefits. Science 383, 860–864 (2024).
Duveiller, G., Hooker, J. & Cescatti, A. The mark of vegetation change on earth’s surface energy balance. Nat. Commun. 9, 679 (2018).
Anderson, R. G. et al. Biophysical considerations in forestry for climate protection. Front. Ecol. Environ. 9, 174–182 (2011).
Schaaf, C. Z. W. MODIS/terra + aqua BRDF/albedo daily L3 Global—500 m V061. NASA Earth Data https://doi.org/10.5067/MODIS/MCD43A3.061 (2021).
Dumont, M. et al. Contribution of light-absorbing impurities in snow to Greenland’s darkening since 2009. Nat. Geosci. 7, 509–512 (2014).
Xu, X., Huang, A., Belle, E., De Frenne, P. & Jia, G. Protected areas provide thermal buffer against climate change. Sci. Adv. 8, 0119 (2022).
Venter, Z. S., Chakraborty, T. & Lee, X. Crowdsourced air temperatures contrast satellite measures of the urban heat island and its mechanisms. Sci. Adv. 7, 9569 (2021).
Guirado, E. et al. The global biogeography and environmental drivers of fairy circles. Proc. Natl Acad. Sci. USA 120, 2304032120 (2023).
Jääskeläinen, E., Manninen, T., Hakkarainen, J. & Tamminen, J. Filling gaps of black-sky surface albedo of the Arctic sea ice using gradient boosting and brightness temperature data. Int. J. Appl. Earth Observ. Geoinf. 107, 102701 (2022).
Zemp, D. C. et al. Self-amplified Amazon forest loss due to vegetation-atmosphere feedbacks. Nat. Commun. 8, 14681 (2017).
Liu, N. F. et al. A statistics-based temporal filter algorithm to map spatiotemporally continuous shortwave albedo from MODIS data. Hydrol. Earth Syst. Sci. 17, 2121–2129 (2013).
Hall, D. K., Salomonson, V. V. & Riggs, G. A. MODIS/terra snow cover daily L3 global 500 m grid. Version 6. NSIDC https://nsidc.org/data/mod10a1/versions/61 (2016).
Friedl, M. & Sulla-Menashe., D. MODIS/terra+aqua land cover type yearly L3 global 500m SIN grid V061. NASA Earth Data https://doi.org/10.5067/MODIS/MCD12Q1.061 (2022).
Casey, K. A., Polashenski, C. M., Chen, J. & Tedesco, M. Impact of MODIS sensor calibration updates on Greenland Ice Sheet surface reflectance and albedo trends. Cryosphere 11, 1781–1795 (2017).
Di Gregorio, A. Land Cover Classification System: Classification Concepts and User Manual Softwave V2 (Food and Agriculture Organization of the United Nations (FAO), 2005).
Di Gregorio, A. & Jansen, L. J. M. A new concept for a land-cover classification system. Land 2, 55–65 (1998).
Loveland, T. R. & Belward, A. S. The igbp-dis global 1km land cover data set, discover: first results. Int. J. Remote Sens. 18, 3289–3295 (1997).
Sulla-Menashe, D., Gray, J. M., Abercrombie, S. P. & Friedl, M. A. Hierarchical mapping of annual global land cover 2001 to present: the MODIS collection 6 land cover product. Remote Sens. Environ. 222, 183–194 (2019).
Tian, J. et al. Simultaneous estimation of fractional cover of photosynthetic and non-photosynthetic vegetation using visible-near infrared satellite imagery. Remote Sens. Environ. 290, 113549 (2023).
Wang, L. & Qu, J. J. NMDI: a normalized multi-band drought index for monitoring soil and vegetation moisture with satellite remote sensing. Geophys. Res. Lett. 34, L20405 (2007).
Zhu, Z. et al. Greening of the Earth and its drivers. Nat. Clim. Change 6, 791–795 (2016).
Cui, J. et al. Global water availability boosted by vegetation-driven changes in atmospheric moisture transport. Nat. Geosci. 15, 982–988 (2022).
Chen, C. et al. China and India lead in greening of the world through land-use management. Nat. Sustain. 2, 122–129 (2019).
Peng, S. et al. Multi-staged NDVI dependent snow-free land-surface shortwave albedo narrowband-to-broadband (NTB) coefficients and their sensitivity analysis. Remote Sens. 9, 93 (2017).
Ma, Z., Xie, Y., Jiao, J., li, L. & Wang, X. The construction and application of an albedo-NDVI based desertification monitoring model. Procedia Environ. Sci. 10, 2029–2035 (2011).
Pang, G., Chen, D., Wang, X. & Lai, H.-W. Spatiotemporal variations of land surface albedo and associated influencing factors on the Tibetan Plateau. Sci. Total Environ. 804, 150100 (2022).
Newnham, G. J., Verbesselt, J., Grant, I. F. & Anderson, S. A. J. Relative Greenness Index for assessing curing of grassland fuel. Remote Sens. Environ. 115, 1456–1463 (2011).
Guerschman, J. P. et al. Estimating fractional cover of photosynthetic vegetation, non-photosynthetic vegetation and bare soil in the Australian tropical savanna region upscaling the EO-1 Hyperion and MODIS sensors. Remote Sens. Environ. 113, 928–945 (2009).
Piao, S. et al. Characteristics, drivers and feedbacks of global greening. Nat. Rev. Earth Environ. 1, 14–27 (2020).
Qiu, B. et al. Dense canopies browning overshadowed by global greening dominant in sparse canopies. Sci. Total Environ. 826, 154222 (2022).
Hall, D. K., Riggs, G. A., DiGirolamo, N. E. & Román, M. O. Evaluation of MODIS and VIIRS cloud-gap-filled snow-cover products for production of an earth science data record. Hydrol. Earth Syst. Sci. 23, 5227–5241 (2019).
Shepard, D. A two-dimensional interpolation function for irregularly-spaced data. In Proc. 1968 23rd ACM National Conf. (eds Blue, R. B. & Rosenberg, A. M.) 517–524 (Association for Computing Machinery, 1968).
Román, M. O. et al. Continuity between NASA MODIS Collection 6.1 and VIIRS Collection 2 land products. Remote Sens. Environ. 302, 113963 (2024).
Kalnay, E. et al. The NCEP/NCAR 40-year reanalysis project. Bull. Am. Meteorological Soc. 77, 437–472 (1996).
Ploton, P. et al. Spatial validation reveals poor predictive performance of large-scale ecological mapping models. Nat. Commun. 11, 4540 (2020).
Strahler, A. H., Muller, J.-P. & Members, M. S. T. MODIS BRDF/Albedo Product: Algorithm Theoretical Basis Document Version 5.0 (USGS, 1999).
Tucker, C. J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 8, 127–150 (1979).
Houldcroft, C. J. et al. New vegetation albedo parameters and global fields of soil background albedo derived from MODIS for use in a climate model. J. Hydrometeorol. 10, 183–198 (2009).
Vermote, E. MODIS/terra surface reflectance 8-day L3 global 500 m SIN Grid V061. NASA Earth Data https://doi.org/10.5067/MODIS/MOD09A1.061 (2021).
Pendergrass, A. G., Conley, A. & Vitt, F. M. Surface and top-of-atmosphere radiative feedback kernels for CESM-CAM5. Earth Syst. Sci. Data 10, 317–324 (2018).
Smith, C. J. et al. Understanding rapid adjustments to diverse forcing agents. Geophys. Res. Lett. 45, 12023–12031 (2018).
Smith, C. J., Kramer, R. J. & Sima, A. The HadGEM3-GA7.1 radiative kernel: the importance of a well-resolved stratosphere. Earth Syst. Sci. Data 12, 2157–2168 (2020).
Block, K. & Mauritsen, T. Forcing and feedback in the MPI-ESM-LR coupled model under abruptly quadrupled CO2. J. Adv. Model. Earth Syst. 5, 676–691 (2013).
Huang, Y., Xia, Y. & Tan, X. On the pattern of CO2 radiative forcing and poleward energy transport. J. Geophys. Res. Atmos. 122, 10578–10593 (2017).
Huang, H. & Huang, Y. Radiative sensitivity quantified by a new set of radiation flux kernels based on the ECMWF Reanalysis v5 (ERA5). Earth Syst. Sci. Data 15, 3001–3021 (2023).
Liu, Q. et al. Preliminary evaluation of the long-term glass albedo product. Int. J. Digital Earth 6, 69–95 (2013).
How, P. et al. PROMICE and GC-Net automated weather station data in Greenland. GEUS Dataverse https://doi.org/10.22008/FK2/IW73UU (2022).
Klein, A. & Stroeve, J. C. Development and validation of a snow albedo algorithm for the MODIS instrument. Ann. Glaciol. 34, 45–52 (2002).
Burakowski, E. A. et al. Spatial scaling of reflectance and surface albedo over a mixed-use, temperate forest landscape during snow-covered periods. Remote Sens. Environ. 158, 465–477 (2015).
Tedesco, M. et al. The darkening of the Greenland ice sheet: trends, drivers, and projections (1981-2100). Cryosphere 10, 477–496 (2016).
Sun, Y., Wang, Y., Zhai, Z. & Zhou, M. Changes in the Antarctic’s summer surface albedo, observed by satellite since 1982 and associated with sea ice anomalies. Remote Sens. 15, 4940 (2023).
Ryan, J. C. et al. How robust are in situ observations for validating satellite-derived albedo over the dark zone of the Greenland Ice Sheet? Geophys. Res. Lett. 44, 6218–6225 (2017).
Ye, F. et al. Reconstructing daily snow and ice albedo series for Greenland by coupling spatiotemporal and physics-informed models. Int. J. Appl. Earth Observ. Geoinf. 124, 103519 (2023).
Franz, B., Kwiatkowska, E., Meister, G. & Mcclain, C. Moderate resolution imaging spectroradiometer on Terra: limitations for ocean color applications. J. Appl. Remote Sens. 2, 023525 (2008).
Xiong, X., Sun, J., Xie, X., Barnes, W. L. & Salomonson, V. V. On-orbit calibration and performance of Aqua MODIS reflective solar bands. IEEE Trans. Geosci. Remote Sens. 48, 535–546 (2010).
Lyapustin, A. et al. Scientific impact of MODIS C5 calibration degradation and C6+ improvements. Atmos. Meas. Tech. 7, 4353–4365 (2014).
Bhatt, R. et al. Response versus scan-angle assessment of MODIS reflective solar bands in Collection 6.1 calibration. IEEE Trans. Geosci. Remote Sens. 58, 2276–2289 (2020).
Angal, A., Xiong, X., Wu, A., Geng, X. & Chen, H. Improvements in the on-orbit response versus scan angle characterization of the Aqua MODIS reflective solar bands. IEEE Trans. Geosci. Remote Sens. 56, 1728–1738 (2018).
Smith, C. J. et al. Effective radiative forcing and adjustments in CMIP6 models. Atmos. Chem. Phys. 20, 9591–9618 (2020).
Hurtt, G. C. et al. Harmonization of land-use scenarios for the period 1500-2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands. Clim. Change 109, 117 (2011).
Feng, G. et al. Multiscale climatological albedo look-up maps derived from moderate resolution imaging spectroradiometer BRDF/albedo products. J. Appl. Remote Sens. 8, 083532 (2014).
Román, M. O. et al. Assessing the coupling between surface albedo derived from MODIS and the fraction of diffuse skylight over spatially-characterized landscapes. Remote Sens. Environ. 114, 738–760 (2010).
Hou, Z. ALLUMs. Zenodo https://doi.org/10.5281/zenodo.13981585 (2025).
Hou, Z. ALLUMs codes. Zenodo https://doi.org/10.5281/zenodo.14955081 (2025).
