Houborg, R., Fisher, J. B. & Skidmore, A. K. Advances in remote sensing of vegetation function and traits. Int. J. Appl. Earth Obs. Geoinf. 43, 1–6 (2015).
Bannari, A., Morin, D., Bonn, F. & Huete, A. A review of vegetation indices. Remote Sens. Rev. 13, 95–120 (1995).
Google Scholar
Gao, X., Huete, A. R., Ni, W. & Miura, T. Optical–biophysical relationships of vegetation spectra without background contamination. Remote Sens. Environ. 74, 609–620 (2000).
Google Scholar
Huete, A. R. A soil-adjusted vegetation index (SAVI). Remote Sens. Environ. 25, 295–309 (1988).
Google Scholar
Badgley, G., Field, C. B. & Berry, J. A. Canopy near-infrared reflectance and terrestrial photosynthesis. Sci. Adv. 3, e1602244 (2017).
Google Scholar
Gamon, J. A. et al. A remotely sensed pigment index reveals photosynthetic phenology in evergreen conifers. Proc. Natl Acad. Sci. USA 113, 13087–13092 (2016).
Google Scholar
Joiner, J. et al. Estimation of terrestrial global gross primary production (GPP) with satellite data-driven models and eddy covariance flux data. Remote Sens. 10, 1346 (2018).
Google Scholar
Piao, S. et al. Characteristics, drivers and feedbacks of global greening. Nat. Rev. Earth Environ. 1, 14–27 (2020).
Google Scholar
Tian, F. et al. Evaluating temporal consistency of long-term global NDVI datasets for trend analysis. Remote Sens. Environ. 163, 326–340 (2015).
Google Scholar
Fan, X. & Liu, Y. A global study of NDVI difference among moderate-resolution satellite sensors. ISPRS J. Photogramm. Remote Sens. 121, 177–191 (2016).
Google Scholar
AghaKouchak, A. et al. Remote sensing of drought: progress, challenges and opportunities. Rev. Geophys. 53, 452–480 (2015).
Google Scholar
Anyamba, A. & Tucker, in Remote Sensing of Drought: Innovative Monitoring Approaches Ch. 2 (eds Wardlow, B. D., Anderson, M. C. & Verdin, J. P.) (Taylor & Francis, 2012).
Veraverbeke, S. et al. Hyperspectral remote sensing of fire: state-of-the-art and future perspectives. Remote Sens. Environ. 216, 105–121 (2018).
Google Scholar
Tucker, C. J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 8, 127–150 (1979).
Google Scholar
Rouse, J. W., Haas, R. H., Schell, J. A. & Deering, D. W. Monitoring vegetation systems in the Great Plains with ERTS. NASA Spec. Publ. 351, 309 (1974).
Rouse, J. W., Haas, R. H., Schell, J. A., Deering, D. W. & Harlan, J. C. Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation. NASA/GSFCT Type III Final Report, 371 (NASA, 1974).
Gutman, G., Skakun, S. & Gitelson, A. Revisiting the use of red and near-infrared reflectances in vegetation studies and numerical climate models. Sci. Remote Sens. 4, 100025 (2021).
Google Scholar
Jackson, R. D. & Huete, A. R. Interpreting vegetation indices. Prev. Vet. Med. 11, 185–200 (1991).
Google Scholar
Richardson, A. J. & Wiegand, C. Distinguishing vegetation from soil background information. Photogramm. Eng. Remote Sens. 43, 1541–1552 (1977).
Baret, F., Guyot, G. & Major, D. in 12th Canadian Symposium on Remote Sensing Geoscience and Remote Sensing Symposium 1355–1358 (IEEE, 1989).
Qi, J., Chehbouni, A., Huete, A. R., Kerr, Y. H. & Sorooshian, S. A modified soil adjusted vegetation index. Remote Sens. Environ. 48, 119–126 (1994).
Google Scholar
Chen, J. M. Evaluation of vegetation indices and a modified simple ratio for boreal applications. Can. J. Remote Sens. 22, 229–242 (1996).
Google Scholar
Brown, L., Chen, J. M., Leblanc, S. G. & Cihlar, J. A shortwave infrared modification to the simple ratio for LAI retrieval in boreal forests: an image and model analysis. Remote Sens. Environ. 71, 16–25 (2000).
Google Scholar
Pinty, B. & Verstraete, M. GEMI: a non-linear index to monitor global vegetation from satellites. Vegetatio 101, 15–20 (1992).
Google Scholar
Huete, A. et al. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 83, 195–213 (2002).
Google Scholar
Kaufman, Y. J. & Tanre, D. Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE Trans. Geosci. Remote Sens. 30, 261–270 (1992).
Google Scholar
Jiang, Z., Huete, A. R., Didan, K. & Miura, T. Development of a two-band enhanced vegetation index without a blue band. Remote Sens. Environ. 112, 3833–3845 (2008).
Google Scholar
Jin, H. & Eklundh, L. A physically based vegetation index for improved monitoring of plant phenology. Remote Sens. Environ. 152, 512–525 (2014).
Google Scholar
Yang, P., van der Tol, C., Campbell, P. K. & Middleton, E. M. Fluorescence Correction Vegetation Index (FCVI): A physically based reflectance index to separate physiological and non-physiological information in far-red sun-induced chlorophyll fluorescence. Remote Sens. Environ. 240, 111676 (2020).
Google Scholar
Badgley, G., Anderegg, L. D., Berry, J. A. & Field, C. B. Terrestrial gross primary production: Using NIRV to scale from site to globe. Glob. Change Biol. 25, 3731–3740 (2019).
Google Scholar
Camps-Valls, G. et al. A unified vegetation index for quantifying the terrestrial biosphere. Sci. Adv. 7, eabc7447 (2021).
Google Scholar
Roberts, D. A., Roth, K. L. & Perroy, R. L. in Hyperspectral Remote Sensing of Vegetation Ch. 14 (eds Thenkabail, P. S., Lyon, J. G. & Huete, A.) (CRC, 2016).
Gitelson, A. A., Vina, A., Ciganda, V., Rundquist, D. C. & Arkebauer, T. J. Remote estimation of canopy chlorophyll content in crops. Geophys. Res. Lett. 32, L08403 (2005).
Google Scholar
Gitelson, A. & Merzlyak, M. N. Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation. J. Plant Physiol. 143, 286–292 (1994).
Google Scholar
Dash, J. & Curran, P. The MERIS terrestrial chlorophyll index. Int. J. Remote Sens. 25, 5403–5413 (2004).
Google Scholar
Penuelas, J., Baret, F. & Filella, I. Semi-empirical indices to assess carotenoids/chlorophyll a ratio from leaf spectral reflectance. Photosynthetica 31, 221–230 (1995).
Peñuelas, J., Gamon, J., Fredeen, A., Merino, J. & Field, C. Reflectance indices associated with physiological changes in nitrogen- and water-limited sunflower leaves. Remote Sens. Environ. 48, 135–146 (1994).
Google Scholar
Merzlyak, M. N., Gitelson, A. A., Chivkunova, O. B. & Rakitin, V. Y. Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening. Physiol. Plant. 106, 135–141 (1999).
Google Scholar
Gitelson, A. A., Merzlyak, M. N. & Chivkunova, O. B. Optical properties and nondestructive estimation of anthocyanin content in plant leaves. Photochem. Photobiol. 74, 38–45 (2001).
Google Scholar
van den Berg, A. K. & Perkins, T. D. Nondestructive estimation of anthocyanin content in autumn sugar maple leaves. HortScience 40, 685–686 (2005).
Google Scholar
Gamon, J. & Surfus, J. Assessing leaf pigment content and activity with a reflectometer. New Phytol. 143, 105–117 (1999).
Google Scholar
Gao, B.-C. NDWI — a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens. Environ. 58, 257–266 (1996).
Google Scholar
Xiao, X., Boles, S., Liu, J., Zhuang, D. & Liu, M. Characterization of forest types in Northeastern China, using multi-temporal SPOT-4 VEGETATION sensor data. Remote Sens. Environ. 82, 335–348 (2002).
Google Scholar
Xiao, X. et al. Satellite-based modeling of gross primary production in an evergreen needleleaf forest. Remote Sens. Environ. 89, 519–534 (2004).
Google Scholar
Yilmaz, M. T., Hunt, E. R. Jr & Jackson, T. J. Remote sensing of vegetation water content from equivalent water thickness using satellite imagery. Remote Sens. Environ. 112, 2514–2522 (2008).
Google Scholar
Cheng, Y.-B., Ustin, S. L., Riaño, D. & Vanderbilt, V. C. Water content estimation from hyperspectral images and MODIS indexes in Southeastern Arizona. Remote Sens. Environ. 112, 363–374 (2008).
Google Scholar
Serrano, L., Penuelas, J. & Ustin, S. L. Remote sensing of nitrogen and lignin in Mediterranean vegetation from AVIRIS data: decomposing biochemical from structural signals. Remote Sens. Environ. 81, 355–364 (2002).
Google Scholar
Filella, I. et al. PRI assessment of long-term changes in carotenoids/chlorophyll ratio and short-term changes in de-epoxidation state of the xanthophyll cycle. Int. J. Remote Sens. 30, 4443–4455 (2009).
Google Scholar
Gamon, J., Penuelas, J. & Field, C. A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sens. Environ. 41, 35–44 (1992).
Google Scholar
Cheng, R. et al. Decomposing reflectance spectra to track gross primary production in a subalpine evergreen forest. Biogeosciences 17, 4523–4544 (2020).
Google Scholar
Seyednasrollah, B. et al. Seasonal variation in the canopy color of temperate evergreen conifer forests. New Phytol. 229, 2586–2600 (2021).
Google Scholar
Merton, R. in Proceedings of the Seventh Annual JPL Airborne Earth Science Workshop 12–16 (NASA, 2004).
Naidu, R. A., Perry, E. M., Pierce, F. J. & Mekuria, T. The potential of spectral reflectance technique for the detection of Grapevine leafroll-associated virus-3 in two red-berried wine grape cultivars. Comput. Electron. Agric. 66, 38–45 (2009).
Google Scholar
Chen, Y. et al. Generation and evaluation of LAI and FPAR products from Himawari-8 Advanced Himawari imager (AHI) data. Remote Sens. 11, 1517 (2019).
Google Scholar
Zhu, Z. et al. Global data sets of vegetation leaf area index (LAI)3g and fraction of photosynthetically active radiation (FPAR)3g derived from global inventory modeling and mapping studies (GIMMS) normalized difference vegetation index (NDVI3g) for the period 1981 to 2011. Remote Sens. 5, 927–948 (2013).
Google Scholar
Liu, Y., Liu, R. & Chen, J. M. Retrospective retrieval of long-term consistent global leaf area index (1981–2011) from combined AVHRR and MODIS data. J. Geophys. Res. 117, G04003 (2012).
Croft, H. et al. The global distribution of leaf chlorophyll content. Remote Sens. Environ. 236, 111479 (2020).
Google Scholar
Bayat, B. et al. Toward operational validation systems for global satellite-based terrestrial essential climate variables. Int. J. Appl. Earth Obs. Geoinf. 95, 102240 (2021).
Cui, Y., Song, L. & Fan, W. Generation of spatio-temporally continuous evapotranspiration and its components by coupling a two-source energy balance model and a deep neural network over the Heihe River Basin. J. Hydrol. 597, 126176 (2021).
Google Scholar
Ali, I., Greifeneder, F., Stamenkovic, J., Neumann, M. & Notarnicola, C. Review of machine learning approaches for biomass and soil moisture retrievals from remote sensing data. Remote Sens. 7, 16398–16421 (2015).
Google Scholar
Gitelson, A. A. et al. Remote estimation of leaf area index and green leaf biomass in maize canopies. Geophys. Res. Lett. 30, 1248 (2003).
Google Scholar
Huang, M. et al. Air temperature optima of vegetation productivity across global biomes. Nat. Ecol. Evol. 3, 772–779 (2019).
Google Scholar
Wang, S. et al. Recent global decline of CO2 fertilization effects on vegetation photosynthesis. Science 370, 1295–1300 (2020).
Google Scholar
Morton, D. C. et al. Amazon forests maintain consistent canopy structure and greenness during the dry season. Nature 506, 221–224 (2014).
Google Scholar
Jiang, Z. et al. Analysis of NDVI and scaled difference vegetation index retrievals of vegetation fraction. Remote Sens. Environ. 101, 366–378 (2006).
Google Scholar
Haboudane, D., Miller, J. R., Pattey, E., Zarco-Tejada, P. J. & Strachan, I. B. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture. Remote Sens. Environ. 90, 337–352 (2004).
Google Scholar
Wu, C., Wang, L., Niu, Z., Gao, S. & Wu, M. Nondestructive estimation of canopy chlorophyll content using Hyperion and Landsat/TM images. Int. J. Remote Sens. 31, 2159–2167 (2010).
Google Scholar
Wang, R. & Gamon, J. A. Remote sensing of terrestrial plant biodiversity. Remote Sens. Environ. 231, 111218 (2019).
Google Scholar
Ustin, S. L. & Gamon, J. A. Remote sensing of plant functional types. New Phytol. 186, 795–816 (2010).
Google Scholar
Hilker, T. et al. Vegetation dynamics and rainfall sensitivity of the Amazon. Proc. Natl Acad. Sci. USA 111, 16041–16046 (2014).
Google Scholar
Zhang, Y., Commane, R., Zhou, S., Williams, A. P. & Gentine, P. Light limitation regulates the response of autumn terrestrial carbon uptake to warming. Nat. Clim. Change 10, 739–743 (2020).
Google Scholar
Weber, M. et al. Exploring the use of DSCOVR/EPIC satellite observations to monitor vegetation phenology. Remote Sens. 12, 2384 (2020).
Google Scholar
Ganguly, S., Friedl, M. A., Tan, B., Zhang, X. & Verma, M. Land surface phenology from MODIS: characterization of the Collection 5 global land cover dynamics product. Remote Sens. Environ. 114, 1805–1816 (2010).
Google Scholar
Gray, J., Sulla-Menashe, D. & Friedl, M. A. User Guide to Collection 6 MODIS Land Cover Dynamics Product (MCD12Q2) (NASA, 2019).
Wang, S., Zhang, Y., Ju, W., Qiu, B. & Zhang, Z. Tracking the seasonal and inter-annual variations of global gross primary production during last four decades using satellite near-infrared reflectance data. Sci. Total Environ. 755, 142569 (2021).
Google Scholar
Tian, F. et al. Calibrating vegetation phenology from Sentinel-2 using eddy covariance, PhenoCam, and PEP725 networks across Europe. Remote Sens. Environ. 260, 112456 (2021).
Google Scholar
Yin, G., Verger, A., Filella, I., Descals, A. & Peñuelas, J. Divergent estimates of forest photosynthetic phenology using structural and physiological vegetation indices. Geophys. Res. Lett. 47, e2020GL089167 (2020).
Google Scholar
Qin, Y. et al. Carbon loss from forest degradation exceeds that from deforestation in the Brazilian Amazon. Nat. Clim. Change 11, 442–448 (2021).
Google Scholar
Samanta, A. et al. Amazon forests did not green-up during the 2005 drought. Geophys. Res. Lett. 37, L05401 (2010).
Google Scholar
Shi, Y., Huang, W., Luo, J., Huang, L. & Zhou, X. Detection and discrimination of pests and diseases in winter wheat based on spectral indices and kernel discriminant analysis. Comput. Electron. Agric. 141, 171–180 (2017).
Google Scholar
Zhang, Z., Liu, M., Liu, X. & Zhou, G. A new vegetation index based on multitemporal Sentinel-2 images for discriminating heavy metal stress levels in rice. Sensors 18, 2172 (2018).
Google Scholar
Yengoh, G. T., Dent, D., Olsson, L., Tengberg, A. E. & Tucker III, C. J. Use of the Normalized Difference Vegetation Index (NDVI) to Assess Land Degradation at Multiple Scales: Current Status, Future Trends, and Practical Considerations (Springer, 2015).
Potter, C. S. et al. Terrestrial ecosystem production: a process model based on global satellite and surface data. Glob. Biogeochem. Cycles 7, 811–841 (1993).
Google Scholar
Running, S. W. et al. A continuous satellite-derived measure of global terrestrial primary production. Bioscience 54, 547–560 (2004).
Google Scholar
Yuan, W. et al. Deriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomes. Agric. For. Meteorol. 143, 189–207 (2007).
Google Scholar
Chen, M. et al. Quantification of terrestrial ecosystem carbon dynamics in the conterminous United States combining a process-based biogeochemical model and MODIS and AmeriFlux data. Biogeosciences 8, 2665–2688 (2011).
Google Scholar
Xiao, J. et al. A continuous measure of gross primary production for the conterminous United States derived from MODIS and AmeriFlux data. Remote Sens. Environ. 114, 576–591 (2010).
Google Scholar
Jiang, C., Guan, K., Wu, G., Peng, B. & Wang, S. A daily, 250 m, and real-time gross primary productivity product (2000–present) covering the contiguous United States. Earth Syst. Sci. Data Discuss. 2020, 1–28 (2020).
Schubert, P. et al. Modeling GPP in the Nordic forest landscape with MODIS time series data — comparison with the MODIS GPP product. Remote Sens. Environ. 126, 136–147 (2012).
Google Scholar
Zeng, Y. et al. A practical approach for estimating the escape ratio of near-infrared solar-induced chlorophyll fluorescence. Remote Sens. Environ. 232, 111209 (2019).
Google Scholar
Baldocchi, D. D. et al. Outgoing near infrared radiation from vegetation scales with canopy photosynthesis across a spectrum of function, structure, physiological capacity and weather. J. Geophys. Res. 125, e2019JG005534 (2020).
Dechant, B. et al. Canopy structure explains the relationship between photosynthesis and sun-induced chlorophyll fluorescence in crops. Remote Sens. Environ. 241, 111733 (2020).
Google Scholar
Rahman, A. F., Gamon, J. A., Fuentes, D. A., Roberts, D. A. & Prentiss, D. Modeling spatially distributed ecosystem flux of boreal forest using hyperspectral indices from AVIRIS imagery. J. Geophys. Res. Atmos. 106, 33579–33591 (2001).
Google Scholar
Zhu, Z. et al. Comment on “Recent global decline of CO2 fertilization effects on vegetation photosynthesis”. Science 373, eabg5673 (2021).
Google Scholar
Doughty, R. et al. Small anomalies in dry-season greenness and chlorophyll fluorescence for Amazon moist tropical forests during El Niño and La Niña. Remote Sens. Environ. 253, 112196 (2021).
Google Scholar
Huang, N. et al. Spatial and temporal variations in global soil respiration and their relationships with climate and land cover. Sci. Adv. 6, eabb8508 (2020).
Google Scholar
Huang, N., He, J.-S. & Niu, Z. Estimating the spatial pattern of soil respiration in Tibetan alpine grasslands using Landsat TM images and MODIS data. Ecol. Indic. 26, 117–125 (2013).
Google Scholar
Neale, C. M., Gonzalez-Dugo, M. P., Serrano-Perez, A., Campos, I. & Mateos, L. Cotton canopy reflectance under variable solar zenith angles: implications of use in evapotranspiration models. Hydrol. Process. 35, e14162 (2021).
Google Scholar
Chen, J. M. & Liu, J. Evolution of evapotranspiration models using thermal and shortwave remote sensing data. Remote Sens. Environ. 237, 111594 (2020).
Google Scholar
Glenn, E. P., Huete, A. R., Nagler, P. L. & Nelson, S. G. Relationship between remotely-sensed vegetation indices, canopy attributes and plant physiological processes: what vegetation indices can and cannot tell us about the landscape. Sensors 8, 2136–2160 (2008).
Google Scholar
Cui, Y., Jia, L. & Fan, W. Estimation of actual evapotranspiration and its components in an irrigated area by integrating the Shuttleworth-Wallace and surface temperature-vegetation index schemes using the particle swarm optimization algorithm. Agric. For. Meteorol. 307, 108488 (2021).
Google Scholar
Glenn, E. P., Neale, C. M., Hunsaker, D. J. & Nagler, P. L. Vegetation index-based crop coefficients to estimate evapotranspiration by remote sensing in agricultural and natural ecosystems. Hydrol. Process. 25, 4050–4062 (2011).
Google Scholar
French, A. N. et al. Satellite-based NDVI crop coefficients and evapotranspiration with eddy covariance validation for multiple durum wheat fields in the US Southwest. Agric. Water Manag. 239, 106266 (2020).
Google Scholar
Lotsch, A., Friedl, M. A., Anderson, B. T. & Tucker, C. J. Coupled vegetation-precipitation variability observed from satellite and climate records. Geophys. Res. Lett. 30, 1774 (2003).
Google Scholar
Nezlin, N. P., Kostianoy, A. G. & Li, B.-L. Inter-annual variability and interaction of remote-sensed vegetation index and atmospheric precipitation in the Aral Sea region. J. Arid Environ. 62, 677–700 (2005).
Google Scholar
Notaro, M., Liu, Z. & Williams, J. W. Observed vegetation–climate feedbacks in the United States. J. Clim. 19, 763–786 (2006).
Google Scholar
Fensholt, R. & Proud, S. R. Evaluation of earth observation based global long term vegetation trends — Comparing GIMMS and MODIS global NDVI time series. Remote Sens. Environ. 119, 131–147 (2012).
Google Scholar
Trishchenko, A. P., Cihlar, J. & Li, Z. Effects of spectral response function on surface reflectance and NDVI measured with moderate resolution satellite sensors. Remote Sens. Environ. 81, 1–18 (2002).
Google Scholar
Ustin, S. L. & Middleton, E. M. Current and near-term advances in Earth observation for ecological applications. Ecol. Process. 10, 1 (2021).
Google Scholar
Wang, D. et al. Impact of sensor degradation on the MODIS NDVI time series. Remote Sens. Environ. 119, 55–61 (2012).
Google Scholar
Zhang, Y., Song, C., Band, L. E., Sun, G. & Li, J. Reanalysis of global terrestrial vegetation trends from MODIS products: browning or greening? Remote Sens. Environ. 191, 145–155 (2017).
Google Scholar
Bhatt, R. et al. A consistent AVHRR visible calibration record based on multiple methods applicable for the NOAA degrading orbits. Part I: Methodology. J. Atmos. Ocean. Technol. 33, 2499–2515 (2016).
Google Scholar
Frankenberg, C., Yin, Y., Byrne, B., He, L. & Gentine, P. Comment on “Recent global decline of CO2 fertilization effects on vegetation photosynthesis”. Science 373, eabg2947 (2021).
Google Scholar
Los, S. O. Estimation of the ratio of sensor degradation between NOAA AVHRR channels 1 and 2 from monthly NDVI composites. IEEE Trans. Geosci. Remote Sens. 36, 206–213 (1998).
Google Scholar
Jiang, C. et al. Inconsistencies of interannual variability and trends in long-term satellite leaf area index products. Glob. Change Biol. 23, 4133–4146 (2017).
Google Scholar
de Beurs, K. M. & Henebry, G. M. Trend analysis of the Pathfinder AVHRR Land (PAL) NDVI data for the deserts of Central Asia. IEEE Geosci. Remote Sens. Lett. 1, 282–286 (2004).
Google Scholar
Wang, Z. et al. Large discrepancies of global greening: indication of multi-source remote sensing data. Global Ecol. Conserv. 34, e02016 (2022).
Google Scholar
Miura, T., Huete, A. R. & Yoshioka, H. Evaluation of sensor calibration uncertainties on vegetation indices for MODIS. IEEE Trans Geosci. Remote Sens. 38, 1399–1409 (2000).
Google Scholar
Lyapustin, A. et al. Scientific impact of MODIS C5 calibration degradation and C6+ improvements. Atmos. Meas. Tech. 7, 4353–4365 (2014).
Google Scholar
Buchhorn, M., Raynolds, M. K. & Walker, D. A. Influence of BRDF on NDVI and biomass estimations of Alaska Arctic tundra. Environ. Res. Lett. 11, 125002 (2016).
Google Scholar
Fensholt, R., Sandholt, I., Proud, S. R., Stisen, S. & Rasmussen, M. O. Assessment of MODIS sun-sensor geometry variations effect on observed NDVI using MSG SEVIRI geostationary data. Int. J. Remote Sens. 31, 6163–6187 (2010).
Google Scholar
Saleska, S. R. et al. Dry-season greening of Amazon forests. Nature 531, E4–E5 (2016).
Google Scholar
Lyapustin, A. I. et al. Multi-angle implementation of atmospheric correction for MODIS (MAIAC): 3. Atmospheric correction. Remote Sens. Environ. 127, 385–393 (2012).
Google Scholar
Norris, J. R. & Walker, J. J. Solar and sensor geometry, not vegetation response, drive satellite NDVI phenology in widespread ecosystems of the western United States. Remote Sens. Environ. 249, 112013 (2020).
Google Scholar
Roy, D. P. et al. A general method to normalize Landsat reflectance data to nadir BRDF adjusted reflectance. Remote Sens. Environ. 176, 255–271 (2016).
Google Scholar
Schaaf, C. B. et al. First operational BRDF, albedo nadir reflectance products from MODIS. Remote Sens. Environ. 83, 135–148 (2002).
Google Scholar
Didan, K., Munoz, A. B., Solano, R. & Huete, A. MODIS Vegetation Index User’s Guide (MOD13 Series) (Univ. Arizona, 2015).
Wang, Z., Schaaf, C. B., Sun, Q., Shuai, Y. & Román, M. O. Capturing rapid land surface dynamics with Collection V006 MODIS BRDF/NBAR/Albedo (MCD43) products. Remote Sens. Environ. 207, 50–64 (2018).
Google Scholar
Saleska, S. R., Didan, K., Huete, A. R. & Da Rocha, H. R. Amazon forests green-up during 2005 drought. Science 318, 612 (2007).
Google Scholar
Vargas, M., Miura, T., Shabanov, N. & Kato, A. An initial assessment of Suomi NPP VIIRS vegetation index EDR. J. Geophys. Res. Atmos. 118, 12,301–12,316 (2013).
Google Scholar
Kobayashi, H. & Dye, D. G. Atmospheric conditions for monitoring the long-term vegetation dynamics in the Amazon using normalized difference vegetation index. Remote Sens. Environ. 97, 519–525 (2005).
Google Scholar
Jiang, C. & Fang, H. GSV: a general model for hyperspectral soil reflectance simulation. Int. J. Appl. Earth Obs. Geoinf. 83, 101932 (2019).
Verrelst, J., Schaepman, M. E., Malenovský, Z. & Clevers, J. G. Effects of woody elements on simulated canopy reflectance: Implications for forest chlorophyll content retrieval. Remote Sens. Environ. 114, 647–656 (2010).
Google Scholar
Huete, A. & Tucker, C. Investigation of soil influences in AVHRR red and near-infrared vegetation index imagery. Int. J. Remote Sens. 12, 1223–1242 (1991).
Google Scholar
Farrar, T., Nicholson, S. & Lare, A. The influence of soil type on the relationships between NDVI, rainfall, and soil moisture in semiarid Botswana. II. NDVI response to soil oisture. Remote Sens. Environ. 50, 121–133 (1994).
Google Scholar
Huete, A. & Warrick, A. Assessment of vegetation and soil water regimes in partial canopies with optical remotely sensed data. Remote Sens. Environ. 32, 155–167 (1990).
Google Scholar
Wang, C. et al. A snow-free vegetation index for improved monitoring of vegetation spring green-up date in deciduous ecosystems. Remote Sens. Environ. 196, 1–12 (2017).
Google Scholar
Myers-Smith, I. H. et al. Complexity revealed in the greening of the Arctic. Nat. Clim. Change 10, 106–117 (2020).
Google Scholar
Shen, M. et al. No evidence of continuously advanced green-up dates in the Tibetan Plateau over the last decade. Proc. Natl Acad. Sci. 110, E2329 (2013).
Hao, D. et al. Modeling anisotropic reflectance over composite sloping terrain. IEEE Trans. Geosci. Remote Sens. 56, 3903–3923 (2018).
Google Scholar
Matsushita, B., Yang, W., Chen, J., Onda, Y. & Qiu, G. Sensitivity of the enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) to topographic effects: a case study in high-density cypress forest. Sensors 7, 2636–2651 (2007).
Google Scholar
Wen, J. et al. Characterizing land surface anisotropic reflectance over rugged terrain: a review of concepts and recent developments. Remote Sens. 10, 370 (2018).
Google Scholar
Friedl, M. A., Davis, F. W., Michaelsen, J. & Moritz, M. Scaling and uncertainty in the relationship between the NDVI and land surface biophysical variables: an analysis using a scene simulation model and data from FIFE. Remote Sens. Environ. 54, 233–246 (1995).
Google Scholar
Tan, B. et al. The impact of gridding artifacts on the local spatial properties of MODIS data: implications for validation, compositing, and band-to-band registration across resolutions. Remote Sens. Environ. 105, 98–114 (2006).
Google Scholar
Wolfe, R. E. et al. Achieving sub-pixel geolocation accuracy in support of MODIS land science. Remote Sens. Environ. 83, 31–49 (2002).
Google Scholar
Ferreira, M. P. et al. Retrieving structural and chemical properties of individual tree crowns in a highly diverse tropical forest with 3D radiative transfer modeling and imaging spectroscopy. Remote Sens. Environ. 211, 276–291 (2018).
Google Scholar
Huete, A. R. et al. Amazon rainforests green-up with sunlight in dry season. Geophys. Res. Lett. 33, L06405 (2006).
Google Scholar
Herrmann, S. M. & Tappan, G. G. Vegetation impoverishment despite greening: a case study from central Senegal. J. Arid Environ. 90, 55–66 (2013).
Google Scholar
Wang, X. et al. No consistent evidence for advancing or delaying trends in spring phenology on the Tibetan Plateau. J. Geophys. Res. Biogeosci. 122, 3288–3305 (2017).
Google Scholar
Donnelly, A., Yu, R. & Liu, L. Comparing in situ spring phenology and satellite-derived start of season at rural and urban sites in Ireland. Int. J. Remote Sens. 42, 7821–7841 (2021).
Google Scholar
Templ, B. et al. Pan European Phenological database (PEP725): a single point of access for European data. Int. J. Biometeorol. 62, 1109–1113 (2018).
Google Scholar
Fu, Y. H. et al. Declining global warming effects on the phenology of spring leaf unfolding. Nature 526, 104–107 (2015).
Google Scholar
Chen, X. & Yang, Y. Observed earlier start of the growing season from middle to high latitudes across the Northern Hemisphere snow-covered landmass for the period 2001–2014. Environ. Res. Lett. 15, 034042 (2020).
Google Scholar
Alatorre, L. C. et al. Temporal changes of NDVI for qualitative environmental assessment of mangroves: shrimp farming impact on the health decline of the arid mangroves in the Gulf of California (1990–2010). J. Arid Environ. 125, 98–109 (2016).
Google Scholar
Jacquemoud, S. & Baret, F. PROSPECT: a model of leaf optical properties spectra. Remote Sens. Environ. 34, 75–91 (1990).
Google Scholar
Wu, S. et al. Quantifying leaf optical properties with spectral invariants theory. Remote Sens. Environ. 253, 112131 (2021).
Google Scholar
Wang, Z. et al. Mapping foliar functional traits and their uncertainties across three years in a grassland experiment. Remote Sens. Environ. 221, 405–416 (2019).
Google Scholar
Van Leeuwen, W. & Huete, A. Effects of standing litter on the biophysical interpretation of plant canopies with spectral indices. Remote Sens. Environ. 55, 123–138 (1996).
Google Scholar
Dechant, B. et al. NIRvP: a robust structural proxy for sun-induced chlorophyll fluorescence and photosynthesis across scales. Remote Sens. Environ. 268, 112763 (2022).
Google Scholar
Zeng, Y. et al. Estimating near-infrared reflectance of vegetation from hyperspectral data. Remote Sens. Environ. 267, 112723 (2021).
Google Scholar
Claverie, M. et al. The Harmonized Landsat and Sentinel-2 surface reflectance data set. Remote Sens. Environ. 219, 145–161 (2018).
Google Scholar
Hantson, S. & Chuvieco, E. Evaluation of different topographic correction methods for Landsat imagery. Int. J. Appl. Earth Obs. Geoinf. 13, 691–700 (2011).
Zhang, H. K. et al. Characterization of Sentinel-2A and Landsat-8 top of atmosphere, surface, and nadir BRDF adjusted reflectance and NDVI differences. Remote Sens. Environ. 215, 482–494 (2018).
Google Scholar
Gao, F., Masek, J., Schwaller, M. & Hall, F. On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance. IEEE Trans. Geosci. Remote Sens. 44, 2207–2218 (2006).
Google Scholar
Zhu, X. et al. A flexible spatiotemporal method for fusing satellite images with different resolutions. Remote Sens. Environ. 172, 165–177 (2016).
Google Scholar
Luo, Y., Guan, K. & Peng, J. STAIR: A generic and fully-automated method to fuse multiple sources of optical satellite data to generate a high-resolution, daily and cloud-/gap-free surface reflectance product. Remote Sens. Environ. 214, 87–99 (2018).
Google Scholar
Houborg, R. & McCabe, M. F. Daily retrieval of NDVI and LAI at 3 m resolution via the fusion of CubeSat, Landsat, and MODIS data. Remote Sens. 10, 890 (2018).
Google Scholar
Kimm, H. et al. Deriving high-spatiotemporal-resolution leaf area index for agroecosystems in the US Corn Belt using Planet Labs CubeSat and STAIR fusion data. Remote Sens. Environ. 239, 111615 (2020).
Google Scholar
Kong, J. et al. Evaluation of four image fusion NDVI products against in-situ spectral-measurements over a heterogeneous rice paddy landscape. Agric. For. Meteorol. 297, 108255 (2021).
Google Scholar
Köhler, P. et al. Global retrievals of solar-induced chlorophyll fluorescence with TROPOMI: first results and intersensor comparison to OCO-2. Geophys. Res. Lett. 45, 10,456–10,463 (2018).
Google Scholar
Sun, Y. et al. OCO-2 advances photosynthesis observation from space via solar-induced chlorophyll fluorescence. Science 358, eaam5747 (2017).
Google Scholar
Joiner, J., Yoshida, Y., Vasilkov, A. & Middleton, E. First observations of global and seasonal terrestrial chlorophyll fluorescence from space. Biogeosciences 8, 637–651 (2011).
Google Scholar
Frankenberg, C. et al. New global observations of the terrestrial carbon cycle from GOSAT: patterns of plant fluorescence with gross primary productivity. Geophys. Res. Lett. 38, L17706 (2011).
Google Scholar
Qiu, B., Ge, J., Guo, W., Pitman, A. J. & Mu, M. Responses of Australian dryland vegetation to the 2019 heat wave at a subdaily scale. Geophys. Res. Lett. 47, e2019GL086569 (2020).
Magney, T. S. et al. Mechanistic evidence for tracking the seasonality of photosynthesis with solar-induced fluorescence. Proc. Natl Acad. Sci. USA 116, 11640–11645 (2019).
Google Scholar
Guanter, L. et al. Potential of the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor for the monitoring of terrestrial chlorophyll fluorescence. Atmos. Meas. Tech. 8, 1337–1352 (2015).
Google Scholar
Knyazikhin, Y. et al. Hyperspectral remote sensing of foliar nitrogen content. Proc. Natl Acad. Sci. USA 110, E185–E192 (2013).
Li, X. & Xiao, J. A global, 0.05-degree product of solar-induced chlorophyll fluorescence derived from OCO-2, MODIS, and reanalysis data. Remote Sens. 11, 517 (2019).
Google Scholar
Zeng, Y. et al. Combining near-infrared radiance of vegetation and fluorescence spectroscopy to detect effects of abiotic changes and stresses. Remote Sens. Environ. 270, 112856 (2022).
Google Scholar
Shi, J. et al. Microwave vegetation indices for short vegetation covers from satellite passive microwave sensor AMSR-E. Remote Sens. Environ. 112, 4285–4300 (2008).
Google Scholar
Talebiesfandarani, S. et al. Microwave vegetation index from multi-angular observations and its application in vegetation properties retrieval: theoretical modelling. Remote Sens. 11, 730 (2019).
Google Scholar
Wigneron, J.-P. et al. SMOS-IC data record of soil moisture and L-VOD: historical development, applications and perspectives. Remote Sens. Environ. 254, 112238 (2021).
Google Scholar
Zhang, Y., Zhou, S., Gentine, P. & Xiao, X. Can vegetation optical depth reflect changes in leaf water potential during soil moisture dry-down events? Remote Sens. Environ. 234, 111451 (2019).
Google Scholar
Frappart, F. et al. Global monitoring of the vegetation dynamics from the vegetation optical depth (VOD): a review. Remote Sens. 12, 2915 (2020).
Google Scholar
Xiao, J., Fisher, J. B., Hashimoto, H., Ichii, K. & Parazoo, N. C. Emerging satellite observations for diurnal cycling of ecosystem processes. Nat. Plants 7, 877–887 (2021).
Google Scholar
Hashimoto, H. et al. New generation geostationary satellite observations support seasonality in greenness of the Amazon evergreen forests. Nat. Commun. 12, 684 (2021).
Google Scholar
Somkuti, P. et al. Solar-induced chlorophyll fluorescence from the Geostationary Carbon Cycle Observatory (GeoCarb): An extensive simulation study. Remote Sens. Environ. 263, 112565 (2021).
Google Scholar
Gorelick, N. et al. Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).
Google Scholar
Richardson, A. D., Braswell, B. H., Hollinger, D. Y., Jenkins, J. P. & Ollinger, S. V. Near-surface remote sensing of spatial and temporal variation in canopy phenology. Ecol. Appl. 19, 1417–1428 (2009).
Google Scholar
Daughtry, C. S. Discriminating crop residues from soil by shortwave infrared reflectance. Agron. J. 93, 125–131 (2001).
Google Scholar
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