Vegetation indices (VIs), which describe remotely sensed vegetation properties such as photosynthetic activity and canopy structure, are widely used to study vegetation dynamics across scales. However, VI-based results can vary between indices, sensors, quality control measures, compositing algorithms, and atmospheric and sun–target–sensor geometry corrections. These variations make it difficult to draw robust conclusions about ecosystem change and highlight the need for consistent VI application and verification. In this Technical Review, we summarize the history and ecological applications of VIs and the linkages and inconsistencies between them. VIs have been used since the early 1970s and have evolved rapidly with the emergence of new satellite sensors with more spectral channels, new scientific demands and advances in spectroscopy. When choosing VIs, the spectral sensitivity and features of VIs and their suitability for target application should be considered. During data analyses, steps must be taken to minimize the impact of artefacts, VI results should be verified with in situ data when possible and conclusions should be based on multiple sets of indicators. Next-generation VIs with higher signal-to-noise ratios and fewer artefacts will be possible with new satellite missions and integration with emerging vegetation metrics such as solar-induced chlorophyll fluorescence, providing opportunities for studying terrestrial ecosystems globally.
Optical vegetation indices (VIs) derived from space-borne Earth observations are widely used for monitoring terrestrial ecosystems and tracking plant biophysical, biochemical and physiological properties, vegetation dynamics and environmental stresses.
Sensor and calibration effects, quality assurance and quality control, bidirectional reflectance distribution function, atmospheric and topographic effects, and snow and soil background effects are among important uncertainty sources of VIs.
Potential artefacts must be carefully considered to avoid biased interpretations of the underlying ecological processes resulting from the improper use of VIs.
VIs based on reflectance ratios such as the normalized difference vegetation index can help reduce sensor calibration, bidirectional effects, atmospheric and topographic effects, but could be sensitive to snow and soil background and scale effects.
Mathematical analysis shows intrinsic similarity among several widely used VIs, including near-infrared reflectance of vegetation, enhanced vegetation index, two-band version of the enhanced vegetation index and difference vegetation index, whereas the ratio-based normalized difference vegetation index behaves differently.
Identifying key sensitive wavelengths for target application is the first step towards the optimal use of VIs, followed by an understanding of potential uncertainty sources in the specific ecosystem.
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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).
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).
Huete, A. R. A soil-adjusted vegetation index (SAVI). Remote Sens. Environ. 25, 295–309 (1988).
Badgley, G., Field, C. B. & Berry, J. A. Canopy near-infrared reflectance and terrestrial photosynthesis. Sci. Adv. 3, e1602244 (2017).
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).
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).
Piao, S. et al. Characteristics, drivers and feedbacks of global greening. Nat. Rev. Earth Environ. 1, 14–27 (2020).
Tian, F. et al. Evaluating temporal consistency of long-term global NDVI datasets for trend analysis. Remote Sens. Environ. 163, 326–340 (2015).
Fan, X. & Liu, Y. A global study of NDVI difference among moderate-resolution satellite sensors. ISPRS J. Photogramm. Remote Sens. 121, 177–191 (2016).
AghaKouchak, A. et al. Remote sensing of drought: progress, challenges and opportunities. Rev. Geophys. 53, 452–480 (2015).
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).
Tucker, C. J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 8, 127–150 (1979).
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).
Jackson, R. D. & Huete, A. R. Interpreting vegetation indices. Prev. Vet. Med. 11, 185–200 (1991).
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).
Chen, J. M. Evaluation of vegetation indices and a modified simple ratio for boreal applications. Can. J. Remote Sens. 22, 229–242 (1996).
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).
Pinty, B. & Verstraete, M. GEMI: a non-linear index to monitor global vegetation from satellites. Vegetatio 101, 15–20 (1992).
Huete, A. et al. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 83, 195–213 (2002).
Kaufman, Y. J. & Tanre, D. Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE Trans. Geosci. Remote Sens. 30, 261–270 (1992).
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).
Jin, H. & Eklundh, L. A physically based vegetation index for improved monitoring of plant phenology. Remote Sens. Environ. 152, 512–525 (2014).
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).
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).
Camps-Valls, G. et al. A unified vegetation index for quantifying the terrestrial biosphere. Sci. Adv. 7, eabc7447 (2021).
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).
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).
Dash, J. & Curran, P. The MERIS terrestrial chlorophyll index. Int. J. Remote Sens. 25, 5403–5413 (2004).
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).
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).
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).
van den Berg, A. K. & Perkins, T. D. Nondestructive estimation of anthocyanin content in autumn sugar maple leaves. HortScience 40, 685–686 (2005).
Gamon, J. & Surfus, J. Assessing leaf pigment content and activity with a reflectometer. New Phytol. 143, 105–117 (1999).
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).
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).
Xiao, X. et al. Satellite-based modeling of gross primary production in an evergreen needleleaf forest. Remote Sens. Environ. 89, 519–534 (2004).
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).
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).
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).
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).
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).
Cheng, R. et al. Decomposing reflectance spectra to track gross primary production in a subalpine evergreen forest. Biogeosciences 17, 4523–4544 (2020).
Seyednasrollah, B. et al. Seasonal variation in the canopy color of temperate evergreen conifer forests. New Phytol. 229, 2586–2600 (2021).
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).
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).
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).
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).
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).
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).
Gitelson, A. A. et al. Remote estimation of leaf area index and green leaf biomass in maize canopies. Geophys. Res. Lett. 30, 1248 (2003).
Huang, M. et al. Air temperature optima of vegetation productivity across global biomes. Nat. Ecol. Evol. 3, 772–779 (2019).
Wang, S. et al. Recent global decline of CO2 fertilization effects on vegetation photosynthesis. Science 370, 1295–1300 (2020).
Morton, D. C. et al. Amazon forests maintain consistent canopy structure and greenness during the dry season. Nature 506, 221–224 (2014).
Jiang, Z. et al. Analysis of NDVI and scaled difference vegetation index retrievals of vegetation fraction. Remote Sens. Environ. 101, 366–378 (2006).
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).
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).
Wang, R. & Gamon, J. A. Remote sensing of terrestrial plant biodiversity. Remote Sens. Environ. 231, 111218 (2019).
Ustin, S. L. & Gamon, J. A. Remote sensing of plant functional types. New Phytol. 186, 795–816 (2010).
Hilker, T. et al. Vegetation dynamics and rainfall sensitivity of the Amazon. Proc. Natl Acad. Sci. USA 111, 16041–16046 (2014).
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).
Weber, M. et al. Exploring the use of DSCOVR/EPIC satellite observations to monitor vegetation phenology. Remote Sens. 12, 2384 (2020).
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).
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).
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).
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).
Qin, Y. et al. Carbon loss from forest degradation exceeds that from deforestation in the Brazilian Amazon. Nat. Clim. Change 11, 442–448 (2021).
Samanta, A. et al. Amazon forests did not green-up during the 2005 drought. Geophys. Res. Lett. 37, L05401 (2010).
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).
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).
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).
Running, S. W. et al. A continuous satellite-derived measure of global terrestrial primary production. Bioscience 54, 547–560 (2004).
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).
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).
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).
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).
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).
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).
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).
Zhu, Z. et al. Comment on “Recent global decline of CO2 fertilization effects on vegetation photosynthesis”. Science 373, eabg5673 (2021).
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).
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).
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).
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).
Chen, J. M. & Liu, J. Evolution of evapotranspiration models using thermal and shortwave remote sensing data. Remote Sens. Environ. 237, 111594 (2020).
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).
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).
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).
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).
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).
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).
Notaro, M., Liu, Z. & Williams, J. W. Observed vegetation–climate feedbacks in the United States. J. Clim. 19, 763–786 (2006).
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).
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).
Ustin, S. L. & Middleton, E. M. Current and near-term advances in Earth observation for ecological applications. Ecol. Process. 10, 1 (2021).
Wang, D. et al. Impact of sensor degradation on the MODIS NDVI time series. Remote Sens. Environ. 119, 55–61 (2012).
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).
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).
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).
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).
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).
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).
Wang, Z. et al. Large discrepancies of global greening: indication of multi-source remote sensing data. Global Ecol. Conserv. 34, e02016 (2022).
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).
Lyapustin, A. et al. Scientific impact of MODIS C5 calibration degradation and C6+ improvements. Atmos. Meas. Tech. 7, 4353–4365 (2014).
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).
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).
Saleska, S. R. et al. Dry-season greening of Amazon forests. Nature 531, E4–E5 (2016).
Lyapustin, A. I. et al. Multi-angle implementation of atmospheric correction for MODIS (MAIAC): 3. Atmospheric correction. Remote Sens. Environ. 127, 385–393 (2012).
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).
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).
Schaaf, C. B. et al. First operational BRDF, albedo nadir reflectance products from MODIS. Remote Sens. Environ. 83, 135–148 (2002).
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).
Saleska, S. R., Didan, K., Huete, A. R. & Da Rocha, H. R. Amazon forests green-up during 2005 drought. Science 318, 612 (2007).
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).
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).
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).
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).
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).
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).
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).
Myers-Smith, I. H. et al. Complexity revealed in the greening of the Arctic. Nat. Clim. Change 10, 106–117 (2020).
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).
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).
Wen, J. et al. Characterizing land surface anisotropic reflectance over rugged terrain: a review of concepts and recent developments. Remote Sens. 10, 370 (2018).
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).
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).
Wolfe, R. E. et al. Achieving sub-pixel geolocation accuracy in support of MODIS land science. Remote Sens. Environ. 83, 31–49 (2002).
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).
Huete, A. R. et al. Amazon rainforests green-up with sunlight in dry season. Geophys. Res. Lett. 33, L06405 (2006).
Herrmann, S. M. & Tappan, G. G. Vegetation impoverishment despite greening: a case study from central Senegal. J. Arid Environ. 90, 55–66 (2013).
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).
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).
Templ, B. et al. Pan European Phenological database (PEP725): a single point of access for European data. Int. J. Biometeorol. 62, 1109–1113 (2018).
Fu, Y. H. et al. Declining global warming effects on the phenology of spring leaf unfolding. Nature 526, 104–107 (2015).
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).
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).
Jacquemoud, S. & Baret, F. PROSPECT: a model of leaf optical properties spectra. Remote Sens. Environ. 34, 75–91 (1990).
Wu, S. et al. Quantifying leaf optical properties with spectral invariants theory. Remote Sens. Environ. 253, 112131 (2021).
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).
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).
Dechant, B. et al. NIRvP: a robust structural proxy for sun-induced chlorophyll fluorescence and photosynthesis across scales. Remote Sens. Environ. 268, 112763 (2022).
Zeng, Y. et al. Estimating near-infrared reflectance of vegetation from hyperspectral data. Remote Sens. Environ. 267, 112723 (2021).
Claverie, M. et al. The Harmonized Landsat and Sentinel-2 surface reflectance data set. Remote Sens. Environ. 219, 145–161 (2018).
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).
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).
Zhu, X. et al. A flexible spatiotemporal method for fusing satellite images with different resolutions. Remote Sens. Environ. 172, 165–177 (2016).
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).
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).
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).
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).
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).
Sun, Y. et al. OCO-2 advances photosynthesis observation from space via solar-induced chlorophyll fluorescence. Science 358, eaam5747 (2017).
Joiner, J., Yoshida, Y., Vasilkov, A. & Middleton, E. First observations of global and seasonal terrestrial chlorophyll fluorescence from space. Biogeosciences 8, 637–651 (2011).
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).
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).
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).
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).
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).
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).
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).
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).
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).
Frappart, F. et al. Global monitoring of the vegetation dynamics from the vegetation optical depth (VOD): a review. Remote Sens. 12, 2915 (2020).
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).
Hashimoto, H. et al. New generation geostationary satellite observations support seasonality in greenness of the Amazon evergreen forests. Nat. Commun. 12, 684 (2021).
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).
Gorelick, N. et al. Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).
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).
Daughtry, C. S. Discriminating crop residues from soil by shortwave infrared reflectance. Agron. J. 93, 125–131 (2001).
Y.Z. and M.C. acknowledges support from the National Aeronautics and Space Administration (NASA) through Remote Sensing Theory and Terrestrial Ecology programmes 80NSSC21K0568 and 80NSSC21K1702. M.C. also acknowledges support by a McIntire–Stennis grant (1027576) from the National Institute of Food and Agriculture (NIFA), United States Department of Agriculture (USDA). B.D. acknowledges support by sDiv, the Synthesis Centre of iDiv (DFG FZT 118, 202548816). J.X. was supported by the National Science Foundation (NSF) (Macrosystems Biology and NEON-Enabled Science programme: DEB-2017870). Y.R. was supported by the National Research Foundation of Korea (NRF-2019R1A2C2084626). The authors thank G. Badgley for fruitful discussions on vegetation indices and P. Köhler for the TROPOMI far-red daily SIF dataset.
The authors declare no competing interests.
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Zeng, Y., Hao, D., Huete, A. et al. Optical vegetation indices for monitoring terrestrial ecosystems globally. Nat Rev Earth Environ 3, 477–493 (2022). https://doi.org/10.1038/s43017-022-00298-5
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