Articles | Volume 15, issue 9
https://doi.org/10.5194/essd-15-4181-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-15-4181-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Spatiotemporally consistent global dataset of the GIMMS Normalized Difference Vegetation Index (PKU GIMMS NDVI) from 1982 to 2022
Muyi Li
School of Urban Planning and Design, Shenzhen Graduate School, Peking
University, Shenzhen 518055, China
Institute of Carbon Neutrality, Peking University, Beijing 100871,
China
Key Laboratory of Earth Surface System and Human–Earth Relations,
Ministry of Natural Resources of China, Shenzhen Graduate School, Peking
University, Shenzhen 518055, China
School of Urban Planning and Design, Shenzhen Graduate School, Peking
University, Shenzhen 518055, China
Institute of Carbon Neutrality, Peking University, Beijing 100871,
China
Key Laboratory of Earth Surface System and Human–Earth Relations,
Ministry of Natural Resources of China, Shenzhen Graduate School, Peking
University, Shenzhen 518055, China
School of Urban Planning and Design, Shenzhen Graduate School, Peking
University, Shenzhen 518055, China
Institute of Carbon Neutrality, Peking University, Beijing 100871,
China
Key Laboratory of Earth Surface System and Human–Earth Relations,
Ministry of Natural Resources of China, Shenzhen Graduate School, Peking
University, Shenzhen 518055, China
Zhe Wang
School of Urban Planning and Design, Shenzhen Graduate School, Peking
University, Shenzhen 518055, China
Institute of Carbon Neutrality, Peking University, Beijing 100871,
China
Key Laboratory of Earth Surface System and Human–Earth Relations,
Ministry of Natural Resources of China, Shenzhen Graduate School, Peking
University, Shenzhen 518055, China
Ranga B. Myneni
Department of Earth & Environment, Boston University, Boston, MA
02215, USA
Shilong Piao
Institute of Carbon Neutrality, Peking University, Beijing 100871,
China
Sino-French Institute for Earth System Science, College of Urban and
Environmental Sciences, Peking University, Beijing 100871, China
State Key Laboratory of Tibetan Plateau Earth System, Environment and
Resources, Institute of Tibetan Plateau Research, Chinese Academy of
Sciences, Beijing 100101, China
Related authors
Sen Cao, Muyi Li, Zaichun Zhu, Zhe Wang, Junjun Zha, Weiqing Zhao, Zeyu Duanmu, Jiana Chen, Yaoyao Zheng, Yue Chen, Ranga B. Myneni, and Shilong Piao
Earth Syst. Sci. Data, 15, 4877–4899, https://doi.org/10.5194/essd-15-4877-2023, https://doi.org/10.5194/essd-15-4877-2023, 2023
Short summary
Short summary
The long-term global leaf area index (LAI) products are critical for characterizing vegetation dynamics under environmental changes. This study presents an updated GIMMS LAI product (GIMMS LAI4g; 1982−2020) based on PKU GIMMS NDVI and massive Landsat LAI samples. With higher accuracy than other LAI products, GIMMS LAI4g removes the effects of orbital drift and sensor degradation in AVHRR data. It has better temporal consistency before and after 2000 and a more reasonable global vegetation trend.
Zhe Jin, Xiangjun Tian, Yilong Wang, Hongqin Zhang, Min Zhao, Tao Wang, Jinzhi Ding, and Shilong Piao
Earth Syst. Sci. Data, 16, 2857–2876, https://doi.org/10.5194/essd-16-2857-2024, https://doi.org/10.5194/essd-16-2857-2024, 2024
Short summary
Short summary
An accurate estimate of spatial distribution and temporal evolution of CO2 fluxes is a critical foundation for providing information regarding global carbon cycle and climate mitigation. Here, we present a global carbon flux dataset for 2015–2022, derived by assimilating satellite CO2 observations into the GONGGA inversion system. This dataset will help improve the broader understanding of global carbon cycle dynamics and their response to climate change.
Kai Yan, Jingrui Wang, Rui Peng, Kai Yang, Xiuzhi Chen, Gaofei Yin, Jinwei Dong, Marie Weiss, Jiabin Pu, and Ranga B. Myneni
Earth Syst. Sci. Data, 16, 1601–1622, https://doi.org/10.5194/essd-16-1601-2024, https://doi.org/10.5194/essd-16-1601-2024, 2024
Short summary
Short summary
Variations in observational conditions have led to poor spatiotemporal consistency in leaf area index (LAI) time series. Using prior knowledge, we leveraged high-quality observations and spatiotemporal correlation to reprocess MODIS LAI, thereby generating HiQ-LAI, a product that exhibits fewer abnormal fluctuations in time series. Reprocessing was done on Google Earth Engine, providing users with convenient access to this value-added data and facilitating large-scale research and applications.
Jiabin Pu, Kai Yan, Samapriya Roy, Zaichun Zhu, Miina Rautiainen, Yuri Knyazikhin, and Ranga B. Myneni
Earth Syst. Sci. Data, 16, 15–34, https://doi.org/10.5194/essd-16-15-2024, https://doi.org/10.5194/essd-16-15-2024, 2024
Short summary
Short summary
Long-term global LAI/FPAR products provide the fundamental dataset for accessing vegetation dynamics and studying climate change. This study develops a sensor-independent LAI/FPAR climate data record based on the integration of Terra-MODIS/Aqua-MODIS/VIIRS LAI/FPAR standard products and applies advanced gap-filling techniques. The SI LAI/FPAR CDR provides a valuable resource for researchers studying vegetation dynamics and their relationship to climate change in the 21st century.
Sen Cao, Muyi Li, Zaichun Zhu, Zhe Wang, Junjun Zha, Weiqing Zhao, Zeyu Duanmu, Jiana Chen, Yaoyao Zheng, Yue Chen, Ranga B. Myneni, and Shilong Piao
Earth Syst. Sci. Data, 15, 4877–4899, https://doi.org/10.5194/essd-15-4877-2023, https://doi.org/10.5194/essd-15-4877-2023, 2023
Short summary
Short summary
The long-term global leaf area index (LAI) products are critical for characterizing vegetation dynamics under environmental changes. This study presents an updated GIMMS LAI product (GIMMS LAI4g; 1982−2020) based on PKU GIMMS NDVI and massive Landsat LAI samples. With higher accuracy than other LAI products, GIMMS LAI4g removes the effects of orbital drift and sensor degradation in AVHRR data. It has better temporal consistency before and after 2000 and a more reasonable global vegetation trend.
Philippe Ciais, Ana Bastos, Frédéric Chevallier, Ronny Lauerwald, Ben Poulter, Josep G. Canadell, Gustaf Hugelius, Robert B. Jackson, Atul Jain, Matthew Jones, Masayuki Kondo, Ingrid T. Luijkx, Prabir K. Patra, Wouter Peters, Julia Pongratz, Ana Maria Roxana Petrescu, Shilong Piao, Chunjing Qiu, Celso Von Randow, Pierre Regnier, Marielle Saunois, Robert Scholes, Anatoly Shvidenko, Hanqin Tian, Hui Yang, Xuhui Wang, and Bo Zheng
Geosci. Model Dev., 15, 1289–1316, https://doi.org/10.5194/gmd-15-1289-2022, https://doi.org/10.5194/gmd-15-1289-2022, 2022
Short summary
Short summary
The second phase of the Regional Carbon Cycle Assessment and Processes (RECCAP) will provide updated quantification and process understanding of CO2, CH4, and N2O emissions and sinks for ten regions of the globe. In this paper, we give definitions, review different methods, and make recommendations for estimating different components of the total land–atmosphere carbon exchange for each region in a consistent and complete approach.
Alexander J. Winkler, Ranga B. Myneni, Alexis Hannart, Stephen Sitch, Vanessa Haverd, Danica Lombardozzi, Vivek K. Arora, Julia Pongratz, Julia E. M. S. Nabel, Daniel S. Goll, Etsushi Kato, Hanqin Tian, Almut Arneth, Pierre Friedlingstein, Atul K. Jain, Sönke Zaehle, and Victor Brovkin
Biogeosciences, 18, 4985–5010, https://doi.org/10.5194/bg-18-4985-2021, https://doi.org/10.5194/bg-18-4985-2021, 2021
Short summary
Short summary
Satellite observations since the early 1980s show that Earth's greening trend is slowing down and that browning clusters have been emerging, especially in the last 2 decades. A collection of model simulations in conjunction with causal theory points at climatic changes as a key driver of vegetation changes in natural ecosystems. Most models underestimate the observed vegetation browning, especially in tropical rainforests, which could be due to an excessive CO2 fertilization effect in models.
Yuanyuan Huang, Phillipe Ciais, Maurizio Santoro, David Makowski, Jerome Chave, Dmitry Schepaschenko, Rose Z. Abramoff, Daniel S. Goll, Hui Yang, Ye Chen, Wei Wei, and Shilong Piao
Earth Syst. Sci. Data, 13, 4263–4274, https://doi.org/10.5194/essd-13-4263-2021, https://doi.org/10.5194/essd-13-4263-2021, 2021
Short summary
Short summary
Roots play a key role in our Earth system. Here we combine 10 307 field measurements of forest root biomass worldwide with global observations of forest structure, climatic conditions, topography, land management and soil characteristics to derive a spatially explicit global high-resolution (~ 1 km) root biomass dataset. In total, 142 ± 25 (95 % CI) Pg of live dry-matter biomass is stored belowground, representing a global average root : shoot biomass ratio of 0.25 ± 0.10.
Yuting Yang, Tim R. McVicar, Dawen Yang, Yongqiang Zhang, Shilong Piao, Shushi Peng, and Hylke E. Beck
Hydrol. Earth Syst. Sci., 25, 3411–3427, https://doi.org/10.5194/hess-25-3411-2021, https://doi.org/10.5194/hess-25-3411-2021, 2021
Short summary
Short summary
This study developed an analytical ecohydrological model that considers three aspects of vegetation response to eCO2 (i.e., stomatal response, LAI response, and rooting depth response) to detect the impact of eCO2 on continental runoff over the past 3 decades globally. Our findings suggest a minor role of eCO2 on the global runoff changes, yet highlight the negative runoff–eCO2 response in semiarid and arid regions which may further threaten the limited water resource there.
Zun Yin, Catherine Ottlé, Philippe Ciais, Feng Zhou, Xuhui Wang, Polcher Jan, Patrice Dumas, Shushi Peng, Laurent Li, Xudong Zhou, Yan Bo, Yi Xi, and Shilong Piao
Hydrol. Earth Syst. Sci., 25, 1133–1150, https://doi.org/10.5194/hess-25-1133-2021, https://doi.org/10.5194/hess-25-1133-2021, 2021
Short summary
Short summary
We improved the irrigation module in a land surface model ORCHIDEE and developed a dam operation model with the aim to investigate how irrigation and dams affect the streamflow fluctuations of the Yellow River. Results show that irrigation mainly reduces the annual river flow. The dam operation, however, mainly affects streamflow variation. By considering two generic operation rules, flood control and base flow guarantee, our dam model can sustainably improve the simulation accuracy.
Cited articles
AghaKouchak, A., Farahmand, A., Melton, F. S., Teixeira, J., Anderson, M.
C., Wardlow, B. D., and Hain, C. R.: Remote sensing of drought: Progress,
challenges and opportunities, Rev. Geophys., 53, 452–480,
https://doi.org/10.1002/2014rg000456, 2015.
Badgley, G., Field, C. B., and Berry, J. A.: Canopy near-infrared
reflectance and terrestrial photosynthesis, Sci. Adv., 3, e1602244,
https://doi.org/10.1126/sciadv.1602244, 2017.
Berner, L. T., Massey, R., Jantz, P., Forbes, B. C., Macias-Fauria, M.,
Myers-Smith, I., Kumpula, T., Gauthier, G., Andreu-Hayles, L., Gaglioti, B.
V., Burns, P., Zetterberg, P., D'Arrigo, R., and Goetz, S. J.: Summer
warming explains widespread but not uniform greening in the Arctic tundra
biome, Nat. Commun., 11, 1–12, https://doi.org/10.1038/s41467-020-18479-5,
2020.
Beurs, K. M. D. and Henebry, G. M.: Trend analysis of the Pathfinder AVHRR
Land (PAL) NDVI data for the deserts of central Asia, IEEE Geosci. Remote,
1, 282–286, https://doi.org/10.1109/LGRS.2004.834805, 2004.
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001.
Cao, B., Yu, L., Naipal, V., Ciais, P., Li, W., Zhao, Y., Wei, W., Chen, D., Liu, Z., and Gong, P.: A 30 m terrace mapping in China using Landsat 8 imagery and digital elevation model based on the Google Earth Engine, Earth Syst. Sci. Data, 13, 2437–2456, https://doi.org/10.5194/essd-13-2437-2021, 2021.
Cao, C., Weinreb, M., and Xu, H.: Predicting Simultaneous Nadir Overpasses
among Polar-Orbiting Meteorological Satellites for the Intersatellite
Calibration of Radiometers, J. Atmos. Ocean. Tech., 21, 537–542,
https://doi.org/10.1175/1520-0426(2004)021<0537:PSNOAP>2.0.CO;2, 2004.
Cao, C., De Luccia, F. J., Xiong, X., Wolfe, R., and Weng, F.: Early
On-Orbit Performance of the Visible Infrared Imaging Radiometer Suite
Onboard the Suomi National Polar-Orbiting Partnership (S-NPP) Satellite,
IEEE T. Geosci. Remote, 52, 1142–1156,
https://doi.org/10.1109/tgrs.2013.2247768, 2014.
Chen, C., Park, T., Wang, X. H., Piao, S. L., Xu, B. D., Chaturvedi, R. K.,
Fuchs, R., Brovkin, V., Ciais, P., Fensholt, R., Tommervik, H., Bala, G.,
Zhu, Z. C., Nemani, R. R., and Myneni, R. B.: China and India lead in
greening of the world through land-use management, Nat. Sustain., 2, 122–129,
https://doi.org/10.1038/s41893-019-0220-7, 2019.
Cui, Y. K., Jia, L., and Fan, W. J.: 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, Agr. Forest Meteorol., 307, 108488,
https://doi.org/10.1016/j.agrformet.2021.108488, 2021.
Didan, K.: MODIS/Terra Vegetation Indices Monthly L3 Global 0.05Deg CMG V061
(V061), NASA EOSDIS Land Processes DAAC [data set],
https://doi.org/10.5067/MODIS/MOD13C2.061, 2021.
Doelling, D. R., Garber, D. P., Avey, L. A., Nguyen, L., and Minnis, P.: The
calibration of AVHRR visible dual gain using Meteosat-8 for NOAA-16 to 18,
Conference on Atmospheric and Enviromental Remote Sensing Data Processing
and Utilization III: Readiness for GEOSS, San Diego, CA, 17–30 August 2007,
WOS:000251483900008, 61–71, https://doi.org/10.1117/12.736080, 2007.
Dong, J., Fu, Y., Wang, J., Tian, H., Fu, S., Niu, Z., Han, W., Zheng, Y., Huang, J., and Yuan, W.: Early-season mapping of winter wheat in China based on Landsat and Sentinel images, Earth Syst. Sci. Data, 12, 3081–3095, https://doi.org/10.5194/essd-12-3081-2020, 2020.
Fan, X. and Liu, Y.: A global study of NDVI difference among
moderate-resolution satellite sensors, ISPRS J. Photogramm., 121, 177–191,
https://doi.org/10.1016/j.isprsjprs.2016.09.008, 2016.
Fensholt, R. and 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,
https://doi.org/10.1016/j.rse.2011.12.015, 2012.
Foga, S., Scaramuzza, P. L., Guo, S., Zhu, Z., Dilley, R. D., Beckmann, T.,
Schmidt, G. L., Dwyer, J. L., Hughes, M. J., and Laue, B.: Cloud detection
algorithm comparison and validation for operational Landsat data products,
Remote Sens. Environ., 194, 379–390,
https://doi.org/10.1016/j.rse.2017.03.026, 2017.
Frankenberg, C., Yin, Y., Byrne, B., He, L. Y., and Gentine, P.: Comment on
“Recent global decline of CO2 fertilization effects on vegetation
photosynthesis” COMMENT, Science, 373, eabg2947,
https://doi.org/10.1126/science.abg2947, 2021.
Friedl, M. and Sulla-Menashe, D.: MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500m SIN Grid V061 (V061), NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MODIS/MCD12Q1.061, 2022a.
Friedl, M. and Sulla-Menashe, D.: MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 0.05Deg CMG V061 (V061), NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MODIS/MCD12C1.061, 2022b.
Friedl, M. A., McIver, D. K., Hodges, J. C. F., Zhang, X. Y., Muchoney, D.,
Strahler, A. H., Woodcock, C. E., Gopal, S., Schneider, A., Cooper, A.,
Baccini, A., Gao, F., and Schaaf, C.: Global land cover mapping from MODIS:
algorithms and early results, Remote Sens. Environ., 83, 287–302,
https://doi.org/10.1016/s0034-4257(02)00078-0, 2002.
Gamon, J. A., Huemmrich, K. F., Wong, C. Y. S., Ensminger, I., Garrity, S.,
Hollinger, D. Y., Noormets, A., and Penuelas, J.: A remotely sensed pigment
index reveals photosynthetic phenology in evergreen conifers, P. Natl. Acad.
Sci. USA, 113, 13087–13092, https://doi.org/10.1073/pnas.1606162113, 2016.
Gao, X., Huete, A. R., Ni, W. G., and Miura, T.: Optical-biophysical
relationships of vegetation spectra without background contamination, Remote Sens. Environ., 74, 609–620, https://doi.org/10.1016/s0034-4257(00)00150-4,
2000.
Helder, D., Thome, K. J., Mishra, N., Chander, G., Xiong, X. X., Angal, A.,
and Choi, T.: Absolute Radiometric Calibration of Landsat Using a Pseudo
Invariant Calibration Site, IEEE T. Geosci. Remote, 51, 1360–1369,
https://doi.org/10.1109/tgrs.2013.2243738, 2013.
Hong, X.-C., Wang, G.-Y., Liu, J., Song, L., and Wu, E. T. Y.: Modeling the
impact of soundscape drivers on perceived birdsongs in urban forests, J.
Clean Prod., 292, 125315, https://doi.org/10.1016/j.jclepro.2020.125315,
2021.
Huang, N. E., Shen, Z., Long, S. R., Wu, M. L. C., Shih, H. H., Zheng, Q.
N., Yen, N. C., Tung, C. C., and Liu, H. H.: The empirical mode
decomposition and the Hilbert spectrum for nonlinear and non-stationary time
series analysis, P. Roy. Soc. A-Math. Phy., 454, 903–995,
https://doi.org/10.1098/rspa.1998.0193, 1998.
Irons, J. R., Dwyer, J. L., and Barsi, J. A.: The next Landsat satellite:
The Landsat Data Continuity Mission, Remote Sens. Environ., 122, 11–21,
https://doi.org/10.1016/j.rse.2011.08.026, 2012.
Jiang, C. Y., Ryu, Y., Fang, H. L., Myneni, R., Claverie, M., and Zhu, Z.
C.: Inconsistencies of interannual variability and trends in long-term
satellite leaf area index products, Global Change Biol., 23, 4133–4146,
https://doi.org/10.1111/gcb.13787, 2017.
Jiang, L., Tarpley, J. D., Mitchell, K. E., Zhou, S., Kogan, F. N., and Guo,
W.: Adjusting for long-term anomalous trends in NOAA's global vegetation
index data sets, IEEE T. Geosci. Remote, 46, 409–422,
https://doi.org/10.1109/tgrs.2007.902844, 2008.
Joiner, J., Yoshida, Y., Zhang, Y., Duveiller, G., Jung, M., Lyapustin, A.,
Wang, Y. J., and Tucker, C. J.: Estimation of Terrestrial Global Gross
Primary Production (GPP) with Satellite Data-Driven Models and Eddy
Covariance Flux Data, Remote Sens., 10, 1346,
https://doi.org/10.3390/rs10091346, 2018.
Justice, C., Belward, A., Morisette, J., Lewis, P., Privette, J., and Baret,
F.: Developments in the 'validation' of satellite sensor products for the
study of the land surface, Int. J. Remote Sens., 21, 3383–3390,
https://doi.org/10.1080/014311600750020000, 2000.
Kogan, F. N.: Application of vegetation index and brightness temperature for
drought detection, in: Natural Hazards: Monitoring and Assessment Using
Remote Sensing Technique, edited by: Singh, R. P. and Furrer, R., Adv. Space Res.-Ser., 11, 91–100,
https://doi.org/10.1016/0273-1177(95)00079-t, 1995.
Li, M., Cao, S., Zhu, Z., Wang, Z., Myneni, R. B., and Piao, S.:
Spatiotemporally consistent global dataset of the GIMMS Normalized
Difference Vegetation Index (PKU GIMMS NDVI) from 1982 to 2022 (V1.2),
Zenodo [data set], https://doi.org/10.5281/zenodo.8253971, 2023.
Li, X., Zhou, Y., Meng, L., Asrar, G. R., Lu, C., and Wu, Q.: A dataset of 30 m annual vegetation phenology indicators (1985–2015) in urban areas of the conterminous United States, Earth Syst. Sci. Data, 11, 881–894, https://doi.org/10.5194/essd-11-881-2019, 2019.
Los, S. O.: Estimation of the ratio of sensor degradation between NOAA AVHRR
channels 1 and 2 from monthly NDVI composites, IEEE T. Geosci. Remote, 36,
206–213, https://doi.org/10.1109/36.655330, 1998.
Maisongrande, P., Duchemin, B., and Dedieu, G.: VEGETATION/SPOT: an
operational mission for the Earth monitoring; presentation of new standard
products, Int. J. Remote Sens., 25, 9–14,
https://doi.org/10.1080/0143116031000115265, 2004.
Mao, D., Wang, Z., Luo, L., and Ren, C.: Integrating AVHRR and MODIS data to
monitor NDVI changes and their relationships with climatic parameters in
Northeast China, Int. J. Appl. Earth Obs., 18, 528–536,
https://doi.org/10.1016/j.jag.2011.10.007, 2012.
Masek, J. G., Vermote, E. F., Saleous, N. E., Wolfe, R., Hall, F. G.,
Huemmrich, K. F., Gao, F., Kutler, J., and Lim, T. K.: A Landsat surface
reflectance dataset for North America, 1990–2000, IEEE Geosci. Remote, 3,
68–72, https://doi.org/10.1109/lgrs.2005.857030, 2006.
Meng, X., Bao, Y., Liu, J., Liu, H., Zhang, X., Zhang, Y., Wang, P., Tang,
H., and Kong, F.: Regional soil organic carbon prediction model based on a
discrete wavelet analysis of hyperspectral satellite data, Int. J. Appl. Earth
Obs., 89, 102111, https://doi.org/10.1016/j.jag.2020.102111, 2020.
Myers-Smith, I. H., Kerby, J. T., Phoenix, G. K., Bjerke, J. W., Epstein, H.
E., Assmann, J. J., John, C., Andreu-Hayles, L., Angers-Blondin, S., Beck,
P. S. A., Berner, L. T., Bhatt, U. S., Bjorkman, A. D., Blok, D., Bryn, A.,
Christiansen, C. T., Cornelissen, J. H. C., Cunliffe, A. M., Elmendorf, S.
C., Forbes, B. C., Goetz, S. J., Hollister, R. D., de Jong, R., Loranty, M.
M., Macias-Fauria, M., Maseyk, K., Normand, S., Olofsson, J., Parker, T. C.,
Parmentier, F. J. W., Post, E., Schaepman-Strub, G., Stordal, F., Sullivan,
P. F., Thomas, H. J. D., Tommervik, H., Treharne, R., Tweedie, C. E.,
Walker, D. A., Wilmking, M., and Wipf, S.: Complexity revealed in the
greening of the Arctic, Nat Clim. Change, 10, 106–117,
https://doi.org/10.1038/s41558-019-0688-1, 2020.
Pedelty, J., Devadiga, S., Masuoka, E., Brown, M., Pinzon, J., Tucker, C.,
Roy, D., Ju, J. C., Vermote, E., Prince, S., Nagol, J., Justice, C., Schaaf,
C., Liu, J. C., Privette, J., Pinheiro, A., and IEEE: Generating a Long-term
Land Data Record from the AVHRR and MODIS instruments, IEEE International
Geoscience and Remote Sensing Symposium (IGARSS), Barcelona, SPAIN,
23–27 July, WOS:000256657301039, 1021–1024,
https://doi.org/10.1109/igarss.2007.4422974, 2007.
Peng, J., Dadson, S., Hirpa, F., Dyer, E., Lees, T., Miralles, D. G., Vicente-Serrano, S. M., and Funk, C.: A pan-African high-resolution drought index dataset, Earth Syst. Sci. Data, 12, 753–769, https://doi.org/10.5194/essd-12-753-2020, 2020.
Piao, S., Wang, X., Park, T., Chen, C., Lian, X., He, Y., Bjerke, J. W.,
Chen, A., Ciais, P., Tommervik, H., Nemani, R. R., and Myneni, R. B.:
Characteristics, drivers and feedbacks of global greening, Nat. Rev. Earth Env., 1, 14–27, https://doi.org/10.1038/s43017-019-0001-x, 2020.
Pinzon, J. E. and Tucker, C. J.: A Non-Stationary 1981-2012 AVHRR NDVI3g
Time Series, Remote Sens., 6, 6929–6960, https://doi.org/10.3390/rs6086929,
2014.
Qin, Y., Xiao, X., Wigneron, J.-P., Ciais, P., Brandt, M., Fan, L., Li, X.,
Crowell, S., Wu, X., Doughty, R., Zhang, Y., Liu, F., Sitch, S., and Moore,
B.: Carbon loss from forest degradation exceeds that from deforestation in
the Brazilian Amazon, Nat. Clim. Change, 11, 442–448,
https://doi.org/10.1038/s41558-021-01026-5, 2021.
Rondeaux, G., Steven, M., and Baret, F.: Optimization of soil-adjusted
vegetation indices, Remote Sens. Environ., 55, 95–107,
https://doi.org/10.1016/0034-4257(95)00186-7, 1996.
Rouse, J. W., Haas, R. H., Schell, J. A., and Deering, D. W.: Monitoring
vegetation systems in the Great Plains with ERTS, NASA Spec. Publ., 351, 309,
1974.
Roy, D. P., Kovalskyy, V., Zhang, H. K., Vermote, E. F., Yan, L., Kumar, S.
S., and Egorov, A.: Characterization of Landsat-7 to Landsat-8 reflective
wavelength and normalized difference vegetation index continuity, Remote Sens. Environ., 185, 57–70, https://doi.org/10.1016/j.rse.2015.12.024, 2016.
Schubert, P., Lagergren, F., Aurela, M., Christensen, T., Grelle, A.,
Heliasz, M., Klemedtsson, L., Lindroth, A., Pilegaard, K., Vesala, T., and
Eklundh, L.: Modeling GPP in the Nordic forest landscape with MODIS time
series data-Comparison with the MODIS GPP product, Remote Sens. Environ., 126,
136–147, https://doi.org/10.1016/j.rse.2012.08.005, 2012.
Shen, M. Wang, S., Jiang, N., Sun, J., Cao, R., Ling, X., Fang, B., Zhang,
Lei, Zhang, Lihao, Xu, X., Lv, W., Li, B., Sun, Q., Meng, F., Jiang, Y.,
Dorji, T., Fu, Y., Iler, A., Vitasse, Y., Steltzer, H., Ji, Z., Zhao, W.,
Piao, S., and Fu, B.: Plant phenology changes and drivers on the
Qinghai–Tibetan Plateau, Nat. Rev. Earth Env., 3, 633–651,
https://doi.org/10.1038/s43017-022-00317-5, 2022.
Storey, J., Choate, M., and Lee, K.: Landsat 8 Operational Land Imager
On-Orbit Geometric Calibration and Performance, Remote Sens., 6,
11127–11152. https://doi.org/10.3390/rs61111127, 2014
Tian, F., Fensholt, R., Verbesselt, J., Grogan, K., Horion, S., and Wang, Y.
J.: Evaluating temporal consistency of long-term global NDVI datasets for
trend analysis, Remote Sens. Environ., 163, 326–340,
https://doi.org/10.1016/j.rse.2015.03.031, 2015.
Trishchenko, A. P., Cihlar, J., and Li, Z.: Effects of spectral response
function on surface reflectance and NDVI measured with moderate resolution
satellite sensors, Remote Sens. Environ., 81, 1–18,
https://doi.org/10.1016/S0034-4257(01)00328-5, 2002.
Tucker, C. J., Pinzon, J. E., Brown, M. E., Slayback, D. A., Pak, E. W.,
Mahoney, R., Vermote, E. F., and El Saleous, N.: An extended AVHRR 8-km NDVI
dataset compatible with MODIS and SPOT vegetation NDVI data, Int. J. Remote Sens., 26, 4485–4498, https://doi.org/10.1080/01431160500168686, 2005.
Vermote, E., Justice, C., Claverie, M., and Franch, B.: Preliminary analysis
of the performance of the Landsat 8/OLI land surface reflectance product,
Remote Sens. Environ., 185, 46–56, https://doi.org/10.1016/j.rse.2016.04.008,
2016.
Wang, S. H., Zhang, Y. G., Ju, W. M., Chen, J. M., Cescatti, A., Sardans,
J., Janssens, I. A., Wu, M. S., Berry, J. A., Campbell, J. E.,
Fernandez-Martinez, M., Alkama, R., Sitch, S., Smith, W. K., Yuan, W. P.,
He, W., Lombardozzi, D., Kautz, M., Zhu, D., Lienert, S., Kato, E., Poulter,
B., Sanders, T. G. M., Kruger, I., Wang, R., Zeng, N., Tian, H. Q.,
Vuichard, N., Jain, A. K., Wiltshire, A., Goll, D. S., and Penuelas, J.:
Response to Comments on “Recent global decline of CO2 fertilization effects
on vegetation photosynthesis” COMMENT, Science, 373, eabg7484,
https://doi.org/10.1126/science.abg7484, 2021.
Wang, Z., Wang, H., Wang, T., Wang, L., Liu, X., Zheng, K., and Huang, X.:
Large discrepancies of global greening: Indication of multi-source remote
sensing data, Glob. Ecol. Conserv., 34, e02016,
https://doi.org/10.1016/j.gecco.2022.e02016, 2022.
Weng, Q., Fu, P., and Gao, F.: Generating daily land surface temperature at
Landsat resolution by fusing Landsat and MODIS data, Remote Sens. Environ.,
145, 55–67, https://doi.org/10.1016/j.rse.2014.02.003, 2014.
Wulder, M. A., White, J. C., Loveland, T. R., Woodcock, C. E., Belward, A.
S., Cohen, W. B., Fosnight, E. A., Shaw, J., Masek, J. G., and Roy, D. P.:
The global Landsat archive: Status, consolidation, and direction, Remote Sens. Environ., 185, 271–283, https://doi.org/10.1016/j.rse.2015.11.032, 2016.
Wulder, M. A., Loveland, T. R., Roy, D. P., Crawford, C. J., Masek, J. G.,
Woodcock, C. E., Allen, R. G., Anderson, M. C., Belward, A. S., Cohen, W.
B., Dwyer, J., Erb, A., Gao, F., Griffiths, P., Helder, D., Hermosillo, T.,
Hipple, J. D., Hostert, P., Hughes, M. J., Huntington, J., Johnson, D. M.,
Kennedy, R., Kilic, A., Li, Z., Lymburner, L., McCorkel, J., Pahlevan, N.,
Scambos, T. A., Schaaf, C., Schott, J. R., Sheng, Y., Storey, J., Vermote,
E., Vogelmann, J., White, J. C., Wynne, R. H., and Zhu, Z.: Current status
of Landsat program, science, and applications, Remote Sens. Environ., 225,
127–147, https://doi.org/10.1016/j.rse.2019.02.015, 2019.
Yang, S., Feng, Q., Liang, T., Liu, B., Zhang, W., and Xie, H.: Modeling
grassland above-ground biomass based on artificial neural network and remote
sensing in the Three-River Headwaters Region, Remote Sens. Environ., 204,
448–455, https://doi.org/10.1016/j.rse.2017.10.011, 2018.
Yang, W., Kogan, F., Guo, W., and Chen, Y.: A novel re-compositing approach
to create continuous and consistent cross-sensor/cross-production global
NDVI datasets, Int. J. Remote Sens., 42, 6025–6049,
https://doi.org/10.1080/01431161.2021.1934597, 2021.
Yin, G., Verger, A., Descals, A., Filella, I., and Peñuelas, J.: A
Broadband Green-Red Vegetation Index for Monitoring Gross Primary Production
Phenology, J. Remote Sens., 2022, 9764982,
https://doi.org/10.34133/2022/9764982, 2022.
Zeng, Y. L., Hao, D. L., Huete, A., Dechant, B., Berry, J., Chen, J. M.,
Joiner, J., Frankenberg, C., Bond-Lamberty, B., Ryu, Y., Xiao, J. F., Asrar,
G. R., and Chen, M.: Optical vegetation indices for monitoring terrestrial
ecosystems globally, Nat Rev. Earth Env., 3, 477–493,
https://doi.org/10.1038/s43017-022-00298-5, 2022.
Zhang, X., Liu, L., Chen, X., Gao, Y., and Jiang, M.: Automatically
Monitoring Impervious Surfaces Using Spectral Generalization and Time Series
Landsat Imagery from 1985 to 2020 in the Yangtze River Delta, J.
Remote Sens., 2021, 9873816, https://doi.org/10.34133/2021/9873816, 2021.
Zhang, X., Xu, M., Wang, S., Huang, Y., and Xie, Z.: Mapping photovoltaic power plants in China using Landsat, random forest, and Google Earth Engine, Earth Syst. Sci. Data, 14, 3743–3755, https://doi.org/10.5194/essd-14-3743-2022, 2022.
Zhang, Y., Song, C., Band, L. E., Sun, G., and Li, J.: Reanalysis of global
terrestrial vegetation trends from MODIS products: Browning or greening?,
Remote Sens. Environ., 191, 145–155,
https://doi.org/10.1016/j.rse.2016.12.018, 2017.
Zhu, Z., Bi, J., Pan, Y., Ganguly, S., Anav, A., Xu, L., Samanta, A., Piao,
S., Nemani, R. R., and Myneni, R. B.: 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, https://doi.org/10.3390/rs5020927, 2013.
Zhu, Z., Wang, S. X., and Woodcock, C. E.: Improvement and expansion of the
Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4-7,
8, and Sentinel 2 images, Remote Sens. Environ., 159, 269–277,
https://doi.org/10.1016/j.rse.2014.12.014, 2015.
Zhu, Z. C., Zeng, H., Myneni, R. B., Chen, C., Zhao, Q., Zha, J. J., Zhan,
S. M., and MacLachlan, I.: Comment on “Recent global decline of CO2
fertilization effects on vegetation photosynthesis” COMMENT, Science, 373,
eabg5673, https://doi.org/10.1126/science.abg5673, 2021.
Short summary
Long-term global Normalized Difference Vegetation Index (NDVI) products support the understanding of changes in vegetation under environmental changes. This study generates a consistent global NDVI product (PKU GIMMS NDVI) from 1982–2022 that eliminates the issue of orbital drift and sensor degradation in Advanced Very High Resolution Radiometer (AVHRR) data. More accurate than its predecessor (GIMMS NDVI3g), it shows high temporal consistency with MODIS NDVI in describing vegetation trends.
Long-term global Normalized Difference Vegetation Index (NDVI) products support the...
Altmetrics
Final-revised paper
Preprint