{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T18:58:12Z","timestamp":1773082692691,"version":"3.50.1"},"reference-count":116,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,17]],"date-time":"2023-03-17T00:00:00Z","timestamp":1679011200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"TIMELINE project"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Remote sensing image composites are crucial for a wide range of remote sensing applications, such as multi-decadal time series analysis. The Advanced Very High Resolution Radiometer (AVHRR) instrument has provided daily data since the early 1980s at a spatial resolution of 1 km, allowing analyses of climate change-related environmental processes. For monitoring vegetation conditions, the Normalized Difference Vegetation Index (NDVI) is the most widely used metric. However, to actually enable such analyses, a consistent NDVI time series over the AVHRR time-span needs to be created. In this context, the aim of this study is to thoroughly assess the effect of different compositing procedures on AVHRR NDVI composites, as no standard procedure has been established. Thirteen different compositing methods have been implemented; daily, decadal, and monthly composites over Europe and Northern Africa have been calculated for the year 2007, and the resulting data sets have been thoroughly evaluated according to six criteria. The median approach was selected as the best-performing compositing algorithm considering all the investigated aspects. However, the combination of the NDVI value and viewing and illumination angles as the criteria for the best-pixel selection proved to be a promising approach, too. The generated NDVI time series, currently ranging from 1981\u20132018, shows a consistent behavior and close agreement to the standard MODIS NDVI product. The conducted analyses demonstrate the strong influence of compositing procedures on the resulting AVHRR NDVI composites.<\/jats:p>","DOI":"10.3390\/rs15061631","type":"journal-article","created":{"date-parts":[[2023,3,20]],"date-time":"2023-03-20T03:09:37Z","timestamp":1679281777000},"page":"1631","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["AVHRR NDVI Compositing Method Comparison and Generation of Multi-Decadal Time Series\u2014A TIMELINE Thematic Processor"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7302-6813","authenticated-orcid":false,"given":"Sarah","family":"Asam","sequence":"first","affiliation":[{"name":"German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), 82234 Wessling, Germany"}]},{"given":"Christina","family":"Eisfelder","sequence":"additional","affiliation":[{"name":"German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), 82234 Wessling, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-5473-9424","authenticated-orcid":false,"given":"Andreas","family":"Hirner","sequence":"additional","affiliation":[{"name":"German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), 82234 Wessling, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4277-9089","authenticated-orcid":false,"given":"Philipp","family":"Reiners","sequence":"additional","affiliation":[{"name":"German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), 82234 Wessling, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7364-7006","authenticated-orcid":false,"given":"Stefanie","family":"Holzwarth","sequence":"additional","affiliation":[{"name":"German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), 82234 Wessling, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8381-7662","authenticated-orcid":false,"given":"Martin","family":"Bachmann","sequence":"additional","affiliation":[{"name":"German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), 82234 Wessling, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1038\/s43017-019-0001-x","article-title":"Characteristics, drivers and feedbacks of global greening","volume":"1","author":"Piao","year":"2020","journal-title":"Nat. Rev. Earth Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1126\/science.aac8083","article-title":"Biophysical climate impacts of recent changes in global forest cover","volume":"351","author":"Alkama","year":"2016","journal-title":"Science"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1897","DOI":"10.1038\/s41559-018-0714-0","article-title":"Enhanced peak growth of global vegetation and its key mechanisms","volume":"2","author":"Huang","year":"2018","journal-title":"Nat. Ecol. Evol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3698","DOI":"10.1038\/s41467-022-31496-w","article-title":"Warming-induced increase in carbon uptake is linked to earlier spring phenology in temperate and boreal forests","volume":"13","author":"Gu","year":"2022","journal-title":"Nat. Commun."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"887","DOI":"10.1126\/science.1173004","article-title":"Phenology Feedbacks on Climate Change","volume":"324","author":"Rutishauser","year":"2009","journal-title":"Science"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.agrformet.2012.09.012","article-title":"Climate change, phenology, and phenological control of vegetation feedbacks to the climate system","volume":"169","author":"Richardson","year":"2013","journal-title":"Agric. For. Meteorol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.cosust.2009.07.006","article-title":"Biodiversity, climate change, and ecosystem services","volume":"1","author":"Mooney","year":"2009","journal-title":"Curr. Opin. Environ. Sustain."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"e01436","DOI":"10.1002\/ecs2.1436","article-title":"Emerging opportunities and challenges in phenology: A review","volume":"7","author":"Tang","year":"2016","journal-title":"Ecosphere"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Kuenzer, C., Dech, S., and Wagner, W. (2015). Remote Sensing Time Series: Revealing Land Surface Dynamics, Springer International Publishing.","DOI":"10.1007\/978-3-319-15967-6"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"875","DOI":"10.1038\/nclimate1908","article-title":"The role of satellite remote sensing in climate change studies","volume":"3","author":"Yang","year":"2013","journal-title":"Nat. Clim. Chang."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1080\/01431169408954056","article-title":"Applications of NOAA-AVHRR 1 km data for environmental monitoring","volume":"15","author":"Ehrlich","year":"1994","journal-title":"Int. J. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Dech, S., Holzwarth, S., Asam, S., Andresen, T., Bachmann, M., Boettcher, M., Dietz, A., Eisfelder, C., Frey, C., and Gesell, G. (2021). Potential and Challenges of Harmonizing 40 Years of AVHRR Data: The TIMELINE Experience. Remote Sens., 13.","DOI":"10.3390\/rs13183618"},{"key":"ref_13","unstructured":"Holzwarth, S. (2022, August 02). TIMELINE DLR Website. Available online: www.timeline.dlr.de."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11676-020-01155-1","article-title":"A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing","volume":"32","author":"Huang","year":"2021","journal-title":"J. For. Res."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1038\/s43017-022-00298-5","article-title":"Optical vegetation indices for monitoring terrestrial ecosystems globally","volume":"3","author":"Zeng","year":"2022","journal-title":"Nat. Rev. Earth Environ."},{"key":"ref_16","unstructured":"Rouse, J.W., Haas, R.H., Schell, J.A., and Deering, D.W. Monitoring vegetation systems in the great plains with ERTS. Proceedings of the Third Symposium on Significant Results Obtained with ERTS-1; NASA SP-351, Available online: https:\/\/ntrs.nasa.gov\/citations\/19740022614."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A soil-adjusted vegetation index (SAVI)","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1126\/science.227.4685.369","article-title":"African Land-Cover Classification Using Satellite Data","volume":"227","author":"Tucker","year":"1985","journal-title":"Science"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"698","DOI":"10.1038\/386698a0","article-title":"Increased plant growth in the northern high latitudes from 1981 to 1991","volume":"386","author":"Myneni","year":"1997","journal-title":"Nature"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5717","DOI":"10.3390\/rs6065717","article-title":"Human Land-Use Practices Lead to Global Long-Term Increases in Photosynthetic Capacity","volume":"6","author":"Mueller","year":"2014","journal-title":"Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"664","DOI":"10.3390\/rs5020664","article-title":"Assessing Land Degradation\/Recovery in the African Sahel from Long-Term Earth Observation Based Primary Productivity and Precipitation Relationships","volume":"5","author":"Fensholt","year":"2013","journal-title":"Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"257","DOI":"10.3390\/rs6010257","article-title":"Phenological Metrics Derived over the European Continent from NDVI3g Data and MODIS Time Series","volume":"6","author":"Atzberger","year":"2014","journal-title":"Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2108","DOI":"10.3390\/rs6032108","article-title":"Comparison of Gross Primary Productivity Derived from GIMMS NDVI3g, GIMMS, and MODIS in Southeast Asia","volume":"6","author":"Wang","year":"2014","journal-title":"Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3446","DOI":"10.3390\/rs6043446","article-title":"Rain-Use-Efficiency: What it Tells us about the Conflicting Sahel Greening and Sahelian Paradox","volume":"6","author":"Dardel","year":"2014","journal-title":"Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3303","DOI":"10.1080\/01431160310001618149","article-title":"European plant phenology and climate as seen in a 20-year AVHRR land-surface parameter dataset","volume":"25","author":"Vidale","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1007\/s100219900056","article-title":"Interannual Variability in Terrestrial Net Primary Production: Exploration of Trends and Controls on Regional to Global Scales","volume":"2","author":"Potter","year":"1999","journal-title":"Ecosystems"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Pedelty, J., Devadiga, S., Masuoka, E., Brown, M., Pinzon, J., Tucker, C., Vermote, E., Prince, S., Nagol, J., and Justice, C. (2007, January 23\u201328). Generating a Long-term Land Data Record from the AVHRR and MODIS Instruments. Proceedings of the 2007 IEEE International Geoscience and Remote Sensing Symposium, Barcelona, Spain.","DOI":"10.1109\/IGARSS.2007.4422974"},{"key":"ref_28","unstructured":"Vermote, E., and NOAA CDR Program (2019). NOAA Climate Data Record (CDR) of AVHRR Normalized Difference Vegetation Index (NDVI), Version 5."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"4485","DOI":"10.1080\/01431160500168686","article-title":"An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data","volume":"26","author":"Tucker","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"6929","DOI":"10.3390\/rs6086929","article-title":"A Non-Stationary 1981\u20132012 AVHRR NDVI3g Time Series","volume":"6","author":"Pinzon","year":"2014","journal-title":"Remote Sens."},{"key":"ref_31","unstructured":"LSA SAF (2021). Normalized Difference Vegetation Index CDR Release 2\u2014Metop, Available online: https:\/\/navigator.eumetsat.int\/product\/EO:EUM:DAT:0385."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2725","DOI":"10.1080\/01431161003743199","article-title":"The Satellite Application Facility on Land Surface Analysis","volume":"32","author":"Trigo","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_33","unstructured":"Government of Canada (2021). Corrected representation of the NDVI using historical AVHRR satellite images (1 km resolution) from 1987 to 2021, Statistics Canada."},{"key":"ref_34","unstructured":"Earth Resources Observation and Science (EROS) Center (2023, March 12). USGS EROS Archive\u2014AVHRR Normalized Difference Vegetation Index (NDVI) Composites, Available online: https:\/\/www.usgs.gov\/centers\/eros\/science\/usgs-eros-archive-avhrr-normalized-difference-vegetation-index-ndvi-composites?qt-science_center_objects=0#qt-science_center_objects."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1417","DOI":"10.1080\/01431168608948945","article-title":"Characteristics of maximum-value composite images from temporal AVHRR data","volume":"7","author":"Holben","year":"1986","journal-title":"Int. J. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.rse.2018.10.031","article-title":"Intra-annual reflectance composites from Sentinel-2 and Landsat for national-scale crop and land cover mapping","volume":"220","author":"Griffiths","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.rse.2016.12.018","article-title":"Reanalysis of global terrestrial vegetation trends from MODIS products: Browning or greening?","volume":"191","author":"Zhang","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1109\/36.295057","article-title":"Evaluation of compositing algorithms for AVHRR data over land","volume":"32","author":"Cihlar","year":"1994","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2383","DOI":"10.1080\/014311697217675","article-title":"Investigation of the maximum Normalized Difference Vegetation Index (NDVI) and the maximum surface temperature (Ts) AVHRR compositing procedures for the extraction of NDVI and Ts over forest","volume":"18","author":"Roy","year":"1997","journal-title":"Int. J. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1080\/02757259409532249","article-title":"A comparison of reflectances and vegetation indices from three methods of compositing the AVHRR-GAC data over Northern Africa","volume":"10","author":"Choudhury","year":"1994","journal-title":"Remote Sens. Rev."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"113375","DOI":"10.1016\/j.rse.2022.113375","article-title":"Evaluation of Landsat image compositing algorithms","volume":"285","author":"Qiu","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_42","first-page":"328","article-title":"Effective Compositing Method to Produce Cloud-Free AVHRR Image","volume":"11","author":"Wang","year":"2014","journal-title":"IEEE GRSL"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1013","DOI":"10.1080\/01431160412331299235","article-title":"AVHRR multitemporal compositing techniques for burned land mapping","volume":"26","author":"Chuvieco","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.rse.2011.08.027","article-title":"Quantifying forest cover loss in Democratic Republic of the Congo, 2000\u20132010, with Landsat ETM+ data","volume":"122","author":"Potapov","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"6254","DOI":"10.1109\/TGRS.2017.2723896","article-title":"High-Dimensional Pixel Composites From Earth Observation Time Series","volume":"55","author":"Roberts","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"6481","DOI":"10.3390\/rs5126481","article-title":"Seasonal Composite Landsat TM\/ETM+ Images Using the Medoid (a Multi-Dimensional Median)","volume":"5","author":"Flood","year":"2013","journal-title":"Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the radiometric and biophysical performance of the MODIS vegetation indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1324","DOI":"10.1109\/36.701082","article-title":"MODIS land data storage, gridding, and compositing methodology: Level 2 grid","volume":"36","author":"Wolfe","year":"1998","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_49","unstructured":"Didan, K., Munoz, A.B., Solano, R., and Huete, A. (2015). MODIS Vegetation Index User\u2019s Guide (MOD13 Series), Version 3.00, June 2015 (Collection 6), Vegetation Index and Phenology Lab, The University of Arizona."},{"key":"ref_50","unstructured":"Pinzon, J.E., Brown, M.E., and Tucker, C.J. (2005). Hilbert-Huang Transform and Its Applications, World Scientific Publishing Co. Pte. Ltd."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"558","DOI":"10.1109\/TGRS.2007.909948","article-title":"Extending the SPOT-VEGETATION NDVI Time Series (1998\u20132006) Back in Time With NOAA-AVHRR Data (1985\u20131998) for Southern Africa","volume":"46","author":"Swinnen","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_52","first-page":"6","article-title":"Composite Method over Land for NOAA\/AVHRR GAC Global Data Set","volume":"40","author":"Matsuoka","year":"2001","journal-title":"J. Jpn. Soc. Photogramm. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.rse.2009.08.011","article-title":"Web-enabled Landsat Data (WELD): Landsat ETM+ composited mosaics of the conterminous United States","volume":"114","author":"Roy","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1080\/07038992.2014.945827","article-title":"Pixel-Based Image Compositing for Large-Area Dense Time Series Applications and Science","volume":"40","author":"White","year":"2014","journal-title":"Can. J. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"2088","DOI":"10.1109\/JSTARS.2012.2228167","article-title":"A Pixel-Based Landsat Compositing Algorithm for Large Area Land Cover Mapping","volume":"6","author":"Griffiths","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/j.rse.2017.01.002","article-title":"Phenology-adaptive pixel-based compositing using optical earth observation imagery","volume":"190","author":"Frantz","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"324","DOI":"10.5589\/m05-024","article-title":"Generating historical AVHRR 1 km baseline satellite data records over Canada suitable for climate change studies","volume":"31","author":"Latifovic","year":"2005","journal-title":"Can. J. Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"5123","DOI":"10.1080\/01431160701253212","article-title":"Mean Compositing, an alternative strategy for producing temporal syntheses. Concepts and performance assessment for SPOT VEGETATION time series","volume":"28","author":"Vancutsem","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2017.11.004","article-title":"Building an exposed soil composite processor (SCMaP) for mapping spatial and temporal characteristics of soils with Landsat imagery (1984\u20132014)","volume":"205","author":"Rogge","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_60","unstructured":"Hagolle, O., Morin, D., and Kadiri, M. (2023, March 12). Detailed Processing Model for the Weighted Average Synthesis Processor (WASP) for Sentinel-2 (1.4). Available online: https:\/\/zenodo.org\/record\/1401360."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.rse.2015.02.009","article-title":"Generating synthetic Landsat images based on all available Landsat data: Predicting Landsat surface reflectance at any given time","volume":"162","author":"Zhu","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_62","first-page":"202","article-title":"Smoothing and gap-filling of high resolution multi-spectral time series: Example of Landsat data","volume":"57","author":"Vuolo","year":"2017","journal-title":"Int. J. Appl. Earth. Obs. Geoinf."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"3403","DOI":"10.1080\/0143116021000021279","article-title":"Evaluating different NDVI composite techniques using NOAA-14 AVHRR data","volume":"24","author":"Chen","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"26669","DOI":"10.1029\/2000JD900380","article-title":"Bidirectional reflectance distribution function signatures of major biomes observed from space","volume":"105","author":"Bicheron","year":"2000","journal-title":"J. Geophys. Res."},{"key":"ref_65","first-page":"452","article-title":"The Global Impact of Clouds on the Production of MODIS Bidirectional Reflectance Model-Based Composites for Terrestrial Monitoring","volume":"3","author":"Roy","year":"2006","journal-title":"IEEE GRSL"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1269","DOI":"10.1109\/TGRS.2002.800241","article-title":"Bidirectional NDVI and atmospherically resistant BRDF inversion for vegetation canopy","volume":"40","author":"Gao","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/0034-4257(91)90017-Z","article-title":"Normalized difference vegetation index measurements from the advanced very high resolution radiometer","volume":"35","author":"Goward","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1016\/j.rse.2003.08.010","article-title":"Intercalibration of vegetation indices from different sensor systems","volume":"88","author":"Steven","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Kuenzer, C., Dech, S., and Wagner, W. (2015). Remote Sensing Time Series: Revealing Land Surface Dynamics, Springer International Publishing.","DOI":"10.1007\/978-3-319-15967-6"},{"key":"ref_70","first-page":"215","article-title":"Effect of NOAA satellite orbital drift on AVHRR-derived phenological metrics","volume":"62","author":"Ji","year":"2017","journal-title":"Int. J. Appl. Earth. Obs. Geoinf."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1080\/01431169408954095","article-title":"Some considerations for using AVHRR data in climatological studies: I. Orbital characteristics of NOAA satellites","volume":"15","author":"McGregor","year":"1994","journal-title":"Int. J. Remote Sens."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"4700","DOI":"10.1080\/01431161.2011.638341","article-title":"Long-term time series of the Earth\u2019s land-surface observations from space","volume":"33","author":"Gutman","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Kern, A., Marjanovi\u0107, H., and Barcza, Z. (2016). Evaluation of the Quality of NDVI3g Dataset against Collection 6 MODIS NDVI in Central Europe between 2000 and 2013. Remote Sens., 8.","DOI":"10.3390\/rs8110955"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.rse.2011.12.015","article-title":"Evaluation of Earth Observation based global long term vegetation trends\u2014Comparing GIMMS and MODIS global NDVI time series","volume":"119","author":"Fensholt","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1633","DOI":"10.5194\/hess-11-1633-2007","article-title":"Updated world map of the K\u00f6ppen-Geiger climate classification","volume":"11","author":"Peel","year":"2007","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_76","first-page":"112","article-title":"The Map of the Natural Vegetation of Europe and its application in the Caucasus Ecoregion","volume":"175","author":"Bohn","year":"2007","journal-title":"Bull. Georgian Natl. Acad. Sci."},{"key":"ref_77","unstructured":"ICOS (2023, January 20). Standardised Greenhouse Gas Measurements throughout Europe. Available online: https:\/\/www.icos-cp.eu\/."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"621","DOI":"10.5194\/bg-18-621-2021","article-title":"Retrieval and validation of forest background reflectivity from daily Moderate Resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF) data across European forests","volume":"18","author":"Pisek","year":"2021","journal-title":"Biogeosciences"},{"key":"ref_79","unstructured":"CEOS Cal\/Val Portal (2023, January 20). PICS: Pseudo-Invariant Calibration Sites. Available online: https:\/\/calvalportal.ceos.org\/pics_sites."},{"key":"ref_80","unstructured":"CEOS Cal\/Val Portal (2023, January 20). LANDNET SITES (CEOS Reference Sites). Available online: https:\/\/calvalportal.ceos.org\/ceos-landnet-sites."},{"key":"ref_81","unstructured":"GHG Europe Database (2023, January 20). GHG Europe Database. Available online: http:\/\/gaia.agraria.unitus.it\/ghg-europe."},{"key":"ref_82","unstructured":"National Physical Laboratory, U.o.S., and EOLab (2023, January 20). Fiducial Reference Measurements for Vegetation. Available online: https:\/\/frm4veg.org\/."},{"key":"ref_83","unstructured":"Forschungszentrum J\u00fclich (2023, January 20). TERENO Northeastern Lowland Observatory Test Sites. Available online: https:\/\/www.tereno.net\/joomla\/index.php\/observatories\/northeast-german-lowland-observatory\/test-sites."},{"key":"ref_84","unstructured":"Davidson, A. (2023, January 20). Joint Experiment for Crop Assessment and Monitoring (JECAM), Germany-DEMMIN. Available online: http:\/\/jecam.org\/studysite\/germany-demmin\/."},{"key":"ref_85","unstructured":"Koslowsky, D. (1996). Mehrj\u00e4hrige Validierte und Homogenisierte Reihen des Reflexionsgrades und des Vegetationsindexes von Landoberfl\u00e4chen aus T\u00e4glichen AVHRR-Daten Hoher Aufl\u00f6sung. [Ph.D. Thesis, Freie Universit\u00e4t Berlin]."},{"key":"ref_86","unstructured":"Defourny, P., Kirches, G., Militzer, J., Boettcher, M., Brockmann, C., and Bontemps, S. (2017). Land Cover CCI Product ValidationAnd Intercomparison Report v2, UCL-Geomatics."},{"key":"ref_87","unstructured":"Cuntz, M., Aiguier, T., Courtois, P., Joetzjer, E., and Lily, J. (2023, January 20). Hesse ICOS Station. Available online: https:\/\/meta.icos-cp.eu\/resources\/stations\/ES_FR-Hes."},{"key":"ref_88","unstructured":"Kidwell, K.B. (1995). NOAA Polar Orbiter Data Users Guide: (TIROS-N, NOAA-6, NOAA-7, NOAA-8, NOAA-9, NOAA-10, NOAA-11, NOAA-12, NOAA-13, and NOAA-14)."},{"key":"ref_89","unstructured":"Robel, J., and Graumann, A. (2014). NOAA KLM User\u2019s Guide with NOAA-N, N Prime, and MetOp Supplements."},{"key":"ref_90","unstructured":"Molch, K., Leone, R., Frey, C., Wolfm\u00fcller, M., and Tungalagsaikhan, P. (2013, January 5\u20137). NOAA AVHRR Data Curation and Reprocessing\u2014TIMELINE. Proceedings of the Big Data from Space (BiDS\u2019 2013), Frascati, Italy."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1016\/j.rse.2008.10.002","article-title":"Effects of spectral response function on surface reflectance and NDVI measured with moderate resolution satellite sensors: Extension to AVHRR NOAA-17, 18 and METOP-A","volume":"113","author":"Trishchenko","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"6519","DOI":"10.1080\/01431161.2010.496473","article-title":"Calibrations for AVHRR channels 1 and 2: Review and path towards consensus","volume":"31","author":"Molling","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_93","first-page":"102317","article-title":"Evaluation of the AVHRR surface reflectance long term data record between 1984 and 2011","volume":"98","author":"Vermote","year":"2021","journal-title":"Int. J. Appl. Earth. Obs. Geoinf."},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Dietz, A.J., Frey, C.M., Ruppert, T., Bachmann, M., Kuenzer, C., and Dech, S. (2017). Automated Improvement of Geolocation Accuracy in AVHRR Data Using a Two-Step Chip Matching Approach\u2014A Part of the TIMELINE Preprocessor. Remote Sens., 9.","DOI":"10.3390\/rs9040303"},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Bachmann, M., and M\u00fcller, T. (2015, January 26\u201331). Using spaceborne hyperspectral data for spectral cross-calibration of multispectral sensors. Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326399"},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Dietz, A.J., Klein, I., Gessner, U., Frey, C.M., Kuenzer, C., and Dech, S. (2017). Detection of Water Bodies from AVHRR Data\u2014A TIMELINE Thematic Processor. Remote Sens., 9.","DOI":"10.3390\/rs9010057"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"2389","DOI":"10.1080\/01431160210163065","article-title":"The cloud analysis tool APOLLO: Improvements and validations","volume":"24","author":"Kriebel","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_98","first-page":"165","article-title":"Optical Properties of Clouds Derived from Fully Cloudy AVHRR Pixels","volume":"62","author":"Kriebel","year":"1989","journal-title":"Beitr\u00e4ge zur Physik der Atmosph\u00e4re"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1080\/01431168808954841","article-title":"An improved method for detecting clear sky and cloudy radiances from AVHRR data","volume":"9","author":"Saunders","year":"1988","journal-title":"Int. J. Remote Sens."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"4155","DOI":"10.5194\/amt-8-4155-2015","article-title":"APOLLO_NG\u2014A probabilistic interpretation of the APOLLO legacy for AVHRR heritage channels","volume":"8","author":"Killius","year":"2015","journal-title":"Atmos. Meas. Tech."},{"key":"ref_101","unstructured":"Berk, A., Anderson, G.P., Acharya, P.K., and Shettle, E.P. (2008). Modtran\u00ae 5.2.0.0 User\u2019s Manual, Air Force Research Laboratory, Space Vehicles Directorate, Air Force Materiel Command Hanscom AFB."},{"key":"ref_102","unstructured":"NASA (2022, November 15). About MODIS, Available online: https:\/\/modis.gsfc.nasa.gov\/about\/."},{"key":"ref_103","unstructured":"NASA (2022, November 15). MODIS Specifications, Available online: https:\/\/modis.gsfc.nasa.gov\/about\/specifications.php."},{"key":"ref_104","unstructured":"Didan, K. (2022, November 15). MODIS\/Terra Vegetation Indices 16-Day L3 Global 1 km SIN Grid V061 [Data Set]. NASA EOSDIS Land Processes DAAC, Available online: https:\/\/lpdaac.usgs.gov\/products\/mod13a2v061\/."},{"key":"ref_105","unstructured":"Didan, K. (2022, November 15). MODIS\/Terra Vegetation Indices Monthly L3 Global 1 km SIN Grid V061 [Data Set]. NASA EOSDIS Land Processes DAAC, Available online: https:\/\/lpdaac.usgs.gov\/products\/mod13a3v061\/."},{"key":"ref_106","unstructured":"Didan, K. (2023, March 03). Status for: Vegetation Indices (MOD13), Available online: https:\/\/modis-land.gsfc.nasa.gov\/ValStatus.php?ProductID=MOD13."},{"key":"ref_107","unstructured":"Huete, A., Justice, C., van Leeuwen, W.J.D., Jacobson, A., Solanos, R., and Laing, T.D. (1999). MODIS VEGETATION INDEX (MOD 13) Algorithm Theoretucal Basis Document, Version 3, Vegetation Index and Phenology Lab."},{"key":"ref_108","unstructured":"NASA, EOSDIS, LAADS, and DAAC (2023, March 06). Long-Term Data Record, Available online: https:\/\/ladsweb.modaps.eosdis.nasa.gov\/missions-and-measurements\/applications\/ltdr\/#project-documentation."},{"key":"ref_109","unstructured":"Vermote, E. (2023, March 12). AVHRR Surface Reflectance and Normalized Difference Vegetation Index\u2014Climate Algorithm Theoretical Basis Document, NOAA Climate Data Record Program CDRP-ATBD-0459 Revsion 2, Available online: https:\/\/www.ncei.noaa.gov\/pub\/data\/sds\/cdr\/CDRs\/Normalized_Difference_Vegetation_Index\/AVHRR\/AlgorithmDescriptionAVHRR_01B-20b.pdf."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/S0034-4257(96)00137-X","article-title":"Multitemporal, multichannel AVHRR data sets for land biosphere studies\u2014Artifacts and corrections","volume":"60","author":"Cihlar","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/0034-4257(91)90005-Q","article-title":"Vegetation indices from AVHRR: An update and future prospects","volume":"35","author":"Gutman","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"1364","DOI":"10.1364\/AO.22.001364","article-title":"Dynamics of directional reflectance factor distributions for vegetation canopies","volume":"22","author":"Kimes","year":"1983","journal-title":"Appl. Opt."},{"key":"ref_113","first-page":"132","article-title":"Effect of atmospheric correction and viewing angle restriction on AVHRR data composites","volume":"20","author":"Cihlar","year":"1994","journal-title":"Can. J. Remote Sens."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1016\/S0034-4257(99)00022-X","article-title":"MODIS Vegetation Index Compositing Approach: A Prototype with AVHRR Data","volume":"69","author":"Huete","year":"1999","journal-title":"Remote Sens. Environ."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1080\/02757259509532272","article-title":"The effect of surface anisotropy and viewing geometry on the estimation of NDVI from AVHRR","volume":"12","author":"Meyer","year":"1995","journal-title":"Remote Sens. Rev."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.rse.2006.06.015","article-title":"Calibration of NOAA16 AVHRR over a desert site using MODIS data","volume":"105","author":"Vermote","year":"2006","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/6\/1631\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:57:43Z","timestamp":1760122663000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/6\/1631"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,17]]},"references-count":116,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["rs15061631"],"URL":"https:\/\/doi.org\/10.3390\/rs15061631","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,17]]}}}