{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T11:27:29Z","timestamp":1782818849635,"version":"3.54.5"},"reference-count":110,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2020,4,23]],"date-time":"2020-04-23T00:00:00Z","timestamp":1587600000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Satellite remote sensing of vegetation at regional to global scales is undertaken at considerable variations in solar zenith angle (SZA) across space and time, yet the extent to which these SZA variations matter for the retrieval of phenology remains largely unknown. Here we examined the effect of seasonal and spatial variations in SZA on retrieving vegetation phenology from time series of the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) across a study area in southeastern Australia encompassing forest, woodland, and grassland sites. The vegetation indices (VI) data span two years and are from the Advanced Himawari Imager (AHI), which is onboard the Japanese Himawari-8 geostationary satellite. The semi-empirical RossThick-LiSparse-Reciprocal (RTLSR) bidirectional reflectance distribution function (BRDF) model was inverted for each spectral band on a daily basis using 10-minute reflectances acquired by H-8 AHI at different sun-view geometries for each site. The inverted RTLSR model was then used to forward calculate surface reflectance at three constant SZAs (20\u00b0, 40\u00b0, 60\u00b0) and one seasonally varying SZA (local solar noon), all normalised to nadir view. Time series of NDVI and EVI adjusted to different SZAs at nadir view were then computed, from which phenological metrics such as start and end of growing season were retrieved. Results showed that NDVI sensitivity to SZA was on average nearly five times greater than EVI sensitivity. VI sensitivity to SZA also varied among sites (biome types) and phenological stages, with NDVI sensitivity being higher during the minimum greenness period than during the peak greenness period. Seasonal SZA variations altered the temporal profiles of both NDVI and EVI, with more pronounced differences in magnitude among NDVI time series normalised to different SZAs. When using VI time series that allowed SZA to vary at local solar noon, the uncertainties in estimating start, peak, end, and length of growing season introduced by local solar noon varying SZA VI time series, were 7.5, 3.7, 6.5, and 11.3 days for NDVI, and 10.4, 11.9, 6.5, and 8.4 days for EVI respectively, compared to VI time series normalised to a constant SZA. Furthermore, the stronger SZA dependency of NDVI compared with EVI, resulted in up to two times higher uncertainty in estimating annual integrated VI, a commonly used remote-sensing proxy for vegetation productivity. Since commonly used satellite products are not generally normalised to a constant sun-angle across space and time, future studies to assess the sun-angle effects on satellite applications in agriculture, ecology, environment, and carbon science are urgently needed. Measurements taken by new-generation geostationary (GEO) satellites offer an important opportunity to refine this assessment at finer temporal scales. In addition, studies are needed to evaluate the suitability of different BRDF models for normalising sun-angle across a broad spectrum of vegetation structure, phenological stages and geographic locations. Only through continuous investigations on how sun-angle variations affect spatiotemporal vegetation dynamics and what is the best strategy to deal with it, can we achieve a more quantitative remote sensing of true signals of vegetation change across the entire globe and through time.<\/jats:p>","DOI":"10.3390\/rs12081339","type":"journal-article","created":{"date-parts":[[2020,4,23]],"date-time":"2020-04-23T10:46:22Z","timestamp":1587638782000},"page":"1339","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["Sun-Angle Effects on Remote-Sensing Phenology Observed and Modelled Using Himawari-8"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1499-8476","authenticated-orcid":false,"given":"Xuanlong","family":"Ma","sequence":"first","affiliation":[{"name":"College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, Gansu 730000, China"},{"name":"School of Life Sciences, University of Technology Sydney, Ultimo NSW 2007, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alfredo","family":"Huete","sequence":"additional","affiliation":[{"name":"School of Life Sciences, University of Technology Sydney, Ultimo NSW 2007, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ngoc","family":"Tran","sequence":"additional","affiliation":[{"name":"School of Life Sciences, University of Technology Sydney, Ultimo NSW 2007, Australia"},{"name":"School of Information and Communication Technology, Hanoi University of Science and Technology, Hanoi 10000, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Bi","sequence":"additional","affiliation":[{"name":"College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, Gansu 730000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6429-1917","authenticated-orcid":false,"given":"Sicong","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Life Sciences, University of Technology Sydney, Ultimo NSW 2007, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4267-1841","authenticated-orcid":false,"given":"Yelu","family":"Zeng","sequence":"additional","affiliation":[{"name":"Department of Global Ecology, Carnegie Institution for Science, Stanford, CA 94305, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"953","DOI":"10.1080\/01431168708948701","article-title":"Influence of topography and sensor view angles on NIR\/red ratio and greenness vegetation indices of wheat","volume":"8","author":"Pinter","year":"1987","journal-title":"Int. J. Remote Sens."},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1109\/36.134076","article-title":"Atmospherically Resistant Vegetation Index (ARVI) for EOS-MODIS","volume":"30","author":"Kaufman","year":"1992","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"706","DOI":"10.1175\/1520-0442(1996)009<0706:ARLSPF>2.0.CO;2","article-title":"A revised land surface parameterizaton (SiB-2) for atmospheric GCMs. Part 2: The generation of global fields of terrestrial biophysical parameters from satellite data","volume":"9","author":"Sellers","year":"1996","journal-title":"J. Clim."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1016\/j.rse.2005.08.017","article-title":"A methood to convert AVHRR Normalized Difference Vegetation Index time series to a standard viewing and illumination geometry","volume":"99","author":"Los","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1016\/j.rse.2005.06.007","article-title":"Atmospheric conditions for monitoring the long-term vegetation dynamics in the Amazon using normalized difference vegetation index","volume":"97","author":"Kobayashi","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1016\/j.rse.2018.02.068","article-title":"Using negative soil adjustment factor in soil-adjusted vegetation index (SAVI) for aboveground living biomass estimation in arid grassland","volume":"209","author":"Ren","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"5017","DOI":"10.1111\/gcb.14427","article-title":"Angle matters: Bidirectional effects impact the slope of relationship between gross primary productivity and sun-induced chlorophyll fluorescence from Orbiting Carbon Observatory-2 across biomes","volume":"24","author":"Zhang","year":"2018","journal-title":"Glob. Chang. Biol."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Jin, Y., Schaaf, C.B., Woodcock, C.E., Gao, F., Li, X., and Strahler, A.H. (2003). Consistency of MODIS surface bidirectional reflectance distribution function and albedo retrievals: 2. Validation. J. Geophys. Res., 108.","DOI":"10.1029\/2002JD002804"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4480","DOI":"10.1109\/JSTARS.2014.2343592","article-title":"Angular effects and correction for medium resolution sensors to support crop monitoring","volume":"7","author":"Gao","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"064014","DOI":"10.1088\/1748-9326\/10\/6\/064014","article-title":"Sunlight mediated seasonality in canopy structure and photosynthetic activity of Amazonian rainforests","volume":"10","author":"Bi","year":"2015","journal-title":"Environ. Res. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/S0034-4257(02)00089-5","article-title":"Atmospheric correction of MODIS data in the visible to middle infrared: First results","volume":"83","author":"Vermote","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_13","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_14","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/S0034-4257(02)00091-3","article-title":"First operational BRDF, albedo, nadir reflectance products from MODIS","volume":"83","author":"Schaaf","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/0034-4257(74)90003-0","article-title":"Vegetation canopy reflectance","volume":"3","author":"Cowell","year":"1974","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1109\/TGRS.1983.350484","article-title":"Diurnal patterns of wheat spectral reflectances","volume":"21","author":"Pinter","year":"1983","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1307","DOI":"10.1080\/01431168708954776","article-title":"Soil and sun-angle interactions on partial canopy spectra","volume":"8","author":"Huete","year":"1987","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/0034-4257(91)90071-D","article-title":"Solar zenith angle effects on vegetation indices in tallgrass prairie","volume":"38","author":"Middleton","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1080\/02757259009532128","article-title":"Measuring vegetation spectral properties","volume":"5","author":"Biehl","year":"1990","journal-title":"Remote Sens. Rev."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","unstructured":"Petri, C.A., and Galv\u00e3o, L.S. (2019). Sensitivity of seven MODIS vegetation indices to BRDF effects during the Amazonian dry season. Remote Sens., 11.","DOI":"10.3390\/rs11141650"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"921","DOI":"10.1109\/36.239916","article-title":"Solar zenith angle effects on forest canopy hemispherical reflectances calculated with a geometric-optical bidirectional reflectance model","volume":"31","author":"Schaaf","year":"1993","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.rse.2015.05.021","article-title":"Suomi NPP VIIRS reflective solar band on-orbit radiometric stability and accuracy assessment using desert and Antarctic Dome C sites","volume":"166","author":"Uprety","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"7513","DOI":"10.1080\/01431161.2010.524675","article-title":"Assessing viewing and illumination geometry effects on the MODIS vegetation index (MOD13Q1) time series: Implications for monitoring phenology and disturbances in forest communities in Queensland, Australia","volume":"32","author":"Bhandari","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_26","first-page":"291","article-title":"View-illumination effects on hyperspectral vegetation indices in the Amazonian tropical forest","volume":"21","author":"Breunig","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1038\/nature13006","article-title":"Amazon forests maintain consistent canopy structure and greenness during the dry season","volume":"506","author":"Morton","year":"2014","journal-title":"Nature"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ma, X., Huete, A., and Tran, N.N. (2019). Interaction of seasonal sun-angle and savanna phenology observed and modelled using MODIS. Remote Sens., 11.","DOI":"10.3390\/rs11121398"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Huete, A.R., Didan, K., Shimabukuro, Y.E., Ratana, P., Saleska, S.R., Hutyra, L.R., Yang, W., Nemani, R.R., and Myneni, R. (2006). Amazon rainforests green-up with sunlight in dry season. Geophys. Res. Lett., 33.","DOI":"10.1029\/2005GL025583"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"7176","DOI":"10.1002\/2014GL061535","article-title":"Can MODIS EVI monitor ecosystem productivity in the Amazon rainforest?","volume":"41","author":"Maeda","year":"2014","journal-title":"Geophys. Res. Lett."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"E4","DOI":"10.1038\/nature16457","article-title":"Dry-season greening of Amazon forests","volume":"531","author":"Saleska","year":"2016","journal-title":"Nature"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"972","DOI":"10.1126\/science.aad5068","article-title":"Leaf development and demography explain photosynthetic seasonality in Amazon evergreen forests","volume":"351","author":"Wu","year":"2016","journal-title":"Science"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"6026","DOI":"10.1109\/TGRS.2013.2294602","article-title":"The normalization of surface anisotropy effects present in SEVIRI reflectances by using the MODIS BRDF method","volume":"52","author":"Proud","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.rse.2005.11.013","article-title":"Analysing NDVI for the African continent using the geostationary meteosat second generation SEVIRI sensor","volume":"101","author":"Fensholt","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1653","DOI":"10.1109\/JSTARS.2013.2259577","article-title":"Phenology estimation from Meteosat second generation data","volume":"6","author":"Sobrino","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.1109\/TGRS.2013.2247611","article-title":"Deriving vegetation phenological time and trajectory information over Africa using SEVIRI daily LAI","volume":"52","author":"Guan","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"4867","DOI":"10.1109\/TGRS.2016.2552462","article-title":"A comparison of tropical rainforest phenology retrieved from geostationary (SEVIRI) and polar-orbiting (MODIS) sensors across the Congo Basin","volume":"54","author":"Yan","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","first-page":"187","article-title":"Retrieval of crop biophysical parameters from Sentinel-2 remote sensing imagery","volume":"80","author":"Xie","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Kalluri, S., Daniels, J., Gunshor, M., Lindsey, D., Schmit, T., and Wu, X. (2018, January 22\u201327). The First Year of Advanced Baseline Imager. Proceedings of the 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8517459"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Zhang, Y., Li, Z., Li, J., and Zhang, X. (August, January 28). The effects of sun-viewer geometry on sun-induced fluorescence and its relationship with gross primary production. Proceedings of the 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8898345"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-019-52076-x","article-title":"Improved characterisation of vegetation and land surface seasonal dynamics in central Japan with Himawari-8 hypertemporal data","volume":"9","author":"Miura","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Li, S., Wang, W., Hashimoto, H., Xiong, J., Vandal, T., Yao, J., Qian, L., Icchi, K., Lyapustin, A., and Wang, Y. (2019). First provisional land surface reflectance product from geostationary satellite Himawari-8 AHI. Remote Sens., 11.","DOI":"10.3390\/rs11242990"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Fang, L., Zhan, X., Schull, M., Kalluri, S., Laszlo, I., Yu, P., Carter, C., Hain, C., and Anderson, M. (2019). Evapotranspiration data product from NESDIS GET-D system upgrated for GOES-16 ABI observations. Remote Sens., 11.","DOI":"10.3390\/rs11222639"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Chen, Y., Sun, K., Chen, C., Bai, T., Park, T., Wang, W., Nemani, R.R., and Myneni, R.B. (2019). Generation and evaluation of LAI and fPAR products from Himawari-8 Advanced Himawari Imager (AHI) data. Remote Sens., 11.","DOI":"10.3390\/rs11131517"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Wheeler, K.I., and Dietz, M.C. (2019). A statistical model for estimating midday NDVI from the geostationary operational environmental satellite (GOES) 16 and 17. Remote Sens., 11.","DOI":"10.3390\/rs11212507"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"151","DOI":"10.2151\/jmsj.2016-009","article-title":"An introduction to Himawari-8\/9-Japan\u2019s new-generation geostationary meteorological satellites","volume":"94","author":"Bessho","year":"2016","journal-title":"J. Meteorol. Soc. Jpn."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"5895","DOI":"10.5194\/bg-13-5895-2016","article-title":"An introduction to the Australian and New Zealand flux tower network-OzFlux","volume":"13","author":"Beringer","year":"2016","journal-title":"Biogeosciences"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1016\/j.rse.2012.09.002","article-title":"Multi-Angle Implementation of Atmospheric Correction for MODIS (MAIAC): 3. Atmospheric Correction","volume":"127","author":"Lyapustin","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_49","unstructured":"Lyapustin, A., and Wang, Y. (2020, February 27). MAIAC-Multi-Angle Implementation of Atmospheric Correction for MODIS: Algorithm Theoretical Basis Document, v1.0, Available online: https:\/\/ladsweb.modaps.eosdis.nasa.gov\/missions-and-measurements\/modis\/MAIAC_ATBD_v1.pdf."},{"key":"ref_50","first-page":"280","article-title":"A method to improve geometric accuracy of Himawari-8\/AHI \u201cJapan Area\u201d data","volume":"54","author":"Matsuoka","year":"2016","journal-title":"J. Jpn. Soc. Photogramm. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"977","DOI":"10.1109\/36.841980","article-title":"An algorithm for the retrieval of albedo from space using semiempirical BRDF models","volume":"38","author":"Lucht","year":"2000","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1080\/02757250009532395","article-title":"Considerations in the parametric modeling of BRDF and albedo from multiangular satellite sensor observations","volume":"18","author":"Lucht","year":"2000","journal-title":"Remote Sens. Rev."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"20455","DOI":"10.1029\/92JD01411","article-title":"A bidirectional reflectance model of the Earth\u2019s surface for the correction of remote sensing data","volume":"97","author":"Roujean","year":"1992","journal-title":"J. Geophys. Res."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1109\/36.134078","article-title":"Geometric-optical bidirectional reflectance modeling of the discrete crown vegetation canopy: Effect of crown shape and mutual shadowing","volume":"30","author":"Li","year":"1992","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_55","unstructured":"Strahler, A.H., Lucht, W., Schaaf, C.B., Tsang, T., Gao, F., Li, X., Muller, J.P., Lewis, P., and Barnsley, M.J. (2020, March 31). MODIS BRDF\/Albedo Product: Algorithm Theoretical Basis Document Versin 5.0, Available online: https:\/\/modis.gsfc.nasa.gov\/data\/atbd\/atbd_mod09.pdf."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"3","DOI":"10.5194\/isprs-annals-III-7-3-2016","article-title":"Bidirectional reflectance modeling of the geostationary sensor Himawari-8\/AHI using a kernal-driven BRDF model","volume":"III-7","author":"Matsuoka","year":"2016","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/S0034-4257(00)00125-5","article-title":"A comparison of satellite-derived spectral albedos to ground-based broadband albedo measurements modeled to satellite spatial scale for a semidesert landscape","volume":"74","author":"Lucht","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"21077","DOI":"10.1029\/95JD02371","article-title":"On the derivation of kernels for kernel-driven models of bidirectional reflectance","volume":"100","author":"Wanner","year":"1995","journal-title":"J. Geophys. Res."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1186","DOI":"10.1109\/36.338367","article-title":"Topographic effects on bidirectional and hemispherical reflectances calculated with a geometric-optical canopy model","volume":"32","author":"Schaaf","year":"1994","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/S0034-4257(02)00135-9","article-title":"Monitoring vegetation phenology using MODIS","volume":"84","author":"Zhang","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/j.rse.2005.10.021","article-title":"Improved monitoring of vegetation dynamics at very high latitudes: A new method using MODIS NDVI","volume":"100","author":"Beck","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"672","DOI":"10.1111\/j.1365-2486.2006.01123.x","article-title":"Variations in satellite-derived phenology in China\u2019s temperate vegetation","volume":"12","author":"Piao","year":"2006","journal-title":"Glob. Chang. Biol."},{"key":"ref_63","unstructured":"Tan, B., Morisette, J., Wolfe, R., Gao, F., Nightingale, J.M., Pedelty, J., and Ederer, G. (2020, February 27). User Guide for MOD09PHN and MOD15PHN Version 3.0. Available online: http:\/\/citeseerx.ist.psu.edu\/viewdoc\/download;jsessionid=416AB95FB7EC158E94B0BB21AFC168F9?doi=10.1.1.492.1979&rep=rep1&type=pdf."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.rse.2014.03.001","article-title":"Modeling growing season phenology in North American forests using seasonal mean vegetation indices from MODIS","volume":"147","author":"Wu","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1109\/TGRS.1995.8746029","article-title":"The interpretation of spectral vegetation indices","volume":"33","author":"Myneni","year":"1995","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_66","first-page":"291","article-title":"Remote Sensing of Ecosystem","volume":"12","author":"Huete","year":"2011","journal-title":"Adv. Environ. Remote Sens. Sens. Algorithms Appl."},{"key":"ref_67","first-page":"309","article-title":"Monitoring vegetation systems in the Great Plains with ERTS","volume":"Volume I","author":"Freden","year":"1973","journal-title":"Third Earth Resources Technology Satellite Symposium. Technical Presentations, Section A"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.rse.2013.07.030","article-title":"Spatial patterns and temporal dynamics in savanna vegetation phenology across the North Australian Tropical Transect","volume":"139","author":"Ma","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1002\/2015JG003144","article-title":"Abrupt shifts in phenology and vegetation productivity under climate extremes","volume":"120","author":"Ma","year":"2015","journal-title":"J. Geophys. Res. Biogeosci."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Wang, D., and Liang, S. (2008, January 8\u201311). Singular spectrum analysis for filling gaps and reducing uncertainties of MODIS Land Products. Proceedings of the IGARSS 2008\u20132008 IEEE International Geoscience and Remote Sensing Symposium, Boston, MA, USA.","DOI":"10.1109\/IGARSS.2008.4780153"},{"key":"ref_71","unstructured":"Aban, J.L.E., and Tateishi, R. (2020, March 31). Application of Singular Spectrum Analysis (SSA) for the Reconstruction of Annual Phenological Profiles of NDVI Time Series Data. The 24th Proceedings of Asian Association of Remote Sensing, Section 11. Data Processing: Data Fusion. Available online: https:\/\/a-a-r-s.org\/proceeding\/ACRS2004\/Papers\/DF204-7.htm."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"151","DOI":"10.5194\/npg-13-151-2006","article-title":"Spatio-temporal filling of missing points in geophysical datasets","volume":"13","author":"Kondrashov","year":"2006","journal-title":"Nonlinear Process. Geophys."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1038\/nature11836","article-title":"Ecosystem resilience despite large-scale altered hydroclimatic conditions","volume":"494","author":"Moran","year":"2013","journal-title":"Nature"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1029\/2012JG002136","article-title":"Extreme precipitation patterns and reductions of terrestrial ecosystem production across biomes","volume":"118","author":"Zhang","year":"2013","journal-title":"J. Geophys. Res. Biogeosci."},{"key":"ref_75","unstructured":"Lymburner, A., Tan, P., McIntyre, A., Thankappan, M., and Sixsmith, J. (2020, April 23). Dynamic Land Cover Dataset Version 2.1. Geoscience Australia, Canberra, Available online: https:\/\/ecat.ga.gov.au\/geonetwork\/srv\/eng\/catalog.search#\/metadata\/83868."},{"key":"ref_76","unstructured":"R Core Team (2019). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, R Core Team."},{"key":"ref_77","unstructured":"Huete, A., Liu, H., and van Leeuwen, W. (1997, January 3\u20138). The use of vegetation indices in forested regions: Issues of linearity and saturation. Proceedings of the 1997 IEEE International Geoscience and Remote Sensing Symposium, Singapore."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Shuai, Y., Schaaf, C., Strahler, A., Liu, J., and Jiao, Z. (2008). Quality assessment of BRDF\/albedo retrievals in MODIS operational system. Geophys. Res. Lett., 35.","DOI":"10.1029\/2007GL032568"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.rse.2013.08.019","article-title":"Comparison of different BRDF correction methods to generate daily normalized MODIS 250 m time series","volume":"140","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.rse.2018.02.001","article-title":"Capturing rapid land surface dynamics with Collection V006 MODIS BRDF\/NBAR\/Albedo (MCD43) products","volume":"207","author":"Wang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Liu, J., Shang, J., Qian, B., Huffman, T., Zhang, Y., Dong, T., Jing, Q., and Martin, T. (2019). Crop yield estimation using time-series MODIS data and the effects of cropland masks in Ontario, Canada. Remote Sens., 11.","DOI":"10.3390\/rs11202419"},{"key":"ref_82","first-page":"71","article-title":"Evaluating land surface phenology from the Advanced Himawari Imager using observations from MODIS and the Phenological Eyes network","volume":"79","author":"Yan","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1016\/j.eja.2006.10.007","article-title":"A simple model of regional wheat yield based on NDVI data","volume":"26","author":"Moriondo","year":"2007","journal-title":"Eur. J. Agron."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.agrformet.2018.06.009","article-title":"Statistical modelling of crop yield in Central Europe using climate data and remote sensing vegetation indeices","volume":"260","author":"Kern","year":"2018","journal-title":"Agric. For. Meteorol."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Kobayashi, H., Nagai, S., Kim, Y., Yang, W., Ikeda, K., Ikawa, H., Nagano, H., and Suzuki, R. (2018). In situ observations reveal how spectral reflectance responds to growing season phenology of an open evergreen forest in Alaska. Remote Sens., 10.","DOI":"10.3390\/rs10071071"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"702","DOI":"10.1016\/j.ecolind.2015.08.013","article-title":"Estimating crop yield using a satellite-based light use efficiency model","volume":"60","author":"Yuan","year":"2016","journal-title":"Ecol. Indic."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"4229","DOI":"10.3390\/rs5094229","article-title":"Recent declines in warming and vegetation greening trends over pan-Arctic tundra","volume":"5","author":"Bhatt","year":"2013","journal-title":"Remote Sens."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Zhang, X., Kondragunta, S., Ram, J., Schmidt, C., and Huang, H.C. (2012). Near-real-time global biomass burning emissions product from geostationary satellite constellation. J. Geophys. Res. Atmos., 117.","DOI":"10.1029\/2012JD017459"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Jin, N., Tao, B., Ren, W., Feng, M., Sun, R., He, L., Zhuang, W., and Yu, Q. (2016). Mapping irrigated and rainfed wheat areas using multi-temporal satellite data. Remote Sens., 8.","DOI":"10.3390\/rs8030207"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"1056","DOI":"10.1109\/TGRS.2012.2228654","article-title":"Overview of intercalibration of satellite instruments","volume":"51","author":"Chander","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1109\/MGRS.2018.2859814","article-title":"Multisensor Normalised Difference Vegetation Index intercalibration: A comprehensive overview of the causes of and solutions for multisensor differences","volume":"6","author":"Fan","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"540","DOI":"10.1016\/j.rse.2018.02.063","article-title":"Spectral band unification and inter-calibration of Himawari AHI with MODIS and VIIRS: Constructing virtual dual-view remote sensors from geostationary and low-Earth-orbiting sensors","volume":"209","author":"Qin","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Adachi, Y., Kikuchi, R., Obata, K., and Yoshioka, H. (2019). Relative Azimuthal-Angle Matching (RAM): A screening method for GEO-LEO reflectance comparison in middle latitude forests. Remote Sens., 11.","DOI":"10.3390\/rs11091095"},{"key":"ref_94","first-page":"229","article-title":"UAV based BRDF-measurements of agricultural surfaces with PFIFFIkus","volume":"38","author":"Niemeyer","year":"2011","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"2415","DOI":"10.1175\/1520-0477(2001)082<2415:FANTTS>2.3.CO;2","article-title":"FLUXNET: A new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities","volume":"82","author":"Baldocchi","year":"2001","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Aubinet, M., Vesala, T., and Papale, D. (2012). Eddy Covariance: A Practical Guide to Measurement and Data Analysis, Springer.","DOI":"10.1007\/978-94-007-2351-1"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.agrformet.2004.12.004","article-title":"Carbon and water fluxes over a temperate Eucalyptus forest and a tropical wet\/dry savanna in Australia: Measurements and comparison with MODIS remote sensing estimates","volume":"129","author":"Leuning","year":"2005","journal-title":"Agric. For. Meteorol."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/j.rse.2012.02.019","article-title":"Intercomparison of MODIS albedo retrievals and in situ measurements across the global FLUXNET network","volume":"121","author":"Cescatti","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1016\/j.agrformet.2015.09.005","article-title":"Improving the performance of remote sensing models for capturing intra-and inter-annual variations in daily GPP: An analysis using global FLUXNET tower data","volume":"214","author":"Verma","year":"2015","journal-title":"Agric. For. Meteorol."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.agrformet.2016.11.193","article-title":"Land surface phenology derived from normalized difference vegetation index (NDVI) at global FLUXNET sites","volume":"233","author":"Wu","year":"2017","journal-title":"Agric. For. Meteorol."},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Gonsamo, A., Chen, J.M., Price, D.T., Kurz, W.A., and Wu, C. (2012). Land surface phenology from optical satellite measurements and CO2 eddy covariance technique. J. Geophys. Res. Biogeosci., 117.","DOI":"10.1029\/2012JG002070"},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"170165","DOI":"10.1038\/sdata.2017.165","article-title":"A global moderate resolution dataset of gross primary production of vegetation for 2000\u20132016","volume":"4","author":"Zhang","year":"2017","journal-title":"Sci. Data"},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"5587","DOI":"10.5194\/bg-13-5587-2016","article-title":"MODIS vegetation products as proxies of photosynthetic potential along a gradient of meteorologically and biologically driven ecosystem productivity","volume":"13","author":"Huete","year":"2016","journal-title":"Biogeosciences"},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Jung, M., Reichstein, M., Margolis, H.A., Cescatti, A., Richardson, A.D., Altaf Arain, M., Arneth, A., Bernhofer, C., Bonal, D., and Chen, J. (2011). Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations. J. Geophys. Res. Biogeosci., 116.","DOI":"10.1029\/2010JG001566"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1016\/j.rse.2015.12.017","article-title":"Matching the phenology of Net Ecosystem Exchange and vegetation indices estimated with MODIS and FLUXNET in-situ observation","volume":"174","author":"Balzarolo","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1093\/treephys\/tpn040","article-title":"Influence of spring phenology on seasonal and annual carbon balance in two contrasting New England forests","volume":"29","author":"Richardson","year":"2009","journal-title":"Tree Physiol."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11284-014-1239-x","article-title":"Development of an in situ observation network for terrestrial ecological remote sensing: The Phenological Eyes Network (PEN)","volume":"30","author":"Nasahara","year":"2015","journal-title":"Ecol. Res."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"16226","DOI":"10.3390\/rs71215825","article-title":"Application-ready expedited MODIS data for operational land surface monitoring of vegetation condition","volume":"7","author":"Brown","year":"2015","journal-title":"Remote Sens."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"7979","DOI":"10.5194\/bg-12-5995-2015","article-title":"Interpreting canopy development and physiology using the EUROPhen camera network at flux sites","volume":"12","author":"Wingate","year":"2015","journal-title":"Biogeosciences"},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1007\/s00484-013-0679-2","article-title":"The spatial pattern of leaf phenology and its response to climate change in China","volume":"58","author":"Dai","year":"2014","journal-title":"Int. J. Biometeorol."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/8\/1339\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T14:09:07Z","timestamp":1760364547000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/8\/1339"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,23]]},"references-count":110,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2020,4]]}},"alternative-id":["rs12081339"],"URL":"https:\/\/doi.org\/10.3390\/rs12081339","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,4,23]]}}}