Performance assessment of the Sentinel-2 LAI products and data fusion techniques for developing new LAI datasets over the high-altitude Himalayan forests

References

• Adams J, Lewis P, Disney M. 2018. Decoupling canopy structure and leaf biochemistry: testing the utility of directional area scattering factor (DASF). Rem Sens. 10(12):1911. doi: 10.3390/rs10121911.
   Google Scholar
• Anderson B, Moore J. 1979. Optimal filtering. Eaglewood cliffs, NJ: Prentice–Hall.
   Google Scholar
• Baret F, Weiss M, Allard D, Garrigues S, Leroy M, Jeanjean H, Fernandes R, Myneni R. 2005. VALERI: a network of sites and a methodology for the validation of medium spatial resolution land satellite products; [accessed 2023 Aug 2]. https://www.academia.edu/download/86725674/VALERI_SiteNetwork.pdf.
   Google Scholar
• Baret F, Morissette JT, Fernandes RA, Champeaux JL, Myneni RB, Chen J, Plummer S, Weiss M, Bacour C, Garrigues S, et al. 2006. Evaluation of the representativeness of networks of sites for the global validation and intercomparison of land biophysical products: proposition of the CEOS-BELMANIP. IEEE Trans Geosci Rem Sens. 44(7):1794–1803. doi: 10.1109/TGRS.2006.876030.
   Web of Science ®Google Scholar
• Bélisle CJP. 1992. Convergence theorems for a class of simulated annealing algorithms on ℝ d. J Appl Probab. 29(4):885–895. doi: 10.2307/3214721.
   Web of Science ®Google Scholar
• Boegh E, Houborg R, Bienkowski J, Braban CF, Dalgaard T, Van Dijk N, Dragosits U, Holmes E Magliulo V, Schelde K, et al. 2013. Remote sensing of LAI, chlorophyll and leaf nitrogen pools of crop- and grasslands in five European landscapes. Biogeosciences. 10:6279–6307. doi: 10.5194/bg-10-6279-2013
   Google Scholar
• Bright RM,Zhao K,Jackson RB,Cherubini F. 2015. Quantifying surface albedo and other direct biogeophysical climate forcings of forestry activities. Glob Chang Biol. 21(9):3246–3266. doi: 10.1111/gcb.12951.25914206
   PubMed Web of Science ®Google Scholar
• Brown LA,Fernandes R,Djamai N,Meier C,Gobron N,Morris H,Canisius F,Bai G,Lerebourg C,Lanconelli C, et al. 2021. Validation of baseline and modified Sentinel-2 Level 2 Prototype Processor leaf area index retrievals over the United States. ISPRS J. Photogramm. Remote Sens. 175:71–87. doi: 10.1016/j.isprsjprs.2021.02.020.
   Web of Science ®Google Scholar
• Brown JF, Tollerud HJ, Barber CP, Zhou Q, Dwyer JL, Vogelmann JE, Loveland TR, Woodcock CE, Stehman SV, Zhu Z, et al. 2020. Lessons learned implementing an operational continuous United States national land change monitoring capability: the Land Change Monitoring, Assessment, and Projection (LCMAP) approach. Rem Sens Environ. 238:111356. doi: 10.1016/j.rse.2019.111356.
   Web of Science ®Google Scholar
• Camacho F, Cernicharo J, Lacaze R, Baret F, Weiss M. 2013. GEOV1: LAI, FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part 2: validation and intercomparison with reference products. Rem Sens Environ. 137:310–329. doi: 10.1016/j.rse.2013.02.030.
   Web of Science ®Google Scholar
• Chen JM, Pavlic G, Brown L, Cihlar J, Leblanc SG, White HP, Hall RJ, Peddle DR, King DJ, Trofymow JA, et al. 2002. Derivation and validation of Canada-wide coarse-resolution leaf area index maps using high-resolution satellite imagery and ground measurements. Rem Sens Environ. 80(1):165–184. doi: 10.1016/S0034-4257(01)00300-5.
   Web of Science ®Google Scholar
• Danner M, Berger K, Wocher M, Mauser W, Hank T. 2021. Efficient RTM-based training of machine learning regression algorithms to quantify biophysical & biochemical traits of agricultural crops. ISPRS J Photogramm Rem Sens. 173:278–296. doi: 10.1016/j.isprsjprs.2021.01.017.
   Web of Science ®Google Scholar
• De Kauwe MG, Disney MI, Quaife T, Lewis P, Williams M. 2011. An assessment of the MODIS collection 5 leaf area index product for a region of mixed coniferous forest. Rem Sens Environ. 115(2):767–780. doi: 10.1016/j.rse.2010.11.004.
   Web of Science ®Google Scholar
• Denny CK, Nielsen SE. 2017. Spatial heterogeneity of the forest canopy scales with the heterogeneity of an understory shrub based on fractal analysis. Forests. 8(5):146. doi: 10.3390/f8050146.
   Web of Science ®Google Scholar
• Djamai N, Fernandes R, Weiss M, McNairn H, Goïta K. 2019. Validation of the Sentinel Simplified Level 2 Product Prototype Processor (SL2P) for mapping cropland biophysical variables using Sentinel-2/MSI and Landsat-8/OLI data. Rem Sens Environ. 225:416–430. doi: 10.1016/j.rse.2019.03.020.
   Web of Science ®Google Scholar
• Durbin J, Koopman SJ. 2013. Time series analysis by state space methods, 2nd edn, Oxford Statistical Science Series, Oxford University Press. doi: 10.1093/ACPROF:OSO/9780199641178.001.0001.
   Google Scholar
• Fang H, Baret F, Plummer S, Schaepman-Strub G. 2019. An overview of global leaf area index (LAI): methods, products, validation, and applications. Rev Geophys. 57(3):739–799. doi: 10.1029/2018RG000608.
   Web of Science ®Google Scholar
• Fang H, Wei S, Liang S. 2012. Validation of MODIS and CYCLOPES LAI products using global field measurement data. Rem Sens Environ. 119:43–54. doi: 10.1016/j.rse.2011.12.006.
   Web of Science ®Google Scholar
• Fang H, Zhang Y, Wei S, Li W, Ye Y, Sun T, Liu W. 2019. Validation of global moderate resolution leaf area index (LAI) products over croplands in northeastern Chinas. Rem Sens Environ. 233:111377. doi: 10.1016/j.rse.2019.111377.
   Web of Science ®Google Scholar
• Frazer GW, Wulder MA, Niemann KO. 2005. Simulation and quantification of the fine-scale spatial pattern and heterogeneity of forest canopy structure: a lacunarity-based method designed for analysis of continuous canopy heights. For Ecol Manage. 214(1–3):65–90. doi: 10.1016/j.foreco.2005.03.056.
   Web of Science ®Google Scholar
• García-Haro FJ, Campos-Taberner M, Muñoz-Marí J, Laparra V, Camacho F, Sánchez-Zapero J, Camps-Valls G. 2018. Derivation of global vegetation biophysical parameters from EUMETSAT polar system. ISPRS J Photogramm Rem Sens. 139:57–74. doi: 10.1016/j.isprsjprs.2018.03.005.
   Web of Science ®Google Scholar
• Goodchild MF. 2011. Scale in GIS: an overview. Geomorphology. 130(1–2):5–9. doi: 10.1016/j.geomorph.2010.10.004.
   Web of Science ®Google Scholar
• Hu Q, Yang J, Xu B, Huang J, Memon MS, Yin G, Zeng Y, Zhao J, Liu K. 2020. Evaluation of global decametric-resolution LAI, FAPAR and FVC estimates derived from sentinel-2 imagery. Rem Sens. 12(6):912. doi: 10.3390/rs12060912.
   Google Scholar
• Jacquemoud S, Baret F. 1990. PROSPECT: a model of leaf optical properties spectra. Rem Sens Environ. 34(2):75–91. doi: 10.1016/0034-4257(90)90100-Z.
   Web of Science ®Google Scholar
• Jin H, Li A, Bian J, Nan X, Zhao W, Zhang Z, Yin G. 2017. Intercomparison and validation of MODIS and GLASS leaf area index (LAI) products over mountain areas: a case study in southwestern China. Int J Appl Earth Obs Geoinf. 55:52–67. doi: 10.1016/j.jag.2016.10.008.
   Web of Science ®Google Scholar
• Joshi N, Baumann M, Ehammer A, Fensholt R, Grogan K, Hostert P, Jepsen MR, Kuemmerle T, Meyfroidt P, Mitchard ETA, et al. 2016. A review of the application of optical and radar remote sensing data fusion to land use mapping and monitoring. Rem Sens. 8(1):70. doi: 10.3390/rs8010070.
   Web of Science ®Google Scholar
• Kishore BSPC, Kumar A, Saikia P, Lele N, Pandey AC, Srivastava P, Bhattacharya BK, Khan ML. 2020. Major forests and plant species discrimination in Mudumalai forest region using airborne hyperspectral sensing. J Asia-Pac Biodivers. 13(4):637–651. doi: 10.1016/j.japb.2020.07.001.
   Web of Science ®Google Scholar
• Knyazikhin Y, Schull MA, Stenberg P, Mõttus M, Rautiainen M, Yang Y, Marshak A, Carmona PL, Kaufmann RK, Lewis P, et al. 2013. Hyperspectral remote sensing of foliar nitrogen content. Proc Natl Acad Sci USA. 110(3):E185–E192. doi: 10.1073/PNAS.1210196109.
   PubMed Web of Science ®Google Scholar
• Koopman SJ, Durbin J. 2003. Filtering and smoothing of state vector for diffuse state–space models. J Time Ser Anal. 24(1):85–98. doi: 10.1111/1467-9892.00294.
   Web of Science ®Google Scholar
• Krzywinski M,Altman N. 2015. Multiple linear regression. Nat Methods. 12(12):1103–1104. doi: 10.1038/nmeth.3665.26962577
   PubMed Web of Science ®Google Scholar
• Le Maire G, François C, Soudani K, Berveiller D, Pontailler JY, Bréda N, Genet H, Davi H, Dufrêne E. 2008. Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content, leaf mass per area, leaf area index and leaf canopy biomass. Rem Sens Environ. 112(10):3846–3864. doi: 10.1016/j.rse.2008.06.005.
   Web of Science ®Google Scholar
• Li XR, Zhu Y, Wang J, Han C. (2003). Optimal linear estimation fusion – part I: unified fusion rules. IEEE Trans Inform Theory, 49(9):2192–2208. doi: 10.1109/TIT.2003.815774.
   Web of Science ®Google Scholar
• Mayr MJ, Samimi C. 2015. Comparing the dry season in-situ leaf area index (LAI) derived from high-resolution rapid eye imagery with MODIS LAI in a Namibian savanna. Rem Sens. 7(4):4834–4857. doi: 10.3390/rs70404834.
   Google Scholar
• Miller JB. 1967. A formula for average foliage density. Aust J Bot. 15(1):141–144. doi: 10.1071/BT9670141.
   Web of Science ®Google Scholar
• Morisette JT, Baret F, Privette JL, Myneni RB, Nickeson JE, Garrigues S, Shabanov NV, Weiss M, Fernandes RA, Leblanc SG, et al. 2006. Validation of global moderate-resolution LAI products: a framework proposed within the CEOS land product validation subgroup. IEEE Trans Geosci Rem Sens. 44(7):1804–1817. doi: 10.1109/TGRS.2006.872529.
   Web of Science ®Google Scholar
• Myneni RB, Hoffman S, Knyazikhin Y, Privette JL, Glassy J, Tian Y, Wang Y, Song X, Zhang Y, Smith GR, et al. 2002. Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data. Rem Sens Environ. 83(1–2):214–231. doi: 10.1016/S0034-4257(02)00074-3.
   Web of Science ®Google Scholar
• Pettorelli N, Schulte To Bühne H, Tulloch A, Dubois G, Macinnis-Ng C, Queirós AM, Keith DA, Wegmann M, Schrodt F, Stellmes M, et al. 2018. Satellite remote sensing of ecosystem functions: opportunities, challenges and way forward. Rem Sens Ecol Conserv. 4(2):71–93. doi: 10.1002/rse2.59.
   Web of Science ®Google Scholar
• Richardson AD, Dail DB, Hollinger DY. 2011. Leaf area index uncertainty estimates for model-data fusion applications. Agric Meteorol. 151(9):1287–1292. doi: 10.1016/j.agrformet.2011.05.009.
   Google Scholar
• Shameem SA, Soni P, Bhat GA. 2010. Comparative study of herb layer diversity in lower Dachigam National Park, Kashmir Himalaya, India. Int J Biodivers Conserv. 2(10):308–315. http://www.academicjournals.org/ijbc.
   Google Scholar
• Simon Blessing RG. 2019. Algorithm theoretical basis document: PROBA-V CDR and ICDR Surface Albedo v1.0. Technical Report Prepared for Copernicus Climate Change Service, July; p. 36–73.
   Google Scholar
• Srivastava PK, Han D, Rico Ramirez MA, Islam T. 2013. Comparative assessment of evapotranspiration derived from NCEP and ECMWF global datasets through weather research and forecasting model. Atmos Sci Lett. 14(2):118–125. doi: 10.1002/asl2.427.
   Web of Science ®Google Scholar
• Stenberg P, Manninen T. 2015. The effect of clumping on canopy scattering and its directional properties: a model simulation using spectral invariants. Int J Rem Sens. 36(19–20):5178–5191. doi: 10.1080/01431161.2015.1049383.
   Web of Science ®Google Scholar
• Sun SL. 2004. Multi-sensor optimal information fusion Kalman filters with applications. Aerosp Sci Technol. 8(1):57–62. doi: 10.1016/j.ast.2003.08.003.
   Web of Science ®Google Scholar
• Tian Y, Woodcock CE, Wang Y, Privette JL, Shabanov NV, Zhou L, Zhang Y, Buermann W, Dong J, Veikkanen B, et al. 2002. Multiscale analysis and validation of the MODIS LAI product II. Sampling strategy. Rem Sens Environ. 83(3):431–441. doi: 10.1016/S0034-4257(02)00058-5.
   Web of Science ®Google Scholar
• Verger A, Baret F, Camacho F. 2011. Optimal modalities for radiative transfer-neural network estimation of canopy biophysical characteristics: evaluation over an agricultural area with CHRIS/PROBA observations. Rem Sens Environ. 115(2):415–426. doi: 10.1016/j.rse.2010.09.012.
   Web of Science ®Google Scholar
• Verger A, Filella I, Baret F, Peñuelas J. 2016. Vegetation baseline phenology from kilometric global LAI satellite 1 products. Rem Sens Environ. 178:1–14. doi: 10.1016/j.rse.2016.02.057.
   Web of Science ®Google Scholar
• Verrelst J, Malenovský Z, Van der Tol C, Camps-Valls G, Gastellu-Etchegorry JP, Lewis P, North P, Moreno J. 2019. Quantifying vegetation biophysical variables from imaging spectroscopy data: a review on retrieval methods. Surv Geophys. 40(3):589–629. doi: 10.1007/s10712-018-9478-y.
   PubMed Web of Science ®Google Scholar
• Weiss M, De Beaufort L, Baret F, Allard D, Bruguier N, Marloie O. 2001. Mapping leaf area index measurements at different scales for the validation of large swath satellite sensors: first results of the VALERI project. Proceedings of the 8th International Symposium on Physical; [accessed 2023 Aug 2]. http://w3.avignon.inra.fr/valeri/documents/valeri-aussois.pdf.
   Google Scholar
• Wu H, Li ZL. 2009. Scale issues in remote sensing: a review on analysis, processing and modeling. Sensors. 9(3):1768–1793. doi: 10.3390/S90301768.
   PubMed Web of Science ®Google Scholar
• Wu J, Jones KB, Li H, Loucks OL. 2006. Scaling and uncertainty analysis in ecology: methods and applications. The Netherlands: Springer; p. 1–351. doi: 10.1007/1-4020-4663-4/COVER.
   Google Scholar
• Wu L, Qin Q, Liu X, Ren H, Wang J, Zheng X, Ye X, Sun Y. 2016. Spatial up-scaling correction for leaf area index based on the fractal theory. Rem Sens. 8(3):197. doi: 10.3390/rs8030197.
   Google Scholar
• Xiao Z, Liang S, Wang J, Xiang Y, Zhao X, Song J. 2016. Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. IEEE Trans Geosci Rem Sens. 54(9):5301–5318. doi: 10.1109/TGRS.2016.2560522.
   Web of Science ®Google Scholar
• Xu B, Li J, Park T, Liu Q, Zeng Y, Yin G, Zhao J, Fan W, Yang L, Knyazikhin Y, et al. 2018. An integrated method for validating long-term leaf area index products using global networks of site-based measurements. Rem Sens Environ. 209:134–151. doi: 10.1016/j.rse.2018.02.049.
   Web of Science ®Google Scholar
• Yan J, Yang Z, Li Z, Li X, Xin L, Sun L. 2016. Drivers of cropland abandonment in mountainous areas: a household decision model on farming scale in Southwest China. Land Use Policy. 57:459–469. doi: 10.1016/j.landusepol.2016.06.014.
   Web of Science ®Google Scholar
• Yan K, Park T, Yan G, Chen C, Yang B, Liu Z, Nemani RR, Knyazikhin Y, Myneni RB. 2016. Evaluation of MODIS LAI/FPAR product collection 6. Part 1: consistency and improvements. Rem Sens. 8(5):359. doi: 10.3390/rs8050359.
   Google Scholar
• Yin G, Li A, Jin H, Zhao W, Bian J, Qu Y, Zeng Y, Xu B. 2017. Derivation of temporally continuous LAI reference maps through combining the LAINet observation system with CACAO. Agric Meteorol. 233:209–221. doi: 10.1016/j.agrformet.2016.11.267.
   Google Scholar
• Zhang J, Atkinson P, Goodchild M. 2014. Scale in spatial information and analysis. https://books.google.com/books?hl=en&lr=&id=mOgxAwAAQBAJ&oi=fnd&pg=PP1&ots=8Oms57CxMt&sig=WruL-0LfG4j_RpkMauEeQMcpsBE.
   Google Scholar
• Zhang Y, Yan G, Bai Y. 2015. Sensitivity of topographic correction to the DEM spatial scale. IEEE Geosci Remote Sens Lett. 12(1):53–57. doi: 10.1109/LGRS.2014.2326000.
   Web of Science ®Google Scholar