{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T06:06:14Z","timestamp":1770271574843,"version":"3.49.0"},"reference-count":91,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,3,27]],"date-time":"2023-03-27T00:00:00Z","timestamp":1679875200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Guangdong Major Project of Basic and Applied Basic Research","award":["2021B0301030007"],"award-info":[{"award-number":["2021B0301030007"]}]},{"name":"Guangdong Major Project of Basic and Applied Basic Research","award":["42075160"],"award-info":[{"award-number":["42075160"]}]},{"name":"Guangdong Major Project of Basic and Applied Basic Research","award":["41730962"],"award-info":[{"award-number":["41730962"]}]},{"name":"Guangdong Major Project of Basic and Applied Basic Research","award":["42088101"],"award-info":[{"award-number":["42088101"]}]},{"name":"Guangdong Major Project of Basic and Applied Basic Research","award":["2017YFA0604300"],"award-info":[{"award-number":["2017YFA0604300"]}]},{"name":"Guangdong Major Project of Basic and Applied Basic Research","award":["311021009"],"award-info":[{"award-number":["311021009"]}]},{"name":"Guangdong Major Project of Basic and Applied Basic Research","award":["YSPTZX202143"],"award-info":[{"award-number":["YSPTZX202143"]}]},{"name":"Natural Science Foundation of China","award":["2021B0301030007"],"award-info":[{"award-number":["2021B0301030007"]}]},{"name":"Natural Science Foundation of China","award":["42075160"],"award-info":[{"award-number":["42075160"]}]},{"name":"Natural Science Foundation of China","award":["41730962"],"award-info":[{"award-number":["41730962"]}]},{"name":"Natural Science Foundation of China","award":["42088101"],"award-info":[{"award-number":["42088101"]}]},{"name":"Natural Science Foundation of China","award":["2017YFA0604300"],"award-info":[{"award-number":["2017YFA0604300"]}]},{"name":"Natural Science Foundation of China","award":["311021009"],"award-info":[{"award-number":["311021009"]}]},{"name":"Natural Science Foundation of China","award":["YSPTZX202143"],"award-info":[{"award-number":["YSPTZX202143"]}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["2021B0301030007"],"award-info":[{"award-number":["2021B0301030007"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["42075160"],"award-info":[{"award-number":["42075160"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["41730962"],"award-info":[{"award-number":["41730962"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["42088101"],"award-info":[{"award-number":["42088101"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["2017YFA0604300"],"award-info":[{"award-number":["2017YFA0604300"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["311021009"],"award-info":[{"award-number":["311021009"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["YSPTZX202143"],"award-info":[{"award-number":["YSPTZX202143"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)","award":["2021B0301030007"],"award-info":[{"award-number":["2021B0301030007"]}]},{"name":"Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)","award":["42075160"],"award-info":[{"award-number":["42075160"]}]},{"name":"Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)","award":["41730962"],"award-info":[{"award-number":["41730962"]}]},{"name":"Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)","award":["42088101"],"award-info":[{"award-number":["42088101"]}]},{"name":"Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)","award":["2017YFA0604300"],"award-info":[{"award-number":["2017YFA0604300"]}]},{"name":"Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)","award":["311021009"],"award-info":[{"award-number":["311021009"]}]},{"name":"Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)","award":["YSPTZX202143"],"award-info":[{"award-number":["YSPTZX202143"]}]},{"name":"the specific research fund of the Innovation Platform for Academicians of Hainan Province","award":["2021B0301030007"],"award-info":[{"award-number":["2021B0301030007"]}]},{"name":"the specific research fund of the Innovation Platform for Academicians of Hainan Province","award":["42075160"],"award-info":[{"award-number":["42075160"]}]},{"name":"the specific research fund of the Innovation Platform for Academicians of Hainan Province","award":["41730962"],"award-info":[{"award-number":["41730962"]}]},{"name":"the specific research fund of the Innovation Platform for Academicians of Hainan Province","award":["42088101"],"award-info":[{"award-number":["42088101"]}]},{"name":"the specific research fund of the Innovation Platform for Academicians of Hainan Province","award":["2017YFA0604300"],"award-info":[{"award-number":["2017YFA0604300"]}]},{"name":"the specific research fund of the Innovation Platform for Academicians of Hainan Province","award":["311021009"],"award-info":[{"award-number":["311021009"]}]},{"name":"the specific research fund of the Innovation Platform for Academicians of Hainan Province","award":["YSPTZX202143"],"award-info":[{"award-number":["YSPTZX202143"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Satellite-based leaf area index (LAI) products, such as the MODIS LAI, play an essential role in land surface and climate modeling research, from regional to global scales. However, data gaps and high-level noise can exist, thus limiting their applications to a broader scope. Our previous work has reprocessed the MODIS LAI Collection 5 (C5) product, and the reprocessed data have been widely used these years. In this study, the MODIS C6.1 LAI data were reprocessed to broaden its application as a successor. We updated the integrated two-step method that is used for MODIS C5 LAI and implemented it into the MODIS C6.1 LAI product. Comprehensive evaluations for the original and reprocessed products were conducted. The results showed that the reprocessed LAI data had better performance in validation against reference maps. In addition, the site scale time series of reprocessed data was much smoother and more consistent with adjacent values. The global scale comparison showed that, though the MODIS C6.1 LAI does have improvements in ground validation with LAI reference maps, its spatial continuity, temporal continuity, and consistency showed little improvement when compared to C5. In contrast, the reprocessed data were more spatiotemporally continuous and consistent. Based on this evaluation, some suggestions for using various MODIS LAI products were given. This study assessed the quality of these different versions of MODIS LAI products and demonstrated the improvement of the reprocessed C6.1 data, which we recommended for use as a substitute for the reprocessed C5 data in land surface and climate modeling.<\/jats:p>","DOI":"10.3390\/rs15071780","type":"journal-article","created":{"date-parts":[[2023,3,27]],"date-time":"2023-03-27T02:18:27Z","timestamp":1679883507000},"page":"1780","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Reprocessed MODIS Version 6.1 Leaf Area Index Dataset and Its Evaluation for Land Surface and Climate Modeling"],"prefix":"10.3390","volume":"15","author":[{"given":"Wanyi","family":"Lin","sequence":"first","affiliation":[{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai 519082, China"}]},{"given":"Hua","family":"Yuan","sequence":"additional","affiliation":[{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai 519082, China"}]},{"given":"Wenzong","family":"Dong","sequence":"additional","affiliation":[{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai 519082, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3395-9878","authenticated-orcid":false,"given":"Shupeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai 519082, China"}]},{"given":"Shaofeng","family":"Liu","sequence":"additional","affiliation":[{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai 519082, China"}]},{"given":"Nan","family":"Wei","sequence":"additional","affiliation":[{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai 519082, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3732-1978","authenticated-orcid":false,"given":"Xingjie","family":"Lu","sequence":"additional","affiliation":[{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai 519082, China"}]},{"given":"Zhongwang","family":"Wei","sequence":"additional","affiliation":[{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai 519082, China"}]},{"given":"Ying","family":"Hu","sequence":"additional","affiliation":[{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai 519082, China"}]},{"given":"Yongjiu","family":"Dai","sequence":"additional","affiliation":[{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai 519082, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1029\/2007JG000635","article-title":"Validation and intercomparison of global Leaf Area Index products derived from remote sensing data","volume":"113","author":"Garrigues","year":"2008","journal-title":"J. Geophys. Res. Biogeosci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1016\/j.rse.2012.12.027","article-title":"GEOV1: LAI and FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part1: Principles of development and production","volume":"137","author":"Baret","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3473","DOI":"10.1109\/JSTARS.2014.2328632","article-title":"Near Real-Time Vegetation Monitoring at Global Scale","volume":"7","author":"Verger","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"32257","DOI":"10.1029\/98JD02462","article-title":"Synergistic algorithm for estimating vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from MODIS and MISR data","volume":"103","author":"Knyazikhin","year":"1998","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/S0034-4257(02)00074-3","article-title":"Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data","volume":"83","author":"Myneni","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Yan, K., Park, T., Yan, G., Chen, C., Yang, B., Liu, Z., Nemani, R.R., Knyazikhin, Y., and Myneni, R.B. (2016). Evaluation of MODIS LAI\/FPAR Product Collection 6. Part 1: Consistency and Improvements. Remote Sens., 8.","DOI":"10.3390\/rs8050359"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1885","DOI":"10.1109\/TGRS.2006.871215","article-title":"MODIS leaf area index products: From validation to algorithm improvement","volume":"44","author":"Yang","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"5301","DOI":"10.1109\/TGRS.2016.2560522","article-title":"Long-Time-Series Global Land Surface Satellite Leaf Area Index Product Derived From MODIS and AVHRR Surface Reflectance","volume":"54","author":"Xiao","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"112985","DOI":"10.1016\/j.rse.2022.112985","article-title":"Development of the GLASS 250-m leaf area index product (version 6) from MODIS data using the bidirectional LSTM deep learning model","volume":"273","author":"Ma","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/2012JG002084","article-title":"Retrospective retrieval of long-term consistent global leaf area index (1981\u20132011) from combined AVHRR and MODIS data","volume":"117","author":"Liu","year":"2012","journal-title":"J. Geophys. Res. Biogeosci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"927","DOI":"10.3390\/rs5020927","article-title":"Global Data Sets of Vegetation Leaf Area Index (LAI)3g and Fraction of Photosynthetically Active Radiation (FPAR)3g Derived from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) for the Period 1981 to 2011","volume":"5","author":"Zhu","year":"2013","journal-title":"Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Claverie, M., Matthews, J.L., Vermote, E.F., and Justice, C.O. (2016). A 30+ Year AVHRR LAI and FAPAR Climate Data Record: Algorithm Description and Validation. Remote Sens., 8.","DOI":"10.3390\/rs8030263"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1016\/j.rse.2013.02.030","article-title":"GEOV1: LAI, FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part 2: Validation and intercomparison with reference products","volume":"137","author":"Camacho","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.rse.2006.12.004","article-title":"Comparison and validation of MODIS and VEGETATION global LAI products over four BigFoot sites in North America","volume":"109","author":"Pisek","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/j.rse.2007.03.001","article-title":"LAI and fAPAR CYCLOPES global products derived from VEGETATION. Part 2: Validation and comparison with MODIS collection 4 products","volume":"110","author":"Weiss","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.rse.2011.12.006","article-title":"Validation of MODIS and CYCLOPES LAI products using global field measurement data","volume":"119","author":"Fang","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4381","DOI":"10.1080\/01431160500113393","article-title":"Time-series validation of MODIS land biophysical products in a Kalahari woodland, Africa","volume":"26","author":"Huemmrich","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Yan, K., Park, T., Yan, G., Liu, Z., Yang, B., Chen, C., Nemani, R.R., Knyazikhin, Y., and Myneni, R.B. (2016). Evaluation of MODIS LAI\/FPAR Product Collection 6. Part 2: Validation and Intercomparison. Remote Sens., 8.","DOI":"10.3390\/rs8060460"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1002\/jgrg.20051","article-title":"Characterization and intercomparison of global moderate resolution leaf area index (LAI) products: Analysis of climatologies and theoretical uncertainties","volume":"118","author":"Fang","year":"2013","journal-title":"J. Geophys. Res. Biogeosci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1038\/s41893-019-0220-7","article-title":"China and India lead in greening of the world through land-use management","volume":"2","author":"Chen","year":"2019","journal-title":"Nat. Sustain."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.foreco.2013.06.032","article-title":"Monitoring the seasonal and interannual variation of the carbon sequestration in a temperate deciduous forest with MODIS time series data","volume":"306","author":"Tang","year":"2013","journal-title":"For. Ecol. Manag."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"108704","DOI":"10.1016\/j.agrformet.2021.108704","article-title":"Revisiting dry season vegetation dynamics in the Amazon rainforest using different satellite vegetation datasets","volume":"312","author":"Xie","year":"2022","journal-title":"Agric. For. Meteorol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1039","DOI":"10.1080\/01431160903505310","article-title":"Integration of MODIS LAI and vegetation index products with the CSM\u2013CERES\u2013Maize model for corn yield estimation","volume":"32","author":"Fang","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1175\/JHM-D-13-063.1","article-title":"Influence of Leaf Area Index Prescriptions on Simulations of Heat, Moisture, and Carbon Fluxes","volume":"15","author":"Kala","year":"2014","journal-title":"J. Hydrometeorol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1029\/2007WR006562","article-title":"A simple surface conductance model to estimate regional evaporation using MODIS leaf area index and the Penman-Monteith equation","volume":"44","author":"Leuning","year":"2008","journal-title":"Water Resour. Res."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1120","DOI":"10.1016\/j.scitotenv.2019.06.516","article-title":"Assessment of five satellite-derived LAI datasets for GPP estimations through ecosystem models","volume":"690","author":"Xie","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1109\/LGRS.2013.2278782","article-title":"Retrieving Leaf Area Index From Landsat Using MODIS LAI Products and Field Measurements","volume":"11","author":"Gao","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_28","first-page":"15","article-title":"A Spatio-Temporal Enhancement Method for medium resolution LAI (STEM-LAI)","volume":"47","author":"Houborg","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinform."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1014","DOI":"10.1002\/eco.1426","article-title":"Performance of LAI-MODIS and the influence on drought simulation in a Mediterranean forest","volume":"7","author":"Chakroun","year":"2014","journal-title":"Ecohydrology"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.ecoinf.2015.12.007","article-title":"Spatio-temporal variation in terminal drought over western India using dryness index derived from long-term MODIS data","volume":"32","author":"Dhorde","year":"2016","journal-title":"Ecol. Inform."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.rse.2018.04.048","article-title":"Use of remote sensing indicators to assess effects of drought and human-induced land degradation on ecosystem health in Northeastern Brazil","volume":"213","author":"Mariano","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1016\/j.agrformet.2008.08.017","article-title":"Effective interpolation of incomplete satellite-derived leaf-area index time series for the continental United States","volume":"149","author":"Borak","year":"2009","journal-title":"Agric. For. Meteorol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.rse.2006.07.026","article-title":"Spatially and temporally continuous LAI data sets based on an integrated filtering method: Examples from North America","volume":"112","author":"Fang","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1109\/LGRS.2007.907971","article-title":"An Algorithm to Produce Temporally and Spatially Continuous MODIS-LAI Time Series","volume":"5","author":"Gao","year":"2008","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Lawrence, P.J., and Chase, T.N. (2007). Representing a new MODIS consistent land surface in the Community Land Model (CLM 3.0). J. Geophys. Res. Biogeosci., 112.","DOI":"10.1029\/2006JG000168"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1016\/j.rse.2010.12.010","article-title":"A simple and effective method for filling gaps in Landsat ETM+ SLC-off images","volume":"115","author":"Chen","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1016\/j.asr.2009.05.009","article-title":"A simplified data assimilation method for reconstructing time-series MODIS NDVI data","volume":"44","author":"Gu","year":"2009","journal-title":"Adv. Space Res."},{"key":"ref_38","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_39","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1016\/j.cageo.2004.05.006","article-title":"TIMESAT\u2014A program for analyzing time-series of satellite sensor data","volume":"30","author":"Eklundh","year":"2004","journal-title":"Comput. Geosci."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1585","DOI":"10.1080\/01431169208904212","article-title":"The Best Index Slope Extraction (BISE): A method for reducing noise in NDVI time-series","volume":"13","author":"Viovy","year":"1992","journal-title":"Int. J. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.proeng.2017.09.596","article-title":"Curve fitting of MODIS NDVI time series in the task of early crops identification by satellite images","volume":"201","author":"Vorobiova","year":"2017","journal-title":"Procedia Eng."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"4771","DOI":"10.1109\/TGRS.2013.2284489","article-title":"Spatial Interpolation to Predict Missing Attributes in GIS Using Semantic Kriging","volume":"52","author":"Bhattacharjee","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1109\/MSP.2013.2273004","article-title":"Image Inpainting: Overview and Recent Advances","volume":"31","author":"Guillemot","year":"2014","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2363","DOI":"10.1109\/TGRS.2008.2010454","article-title":"A Bandelet-Based Inpainting Technique for Clouds Removal From Remotely Sensed Images","volume":"47","author":"Maalouf","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1109\/MGRS.2015.2441912","article-title":"Missing Information Reconstruction of Remote Sensing Data: A Technical Review","volume":"3","author":"Shen","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1109\/LGRS.2006.869966","article-title":"A New Method for Retrieving Band 6 of Aqua MODIS","volume":"3","author":"Wang","year":"2006","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1109\/TGRS.2008.2003436","article-title":"Restoration of Aqua MODIS Band 6 Using Histogram Matching and Local Least Squares Fitting","volume":"47","author":"Rakwatin","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2015.03.009","article-title":"Sparse-based reconstruction of missing information in remote sensing images from spectral\/temporal complementary information","volume":"106","author":"Li","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"112632","DOI":"10.1016\/j.rse.2021.112632","article-title":"Long time-series NDVI reconstruction in cloud-prone regions via spatio-temporal tensor completion","volume":"264","author":"Chu","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"105100","DOI":"10.1016\/j.jastp.2019.105100","article-title":"Application of Long Short-Term Memory neural network model for the reconstruction of MODIS Land Surface Temperature images","volume":"194","author":"Arslan","year":"2019","journal-title":"J. Atmos. Sol.-Terr. Phys."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1109\/TGRS.2013.2237780","article-title":"Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance","volume":"52","author":"Xiao","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Yu, W., Li, J., Liu, Q., Zhao, J., Dong, Y., Zhu, X., Lin, S., Zhang, H., and Zhang, Z. (2021). Gap Filling for Historical Landsat Ndvi Time Series by Integrating Climate Data. Remote Sens., 13.","DOI":"10.3390\/rs13030484"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"111716","DOI":"10.1016\/j.rse.2020.111716","article-title":"Deep learning in environmental remote sensing: Achievements and challenges","volume":"241","author":"Yuan","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_54","first-page":"102640","article-title":"High-quality vegetation index product generation: A review of NDVI time series reconstruction techniques","volume":"105","author":"Li","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinform."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1171","DOI":"10.1016\/j.rse.2011.01.001","article-title":"Reprocessing the MODIS Leaf Area Index products for land surface and climate modelling","volume":"115","author":"Yuan","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1021\/ac60214a047","article-title":"Smoothing and Differentiation of Data by Simplified Least Squares Procedures","volume":"36","author":"Savitzky","year":"1964","journal-title":"Anal. Chem."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Fang, H., Liang, S., Kim, H.-Y., Townshend, J.R., Schaaf, C.L., Strahler, A.H., and Dickinson, R.E. (2007). Developing a spatially continuous 1 km surface albedo data set over North America from Terra MODIS products. J. Geophys. Res. Atmos., 112.","DOI":"10.1029\/2006JD008377"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1002\/2016MS000832","article-title":"New turbulent resistance parameterization for soil evaporation based on a pore-scale model: Impact on surface fluxes in CABLE: Cable soil evaporation parameterization","volume":"9","author":"Decker","year":"2017","journal-title":"J. Adv. Model. Earth Syst."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"744","DOI":"10.1175\/JHM-D-12-0183.1","article-title":"Evaluated Crop Evapotranspiration over a Region of Irrigated Orchards with the Improved ACASA\u2013WRF Model","volume":"15","author":"Falk","year":"2014","journal-title":"J. Hydrometeorol."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1341","DOI":"10.5194\/gmd-5-1341-2012","article-title":"Development of high resolution land surface parameters for the Community Land Model","volume":"5","author":"Ke","year":"2012","journal-title":"Geosci. Model Dev."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"e2020MS002062","DOI":"10.1029\/2020MS002062","article-title":"Enhancing the Noah-MP Ecosystem Response to Droughts With an Explicit Representation of Plant Water Storage Supplied by Dynamic Root Water Uptake","volume":"12","author":"Niu","year":"2020","journal-title":"J. Adv. Model. Earth Syst."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"251","DOI":"10.5194\/essd-14-251-2022","article-title":"High-resolution biogenic global emission inventory for the time period 2000\u20132019 for air quality modelling","volume":"14","author":"Sindelarova","year":"2022","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_63","unstructured":"Myneni, R., Knyazikhin, Y., and Park, T. (2023, March 20). MODIS\/Terra + Aqua Leaf Area Index\/FPAR 8-Day L4 Global 500 m SIN Grid V061, NASA EOSDIS Land Processes DAAC [Data Set]. Available online: https:\/\/doi.org\/10.5067\/MODIS\/MCD15A2H.061."},{"key":"ref_64","unstructured":"Myneni, R. (2023, March 20). MODIS Collection 6.1 (C6.1) LAI\/FPAR Product User\u2019s Guide, Available online: https:\/\/Modis-Land.Gsfc.Nasa.Gov\/Pdf\/MOD15_C61_UserGuide_April2020.Pdf."},{"key":"ref_65","unstructured":"Friedl, M., and Sulla-Menashe, D. (2023, March 20). MODIS\/Terra + Aqua Land Cover Type Yearly L3 Global 500 m SIN Grid V061 [Data Set]. NASA EOSDIS Land Processes DAAC. Available online: https:\/\/doi.org\/10.5067\/MODIS\/MCD12Q1.061."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1804","DOI":"10.1109\/TGRS.2006.872529","article-title":"Validation of global moderate-resolution LAI products: A framework proposed within the CEOS land product validation subgroup","volume":"44","author":"Morisette","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_67","unstructured":"Cochran, W.G. (1977). Sampling Techniques, Wiley & Sons. [3rd ed.]."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Cohen, W.B., Maiersperger, T.K., and Pflugmacher, D. (2006). BigFoot Land Cover Surfaces for North and South American Sites, 2000\u20132003, Oak Ridge National Laboratory Distributed Active Archive Center.","DOI":"10.3334\/ORNLDAAC\/748"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Fuster, B., S\u00e1nchez-Zapero, J., Camacho, F., Garc\u00eda-Santos, V., Verger, A., Lacaze, R., Weiss, M., Baret, F., and Smets, B. (2020). Quality Assessment of PROBA-V LAI, fAPAR and fCOVER Collection 300 m Products of Copernicus Global Land Service. Remote Sens., 12.","DOI":"10.3390\/rs12061017"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.rse.2004.02.007","article-title":"Evaluation of the MODIS LAI algorithm at a coniferous forest site in Finland","volume":"91","author":"Wang","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1016\/j.rse.2004.03.019","article-title":"Upscaling ground observations of vegetation water content, canopy height, and leaf area index during SMEX02 using aircraft and Landsat imagery","volume":"92","author":"Anderson","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Bai, G., Dash, J., Brown, L., Meier, C., Lerebourg, C., Ronco, E., Lamquin, N., Bruniquel, V., Clerici, M., and Gobron, N. (August, January 28). GBOV (Ground-Based Observation for Validation): A Copernicus Service for Validation of Vegetation Land Products. Proceedings of the IGARSS 2019\u20132019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8898634"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1824","DOI":"10.1109\/TGRS.2002.802519","article-title":"Seasonality extraction by function fitting to time-series of satellite sensor data","volume":"40","author":"Eklundh","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"111935","DOI":"10.1016\/j.rse.2020.111935","article-title":"Evaluation of global leaf area index and fraction of absorbed photosynthetically active radiation products over North America using Copernicus Ground Based Observations for Validation data","volume":"247","author":"Brown","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.rse.2018.08.022","article-title":"A simple method to improve the quality of NDVI time-series data by integrating spatiotemporal information with the Savitzky-Golay filter","volume":"217","author":"Cao","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1111\/j.1469-8137.2010.03579.x","article-title":"Evidence of a universal scaling relationship for leaf CO 2 drawdown along an aridity gradient","volume":"190","author":"Prentice","year":"2011","journal-title":"New Phytol."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41597-020-0453-3","article-title":"Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset","volume":"7","author":"Harris","year":"2020","journal-title":"Sci. Data"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Campos-Taberner, M., Garc\u00eda-Haro, F.J., Busetto, L., Ranghetti, L., Mart\u00ednez, B., Gilabert, M.A., Camps-Valls, G., Camacho, F., and Boschetti, M. (2018). A Critical Comparison of Remote Sensing Leaf Area Index Estimates over Rice-Cultivated Areas: From Sentinel-2 and Landsat-7\/8 to MODIS, GEOV1 and EUMETSAT Polar System. Remote Sens., 10.","DOI":"10.3390\/rs10050763"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.rse.2017.12.024","article-title":"Satellite-derived LAI products exhibit large discrepancies and can lead to substantial uncertainty in simulated carbon and water fluxes","volume":"206","author":"Liu","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"4820","DOI":"10.1073\/pnas.0611338104","article-title":"Large seasonal swings in leaf area of Amazon rainforests","volume":"104","author":"Myneni","year":"2007","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"2365","DOI":"10.5194\/hess-26-2365-2022","article-title":"Towards effective drought monitoring in the Middle East and North Africa (MENA) region: Implications from assimilating leaf area index and soil moisture into the Noah-MP land surface model for Morocco","volume":"26","author":"Nie","year":"2022","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.isprsjprs.2019.06.007","article-title":"Estimating leaf area index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images","volume":"154","author":"Wang","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.rse.2018.02.049","article-title":"An integrated method for validating long-term leaf area index products using global networks of site-based measurements","volume":"209","author":"Xu","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"241","DOI":"10.5589\/m02-092","article-title":"Landsat-5 TM and Landsat-7 ETM+ based accuracy assessment of leaf area index products for Canada derived from SPOT-4 VEGETATION data","volume":"29","author":"Fernandes","year":"2003","journal-title":"Can. J. Remote Sens."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"29429","DOI":"10.1029\/97JD01107","article-title":"Leaf area index of boreal forests: Theory, techniques, and measurements","volume":"102","author":"Chen","year":"1997","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/j.agrformet.2006.08.005","article-title":"Leaf area index measurements at Fluxnet-Canada forest sites","volume":"140","author":"Chen","year":"2006","journal-title":"Agric. For. Meteorol."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/S0034-4257(99)00056-5","article-title":"Direct and Indirect Estimation of Leaf Area Index, fAPAR, and Net Primary Production of Terrestrial Ecosystems","volume":"70","author":"Gower","year":"1999","journal-title":"Remote Sens. Environ."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.agrformet.2003.08.001","article-title":"Review of methods for in situ leaf area index (LAI) determination: Part II. Estimation of LAI, errors and sampling","volume":"121","author":"Weiss","year":"2004","journal-title":"Agric. For. Meteorol."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"1397","DOI":"10.3390\/rs70201397","article-title":"Uncertainty Analysis in the Creation of a Fine-Resolution Leaf Area Index (LAI) Reference Map for Validation of Moderate Resolution LAI Products","volume":"7","author":"Iiames","year":"2015","journal-title":"Remote Sens."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"4133","DOI":"10.1111\/gcb.13787","article-title":"Inconsistencies of interannual variability and trends in long-term satellite leaf area index products","volume":"23","author":"Jiang","year":"2017","journal-title":"Glob. Chang. Biol."},{"key":"ref_91","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."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/7\/1780\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:03:43Z","timestamp":1760123023000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/7\/1780"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,27]]},"references-count":91,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2023,4]]}},"alternative-id":["rs15071780"],"URL":"https:\/\/doi.org\/10.3390\/rs15071780","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,27]]}}}