{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T05:10:55Z","timestamp":1768799455343,"version":"3.49.0"},"reference-count":72,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,2,22]],"date-time":"2024-02-22T00:00:00Z","timestamp":1708560000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42171313"],"award-info":[{"award-number":["42171313"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42090012"],"award-info":[{"award-number":["42090012"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Owing to advancements in satellite remote sensing technology, the acquisition of global land surface parameters, notably, the leaf area index (LAI), has become increasingly accessible. The Sentinel-2 (S2) satellite plays an important role in the monitoring of ecological environments and resource management. The prevalent use of the 20 m spatial resolution band in S2-based inversion models imposes significant limitations on the applicability of S2 data in applications requiring finer spatial resolution. Furthermore, although a substantial body of research on LAI retrieval using S2 data concentrates on agricultural landscapes, studies dedicated to forest ecosystems, although increasing, remain relatively less prevalent. This study aims to establish a viable methodology for retrieving 10 m resolution LAI data in forested regions. The empirical model of the soil adjusted vegetation index (SAVI), the backpack neural network based on simulated annealing (SA-BP) algorithm, and the variational heteroscedastic Gaussian process regression (VHGPR) model are established in this experiment based on the LAI data measured and the corresponding 10 m spatial resolution S2 satellite surface reflectance data in the Saihanba Forestry Center (SFC). The LAI retrieval performance of the three models is then validated using field data, and the error sources of the best performing VHGPR models (R2 of 0.8696 and RMSE of 0.5078) are further analyzed. Moreover, the VHGPR model stands out for its capacity to quantify the uncertainty in LAI estimation, presenting a notable advantage in assessing the significance of input data, eliminating redundant bands, and being well suited for uncertainty estimation. This feature is particularly valuable in generating accurate LAI products, especially in regions characterized by diverse forest compositions.<\/jats:p>","DOI":"10.3390\/rs16050764","type":"journal-article","created":{"date-parts":[[2024,2,22]],"date-time":"2024-02-22T11:28:47Z","timestamp":1708601327000},"page":"764","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["High Spatial Resolution Leaf Area Index Estimation for Woodland in Saihanba Forestry Center, China"],"prefix":"10.3390","volume":"16","author":[{"given":"Changjing","family":"Wang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Beijing Engineering Research Center for Global Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"},{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 610031, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7384-6152","authenticated-orcid":false,"given":"Hongmin","family":"Zhou","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Beijing Engineering Research Center for Global Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]},{"given":"Guodong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 610031, China"}]},{"given":"Jianguo","family":"Duan","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Beijing Engineering Research Center for Global Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]},{"given":"Moxiao","family":"Lin","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Beijing Engineering Research Center for Global Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1111\/j.1365-3040.1992.tb00992.x","article-title":"Defining leaf area index for non-flat leaves","volume":"15","author":"Chen","year":"1992","journal-title":"Plant Cell Environ."},{"key":"ref_2","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_3","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_4","doi-asserted-by":"crossref","first-page":"739","DOI":"10.1029\/2018RG000608","article-title":"An overview of global leaf area index (LAI): Methods, products, validation, and applications","volume":"57","author":"Fang","year":"2019","journal-title":"Rev. Geophys."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.ecolind.2015.05.036","article-title":"Vegetation dynamics and responses to recent climate change in Xinjiang using leaf area index as an indicator","volume":"58","author":"Liang","year":"2015","journal-title":"Ecol. Indic."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.eja.2018.09.006","article-title":"Improving regional winter wheat yield estimation through assimilation of phenology and leaf area index from remote sensing data","volume":"101","author":"Chen","year":"2018","journal-title":"Eur. J. Agron."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Tripathi, A.M., Pohankov\u00e1, E., Fischer, M., Ors\u00e1g, M., Trnka, M., Klem, K., and Marek, M.V. (2018). The evaluation of radiation use efficiency and leaf area index development for the estimation of biomass accumulation in short rotation poplar and annual field crops. Forests, 9.","DOI":"10.3390\/f9040168"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2219","DOI":"10.1109\/TGRS.2006.872100","article-title":"Algorithm for global leaf area index retrieval using satellite imagery","volume":"44","author":"Deng","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.isprsjprs.2015.04.013","article-title":"Experimental Sentinel-2 LAI estimation using parametric, non-parametric and physical retrieval methods\u2014A comparison","volume":"108","author":"Verrelst","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.1007\/s12040-011-0126-x","article-title":"Development of regional wheat VI-LAI models using Resourcesat-1 AWiFS data","volume":"120","author":"Chaurasia","year":"2011","journal-title":"J. Earth Syst. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Goswami, S., Gamon, J., Vargas, S., and Tweedie, C. (2015). Relationships of NDVI, Biomass, and Leaf Area Index (LAI) for Six Key Plant Species in Barrow, Alaska, PeerJ PrePrints. 2167-9843.","DOI":"10.7287\/peerj.preprints.913"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1016\/j.rse.2004.06.003","article-title":"A comparison of empirical and neural network approaches for estimating corn and soybean leaf area index from Landsat ETM+ imagery","volume":"92","author":"Walthall","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"109058","DOI":"10.1016\/j.ecolmodel.2020.109058","article-title":"Evaluation of models to determine LAI on poplar stands using spectral indices from Sentinel-2 satellite images","volume":"428","author":"Zamudio","year":"2020","journal-title":"Ecol. Model."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Qiao, K., Zhu, W., Xie, Z., and Li, P. (2019). Estimating the seasonal dynamics of the leaf area index using piecewise LAI-VI relationships based on phenophases. Remote Sens., 11.","DOI":"10.3390\/rs11060689"},{"key":"ref_15","first-page":"67","article-title":"A comparison of different methods for assessing leaf area index in four canopy types","volume":"65","author":"Roland","year":"2019","journal-title":"Cent. Eur. For. J."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.isprsjprs.2017.10.004","article-title":"A hybrid training approach for leaf area index estimation via Cubist and random forests machine-learning","volume":"135","author":"Houborg","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1482","DOI":"10.1109\/JSTARS.2018.2813281","article-title":"Vegetation indices combining the red and red-edge spectral information for leaf area index retrieval","volume":"11","author":"Xie","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2874","DOI":"10.1109\/JSTARS.2020.2995577","article-title":"Estimating effective leaf area index of winter wheat using simulated observation on unmanned aerial vehicle-based point cloud data","volume":"13","author":"Song","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1016\/j.isprsjprs.2021.01.017","article-title":"Efficient RTM-based training of machine learning regression algorithms to quantify biophysical & biochemical traits of agricultural crops","volume":"173","author":"Danner","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3529","DOI":"10.1109\/JSTARS.2017.2690623","article-title":"Retrieval of specific leaf area from landsat-8 surface reflectance data using statistical and physical models","volume":"10","author":"Ali","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"894","DOI":"10.1016\/j.isprsjprs.2011.09.013","article-title":"Mapping grassland leaf area index with airborne hyperspectral imagery: A comparison study of statistical approaches and inversion of radiative transfer models","volume":"66","author":"Darvishzadeh","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.rse.2016.05.023","article-title":"Quantifying the influences of spectral resolution on uncertainty in leaf trait estimates through a Bayesian approach to RTM inversion","volume":"183","author":"Shiklomanov","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"7442","DOI":"10.1109\/TGRS.2016.2604007","article-title":"Fractional vegetation cover estimation method through dynamic Bayesian network combining radiative transfer model and crop growth model","volume":"54","author":"Wang","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1040","DOI":"10.1109\/TGRS.2018.2864517","article-title":"Gradient-based automatic lookup table generator for radiative transfer models","volume":"57","author":"Servera","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.isprsjprs.2019.02.013","article-title":"Inversion of rice canopy chlorophyll content and leaf area index based on coupling of radiative transfer and Bayesian network models","volume":"150","author":"Xu","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1109\/TGRS.2013.2238242","article-title":"Optimizing LUT-Based RTM Inversion for Semiautomatic Mapping of Crop Biophysical Parameters from Sentinel-2 and -3 Data: Role of Cost Functions","volume":"52","author":"Verrelst","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1109\/JSTARS.2018.2855564","article-title":"Modeling winter wheat leaf area index and canopy water content with three different approaches using Sentinel-2 multispectral instrument data","volume":"12","author":"Pan","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_28","unstructured":"Myneni, R., and Park, Y. (2024, February 15). MODIS Collection 6 (C6) LAI\/FPAR Product User\u2019s Guide, Available online: https:\/\/lpdaac.usgs.gov\/documents\/624\/-MOD15_User_Guide_V6.pdf."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.compag.2019.03.017","article-title":"Monitoring maize growth conditions by training a BP neural network with remotely sensed vegetation temperature condition index and leaf area index","volume":"160","author":"Wang","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wang, L., Chang, Q., Yang, J., Zhang, X., and Li, F. (2018). Estimation of paddy rice leaf area index using machine learning methods based on hyperspectral data from multi-year experiments. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0207624"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2789","DOI":"10.1016\/j.rse.2008.01.006","article-title":"Performances of neural networks for deriving LAI estimates from existing CYCLOPES and MODIS products","volume":"112","author":"Verger","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_32","first-page":"1057","article-title":"BP Neural Network Based on Simulated Annealing Algorithm for High Resolution LAI Retrieval","volume":"35","author":"Xue","year":"2020","journal-title":"Remote Sens. Technol. Appl."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.ecoinf.2019.05.008","article-title":"Estimating leaf area index and light extinction coefficient using Random Forest regression algorithm in a tropical moist deciduous forest, India","volume":"52","author":"Srinet","year":"2019","journal-title":"Ecol. Inform."},{"key":"ref_34","first-page":"102027","article-title":"Estimation of leaf area index using PROSAIL based LUT inversion, MLRA-GPR and empirical models: Case study of tropical deciduous forest plantation, North India","volume":"86","author":"Sinha","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Yang, J., Liu, X., Du, L., Shi, S., Sun, J., and Chen, B. (2020). Estimation of multi-species leaf area index based on Chinese GF-1 satellite data using look-up table and gaussian process regression methods. Sensors, 20.","DOI":"10.3390\/s20092460"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.isprsjprs.2015.05.005","article-title":"Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties\u2014A review","volume":"108","author":"Verrelst","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.isprsjprs.2020.07.004","article-title":"Gaussian processes retrieval of LAI from Sentinel-2 top-of-atmosphere radiance data","volume":"167","author":"Vicent","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_38","first-page":"838","article-title":"Retrieval of biophysical parameters with heteroscedastic Gaussian processes","volume":"11","author":"Titsias","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_39","unstructured":"L\u00e1zaro-Gredilla, M., and Titsias, M.K. (July, January 28). Variational heteroscedastic Gaussian process regression. Proceedings of the ICML, Bellevue, WA, USA."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Mu\u00f1oz-Mar\u00ed, J., Verrelst, J., L\u00e1zaro-Gredilla, M., and Camps-Vails, G. (2015, January 26\u201331). Biophysical parameter retrieval with warped Gaussian processes. Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7325685"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2038","DOI":"10.1109\/LGRS.2020.3014676","article-title":"Intelligent sampling for vegetation nitrogen mapping based on hybrid machine learning algorithms","volume":"18","author":"Verrelst","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"108153","DOI":"10.1016\/j.agrformet.2020.108153","article-title":"Investigating the urban-induced microclimate effects on winter wheat spring phenology using Sentinel-2 time series","volume":"294","author":"Tian","year":"2020","journal-title":"Agric. For. Meteorol."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Li, L., Zhou, X., Chen, L., Chen, L., Zhang, Y., and Liu, Y. (2020). Estimating urban vegetation biomass from Sentinel-2A image data. Forests, 11.","DOI":"10.3390\/f11020125"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zhang, X., and Song, P. (2021). Estimating urban evapotranspiration at 10 m resolution using vegetation information from Sentinel-2: A case study for the Beijing Sponge City. Remote Sens., 13.","DOI":"10.3390\/rs13112048"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1702","DOI":"10.3390\/rs70201702","article-title":"Mapping spatial distribution of larch plantations from multi-seasonal Landsat-8 OLI imagery and multi-scale textures using random forests","volume":"7","author":"Gao","year":"2015","journal-title":"Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"856","DOI":"10.11834\/jrs.20210341","article-title":"Comprehensive remote sensing experiment of carbon cycle, water cycle and energy balance in Luan River Basin","volume":"25","author":"Yan","year":"2021","journal-title":"Natl. Remote Sens. Bull."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"888","DOI":"10.11834\/jrs.20210305","article-title":"Airborne comprehensive remote sensing experiment of forest and grass resources in Xiaoluan River Basin","volume":"25","author":"Mu","year":"2021","journal-title":"Natl. Remote Sens. Bull."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1000","DOI":"10.11834\/jrs.20219447","article-title":"Time series high-resolution leaf area index estimation and change monitoring in the Saihanba area","volume":"25","author":"Zhou","year":"2021","journal-title":"Natl. Remote Sens. Bull."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Bak\u00f3, G., F\u00fcl\u00f6p, G., and Szab\u00f3, B. (2014, January 5\u20136). Detection of Invasive Woody Increment with the Analysis of Landsat Images. Proceedings of the Forum of Young Geoinformaticians 2014, Technical University, Zvolen, Slovakia.","DOI":"10.17700\/jai.2015.6.1.157"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jmp.2018.03.001","article-title":"A tutorial on Gaussian process regression: Modelling, exploring, and exploiting functions","volume":"85","author":"Schulz","year":"2018","journal-title":"J. Math. Psychol."},{"key":"ref_51","first-page":"130","article-title":"Variable selection for nonparametric Gaussian process priors: Models and computational strategies","volume":"26","author":"Savitsky","year":"2011","journal-title":"Stat. Sci. Rev. J. Inst. Math. Stat."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1109\/TGRS.2009.2023983","article-title":"Gaussian process approach to remote sensing image classification","volume":"48","author":"Bazi","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1109\/MGRS.2015.2510084","article-title":"A survey on Gaussian processes for earth-observation data analysis: A comprehensive investigation","volume":"4","author":"Verrelst","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"104394","DOI":"10.1016\/j.catena.2019.104394","article-title":"A remote sensing and artificial neural network-based integrated agricultural drought index: Index development and applications","volume":"186","author":"Liu","year":"2020","journal-title":"Catena"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"2719","DOI":"10.3390\/s90402719","article-title":"Retrieving leaf area index (LAI) using remote sensing: Theories, methods and sensors","volume":"9","author":"Zheng","year":"2009","journal-title":"Sensors"},{"key":"ref_56","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_57","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2020.2991573","article-title":"A gaussian approximation of marginal likelihood in relevance vector machine for industrial data with input noise","volume":"70","author":"Chen","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"113385","DOI":"10.1016\/j.rse.2022.113385","article-title":"A hybrid model to predict nitrogen concentration in heterogeneous grassland using field spectroscopy","volume":"285","author":"Orsi","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"8522","DOI":"10.1109\/TGRS.2019.2921392","article-title":"Exploration of machine learning techniques in emulating a coupled soil\u2013canopy\u2013atmosphere radiative transfer model for multi-parameter estimation from satellite observations","volume":"57","author":"Shi","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_60","first-page":"52","article-title":"Intercomparison and validation of MODIS and GLASS leaf area index (LAI) products over mountain areas: A case study in southwestern China","volume":"55","author":"Jin","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"794","DOI":"10.1109\/JSTARS.2020.2970999","article-title":"A simulation-based analysis of topographic effects on LAI inversion over sloped terrain","volume":"13","author":"Yu","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.isprsjprs.2019.06.008","article-title":"Evaluation of topographic effects on multiscale leaf area index estimation using remotely sensed observations from multiple sensors","volume":"154","author":"Jin","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"2207","DOI":"10.1109\/TGRS.2006.872081","article-title":"On the blending of the Landsat and MODIS surface reflectance: Predicting daily Landsat surface reflectance","volume":"44","author":"Gao","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_64","first-page":"1","article-title":"The improved winter wheat yield estimation by assimilating GLASS LAI into a crop growth model with the proposed Bayesian posterior-based ensemble Kalman filter","volume":"61","author":"Huang","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"3119","DOI":"10.5194\/gmd-12-3119-2019","article-title":"Comparison of different sequential assimilation algorithms for satellite-derived leaf area index using the Data Assimilation Research Testbed (version Lanai)","volume":"12","author":"Ling","year":"2019","journal-title":"Geosci. Model Dev."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Zhang, K., Zhou, H., Wang, J., and Xue, H. (2013, January 21\u201326). Estimation and validation of high temporal and spatial resolution albedo. Proceedings of the 2013 IEEE International Geoscience and Remote Sensing Symposium-IGARSS, Melbourne, Australia.","DOI":"10.1109\/IGARSS.2013.6723428"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"36","DOI":"10.2112\/SI80-007.1","article-title":"Spatial-temporal variation characteristics of NPP in the Heihe River Basin, northwestern China, in a recent 10-year period calculated by casa model","volume":"80","author":"Liu","year":"2017","journal-title":"J. Coast. Res."},{"key":"ref_68","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_69","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_70","doi-asserted-by":"crossref","first-page":"3147","DOI":"10.1111\/gcb.12647","article-title":"Vegetation productivity patterns at high northern latitudes: A multi-sensor satellite data assessment","volume":"20","author":"Guay","year":"2014","journal-title":"Glob. Change Biol."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"9025","DOI":"10.1080\/01431161.2018.1504342","article-title":"Mapping daily leaf area index at 30 m resolution over a meadow steppe area by fusing Landsat, Sentinel-2A and MODIS data","volume":"39","author":"Li","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1080\/19479830903561035","article-title":"Multi-source remote sensing data fusion: Status and trends","volume":"1","author":"Zhang","year":"2010","journal-title":"Int. J. Image Data Fusion"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/5\/764\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:03:12Z","timestamp":1760104992000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/5\/764"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,22]]},"references-count":72,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2024,3]]}},"alternative-id":["rs16050764"],"URL":"https:\/\/doi.org\/10.3390\/rs16050764","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,22]]}}}