{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T09:16:10Z","timestamp":1762506970659,"version":"build-2065373602"},"reference-count":57,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2016,12,27]],"date-time":"2016-12-27T00:00:00Z","timestamp":1482796800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Basic Research Program of China","doi-asserted-by":"publisher","award":["2013CB733403"],"award-info":[{"award-number":["2013CB733403"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41171263"],"award-info":[{"award-number":["41171263"]}],"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>High-resolution leaf area index (LAI) maps from remote sensing data largely depend on empirical models, which link field LAI measurements to the vegetation index. The existing empirical methods often require the field measurements to be sufficient for constructing a reliable model. However, in many regions of the world, there are limited field measurements available. This paper presents a prior knowledge-based (PKB) method to derivate LAI with limited field measurements, in an effort to improve the accuracy of empirical model. Based on the assumption that the experimental sites with the same vegetation type can be represented by similar models, a priori knowledge for crops was extracted from the published models in various cropland sites. The knowledge, composed of an initial guess of each model parameter with the associated uncertainty, was then combined with the local field measurements to determine a semi-empirical model using the Bayesian inversion method. The proposed method was evaluated at a cropland site in the Huailai region of Hebei Province, China. Compared with the regression method, the proposed PKB method can effectively improve the accuracy of empirical model and LAI estimation, when the field measurements were limited. The results demonstrate that a priori knowledge extracted from the universal sites can provide important auxiliary information to improve the representativeness of the empirical model in a given study area.<\/jats:p>","DOI":"10.3390\/rs9010013","type":"journal-article","created":{"date-parts":[[2016,12,28]],"date-time":"2016-12-28T11:22:14Z","timestamp":1482924134000},"page":"13","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A Prior Knowledge-Based Method to Derivate High-Resolution Leaf Area Index Maps with Limited Field Measurements"],"prefix":"10.3390","volume":"9","author":[{"given":"Yuechan","family":"Shi","sequence":"first","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Research Center for Remote Sensing and GIS, School of Geography, Beijing Normal University, Beijing 100875, China"},{"name":"Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4962-4888","authenticated-orcid":false,"given":"Jindi","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Research Center for Remote Sensing and GIS, School of Geography, Beijing Normal University, Beijing 100875, China"},{"name":"Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Research Center for Remote Sensing and GIS, School of Geography, Beijing Normal University, Beijing 100875, China"},{"name":"Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yonghua","family":"Qu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Research Center for Remote Sensing and GIS, School of Geography, Beijing Normal University, Beijing 100875, China"},{"name":"Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2016,12,27]]},"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":"1872","DOI":"10.1109\/TGRS.2006.874794","article-title":"Evaluation of national and global LAI products derived from optical remote sensing instruments over Canada","volume":"44","author":"Abuelgasim","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","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_4","doi-asserted-by":"crossref","first-page":"1843","DOI":"10.1016\/j.agrformet.2011.08.002","article-title":"Potential performances of remote sensing LAI assimilation in WOFOST model based on an OSS Experiment","volume":"151","author":"Curnel","year":"2011","journal-title":"Agric. For. Meteorol."},{"key":"ref_5","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_6","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_7","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.agrformet.2004.06.011","article-title":"Inter-annual variability in the leaf area index of a boreal aspen-hazelnut forest in relation to net ecosystem production","volume":"126","author":"Barr","year":"2004","journal-title":"Agric. For. Meteorol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"5605","DOI":"10.1080\/01431160802060904","article-title":"Extracting forest canopy structure from spatial information of high resolution optical imagery: Tree crown size versus leaf area index","volume":"29","author":"Song","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.advwatres.2012.06.005","article-title":"Mapping daily evapotranspiration at Landsat spatial scales during the BEAREX\u201908 field campaign","volume":"50","author":"Anderson","year":"2012","journal-title":"Adv. Water Resour."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.agrformet.2012.07.014","article-title":"Remote sensing green area index for winter wheat crop monitoring: 10-year assessment at regional scale over a fragmented landscape","volume":"152","author":"Duveiller","year":"2012","journal-title":"Agric. For. Meteorol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.rse.2014.01.004","article-title":"Relationships between gross primary production, green LAI, and canopy chlorophyll content in maize: Implications for remote sensing of primary production","volume":"144","author":"Gitelson","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_12","first-page":"165","article-title":"A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling","volume":"9","author":"Dorigo","year":"2007","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_13","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. Geoinf."},{"key":"ref_14","first-page":"72","article-title":"Evaluation of SPOT imagery for the estimation of grassland biomass","volume":"38","author":"Dusseux","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.rse.2011.10.032","article-title":"Generating global leaf area index from Landsat: Algorithm formulation and demonstration","volume":"122","author":"Ganguly","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1804","DOI":"10.1109\/TGRS.2006.872529","article-title":"Validation of global medium-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_17","doi-asserted-by":"crossref","first-page":"4190","DOI":"10.3390\/rs6054190","article-title":"On Line Validation Exercise (OLIVE): A web based service for the validation of medium resolution land products. Application to FAPAR products","volume":"6","author":"Weiss","year":"2014","journal-title":"Remote Sens."},{"key":"ref_18","unstructured":"Fernandes, R., Plummer, S., Nightingale, J., Baret, F., Camacho, F., Fang, H., Garrigues, S., Gobron, N., Lang, M., and Lacaze, R. Global leaf area index product validation good practices, Available online: http:\/\/lpvs.gsfc.nasa.gov\/LAI_home.html."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Liang, S. (2008). Advances in Land Remote Sensing: System, Modeling, Inversion and Application, Springer Science and Business Media B.V.","DOI":"10.1007\/978-1-4020-6450-0_1"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"4927","DOI":"10.3390\/rs6064927","article-title":"On the semi-automatic retrieval of biophysical parameters based on spectral index optimization","volume":"6","author":"Rivera","year":"2014","journal-title":"Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/S0034-4257(01)00300-5","article-title":"Derivation and validation of Canada-wide coarse-resolution leaf area index maps using high-resolution satellite imagery and ground measurements","volume":"80","author":"Chen","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1016\/S0034-4257(02)00173-6","article-title":"An improved strategy for regression of biophysical variables and Landsat ETM+ data","volume":"84","author":"Cohen","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.agrformet.2008.07.014","article-title":"Derivation of high-resolution leaf area index maps in support of validation activities: Application to the cropland Barrax site","volume":"149","author":"Martinez","year":"2009","journal-title":"Agric. For. Meteorol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"12887","DOI":"10.3390\/rs71012887","article-title":"An upscaling algorithm to obtain the representative ground truth of LAI time series in heterogeneous land surface","volume":"7","author":"Shi","year":"2015","journal-title":"Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3846","DOI":"10.1016\/j.rse.2008.06.005","article-title":"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","volume":"112","author":"Soudani","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1547","DOI":"10.2135\/cropsci2002.1547","article-title":"Relationship between growth traits and spectral vegetation indices in durum wheat","volume":"42","author":"Aparicio","year":"2002","journal-title":"Crop Sci."},{"key":"ref_27","first-page":"19","article-title":"Comparative analysis of different retrieval methods for mapping grassland leaf area index using airborne imaging spectroscopy","volume":"43","author":"Atzberger","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_28","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\u2013A comparison","volume":"108","author":"Verrelst","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.rse.2003.12.013","article-title":"Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture","volume":"90","author":"Haboudane","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_30","first-page":"104","article-title":"Quantification winter wheat LAI with HJ-1CCD image features over multiple growing seasons","volume":"44","author":"Li","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"7063","DOI":"10.3390\/s110707063","article-title":"Evaluation of sentinel-2 red edge bands for empirical estimation of green LAI and chlorophyll content","volume":"11","author":"Delegido","year":"2011","journal-title":"Sensors"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Ross, J. (1981). The Radiation Regime and Architecture of Plant Stands, Dr. W. Junk.","DOI":"10.1007\/978-94-009-8647-3"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/0034-4257(91)90009-U","article-title":"Potentials and limits of vegetation indices for LAI and FAPAR assessment","volume":"35","author":"Baret","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/0002-1571(71)90092-6","article-title":"A theoretical analysis of the frequency of gaps in plant stands","volume":"8","author":"Nilson","year":"1971","journal-title":"Agric. Meteorol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/S0034-4257(97)00104-1","article-title":"On the relation between NDVI, fractional vegetation cover, and leaf area index","volume":"62","author":"Carlson","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1016\/j.rse.2012.04.002","article-title":"Assessment of vegetation indices for regional crop green LAI estimation from Landsat images over multiple growing seasons","volume":"123","author":"Liu","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1016\/j.rse.2008.11.014","article-title":"Albedo and LAI estimates from FORMOSAT-2 data for crop monitoring","volume":"113","author":"Bsaibes","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/j.rse.2010.09.012","article-title":"Optimal modalities for radiative transfer-neural network estimation of canopy biophysical characteristics: Evaluation over an agricultural area with CHRIS\/PROBA observations","volume":"115","author":"Verger","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1051\/agro:2002036","article-title":"Validation of neural net techniques to estimate canopy biophysical variables from remote sensing data","volume":"22","author":"Weiss","year":"2002","journal-title":"Agronomie"},{"key":"ref_40","first-page":"1189","article-title":"LAI measuring data processing, analysis and spatial scaling in the middle reaches of heihe experimental research region","volume":"18","author":"Liu","year":"2010","journal-title":"Remote Sens. Technol. Appl."},{"key":"ref_41","unstructured":"Zhang, R.H. (2013). Quantitative Thermal Infrared Remote Sensing Model and Ground Experimental Base, Science Press. (In Chinese)."},{"key":"ref_42","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 maize and soybean leaf area index from Landsat ETM+ imagery","volume":"92","author":"Walthall","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1016\/j.agrformet.2007.04.001","article-title":"Determining vegetation indices from solar and photosynthetically active radiation fluxes","volume":"144","author":"Wilson","year":"2007","journal-title":"Agric. For. Meteorol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.agrformet.2014.08.005","article-title":"Seasonal variation of leaf area index (LAI) over paddy rice fields in NE China: Intercomparison of destructive sampling, LAI-2200, digital hemispherical photography (DHP), and AccuPAR methods","volume":"198","author":"Fang","year":"2014","journal-title":"Agric. For. Meteorol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1109\/JSTARS.2013.2289931","article-title":"Crop leaf area index observations with a wireless sensor network and its potential for validating remote sensing products","volume":"7","author":"Qu","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Li, X., Wang, J., Hu, B., and Strahler, A. (1998). On utilization of prior knowledge in inversion 556 of remote sensing models. Sci. China Ser. D, 580\u2013586.","DOI":"10.1007\/BF02878739"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"11925","DOI":"10.1029\/2000JD900639","article-title":"A priori knowledge accumulation and its application to linear BRDF model inversion","volume":"106","author":"Li","year":"2001","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0034-4257(02)00035-4","article-title":"Retrieval of canopy biophysical variables from bidirectional reflectance: Using prior information to solve the ill-posed inverse problem","volume":"84","author":"Combal","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"4927","DOI":"10.1080\/01431160802036334","article-title":"A strategy to integrate a priori knowledge for an improved inversion of the LAI from BRDF modelling","volume":"29","author":"Yan","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_50","first-page":"4","article-title":"The spectrum knowledge base of typical objects and remote sensing inversion of land surface parameters","volume":"8","author":"Wang","year":"2004","journal-title":"J. Remote Sens."},{"key":"ref_51","first-page":"160","article-title":"Design and experiment of crop structural parameters automatic measurement system","volume":"28","author":"Qu","year":"2012","journal-title":"Trans. CSAE"},{"key":"ref_52","first-page":"463","article-title":"On the correct estimation of effective leaf area index: Does it reveal information on clumping effects?","volume":"150","author":"Ryu","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_53","unstructured":"Earth Explorer, Available online: http:\/\/earthexplorer.usgs.gov\/."},{"key":"ref_54","unstructured":"Tarantola, A. (1987). Inverse Problem Theory: Methods for Data Fitting and Model Parameter Estimation, Elsevier Science."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1015","DOI":"10.1029\/91WR02985","article-title":"Effective and efficient global optimization for conceptual rainfall-runoff models","volume":"28","author":"Duan","year":"1992","journal-title":"Water Resour. Res."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"907","DOI":"10.1080\/02652030902788946","article-title":"Rapid and non-invasive analysis of deoxynivalenol in durum and common wheat by Fourier-transform near infrared (FT-NIR) spectroscopy","volume":"26","author":"De","year":"2009","journal-title":"Food Addit. Contam."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"865","DOI":"10.1007\/s11947-014-1454-z","article-title":"Value adding to red grape pomace exploiting eco-friendly FT-NIR spectroscopy technique","volume":"8","author":"Machado","year":"2015","journal-title":"Food Bioprocess Technol."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/1\/13\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:29:23Z","timestamp":1760210963000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/1\/13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,12,27]]},"references-count":57,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2017,1]]}},"alternative-id":["rs9010013"],"URL":"https:\/\/doi.org\/10.3390\/rs9010013","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2016,12,27]]}}}