{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:21:24Z","timestamp":1760239284202,"version":"build-2065373602"},"reference-count":89,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2020,10,15]],"date-time":"2020-10-15T00:00:00Z","timestamp":1602720000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In recent decades, technological advancements in sensors have generated increasing interest in remote sensing data for the study of vegetation features. Image pixel resolution can affect data analysis and results. This study evaluated the potential of three satellite images of differing resolution (Landsat 8, 30 m; Sentinel-2, 10 m; and Pleiades 1A, 2 m) in assessing the Leaf Area Index (LAI) of riparian vegetation in two Mediterranean streams, and in both a winter wheat field and a deciduous forest used to compare the accuracy of the results. In this study, three different retrieval methods\u2014the Caraux-Garson, the Lambert-Beer, and the Campbell and Norman equations\u2014are used to estimate LAI from the Normalized Difference Vegetation Index (NDVI). To validate sensor data, LAI values were measured in the field using the LAI 2200 Plant Canopy Analyzer. The statistical indices showed a better performance for Pleiades 1A and Landsat 8 images, the former particularly in sites characterized by high canopy closure, such as deciduous forests, or in areas with stable riparian vegetation, the latter where stable reaches of riparian vegetation cover are almost absent or very homogenous, as in winter wheat fields. Sentinel-2 images provided more accurate results in terms of the range of LAI values. Considering the different types of satellite imagery, the Lambert-Beer equation generally performed best in estimating LAI from the NDVI, especially in areas that are geomorphologically stable or have a denser vegetation cover, such as deciduous forests.<\/jats:p>","DOI":"10.3390\/rs12203376","type":"journal-article","created":{"date-parts":[[2020,10,15]],"date-time":"2020-10-15T21:42:21Z","timestamp":1602798141000},"page":"3376","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Influence of Different Satellite Imagery on the Analysis of Riparian Leaf Density in a Mountain Stream"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1155-8953","authenticated-orcid":false,"given":"Giovanni","family":"Romano","sequence":"first","affiliation":[{"name":"Department of Agricultural and Environmental Sciences, University of Bari Aldo Moro, 70032 Bari, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6724-8789","authenticated-orcid":false,"given":"Giovanni Francesco","family":"Ricci","sequence":"additional","affiliation":[{"name":"Department of Agricultural and Environmental Sciences, University of Bari Aldo Moro, 70032 Bari, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4462-0466","authenticated-orcid":false,"given":"Francesco","family":"Gentile","sequence":"additional","affiliation":[{"name":"Department of Agricultural and Environmental Sciences, University of Bari Aldo Moro, 70032 Bari, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Horning, N. (2008). Remote Sensing. Encyclopedia of Ecology, Elsevier.","DOI":"10.1016\/B978-008045405-4.00237-8"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1080\/02757250109532436","article-title":"Remote sensing of impervious surfaces: A review","volume":"20","author":"Slonecker","year":"2001","journal-title":"Remote Sens. Rev."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Balsamo, G., Agusti-Panareda, A., Albergel, C., Arduini, G., Beljaars, A., Bidlot, J., Blyth, E., Bousserez, N., Boussetta, S., and Brown, A. (2018). Satellite and In Situ Observations for Advancing Global Earth Surface Modelling: A Review. Remote Sens., 10.","DOI":"10.3390\/rs10122038"},{"key":"ref_4","unstructured":"Schowengerdt, R.A. (2007). Remote Sensing, Elsevier."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"11051","DOI":"10.3390\/rs61111051","article-title":"UAV Flight Experiments Applied to the Remote Sensing of Vegetated Areas","volume":"6","author":"Barrado","year":"2014","journal-title":"Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"111207","DOI":"10.1016\/j.rse.2019.05.026","article-title":"Data and resolution requirements in mapping vegetation in spatially heterogeneous landscapes","volume":"230","author":"Virtanen","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Yao, H., Qin, R., and Chen, X. (2019). Unmanned Aerial Vehicle for Remote Sensing Applications\u2014A Review. Remote Sens., 11.","DOI":"10.3390\/rs11121443"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"800","DOI":"10.1002\/esp.4769","article-title":"Evaluating functional connectivity in a small agricultural catchment under contrasting flood events by using UAV","volume":"45","author":"Calsamiglia","year":"2020","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2971","DOI":"10.3390\/rs70302971","article-title":"Intercomparison of UAV, Aircraft and Satellite Remote Sensing Platforms for Precision Viticulture","volume":"7","author":"Matese","year":"2015","journal-title":"Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1016\/j.apenergy.2019.02.027","article-title":"Remote sensing for vegetation monitoring in carbon capture storage regions: A review","volume":"240","author":"Chen","year":"2019","journal-title":"Appl. Energy"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1353691","DOI":"10.1155\/2017\/1353691","article-title":"Significant remote sensing vegetation indices: A review of developments and applications","volume":"2017","author":"Xue","year":"2017","journal-title":"J. Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1007\/978-3-030-39299-4_12","article-title":"Comparing LAI Field Measurements and Remote Sensing to Assess the Influence of Check Dams on Riparian Vegetation Cover","volume":"Volume 67","author":"Coppola","year":"2020","journal-title":"Lecture Notes in Civil Engineering"},{"key":"ref_13","unstructured":"Liang, S., and Wang, J. (2020). Advanced Remote Sensing Terrestrial Information Extraction and Applications, Elsevier. [2nd ed.]."},{"key":"ref_14","first-page":"1","article-title":"Satellite Remote Sensing in Environmental Impact Assessment: An Overview","volume":"4","author":"Vorovencii","year":"2011","journal-title":"Bull. Transilv. Univ. Bra\u015fov"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.isprsjprs.2015.10.004","article-title":"Remote sensing platforms and sensors: A survey","volume":"115","author":"Toth","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Piermattei, L., Marty, M., Karel, W., Ressl, C., Hollaus, M., Ginzler, C., and Pfeifer, N. (2018). Impact of the Acquisition Geometry of Very High-Resolution Pl\u00e9iades Imagery on the Accuracy of Canopy Height Models over Forested Alpine Regions. Remote Sens., 10.","DOI":"10.3390\/rs10101542"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.rse.2007.02.014","article-title":"Application of high spatial resolution satellite imagery for riparian and forest ecosystem classification","volume":"110","author":"Johansen","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Lopez, R.R.D., and Frohn, R.C. (2017). Remote Sensing for Landscape Ecology: New Metric Indicators: Monitoring, Modeling, and Assessment of Ecosystems, CRC Press. [2nd ed.].","DOI":"10.1201\/9781315152714"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1297","DOI":"10.1080\/01431160500486732","article-title":"The potential and challenge of remote sensing-based biomass estimation","volume":"27","author":"Lu","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1080\/17538947.2014.990526","article-title":"A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems","volume":"9","author":"Lu","year":"2016","journal-title":"Int. J. Digit. Earth"},{"key":"ref_21","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_22","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1006\/jare.2001.0844","article-title":"Assessment of spectral vegetation indices for riparian vegetation in the Colorado River delta, Mexico","volume":"49","author":"Nagler","year":"2001","journal-title":"J. Arid Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1762","DOI":"10.2307\/1941154","article-title":"Rapid estimation of coniferous forest leaf area index using a portable integrating radiometer","volume":"69","author":"Pierce","year":"1988","journal-title":"Ecology"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Fang, H., and Liang, S. (2014). Leaf Area Index Models. Reference Module in Earth Systems and Environmental Sciences, Elsevier.","DOI":"10.1016\/B978-0-12-409548-9.09076-X"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4743","DOI":"10.1080\/01431160410001726067","article-title":"Post-fire recovery of leaf area index in California chaparral: A remote sensing-chronosequence approach","volume":"25","author":"McMichael","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_26","first-page":"1","article-title":"Biogeochemistry of Terrestrial Net Primary Production","volume":"Volumes 8\u20139","author":"Chapin","year":"2007","journal-title":"Treatise on Geochemistry"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"827","DOI":"10.1016\/j.scitotenv.2018.12.081","article-title":"Effect of check dams on riparian vegetation cover: A multiscale approach based on field measurements and satellite images for Leaf Area Index assessment","volume":"657","author":"Ricci","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Richards, D., and Wang, J.W. (2020). Fusing street level photographs and satellite remote sensing to map leaf area index. Ecol. Indic., 115.","DOI":"10.1016\/j.ecolind.2020.106342"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.rse.2004.10.006","article-title":"On the relationship of NDVI with leaf area index in a deciduous forest site","volume":"94","author":"Wang","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"968","DOI":"10.2134\/agronj2005.0200","article-title":"Aerial Color Infrared Photography for Determining Late-Season Nitrogen Requirements in Corn","volume":"98","author":"Sripada","year":"2005","journal-title":"Reprod. Agron. J."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1016\/j.jhydrol.2012.09.039","article-title":"Diagnostic analysis of distributed input and parameter datasets in Mediterranean basin streamflow modeling","volume":"472\u2013473","author":"Milella","year":"2012","journal-title":"J. Hydrol."},{"key":"ref_32","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_33","doi-asserted-by":"crossref","first-page":"1661","DOI":"10.1080\/01431160310001620803","article-title":"Mapping leaf area index through spectral vegetation indices in a subtropical watershed","volume":"25","author":"Xavier","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Malanson, G.P. (1993). Riparian Landscapes, Cambridge University Press.","DOI":"10.1017\/CBO9780511565434"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/S0378-1127(96)03761-9","article-title":"Biodiversity and management of riparian vegetation in western Australia","volume":"85","author":"Hancock","year":"1996","journal-title":"For. Ecol. Manag."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/S0169-2046(00)00145-6","article-title":"The effects of human impact on spatial structure of the riparian vegetation along the Ashida river, Japan","volume":"53","author":"Inoue","year":"2001","journal-title":"Landsc. Urban Plan."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"873","DOI":"10.1016\/j.ecolind.2016.05.048","article-title":"Evaluation of ecosystem service value of riparian zone using land use data from 1986 to 2012","volume":"69","author":"Fu","year":"2016","journal-title":"Ecol. Indic."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"678","DOI":"10.1002\/eco.1389","article-title":"Check dam influence on vegetation species diversity in mountain torrents of the Mediterranean environment","volume":"7","author":"Bombino","year":"2014","journal-title":"Ecohydrology"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/j.scitotenv.2018.06.035","article-title":"Evaluating the effects of check dams on channel geometry, bed sediment size and riparian vegetation in Mediterranean mountain torrents","volume":"642","author":"Zema","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.swaqe.2016.08.001","article-title":"Impact of current riparian land on sediment retention in the Danube River Basin","volume":"8","author":"Vigiak","year":"2016","journal-title":"Sustain. Water Qual. Ecol."},{"key":"ref_41","unstructured":"European Commission Directive 2000\/60\/EC (2020, September 08). Official Journal of the European Communities. Available online: https:\/\/eur-lex.europa.eu\/legal-content\/EN\/TXT\/?uri=celex:32000L0060."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1007\/s10750-012-1304-9","article-title":"Riparian vegetation research in Mediterranean-climate regions: Common patterns, ecological processes, and considerations for management","volume":"719","author":"Stella","year":"2013","journal-title":"Hydrobiologia"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/j.ecolind.2012.06.002","article-title":"Pan-European distribution modelling of stream riparian zones based on multi-source Earth Observation data","volume":"24","author":"Clerici","year":"2013","journal-title":"Ecol. Indic."},{"key":"ref_44","unstructured":"(2020, September 08). LI-COR LI-2200C Plant Canopy Analyzer. Available online: https:\/\/licor.app.boxenterprise.net\/s\/fqjn5mlu8c1a7zir5qel."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1097\/SS.0000000000000162","article-title":"Evaluation of Alternative Management Practices with the AnnAGNPS Model in the Carapelle Watershed","volume":"181","author":"Abdelwahab","year":"2016","journal-title":"Soil Sci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1061\/(ASCE)HY.1943-7900.0000125","article-title":"Effect of Time Scale on the Performance of Different Sediment Transport Formulas in a Semiarid Region","volume":"136","author":"Bisantino","year":"2010","journal-title":"J. Hydraul. Eng."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1233","DOI":"10.1002\/ldr.2889","article-title":"Identifying sediment source areas in a Mediterranean watershed using the SWAT model","volume":"29","author":"Ricci","year":"2018","journal-title":"Land Degrad. Dev."},{"key":"ref_48","first-page":"92392B","article-title":"Evaluating the Potential of GeoEye Data in Retrieving LAI at Watershed Scale","volume":"Volume 9239","author":"Neale","year":"2014","journal-title":"Remote Sensing for Agriculture, Ecosystems, and Hydrology XVI, Proceedings of the SPIE Remote Sensing, Amsterdam, The Netherlands, 22\u201325 September 2014"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1016\/j.agrformet.2018.11.033","article-title":"Review of indirect optical measurements of leaf area index: Recent advances, challenges, and perspectives","volume":"265","author":"Yan","year":"2019","journal-title":"Agric. For. Meteorol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.rse.2019.01.030","article-title":"Estimating fractional cover of tundra vegetation at multiple scales using unmanned aerial systems and optical satellite data","volume":"224","author":"Luoto","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1007\/978-3-319-73383-8_11","article-title":"Evaluation of sentinel-2 MSI and pleiades 1B imagery in forest fire susceptibility assessment in temperate regions of Central and Eastern Europe. A Case study of Romania","volume":"Volume 48","author":"Mihai","year":"2019","journal-title":"Advances in Natural and Technological Hazards Research"},{"key":"ref_52","first-page":"268","article-title":"Evaluating seasonal effect on forest leaf area index mapping using multi-seasonal high resolution satellite pl\u00e9iades imagery","volume":"80","author":"Pu","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Wang, D., Wan, B., Qiu, P., Su, Y., Guo, Q., Wang, R., Sun, F., and Wu, X. (2018). Evaluating the Performance of Sentinel-2, Landsat 8 and Pl\u00e9iades-1 in Mapping Mangrove Extent and Species. Remote Sens., 10.","DOI":"10.3390\/rs10091468"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"893","DOI":"10.1016\/j.rse.2009.01.007","article-title":"Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors","volume":"113","author":"Chander","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1016\/j.catena.2017.12.039","article-title":"Modeling land use changes and their impact on sediment load in a Mediterranean watershed","volume":"163","author":"Romano","year":"2018","journal-title":"Catena"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"586","DOI":"10.1016\/j.rse.2018.07.015","article-title":"Atmospheric correction of metre-scale optical satellite data for inland and coastal water applications","volume":"216","author":"Vanhellemont","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"765","DOI":"10.1016\/j.proenv.2012.01.347","article-title":"The Relationship between NDVI and Terrain Factors\u2014A Case Study of Chongqing","volume":"12","author":"Zhan","year":"2012","journal-title":"Procedia Environ. Sci."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Ayalew, D.A., Deumlich, D., \u0160arapatka, B., and Doktor, D. (2020). Quantifying the Sensitivity of NDVI-Based C Factor Estimation and Potential Soil Erosion Prediction using Spaceborne Earth Observation Data. Remote Sens., 12.","DOI":"10.3390\/rs12071136"},{"key":"ref_59","unstructured":"Rouse, J.W.J., Haas, R., Schell, J., Deering, D., and Harlan, J.C. (2020, September 12). Monitoring the Vernal Advancement and Retrogradation (Green Wave Effect) of Natural Vegetation, Available online: https:\/\/ntrs.nasa.gov\/citations\/19740008955."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"2519","DOI":"10.1080\/01431169308904290","article-title":"Forest ecosystem processes at the watershed scale: Sensitivity to remotely-sensed leaf area index estimates","volume":"14","author":"Nemani","year":"1993","journal-title":"Int. J. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.isprsjprs.2015.10.005","article-title":"Examining the potential of Sentinel-2 MSI spectral resolution in quantifying above ground biomass across different fertilizer treatments","volume":"110","author":"Sibanda","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"111414","DOI":"10.1016\/j.rse.2019.111414","article-title":"A comparison of Landsat 8, RapidEye and Pleiades products for improving empirical predictions of satellite-derived bathymetry","volume":"233","author":"Cahalane","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_63","unstructured":"Caraux-Garson, D., Lacaze, B., Scala, F., Hill, J., and Mehel, W. (1998, January 11\u201314). Ten years of vegetation cover monitoring with Landsat TM remote sensing, an operational approach of DeMon-2 in Languedoc, France. Proceedings of the 18th EARSeL Symposium on Operational Remote Sensing for Sustainable Development, Enschede, The Netherlands."},{"key":"ref_64","unstructured":"Lacaze, B. (1996). Integrated Approaches to Desertification Mapping and Monitoring in the Mediterranean Basin: Final Report of the DEMON-1 Project, Space Applications Institute, Environmental Mapping and Modelling Unit."},{"key":"ref_65","first-page":"241","article-title":"Alternative Approaches for Estimating Leaf Area Index (LAI) from Remotely Sensed Satellite and Aircraft Imagery","volume":"Volume 5544","author":"Gao","year":"2004","journal-title":"Remote Sensing and Modeling of Ecosystems for Sustainability, Proceedings of the Optical Science And Technology, The Spie 49th Annual Meeting, Denver, CO, USA, 2\u20136 August 2004"},{"key":"ref_66","first-page":"167","article-title":"Percent Bias and Standard Error in Logarithmic Regression","volume":"25","author":"Wiant","year":"1979","journal-title":"For. Sci."},{"key":"ref_67","first-page":"557","article-title":"Correlation and Causation","volume":"20","author":"Wright","year":"1921","journal-title":"J. Agric. Res."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/0022-1694(70)90255-6","article-title":"River flow forecasting through conceptual models part I\u2014A discussion of principles","volume":"10","author":"Nash","year":"1970","journal-title":"J. Hydrol."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"e1891","DOI":"10.1002\/eco.1891","article-title":"Riparian and geomorphic controls on thermal habitat dynamics of pools in a temperate headwater stream","volume":"10","author":"Ouellet","year":"2017","journal-title":"Ecohydrology"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.rse.2005.07.008","article-title":"Vegetation water content estimation for corn and soybeans using spectral indices derived from MODIS near\u2014And short-wave infrared bands","volume":"98","author":"Chen","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.14358\/PERS.70.9.1053","article-title":"Spectral mixture analysis of the urban landscape in Indianapolis with Landsat ETM+ imagery","volume":"70","author":"Lu","year":"2004","journal-title":"Photogramm. Eng. Remote Sensing"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Kang, Y., \u00d6zdo\u011fan, M., Zipper, S.C., Rom\u00e1n, M.O., Walker, J., Hong, S.Y., Marshall, M., Magliulo, V., Moreno, J., and Alonso, L. (2016). How universal is the relationship between remotely sensed vegetation indices and crop leaf area index? A global assessment. Remote Sens., 8.","DOI":"10.3390\/rs8070597"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Dhakar, R., Sehgal, V.K., Chakraborty, D., Sahoo, R.N., and Mukherjee, J. (2019). Field scale wheat LAI retrieval from multispectral Sentinel 2A-MSI and LandSat 8-OLI imagery: Effect of atmospheric correction, image resolutions and inversion techniques. Geocarto Int., 1\u201321.","DOI":"10.1080\/10106049.2019.1687591"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"887","DOI":"10.3389\/fpls.2017.00887","article-title":"Timing Is Important: Unmanned Aircraft vs. Satellite Imagery in Plant Invasion Monitoring","volume":"8","year":"2017","journal-title":"Front. Plant Sci."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/S0034-4257(96)00248-9","article-title":"Effects of spectral, spatial, and radiometric characteristics on remote sensing vegetation indices of forested regions","volume":"61","author":"Teillet","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_76","first-page":"328","article-title":"Relationships between leaf area index (LAI) and vegetation indices to analyze and monitor Mediterranean ecosystems","volume":"Volume 4171","author":"Owe","year":"2001","journal-title":"Remote Sensing for Agriculture, Ecosystems, and Hydrology II, Proceedings of the Europto Remote Sensing, Barcelona, Spain, 25\u201329 September 2000"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"1157","DOI":"10.1007\/s11119-019-09648-8","article-title":"An optimized non-linear vegetation index for estimating leaf area index in winter wheat","volume":"20","author":"Feng","year":"2019","journal-title":"Precis. Agric."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Towers, P.C., Strever, A., and Poblete-Echeverr\u00eda, C. (2019). Comparison of Vegetation Indices for Leaf Area Index Estimation in Vertical Shoot Positioned Vine Canopies with and without Grenbiule Hail-Protection Netting. Remote Sens., 11.","DOI":"10.3390\/rs11091073"},{"key":"ref_79","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_80","doi-asserted-by":"crossref","unstructured":"Clevers, J., Kooistra, L., and van den Brande, M. (2017). Using Sentinel-2 Data for Retrieving LAI and Leaf and Canopy Chlorophyll Content of a Potato Crop. Remote Sens., 9.","DOI":"10.3390\/rs9050405"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Pasqualotto, N., Delegido, J., Van Wittenberghe, S., Rinaldi, M., and Moreno, J. (2019). Multi-Crop Green LAI Estimation with a New Simple Sentinel-2 LAI Index (SeLI). Sensors, 19.","DOI":"10.3390\/s19040904"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/0034-4257(94)00111-Y","article-title":"Leaf area index estimation from visible and near-infrared reflectance data","volume":"52","author":"Price","year":"1995","journal-title":"Remote Sens. Environ."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"4834","DOI":"10.3390\/rs70404834","article-title":"Comparing the Dry Season In-Situ Leaf Area Index (LAI) Derived from High-Resolution RapidEye Imagery with MODIS LAI in a Namibian Savanna","volume":"7","author":"Mayr","year":"2015","journal-title":"Remote Sens."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.fcr.2007.01.003","article-title":"Assessing broadband vegetation indices and QuickBird data in estimating leaf area index of corn and potato canopies","volume":"102","author":"Wu","year":"2007","journal-title":"F. Crop. Res."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"979","DOI":"10.5194\/nhess-9-979-2009","article-title":"Influences of leaf area index estimations on water balance modeling in a mediterranean semi-arid basin","volume":"9","author":"Gigante","year":"2009","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1002\/esp.3290190207","article-title":"Derivation of vegetative variables from a landsat tm image for modelling soil erosion","volume":"19","year":"1994","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/S1006-1266(07)60025-X","article-title":"Estimation of Fractional Vegetation Cover Based on Digital Camera Survey Data and a Remote Sensing Model","volume":"17","author":"Hu","year":"2007","journal-title":"J. China Univ. Min. Technol."},{"key":"ref_88","unstructured":"Wu, B., Li, M., Yan, C., Zhou, W., and Yan, C. (2004, January 20\u201324). Developing method of vegetation fraction estimation by remote sensing for soil loss equation: A case in the Upper Basin of Miyun Reservoir. Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Anchorage, AK, USA."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"16164","DOI":"10.3390\/rs71215817","article-title":"Evaluation of sampling methods for validation of remotely sensed fractional vegetation cover","volume":"7","author":"Mu","year":"2015","journal-title":"Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/20\/3376\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:22:03Z","timestamp":1760178123000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/20\/3376"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,15]]},"references-count":89,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2020,10]]}},"alternative-id":["rs12203376"],"URL":"https:\/\/doi.org\/10.3390\/rs12203376","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2020,10,15]]}}}