{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T04:06:42Z","timestamp":1777608402057,"version":"3.51.4"},"reference-count":60,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,9,27]],"date-time":"2023-09-27T00:00:00Z","timestamp":1695772800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Plant Sci."],"abstract":"<jats:p>Accurate estimation of fractional vegetation cover (FVC) is essential for crop growth monitoring. Currently, satellite remote sensing monitoring remains one of the most effective methods for the estimation of crop FVC. However, due to the significant difference in scale between the coarse resolution of satellite images and the scale of measurable data on the ground, there are significant uncertainties and errors in estimating crop FVC. Here, we adopt a Strategy of Upscaling-Downscaling operations for unmanned aerial systems (UAS) and satellite data collected during 2 growing seasons of winter wheat, respectively, using backpropagation neural networks (BPNN) as support to fully bridge this scale gap using highly accurate the UAS-derived FVC (FVC<jats:sub>UAS<\/jats:sub>) to obtain wheat accurate FVC. Through validation with an independent dataset, the BPNN model predicted FVC with an RMSE of 0.059, which is 11.9% to 25.3% lower than commonly used Long Short-Term Memory (LSTM), Random Forest Regression (RFR), and traditional Normalized Difference Vegetation Index-based method (NDVI-based) models. Moreover, all those models achieved improved estimation accuracy with the Strategy of Upscaling-Downscaling, as compared to only upscaling UAS data. Our results demonstrate that: (1) establishing a nonlinear relationship between FVC<jats:sub>UAS<\/jats:sub> and satellite data enables accurate estimation of FVC over larger regions, with the strong support of machine learning capabilities. (2) Employing the Strategy of Upscaling-Downscaling is an effective strategy that can improve the accuracy of FVC estimation, in the collaborative use of UAS and satellite data, especially in the boundary area of the wheat field. This has significant implications for accurate FVC estimation for winter wheat, providing a reference for the estimation of other surface parameters and the collaborative application of multisource data.<\/jats:p>","DOI":"10.3389\/fpls.2023.1220137","type":"journal-article","created":{"date-parts":[[2023,9,28]],"date-time":"2023-09-28T09:59:32Z","timestamp":1695895172000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":16,"title":["Accurate estimation of fractional vegetation cover for winter wheat by integrated unmanned aerial systems and satellite images"],"prefix":"10.3389","volume":"14","author":[{"given":"Songlin","family":"Yang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shanshan","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bing","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruyi","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cunjun","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinkang","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shengwei","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Enhui","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zihang","family":"Lou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dailiang","family":"Peng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2023,9,27]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"3515","DOI":"10.3390\/rs14153515","article-title":"A review of hybrid approaches for quantitative assessment of crop traits using optical remote sensing: research trends and future directions","volume":"14","author":"Abdelbaki","year":"2022","journal-title":"Remote Sens."},{"key":"B2","doi-asserted-by":"publisher","first-page":"100019","DOI":"10.1016\/j.srs.2021.100019","article-title":"UAV & satellite synergies for optical remote sensing applications: A literature review","volume":"3","author":"Alvarez-Vanhard","year":"2021","journal-title":"Sci. Remote Sens."},{"key":"B3","doi-asserted-by":"publisher","first-page":"1242","DOI":"10.1007\/s11119-020-09717-3","article-title":"Vineyard yield estimation by combining remote sensing, computer vision and artificial neural network techniques","volume":"21","author":"Ballesteros","year":"2020","journal-title":"Precis. Agric."},{"key":"B4","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"B5","doi-asserted-by":"publisher","first-page":"100055","DOI":"10.1016\/j.xinn.2020.100055","article-title":"Future earth and sustainable developments","volume":"1","author":"Cheng","year":"2020","journal-title":"Innovation"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2022.1090970","article-title":"Wheat yield estimation using remote sensing data based on machine learning approaches","volume":"13","author":"Cheng","year":"2022","journal-title":"Front. Plant Sci."},{"key":"B7","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1016\/j.isprsjprs.2018.10.018","article-title":"Soybean crop coverage estimation from NDVI images with different spatial resolution to evaluate yield variability in a plot","volume":"146","author":"De la Casa","year":"2018","journal-title":"ISPRS J. Photogrammetry Remote Sens."},{"key":"B8","doi-asserted-by":"publisher","first-page":"364","DOI":"10.1016\/j.isprsjprs.2019.11.018","article-title":"Remote sensing algorithms for estimation of fractional vegetation cover using pure vegetation index values: A review","volume":"159","author":"Gao","year":"2020","journal-title":"ISPRS J. Photogrammetry Remote Sens."},{"key":"B9","doi-asserted-by":"publisher","first-page":"102281","DOI":"10.1016\/j.jag.2020.102281","article-title":"Mapping the fractional coverage of the invasive shrub Ulex europaeus with multi-temporal Sentinel-2 imagery utilizing UAV orthoimages and a new spatial optimization approach","volume":"96","author":"Graenzig","year":"2021","journal-title":"Int. J. Appl. Earth Observ. Geoinform."},{"key":"B10","doi-asserted-by":"publisher","first-page":"100079","DOI":"10.1016\/j.xinn.2021.100079","article-title":"A credit system to solve agricultural nitrogen pollution","volume":"2","author":"Gu","year":"2021","journal-title":"Innovation"},{"key":"B11","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2022.1075856","article-title":"Estimation of wheat tiller density using remote sensing data and machine learning methods","volume":"13","author":"Hu","year":"2022","journal-title":"Front. Plant Sci."},{"key":"B12","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1016\/j.rse.2016.02.019","article-title":"Fractional vegetation cover estimation algorithm for Chinese GF-1 wide field view data","volume":"177","author":"Jia","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"B13","doi-asserted-by":"publisher","first-page":"1559","DOI":"10.1007\/s11128-014-0841-8","article-title":"Quantum image scaling using nearest neighbor interpolation","volume":"14","author":"Jiang","year":"2015","journal-title":"Quantum Inf. Process."},{"key":"B14","volume-title":"Remote sensing of vegetation: principles, techniques, and applications","author":"Jones","year":"2010"},{"key":"B15","doi-asserted-by":"publisher","first-page":"1153","DOI":"10.1109\/TASSP.1981.1163711","article-title":"Cubic convolution interpolation for digital image processing","volume":"29","author":"Keys","year":"1981","journal-title":"IEEE Trans. acoustics speech Signal Process."},{"key":"B16","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1007\/978-1-4419-6533-2_12","article-title":"Bilinear interpolation","author":"Kirkland","year":"2010","journal-title":"Adv. Computing Electron Microscopy"},{"key":"B17","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/j.inffus.2022.10.007","article-title":"Image super-resolution: A comprehensive review, recent trends, challenges and applications","volume":"91","author":"Lepcha","year":"2023","journal-title":"Inf. Fusion."},{"key":"B18","doi-asserted-by":"publisher","first-page":"111537","DOI":"10.1016\/j.rse.2019.111537","article-title":"SFSDAF: An enhanced FSDAF that incorporates sub-pixel class fraction change information for spatio-temporal image fusion","volume":"237","author":"Li","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"B19","doi-asserted-by":"publisher","first-page":"102926","DOI":"10.1016\/j.jag.2022.102926","article-title":"Deep learning in multimodal remote sensing data fusion: A comprehensive review","volume":"112","author":"Li","year":"2022","journal-title":"Int. J. Appl. Earth Observ. Geoinform."},{"key":"B20","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1016\/j.isprsjprs.2023.03.020","article-title":"Review of ground and aerial methods for vegetation cover fraction (fCover) and related quantities estimation: definitions, advances, challenges, and future perspectives","volume":"199","author":"Li","year":"2023","journal-title":"ISPRS J. Photogrammetry Remote Sens."},{"key":"B21","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1016\/j.agrformet.2018.07.028","article-title":"A half-Gaussian fitting method for estimating fractional vegetation cover of corn crops using unmanned aerial vehicle images","volume":"262","author":"Li","year":"2018","journal-title":"Agric. For. Meteorol."},{"key":"B22","doi-asserted-by":"publisher","first-page":"4426","DOI":"10.3390\/rs14174426","article-title":"Crop monitoring using sentinel-2 and UAV multispectral imagery: A comparison case study in Northeastern Germany","volume":"14","author":"Li","year":"2022","journal-title":"Remote Sens."},{"key":"B23","first-page":"1163","article-title":"Prospects on future developments of quantitative remote sensing","volume":"68","author":"Li","year":"2013","journal-title":"Acta Geogr. Sin."},{"key":"B24","volume-title":"Advanced remote sensing: terrestrial information extraction and applications","author":"Liang","year":"2019"},{"key":"B25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13007-021-00796-5","article-title":"Improving the estimation of alpine grassland fractional vegetation cover using optimized algorithms and multi-dimensional features","volume":"17","author":"Lin","year":"2021","journal-title":"Plant Methods"},{"key":"B26","doi-asserted-by":"publisher","first-page":"6532","DOI":"10.1109\/JSTARS.2021.3075624","article-title":"Fractional vegetation cover estimation algorithm based on recurrent neural network for MODIS 250 m reflectance data","volume":"14","author":"Liu","year":"2021","journal-title":"IEEE J. Selected Topics Appl. Earth Observ. Remote Sens."},{"key":"B27","doi-asserted-by":"publisher","first-page":"406","DOI":"10.1111\/j.1654-1103.2011.01373.x","article-title":"A novel method for extracting green fractional vegetation cover from digital images","volume":"23","author":"Liu","year":"2012","journal-title":"J. Vegetation Sci."},{"key":"B28","doi-asserted-by":"publisher","first-page":"1764","DOI":"10.3390\/rs10111764","article-title":"From geometric-optical remote sensing modeling to quantitative remote sensing science\u2014In memory of Academician Xiaowen Li","volume":"10","author":"Liu","year":"2018","journal-title":"Remote Sens."},{"key":"B29","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1007\/s11769-018-1010-2","article-title":"Comparative analysis of fractional vegetation cover estimation based on multi-sensor data in a semi-arid sandy area","volume":"29","author":"Liu","year":"2019","journal-title":"Chin. Geogr. Sci."},{"key":"B30","doi-asserted-by":"publisher","first-page":"1795","DOI":"10.1016\/j.asr.2022.06.039","article-title":"Efficient selection of SAR features using ML-based algorithms for accurate FVC estimation","volume":"70","author":"Maurya","year":"2022","journal-title":"Adv. Space Res."},{"key":"B31","first-page":"7448","article-title":"Development of fusion approach for estimation of vegetation fraction cover with drone and sentinel-2 data","author":"Maurya","year":"2018"},{"key":"B32","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.jag.2019.01.013","article-title":"Ultra-high spatial resolution fractional vegetation cover from unmanned aerial multispectral imagery","volume":"78","author":"Melville","year":"2019","journal-title":"Int. J. Appl. Earth Observ. Geoinform."},{"key":"B33","doi-asserted-by":"publisher","first-page":"106414","DOI":"10.1016\/j.compag.2021.106414","article-title":"Estimating fractional vegetation cover of maize under water stress from UAV multispectral imagery using machine learning algorithms","volume":"189","author":"Niu","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"B34","doi-asserted-by":"publisher","first-page":"380","DOI":"10.3390\/rs14020380","article-title":"Fractional vegetation cover derived from UAV and Sentinel-2 imagery as a proxy for in situ FAPAR in a dense mixed-coniferous forest","volume":"14","author":"Putzenlechner","year":"2022","journal-title":"Remote Sens."},{"key":"B35","doi-asserted-by":"publisher","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":"Riihim\u00e4ki","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"B36","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning representations by back-propagating errors","volume":"323","author":"Rumelhart","year":"1986","journal-title":"Nature"},{"key":"B37","doi-asserted-by":"publisher","first-page":"100058","DOI":"10.1016\/j.srs.2022.100058","article-title":"Estimation and validation of 30 m fractional vegetation cover over China through integrated use of Landsat 8 and Gaofen 2 data","volume":"6","author":"Song","year":"2022","journal-title":"Sci. Remote Sens."},{"key":"B38","doi-asserted-by":"publisher","first-page":"2657","DOI":"10.3390\/plants10122657","article-title":"Solar Radiation Flux Provides a Method of Quantifying Weed-Crop Balance in Present and Future Climates","volume":"10","author":"Squire","year":"2021","journal-title":"Plants"},{"key":"B39","doi-asserted-by":"publisher","DOI":"10.3390\/rs12111742","article-title":"Verification of fractional vegetation coverage and NDVI of desert vegetation via UAVRS technology","volume":"12","author":"Tang","year":"2020","journal-title":"Remote Sens."},{"key":"B40","first-page":"1182","article-title":"Canopy characteristic scale model and quantitative calculation","volume":"18","author":"Tang","year":"2014","journal-title":"J. Remote Sens."},{"key":"B41","doi-asserted-by":"publisher","first-page":"102362","DOI":"10.1016\/j.jag.2021.102362","article-title":"Improving the spatiotemporal fusion accuracy of fractional vegetation cover in agricultural regions by combining vegetation growth models","volume":"101","author":"Tao","year":"2021","journal-title":"Int. J. Appl. Earth Observ. Geoinform."},{"key":"B42","first-page":"106038","article-title":"Using NDVI for the assessment of canopy cover in agricultural crops within modelling research","volume-title":"Comp. Electron. Agricult.","author":"Tenreiro","year":"2021"},{"key":"B43","doi-asserted-by":"crossref","DOI":"10.1201\/b11222","volume-title":"Hyperspectral remote sensing of vegetation","author":"Thenkabail","year":"2016"},{"key":"B44","doi-asserted-by":"publisher","first-page":"1672","DOI":"10.1109\/LGRS.2019.2954291","article-title":"A time-efficient fractional vegetation cover estimation method using the dynamic vegetation growth information from time series Glass FVC product","volume":"17","author":"Tu","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"B45","doi-asserted-by":"publisher","first-page":"055005","DOI":"10.1088\/1748-9326\/11\/5\/055005","article-title":"Circumpolar Arctic vegetation: a hierarchic review and roadmap toward an internationally consistent approach to survey, archive and classify tundra plot data","volume":"11","author":"Walker","year":"2016","journal-title":"Environ. Res. Lett."},{"key":"B46","doi-asserted-by":"publisher","first-page":"4691","DOI":"10.1093\/jxb\/erab194","article-title":"A model for phenotyping crop fractional vegetation cover using imagery from unmanned aerial vehicles","volume":"72","author":"Wan","year":"2021","journal-title":"J. Exp. Bot."},{"key":"B47","doi-asserted-by":"publisher","first-page":"107665","DOI":"10.1016\/j.agrformet.2019.107665","article-title":"Landscape-level vegetation classification and fractional woody and herbaceous vegetation cover estimation over the dryland ecosystems by unmanned aerial vehicle platform","volume":"278","author":"Wang","year":"2019","journal-title":"Agric. For. Meteorol."},{"key":"B48","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1016\/j.isprsjprs.2020.07.006","article-title":"Generating spatiotemporally consistent fractional vegetation cover at different scales using spatiotemporal fusion and multiresolution tree methods","volume":"167","author":"Wang","year":"2020","journal-title":"ISPRS J. Photogrammetry Remote Sens."},{"key":"B49","doi-asserted-by":"publisher","first-page":"111865","DOI":"10.1016\/j.rse.2020.111865","article-title":"Multi-scale integration of satellite remote sensing improves characterization of dry-season green-up in an Amazon tropical evergreen forest","volume":"246","author":"Wang","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"B50","doi-asserted-by":"publisher","first-page":"3373","DOI":"10.1109\/JSTARS.2021.3064580","article-title":"Improving the accuracy of fractional evergreen forest cover estimation at subpixel scale in cloudy and rainy areas by harmonizing landsat-8 and sentinel-2 time-series data","volume":"14","author":"Wu","year":"2021","journal-title":"IEEE J. Selected Topics Appl. Earth Observ. Remote Sens."},{"key":"B51","doi-asserted-by":"publisher","first-page":"31","DOI":"10.3390\/rs9010031","article-title":"Subpixel inundation mapping using landsat-8 OLI and UAV data for a wetland region on the zoige plateau, China","volume":"9","author":"Xia","year":"2017","journal-title":"Remote Sens."},{"key":"B52","doi-asserted-by":"publisher","first-page":"286","DOI":"10.1016\/j.rse.2015.07.014","article-title":"Characterization of shrubland ecosystem components as continuous fields in the northwest United States","volume":"168","author":"Xian","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"B53","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1016\/j.rse.2005.07.011","article-title":"A comparison of methods for estimating fractional green vegetation cover within a desert-to-upland transition zone in central New Mexico, USA","volume":"98","author":"Xiao","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"B54","doi-asserted-by":"publisher","first-page":"100179","DOI":"10.1016\/j.xinn.2021.100179","article-title":"Artificial intelligence: A powerful paradigm for scientific research","volume":"2","author":"Xu","year":"2021","journal-title":"Innovation"},{"key":"B55","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/j.isprsjprs.2019.09.017","article-title":"Improving the estimation of fractional vegetation cover from UAV RGB imagery by colour unmixing","volume":"158","author":"Yan","year":"2019","journal-title":"ISPRS J. Photogrammetry Remote Sens."},{"key":"B56","first-page":"107","article-title":"Sky-farmers: Applications of unmanned aerial vehicles (UAV) in agriculture","author":"Yinka-Banjo","year":"2019","journal-title":"Autonomous vehicles"},{"key":"B57","first-page":"1","article-title":"A deep transfer learning method for estimating fractional vegetation cover of sentinel-2 multispectral images","volume":"19","author":"Yu","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"B58","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2022.3148139","article-title":"Progress and challenges in intelligent remote sensing satellite systems","author":"Zhang","year":"2022","journal-title":"IEEE J. Selected Topics Appl. Earth Observ. Remote Sens"},{"key":"B59","doi-asserted-by":"publisher","first-page":"1485","DOI":"10.3390\/s19071485","article-title":"Integrated satellite, unmanned aerial vehicle (UAV) and ground inversion of the SPAD of winter wheat in the reviving stage","volume":"19","author":"Zhang","year":"2019","journal-title":"Sensors"},{"key":"B60","doi-asserted-by":"publisher","first-page":"2678","DOI":"10.3390\/rs11222678","article-title":"Estimating maize above-ground biomass using 3D point clouds of multi-source unmanned aerial vehicle data at multi-spatial scales","volume":"11","author":"Zhu","year":"2019","journal-title":"Remote Sens."}],"container-title":["Frontiers in Plant Science"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpls.2023.1220137\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,28]],"date-time":"2023-09-28T09:59:42Z","timestamp":1695895182000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpls.2023.1220137\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,27]]},"references-count":60,"alternative-id":["10.3389\/fpls.2023.1220137"],"URL":"https:\/\/doi.org\/10.3389\/fpls.2023.1220137","relation":{},"ISSN":["1664-462X"],"issn-type":[{"value":"1664-462X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,27]]},"article-number":"1220137"}}