{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T01:32:38Z","timestamp":1774315958844,"version":"3.50.1"},"reference-count":52,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,10,24]],"date-time":"2022-10-24T00:00:00Z","timestamp":1666569600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key scientific and technological projects of Heilongjiang province","award":["2021ZXJ05A05"],"award-info":[{"award-number":["2021ZXJ05A05"]}]},{"name":"Key scientific and technological projects of Heilongjiang province","award":["41601346"],"award-info":[{"award-number":["41601346"]}]},{"name":"Key scientific and technological projects of Heilongjiang province","award":["PT2022-24"],"award-info":[{"award-number":["PT2022-24"]}]},{"name":"Key scientific and technological projects of Heilongjiang province","award":["2019B020216001"],"award-info":[{"award-number":["2019B020216001"]}]},{"name":"National Natural Science Foundation of China","award":["2021ZXJ05A05"],"award-info":[{"award-number":["2021ZXJ05A05"]}]},{"name":"National Natural Science Foundation of China","award":["41601346"],"award-info":[{"award-number":["41601346"]}]},{"name":"National Natural Science Foundation of China","award":["PT2022-24"],"award-info":[{"award-number":["PT2022-24"]}]},{"name":"National Natural Science Foundation of China","award":["2019B020216001"],"award-info":[{"award-number":["2019B020216001"]}]},{"name":"Platform Construction Funded Program of Beijing Academy of Agriculture and Forestry Sciences","award":["2021ZXJ05A05"],"award-info":[{"award-number":["2021ZXJ05A05"]}]},{"name":"Platform Construction Funded Program of Beijing Academy of Agriculture and Forestry Sciences","award":["41601346"],"award-info":[{"award-number":["41601346"]}]},{"name":"Platform Construction Funded Program of Beijing Academy of Agriculture and Forestry Sciences","award":["PT2022-24"],"award-info":[{"award-number":["PT2022-24"]}]},{"name":"Platform Construction Funded Program of Beijing Academy of Agriculture and Forestry Sciences","award":["2019B020216001"],"award-info":[{"award-number":["2019B020216001"]}]},{"name":"Key Field Research and Development Program of Guangdong Province","award":["2021ZXJ05A05"],"award-info":[{"award-number":["2021ZXJ05A05"]}]},{"name":"Key Field Research and Development Program of Guangdong Province","award":["41601346"],"award-info":[{"award-number":["41601346"]}]},{"name":"Key Field Research and Development Program of Guangdong Province","award":["PT2022-24"],"award-info":[{"award-number":["PT2022-24"]}]},{"name":"Key Field Research and Development Program of Guangdong Province","award":["2019B020216001"],"award-info":[{"award-number":["2019B020216001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Aboveground biomass (AGB) is an important indicator to evaluate crop growth, which is closely related to yield and plays an important role in guiding fine agricultural management. Compared with traditional AGB measurements, unmanned aerial vehicle (UAV) hyperspectral remote sensing technology has the advantages of being non-destructive, highly mobile, and highly efficient in precision agriculture. Therefore, this study uses a hyperspectral sensor carried by a UAV to obtain hyperspectral images of potatoes in stages of tuber formation, tuber growth, starch storage, and maturity. Linear regression, partial least squares regression (PLSR), and random forest (RF) based on vegetation indices (Vis), green-edge parameters (GEPs), and combinations thereof are used to evaluate the accuracy of potato AGB estimates in the four growth stages. The results show that (i) the selected VIs and optimal GEPs correlate significantly with AGB. Overall, VIs correlate more strongly with AGB than do GEPs. (ii) AGB estimates made by linear regression based on the optimal VIs, optimal GEPs, and combinations thereof gradually improve in going from the tuber-formation to the tuber-growth stage and then gradually worsen in going from the starch-storage to the maturity stage. Combining the optimal GEPs with the optimal VIs produces the best estimates, followed by using the optimal VIs alone, and using the optimal GEPs produces the worst estimates. (iii) Compared with the single-parameter model, which uses the PLSR and RF methods based on VIs, the combination of VIs with the optimal GEPs significantly improves the estimation accuracy, which gradually improves in going from the tuber-formation to the tuber-growth stage, and then gradually deteriorates in going from the starch-storage to the maturity stage. The combination of VIs with the optimal GEPs produces the most accurate estimates. (iv) The PLSR method is better than the RF method for estimating AGB in each growth period. Therefore, combining the optimal GEPs and VIs and using the PLSR method improves the accuracy of AGB estimates, thereby allowing for non-destructive dynamic monitoring of potato growth.<\/jats:p>","DOI":"10.3390\/rs14215323","type":"journal-article","created":{"date-parts":[[2022,10,24]],"date-time":"2022-10-24T11:53:55Z","timestamp":1666612435000},"page":"5323","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Estimation of Potato Above-Ground Biomass Based on Vegetation Indices and Green-Edge Parameters Obtained from UAVs"],"prefix":"10.3390","volume":"14","author":[{"given":"Yang","family":"Liu","sequence":"first","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"},{"name":"Key Lab of Smart Agriculture System, Ministry of Education, China Agricultural University, Beijing 100083, China"},{"name":"Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3312-6200","authenticated-orcid":false,"given":"Haikuan","family":"Feng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"},{"name":"College of Agriculture, Nanjing Agricultural University, Nanjing 210095, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9766-5313","authenticated-orcid":false,"given":"Jibo","family":"Yue","sequence":"additional","affiliation":[{"name":"College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China"}]},{"given":"Yiguang","family":"Fan","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6769-214X","authenticated-orcid":false,"given":"Xiuliang","family":"Jin","sequence":"additional","affiliation":[{"name":"Institute of Crop Sciences, Chinese Academy of Agricultural Sciences\/Key Laboratory of Crop Physiology and Ecology, Ministry of Agriculture, Beijing 100081, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0294-5705","authenticated-orcid":false,"given":"Xiaoyu","family":"Song","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8506-7295","authenticated-orcid":false,"given":"Hao","family":"Yang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"}]},{"given":"Guijun","family":"Yang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"107089","DOI":"10.1016\/j.compag.2022.107089","article-title":"Remote-sensing estimation of potato above-ground biomass based on spectral and spatial features extracted from high-definition digital camera images","volume":"198","author":"Liu","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Brocks, S., and Bareth, G. (2018). Estimating barley biomass with crop surface models from Oblique RGB imagery. Remote Sens., 10.","DOI":"10.3390\/rs10020268"},{"key":"ref_3","first-page":"79","article-title":"Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley","volume":"39","author":"Bendig","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.fcr.2013.09.023","article-title":"Hyperspectral canopy sensing of paddy rice aboveground biomass at different growth stages","volume":"155","author":"Gnyp","year":"2014","journal-title":"Field Crops Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1016\/j.ecolind.2016.03.036","article-title":"Remote estimation of canopy height and aboveground biomass of maize using high-resolution stereo images from a low-cost unmanned aerial vehicle system","volume":"67","author":"Li","year":"2016","journal-title":"Ecol. Indic."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zhu, W., Sun, Z., Peng, J., Huang, Y., Li, J., Zhang, J., Yang, B., and Liao, X. (2019). Estimating maize above-ground biomass using 3D point clouds of multi-source unmanned aerial vehicle data at multi-spatial scales. Remote Sens., 11.","DOI":"10.3390\/rs11222678"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.3389\/fpls.2019.01145","article-title":"Estimating biomass and canopy height with LiDAR for field crop breeding","volume":"10","author":"Walter","year":"2019","journal-title":"Front. Plant Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.compag.2013.10.010","article-title":"Winter wheat biomass estimation based on spectral indices, band depth analysis and partial least squares regression using hyperspectral measurements","volume":"100","author":"Fu","year":"2014","journal-title":"Comput. Electron. Agric."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Yue, J., Feng, H., Jin, X., Yuan, H., Li, Z., Zhou, C., Yang, G., and Tian, Q. (2018). A comparison of crop parameters estimation using images from UAV-mounted snapshot hyperspectral sensor and high-definition digital camera. Remote Sens., 10.","DOI":"10.3390\/rs10071138"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.rse.2015.02.023","article-title":"Estimating aboveground biomass and leaf area of low-stature Arctic shrubs with terrestrial LiDAR","volume":"164","author":"Greaves","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.rse.2017.06.007","article-title":"Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery","volume":"198","author":"Jin","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1016\/j.compag.2016.01.007","article-title":"Estimating wheat biomass by combining image clustering with crop height","volume":"121","author":"Schirrmann","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Chen, Y., Li, L., Lu, D., and Li, D. (2019). Exploring bamboo forest above ground biomass estimation using sentinel-2 Data. Remote Sens., 11.","DOI":"10.3390\/rs11010007"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"108148","DOI":"10.1016\/j.fcr.2021.108148","article-title":"Remote estimation of grain yield based on UAV data in different rice cultivars under contrasting climatic zone","volume":"267","author":"Duan","year":"2021","journal-title":"Field Crop. Res."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1296","DOI":"10.3390\/s20051296","article-title":"Estimation of crop growth parameters using UAV-based hyperspectral remote sensing data","volume":"20","author":"Tao","year":"2020","journal-title":"Sensors"},{"key":"ref_16","first-page":"235","article-title":"Assessment of RapidEye vegetation indices for estimation of leaf area index and biomass in corn and soybean crops","volume":"34","author":"Kross","year":"2015","journal-title":"Int. J. Appl. earth Obs."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"634","DOI":"10.1016\/j.mcm.2011.10.038","article-title":"Assimilation of MODIS-LAI into the WOFOST model for forecasting regional winter wheat yield","volume":"58","author":"Ma","year":"2013","journal-title":"Math. Comput. Model."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.ecolmodel.2013.08.016","article-title":"Assimilating remote sensing information with crop model using Ensemble Kalman Filter for improving LAI monitoring and yield estimation","volume":"270","author":"Zhao","year":"2013","journal-title":"Ecol. Model."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhou, G., Liu, X., and Liu, M. (2019). Assimilating remote sensing phenological information into the WOFOST model for rice growth simulation. Remote Sens., 11.","DOI":"10.3390\/rs11030268"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1016\/j.agwat.2018.03.029","article-title":"Parametrization of Cropsyst model for the simulation of a potato crop in a Mediterranean environment","volume":"203","author":"Montoya","year":"2018","journal-title":"Agric. Water Manag."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.ecolmodel.2005.11.030","article-title":"Regional importance of crop yield constraints: Linking simulation models and geostatistics to interpret spatial patterns","volume":"196","author":"Lobell","year":"2005","journal-title":"Ecol. Model."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1004","DOI":"10.1080\/01431161.2013.875629","article-title":"Estimation of the dynamics and yields of cereals in a semi-arid area using remote sensing and the SAFY growth model","volume":"35","author":"Chahbi","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Choudhury, M.R., Das, S., Christopher, J., Apan, A., Chapman, S., Menzies, N.W., and Dang, Y.P. (2021). Improving biomass and grain yield prediction of wheat genotypes on sodic soil using integrated high-resolution multispectral, hyperspectral, 3D point cloud, and machine learning techniques. Remote Sens., 13.","DOI":"10.3390\/rs13173482"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Berger, K., Atzberger, C., Danner, M., D\u2019Urso, G., Mauser, W., Vuolo, F., Hank, T., Berger, K., Atzberger, C., and Danner, M. (2018). Evaluation of the PROSAIL model capabilities for future hyperspectral model environments: A review study. Remote Sens., 10.","DOI":"10.3390\/rs10010085"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1016\/j.rse.2004.03.010","article-title":"Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data","volume":"91","author":"Xiao","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_26","first-page":"102177","article-title":"Fine-scale prediction of biomass and leaf nitrogen content in sugarcane using UAV LiDAR and multispectral imaging","volume":"92","author":"Shendryk","year":"2020","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1080\/15481603.2020.1799546","article-title":"Evaluation of machine learning models for rice dry biomass estimation and mapping using quad-source optical imagery","volume":"57","author":"Mansaray","year":"2020","journal-title":"GISci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1304","DOI":"10.1007\/s11119-020-09722-6","article-title":"Accuracy of carrot yield forecasting using proximal hyperspectral and satellite multispectral data","volume":"21","author":"Suarez","year":"2020","journal-title":"Precis. Agric."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.cj.2019.06.005","article-title":"Deep neural network algorithm for estimating maize biomass based on simulated Sentinel 2A vegetation indices and leaf area index","volume":"8","author":"Jin","year":"2020","journal-title":"Crop J."},{"key":"ref_30","first-page":"2501","article-title":"Estimating above ground biomass of winter wheat at early growth stages based on visual spectral","volume":"39","author":"Zhang","year":"2018","journal-title":"Spectrosc. Spect. Anal."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1007\/s10846-019-01001-5","article-title":"High-throughput biomass estimation in rice crops using UAV multispectral imagery","volume":"96","author":"Devia","year":"2019","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1186\/s13007-019-0394-z","article-title":"Modeling maize above-ground biomass based on machine learning approaches using UAV remote-sensing data","volume":"15","author":"Han","year":"2019","journal-title":"Plant Methods"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"4741","DOI":"10.1080\/01431161.2017.1325533","article-title":"Modelling and mapping of above ground biomass (AGB) of oil palm plantations in Malaysia using remotely-sensed data","volume":"38","author":"Asari","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Yue, J., Yang, G., Li, C., Li, Z., Wang, Y., Feng, H., and Xu, B. (2017). Estimation of winter wheat above-ground biomass using unmanned aerial vehicle-based snapshot hyperspectral sensor and crop height improved models. Remote Sens., 9.","DOI":"10.3390\/rs9070708"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"7593","DOI":"10.7717\/peerj.7593","article-title":"Estimation of maize above-ground biomass based on stem-leaf separation strategy integrated with LiDAR and optical remote sensing data","volume":"7","author":"Zhu","year":"2019","journal-title":"PeerJ"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1016\/j.eja.2006.01.001","article-title":"Assessment of rice leaf growth and nitrogen status by hyperspectral canopy reflectance and partial least square regression","volume":"24","author":"Nguyen","year":"2006","journal-title":"Eur. J. Agron."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"620","DOI":"10.3390\/rs12040620","article-title":"A novel approach for estimation of above-ground biomass of sugar beet based on wavelength selection and optimized support vector machine","volume":"12","author":"Zhang","year":"2020","journal-title":"Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Yue, J., Feng, H., Yang, G., and Li, Z. (2018). A Comparison of regression techniques for estimation of above-ground winter wheat biomass using near-surface spectroscopy. Remote Sens., 10.","DOI":"10.3390\/rs10010066"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"106671","DOI":"10.1016\/j.compag.2021.106671","article-title":"Monitoring maize canopy chlorophyll density under lodging stress based on UAV hyperspectral imagery","volume":"193","author":"Sun","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1360","DOI":"10.3389\/fpls.2018.01360","article-title":"Hyperspectral estimation of canopy leaf biomass phenotype per ground area using a continuous wavelet analysis in wheat","volume":"9","author":"Yao","year":"2018","journal-title":"Front. Plant Sci."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"795","DOI":"10.1007\/s12524-016-0632-z","article-title":"Estimation of sugar beet aboveground biomass by band depth optimization of hyperspectral canopy reflectance","volume":"45","author":"Tian","year":"2017","journal-title":"J. Indian Soc. Remote"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1150","DOI":"10.1111\/ele.12653","article-title":"Climate change\u2014Associated trends in net biomass change are age dependent in western boreal forests of Canada","volume":"19","author":"Chen","year":"2016","journal-title":"Ecol. Lett."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"10395","DOI":"10.3390\/rs61110395","article-title":"Estimating biomass of barley using crop surface models (CSMs) derived from UAV-based RGB imaging","volume":"6","author":"Bendig","year":"2014","journal-title":"Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Banerjee, B., Spangenberg, G., and Kant, S. (2020). Fusion of spectral and structural information from aerial images for improved biomass estimation. Remote Sens., 12.","DOI":"10.3390\/rs12193164"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"105026","DOI":"10.1016\/j.compag.2019.105026","article-title":"Estimating biomass of winter oilseed rape using vegetation indices and texture metrics derived from UAV multispectral images","volume":"166","author":"Liu","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.isprsjprs.2019.02.022","article-title":"Estimate of winter-wheat above-ground biomass based on UAV ultrahigh-ground-resolution image textures and vegetation indices","volume":"150","author":"Yue","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1007\/s11119-018-9600-7","article-title":"Improved estimation of rice aboveground biomass combining textural and spectral analysis of UAV imagery","volume":"20","author":"Zheng","year":"2019","journal-title":"Precis. Agric."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Quir\u00f3s Vargas, J.J., Zhang, C., Smitchger, J.A., McGee, R.J., and Sankaran, S. (2019). Phenotyping of plant biomass and performance traits using remote sensing techniques in pea. Sensors, 19.","DOI":"10.3390\/s19092031"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.compag.2018.05.034","article-title":"High-throughput field phenotyping in dry bean using small unmanned aerial vehicle based multispectral imagery","volume":"151","author":"Sankaran","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_50","first-page":"208","article-title":"Estimation of spring wheat chlorophyll content based on hyperspectral features and PLSR model","volume":"33","author":"Kasim","year":"2017","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.isprsjprs.2020.02.013","article-title":"Above-ground biomass estimation and yield prediction in potato by using UAV-based RGB and hyperspectral imaging","volume":"162","author":"Li","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"6221","DOI":"10.3390\/rs6076221","article-title":"Exploring the best hyperspectral features for LAI estimation using partial least squares regression","volume":"6","author":"Li","year":"2014","journal-title":"Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/21\/5323\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:01:57Z","timestamp":1760144517000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/21\/5323"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,24]]},"references-count":52,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["rs14215323"],"URL":"https:\/\/doi.org\/10.3390\/rs14215323","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,24]]}}}