{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T05:05:09Z","timestamp":1787029509301,"version":"build-2736575974"},"reference-count":66,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2020,1,26]],"date-time":"2020-01-26T00:00:00Z","timestamp":1579996800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["(Grant No. 2018YFD0200900)."],"award-info":[{"award-number":["(Grant No. 2018YFD0200900)."]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Unmanned aerial vehicles (UAVs) equipped with spectral sensors have become useful in the fast and non-destructive assessment of crop growth, endurance and resource dynamics. This study is intended to inspect the capabilities of UAV-onboard multispectral sensors for non-destructive phenotype variables, including leaf area index (LAI), leaf mass per area (LMA) and specific leaf area (SLA) of rapeseed oil at different growth stages. In addition, the raw image data with high ground resolution (20 cm) were resampled to 30, 50 and 100 cm to determine the influence of resolution on the estimation of phenotype variables by using vegetation indices (VIs). Quadratic polynomial regression was applied to the quantitative analysis at different resolutions and growth stages. The coefficient of determination (R2) and root mean square error results indicated the significant accuracy of the LAI estimation, wherein the highest R2 values were attained by RVI = 0.93 and MTVI2 = 0.89 at the elongation stage. The noise equivalent of sensitivity and uncertainty analyses at the different growth stages accounted for the sensitivity of VIs, which revealed the optimal VIs of RVI, MTVI2 and MSAVI in the LAI estimation. LMA and SLA, which showed significant accuracies at (R2 = 0.85, 0.81) and (R2 = 0.85, 0.71), were estimated on the basis of the predicted leaf dry weight and LAI at the elongation and flowering stages, respectively. No significant variations were observed in the measured regression coefficients using different resolution images. Results demonstrated the significant potential of UAV-onboard multispectral sensor and empirical method for the non-destructive retrieval of crop canopy variables.<\/jats:p>","DOI":"10.3390\/rs12030397","type":"journal-article","created":{"date-parts":[[2020,1,27]],"date-time":"2020-01-27T07:41:11Z","timestamp":1580110871000},"page":"397","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Assessment of UAV-Onboard Multispectral Sensor for Non-Destructive Site-Specific Rapeseed Crop Phenotype Variable at Different Phenological Stages and Resolutions"],"prefix":"10.3390","volume":"12","author":[{"given":"Sadeed","family":"Hussain","sequence":"first","affiliation":[{"name":"College of Resource and Environment, Huazhong Agricultural University, Wuhan 430070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaixiu","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Resource and Environment, Huazhong Agricultural University, Wuhan 430070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mairaj","family":"Din","sequence":"additional","affiliation":[{"name":"Department of Agronomy, University of Agriculture Faisalabad, Burewala 61010, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongkang","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Resource and Environment, Huazhong Agricultural University, Wuhan 430070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhihua","family":"Shi","sequence":"additional","affiliation":[{"name":"College of Resource and Environment, Huazhong Agricultural University, Wuhan 430070, China"},{"name":"Key Laboratory of Arable Land Conservation (Middle and Lower Reaches of Yangtze River), Ministry of Agriculture, Wuhan 430070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shanqin","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Resource and Environment, Huazhong Agricultural University, Wuhan 430070, China"},{"name":"Key Laboratory of Arable Land Conservation (Middle and Lower Reaches of Yangtze River), Ministry of Agriculture, Wuhan 430070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,1,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1392","DOI":"10.3390\/rs4051392","article-title":"An Automated Technique for Generating Georectified Mosaics from Ultra-High Resolution Unmanned Aerial Vehicle (UAV) Imagery, Based on Structure from Motion (SfM) Point Clouds","volume":"4","author":"Turner","year":"2012","journal-title":"Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.biosystemseng.2015.01.008","article-title":"ScienceDirect Multi-temporal imaging using an unmanned aerial vehicle for monitoring a sunflower crop","volume":"132","author":"Vega","year":"2015","journal-title":"Biosyst. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1007\/s11119-012-9274-5","article-title":"The application of small unmanned aerial systems for precision agriculture: A review","volume":"13","author":"Zhang","year":"2015","journal-title":"Precis. Agric."},{"key":"ref_4","unstructured":"Tremblay, N., Vigneault, P., B\u00e9lec, C., Fallon, E., and Bouroubi, M.Y. (2014, January 20\u201323). A comparison of performance between UAV and satellite imagery for N status assessment in corn. Proceedings of the 12th International Conference on Precision Agriculture, Sacramento, CA, USA."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Hunt, E.R., Daughtry, C.S.T., Mirsky, S.B., and Hively, W.D. (2013, January 12\u201316). Remote sensing with unmanned aircraft systems for precision agriculture applications. Proceedings of the 2013 Second International Conference on Agro-Geoinformatics (Agro-Geoinformatics), Fairfax, VA, USA.","DOI":"10.1109\/Argo-Geoinformatics.2013.6621894"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.fcr.2013.07.019","article-title":"A preliminary precision rice management system for increasing both grain yield and nitrogen use efficiency","volume":"154","author":"Zhao","year":"2013","journal-title":"Field Crops Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"562","DOI":"10.3390\/rs2020562","article-title":"Value of Using Different Vegetative Indices to Quantify Agricultural Crop Characteristics at Different Growth Stages under Varying Management Practices","volume":"2","author":"Hatfield","year":"2010","journal-title":"Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"595","DOI":"10.14358\/PERS.71.5.595","article-title":"Agricultural Applications of High-Resolution Digital Multispectral Imagery: Evaluating Within-Field Spatial Variability of Canola (Brassica napus) in Western Australia","volume":"71","author":"Warren","year":"2005","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1007\/s11119-012-9257-6","article-title":"A flexible unmanned aerial vehicle for precision agriculture","volume":"13","author":"Primicerio","year":"2012","journal-title":"Precis. Agric."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/fpls.2017.00820","article-title":"Evaluating Hyperspectral Vegetation Indices for Leaf Area Index Estimation of Oryza sativa L. at Diverse Phenological Stages","volume":"8","author":"Din","year":"2017","journal-title":"Front. Plant Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.compag.2010.07.001","article-title":"Current status and future directions of precision aerial application for site-specific crop management in the USA","volume":"74","author":"Lan","year":"2010","journal-title":"Comput. Electron. Agric."},{"key":"ref_12","unstructured":"Hunt, E.-R., Horneck, D., Gadler, D., Bruce, A., Turner, R., Spinelli, C., Brungardt, J., and Hamm, P. (2014, January 20\u201323). Detection of nitrogen deficiency in potatoes using small unmanned aircraft systems. Proceedings of the 12th International Conference on Precision Agriculture, Sacramento, CA, USA."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Fatoyinbo, T. (2012). Rice Crop Monitoring with Unmanned Helicopter Remote Sensing Images. Remote Sensing of Biomass\u2014Principles and Applications, InTech.","DOI":"10.5772\/696"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"033542","DOI":"10.1117\/1.3216822","article-title":"Unmanned aerial vehicle-based remote sensing for rangeland assessment, monitoring, and management","volume":"3","author":"Laliberte","year":"2009","journal-title":"J. Appl. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3557","DOI":"10.3390\/s8053557","article-title":"Assessment of Unmanned Aerial Vehicles Imagery for Quantitative Monitoring of Wheat Crop in Small Plots","volume":"8","author":"Lelong","year":"2008","journal-title":"Sensors"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/B0-12-226865-2\/00132-2","article-title":"Functional Diversity","volume":"Volume 3","author":"Tilman","year":"2001","journal-title":"Encyclopedia of Biodiversity"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"277","DOI":"10.5194\/isprsarchives-XL-7-W3-277-2015","article-title":"Prospect inversion for indirect estimation of leaf dry matter content and specific leaf area","volume":"40","author":"Ali","year":"2015","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"e72736","DOI":"10.1371\/journal.pone.0072736","article-title":"Estimation of Wheat Agronomic Parameters using New Spectral Indices","volume":"8","author":"Jin","year":"2013","journal-title":"PLoS ONE"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.isprsjprs.2013.10.009","article-title":"Deriving leaf mass per area (LMA) from foliar reflectance across a variety of plant species using continuous wavelet analysis","volume":"87","author":"Cheng","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_20","first-page":"1","article-title":"Leaf Mass per Area (LMA) and Its Relationship with Leaf Structure and Anatomy in 34 Mediterranean Woody Species along a Water Availability Gradient","volume":"11","author":"Olmo","year":"2016","journal-title":"PloS ONE"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ecocom.2013.06.003","article-title":"Review of optical-based remote sensing for plant trait mapping","volume":"15","author":"Homolova","year":"2013","journal-title":"Ecol. Complex."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wei, Z., Zhang, B., Liu, Y., and Xu, D. (2018). The Application of a Modified Version of the SWAT Model at the Daily Temporal Scale and the Hydrological Response unit Spatial Scale: A Case Study Covering an Irrigation District in the Hei River Basin. Water, 10.","DOI":"10.3390\/w10081064"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.envsoft.2015.12.001","article-title":"Development and evaluation of targeted marginal land mapping approach in SWAT model for simulating water quality impacts of selected second generation biofeedstock","volume":"81","author":"Singh","year":"2016","journal-title":"Environ. Model. Softw."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3586","DOI":"10.1109\/JSTARS.2014.2342291","article-title":"Leaf area index estimation using vegetation indices derived from airborne hyperspectral images in winter wheat","volume":"7","author":"Xie","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3468","DOI":"10.1016\/j.rse.2011.08.010","article-title":"Comparison of different vegetation indices for the remote assessment of green leaf area index of crops","volume":"115","author":"Gitelson","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"90","DOI":"10.25103\/jestr.111.11","article-title":"Communication Antenas for UAVs","volume":"11","author":"Marques","year":"2018","journal-title":"J. Eng. Sci. Technol. Rev."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1297","DOI":"10.1111\/j.1749-8198.2010.00381.x","article-title":"Small-Scale Remotely Piloted Vehicles in Environmental Research","volume":"4","author":"Hardin","year":"2010","journal-title":"Geogr. Compass"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.geoderma.2012.09.009","article-title":"The utility of remotely-sensed vegetative and terrain covariates at different spatial resolutions in modelling soil and watertable depth (for digital soil mapping)","volume":"193\u2013194","author":"Taylor","year":"2013","journal-title":"Geoderma"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1016\/j.jenvman.2017.06.017","article-title":"Evaluating the effect of remote sensing image spatial resolution on soil exchangeable potassium prediction models in smallholder farm settings","volume":"200","author":"Xu","year":"2017","journal-title":"J. Environ. Manag."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"6724","DOI":"10.1109\/TGRS.2014.2301443","article-title":"Soil Phosphorus and Nitrogen Predictions Across Spatial Escalating Scales in an Aquatic Ecosystem Using Remote Sensing Images","volume":"52","author":"Kim","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","first-page":"1","article-title":"A Novel Remote Sensing Approach for Prediction of Maize Yield Under Different Conditions of Nitrogen Fertilization","volume":"7","author":"Masuka","year":"2016","journal-title":"Front. Plant Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.compag.2018.05.026","article-title":"Diagnosis of Nitrogen Status In Winter Oilseed Rape (Brassica napus L.) Using In-situ Hyperspectral Data and Unmanned Aerial Vehicle (UAV) Multispectral Images","volume":"151","author":"Liu","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_33","unstructured":"Pearson, R.L., and Miller, L.D. (1972). Remote Mapping of Standing Crop Biomass for Estimation of the Productivity of the Shortgrass Prairie, Colorado State University."},{"key":"ref_34","unstructured":"J Rouse, J.W., Haas, R.W., Schell, J.A., Deering, D.H., and Harlan, J.C. (1974). Monitoring the Vernal Advancement and Retrogradation (Greenwave effect) of Natural Vegetation."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/S0034-4257(96)00072-7","article-title":"Use of a green channel in remote sensing of global vegetation from EOS-MODIS","volume":"58","author":"Gitelson","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S1672-6308(07)60027-4","article-title":"New Vegetation Index and Its Application in Estimating Leaf Area Index of Rice","volume":"14","author":"Wang","year":"2007","journal-title":"Rice Sci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A soil-adjusted vegetation index (SAVI)","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_38","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_39","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/0034-4257(95)00186-7","article-title":"Optimization of soil-adjusted vegetation indices","volume":"55","author":"Rondeaux","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_40","first-page":"62980B","article-title":"Hyperspectral mapping of crop and soils for precision agriculture","volume":"6298","author":"Gao","year":"2006","journal-title":"Remote Sensing and Modeling of Ecosystems for Sustainability III"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/0034-4257(94)90134-1","article-title":"A modified soil adusted vegetation index","volume":"48","author":"Qi","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1007\/s11119-010-9165-6","article-title":"Evaluating hyperspectral vegetation indices for estimating nitrogen concentration of winter wheat at different growth stages","volume":"11","author":"Li","year":"2010","journal-title":"Precis. Agric."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/fpls.2018.01883","article-title":"Estimation of dynamic canopy variables using hyperspectral derived vegetation indices under varying N rates at diverse phenological stages of rice","volume":"9","author":"Din","year":"2019","journal-title":"Front. Plant Sci."},{"key":"ref_44","first-page":"37","article-title":"Remote estimation of crop fractional vegetation cover\u202f: the use of noise equivalent as an indicator of performance of vegetation indices","volume":"34","author":"Taylor","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_45","first-page":"140","article-title":"Estimating green LAI in four crops: Potential of determining optimal spectral bands for a universal algorithm","volume":"192\u2013193","author":"Peng","year":"2014","journal-title":"Agric. For. Meteorol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"706","DOI":"10.1002\/ece3.932","article-title":"Predicting leaf traits of herbaceous species from their spectral characteristics","volume":"4","author":"Roelofsen","year":"2014","journal-title":"Ecol. Evol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/j.compag.2018.07.023","article-title":"A TPE based inversion of PROSAIL for estimating canopy biophysical and biochemical variables of oilseed rape","volume":"152","author":"Wang","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.rse.2008.09.014","article-title":"Utility of an image-based canopy reflectance modeling tool for remote estimation of LAI and leaf chlorophyll content at the field scale","volume":"113","author":"Houborg","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.agrformet.2013.05.003","article-title":"Comparing hyperspectral index optimization algorithms to estimate aerial N uptake using multi-temporal winter wheat datasets from contrasting climatic and geographic zones in China and Germany","volume":"180","author":"Li","year":"2013","journal-title":"Agric. For. Meteorol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"963","DOI":"10.1093\/aob\/mcw022","article-title":"Leaf density explains variation in leaf mass per area in rice between cultivars and nitrogen treatments","volume":"117","author":"Xiong","year":"2016","journal-title":"Ann. Bot."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.fcr.2012.02.012","article-title":"Using Leaf Area Index, retrieved from optical imagery, in the STICS crop model for predicting yield and biomass of field crops","volume":"131","author":"Pattey","year":"2012","journal-title":"Field Crops Res."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/fpls.2016.00759","article-title":"The Potential of Hyperspectral Patterns of Winter Wheat to Detect Changes in Soil Microbial Community Composition","volume":"7","author":"Carvalho","year":"2016","journal-title":"Front. Plant Sci."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.fcr.2013.04.029","article-title":"Tillering responses of rice to plant density and nitrogen rate in a subtropical environment of southern China","volume":"149","author":"Huang","year":"2013","journal-title":"Field Crops Res."},{"key":"ref_54","first-page":"1685","article-title":"Quantitative relationships between hyper-spectral vegetation indices and leaf area index of rice","volume":"20","author":"Tian","year":"2009","journal-title":"J. Appl. Ecol."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1093\/jpe\/rtu027","article-title":"Remote estimation of the fraction of absorbed photosynthetically active radiation for a maize canopy in Northeast China","volume":"8","author":"Zhang","year":"2015","journal-title":"J. Plant Ecol."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"6199","DOI":"10.1080\/01431160902842342","article-title":"Leaf Area Index derivation from hyperspectral vegetation indicesand the red edge position","volume":"30","author":"Darvishzadeh","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"4014","DOI":"10.1109\/TGRS.2013.2278838","article-title":"Sensitivity Analysis of Vegetation Reflectance to Biochemical and Biophysical Variables at Leaf, Canopy, and Regional Scales","volume":"52","author":"Xiao","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.agrformet.2015.12.025","article-title":"Hyperspectral narrowband and multispectral broadband indices for remote sensing of crop evapotranspiration and its components (transpiration and soil evaporation)","volume":"218\u2013219","author":"Marshall","year":"2016","journal-title":"Agric. For. Meteorol."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"948","DOI":"10.1046\/j.1365-2435.1998.00274.x","article-title":"Leaf structure (specific leaf area) modulates photosynthesis\u2013nitrogen relations\u202f: Evidence from within and across species and functional groups","volume":"12","author":"Reich","year":"1998","journal-title":"Funct. Ecol."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/S0378-4290(01)00199-X","article-title":"Moisture-deficit-induced changes in leaf-water content, leaf carbon exchange rate and biomass production in groundnut cultivars differing in specific leaf area","volume":"74","author":"Nautiyal","year":"2002","journal-title":"Field Crops Res."},{"key":"ref_61","first-page":"1","article-title":"A Novel Remote Sensing Approach for Prediction of Maize Yield Under Different Conditions of Nitrogen Fertilization","volume":"7","author":"Masuka","year":"2016","journal-title":"Front. Plant Sci."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/JSTARS.2018.2824901","article-title":"Estimating Plant Traits of Alpine Grasslands on the Qinghai-Tibetan Plateau Using Remote Sensing","volume":"11","author":"Li","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1890\/070152","article-title":"Airborne spectranomics\u202f: mapping canopy chemical and taxonomic diversity in tropical forests","volume":"7","author":"Anser","year":"2009","journal-title":"Environ. Front. Ecol."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/0034-4257(94)90016-7","article-title":"On the relationship between FAPAR and NDVI","volume":"49","author":"Myneni","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Steinberg, A., Chabrillat, S., Stevens, A., Segl, K., and Foerster, S. (2016). Prediction of common surface soil properties based on Vis-NIR airborne and simulated EnMAP imaging spectroscopy data: Prediction accuracy and influence of spatial resolution. Remote Sens., 8.","DOI":"10.3390\/rs8070613"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.geoderma.2014.03.021","article-title":"Scale-dependency of LiDAR derived terrain attributes in quantitative soil-landscape modeling: Effects of grid resolution vs. neighborhood extent","volume":"230\u2013231","author":"Maynard","year":"2014","journal-title":"Geoderma"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/3\/397\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:20:40Z","timestamp":1760361640000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/3\/397"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,26]]},"references-count":66,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2020,2]]}},"alternative-id":["rs12030397"],"URL":"https:\/\/doi.org\/10.3390\/rs12030397","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,26]]}}}