{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T09:23:14Z","timestamp":1773998594333,"version":"3.50.1"},"reference-count":59,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2023,11,18]],"date-time":"2023-11-18T00:00:00Z","timestamp":1700265600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2021YFD1201601"],"award-info":[{"award-number":["2021YFD1201601"]}]},{"name":"National Key Research and Development Program of China","award":["2023YFD2000009"],"award-info":[{"award-number":["2023YFD2000009"]}]},{"name":"National Key Research and Development Program of China","award":["41671411"],"award-info":[{"award-number":["41671411"]}]},{"name":"National Natural Science Foundation of China","award":["2021YFD1201601"],"award-info":[{"award-number":["2021YFD1201601"]}]},{"name":"National Natural Science Foundation of China","award":["2023YFD2000009"],"award-info":[{"award-number":["2023YFD2000009"]}]},{"name":"National Natural Science Foundation of China","award":["41671411"],"award-info":[{"award-number":["41671411"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurate and high-throughput identification of the initial anthesis of soybean varieties is important for the breeding and screening of high-quality soybean cultivars in field trials. The objectives of this study were to identify the initial day of anthesis (IADAS) of soybean varieties based on remote sensing multispectral time-series images acquired by unmanned aerial vehicles (UAVs), and analyze the differences in the initial anthesis of the same soybean varieties between two different climatic regions, Shijiazhuang (SJZ) and Xuzhou (XZ). First, the temporal dynamics of several key crop growth indicators and spectral indices were analyzed to find an effective indicator that favors the identification of IADAS, including leaf area index (LAI), above-ground biomass (AGB), canopy height (CH), normalized-difference vegetation index (NDVI), red edge chlorophyll index (CIred edge), green normalized-difference vegetation index (GNDVI), enhanced vegetation index (EVI), two-band enhanced vegetation index (EVI2) and normalized-difference red-edge index (NDRE). Next, this study compared several functions, like the symmetric gauss function (SGF), asymmetric gauss function (AGF), double logistic function (DLF), and fourier function (FF), for time-series curve fitting, and then estimated the IADAS of soybean varieties with the first-order derivative maximal feature (FDmax) of the CIred edge phenology curves. The relative thresholds of the CIred edge curves were also used to estimate IADAS, in two ways: a single threshold for all of the soybean varieties, and three different relative thresholds for early, middle, and late anthesis varieties, respectively. Finally, this study presented the variations in the IADAS of the same soybean varieties between two different climatic regions and discussed the probable causal factors. The results showed that CIred edge was more suitable for soybean IADAS identification compared with the other investigated indicators because it had no saturation during the whole crop lifespan. Compared with DLF, AGF and FF, SGF provided a better fitting of the CIred edge time-series curves without overfitting problems, although the coefficient of determination (R2) and root mean square error (RMSE) were not the best. The FDmax of the SGF-fitted CIred edge curve (SGF_CIred edge) provided good estimates of the IADAS, with an RMSE and mean average error (MAE) of 3.79 days and 3.00 days, respectively. The SGF-fitted_CIred edge curve can be used to group the soybean varieties into early, middle and late groups. Additionally, the accuracy of the IADAS was improved (RMSE = 3.69 days and MAE = 3.09 days) by using three different relative thresholds (i.e., RT50, RT55, RT60) for the three flowering groups compared to when using a single threshold (RT50). In addition, it was found that the IADAS of the same soybean varieties varied greatly when planted in two different climatic regions due to the genotype\u2013environment interactions. Overall, this study demonstrated that the IADAS of soybean varieties can be identified efficiently and accurately based on UAV remote sensing multispectral time-series data.<\/jats:p>","DOI":"10.3390\/rs15225413","type":"journal-article","created":{"date-parts":[[2023,11,18]],"date-time":"2023-11-18T07:33:18Z","timestamp":1700292798000},"page":"5413","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Identification of the Initial Anthesis of Soybean Varieties Based on UAV Multispectral Time-Series Images"],"prefix":"10.3390","volume":"15","author":[{"given":"Di","family":"Pan","sequence":"first","affiliation":[{"name":"School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China"},{"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":"Changchun","family":"Li","sequence":"additional","affiliation":[{"name":"School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, 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"},{"name":"School of Geological Engineering and Geomatics, Chang\u2019an University, Xi\u2019an 710054, China"}]},{"given":"Pengting","family":"Ren","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":"Yuanyuan","family":"Ma","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":"Weinan","family":"Chen","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\/0000-0003-3312-6200","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"}]},{"given":"Riqiang","family":"Chen","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":"Xin","family":"Chen","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":"Heli","family":"Li","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":[[2023,11,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"179","DOI":"10.9787\/PBB.2015.3.3.179","article-title":"Soybean [Glycine max (L.) Merrill]: Importance as a crop and pedigree reconstruction of Korean varieties","volume":"3","author":"Lee","year":"2015","journal-title":"Plant Breed. Biotechnol."},{"key":"ref_2","first-page":"100233","article-title":"An applied deep learning approach for estimating soybean relative maturity from UAV imagery to aid plant breeding decisions","volume":"7","author":"Moeinizade","year":"2022","journal-title":"Mach. Learn. Appl."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"688","DOI":"10.1016\/j.molp.2020.03.002","article-title":"Toward a \u201cgreen revolution\u201d for soybean","volume":"13","author":"Liu","year":"2020","journal-title":"Mol. Plant"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.tplants.2013.09.008","article-title":"Field high-throughput phenotyping: The new crop breeding frontier","volume":"19","author":"Araus","year":"2014","journal-title":"Trends Plant Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1109\/MGRS.2020.2998816","article-title":"High-throughput estimation of crop traits: A review of ground and aerial phenotyping platforms","volume":"9","author":"Jin","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1714","DOI":"10.1111\/nph.15817","article-title":"Field crop phenomics: Enabling breeding for radiation use efficiency and biomass in cereal crops","volume":"223","author":"Furbank","year":"2019","journal-title":"New Phytol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1007\/s43657-020-00007-6","article-title":"High-throughput phenotyping: A platform to accelerate crop improvement","volume":"1","author":"Jangra","year":"2021","journal-title":"Phenomics"},{"key":"ref_8","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 Crops Res."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"105731","DOI":"10.1016\/j.compag.2020.105731","article-title":"A review on plant high-throughput phenotyping traits using UAV-based sensors","volume":"178","author":"Xie","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"106033","DOI":"10.1016\/j.compag.2021.106033","article-title":"A comprehensive review on recent applications of unmanned aerial vehicle remote sensing with various sensors for high-throughput plant phenotyping","volume":"182","author":"Feng","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Tayade, R., Yoon, J., Lay, L., Khan, A.L., Yoon, Y., and Kim, Y. (2022). Utilization of spectral indices for high-throughput phenotyping. Plants, 11.","DOI":"10.3390\/plants11131712"},{"key":"ref_12","first-page":"14","article-title":"Improved estimation of leaf area index and leaf chlorophyll content of a potato crop using multi-angle spectral data\u2013potential of unmanned aerial vehicle imagery","volume":"66","author":"Roosjen","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_13","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_14","doi-asserted-by":"crossref","first-page":"106775","DOI":"10.1016\/j.compag.2022.106775","article-title":"UAV-based chlorophyll content estimation by evaluating vegetation index responses under different crop coverages","volume":"196","author":"Qiao","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1638","DOI":"10.3389\/fpls.2018.01638","article-title":"Clustering field-based maize phenotyping of plant-height growth and canopy spectral dynamics using a UAV remote-sensing approach","volume":"9","author":"Han","year":"2018","journal-title":"Front. Plant Sci."},{"key":"ref_16","first-page":"103121","article-title":"Identifying crop phenology using maize height constructed from multi-sources images","volume":"115","author":"Guo","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"109646","DOI":"10.1016\/j.agrformet.2023.109646","article-title":"UAV time-series imagery with novel machine learning to estimate heading dates of rice accessions for breeding","volume":"341","author":"Lyu","year":"2023","journal-title":"Agric. For. Meteorol."},{"key":"ref_18","first-page":"19","article-title":"Spatially detailed retrievals of spring phenology from single-season high-resolution image time series","volume":"59","author":"Vrieling","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1016\/j.rse.2010.09.009","article-title":"Monitoring fall foliage coloration dynamics using time-series satellite data","volume":"115","author":"Zhang","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"108019","DOI":"10.1016\/j.agrformet.2020.108019","article-title":"Comparison of MODIS-based vegetation indices and methods for winter wheat green-up date detection in Huanghuai region of China","volume":"288","author":"Gan","year":"2020","journal-title":"Agric. For. Meteorol."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhao, F., Yang, G., Yang, X., Cen, H., Zhu, Y., Han, S., Yang, H., He, Y., and Zhao, C. (2021). Determination of key phenological phases of winter wheat based on the time-weighted dynamic time warping algorithm and MODIS time-series data. Remote Sens., 13.","DOI":"10.3390\/rs13091836"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1824","DOI":"10.1109\/TGRS.2002.802519","article-title":"Seasonality extraction by function fitting to time-series of satellite sensor data","volume":"40","author":"Jonsson","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/S0034-4257(02)00135-9","article-title":"Monitoring vegetation phenology using MODIS","volume":"84","author":"Zhang","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.rse.2018.08.022","article-title":"A simple method to improve the quality of NDVI time-series data by integrating spatiotemporal information with the Savitzky-Golay filter","volume":"217","author":"Cao","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1016\/j.rse.2005.03.008","article-title":"A crop phenology detection method using time-series MODIS data","volume":"96","author":"Sakamoto","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3735","DOI":"10.1080\/01431169408954355","article-title":"Fourier series for analysis of temporal sequences of satellite sensor imagery","volume":"15","author":"Olsson","year":"1994","journal-title":"Int. J. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.fcr.2016.08.027","article-title":"Detection of rice phenology through time series analysis of ground-based spectral index data","volume":"198","author":"Zheng","year":"2016","journal-title":"Field Crops Res."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ma, Y., Jiang, Q., Wu, X., Zhu, R., Gong, Y., Peng, Y., Duan, B., and Fang, S. (2020). Monitoring hybrid rice phenology at initial heading stage based on low-altitude remote sensing data. Remote Sens., 13.","DOI":"10.3390\/rs13010086"},{"key":"ref_29","first-page":"102435","article-title":"Integrating spectral and textural information for identifying the tasseling date of summer maize using UAV based RGB images","volume":"102","author":"Guo","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"111511","DOI":"10.1016\/j.rse.2019.111511","article-title":"A review of vegetation phenological metrics extraction using time-series, multispectral satellite data","volume":"237","author":"Zeng","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2146","DOI":"10.1016\/j.rse.2010.04.019","article-title":"A two-step filtering approach for detecting maize and soybean phenology with time-series MODIS data","volume":"114","author":"Sakamoto","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"107938","DOI":"10.1016\/j.agrformet.2020.107938","article-title":"A near real-time deep learning approach for detecting rice phenology based on UAV images","volume":"287","author":"Yang","year":"2020","journal-title":"Agric. For. Meteorol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"113060","DOI":"10.1016\/j.rse.2022.113060","article-title":"Detecting crop phenology from vegetation index time-series data by improved shape model fitting in each phenological stage","volume":"277","author":"Liu","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"105398","DOI":"10.1016\/j.compag.2020.105398","article-title":"Detection of phenology using an improved shape model on time-series vegetation index in wheat","volume":"173","author":"Zhou","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.rse.2016.10.005","article-title":"Development of methods to improve soybean yield estimation and predict plant maturity with an unmanned aerial vehicle based platform","volume":"187","author":"Yu","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zhang, S., Feng, H., Han, S., Shi, Z., Xu, H., Liu, Y., Feng, H., Zhou, C., and Yue, J. (2022). Monitoring of Soybean Maturity Using UAV Remote Sensing and Deep Learning. Agriculture, 13.","DOI":"10.3390\/agriculture13010110"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1038\/s43017-022-00298-5","article-title":"Optical vegetation indices for monitoring terrestrial ecosystems globally","volume":"3","author":"Zeng","year":"2022","journal-title":"Nat. Rev. Earth Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.eja.2014.04.006","article-title":"Evaluation of optical sensor measurements of canopy reflectance and of leaf flavonols and chlorophyll contents to assess crop nitrogen status of muskmelon","volume":"58","author":"Padilla","year":"2014","journal-title":"Eur. J. Agron."},{"key":"ref_39","first-page":"100311","article-title":"Applicability of MODIS land cover and Enhanced Vegetation Index (EVI) for the assessment of spatial and temporal changes in strength of vegetation in tropical rainforest region of Borneo","volume":"18","author":"Vijith","year":"2020","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"112456","DOI":"10.1016\/j.rse.2021.112456","article-title":"Calibrating vegetation phenology from Sentinel-2 using eddy covariance, PhenoCam, and PEP725 networks across Europe","volume":"260","author":"Tian","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1080\/22797254.2019.1572459","article-title":"Detection of irrigation inhomogeneities in an olive grove using the NDRE vegetation index obtained from UAV images","volume":"52","author":"Jorge","year":"2019","journal-title":"Eur. J. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and photographic infrared linear combinations for monitoring vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_43","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_44","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the radiometric and biophysical performance of the MODIS vegetation indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"3833","DOI":"10.1016\/j.rse.2008.06.006","article-title":"Development of a two-band enhanced vegetation index without a blue band","volume":"112","author":"Jiang","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/S0034-4257(00)00113-9","article-title":"Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance","volume":"74","author":"Daughtry","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1078\/0176-1617-00887","article-title":"Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves","volume":"160","author":"Gitelson","year":"2003","journal-title":"J. Plant Physiol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"4055","DOI":"10.5194\/bg-10-4055-2013","article-title":"A comparison of methods for smoothing and gap filling time series of remote sensing observations\u2013application to MODIS LAI products","volume":"10","author":"Kandasamy","year":"2013","journal-title":"Biogeosciences"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2327","DOI":"10.1007\/s11119-023-10042-8","article-title":"Monitoring leaf nitrogen content in rice based on information fusion of multi-sensor imagery from UAV","volume":"24","author":"Xu","year":"2023","journal-title":"Precis. Agric."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Xu, S., Xu, X., Blacker, C., Gaulton, R., Zhu, Q., Yang, M., Yang, G., Zhang, J., Yang, Y., and Yang, M. (2023). Estimation of Leaf Nitrogen Content in Rice Using Vegetation Indices and Feature Variable Optimization with Information Fusion of Multiple-Sensor Images from UAV. Remote Sens., 15.","DOI":"10.3390\/rs15030854"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"L08403","DOI":"10.1029\/2005GL022688","article-title":"Remote estimation of canopy chlorophyll content in crops","volume":"32","author":"Gitelson","year":"2005","journal-title":"Geophys. Res. Lett."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.jplph.2008.03.004","article-title":"Non-destructive determination of maize leaf and canopy chlorophyll content","volume":"166","author":"Ciganda","year":"2009","journal-title":"J. Plant Physiol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"113314","DOI":"10.1016\/j.rse.2022.113314","article-title":"Estimating leaf nitrogen content by coupling a nitrogen allocation model with canopy reflectance","volume":"283","author":"Li","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1353","DOI":"10.1016\/j.cj.2022.03.001","article-title":"Detecting winter canola (Brassica napus) phenological stages using an improved shape-model method based on time-series UAV spectral data","volume":"10","author":"Zhang","year":"2022","journal-title":"Crop J."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1769","DOI":"10.2134\/agronj2013.0242","article-title":"Continuous monitoring of crop reflectance, vegetation fraction, and identification of developmental stages using a four band radiometer","volume":"105","author":"Gitelson","year":"2013","journal-title":"Agron. J."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Kleinsmann, J., Verbesselt, J., and Kooistra, L. (2023). Monitoring Individual Tree Phenology in a Multi-Species Forest Using High Resolution UAV Images. Remote Sens., 15.","DOI":"10.3390\/rs15143599"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.rse.2017.11.009","article-title":"Remote sensing of mangrove forest phenology and its environmental drivers","volume":"205","author":"Dash","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"2316","DOI":"10.1007\/s11427-022-2094-6","article-title":"Climate warming shifts the time interval between flowering and leaf unfolding depending on the warming period","volume":"65","author":"Wang","year":"2022","journal-title":"Sci. China Life Sci."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.ecoinf.2018.05.006","article-title":"CropPhenology: An R package for extracting crop phenology from time series remotely sensed vegetation index imagery","volume":"46","author":"Araya","year":"2018","journal-title":"Ecol. Inform."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/22\/5413\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:25:25Z","timestamp":1760131525000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/22\/5413"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,18]]},"references-count":59,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2023,11]]}},"alternative-id":["rs15225413"],"URL":"https:\/\/doi.org\/10.3390\/rs15225413","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,18]]}}}