{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T15:40:46Z","timestamp":1766158846101,"version":"build-2065373602"},"reference-count":57,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,12,28]],"date-time":"2021-12-28T00:00:00Z","timestamp":1640649600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41861056","61631011"],"award-info":[{"award-number":["41861056","61631011"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Inner Mongolia Autonomous Region Science and Technology Plan Project","award":["2021GG0183"],"award-info":[{"award-number":["2021GG0183"]}]},{"name":"the Introduction of high levels talents to start scientific research projects of Inner Mongolia Normal University","award":["2020YJRC051"],"award-info":[{"award-number":["2020YJRC051"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Detection of forest pest outbreaks can help in controlling outbreaks and provide accurate information for forest management decision-making. Although some needle injuries occur at the beginning of the attack, the appearance of the trees does not change significantly from the condition before the attack. These subtle changes cannot be observed with the naked eye, but usually manifest as small changes in leaf reflectance. Therefore, hyperspectral remote sensing can be used to detect the different stages of pest infection as it offers high-resolution reflectance. Accordingly, this study investigated the response of a larch forest to Jas\u2019s Larch Inchworm (Erannis jacobsoni Djak) and performed the different infection stages detection and identification using ground hyperspectral data and data on the forest biochemical components (chlorophyll content, fresh weight moisture content and dry weight moisture content). A total of 80 sample trees were selected from the test area, covering the following three stages: before attack, early-stage infection and middle- to late-stage infection. Combined with the Findpeaks-SPA function, the response relationship between biochemical components and spectral continuous wavelet coefficients was analyzed. The support vector machine classification algorithm was used for detection infection. The results showed that there was no significant difference in the biochemical composition between healthy and early-stage samples, but the spectral continuous wavelet coefficients could reflect these subtle changes with varying degrees of sensitivity. The continuous wavelet coefficients corresponding to these stresses may have high potential for infection detection. Meanwhile, the highest overall accuracy of the model based on chlorophyll content, fresh weight moisture content and dry weight moisture content were 90.48%, 85.71% and 90.48% respectively, and the Kappa coefficients were 0.85, 0.79 and 0.86 respectively.<\/jats:p>","DOI":"10.3390\/rs14010124","type":"journal-article","created":{"date-parts":[[2021,12,29]],"date-time":"2021-12-29T02:31:27Z","timestamp":1640745087000},"page":"124","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Detection of Larch Forest Stress from Jas\u2019s Larch Inchworm (Erannis jacobsoni Djak) Attack Using Hyperspectral Remote Sensing"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7015-4668","authenticated-orcid":false,"given":"Guilin","family":"Xi","sequence":"first","affiliation":[{"name":"College of Geographical Science, Inner Mongolia Normal University, Hohhot 010022, China"},{"name":"College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaojun","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Geographical Science, Inner Mongolia Normal University, Hohhot 010022, China"},{"name":"Inner Mongolia Key Laboratory of Remote Sensing & Geography Information System, Hohhot 010022, China"},{"name":"Inner Mongolia Key Laboratory of Disaster and Ecological Security on the Mongolia Plateau, Hohhot 010022, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yaowen","family":"Xie","sequence":"additional","affiliation":[{"name":"College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bao","family":"Gang","sequence":"additional","affiliation":[{"name":"College of Geographical Science, Inner Mongolia Normal University, Hohhot 010022, China"},{"name":"Inner Mongolia Key Laboratory of Remote Sensing & Geography Information System, Hohhot 010022, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuhai","family":"Bao","sequence":"additional","affiliation":[{"name":"College of Geographical Science, Inner Mongolia Normal University, Hohhot 010022, China"},{"name":"Inner Mongolia Key Laboratory of Remote Sensing & Geography Information System, Hohhot 010022, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ganbat","family":"Dashzebeg","sequence":"additional","affiliation":[{"name":"Institute of Geography and Geoecology, Mongolian Academy of Sciences, Ulaanbaatar 15170, Mongolia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tsagaantsooj","family":"Nanzad","sequence":"additional","affiliation":[{"name":"Institute of Geography and Geoecology, Mongolian Academy of Sciences, Ulaanbaatar 15170, Mongolia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Altanchimeg","family":"Dorjsuren","sequence":"additional","affiliation":[{"name":"Institute of Biology, Mongolian Academy of Sciences, Ulaanbaatar 13330, Mongolia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Davaadorj","family":"Enkhnasan","sequence":"additional","affiliation":[{"name":"Institute of Biology, Mongolian Academy of Sciences, Ulaanbaatar 13330, Mongolia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mungunkhuyag","family":"Ariunaa","sequence":"additional","affiliation":[{"name":"Institute of Geography and Geoecology, Mongolian Academy of Sciences, Ulaanbaatar 15170, Mongolia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,28]]},"reference":[{"key":"ref_1","first-page":"2732","article-title":"Estimation of Leaf Loss Rate in Larch Infested with Erannis Jacobsoni Djak Based on Differential Spectral Continuous Wavelet Coefficient","volume":"39","author":"Huang","year":"2019","journal-title":"Spectrosc. Spect. Anal."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1046\/j.1523-1739.1996.10010235.x","article-title":"Ranking conservation and timber management options for leadbeater\u2019s possum in southeastern Australia using population viability analysis","volume":"10","author":"Lindenmayer","year":"1996","journal-title":"Conserv. Biol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e1600821","DOI":"10.1126\/sciadv.1600821","article-title":"The last frontiers of wilderness: Tracking loss of intact forest landscapes from 2000 to 2013","volume":"3","author":"Potapov","year":"2017","journal-title":"Sci. Adv."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"111264","DOI":"10.1016\/j.rse.2019.111264","article-title":"The potential of dual-wavelength terrestrial lidar in early detection of Ips typographus (L.) infestation\u2014Leaf water content as a proxy","volume":"231","author":"Junttila","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"e01257","DOI":"10.1002\/ecs2.1257","article-title":"Local colonization-extinction dynamics of a tree-killing bark beetle during a large-scale outbreak","volume":"7","author":"Karvemo","year":"2016","journal-title":"Ecosphere"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"112240","DOI":"10.1016\/j.rse.2020.112240","article-title":"Early detection of forest stress from European spruce bark beetle attack, and a new vegetation index: Normalized distance red & SWIR (NDRS)","volume":"255","author":"Huo","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1016\/j.foreco.2016.11.004","article-title":"Spectral evidence of early-stage spruce beetle infestation in Engelmann spruce","volume":"384","author":"Foster","year":"2017","journal-title":"For. Ecol. Manag."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1080\/07038992.2015.1065707","article-title":"Hyperspectral Remote Sensing of Mountain Pine Beetle with an Emphasis on Previsual Assessment","volume":"41","author":"Niemann","year":"2015","journal-title":"Can. J. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.rse.2013.11.018","article-title":"Detecting diurnal and seasonal variation in canopy water content of nut tree orchards from airborne imaging spectroscopy data using continuous wavelet analysis","volume":"143","author":"Cheng","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_10","first-page":"2925","article-title":"Hyperspectral Discrimination of Different Canopy Colors in Erannis Jacobsoni Djak-Infested Larch","volume":"40","author":"Xi","year":"2020","journal-title":"Spectrosc. Spect. Anal."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1912","DOI":"10.3390\/rs5041912","article-title":"Early Detection of Bark Beetle Green Attack Using TerraSAR-X and RapidEye Data","volume":"5","author":"Ortiz","year":"2013","journal-title":"Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.rse.2005.12.010","article-title":"Estimating the probability of mountain pine beetle red-attack damage","volume":"101","author":"Wulder","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.rse.2018.02.073","article-title":"A framework for detecting conifer mortality across an ecoregion using high spatial resolution spaceborne imaging spectroscopy","volume":"209","author":"Tane","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.rse.2013.01.002","article-title":"Evaluating methods to detect bark beetle-caused tree mortality using single-date and multi-date Landsat imagery","volume":"132","author":"Meddens","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1632","DOI":"10.1016\/j.rse.2011.02.018","article-title":"Evaluating the potential of multispectral imagery to map multiple stages of tree mortality","volume":"115","author":"Meddens","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.foreco.2005.09.021","article-title":"Surveying mountain pine beetle damage of forests: A review of remote sensing opportunities","volume":"221","author":"Wulder","year":"2006","journal-title":"For. Ecol. Manag."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"32","DOI":"10.5558\/tfc85032-1","article-title":"Challenges for the operational detection of mountain pine beetle green attack with remote sensing","volume":"85","author":"Wulder","year":"2009","journal-title":"For. Chron."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1799","DOI":"10.1080\/01431160210155965","article-title":"Spectral absorption features as indicators of water status in coast live oak (Quercus agrifolia) leaves","volume":"24","author":"Pu","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhang, J.Y., Sun, H., Gao, D.H., Qiao, L., Liu, N., Li, M.Z., and Zhang, Y. (2020). Detection of Canopy Chlorophyll Content of Corn Based on Continuous Wavelet Transform Analysis. Remote Sens., 12.","DOI":"10.3390\/rs12172741"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Asner, G.P., Martin, R.E., Keith, L.M., Heller, W.P., Hughes, M.A., Vaughn, N.R., Hughes, R.F., and Balzotti, C. (2018). A Spectral Mapping Signature for the Rapid Ohia Death (ROD) Pathogen in Hawaiian Forests. Remote Sens., 10.","DOI":"10.3390\/rs10030404"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Huang, L.S., Wu, K., Huang, W.J., Dong, Y.Y., Ma, H.Q., Liu, Y., and Liu, L.Y. (2021). Detection of Fusarium Head Blight in Wheat Ears Using Continuous Wavelet Analysis and PSO-SVM. Agriculture, 11.","DOI":"10.3390\/agriculture11100998"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"101181","DOI":"10.1016\/j.ecoinf.2020.101181","article-title":"Improving the classification of invasive plant species by using continuous wavelet analysis and feature reduction techniques","volume":"61","author":"Omeer","year":"2021","journal-title":"Ecol. Inform."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"899","DOI":"10.1016\/j.rse.2009.12.005","article-title":"Continuous wavelet analysis for the detection of green attack damage due to mountain pine beetle infestation","volume":"114","author":"Cheng","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Jia, M., Li, D., Colombo, R., Wang, Y., Wang, X., Cheng, T., Zhu, Y., Yao, X., Xu, C.J., and Ouer, G. (2019). Quantifying Chlorophyll Fluorescence Parameters from Hyperspectral Reflectance at the Leaf Scale under Various Nitrogen Treatment Regimes in Winter Wheat. Remote Sens., 11.","DOI":"10.3390\/rs11232838"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4059","DOI":"10.1080\/01431161.2018.1454620","article-title":"Using wavelet analysis of hyperspectral remote-sensing data to estimate canopy chlorophyll content of winter wheat under stripe rust stress","volume":"39","author":"He","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1016\/j.rse.2010.11.001","article-title":"Spectroscopic determination of leaf water content using continuous wavelet analysis","volume":"115","author":"Cheng","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"S22","DOI":"10.1038\/nmeth.1371","article-title":"Computation for ChIP-seq and RNA-seq studies","volume":"6","author":"Pepke","year":"2009","journal-title":"Nat. Methods"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/j.rse.2018.08.024","article-title":"Assessment of defoliation during the Dendrolimus tabulaeformis Tsai et Liu disaster outbreak using UAV-based hyperspectral images","volume":"217","author":"Zhang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"112350","DOI":"10.1016\/j.rse.2021.112350","article-title":"Spectroscopic detection of rice leaf blast infection from asymptomatic to mild stages with integrated machine learning and feature selection","volume":"257","author":"Tian","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Sarangdhar, A.A., and Pawar, V.R. (2017, January 20\u201322). Machine Learning Regression Technique for Cotton Leaf Disease Detection and Controlling using IoT. Proceedings of the 2017 International Conference of Electronics, Communication and Aerospace Technology (Iceca), Coimbatore, India.","DOI":"10.1109\/ICECA.2017.8212855"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Ulziibaatar, M., and Matsui, K. (2021). Herders\u2019 Perceptions about Rangeland Degradation and Herd Management: A Case among Traditional and Non-Traditional Herders in Khentii Province of Mongolia. Sustainability, 13.","DOI":"10.3390\/su13147896"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"7373","DOI":"10.1073\/pnas.0610555104","article-title":"Mutational reconstructed ferric chelate reductase confers enhanced tolerance in rice to iron deficiency in calcareous soil","volume":"104","author":"Ishimaru","year":"2007","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2419","DOI":"10.1016\/S2095-3119(20)63259-2","article-title":"Key indicators affecting maize stalk lodging resistance of different growth periods under different sowing dates","volume":"19","author":"Wang","year":"2020","journal-title":"J. Integr. Agric."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Bayer, A. (2020). Fertilizer Rate and Substrate Water Content Effect on Growth and Flowering of Beardtongue. Horticulturae, 6.","DOI":"10.3390\/horticulturae6030057"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"783","DOI":"10.30848\/PJB2020-3(17)","article-title":"Plant Growth Promoting Rhizobacteria in Combination with Plant Growth Regulators Attenuate the Effect of Drought Stress","volume":"52","author":"Bashir","year":"2020","journal-title":"Pak. J. Bot"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2136\/vzj2018.04.0073","article-title":"Estimating Field Capacity from Volumetric Soil Water Content Time Series Using Automated Processing Algorithms","volume":"17","author":"Bean","year":"2018","journal-title":"Vadose Zone J."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2517","DOI":"10.1093\/bioinformatics\/bts463","article-title":"Nebula--a web-server for advanced ChIP-seq data analysis","volume":"28","author":"Boeva","year":"2012","journal-title":"Bioinformatics"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Malone, B.M., Tan, F., Bridges, S.M., and Peng, Z. (2011). Comparison of four ChIP-Seq analytical algorithms using rice endosperm H3K27 trimethylation profiling data. PLoS ONE, 6.","DOI":"10.1371\/journal.pone.0025260"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"5901","DOI":"10.3390\/rs70505901","article-title":"Evaluating Different Methods for Grass Nutrient Estimation from Canopy Hyperspectral Reflectance","volume":"7","author":"Wang","year":"2015","journal-title":"Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"119585","DOI":"10.1016\/j.saa.2021.119585","article-title":"Hyperspectral prediction of sugarbeet seed germination based on gauss kernel SVM","volume":"253","author":"Yang","year":"2021","journal-title":"Spectrochim. Acta A Mol. Biomol. Spectrosc."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhang, T., Huang, Y.B., Reddy, K.N., Yang, P.T., Zhao, X.H., and Zhang, J.C. (2021). Using Machine Learning and Hyperspectral Images to Assess Damages to Corn Plant Caused by Glyphosate and to Evaluate Recoverability. Agronomy, 11.","DOI":"10.3390\/agronomy11030583"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"631573","DOI":"10.3389\/fpls.2021.631573","article-title":"Potential of Multivariate Statistical Technique Based on the Effective Spectra Bands to Estimate the Plant Water Content of Wheat Under Different Irrigation Regimes","volume":"12","author":"Sun","year":"2021","journal-title":"Front. Plant Sci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40663-021-00328-6","article-title":"Early detection of pine wilt disease in Pinus tabuliformis in North China using a field portable spectrometer and UAV-based hyperspectral imagery","volume":"8","author":"Yu","year":"2021","journal-title":"For. Ecosyst."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"111323","DOI":"10.1016\/j.rse.2019.111323","article-title":"Combining LiDAR and hyperspectral data for aboveground biomass modeling in the Brazilian Amazon using different regression algorithms","volume":"232","author":"Ometto","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"690","DOI":"10.1016\/j.rse.2017.09.031","article-title":"Fractional cover mapping of spruce and pine at 1 ha resolution combining very high and medium spatial resolution satellite imagery","volume":"204","author":"Immitzer","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1016\/j.rse.2013.09.014","article-title":"Assessing the potential of hyperspectral imagery to map bark beetle-induced tree mortality","volume":"140","author":"Fassnacht","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"111232","DOI":"10.1016\/j.rse.2019.111232","article-title":"The capability of species-related forest stand characteristics determination with the use of hyperspectral data","volume":"231","author":"Wietecha","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1016\/j.rse.2018.10.004","article-title":"Characterizing 32 years of shrub cover dynamics in southern Portugal using annual Landsat composites and machine learning regression modeling","volume":"219","author":"Suess","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.rse.2015.06.015","article-title":"Detection of spruce beetle-induced tree mortality using high- and medium-resolution remotely sensed imagery","volume":"168","author":"Hart","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1186","DOI":"10.1038\/s41467-020-15013-5","article-title":"Trade-offs between multifunctionality and profit in tropical smallholder landscapes","volume":"11","author":"Grass","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"21213","DOI":"10.1073\/pnas.0906457106","article-title":"Derivation of burn scar depths and estimation of carbon emissions with LIDAR in Indonesian peatlands","volume":"106","author":"Ballhorn","year":"2009","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1016\/j.rse.2019.01.031","article-title":"Chlorophyll content estimation in an open-canopy conifer forest with Sentinel-2A and hyperspectral imagery in the context of forest decline","volume":"223","author":"Hornero","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Liu, N., Xing, Z.Z., Zhao, R.M., Qiao, L., Li, M.Z., Liu, G., and Sun, H. (2020). Analysis of Chlorophyll Concentration in Potato Crop by Coupling Continuous Wavelet Transform and Spectral Variable Optimization. Remote Sens., 12.","DOI":"10.3390\/rs12172826"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"An, G.Q., Xing, M.F., Liao, C.H., and He, B.B. (October, January 26). Estimating Chlorophyll Content of Rice Based on Uav-Based Hyperspectral Imagery and Continuous Wavelet Transform. Proceedings of the Igarss 2020-2020 Ieee International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA.","DOI":"10.1109\/IGARSS39084.2020.9323597"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"103542","DOI":"10.1016\/j.infrared.2020.103542","article-title":"Rapid estimation of leaf nitrogen content in apple-trees based on canopy hyperspectral reflectance using multivariate methods","volume":"111","author":"Chen","year":"2020","journal-title":"Infrared Phys. Technol."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"105066","DOI":"10.1016\/j.compag.2019.105066","article-title":"Early detection of tomato spotted wilt virus infection in tobacco using the hyperspectral imaging technique and machine learning algorithms","volume":"167","author":"Gu","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"105025","DOI":"10.1016\/j.compag.2019.105025","article-title":"Optical sensing for early spring freeze related blueberry bud damage detection: Hyperspectral imaging for salient spectral wavelengths identification","volume":"167","author":"Gao","year":"2019","journal-title":"Comput. Electron. Agric."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/1\/124\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:54:59Z","timestamp":1760169299000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/1\/124"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,28]]},"references-count":57,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,1]]}},"alternative-id":["rs14010124"],"URL":"https:\/\/doi.org\/10.3390\/rs14010124","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2021,12,28]]}}}