{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T02:09:21Z","timestamp":1769047761979,"version":"3.49.0"},"reference-count":66,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,5,8]],"date-time":"2022-05-08T00:00:00Z","timestamp":1651968000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41701398"],"award-info":[{"award-number":["41701398"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2013AA102401-2"],"award-info":[{"award-number":["2013AA102401-2"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National High Technology Research and Development Program","award":["41701398"],"award-info":[{"award-number":["41701398"]}]},{"name":"National High Technology Research and Development Program","award":["2013AA102401-2"],"award-info":[{"award-number":["2013AA102401-2"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>To explore rapid anthocyanin (Anth) detection technology based on remote sensing (RS) in tree peony leaves, we considered 30 species of tree peonies located in Shaanxi Province, China. We used an SVC HR~1024i portable ground object spectrometer and mini-unmanned aerial vehicle (UAV)-borne RS systems to obtain hyperspectral (HS) reflectance and images of canopy leaves. First, we performed principal component analysis (PCA), first-order differential (FD), and continuum removal (CR) transformations on the original ground-based spectra; commonly used spectral parameters were implemented to estimate Anth content using multiple stepwise regression (MSR), partial least squares (PLS), back-propagation neural network (BPNN), and random forest (RF) models. The spectral transformation highlighted the characteristics of spectral curves and improved the relationship between spectral reflectance and Anth, and the RF model based on the FD spectrum portrayed the best estimation accuracy (R2c = 0.91; R2v = 0.51). Then, the RGB (red-green-blue) gray vegetation index (VI) and the texture parameters were constructed using UAV images, and an Anth estimation model was constructed using UAV parameters. Finally, the UAV image was fused with the ground spectral data, and a multisource RS model of Anth estimation was constructed, based on PCA + UAV, FD + UAV, and CR + UAV, using MSR, PLS, BPNN, and RF methods. The RF model based on FD+UAV portrayed the best modeling and verification effect (R2c = 0.93; R2v = 0.76); compared with the FD-RF model, R2c increased only slightly, but R2v increased greatly from 0.51 to 0.76, indicating improved modeling and testing accuracy. The optimal spectral transformation for the Anth estimation of tree peony leaves was obtained, and a high-precision Anth multisource RS model was constructed. Our results can be used for the selection of ground-based HS transformation in future plant Anth estimation, and as a theoretical basis for plant growth monitoring based on ground and UAV multisource RS.<\/jats:p>","DOI":"10.3390\/rs14092271","type":"journal-article","created":{"date-parts":[[2022,5,8]],"date-time":"2022-05-08T23:27:25Z","timestamp":1652052445000},"page":"2271","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Combining Different Transformations of Ground Hyperspectral Data with Unmanned Aerial Vehicle (UAV) Images for Anthocyanin Estimation in Tree Peony Leaves"],"prefix":"10.3390","volume":"14","author":[{"given":"Lili","family":"Luo","sequence":"first","affiliation":[{"name":"College of Nature Resources and Environment, Northwest A&F University, Yangling 712100, China"}]},{"given":"Qinrui","family":"Chang","sequence":"additional","affiliation":[{"name":"College of Nature Resources and Environment, Northwest A&F University, Yangling 712100, China"}]},{"given":"Yifan","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Nature Resources and Environment, Northwest A&F University, Yangling 712100, China"}]},{"given":"Danyao","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Nature Resources and Environment, Northwest A&F University, Yangling 712100, China"}]},{"given":"Fenling","family":"Li","sequence":"additional","affiliation":[{"name":"College of Nature Resources and Environment, Northwest A&F University, Yangling 712100, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.envexpbot.2015.05.012","article-title":"Multiple functional roles of anthocyanins in plant-environment interactions","volume":"119","author":"Landi","year":"2015","journal-title":"Environ. Exp. Bot."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/S1011-1344(00)00042-7","article-title":"Light-stress-induced pigment changes and evidence for anthocyanin photoprotection in apples","volume":"55","author":"Merzlyak","year":"2000","journal-title":"J. Photochem. Photobiol. B Biol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1562\/0031-8655(2001)074<0038:OPANEO>2.0.CO;2","article-title":"Optical properties and nondestructive estimation of anthocyanin content in plant leaves","volume":"74","author":"Gitelson","year":"2001","journal-title":"Photochem. Photobiol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/j.foodchem.2015.05.099","article-title":"Effects of cold atmospheric gas phase plasma on anthocyanins and color in pomegranate juice","volume":"190","author":"Kovaevi","year":"2016","journal-title":"Food Chem."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2449","DOI":"10.1002\/jsfa.10869","article-title":"Application of near infrared spectroscopy as an instantaneous and simultaneous prediction tool for anthocyanins and sugar in whole fresh raspberry","volume":"101","author":"Gales","year":"2021","journal-title":"J. Sci. Food Agric."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/j.jfoodeng.2011.02.018","article-title":"Determination of anthocyanin concentration in whole grape skins using hyperspectral imaging and adaptive boosting neural networks","volume":"105","author":"Fernandes","year":"2011","journal-title":"J. Food Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1007\/s00280-014-2656-9","article-title":"Endostar in combination with modified FOLFOX6 as an initial therapy in advanced colorectal cancer patients: A phase I clinical trial","volume":"75","author":"Chen","year":"2015","journal-title":"Cancer Chemother. Pharmacol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"112303","DOI":"10.1016\/j.rse.2021.112303","article-title":"Hyperspectral imagery to monitor crop nutrient status within and across growing seasons","volume":"255","author":"Liu","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wu, T., Zhang, L., Peng, B., Zhang, H., Chen, Z., and Gao, M. (2016). Real-time progressive hyperspectral remote sensing detection methods for crop pest and diseases. Remotely Sensed Data Compression, Communications, and Processing XII, SPIE.","DOI":"10.1117\/12.2225874"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"105321","DOI":"10.1016\/j.compag.2020.105321","article-title":"Progress of hyperspectral data processing and modelling for cereal crop nitrogen monitoring","volume":"172","author":"Fu","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhang, F., and Zhou, G. (2019). Estimation of vegetation water content using hyperspectral vegetation indices: A comparison of crop water indicators in response to water stress treatments for summer maize. BMC Ecol., 19.","DOI":"10.1186\/s12898-019-0233-0"},{"key":"ref_12","first-page":"3231","article-title":"Analysis of hyperspectral variation of different potato cultivars based on continuum removed spectra","volume":"38","author":"Luo","year":"2018","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_13","first-page":"174","article-title":"Estimation of Winter Wheat Leaf Nitrogen Content Based on Continuum Removed Spectra","volume":"48","author":"Li","year":"2017","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_14","first-page":"393","article-title":"Comparison on Hyperspectral Estimation Method of Nitrogen Content in Bamboo Leaf","volume":"49","author":"Zheng","year":"2018","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_15","first-page":"141","article-title":"Influence of spectral transformation methods on nutrient content inversion accuracy by hyperspectral remote sensing in black soil","volume":"34","author":"Zhang","year":"2018","journal-title":"Nongye Gongcheng Xuebao\/ Trans. Chin. Soc. Agric. Eng."},{"key":"ref_16","first-page":"399","article-title":"Applications and trends of unmanned aerial vehicle in agriculture","volume":"44","author":"Chen","year":"2018","journal-title":"J. Zhejiang Univ. Sci. (Agric. Life Sci.)"},{"key":"ref_17","first-page":"42","article-title":"Impact of Multispectral Bands Texture on Leaf Area Index Using Landsat_8","volume":"30","author":"Jiao","year":"2014","journal-title":"Geogr. Inf. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1111\/j.1600-0587.2008.05512.x","article-title":"Satellite image texture and a vegetation index predict avian biodiversity in the Chihuahuan Desert of New Mexico","volume":"32","author":"Pidgeon","year":"2009","journal-title":"Ecography"},{"key":"ref_19","first-page":"3060","article-title":"Biomass estimation in winter wheat by UAV spectral information and texture information fusion","volume":"51","author":"Liu","year":"2018","journal-title":"Sci. Agric. Sin."},{"key":"ref_20","first-page":"2220","article-title":"Cotton nitrogen nutrition diagnosis based on spectrum and texture feature of images from low altitude unmanned aerial vehicle","volume":"52","author":"Chen","year":"2019","journal-title":"Sci. Agric. Sin."},{"key":"ref_21","first-page":"1205","article-title":"Estimation of Above-Ground Biomass of Potato Based on Wavelet Analysis","volume":"41","author":"Yang","year":"2021","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"4488","DOI":"10.1364\/AO.43.004488","article-title":"Dualex: A new instrument for field measurements of epidermal ultraviolet absorbance by chlorophyll fluorescence","volume":"43","author":"Goulas","year":"2004","journal-title":"Appl. Opt."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1007\/s11120-007-9135-7","article-title":"Investigating UV screening in leaves by two different types of portable UV fluorimeter reveals in vivo screening by anthocyanins and carotenoids","volume":"93","author":"Ghozlen","year":"2007","journal-title":"Photosynth. Res."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"650","DOI":"10.1016\/j.jfca.2008.03.012","article-title":"New portable optical sensors for the assessment of winegrape phenolic maturity based on berry fluorescence","volume":"21","author":"Cerovic","year":"2008","journal-title":"J. Food Compos. Anal."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2812","DOI":"10.1039\/C3AY41907J","article-title":"Principal component analysis","volume":"6","author":"Bro","year":"2014","journal-title":"Anal. Methods"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"83","DOI":"10.4995\/msel.2013.1905","article-title":"Principal component analysis applied to remote sensing","volume":"6","author":"Estornell","year":"2013","journal-title":"Model. Sci. Educ. Learn."},{"key":"ref_27","first-page":"1965","article-title":"Monitoring of Corn Canopy Blight Disease Based on UAV Hyperspectral Method","volume":"40","author":"Liang","year":"2020","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_28","first-page":"145","article-title":"Extracting oilseed rape growing regions based on variation characteristics of red edge position","volume":"29","author":"She","year":"2013","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/s12524-017-0667-9","article-title":"Using Red Edge Position Shift to Monitor Grassland Grazing Intensity in Inner Mongolia","volume":"46","author":"Zheng","year":"2018","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_30","first-page":"2644","article-title":"Estimation of fraction of absorbed photosynthetically active radiation for winter wheat based on hyperspectral characteristic parameters","volume":"35","author":"Zhang","year":"2015","journal-title":"Guang Pu Xue Yu Guang Pu Fen Xi Guang Pu"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2487","DOI":"10.1007\/s40808-020-00829-3","article-title":"Hyperspectral remote sensing for extraction of soil salinization in the northern region of Ningxia","volume":"6","author":"Guan","year":"2020","journal-title":"Model. Earth Syst. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"557","DOI":"10.3724\/SP.J.1006.2020.94045","article-title":"Estimation of total nitrogen content in sugarbeet leaves under drip irrigation based on hyperspectral characteristic parameters and vegetation index","volume":"46","author":"Li","year":"2020","journal-title":"Acta Agron. Sin."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.rse.2012.12.015","article-title":"Retrieval of spruce leaf chlorophyll content from airborne image data using continuum removal and radiative transfer","volume":"131","author":"Kaplan","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11119-021-09804-z","article-title":"Hyperspectral assessment of leaf nitrogen accumulation for winter wheat using different regression modeling","volume":"22","author":"Guo","year":"2021","journal-title":"Precis. Agric."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.geoderma.2008.09.016","article-title":"Continuum removal versus PLSR method for clay and calcium carbonate content estimation from laboratory and airborne hyperspectral measurements","volume":"148","author":"Gomez","year":"2008","journal-title":"Geoderma"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"e00399","DOI":"10.1016\/j.geodrs.2021.e00399","article-title":"Inversion of soil pH during the dry and wet seasons in the Yinbei region of Ningxia, China, based on multi-source remote sensing data","volume":"25","author":"Jia","year":"2021","journal-title":"Geoderma Reg."},{"key":"ref_37","first-page":"11","article-title":"Assimilating multi-source remotely sensed data into a light use efficiency model for net primary productivity estimation","volume":"72","author":"Yan","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_38","first-page":"55","article-title":"Analysis of spectral absorption features in hyperspectral imagery","volume":"5","year":"2004","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1016\/S0034-4257(01)00182-1","article-title":"Estimating the foliar biochemical concentration of leaves with reflectance spectrometry: Testing the Kokaly and Clark methodologies","volume":"76","author":"Curran","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1016\/j.rse.2003.11.001","article-title":"Predicting in situ pasture quality in the Kruger National Park, South Africa, using continuum-removed absorption features","volume":"89","author":"Mutanga","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1946","DOI":"10.1080\/00103624.2020.1808013","article-title":"Evaluation of a digital camera and a smartphone application, using the dark green color index, in assessing maize nitrogen status","volume":"51","author":"Rhezali","year":"2020","journal-title":"Commun. Soil Sci. Plant Anal."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/S0034-4257(01)00289-9","article-title":"Novel algorithms for remote estimation of vegetation fraction","volume":"80","author":"Gitelson","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"719","DOI":"10.2135\/cropsci1999.0011183X003900030019x","article-title":"Measuring wheat senescence with a digital camera","volume":"39","author":"Adamsen","year":"1999","journal-title":"Crop Sci."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/0034-4257(74)90037-6","article-title":"Seasonal canopy reflectance patterns of wheat, sorghum, and soybean","volume":"3","author":"Kanemasu","year":"1974","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1006\/anbo.1997.0544","article-title":"An algorithm for estimating chlorophyll content in leaves using a video camera","volume":"81","author":"Kawashima","year":"1998","journal-title":"Ann. Bot."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"943","DOI":"10.2135\/cropsci2003.9430","article-title":"Quantifying turfgrass color using digital image analysis","volume":"43","author":"Karcher","year":"2003","journal-title":"Crop Sci."},{"key":"ref_47","first-page":"147","article-title":"Estimation of leaf area index of soybean breeding materials based on UAV digital images","volume":"48","author":"Li","year":"2017","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"111702","DOI":"10.1016\/j.rse.2020.111702","article-title":"A remote sensing approach to mapping fire severity in south-eastern Australia using sentinel 2 and random forest","volume":"240","author":"Gibson","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1109\/TGRS.2004.842022","article-title":"Partially supervised classification of remote sensing images using SVM-based probability density estimation","volume":"43","author":"Mantero","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/S0034-4257(98)00032-7","article-title":"Derivative analysis of hyperspectral data","volume":"66","author":"Tsai","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Kanemasu, E.T., Demetriades-Shah, T.H., Su, H., and Lang, A.R.G. (1990). Estimating Grassland Biomass Using Remotely Sensed Data. Applications of Remote Sensing in Agriculture, Butterworths.","DOI":"10.1016\/B978-0-408-04767-8.50017-7"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"3239","DOI":"10.1007\/s12652-018-1043-5","article-title":"Estimating leaf chlorophyll content in tobacco based on various canopy hyperspectral parameters","volume":"10","author":"Guo","year":"2019","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/S0034-4257(00)00197-8","article-title":"Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density","volume":"76","author":"Broge","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"6407","DOI":"10.3390\/rs6076407","article-title":"Estimates of aboveground biomass from texture analysis of Landsat imagery","volume":"6","author":"Kelsey","year":"2014","journal-title":"Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"810","DOI":"10.3390\/rs4040810","article-title":"Improved forest biomass and carbon estimations using texture measures from WorldView-2 satellite data","volume":"4","author":"Eckert","year":"2012","journal-title":"Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"936","DOI":"10.3389\/fpls.2018.00936","article-title":"Combining unmanned aerial vehicle (UAV)-based multispectral imagery and ground-based hyperspectral data for plant nitrogen concentration estimation in rice","volume":"9","author":"Zheng","year":"2018","journal-title":"Front. Plant Sci."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Liu, K., Liu, L., Myint, S.W., Wang, S., Liu, H., and He, Z. (2017). Exploring the potential of worldview-2 red-edge band-based vegetation indices for estimation of mangrove leaf area index with machine learning algorithms. Remote Sens., 9.","DOI":"10.3390\/rs9101060"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"2038","DOI":"10.1109\/LGRS.2020.3014676","article-title":"Intelligent sampling for vegetation nitrogen mapping based on hybrid machine learning algorithms","volume":"18","author":"Verrelst","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Lu, B., and He, Y. (2019). Evaluating empirical regression, machine learning, and radiative transfer modelling for estimating vegetation chlorophyll content using bi-seasonal hyperspectral images. Remote Sens., 11.","DOI":"10.3390\/rs11171979"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"16398","DOI":"10.3390\/rs71215841","article-title":"Review of machine learning approaches for biomass and soil moisture retrievals from remote sensing data","volume":"7","author":"Ali","year":"2015","journal-title":"Remote Sens."},{"key":"ref_61","first-page":"1722","article-title":"Spatial heterogeneity of estuarine wetland ecosystem health influenced by complex natural and anthropogenic factors","volume":"41","author":"Wang","year":"2021","journal-title":"Spectrosc. Spectral Anal."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"136","DOI":"10.3390\/rs14010136","article-title":"Estimation of Cotton Leaf Area Index (LAI) Based on Spectral Transformation and Vegetation Index","volume":"14","author":"Lv","year":"2021","journal-title":"Remote Sens."},{"key":"ref_63","first-page":"135","article-title":"Estimating Nitrogen Concentrations in Wetland Reeds Based on Reducing Foliar Water Effect by Hyperspectral Data","volume":"36","author":"Wang","year":"2016","journal-title":"Sci. Geogr. Sin."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1016\/j.scitotenv.2018.02.052","article-title":"Simulating spatial distribution of coastal soil carbon content using a comprehensive land surface factor system based on remote sensing","volume":"628","author":"Chi","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2004.06.008","article-title":"Estimating foliage nitrogen concentration from HYMAP data using continuum removal analysis","volume":"93","author":"Huang","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1445","DOI":"10.1016\/j.scitotenv.2018.04.085","article-title":"Spatial heterogeneity of estuarine wetland ecosystem health influenced by complex natural and anthropogenic factors","volume":"634","author":"Chi","year":"2018","journal-title":"Sci. Total Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/9\/2271\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:07:56Z","timestamp":1760137676000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/9\/2271"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,8]]},"references-count":66,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["rs14092271"],"URL":"https:\/\/doi.org\/10.3390\/rs14092271","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,8]]}}}