{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,26]],"date-time":"2026-10-26T17:20:17Z","timestamp":1793035217583,"version":"4.5.0"},"reference-count":67,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,8,1]],"date-time":"2022-08-01T00:00:00Z","timestamp":1659312000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["2019A1515010741"],"award-info":[{"award-number":["2019A1515010741"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["JCYJ20210324093210029"],"award-info":[{"award-number":["JCYJ20210324093210029"]}]},{"name":"Shenzhen Science and Technology Program","award":["2019A1515010741"],"award-info":[{"award-number":["2019A1515010741"]}]},{"name":"Shenzhen Science and Technology Program","award":["JCYJ20210324093210029"],"award-info":[{"award-number":["JCYJ20210324093210029"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Monitoring the seasonal leaf nutrients of mangrove forests helps one to understand the dynamics of carbon (C) sequestration and to diagnose the availability and limitation of nitrogen (N) and phosphorus (P). To date, very little attention has been paid to mapping the seasonal leaf C, N, and P of mangrove forests with remote sensing techniques. Based on Sentinel-2 images taken in spring, summer, and winter, this study aimed to compare three machine learning models (XGBoost, extreme gradient boosting; RF, random forest; LightGBM, light gradient boosting machine) in estimating the three leaf nutrients and further to apply the best-performing model to map the leaf nutrients of 15 seasons from 2017 to 2021. The results showed that there were significant differences in leaf nutrients (p &lt; 0.05) across the three seasons. Among the three machine learning models, XGBoost with sensitive spectral features of Sentinel-2 images was optimal for estimating the leaf C (R2 = 0.655, 0.799, and 0.829 in spring, summer, and winter, respectively), N (R2 = 0.668, 0.743, and 0.704) and P (R2 = 0.539, 0.622, and 0.596) over the three seasons. Moreover, the red-edge (especially B6) and near-infrared bands (B8 and B8a) of Sentinel-2 images were efficient estimators of mangrove leaf nutrients. The information of species, elevation, and canopy structure (leaf area index [LAI] and canopy height) would be incorporated into the present model to improve the model accuracy and transferability in future studies.<\/jats:p>","DOI":"10.3390\/rs14153679","type":"journal-article","created":{"date-parts":[[2022,8,1]],"date-time":"2022-08-01T21:01:24Z","timestamp":1659387684000},"page":"3679","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Mapping Seasonal Leaf Nutrients of Mangrove with Sentinel-2 Images and XGBoost Method"],"prefix":"10.3390","volume":"14","author":[{"given":"Jing","family":"Miao","sequence":"first","affiliation":[{"name":"MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area, Guangdong Key Laboratory of Urban Informatics, Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China"},{"name":"School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianing","family":"Zhen","sequence":"additional","affiliation":[{"name":"Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen 518000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4839-7724","authenticated-orcid":false,"given":"Junjie","family":"Wang","sequence":"additional","affiliation":[{"name":"MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area, Guangdong Key Laboratory of Urban Informatics, Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China"},{"name":"College of Life Sciences and Oceanography, Shenzhen University, Shenzhen 518060, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Demei","family":"Zhao","sequence":"additional","affiliation":[{"name":"MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area, Guangdong Key Laboratory of Urban Informatics, Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China"},{"name":"School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiapeng","family":"Jiang","sequence":"additional","affiliation":[{"name":"MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area, Guangdong Key Laboratory of Urban Informatics, Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China"},{"name":"School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhen","family":"Shen","sequence":"additional","affiliation":[{"name":"MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area, Guangdong Key Laboratory of Urban Informatics, Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China"},{"name":"School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changjun","family":"Gao","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Silviculture, Protection and Utilization, Guangdong Academy of Forestry, Guangzhou 510520, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guofeng","family":"Wu","sequence":"additional","affiliation":[{"name":"MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area, Guangdong Key Laboratory of Urban Informatics, Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China"},{"name":"School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"674932","DOI":"10.3389\/fpls.2021.674932","article-title":"Relationships between Leaf Carbon and Macronutrients across Woody Species and Forest Ecosystems Highlight How Carbon Is Allocated to Leaf Structural Function","volume":"12","author":"Xing","year":"2021","journal-title":"Front. Plant Sci."},{"key":"ref_2","first-page":"209","article-title":"Role of nitrogen for plant growth and development: A review","volume":"10","author":"Leghari","year":"2016","journal-title":"Adv. Environ. Biol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1146\/annurev-environ-101718-033302","article-title":"The State of the World\u2019s Mangrove Forests: Past, Present, and Future","volume":"44","author":"Friess","year":"2019","journal-title":"Annu. Rev. Environ. Resour."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"552","DOI":"10.1890\/110004","article-title":"A blueprint for blue carbon: Toward an improved understanding of the role of vegetated coastal habitats in sequestering CO2","volume":"9","author":"McLeod","year":"2011","journal-title":"Front. Ecol. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Chen, H., Xu, B.B., Wei, S.D., Zhang, L.H., Zhou, H.C., and Lin, Y.M. (2016). Nutrient Resorption and Phenolics Concentration Associated with Leaf Senescence of the Subtropical Mangrove Aegiceras corniculatum: Implications for Nutrient Conservation. Forests, 7.","DOI":"10.3390\/f7110290"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"364","DOI":"10.4319\/lo.2006.51.1_part_2.0364","article-title":"Nitrogen as the limiting nutrient for eutrophication in coastal marine ecosystems: Evolving views over three decades","volume":"51","author":"Howarth","year":"2006","journal-title":"Limnol. Oceanogr."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"878","DOI":"10.3390\/rs3050878","article-title":"Remote Sensing of Mangrove Ecosystems: A Review","volume":"3","author":"Kuenzer","year":"2011","journal-title":"Remote Sens."},{"key":"ref_8","first-page":"102493","article-title":"Estimating mangrove leaf area index based on red-edge vegetation indices: A comparison among UAV, WorldView-2 and Sentinel-2 imagery","volume":"103","author":"Guo","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"108978","DOI":"10.1016\/j.ecolind.2022.108978","article-title":"Newly-developed three-band hyperspectral vegetation index for estimating leaf relative chlorophyll content of mangrove under different severities of pest and disease","volume":"140","author":"Jiang","year":"2022","journal-title":"Ecol. Indic."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.isprsjprs.2017.03.013","article-title":"Monitoring mangrove biomass change in Vietnam using SPOT images and an object-based approach combined with machine learning algorithms","volume":"128","author":"Pham","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/0034-4257(89)90069-2","article-title":"Remote sensing of foliar chemistry","volume":"30","author":"Curran","year":"1989","journal-title":"Remote Sens. Environ."},{"key":"ref_12","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_13","first-page":"344","article-title":"Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and-3","volume":"23","author":"Clevers","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1080\/01431160305012","article-title":"Remote sensing of mangrove biophysical properties: Evidence from a laboratory simulation of the possible effects of background variation on spectral vegetation indices","volume":"24","author":"Diaz","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_15","first-page":"102320","article-title":"Optimal and robust vegetation mapping in complex environments using multiple satellite imagery: Application to mangroves in Southeast Asia","volume":"99","author":"Xiao","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"14530","DOI":"10.3390\/rs71114530","article-title":"Spatiotemporal Variation in Mangrove Chlorophyll Concentration Using Landsat 8","volume":"7","author":"Atkinson","year":"2015","journal-title":"Remote Sens."},{"key":"ref_17","first-page":"101924","article-title":"The effects of water depth on estimating Fractional Vegetation Cover in mangrove forests","volume":"83","author":"Younes","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Annala, L., Ayramo, S., and Polonen, I. (2020). Comparison of Machine Learning Methods in Stochastic Skin Optical Model Inversion. Appl. Sci., 10.","DOI":"10.3390\/app10207097"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.compag.2018.05.012","article-title":"Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review","volume":"151","author":"Chlingaryan","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"146816","DOI":"10.1016\/j.scitotenv.2021.146816","article-title":"Aboveground mangrove biomass estimation in Beibu Gulf using machine learning and UAV remote sensing","volume":"781","author":"Tian","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_21","first-page":"102618","article-title":"Winter wheat SPAD estimation from UAV hyperspectral data using cluster-regression methods","volume":"105","author":"Yang","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1459","DOI":"10.1071\/AR9951459","article-title":"Soil carbon fractions based on their degree of oxidation, and the development of a carbon management index for agricultural systems","volume":"46","author":"Blair","year":"1995","journal-title":"Aust. J. Agric. Res."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1401","DOI":"10.1016\/S0021-9258(18)57154-4","article-title":"A study of conditions for Kjeldahl determination of nitrogen in proteins; description of methods with mercury as catalyst, and titrimetric and gasometric measurements of the ammonia formed","volume":"176","author":"Hiller","year":"1948","journal-title":"J. Biol. Chem."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Harborne, J.B. (1984). Methods of Plant Analysis. Phytochemical Methods: A Guide to Modern Techniques of Plant Analysis, Springer.","DOI":"10.1007\/978-94-009-5570-7_1"},{"key":"ref_25","first-page":"4610","article-title":"Super-Resolving Multiresolution Images With Band-Independent Geometry of Multispectral Pixels","volume":"55","author":"Brodu","year":"2017","journal-title":"IEEE Trans. Geosci. Electron."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"104762","DOI":"10.1016\/j.envexpbot.2021.104762","article-title":"How far can chlorophyll a fluorescence detect phosphorus status in wheat leaves (Triticum durum L.)","volume":"194","author":"Lahrir","year":"2022","journal-title":"Environ. Exp. Bot."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/S0034-4257(98)00046-7","article-title":"Remote Sensing of Chlorophyll a, Chlorophyll b, Chlorophyll a+b, and Total Carotenoid Content in Eucalyptus Leaves","volume":"66","author":"Datt","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_28","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_29","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1080\/01431168308948546","article-title":"The red edge of plant leaf reflectance","volume":"4","author":"Horler","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_30","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_31","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1016\/S0034-4257(96)00112-5","article-title":"A comparison of vegetation indices global set of TM images for EOS-MODIS","volume":"59","author":"Huete","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_32","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_33","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1016\/S0034-4257(02)00018-4","article-title":"Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture","volume":"81","author":"Haboudane","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1230","DOI":"10.1016\/j.agrformet.2008.03.005","article-title":"Estimating chlorophyll content from hyperspectral vegetation indices: Modeling and validation","volume":"148","author":"Wu","year":"2008","journal-title":"Agric. For. Meteorol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/0034-4257(88)90078-8","article-title":"Complementarity of middle-infrared with visible and near-infrared reflectance for monitoring wheat canopies","volume":"26","author":"Baret","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1080\/014311698215919","article-title":"Spectral indices for estimating photosynthetic pigment concentrations: A test using senescent tree leaves","volume":"19","author":"Blackburn","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_37","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_38","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1016\/j.rse.2004.11.012","article-title":"Discrimination of sugarcane varieties in Southeastern Brazil with EO-1 Hyperion data","volume":"94","author":"Formaggio","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/0034-4257(89)90046-1","article-title":"Detection of changes in leaf water content using near-and middle-infrared reflectances","volume":"30","author":"Hunt","year":"1989","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.rse.2005.09.002","article-title":"Assessing vineyard condition with hyperspectral indices: Leaf and canopy reflectance simulation in a row-structured discontinuous canopy","volume":"99","author":"Zarcotejada","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1080\/01431169408954109","article-title":"Ratios of leaf reflectances in narrow wavebands as indicators of plant stress","volume":"15","author":"Carter","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2018.06.022","article-title":"Estimation of the directional and whole apparent clumping index (ACI) from indirect optical measurements","volume":"144","author":"Fang","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_43","first-page":"97","article-title":"Narrow band based and broadband derived vegetation indices using Sentinel-2 Imagery to estimate vegetation biomass","volume":"6","author":"Imran","year":"2020","journal-title":"Glob. J. Environ. Sci. Manag."},{"key":"ref_44","unstructured":"Darmawan, S., Takeuchi, W., Vetrita, Y., Winarso, G., Wikantika, K., and Sari, D.K. (2014, January 22\u201323). Iop Characterization. Proceedings of the 7th IGRSM International Remote Sensing and GIS Conference and Exhibition, Kuala Lumpur, Malaysia."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Jia, M., Wang, Z., Wang, C., Mao, D., and Zhang, Y. (2019). A New Vegetation Index to Detect Periodically Submerged Mangrove Forest Using Single-Tide Sentinel-2 Imagery. Remote Sens., 11.","DOI":"10.3390\/rs11172043"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2353","DOI":"10.1021\/acs.jcim.6b00591","article-title":"Extreme Gradient Boosting as a Method for Quantitative Structure-Activity Relationships","volume":"56","author":"Sheridan","year":"2016","journal-title":"J. Chem. Inf. Model."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"101360","DOI":"10.1016\/j.ecoinf.2021.101360","article-title":"Application of hyperspectral analysis of chlorophyll a concentration inversion in Nansi Lake","volume":"64","author":"Cui","year":"2021","journal-title":"Ecol. Inform."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Yuan, Z.R., Ye, Y., Wei, L.F., Yang, X., and Huang, C. (2022). Study on the Optimization of Hyperspectral Characteristic Bands Combined with Monitoring and Visualization of Pepper Leaf SPAD Value. Sensors, 22.","DOI":"10.3390\/s22010183"},{"key":"ref_50","unstructured":"Ke, G.L., Meng, Q., Finley, T., Wang, T.F., Chen, W., Ma, W.D., Ye, Q.W., and Liu, T.Y. (2017, January 4\u20139). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Proceedings of the 31st Annual Conference on Neural Information Processing Systems (NIPS), Long Beach, CA, USA."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1038\/35046058","article-title":"Nutritional constraints in terrestrial and freshwater food webs","volume":"408","author":"Elser","year":"2000","journal-title":"Nature"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"852","DOI":"10.1111\/geb.13254","article-title":"Global patterns in leaf stoichiometry across coastal wetlands","volume":"30","author":"Hu","year":"2021","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Abbas, M., Ebeling, A., Oelmann, Y., Ptacnik, R., Roscher, C., Weigelt, A., Weisser, W.W., Wilcke, W., and Hillebrand, H. (2013). Biodiversity Effects on Plant Stoichiometry. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0058179"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1758","DOI":"10.4319\/lo.1996.41.8.1758","article-title":"The fate of marine autotrophic production","volume":"41","author":"Duarte","year":"1996","journal-title":"Limnol. Oceanogr."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1441","DOI":"10.2307\/2404783","article-title":"The vegetation N:P ratio: A new tool to detect the nature of nutrient limitation","volume":"33","author":"Koerselman","year":"1996","journal-title":"J. Appl. Ecol."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1715","DOI":"10.1007\/s11676-018-0742-2","article-title":"Seasonal variations of leaf traits and drought adaptation strategies of four common woody species in South Texas, USA","volume":"30","author":"Qin","year":"2019","journal-title":"J. For. Res."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1093\/aob\/mch094","article-title":"Seasonal branch nutrient dynamics in two Mediterranean woody shrubs with contrasted phenology","volume":"93","author":"Milla","year":"2004","journal-title":"Ann. Bot."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Liu, F.D., Liu, Y.H., Wang, G.M., Song, Y., Liu, Q., Li, D.S., Mao, P.L., and Zhang, H. (2015). Seasonal Variations of C: N: P Stoichiometry and Their Trade-offs in Different Organs of Suaeda salsa in Coastal Wetland of Yellow River Delta, China. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0138169"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"271","DOI":"10.3389\/fmars.2016.00271","article-title":"Nutrient Limitation in Central Red Sea Mangroves","volume":"3","author":"Almahasheer","year":"2016","journal-title":"Front. Mar. Sci."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"693","DOI":"10.5194\/bg-15-693-2018","article-title":"Variations and determinants of carbon content in plants: A global synthesis","volume":"15","author":"Ma","year":"2018","journal-title":"Biogeosciences"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"906","DOI":"10.1126\/science.1083140","article-title":"Ecological stoichiometry\u2014The biology of elements from molecules to the biosphere","volume":"300","author":"Michaels","year":"2003","journal-title":"Science"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"821","DOI":"10.1038\/nature02403","article-title":"The worldwide leaf economics spectrum","volume":"428","author":"Wright","year":"2004","journal-title":"Nature"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"891","DOI":"10.3390\/rs5020891","article-title":"Relationship between Hyperspectral Measurements and Mangrove Leaf Nitrogen Concentrations","volume":"5","author":"Zhang","year":"2013","journal-title":"Remote Sens."},{"key":"ref_64","first-page":"102387","article-title":"Mapping leaf chlorophyll content of mangrove forests with Sentinel-2 images of four periods","volume":"102","author":"Zhen","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1016\/j.biosystemseng.2005.05.001","article-title":"Potential for Onsite and Online Analysis of Pig Manure using Visible and Near Infrared Reflectance Spectroscopy","volume":"91","author":"Saeys","year":"2005","journal-title":"Biosyst. Eng."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"17911","DOI":"10.1038\/s41598-021-97131-8","article-title":"Modeling hydrogen solubility in hydrocarbons using extreme gradient boosting and equations of state","volume":"11","author":"Mohammadi","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Jia, Y., Jin, S.G., Savi, P., Gao, Y., Tang, J., Chen, Y.X., and Li, W.M. (2019). GNSS-R Soil Moisture Retrieval Based on a XGboost Machine Learning Aided Method: Performance and Validation. Remote Sens., 11.","DOI":"10.3390\/rs11141655"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/15\/3679\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:00:46Z","timestamp":1760140846000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/15\/3679"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,1]]},"references-count":67,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["rs14153679"],"URL":"https:\/\/doi.org\/10.3390\/rs14153679","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,1]]}}}