{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T12:02:56Z","timestamp":1775736176959,"version":"3.50.1"},"reference-count":100,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2024,8,21]],"date-time":"2024-08-21T00:00:00Z","timestamp":1724198400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Tianchi Talent Project, Science Foundation of Xinjiang Uygur Autonomous Region","award":["2023D01B04"],"award-info":[{"award-number":["2023D01B04"]}]},{"name":"Tianchi Talent Project, Science Foundation of Xinjiang Uygur Autonomous Region","award":["41902307"],"award-info":[{"award-number":["41902307"]}]},{"name":"Tianchi Talent Project, Science Foundation of Xinjiang Uygur Autonomous Region","award":["2223PTKFKT"],"award-info":[{"award-number":["2223PTKFKT"]}]},{"name":"National Natural Science Foundation of China","award":["2023D01B04"],"award-info":[{"award-number":["2023D01B04"]}]},{"name":"National Natural Science Foundation of China","award":["41902307"],"award-info":[{"award-number":["41902307"]}]},{"name":"National Natural Science Foundation of China","award":["2223PTKFKT"],"award-info":[{"award-number":["2223PTKFKT"]}]},{"name":"Open Project of the Xinjiang Planting Industry Green Production Engineering Technology Research Center","award":["2023D01B04"],"award-info":[{"award-number":["2023D01B04"]}]},{"name":"Open Project of the Xinjiang Planting Industry Green Production Engineering Technology Research Center","award":["41902307"],"award-info":[{"award-number":["41902307"]}]},{"name":"Open Project of the Xinjiang Planting Industry Green Production Engineering Technology Research Center","award":["2223PTKFKT"],"award-info":[{"award-number":["2223PTKFKT"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Quickly determining the metal content in plants and subsequently identifying geochemical anomalies can provide clues and guidance for predicting the location and scale of concealed ore bodies in vegetation-covered areas. Although visible, near-infrared and shortwave infrared (VNIR\u2013SWIR) reflectance spectroscopy at wavelengths ranging from 400 to 2500 nm has been proven by many researchers to be a fast, accurate and nondestructive approach for estimating the contents of copper (Cu), lead (Pb), zinc (Zn) and other metal elements in plants, relatively few studies have been conducted on the estimation of lithium (Li) in plants. Therefore, the potential of applying VNIR\u2013SWIR spectroscopy techniques for estimating the Li content in plants was explored in this study. The Jingerquan Li mining area in Hami, Xinjiang, China, was chosen. Three sampling lines were established near a pegmatite deposit and in a background region, canopy reflectance spectra were obtained for desert plants and Li contents were determined in the laboratory; then, quantitative relationships were established between nine different transformed spectra (including both integer and fractional orders) and the Li content was estimated using partial least squares regression (PLSR). The results showed that models constructed using high-order derivative spectra (with an order greater than or equal to 1) significantly outperformed those based on original and low-order derivative spectra (with an order less than 1). Notably, the model based on a 1.1-order derivative spectrum displayed the best performance. Furthermore, the performance of the model based on the two-layer wavelet coefficients of the 1.1-order derivative spectrum was further improved compared with that of the model based on only the 1.1-order derivative spectrum. The coefficient of determination (Rpre2) and the ratio of performance to deviation (RPD) for the validation set increased from 0.6977 and 1.7656 to 0.7044 and 1.8446, respectively, and the root mean square error (RMSEpre) decreased from 2.5735 to 2.4633 mg\/kg. These results indicate that quickly and accurately estimating the Li content in plants via the proposed spectroscopic analysis technique is feasible and effective; however, appropriate spectral preprocessing methods should be selected before hyperspectral estimation models are constructed. Overall, the developed hybrid spectral transformation approach, which combines wavelet coefficients and derivative spectra, displayed excellent application potential for estimating the Li content in plants.<\/jats:p>","DOI":"10.3390\/rs16163071","type":"journal-article","created":{"date-parts":[[2024,8,22]],"date-time":"2024-08-22T04:26:57Z","timestamp":1724300817000},"page":"3071","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Rapid Prediction of the Lithium Content in Plants by Combining Fractional-Order Derivative Spectroscopy and Wavelet Transform Analysis"],"prefix":"10.3390","volume":"16","author":[{"given":"Shichao","family":"Cui","sequence":"first","affiliation":[{"name":"College of Resources and Environment, Xinjiang Agricultural University, Urumqi 830052, China"},{"name":"Xinjiang Planting Industry Green Production Engineering Technology Research Center, Urumqi 830052, China"}]},{"given":"Guo","family":"Jiang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China"},{"name":"Xinjiang Research Centre for Mineral Resources, Chinese Academy of Sciences, Urumqi 830011, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5359-5499","authenticated-orcid":false,"given":"Yong","family":"Bai","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China"},{"name":"Xinjiang Research Centre for Mineral Resources, Chinese Academy of Sciences, Urumqi 830011, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1007\/s00425-003-1017-4","article-title":"Lithium treatment induces a hypersensitive-like response in tobacco","volume":"217","author":"Naranjo","year":"2003","journal-title":"Planta"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1007\/s12011-012-9435-4","article-title":"A Study on Selected Physiological Parameters of Plants Grown Under Lithium Supplementation","volume":"149","author":"Kalinowska","year":"2012","journal-title":"Biol. Trace Elem. Res."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Martinez, N.E., Sharp, J.L., Johnson, T.E., Kuhne, W.W., Stafford, C.T., and Duff, M.C. (2018). Reflectance-Based Vegetation Index Assessment of Four Plant Species Exposed to Lithium Chloride. Sensors, 18.","DOI":"10.3390\/s18092750"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Hayyat, M.U., Nawaz, R., Siddiq, Z., Shakoor, M.B., Mushtaq, M., Ahmad, S.R., Ali, S., Hussain, A., Irshad, M.A., and Alsahli, A.A. (2021). Investigation of Lithium Application and Effect of Organic Matter on Soil Health. Sustainability, 13.","DOI":"10.3390\/su13041705"},{"key":"ref_5","first-page":"106","article-title":"Crtical metal mineral resources: Current research status and scientific issues","volume":"33","author":"Zhai","year":"2019","journal-title":"Bull. Natl. Nat. Sci. Found. China"},{"key":"ref_6","first-page":"1189","article-title":"Study on critical mineral resources: Significance of research, determination of types, attributes of resources, progress of prospecting, problem of utilization and direction of exploitation","volume":"93","author":"Wang","year":"2019","journal-title":"Acta Geol. Sin."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"K\u00f6hler, M., Hanelli, D., Schaefer, S., Barth, A., Knobloch, A., Hielscher, P., Cardoso-Fernandes, J., Lima, A., and Teodoro, A.C. (2021). Lithium Potential Mapping Using Artificial Neural Networks: A Case Study from Central Portugal. Minerals, 11.","DOI":"10.3390\/min11101046"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"723","DOI":"10.3749\/canmin.AB00004","article-title":"Constraints and potentials of remote sensing data\/techniques applied to lithium (Li)-pegmatites","volume":"57","author":"Lima","year":"2019","journal-title":"Can. Miner."},{"key":"ref_9","first-page":"10","article-title":"Remote sensing data in lithium (Li) exploration: A new approach for the detection of Li-bearing pegmatites","volume":"76","author":"Teodoro","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Cardoso-Fernandes, J., Silva, J., Dias, F., Lima, A., Teodoro, A.C., Barr\u00e8s, O., Cauzid, J., Perrotta, M., Roda-Robles, E., and Ribeiro, M.A. (2021). Tools for Remote Exploration: A Lithium (Li) Dedicated Spectral Library of the Fregeneda-Almendra Aplite-Pegmatite Field. Data, 6.","DOI":"10.3390\/data6030033"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Gao, Y.B., Bagas, L., Li, K., Jin, M.S., Liu, Y.G., and Teng, J.X. (2020). Newly Discovered Triassic Lithium Deposits in the Dahongliutan Area, North West China: A Case Study for the Detection of Lithium-Bearing Pegmatite Deposits in Rugged Terrains Using Remote-Sensing Data and Images. Front. Earth Sci., 8.","DOI":"10.3389\/feart.2020.591966"},{"key":"ref_12","first-page":"2971","article-title":"Progress in geological study of pegmatite-type lithium deposits in the world","volume":"95","author":"Chen","year":"2021","journal-title":"Acta Geol. Sin."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Cardoso-Fernandes, J., Teodoro, A.C., Lima, A., and Roda-Robles, E. (2020). Semi-Automatization of Support Vector Machines to Map Lithium (Li) Bearing Pegmatites. Remote Sens., 12.","DOI":"10.3390\/rs12142319"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Cardoso-Fernandes, J., Teodoro, A.C., Lima, A., Perrotta, M., and Roda-Robles, E. (2020). Detecting Lithium (Li) Mineralizations from Space: Current Research and Future Perspectives. Appl. Sci., 10.","DOI":"10.3390\/app10051785"},{"key":"ref_15","unstructured":"Perrotta, M.M., Souza Filho, C.R., and Leite, C.A.S. (2005, January 16\u201321). Mapeamento espectral de intrus\u00f5es pegmat\u00edticas relacionadas a mineraliza\u00e7\u00f5es de l\u00edtio, gemas e minerais industriais na regi\u00e3o do vale do Jequitinhonha (MG) a partir de imagens ASTER. Proceedings of the Anais do XII Simp\u00f3sio Brasileiro de Sensoriamento Remoto, Goi\u00e2nia, Brazil."},{"key":"ref_16","unstructured":"Mendes, D., Perrotta, M.M., Costa, M.A.C., and Paes, V.J.C. (2017, January 28\u201329). Mapeamento espectral para identifica\u00e7\u00e3o de assinaturas espectrais de minerais de l\u00edtio em imagens ASTER (NE\/MG). Proceedings of the Anais do XVIII Simp\u00f3sio Brasileiro de Sensoriamento Remoto, Santos-SP, Brazil."},{"key":"ref_17","unstructured":"Michel, U., and Schulz, K. (2018, January 10\u201313). Potential of Sentinel-2 data in the detection of lithium (Li)-bearing pegmatites: A study case. Proceedings of the SPIE, SPIE Remote Sensing, Berlin, Germany."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Cardoso-Fernandes, J., Silva, J., Perrotta, M.M., Lima, A., Teodoro, A.C., Ribeiro, M.A., Dias, F., Barr\u00e8s, O., Cauzid, J., and Roda-Robles, E. (2021). Interpretation of the Reflectance Spectra of Lithium (Li) Minerals and Pegmatites: A Case Study for Mineralogical and Lithological Identification in the Fregeneda-Almendra Area. Remote Sens., 13.","DOI":"10.3390\/rs13183688"},{"key":"ref_19","unstructured":"Schulz, K., Michel, U., and Nikolakopoulos, K.G. (2019, January 9\u201312). Remote sensing techniques to detect areas with potential for lithium exploration in Minas Gerais, Brazil. Proceedings of the SPIE, SPIE Remote Sensing, Strasbourg, France."},{"key":"ref_20","first-page":"507","article-title":"Reflectance spectral characteristics of rocks and mineral in Jiajika lithium deposits in west Sichuan","volume":"37","author":"Dai","year":"2019","journal-title":"Rock Miner. Anal."},{"key":"ref_21","first-page":"389","article-title":"Geological mapping and ore-prospecting study using remote sensing technology in Jiajika area of Western Sichuan Province","volume":"44","author":"Dai","year":"2017","journal-title":"Geol. China"},{"key":"ref_22","first-page":"992","article-title":"Quantitative estimation of content of lithium using reflectance spectroscopy","volume":"34","author":"Dai","year":"2019","journal-title":"Remote Sens. Technol. Appl."},{"key":"ref_23","unstructured":"Michel, U., and Schulz, K. (2018, January 10\u201313). Pegmatite spectral behavior considering ASTER and Landsat 8 OLI data in Naipa and Muiane mines (Alto Ligonha, Mozambique). Proceedings of the SPIE, SPIE Remote Sensing, Berlin, Germany."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Gemusse, U., Lima, A., Teodoro, A.C.M., Schulz, K., Nikolakopoulos, K.G., and Michel, U. (2019, January 10\u201312). Comparing different techniques of satellite imagery classification to mineral mapping pegmatite of Muiane and Naipa: Mozambique). Proceedings of the Earth Resources and Environmental Remote Sensing\/GIS Applications X, Strasbourg, France.","DOI":"10.1117\/12.2532570"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"106900","DOI":"10.1016\/j.gexplo.2021.106900","article-title":"Mineral prospecting from biogeochemical and geological information using hyperspectral remote sensing-Feasibility and challenges","volume":"232","author":"Chakraborty","year":"2022","journal-title":"J. Geochem. Explor."},{"key":"ref_26","first-page":"610","article-title":"Method of plant geochemical measurement and its prospecting result","volume":"19","author":"Hu","year":"2005","journal-title":"Miner. Resour. Geol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/S0375-6742(96)00039-8","article-title":"Biogeochemical exploration for gold in tropical rain forest regions of Papua New Guinea","volume":"57","author":"McInnes","year":"1996","journal-title":"J. Geochem. Explor."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1016\/S1367-9120(99)00065-6","article-title":"Salix acmophylla, Tamarix smyrnensis and Phragmites australis as biogeochemical indicators for copper deposits in Elaz\u0131\u01e7, Turkey","volume":"18","year":"2000","journal-title":"J. Asian Earth Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.chemer.2003.09.001","article-title":"Pinus brutia as a biogeochemical medium to detect iron and zinc in soil analysis, chromite deposits of the area mersin, Turkey","volume":"65","year":"2005","journal-title":"Geochemistry"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.gexplo.2004.11.003","article-title":"Plants growing in abandoned mines of portugal are useful for biogeochemical exploration of arsenic, antimony, tungsten and mine reclamation","volume":"85","author":"Pratas","year":"2005","journal-title":"J. Geochem. Explor."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"723","DOI":"10.1016\/j.apgeochem.2007.12.001","article-title":"Biogeochemistry of Pb\u2013Zn gossans, northwest Queensland, Australia: Implications for mineral exploration and mine site rehabilitation","volume":"23","author":"Lottermoser","year":"2008","journal-title":"Appl. Geochem."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.gexplo.2007.07.002","article-title":"Biogeochemical characteristics of the Hetai goldfield, Guangdong Province. China","volume":"96","author":"Miao","year":"2008","journal-title":"J. Geochem. Explor."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.gexplo.2010.01.004","article-title":"Biogeochemical sampling for mineral exploration in arid terrains: Tanami Gold Province. Australia","volume":"104","author":"Reid","year":"2010","journal-title":"J. Geochem. Explor."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.apgeochem.2012.10.034","article-title":"Spinifex biogeochemistry across Arid Australia: Mineral exploration potential and chromium accumulation","volume":"29","author":"Reid","year":"2013","journal-title":"Appl. Geochem."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.chemer.2011.11.003","article-title":"The species of Silene compacta Fischer as indicator of zinc. iron and copper mineralization","volume":"72","author":"Filippidis","year":"2012","journal-title":"Geochemistry"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1144\/geochem2016-416","article-title":"Metal migration at the DeGrussa Cu-Au sulphide deposit, Western Australia: Soil, vegetation and groundwater studies","volume":"17","author":"Noble","year":"2017","journal-title":"Geochem. Explor. Environ. Anal."},{"key":"ref_37","first-page":"122","article-title":"Phytogeochemical Characteristics of Seriphidium terrae-albae (Krasch) Poljak in the Metallic Ore Deposits in North Part of East Junggar Desert Area, Xinjiang and their Prospecting Significance","volume":"41","author":"Song","year":"2017","journal-title":"Geotecton. Metallog."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"106665","DOI":"10.1016\/j.gexplo.2020.106665","article-title":"Test of vegetation-based surface exploration for detection of Arctic mineralizations: The deep buried Kangerluarsuk Zn-Pb-Ag anomaly","volume":"220","author":"Johnsen","year":"2021","journal-title":"J. Geochem. Explor."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.saa.2018.12.051","article-title":"Research and analysis of cadmium residue in tomato leaves based on WT-LSSVR and Vis-NIR hyperspectral imaging","volume":"212","author":"Sun","year":"2019","journal-title":"Spectrochim. Acta A Mol. Biomol. Spectrosc."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"911","DOI":"10.1080\/01431161.2019.1651949","article-title":"Study of vegetation spectral anomaly behaviour in a porphyry copper mine area based on hyperspectral indices","volume":"41","author":"He","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"106293","DOI":"10.1016\/j.compag.2021.106293","article-title":"Predicting copper content in chicory leaves using hyperspectral data with continuous wavelet transforms and partial least squares","volume":"187","author":"Lin","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1021","DOI":"10.1016\/j.scitotenv.2018.12.458","article-title":"Rapid detection of cadmium and its distribution in Miscanthus sacchariflorus based on visible and near-infrared hyperspectral imaging","volume":"659","author":"Feng","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Mirzaei, M., Verrelst, J., Marofi, S., Abbasi, M., and Azadi, H. (2019). Eco-Friendly Estimation of Heavy Metal Contents in Grapevine Foliage Using In-Field Hyperspectral Data and Multivariate Analysis. Remote Sens., 11.","DOI":"10.3390\/rs11232731"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"e13793","DOI":"10.1111\/jfpe.13793","article-title":"Non-destructive detection of lead content in oilseed rape leaves based on MRF-HHO-SVR and hyperspectral technology","volume":"44","author":"Cao","year":"2021","journal-title":"J. Food Process Eng."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.rse.2015.10.006","article-title":"A new vegetation index for detecting vegetation anomalies due to mineral deposits with application to a tropical forest area","volume":"171","author":"Hede","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"576","DOI":"10.1080\/2150704X.2017.1306135","article-title":"A new narrow band vegetation index for characterizing the degree of vegetation stress due to copper: The copper stress vegetation index (CSVI)","volume":"8","author":"Zhang","year":"2017","journal-title":"Remote Sens. Lett."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"014511","DOI":"10.1117\/1.JRS.13.014511","article-title":"Assessment of the application of copper stress vegetation index on Hyperion image in Dexing Copper Mine, China","volume":"13","author":"Zhang","year":"2019","journal-title":"J. Appl. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"4473","DOI":"10.1080\/01431161.2018.1563842","article-title":"Spectral characteristics of copper-stressed vegetation leaves and further understanding of the copper stress vegetation index","volume":"40","author":"Zhang","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"39029","DOI":"10.1007\/s11356-020-09973-w","article-title":"Predicting copper contamination in wheat canopy during the full growth period using hyperspectral data. Environ","volume":"27","author":"Wang","year":"2021","journal-title":"Sci. Pollut. Res. Int."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Lassalle, G., Fabre, S., Credoz, A., H\u00e9dacq, R., Dubucq, D., and Elger, A. (2021). Mapping leaf metal content over industrial brownfields using airborne hyperspectral imaging and optimized vegetation indices. Sci. Rep., 11.","DOI":"10.1038\/s41598-020-79439-z"},{"key":"ref_51","first-page":"3234","article-title":"Spectral red edge position responding and pollution moitoring of core leaves stressed by heavy metal copper","volume":"54","author":"Shi","year":"2015","journal-title":"Hubei Agric. Sci."},{"key":"ref_52","first-page":"744","article-title":"Relational model between the hyperspectral variability of Celosia argentea L. growing in manganese stress environment and the content of metal element in the canopy","volume":"38","author":"Chen","year":"2018","journal-title":"J. Guilin Univ. Technol."},{"key":"ref_53","first-page":"546","article-title":"Effects of Cuprum Stress on Position of Red Edge of Maize Leaf Reflection Hyperspectra and Relations to Chlorophyll Content","volume":"38","author":"Li","year":"2018","journal-title":"Spectrosc. Spect. Anal."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.scitotenv.2018.04.415","article-title":"Heavy metal-induced stress in rice crops detected using multi-temporal Sentinel-2 satellite images","volume":"637","author":"Liu","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_55","first-page":"2091","article-title":"LD-CR-SIDSCA(tan) Detection Model for the Weak Spectral Information of Maize Leaves under Copper and Lead Stresses","volume":"39","author":"Zhang","year":"2019","journal-title":"Spectrosc. Spect. Anal."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.plantsci.2009.03.013","article-title":"Analysis of the metabolome and transcriptome of Brassica carinata seedlings after lithium chloride exposure","volume":"177","author":"Li","year":"2009","journal-title":"Plant. Sci."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1007\/s12011-013-9606-y","article-title":"The influence of two lithium forms on the growth, L-ascorbic acid content and lithium accumulation in lettuce plants","volume":"152","author":"Kalinowska","year":"2013","journal-title":"Biol. Trace Elem. Res."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.plaphy.2016.05.034","article-title":"Lithium toxicity in plants: Reasons, mechanisms and remediation possibilities-A review","volume":"107","author":"Shahzad","year":"2016","journal-title":"Plant Physiol. Biochem."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"7989","DOI":"10.15666\/aeer\/1806_79898008","article-title":"Alleviation of lithium toxicity in sorghum (Sorghum vulgare pers.) by inoculation with lithium resistant bacteria","volume":"18","author":"Hayyat","year":"2020","journal-title":"Appl. Ecol. Environ. Res."},{"key":"ref_60","first-page":"385","article-title":"Genetic linkage between pegmatites and granites from Jingerquan, East Tianshan Mountains: Evidence from zircon U-Pb geochronological and Hf isotopic data","volume":"49","author":"Li","year":"2020","journal-title":"Geochinica"},{"key":"ref_61","first-page":"686","article-title":"A technology for identifying Li-Be pegmatite using ASTER remote sensing data in granite of Gobi shallow-covered area: A case study of recognition and prediction of Li-Be pegmatite in Jingerquan, Xinjiang","volume":"39","author":"Yao","year":"2020","journal-title":"Miner. Deposit."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.geoderma.2013.10.024","article-title":"Prediction of low heavy metal concentrations in agricultural soils using visible and near-infrared reflectance spectroscopy","volume":"216","author":"Wang","year":"2014","journal-title":"Geoderma"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"758","DOI":"10.1016\/j.geoderma.2018.10.025","article-title":"Application of fractional-order derivative in the quantitative estimation of soil organic matter content through visible and near-infrared spectroscopy","volume":"337","author":"Hong","year":"2019","journal-title":"Geoderma"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"115845","DOI":"10.1016\/j.envpol.2020.115845","article-title":"VIRS based detection in combination with machine learning for mapping soil pollution","volume":"268","author":"Jia","year":"2021","journal-title":"Environ. Pollut."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Wang, J., Tiyip, T., Ding, J., Zhang, D., Liu, W., Wang, F., and Tashpolat, N. (2017). Desert soil clay content estimation using reflectance spectroscopy preprocessed by fractional derivative. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0184836"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Zhao, L., Hu, Y.-M., Zhou, W., Liu, Z.-H., Pan, Y.-C., Shi, Z., Wang, L., and Wang, G.-X. (2018). Estimation Methods for Soil Mercury Content Using Hyperspectral Remote Sensing. Sustainability., 10.","DOI":"10.3390\/su10072474"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"105222","DOI":"10.1016\/j.catena.2021.105222","article-title":"Hyperspectral inversion of soil heavy metals in Three-River Source Region based on random forest model","volume":"202","author":"Zhou","year":"2021","journal-title":"Catena"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"840","DOI":"10.1366\/0003702981944580","article-title":"Fractional derivative analysis of diffuse reflectance spectra","volume":"52","author":"Schmitt","year":"1998","journal-title":"Appl. Spectrosc."},{"key":"ref_69","first-page":"172","article-title":"Estimation of Chlorophyll Content in Winter Wheat Based on Wavelet Transform and Fractional Differential","volume":"52","author":"Li","year":"2019","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_70","first-page":"33","article-title":"Hyperspectral estimation of black soil organic matter content based on wavelet transform and successive projections algorithm","volume":"33","author":"Xiao","year":"2021","journal-title":"Remote Sens. Land Resour."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/S0169-7439(01)00155-1","article-title":"PLS-regression: A basic tool of chemometrics","volume":"58","author":"Wold","year":"2021","journal-title":"Chemometr. Intell. Lab."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.geoderma.2005.03.007","article-title":"Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties","volume":"131","author":"Walvoort","year":"2006","journal-title":"Geoderma"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.geoderma.2008.04.007","article-title":"Comparison of multivariate methods for inferential modeling of soil carbon using visible\/near-infrared spectra","volume":"146","author":"Vasques","year":"2008","journal-title":"Geoderma"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.jhazmat.2013.11.059","article-title":"Visible and near-infrared reflectance spectroscopy\u2014An alternative for monitoring soil contamination by heavy metals","volume":"265","author":"Shi","year":"2014","journal-title":"J. Hazard. Mater."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.geoderma.2019.06.040","article-title":"Capability of Sentinel-2 MSI data for monitoring and mapping of soil salinity in dry and wet seasons in the Ebinur Lake region, Xinjiang, China","volume":"353","author":"Wang","year":"2019","journal-title":"Geoderma."},{"key":"ref_76","first-page":"102420","article-title":"Predicting the abundance of copper in soil using reflectance spectroscopy and GF5 hyperspectral imagery","volume":"102","author":"Yin","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1016\/j.saa.2018.12.032","article-title":"Hyperspectral inversion of heavy metal content in reclaimed soil from a mining wasteland based on different spectral transformation and modeling method","volume":"211","author":"Zhang","year":"2019","journal-title":"Spectrochim. Acta Part A"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.isprsjprs.2017.12.003","article-title":"Hyperspectral sensing of heavy metals in soil and vegetation: Feasibility and challenges","volume":"136","author":"Wang","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"106031","DOI":"10.1016\/j.compag.2021.106031","article-title":"Predicting the contents of soil salt and major water-soluble ions with fractional-order derivative spectral indices and variable selection","volume":"182","author":"Lao","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Hong, Y., Chen, Y., Yu, L., Liu, Y., Liu, Y., Zhang, Y., Liu, Y., and Cheng, H. (2018). Combining Fractional Order Derivative and Spectral Variable Selection for Organic Matter Estimation of Homogeneous Soil Samples by VIS\u2013NIR Spectroscopy. Remote Sens., 10.","DOI":"10.3390\/rs10030479"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"2125","DOI":"10.1016\/j.saa.2003.11.013","article-title":"A simple method to extract spectral parameters using fractional derivative spectrometry","volume":"60","author":"Kharintsev","year":"2004","journal-title":"Spectrochim. Acta Part A"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.chemolab.2015.02.017","article-title":"Improvement of NIR model by fractional order Savitzky\u2013Golay derivation (FOSGD) coupled with wavelength selection","volume":"143","author":"Tong","year":"2015","journal-title":"Chemometr. Intell. Lab."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1007\/s10661-017-6261-3","article-title":"Estimating cadmium concentration in the edible part of Capsicum annuum using hyperspectral models","volume":"189","author":"Wang","year":"2017","journal-title":"Environ. Monit. Assess."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"510","DOI":"10.1016\/j.still.2015.07.021","article-title":"Estimating the soil clay content and organic matter by means of different calibration methods of vis-NIR diffuse reflectance spectroscopy","volume":"155","author":"Nawar","year":"2016","journal-title":"Soil Tillage Res."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.catena.2018.10.051","article-title":"Combination of fractional order derivative and memory-based learning algorithm to improve the estimation accuracy of soil organic matter by visible and near-infrared spectroscopy","volume":"174","author":"Hong","year":"2019","journal-title":"Catena"},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Zhou, W., Zhang, J., Zou, M., Liu, X., Du, X., Wang, Q., Liu, Y., Liu, Y., and Li, J. (2019). Feasibility of Using Rice Leaves Hyperspectral Data to estimate CaCl2-extractable concentrations of Heavy Metals in Agricultural Soil. Sci. Rep., 9.","DOI":"10.1038\/s41598-019-52503-z"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"123288","DOI":"10.1016\/j.jhazmat.2020.123288","article-title":"Estimating the distribution trend of soil heavy metals in mining area from HyMap airborne hyperspectral imagery based on ensemble learning","volume":"401","author":"Tan","year":"2021","journal-title":"J. Hazard. Mater."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"036003","DOI":"10.1117\/1.JRS.12.036003","article-title":"Comparing the effects of different spectral transformations on the estimation of the copper content of Seriphidium terrae-albae","volume":"12","author":"Cui","year":"2018","journal-title":"J. Appl. Remote Sens."},{"key":"ref_89","first-page":"309","article-title":"Estimating Heavy Metal Contents in Anabasis L. Using Hyperspectral Data","volume":"37","author":"Abdugheni","year":"2020","journal-title":"J. Xinjiang Univ."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"104257","DOI":"10.1016\/j.catena.2019.104257","article-title":"Prediction of soil organic matter in northwestern China using fractional order derivative spectroscopy and modified normalized difference indices","volume":"185","author":"Zhang","year":"2020","journal-title":"Catena"},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Ge, X.Y., Ding, J.L., Jin, X.L., Wang, J.Z., Chen, X.Y., and Li, X.H. (2021). Estimating Agricultural Soil Moisture Content through UAV-Based Hyperspectral Images in the Arid Region. Remote Sens., 13.","DOI":"10.3390\/rs13081562"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"114228","DOI":"10.1016\/j.geoderma.2020.114228","article-title":"Exploring the potential of airborne hyperspectral image for estimating topsoil organic carbon: Effects of fractional-order derivative and optimal band combination algorithm","volume":"365","author":"Hong","year":"2020","journal-title":"Geoderma"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"1969","DOI":"10.1016\/j.scitotenv.2018.09.391","article-title":"Estimating lead and zinc concentrations in peri-urban agricultural soils through reflectance spectroscopy: Effects of fractional-order derivative and random forest","volume":"651","author":"Hong","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Shi, T.Z., Liu, H.Z., Chen, Y.Y., Fei, T., Wang, J.J., and Wu, G.F. (2017). Spectroscopic Diagnosis of Arsenic Contamination in Agricultural Soils. Sensors, 17.","DOI":"10.3390\/s17051036"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"2742","DOI":"10.1021\/es015747j","article-title":"Estimate of heavy metal contamination in soils after a mining accident using reflectance spectroscopy","volume":"36","author":"Kemper","year":"2002","journal-title":"Environ. Sci. Technol."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"6264","DOI":"10.1021\/es405361n","article-title":"Monitoring Arsenic Contamination in Agricultural Soils with Reflectance Spectroscopy of Rice Plants","volume":"48","author":"Shi","year":"2014","journal-title":"Environ. Sci. Technol."},{"key":"ref_97","first-page":"95","article-title":"Improving the prediction of arsenic contents in agricultural soils by combining the reflectance spectroscopy of soils and rice plants","volume":"52","author":"Shi","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.geoderma.2018.08.010","article-title":"Estimating heavy metal concentrations in suburban soils with reflectance spectroscopy","volume":"336","author":"Cheng","year":"2019","journal-title":"Geoderma"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"115422","DOI":"10.1016\/j.envpol.2020.115412","article-title":"Ensemble machine-learning-based framework for estimating total nitrogen concentration in water using drone-borne hyperspectral imagery of emergent plants: A case study in an arid oasis, NW China","volume":"266","author":"Wang","year":"2020","journal-title":"Environ. Pollut."},{"key":"ref_100","first-page":"246","article-title":"Monitoring stress levels on rice with heavy metal pollution from hyperspectral reflectance data using wavelet-fractal analysis","volume":"13","author":"Liu","year":"2011","journal-title":"Int. J. Appl. Earth Obs. Geoinf."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/16\/3071\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:40:09Z","timestamp":1760110809000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/16\/3071"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,21]]},"references-count":100,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["rs16163071"],"URL":"https:\/\/doi.org\/10.3390\/rs16163071","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,21]]}}}