Hyperspectral estimation of mercury content of soil in Oasis city in arid zones of China

References

• Ajay KP, Jayanta GK, Sameer SU. 2020. Fractional abundances study of macronutrients in soil using hyperspectral remote sensing. Geocarto Int. 37(2):1–20.
   Web of Science ®Google Scholar
• Chang RC, Chen Z, Wang DM, Guo K. 2022. Hyperspectral remote sensing inversion and monitoring of organic matter in black soil based on dynamic fitness inertia weight particle swarm optimization neural network. Remote Sens. 14(17):4316. doi: 10.3390/rs14174316.
   Google Scholar
• Datta D, Paul M, Murshed M, Teng SW, Schmidtke L. 2023. Comparative analysis of machine and deep learning models for soil properties prediction from hyperspectral visual band. Environments. 10(5):77. doi: 10.3390/environments10050077.
   Web of Science ®Google Scholar
• Dong JH, Dai WT, Xu JR, Li SN. 2016. Spectral estimation model construction of heavy metals in mining reclamation areas. Int J Environ Res Public Health. 13(7):640. doi: 10.3390/ijerph13070640.
   PubMed Web of Science ®Google Scholar
• Idowu OJ, Van EHM, Abawi GS, Wolfe DW, Ball JI, Gugino BK, Moebius BN, Schindelbeck RR, Bilgili AV. 2008. Farmer-oriented assessment of soil quality using field, laboratory, and VNIR spectroscopy methods. Plant Soil. 307(1–2):243–253. doi: 10.1007/s11104-007-9521-0.
   Web of Science ®Google Scholar
• Jia P, Zhang J, He W, Yuan D, Hu Y, Zamanian K, Jia K, Zhao X. 2022. Inversion of different cultivated soil types’ salinity using hyperspectral data and machine learning. Remote Sens. 14(22):5639. doi: 10.3390/rs14225639.
   Google Scholar
• Jia PP, Zhang JH, He W, Hu Y, Zeng R, Zamanian K, Jia KL, Zhao XN. 2022. Combination of hyperspectral and machine learning to invert soil electrical conductivity. Remote Sens. 14(11):2602. doi: 10.3390/rs14112602.
   Google Scholar
• Levy JS, Johnson JTE. 2021. Remote soil moisture measurement from drone-borne reflectance spectroscopy: applications to hydroperiod measurement in desert playas. Remote Sens. 13(5):1035. doi: 10.3390/rs13051035.
   Google Scholar
• Lin X, Su YC, Shang J, Sha J, Li X, Sun YY, Ji J, Jin B. 2019. Geographically weighted regression effects on soil zinc content hyperspectral modeling by applying the fractional-order differential. Remote Sens. 11(6):636. doi: 10.3390/rs11060636.
   Google Scholar
• Liu K, Zhao D, Fang JY, Zhang X, Zhang QY, Li XK. 2017. Estimation of heavy metal contamination in soil using remote sensing spectroscopy and a statistical approach. J Indian Soc Remote Sens. 45(5):805–813. doi: 10.1007/s12524-016-0648-4.
   Web of Science ®Google Scholar
• Liu W, Li M, Zhang M, Long S, Guo Z, Wang H, Li W, Wang D, Hu Y, Wei Y, et al. 2020. Hyperspectral inversion of mercury in reed leaves under different levels of soil mercury contamination. Environ Sci Pollut Res Int. 27(18):22935–22945. doi: 10.1007/s11356-020-08807-z.
   PubMed Web of Science ®Google Scholar
• Mamattursun E, Anwar M, Ajigul M, Gulbanu H. 2018. A human health risk assessment of heavy metals in agricultural soils of Yanqi Basin, Silk Road Economic Belt, China. Hum Ecol Risk Assess. 24:1352–1366.
   Web of Science ®Google Scholar
• [MAPRC] Ministry of Agriculture of the People’s Republic of China. 2008. Beijing, China. GB/T 22105.2—2008. Soil quality—analysis of total mercury, arsenic, and lead contents—atomic fluorescence spectrometry—Part 2: analysis of total mercury contents in soils. Melbourne, Australia: MAPRC.
   Google Scholar
• Michelle D, Onisimo M, Riyad I. 2011. Examining the utility of random forest and AISA Eagle hyperspectral image data to predict Pinus patula age in KwaZulu-Natal, South Africa. Geocarto Int. 26:275–289.
   Web of Science ®Google Scholar
• Nawar S, Mohamed ES, Essam ESS, Mohamed WS, Rebouh NY, Hammam AA. 2023. Estimation of key potentially toxic elements in arid agricultural soils using Vis-NIR spectroscopy with variable selection and PLSR algorithms. Front Environ Sci. 11:1222871. doi: 10.3389/fenvs.2023.1222871.
   Web of Science ®Google Scholar
• Nazupar S, Mamattursun E, Li XG, Wang YH. 2022. Spatial distribution, contamination levels, and health risks of trace elements in topsoil along an urbanization gradient in the city of Urumqi, China. Sustainability. 14(19):12646–12663. doi: 10.3390/su141912646.
   Web of Science ®Google Scholar
• Pirrone N, Cinnirella S, Feng X, Finkelman RB, Friedli HR, Leaner J, Mason R, Mukherjee AB, Stracher GB, Streets DG, et al. 2010. Global mercury emissions to the atmosphere from anthropogenic and natural sources. Atmos Chem Phys. 10(13):5951–5964. doi: 10.5194/acp-10-5951-2010.
   Web of Science ®Google Scholar
• Shekhar C, Negi HS, Srivastava S, Dwivedi M. 2018. Hyper-spectral data based investigations for snow wetness mapping. Geocarto Int. 34(6):664–687. doi: 10.1080/10106049.2018.1438528.
   Web of Science ®Google Scholar
• Shi TZ, Chen YY, Liu YL, Wu GF. 2014. Visible and near-infrared reflectance spectroscopy—An alternative for monitoring soil contamination by heavy metals. J Hazard Mater. 265:166–176. doi: 10.1016/j.jhazmat.2013.11.059.
   PubMed Web of Science ®Google Scholar
• Song L, Jian J, Tan DJ, Xie HB, Luo ZF, Gao B. 2015. Estimate of heavy metals in soil and streams using combined geochemistry and field spectroscopy in Wan-sheng mining area, Chongqing, China. Int J Appl Earth Obs Geoinf. 34:1–9. doi: 10.1016/j.jag.2014.06.013.
   Web of Science ®Google Scholar
• Tan K, Ma W, Chen L, Wang H, Du Q, Du P, Yan B, Liu R, Li H. 2021. Estimating the distribution trend of soil heavy metals in mining area from HyMap airborne hyperspectral imagery based on ensemble learning. J Hazard Mater. 401:123288. doi: 10.1016/j.jhazmat.2020.123288.
   PubMed Web of Science ®Google Scholar
• Tao C, Wang YJ, Zou B, Tu YL, Jiang XL. 2018. Assessment and analysis of heavy metal lead and zinc in soil with hyperspectral inversion model. Spectro Spectr Anal. 38:1850–1855.
   Web of Science ®Google Scholar
• Tian S, Wang S, Bai X, Zhou D, Lu Q, Wang M, Wang J. 2020. Hyperspectral estimation model of soil Pb content and its applicability in different soil types. Acta Geochim. 39(3):423–433. doi: 10.1007/s11631-019-00388-0.
   Web of Science ®Google Scholar
• Vohland M, Besold J, Hill J, Fründ HC. 2011. Comparing different multivariate calibration methods for the determination of soil organic carbon pools with visible to near infrared spectroscopy. Geoderma. 166(1):198–205. doi: 10.1016/j.geoderma.2011.08.001.
   Web of Science ®Google Scholar
• Wang F, Gao J, Zha Y. 2018. Hyperspectral sensing of heavy metals in soil and vegetation: feasibility and challenges. ISPRS J Photo Remote Sens. 136:73–84. doi: 10.1016/j.isprsjprs.2017.12.003.
   Web of Science ®Google Scholar
• Wang YY, Niu RQ, Guo L, Xiao YX, Ma HL, Zhao LR. 2023. Estimate of soil heavy metal in a mining region using PCC SVM RFECV AdaBoost combined with reflectance spectroscopy. Environ Geochem Health. 45(12):9103–9121. doi: 10.1007/s10653-023-01488-w.
   PubMedGoogle Scholar
• Wei LF, Pu HC, Wang ZX, Yuan ZR, Yan XR, Cao LQ. 2020. Estimation of soil arsenic content with hyperspectral remote sensing. Sensors (Basel). 20(14):4056–4071. doi: 10.3390/s20144056.
   PubMed Web of Science ®Google Scholar
• Xu L, Chen Y, Feng A, Shi X, Feng Y, Yang Y, Wang Y, Wu Z, Zou Z, Ma W, et al. 2023. Study on detection method of microplastics in farmland soil based on hyperspectral imaging technology. Environ Res. 232:116389–116389. doi: 10.1016/j.envres.2023.116389.
   PubMed Web of Science ®Google Scholar
• Xue Y, Zou B, Wen Y, Tu Y, Xiong L. 2020. Hyperspectral inversion of chromium content in soil using support vector machine combined with lab and field spectra. Sustainability. 12(11):4441–4456. doi: 10.3390/su12114441.
   Web of Science ®Google Scholar
• Yang HF, Xu H, Zhong XN. 2022. Prediction of soil heavy metal concentrations in copper tailings area using hyperspectral reflectance. Environ Earth Sci. 81(6):183–193. doi: 10.1007/s12665-022-10307-x.
   Web of Science ®Google Scholar
• Yang PM, Hu J, Hu BF, Luo DF, Peng J. 2022. Estimating soil organic matter content in desert areas using in situ hyperspectral data and feature variable selection algorithms in southern Xinjiang, China. Remote Sens. 14(20):5221 doi: 10.3390/rs14205221.
   Google Scholar
• Zhang SW, Shen Q, Nie CJ, , Huang YF, Wang JH, Hu QQ, Ding XJ, Zhou Y, Chen YP. 2019. Hyperspectral inversion of heavy metal content in reclaimed soil from a mining wasteland based on different spectral transformation and modeling methods. Spectrochim Acta A Mol Biomol Spectrosc. 211:393–400. doi: 10.1016/j.saa.2018.12.032.
   PubMed Web of Science ®Google Scholar
• Zhang TY, Fu Q, Tian RQ, Zhang Y, Sun ZH. 2023. A spectrum contextual self-attention deep learning network for hyperspectral inversion of soil metals. Eco Indic. 152:110351. doi: 10.1016/j.ecolind.2023.110351.
   Web of Science ®Google Scholar
• Zhao L, Hu YM, Zhou W, Liu ZH, Pan YC, Shi Z, Wang L, Wang GX. 2018. Estimation methods for soil mercury content using hyperspectral remote sensing. Sustainability. 10(7):2474–2487. doi: 10.3390/su10072474.
   Web of Science ®Google Scholar
• Zhao LY, Tan K, Wang X, Ding JW, Liu ZX, Ma HL, Han B. 2022. Hyperspectral feature selection for SOM prediction using deep reinforcement learning and multiple subset evaluation strategies. Remote Sens. 15(1):127. doi: 10.3390/rs15010127.
   Google Scholar
• Zhou M, Zou B, Tu Y, Feng H, He C, Ma X, Ning J. 2022. Spectral response feature bands extracted from near standard soil samples for estimating soil Pb in a mining area. Geocarto Int. 37(26):13248–13267. doi: 10.1080/10106049.2022.2076921.
   Web of Science ®Google Scholar
• Zhou W, Yang H, Xie L, Li H, Huang L, Zhao Y, Yue T. 2021. Hyperspectral inversion of soil heavy metals in Three-River Source Region based on random forest model. Catena. 202:105222. doi: 10.1016/j.catena.2021.105222.
   Web of Science ®Google Scholar