Abstract
Phytoremediation is an effective tool which can be employed to revive the degraded or metal-contaminated soils. However, assessment of contamination caused by heavy metal in soil and its monitoring on long-term basis is essential to assess the efficacy of phytoremediation processes. Conventional techniques for monitoring the contaminated sites are noticeably expensive, time intensive, and destructive in nature. Remote sensing (RS) may assist as an efficient alternative technique for detecting metal contamination and monitoring phytoremediation on a long-term basis. The RS data from various sources at various scales such as proximal sensing data (laboratory and field-based spectroradiometric data), airborne data (dronecollected data), and space-borne data (satellite data) are crucial for monitoring the extent of contamination and to detect changes in land use pattern and surface cover of the polluted site over a time period. Most of the RS based techniques use vegetation reflectivity within the red-edge position of the electromagnetic radiation for indirect estimation of contamination level that is associated with heavy metal and organic carbon (hydrocarbon) concentration in soil. In proximal sensing, laboratory- and field-based spectroscopic data are employed to predict the level of contamination through correlating the characteristic reflectance spectra of the spectrally active soil constituents with metals. To determine the efficiency of phytoremediation, monitoring of revegetation or biorecultivation is also necessary using RS data. One of the most promising techniques to monitor revegetation is to calculate various indices related to soil, vegetation, and moisture through interpreting the remote sensing-based data product. The most frequently used vegetation index such as normalized difference vegetation index (NDVI) helps to measure the phytoproductivity of the polluted area. RS based indices are useful to detect metal-induced vegetation stress. However, a few key limitations are there in obtaining satisfactory results using RS based methods such as complexity of spectra, non-availability of unique spectral feature for particular metal, and noisy spectra due to variation in atmospheric conditions. In spite of so many challenges, RS based techniques are considered as non-destructive, time-saving, and cost-effective alternative techniques especially for large phytoremediation areas. Recently both airborne and space-borne hyperspectral RS data are used for continuous and detailed monitoring of the contaminated areas.
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References
Adam E, Mutanga O, Rugege D (2010) Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: a review. Wetlands Eco Manag 18:281–296. https://doi.org/10.1007/s11273-009-9169-z
Adam EM, Mutanga O, Rugege D, Ismail R (2012) Discriminating the papyrus vegetation (Cyperus papyrus L.) and its co-existent species using random forest and hyperspectral data resampled to HYMAP. Int J Remote Sens 33:552–569. https://doi.org/10.1080/01431161.2010.543182
Aryal R, Nirola R, Beecham S, Kamruzzaman M (2016) Impact of elemental uptake in the root chemistry of wetland plants. Int J Phytoremediation 18:936–942. https://doi.org/10.1080/15226514.2015.1131239
Bandaru V, Hansen DJ, Codling EE, Daughtry CS, White-Hanson S, Green CE (2010) Quantifying arsenic-induced morphological changes in spinach leaves: implications for remote sensing. Int J Remote Sens 31(15):4163–4177. https://doi.org/10.1080/01431161.2010.498453
Bonanno G (2013) Comparative performance of trace element bioaccumulation and biomonitoring in the plant species Typha domingensis, Phragmites australis (Cav.) Trin. ex Steud. and Arundo donax L. Ecotoxicol Environ Saf 97:124–130. https://doi.org/101016/jecoenv201307017
Bragato C, Brix H, Malagoli M (2006) Accumulation of nutrients and heavy metals in Phragmites australis (Cav.) Trin. ex Steudel. and Bolboschoenus maritimus (L.) Palla in a constructed wetland of the Venice lagoon watershed. Environ Pollut 144(3):967–975. https://doi.org/101016/jenvpol200601046
Chakraborty S, Weindorf D (2010) Rapid identification of oil-contaminated soils using visible near infrared diffuse reflectance spectroscopy. J Environ Qual 39:1378–1387. https://doi.org/10.2134/jeq2010.0183
Chmielewska EW, Chwil M (2005) Lead induced histological and ultrastructural changes in the leaves of soybean (Glycine max). Soil Sci Plant Nutr 51(2):203–212. https://doi.org/10.1111/j.1747-0765.2005.tb00024.x
Choe E, Kim KW, Bang S, Yoon IH, Lee KY (2009) Qualitative analysis and mapping of heavy metals in an abandoned Au–Ag mine area using NIR spectroscopy. Environ Geol 58(3):477–482. https://doi.org/10.1007/s00254-008-1520-9
Cocks T, Jenssen R, Stewart A, Wilson I, Shields T (1998) The HyMap airborne hyperspectral sensor: the system calibration and performance. In: Schaepman M, Schläpfer D, Itten K (eds) Proceeding of 1st EARSeL workshop on imaging spectroscopy. Zürich, Switzerland, pp 37–42. http://artefacts.ceda.ac.uk/neodc_docs/Hymap_specs.pdf
Conforti M, Castrignanò A, Robustelli G, Scarciglia F, Stelluti M, Buttafuoco G (2015) Laboratory-based Vis–NIR spectroscopy and partial least square regression with spatially correlated errors for predicting spatial variation of soil organic matter content. CATENA 124:60–67. https://doi.org/10.1016/j.catena.2014.09.004
Das B, Manohara KK, Mahajan GR, Sahoo RN (2020) Spectroscopy based novel spectral indices PCA-and PLSR-coupled machine learning models for salinity stress phenotyping of rice. Spectrochim Acta a: Mol Biomol Spectrosc 229:117983. https://doi.org/10.1016/j.saa.2019.117983
Davidson A, Csillag F (2001) The influence of vegetation index and spatial resolution on a two-date remote sensing-derived relation to C4 cpecies coverage. Remote Sens Environ 75:138–151. https://doi.org/10.1016/S0034-4257(00)00162-0
Davranche A, Lefebvre G, Poulin B (2010) Wetland monitoring using classification trees and SPOT-5 seasonal time series. Remote Sens Environ 114(3):552–562. https://doi.org/10.1016/j.rse.2009.10.009
Dubula B, Tesfamichael SG, Rampedi IT (2016) Assessing the potential of remote sensing to discriminate invasive Asparagus laricinus from adjacent land cover types. Cogent Geosci 2:1–17. https://doi.org/101080/2331204120161154650
Dunagan SC, Gilmore MS, Varekamp JC (2007) Effects of mercury on visible/near-infrared reflectance spectra of mustard spinach plants (Brassica rapa P.). Environ Pollut 148(1):301–311. https://doi.org/10.1016/j.envpol.2006.10.023
Dupuy N, Douay F (2001) Infrared and chemometrics study of the interaction between heavy metals and organic matter in soils. Spectrochim Acta Part A Mol Biomol Spectrosc 57(5):1037–1047. https://doi.org/10.1016/S1386-1425(00)00420-0
Ermolaev NR, Belobrov VP, Yudin SA, Torochkov EL (2019) Monitoring the process of bioremediation and revegetation of a phosphogypsum waste stack by remote sensing. In:Â IOP Conference series: earth and environmental science, vol 350, no 1. IOP Publishing pp 012057. https://doi.org/10.1088/1755-1315/350/1/012057
Farifteh J, Van der Meer F, Van der Meijde M, Atzberger C (2008) Spectral characteristics of salt- affected soils: A laboratory experiment. Geoderma 145(3–4):196–206. https://doi.org/10.1016/j.geoderma.2008.03.011
Foody GM, Mathur A (2004) A relative evaluation of multiclass image classifica- tion by support vector machines. IEEE Trans Geosci Remote Sens 42:1336–1343. https://doi.org/10.1109/TGRS.2004.827257
Götze C, Jung A, Merbach I, Wennrich R, Gläßer C (2010) Spectrometric analyses in comparison to the physiological condition of heavy metal stressed floodplain vegetation in a standardized experiment. Open Geosci 2(2):132–137. https://doi.org/10.2478/v10085-010-0002-y
Gallagher FJ, Pechmann I, Bogden JD, Grabosky J, Weis P (2008) Soil metal concentrations and productivity of Betula populifolia (gray birch) as measured by field spectrometry and incremental annual growth in an abandoned urban Brownfield in New Jersey. Environ Pollut 156(3):699–706. https://doi.org/10.1016/j.envpol.2008.06.013
Gras JP, Barthès BG, Mahaut B, Trupin S (2014) Best practices for obtaining and processing field visible and near infrared (VNIR) spectra of topsoils. Geoderma 214:126–134. https://doi.org/10.1016/j.geoderma.2013.09.021
Guyot G, Baret F (1988) Use of high spectral resolution to follow the state of vegetation cover. In: Spectral signatures of objects in remote sensing, vol 287, pp 279. http://adsabs.harvard.edu/full/1988esasp.287..279g
Horler DNH, Dockray M, Barber J (1983) The red edge of plant leaf reflectance. Int J Remote Sens 4(2):273–288. https://doi.org/10.1080/01431168308948546
Kaewtubtim P, Meeinkuirt W, Seepom S, Pichtel J (2016) Heavy metal phytoremediation potential of plant species in a mangrove ecosystem in Pattani Bay, Thailand. Appl Ecol Environ Res 14(1):367–382. https://doi.org/10.15666/aeer/1401_367382
Knadel M, Gislum R, Hermansen C, Peng Y, de Moldrup P, Jonge LW, Greve MH (2017) Comparing predictive ability of laser-induced breakdown spectroscopy to visible near-infrared spectroscopy for soil property determination. Biosys Eng 156:157–172. https://doi.org/10.1016/j.biosystemseng.2017.01.007
Kooistra L, Leuven R, Wehrens R, Nienhuis PH, Buydens LMC (2003) A comparison of methods to relate grass reflectance to soil metal contamination. Int J Remote Sens 24(24):4995–5010. https://doi.org/10.1080/0143116031000080769
Kooistra L, Salas EAL, Clevers J, Wehrens R, Leuven R, Nienhuis PH, Buydens LMC (2004) Exploring field vegetation reflectance as an indicator of soil contamination in river floodplains. Environ Pollut 127(2):281–290. https://doi.org/10.1016/S0269-7491(03)00266-5
Li QT, Yang FJ, Zhang B, Zhang X, Zhou GZ (2008) Biogeochemistry responses and spectral characteristics of Rhus chinensis Mill under heavy metal contamination stress. J Remote Sens-Beijing 12(2):290. http://en.cnki.com.cn/Article_en/CJFDTOTAL-YGXB200802013.htm
Liu M, Liu X, Li M, Fang M, Chi W (2010) Neural-network model for estimating leaf chlorophyll concentration in rice under stress from heavy metals using four spectral indices. Biosys Eng 106(3):223–233. https://doi.org/10.1016/j.biosystemseng.2009.12.008
Liu M, Liu X, Wu M, Li L, Xiu L (2011) Integrating spectral indices with environmental parameters for estimating heavy metal concentrations in rice using a dynamic fuzzy neural-network model. Comput Geosci 37(10):1642–1652. https://doi.org/10.1016/j.cageo.2011.03.009
Mabhungu L, Adam E, Newete SW (2019) Monitoring of phytoremediating wetland macrophytes using remote sensing: the case of common reed (Phragmites australis (cav.) Trin. Ex steud.) And the giant reed (Aarundo donax L.) A. Appl Eco Environ Res 17(4):7957–7972. https://doi.org/10.15666/AEER/1704_79577972
Malley DF, Williams PC (1997) Use of near-infrared reflectance spectroscopy in prediction of heavy metals in freshwater sediment by their association with organic matter. Environ Sci Technol 31(12):3461–3467. https://doi.org/10.1021/es970214p
Mondal BP, Sekhon BS (2019) Using diffuse reflectance spectroscopy for assessment of soil phosphorus status of an intensively cropped region. Agric Res J 56:657–661. https://doi.org/10.5958/2395-146X.2019.00102.9
Nanni MR, Dematte JAM (2006) Spectral reflectance methodology in comparison to traditional soil analysis. Soil Sci Soc Am J 70(2):393. https://doi.org/10.2136/sssaj2003.0285
Nawar S, Mouazen AM (2017) Comparison between random forests artificial neural networks and gradient boosted machines methods of on-line Vis-NIR spectroscopy measurements of soil total nitrogen and total carbon. Sensors 17(10):2428. https://doi.org/10.3390/s17102428
Newete SW, Byrne MJ (2016) The capacity of aquatic macrophytes for phytoremediation and their disposal with specific reference to water hyacinth. Environ Sci Pollut Res 23:10630–10643. https://doi.org/101007/s11356-016-6329-6
Noomen M, Hakkarainen A, Van der Meijde M, Van der Werff H (2015) Evaluating the feasibility of multitemporal hyperspectral remote sensing for monitoring bioremediation. Int J Appl Earth Obs Geoinf 34:217–225. https://doi.org/10.1016/j.jag.2014.08.016
Noomen MF, Skidmore AK (2009) The effects of high soil CO2 concentrations on the first derivative of maize leaf reflectance. Int J Remote Sens 30:481–497. https://doi.org/10.1080/01431160802339431
Panigada C, Colombo R, Meroni M, Rossini M, Cogliati S, Busetto L, Fava F, Picchi V, Migli-Avacca M, Marchesi A (2010) Remote sensing of vegetation status using hyperspectral data. Hyperspectral workshop, Frascati, Italy, ESA SP-683
Penuelas J, Filella I (1998) Visible and near-infrared reflectance techniques for diagnosing plant physiological status. Trends Plant Sci 3(4):151–156. https://doi.org/10.1016/S1360-1385(98)01213-8
Prasad M (2004) Heavy metal stress in plants: from molecules to ecosystems. Springer and Narosa Publishing House, New Delhi, India, p 462. https://ci.nii.ac.jp/ncid/BA73484138?l=en
Pu R (2009) Broadleaf species recognition with in situ hyperspectral data. Int J Remote Sens 30(11):2759–2779. https://doi.org/10.1080/01431160802555820
Rathod PH, Rossiter DG, Noomen MF, Van der Meer FD (2012) Proximal spectral sensing to monitor phytoremediation of metal-contaminated soils. Int J Phytorem 15:405–426. https://doi.org/10.1080/15226514.2012.702805
Ren HY, Zhuang DF, Pan JJ, Shi XZ, Shi RH, Wang HJ (2010) Study on canopy spectral characteristics of paddy polluted by heavy metals. Spectrosc Spectral Anal 30(2):430–434. https://doi.org/10.3964/j.issn.1000-0593(2010)02-0430-05
Ren HY, Zhuang DF, Singh AN, Pan JJ, Qiu DS, Shi RH (2009) Estimation of As and Cu contamination in agricultural soils around a mining area by reflectance spectroscopy: a case study. Pedosphere 19(6):719–726. https://doi.org/10.1016/S1002-0160(09)60167-3
Schmidt T, Rico C, Rodrigues-Rastrero M, Sierra MJ, Diaz-Puente FJ, Pelayo M, Millan R (2013) Monitoring of the mercury mining site Almadèn implementing remote sensing technologies. Environ Res 125:92–102. https://doi.org/101016/jenvres201212014i
Schowengerdt RA (2006) Remote sensing: models and methods for image processing. Elsevier, New York, NY, UK
Schuerger AC, Capelle GA, Di Benedetto JA, Mao C, Thai CN, Evans MD, Richards JT, Blank TA, Stryjewski EC (2003) Comparison of two hyperspectral imaging and two laser-induced fluorescence instruments for the detection of zinc stress and chlorophyll concentration in bahia grass (Paspalum notatum Flugge.). Remote Sens Environ 84(4):572–588. https://doi.org/10.1016/S0034-4257(02)00181-5
Shakya K, Chettri MK, Sawidis T (2008) Impact of heavy metals (Copper Zinc and Lead) on the chlorophyll content of some mosses. Arch Environ Contam Toxicol 54:412–421. https://doi.org/101007/s00244-007-9060-y
Shepherd KD, Walsh MG (2002) Development of reflectance spectral libraries for characterization of soil properties. Soil Sci Soc Am J 66:988–998. https://doi.org/10.2136/sssaj2002.9880
Shuping LS, Snyman RG, Odendaal JP, Ndakidemi PA (2011) Accumulation and distribution of metals in Bolboschoenus maritimus (Cyperaceae) from a South African river. Water Air Soil Pollut 216(1–4):319–328. https://doi.org/10.1007/s11270-010-0535-5
Siebielec G, McCarty GW, Stuczynski TI, Reeves JB III (2004) Near-and mid-infrared diffuse reflectance spectroscopy for measuring soil metal content. J Environ Qual 33(6):2056. https://doi.org/10.2134/jeq2004.2056
Slonecker T, Haack B, Price S (2009) Spectroscopic analysis of arsenic uptake in Pteris ferns. Remote Sens 1:644–675. https://doi.org/10.3390/rs1040644
Smith AM, Blackshaw RE (2003) Weed-crop discrimination using remote sensing: a detached leaf experiment. Weed Technol 17(4):811–820. https://doi.org/10.1614/WT02-179
Smith KL, Steven MD, Colls JJ (2004) Use of hyperspectral derivative ratios in the red-edge region to identify plant stress responses to gas leaks. Remote Sens Environ 92:207–217. https://doi.org/10.1016/j.rse.2004.06.002
Song H, He Y (2005) Evaluating soil organic matter with visible spectroscopy. In: IMTC—instrumentation and measurement technology conference. IEEE, Ottawa (Canada), pp 1321–1324. https://doi.org/10.1109/IMTC.2005.1604362
Sridhar BB, Diehl SV, Han FX, Monts DL, Su Y (2005) Anatomical changes due to uptake and accumulation of Zn and Cd in Indian mustard (Brassica juncea). Environ Exp Bot 54(2):131–141. https://doi.org/10.1016/j.envexpbot.2004.06.011
Sridhar BB, Han FX, Diehl SV, Monts DL, Su Y (2007) Monitoring the effects of arsenic and chromium accumulation in Chinese brake fern (Pteris vittata). Int J Remote Sens 28(5):1055–1067. https://doi.org/10.1080/01431160600868466
Stenberg B, Viscarra Rossel RA, Mouazen A, Wetterlind J (2010) Visible and near infrared spec- troscopy in soil science. In: Donald LS (ed) Advances in agronomy. Academic Press, Burlington (MA), pp 163–215. https://doi.org/10.1016/S0065-2113(10)07005-7
US-DOE (2000) Appendix D: monitoring report proceedings from the workshop on phytoremediation of inorganic contaminants (INEEL/EXT-2000–00207) Idaho Falls (ID): US Department of Energy–Subsurface Contaminants Focus Area Idaho National Engineering and Environmental Laboratory Argonne National Laboratory and Enviro Issues, pp 1–18
USEPA (2000) Introduction to phytoremediation. United State Environment Protection Agency, National Risk Management Research Laboratory Office of Research and Development, pp 56–58
Vaiphasa C, Ongsomwang S, Vaiphasa T, Skidmore AK (2005) Tropical mangrove species discrimination using hyperspectral data: a laboratory study. Estuar Coast Shelf Sci 65:371–379. https://doi.org/101016/jecss200506014
Van der Merwe CG, Schoonbee HJ, Pretorius J (1990) Observations on concentrations of the heavy metals zinc manganese nickel and iron in the water in the sediments and in two aquatic macrophytes Typha capensis (Rohrb.) NE Br. and Arundo donax L. of a stream affected by goldmine and industrial effluents. Water SA 16(2):119–124. https://www.cabdirect.org/cabdirect/abstract/19911953045
Vasques GM, Grunwald S, Sickman JO (2008) Comparison of multivariate methods for inferential modeling of soil carbon using visible/near-infrared spectra. Geoderma 146:14–25. https://doi.org/10.1016/j.geoderma.2008.04.007
Viscarra Rossel RA, Behrens T (2010) Using data mining to model and interpret soil diffuse reflectance spectra. Geoderma 158(1–2):46–54. https://doi.org/10.1016/j.geoderma.2008.04.007
Viscarra Rossel RA, McBratney A, Minasny B (2010) Proximal soil sensing, 1st edn. Springer Verlag, New York https://doi.org/10.1007/978-90-481-8859-8
Vohland M, Bossung C, Fr HC (2009) A spectroscopic approach to assess trace-heavy metal contents in contaminated floodplain soils via spectrally active soil components. J Plant Nutr Soil Sci 172(2):201–209. https://doi.org/10.1002/jpln.200700087
Wenjun J, Zhou S, Jingyi H, Shuo L (2014) In situ measurement of some soil properties in paddy soil using visible and near-infrared spectroscopy. PLoS ONE 9(8):105708. https://doi.org/10.1371/journal.pone.0105708
Wu Y, Zhang X, Liao Q, Ji J (2011) Can contaminant elements in soils be assessed by remote sensing technology: a case study with simulated data. Soil Sci 176(4):196–205. https://doi.org/10.1097/SS.0b013e3182114717
Wu YZ, Chen J, Ji J, Gong P, Liao Q, Tian Q, Ma HR (2007) A mechanism study of reflectance spectroscopy for investigating heavy metals in soils. Soil Sci Soc Am J 71(3):918–926. https://doi.org/10.2136/sssaj2006.0285
Wuana RA, Okieimen FE (2011) Heavy metals in contaminated soils: a review of sources chemistry risks and best available strategies for remediation. ISRN Ecol. https://doi.org/10.5402/2011/402647
Xia XQ, Mao YQ, Ji JF, Ma HR, Chen J, Liao QL (2007) Reflectance spectroscopy study of Cd contamination in the sediments of the Changjiang River China. Environ Sci Technol 41(10):3449–3454. https://doi.org/10.1021/es0624422
Xu L, Zhou YP, Tang LJ, Wu H-L, Jiang JH, Shen GL, Yu RQ (2008) Ensemble preprocessing of near-infrared (NIR) spectra for multivariate calibration. Anal Chim Acta 616(2):138–143. https://doi.org/10.1016/j.aca.2008.04.031
Yang H, Zhang J, Van der Meer FD, Kroonenberg SB (2000) Imaging spectrometry data correlated to hydrocarbon microseepage. Int J Remote Sens 21:197–202. https://doi.org/10.1080/014311600211091
Zengin FK, Munzuroglu O (2005) Effects of some heavy metals on content of chlorophyll proline and some antioxidant chemicals in bean (Phaseolus vulgaris L.) seedlings. Acta Biologica Cracoviensia Series Botanica 47(2):157–164 bwmeta1.element.agro-article-194d9de5-de29–488e-8a51–3ae04e829833
Zomer RJ, Trabucco A, Ustin SL (2009) Building spectral libraries for wetlands land cover classification and hyperspectral remote sensing. J Environ Manage 90:2170–2177. https://doi.org/101016/jjenvman200706028
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Mondal, B.P., Sahoo, R.N., Das, B., Paul, P., Chattopadhyay, A., Devi, S. (2022). Monitoring Phytoremediation of Metal-Contaminated Soil Using Remote Sensing. In: Malik, J.A. (eds) Advances in Bioremediation and Phytoremediation for Sustainable Soil Management. Springer, Cham. https://doi.org/10.1007/978-3-030-89984-4_25
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