Application of PROSPECT for estimating total petroleum hydrocarbons in contaminated soils from leaf optical properties.

Recent advances in hyperspectral spectroscopy suggest making use of leaf optical properties for monitoring soil contamination in oil production regions by detecting pigment alterations induced by Total Petroleum Hydrocarbons (TPH). However, this provides no quantitative information about the level of contamination. To achieve this, we propose an approach based on the inversion of the PROSPECT model. 1620 leaves from five species were collected on a site contaminated by 16 to 77 g.kg-1 of TPH over a 14-month period. Their spectral signature was measured and used in PROSPECT model inversions to retrieve leaf biochemistry. The model performed well for simulating the spectral signatures (RMSE < 2%) and for estimating leaf pigment contents (RMSE ≤ 2.95 μg.cm-2 for chlorophylls). Four out of the five species exhibited alterations in pigment contents when exposed to TPH. A strong correlation was established between leaf chlorophyll content and soil TPH concentrations (R2 ≥ 0.74) for three of them, allowing accurate predictions of TPH (RMSE =3.20 g.kg-1 and RPD = 5.17). The accuracy of predictions varied by season and improved after the growing period. This study demonstrates the capacity of PROSPECT to estimate oil contamination and opens up promising perspectives for larger-scale applications.

[1]  P. Alam ‘G’ , 2021, Composites Engineering: An A–Z Guide.

[2]  Jan G. P. W. Clevers,et al.  Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties - A review , 2015 .

[3]  P. Alam ‘S’ , 2021, Composites Engineering: An A–Z Guide.

[4]  S. Deka,et al.  Effect of crude oil contamination on the chlorophyll content and morpho-anatomy of Cyperus brevifolius (Rottb.) Hassk , 2014, Environmental Science and Pollution Research.

[5]  Wolfram Mauser,et al.  Evaluation of the PROSAIL Model Capabilities for Future Hyperspectral Model Environments: A Review Study , 2018, Remote. Sens..

[6]  I. Auby,et al.  Toxicity effects of an environmental realistic herbicide mixture on the seagrass Zostera noltei. , 2017, Environmental pollution.

[7]  Ieda Del'Arco Sanches,et al.  Assessing the impact of hydrocarbon leakages on vegetation using reflectance spectroscopy , 2013 .

[8]  Guofeng Wu,et al.  Monitoring arsenic contamination in agricultural soils with reflectance spectroscopy of rice plants. , 2014, Environmental science & technology.

[9]  Carlos Roberto de Souza Filho,et al.  Spectroscopic characterization of red latosols contaminated by petroleum-hydrocarbon and empirical model to estimate pollutant content and type , 2016 .

[10]  N. Merkl,et al.  Phytoremediation in the Tropics—The Effect of Crude Oil on the Growth of Tropical Plants , 2004 .

[11]  Dominique Dubucq,et al.  Experimental study of hyperspectral responses of plants grown on mud pit soil , 2016, Remote Sensing.

[12]  G. A. Blackburn,et al.  Quantifying Chlorophylls and Caroteniods at Leaf and Canopy Scales: An Evaluation of Some Hyperspectral Approaches , 1998 .

[13]  Benoit Rivard,et al.  Characterization of mineral substrates impregnated with crude oils using proximal infrared hyperspectral imaging , 2016 .

[14]  I. D. Sanches,et al.  Unravelling remote sensing signatures of plants contaminated with gasoline and diesel: an approach using the red edge spectral feature. , 2013, Environmental pollution.

[15]  Dominique Dubucq,et al.  Hyperspectral signature analysis of three plant species to long-term hydrocarbon and heavy metal exposure , 2017, Remote Sensing.

[16]  Hankui K. Zhang,et al.  An extended PROSPECT: Advance in the leaf optical properties model separating total chlorophylls into chlorophyll a and b , 2017, Scientific Reports.

[17]  Liangpei Zhang,et al.  An Adaptive Differential Evolution Endmember Extraction Algorithm for Hyperspectral Remote Sensing Imagery , 2014, IEEE Geoscience and Remote Sensing Letters.

[18]  Weimin Ju,et al.  Improving the PROSPECT Model to Consider Anisotropic Scattering of Leaf Internal Materials and Its Use for Retrieving Leaf Biomass in Fresh Leaves , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[19]  Susan L. Ustin,et al.  Comparing the Potential of Multispectral and Hyperspectral Data for Monitoring Oil Spill Impact , 2018, Sensors.

[20]  Guofeng Wu,et al.  Estimation of arsenic in agricultural soils using hyperspectral vegetation indices of rice. , 2016, Journal of hazardous materials.

[21]  Miss A.O. Penney (b) , 1974, The New Yale Book of Quotations.

[22]  J. Fletcher Distribution , 2009, BMJ : British Medical Journal.

[23]  Carlos Roberto de Souza Filho,et al.  Hyperspectral remote sensing detection of petroleum hydrocarbons in mixtures with mineral substrates: Implications for onshore exploration and monitoring , 2017 .

[24]  Wei Gong,et al.  Analyzing the performance of PROSPECT model inversion based on different spectral information for leaf biochemical properties retrieval , 2018 .

[25]  Y. Perrodin,et al.  Ecological risk assessment of urban and industrial systems: a review. , 2011, The Science of the total environment.

[26]  C Bona,et al.  Development of Canavalia ensiformis in soil contaminated with diesel oil , 2016, Environmental Science and Pollution Research.

[27]  Roberta E. Martin,et al.  Genetic variation in leaf pigment, optical and photosynthetic function among diverse phenotypes of Metrosideros polymorpha grown in a common garden , 2007, Oecologia.

[28]  E. Hunt,et al.  Estimating near-infrared leaf reflectance from leaf structural characteristics. , 2001, American journal of botany.

[29]  K. Tansey,et al.  Detecting the effects of hydrocarbon pollution in the Amazon forest using hyperspectral satellite images. , 2015, Environmental pollution.

[30]  Zhongxin Chen,et al.  Monitoring plant response to phenanthrene using the red edge of canopy hyperspectral reflectance. , 2014, Marine pollution bulletin.

[31]  Harald van der Werff,et al.  Spectral and spatial indicators of botanical changes caused by long-term hydrocarbon seepage , 2012, Ecol. Informatics.

[32]  E. Nemeth,et al.  Physiological and molecular responses to heavy metal stresses suggest different detoxification mechanism of Populus deltoides and P. x canadensis. , 2016, Journal of plant physiology.

[33]  Dawei Liu,et al.  Evaluating Metal Effects on the Reflectance Spectra of Plant Leaves during Different Seasons in Post-Mining Areas, China , 2018, Remote. Sens..

[34]  Margaret Kalacska,et al.  Differences in leaf traits, leaf internal structure, and spectral reflectance between two communities of lianas and trees: Implications for remote sensing in tropical environments , 2009 .

[35]  Xavier Briottet,et al.  Criteria Comparison for Classifying Peatland Vegetation Types Using In Situ Hyperspectral Measurements , 2017, Remote. Sens..

[36]  W. Oechel,et al.  Seasonal patterns of reflectance indices, carotenoid pigments and photosynthesis of evergreen chaparral species , 2002, Oecologia.

[37]  Njike Chigbu,et al.  Comparative Analysis of Spectral Responses of Varied Plant Species to Oil Stress , 2013 .

[38]  Hidetoshi Asai,et al.  Vis-NIR Spectroscopy and PLS Regression with Waveband Selection for Estimating the Total C and N of Paddy Soils in Madagascar , 2017, Remote. Sens..

[39]  H. Athar,et al.  Influence of sub-lethal crude oil concentration on growth, water relations and photosynthetic capacity of maize (Zea mays L.) plants , 2016, Environmental Science and Pollution Research.

[40]  George Alan Blackburn,et al.  Early detection of oil-induced stress in crops using spectral and thermal responses , 2013 .

[41]  Jason Levy,et al.  Advances in Remote Sensing for Oil Spill Disaster Management: State-of-the-Art Sensors Technology for Oil Spill Surveillance , 2008, Sensors.

[42]  A. Skidmore,et al.  Applicability of the PROSPECT model for estimating protein and cellulose + lignin in fresh leaves , 2015 .

[43]  L. A. Stone,et al.  Computer Aided Design of Experiments , 1969 .

[44]  K. Tansey,et al.  Field spectroscopy and radiative transfer modelling to assess impacts of petroleum pollution on biophysical and biochemical parameters of the Amazon rainforest , 2017, Environmental Earth Sciences.

[45]  Stéphane Jacquemoud,et al.  PROSPECT-D: towards modeling leaf optical properties through a complete lifecycle , 2017 .

[46]  M M Nujkić,et al.  Impact of metallurgical activities on the content of trace elements in the spatial soil and plant parts of Rubus fruticosus L. , 2016, Environmental science. Processes & impacts.

[47]  A. Skidmore,et al.  Inversion of a radiative transfer model for estimating vegetation LAI and chlorophyll in a heterogeneous grassland , 2008 .

[48]  Hamad Karki,et al.  Application of robotics in onshore oil and gas industry - A review Part I , 2016, Robotics Auton. Syst..

[49]  F. Baret,et al.  PROSPECT: A model of leaf optical properties spectra , 1990 .

[50]  P. Harvey,et al.  Scanning electron microscopic investigations of root structural modifications arising from growth in crude oil-contaminated sand , 2014, Environmental Science and Pollution Research.

[51]  George Kvesitadze...,et al.  Biochemical Mechanisms of Detoxification in Higher Plants: Basis of Phytoremediation , 2006 .

[52]  Mui Lay,et al.  Reflectance properties and physiological responses of Salicornia virginica to heavy metal and petroleum contamination. , 2005, Environmental pollution.

[53]  Rei Sonobe,et al.  Using spectral reflectance to estimate leaf chlorophyll content of tea with shading treatments , 2018, Biosystems Engineering.

[54]  Roberta E. Martin,et al.  PROSPECT-4 and 5: Advances in the leaf optical properties model separating photosynthetic pigments , 2008 .

[55]  V. Ochoa-Herrera,et al.  Distribution, contents and health risk assessment of metal(loid)s in small-scale farms in the Ecuadorian Amazon: An insight into impacts of oil activities. , 2018, The Science of the total environment.

[56]  Su Zhang,et al.  A Novel Principal Component Analysis Method for the Reconstruction of Leaf Reflectance Spectra and Retrieval of Leaf Biochemical Contents , 2017, Remote. Sens..

[57]  N. Goel,et al.  Needle chlorophyll content estimation through model inversion using hyperspectral data from boreal conifer forest canopies , 2004 .

[58]  David W. Lee,et al.  Why leaves turn red in autumn. The role of anthocyanins in senescing leaves of red-osier dogwood. , 2001, Plant physiology.

[59]  Heiko Balzter,et al.  Plant Family-Specific Impacts of Petroleum Pollution on Biodiversity and Leaf Chlorophyll Content in the Amazon Rainforest of Ecuador , 2017, PloS one.

[60]  Simcha Lev-Yadun,et al.  Unravelling the evolution of autumn colours: an interdisciplinary approach. , 2009, Trends in ecology & evolution.

[61]  F. M. Danson,et al.  Extraction of vegetation biophysical parameters by inversion of the PROSPECT + SAIL models on sugar beet canopy reflectance data. Application to TM and AVIRIS sensors , 1995 .

[62]  Ying Li,et al.  Factors Influencing Leaf Chlorophyll Content in Natural Forests at the Biome Scale , 2018, Front. Ecol. Evol..

[63]  D. Sims,et al.  Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages , 2002 .

[64]  Dominique Dubucq,et al.  Detection and discrimination of various oil-contaminated soils using vegetation reflectance. , 2019, The Science of the total environment.

[65]  S. Khalid,et al.  Foliar heavy metal uptake, toxicity and detoxification in plants: A comparison of foliar and root metal uptake. , 2017, Journal of hazardous materials.

[66]  Quan Wang,et al.  Retrieval of Leaf Biochemical Parameters Using PROSPECT Inversion: A New Approach for Alleviating Ill-Posed Problems , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[67]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[68]  Dominique Dubucq,et al.  Assessing Soil Contamination Due to Oil and Gas Production Using Vegetation Hyperspectral Reflectance. , 2018, Environmental science & technology.

[69]  A. Gitelson,et al.  Detection of Red Edge Position and Chlorophyll Content by Reflectance Measurements Near 700 nm , 1996 .

[70]  Benoit Rivard,et al.  Foliar spectral properties following leaf clipping and implications for handling techniques , 2006 .

[71]  Ponnuthurai N. Suganthan,et al.  Recent advances in differential evolution - An updated survey , 2016, Swarm Evol. Comput..

[72]  Xiao Zhang,et al.  The effects of petroleum-contaminated soil on photosynthesis of Amorpha fruticosa seedlings , 2016, International Journal of Environmental Science and Technology.

[73]  Jun-sheng Li,et al.  Soil TPH Concentration Estimation Using Vegetation Indices in an Oil Polluted Area of Eastern China , 2013, PloS one.

[74]  Rosa Elvira Correa Pabón,et al.  Reflectance and imaging spectroscopy applied to detection of petroleum hydrocarbon pollution in bare soils. , 2019, The Science of the total environment.

[75]  Valérie Demarez,et al.  Seasonal variation of leaf chlorophyll content of a temperate forest. Inversion of the PROSPECT model , 1999 .

[76]  Michael E. Schaepman,et al.  Using spectral information from the NIR water absorption features for the retrieval of canopy water content , 2008, Int. J. Appl. Earth Obs. Geoinformation.

[77]  Ming Xiao,et al.  Plants' use of different nitrogen forms in response to crude oil contamination. , 2011, Environmental pollution.

[78]  Frédéric Baret,et al.  Estimation of leaf traits from reflectance measurements: comparison between methods based on vegetation indices and several versions of the PROSPECT model , 2018, Plant Methods.

[79]  W. Verhoef,et al.  PROSPECT+SAIL models: A review of use for vegetation characterization , 2009 .

[80]  M. MacKinnon,et al.  Growth and Physiological Responses of Triticum aestivum and Deschampsia caespitosa Exposed to Petroleum Coke , 2011 .

[81]  Ivica Kisić,et al.  The effect of drilling fluids and crude oil on some chemical characteristics of soil and crops , 2009 .

[82]  T. Jackson,et al.  Remote sensing of vegetation water content from equivalent water thickness using satellite imagery , 2008 .

[83]  Ashish Ghosh,et al.  Self-adaptive differential evolution for feature selection in hyperspectral image data , 2013, Appl. Soft Comput..

[84]  G. Carter,et al.  Variability in leaf optical properties among 26 species from a broad range of habitats. , 1998, American journal of botany.

[85]  Jean-Yves Tourneret,et al.  Supervised Nonlinear Spectral Unmixing Using a Postnonlinear Mixing Model for Hyperspectral Imagery , 2012, IEEE Transactions on Image Processing.

[86]  Shantanu Datta,et al.  A review on different pipeline fault detection methods , 2016 .

[87]  Serge Rambal,et al.  Exploring the relationships between reflectance and anatomical and biochemical properties in Quercus ilex leaves , 1999 .