Evaluation of EnMAP Hyperspectral Data for the Identification of

Placers in the Rias Baixas Region (Spain)

Beatriz L. Araújo

, Joana Cardoso-Fernandes

1,2 b

, Antonio Azzalini

, Morgana Carvalho

1,2 d

Alexandre Lima

1,2 e

, Francisco J. González

and Ana Cláudia Teodoro

1,2 g

Department of Geosciences, Environmental and Spatial Planning, Faculty of Sciences of the University of Porto,

Rua do Campo Alegre s/n, Porto, Portugal

ICT (Institute of Earth Sciences) – Porto Pole, Portugal

Marine Geology Resources and Extreme Environments, Geological Survey of Spain (IGME-CSIC), Madrid, Spain

Keywords: Vigo, CRMs, Heavy-Mineral, Placers, EnMAP, Band Ratios, Spectral Unmixing, MTMF, Remote Sensing.

Abstract: Critical Raw Materials are crucial to achieve the European Union’s (EU) goals of a climate-neutral economy

by 2050. The high supply risk led the EU to prioritise domestic mineral exploration. This study, part of the

S34I – SECURE AND SUSTAINABLE SUPPLY OF RAW MATERIALS project, utilised remote-sensing-

based methods to identify and map heavy-mineral (HM) placer deposits in the Ria de Vigo, located in Galicia,

Spain. Documented since the 70s, the sands of the Vigo beaches contain placers rich in Ti, Sn, Li, Rare Earth

Elements (REE), Au, Fe and Cu. Mineral mapping was performed using hyperspectral EnMAP data. Band

ratios were applied to identify possible mineralisation areas. Additionally, spectral unmixing was performed

through the Mixture Tuned Matched Filtering (MTMF) workflow, included in ENVI 6.0 software, and two

classification maps were obtained: one utilising the USGS spectral library and the other employing an HM

concentrate spectral library. Band ratios were able to distinguish possible areas of hydrothermal alteration.

MTMF classifications mapped most HM known to occur in the Ria, namely sillimanite, garnet, tourmaline,

ilmenite, rutile, and monazite, were identified. This first approach will allow the selection of areas of interest

for field validation and verification. The results will also be confronted with existing geological data.

1 INTRODUCTION

Critical Raw Materials (CRMs) are vital in key

industries, such as renewable energy, electronics,

defence and space exploration (Hool et al., 2023).

These materials are crucial for achieving the

European Union (EU) goals of a climate-neutral

economy by 2050 and supporting the green and

digital transition (European Commission et al., 2023)

The S34I – Secure and Sustainable Supply of Raw

Materials for EU Industry project, funded by the

European Union under grant agreement no.

https://orcid.org/0000-0002-5734-6155

https://orcid.org/0000-0001-8265-3897

https://orcid.org/0000-0003-1058-5463

https://orcid.org/0000-0001-9920-0886

https://orcid.org/0000-0002-6598-5934

https://orcid.org/0000-0002-6311-1950

https://orcid.org/0000-0002-8043-6431

101091616 (https://doi.org/10.3030/101091616),

addresses these European needs through the

development of data-driven methods to analyse Earth

Observation (EO) data to support mineral exploration

and monitoring of the complete mining cycle in its

exploration, closure and post-closure phases. This

project aims to enhance EU autonomy regarding

CRMs, support the green transition, as well as address

environmental challenges within the mining cycle.

To contribute to those goals, this work applied

remote sensing techniques and hyperspectral data to

identify Heavy Mineral (HM) beach placer deposits

Araújo, B. L., Cardoso-Fernandes, J., Azzalini, A., Carvalho, M., Lima, A., González, F. J. and Teodoro, A. C.

Evaluation of EnMAP Hyperspectral Data for the Identiﬁcation of Placers in the Rias Baixas Region (Spain).

DOI: 10.5220/0013496200003935

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 11th International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2025), pages 297-304

ISBN: 978-989-758-741-2; ISSN: 2184-500X

297

in the Ria de Vigo, located in the Rias Baixas region,

Spain. These HM placer deposits, known to occur in

this region since the 70s (IGME, 1976), are rich in Ti,

Sn, Li, Rare Earth Elements (REE), Au, Fe and Cu –

some of which are classified as CRMs by the

European Commission.

Several studies concerning the identification and

characterisation of HM placer deposits using remote

sensing have been carried out using multiple

approaches.

Chandrasekar et al. (2011) used Landsat 7

Enhanced Thematic Mapper Plus data and the

Environment for Visualising Images (ENVI)

software to successfully map minerals like garnet,

zircon and monazite along India’s South Tamil Nadu

coast. Gazi et al. (2019) similarly used Landsat 8,

ENVI, ArcGIS and Erdas Imagine software to

identify and determine the concentration of HM

placer deposits on the Cox Bazar beaches. Rejith et

al. (2020) utilised ASTER and Landsat 8 data to map

beach sediments on the coast of Thiruvananthapuram,

India, by applying the Spectral Angle Mapper

algorithm (SAM). Later, Rejith et al. (2022) also

mapped and studied the beach placers on the east

coast of Nadu, India, using EO-1 Hyperion data and

employing SAM image classification and Random

Forest regression.

In this study, we made a first approach to the use

of hyperspectral Environmental Mapping and

Analysis Program (EnMAP) data used for mineral

mapping to evaluate the potential of such data for

placer exploration.

2 STUDY AREA

The Rias Baixas region is located on the Atlantic

margin of southwestern Galicia, in north-western

Spain, close to the border with Portugal (Fig. 1). It is

composed of four morphologically identical

estuaries, known as Rias, of which the Ria de Vigo,

the target of this study, is the southernmost.

The Ria de Vigo is 32.5 km long, measuring 1 km

in width at its inner part, gradually expanding as it

extends towards the mouth of the Ria, where it

becomes 10 km wide, and therefore exhibiting a

distinctive funnel-shaped morphology, positioned

with a NE-SW direction (Méndez & Vilas, 2005).

The geology of this region, which is a part of the

Galicia Trás-os-Montes zone of the Iberian Massif,

consists mainly of metasedimentary sequences,

amphibolites, gneisses and Variscan granitoids

(Capdevila & Floor, 1970; Julivert et al., 1974;

Navas & Corretgé,

1997) (Fig. 2). The geological,

Figure 1: Geographical location of the Ria de Vigo in

Galicia, Spain.

geomorphological and hydrodynamic characteristics

of the Galician beaches allow for concentration of

economically valuable HM, such as magnetite,

ilmenite, garnet, zircon, cassiterite, monazite and

spodumene, forming placer deposits in the land-sea

transition and shallow waters (Galván, 2002; Gent et

al., 2005; IGME, 1976; Méndez et al., 2000; Pérez et

al., 2008; Prego et al., 2009).

The HM sands form elongated layers aligned

parallel to the coastline and are present in Límens,

Santa Marta, Alemáns, Canabal, Ratas, Patos and Vao

beaches. These placer deposits vary seasonally due to

changes in the hydrographic conditions of the Ria. In

late summer and early autumn, HM accumulation is

higher in the Ria’s northern margin, while in late

winter and early spring, it is higher in the southern

margin (Ng-Cutipa et al., 2024).

3 DATA AND METHODS

3.1 Data

For mineral mapping, EnMAP data was employed.

The EnMAP image, a Level-2A product consisting of

224 spectral bands (out of the original 246), was

acquired on 26 June 2024 with a small cloud cover

(<10%). EnMAP data can be accessed by submission

of an acquisition request through the EnMAP

Instrument Planning portal

(https://planning.enmap.org).

It is equipped with a passive push-broom type

hyperspectral imager (HSI) that records reflected

radiation

between 420 nm to 2450 nm in 246 bands,

S34I 2025 - Special Session on S34I - From the Sky to the Soil

298

Figure 2: Geological map of the study area, based on the MAGNA 50 – Geological map of Spain, at scale 1:50000.

covering the Visible-Near Infrared (NIR) and the

Short-Wave Infrared (SWIR) range of the

electromagnetic spectrum (Storch et al., 2023). The

spectral resolution is 6.5 nm in the VNIR range and

10 nm in the SWIR, while the spatial resolution is 30

m, providing a swath width of 30 km (Guanter et al.,

2016).

3.2 Processing

EnMAP was processed using two platforms: the

Sentinel Application Platform (SNAP) and the

Environment for Visualising Images (ENVI) 6.0.

EnMAP’s L2A products are considered analysis-

ready, so no further processing is needed except for

spectral subsetting to remove bad-quality bands,

which are corrupted data layers containing no data

pixels (Alicandro et al., 2022). In this case, bands

number 131 (1342.82 nm), 132 (1354.76 nm), 133

(1366.69 nm), 134 (1378.60 nm) and 135 (1390.48

nm) were manually identified and removed. As a

result, the final product consists of 219 bands out of

the original 224.

3.2.1 Band Ratios

For mineral mapping, band ratios were performed in

the first instance to detect zones of alteration and

possible mineralisation.

Different band ratios were tested in this work to

highlight areas of possible hydrothermal alteration,

mainly several ratios for clay minerals, iron oxides

and silicates, as well as carbonates. Hydrothermal

alteration zones in coastal zones can indicate potential

source areas for HM (cassiterite, gold, etc).

Henrich et al. (2012) previously defined several

band ratios, which can be consulted at

https://www.indexdatabase.de/. These ratios are

described in Table 1.

3.2.2 Spectral unmixing

The Mixture Tuned Matched Filtering (MTMF)

algorithm was employed to detect specific mineral

signatures within the image through a spectral

unmixing approach. The MTMF supervised

classification is based on partial unmixing since it can

unmix an endmember from an unknown background.

Evaluation of EnMAP Hyperspectral Data for the Identiﬁcation of Placers in the Rias Baixas Region (Spain)

299

Table 1: Description of the band ratios performed.

Band ratio T

e of mineral feature

𝟏𝟓𝟑

𝟏𝟖𝟗

Alteration

(𝟏𝟓𝟑 + 𝟏𝟗𝟔)

𝟏𝟖𝟗

Alunite/Kaolinite/Pyrophyllite

𝟏𝟖𝟗 ∗ 𝟐𝟎𝟒

𝟏𝟗𝟔

𝟐

Clay

𝟐𝟎𝟒

𝟏𝟖𝟗

Kaolinite

(𝟐𝟎𝟒 + 𝟐𝟏𝟕)

𝟐𝟎𝟖

Carbonate/Chlorite/Epidote

(𝟏𝟗𝟔 + 𝟐𝟏𝟕)

(

𝟐𝟎𝟒 + 𝟐𝟎𝟖

)

Epidote/Chlorite/Amphibole

𝟒𝟔

𝟐𝟗

Ferric iron

𝟏𝟖𝟗

𝟔𝟖

𝟐𝟗

𝟒𝟓

Ferrous Iron

𝟏𝟓𝟑

𝟔𝟖

Ferric Oxides

𝟏𝟖𝟗

𝟏𝟓𝟑

Ferrous Silicates

𝟏𝟖𝟕 + 𝟏𝟗𝟕

𝟏𝟗𝟒

Al-sheetsilicates

𝟏𝟗𝟔

𝟐𝟎𝟖

Amphibole

𝟏𝟗𝟔 + 𝟐𝟏𝟕

𝟐𝟎𝟖

Amphibole/ MgOH

It uses a Matched Filter (MF) to match the known

spectral signature to the image spectra, maximising

the response of the target spectra and suppressing the

response of unknown elements in the image (Kumar

et al., 2022). The result is a target abundance image

where each pixel has an MF score. The other part of

the classification consists of applying a Mixture

Tuning (MT) method to reduce false positives by

using a linear spectral mixing model to add an

infeasibility image to the results. The results are a set

of rule images that correlate to the MF and

infeasibility scores, for each pixel, when compared to

each endmember spectra. In this work, endmembers

were selected from two different spectral libraries,

one used for each classification attempt (Table 2).

Table 2: Independent spectral libraries used for the MTMF

classification and description of the endmembers.

First

Classification

(USGS library)

Second Classification (HM library)

Spectrum

code

Manual interpretation

Almandine UPO_1

Montmorillonite,

Illite

Goethite UPO_2

Amphibole,

Montmorillonite

Grossular UPO_3

Amphibole,

Montmorillonite

Ilmenite UPO_4

Amphibole,

Montmorillonite

Monazite UPO_5 Chlorite, Biotite

Rutile UPO_6

Montmorillonite,

Illite

Sillimanite UPO_7 Tourmaline, Garnet

Spessartine UPO_8 Tourmaline, Garnet

Tourmaline UPO_9 Tourmaline, Garnet

Zircon UPO_10 Tourmaline, Garnet

The first spectral library was derived from the

USGS spectral library (Kokaly et al., 2017), and the

minerals that constitute the endmembers were

selected due to their known occurrence in the Ria de

Vigo. The second was obtained in the scope of the

S34I project, where HM concentrates of Vigo beach

placer samples were collected in a laboratory, and

their spectral signature was identified with an ASD

FieldSpec4 spectroradiometer. While these HM

concentrates are not spectrally pure, they were used

as endmembers since they represent the mix of

materials found in Vigo beaches.

4 RESULTS

4.1 Band Ratios

The best results were achieved with the Alteration,

Ferric oxides and Amphibole band ratios (Fig. 3). The

alteration band ratio successfully identified areas

known to be affected by metamorphism and,

therefore,

more likely to have significant

S34I 2025 - Special Session on S34I - From the Sky to the Soil

300

Figure 3: A) Alteration band ratio; B) Ferric oxides band

ratio; C) Amphibole band ratio.

hydrothermal alteration. These areas are

characterised by the vast presence of granitoids,

which, through chemical weathering, lead to the

formation of clay minerals. The ferric oxide ratio

shows a relatively uniform distribution throughout

the image, with a slight increase in ferric oxides

within igneous rock formations. Finally, the

Amphibole band ratio displayed higher values

(corresponding to warmer colours) in areas where

amphibolitic rocks are known to outcrop.

Band ratios were useful for identifying broad

areas of interest despite the limits imposed by the

dense vegetation across the entire EnMAP image

area.

4.2 Spectral Unmixing - MTMF

For both MTMF classifications, two distinct maps

were produced using the Rule Classifier incorporated

in the ENVI software. The maps contain all identified

endmembers, each class represented by a different

colour. The classification maps were performed for

Límens-Santa Marta, Vao-Samil, Patos and Alemáns-

Ratas-Canabal beaches (Fig. 4).

Figure 4: ESRI Basemap imagery for all target Vigo

beaches; A – Límens-Santa Marta beaches; V- Vao-Samil

beaches; C – Patos beach; D – Alemáns-Ratas-Canabal

beaches.

In the first classification, using the USGS spectral

library (Fig. 5), several mineral signatures were

identified across the Vigo beaches, being the main

ones Límens-Santa Marta and Vao. In the Límens-

Santa Marta beaches, signatures from rutile, goethite,

sillimanite, tourmaline, and all three varieties of

garnet, almandine, grossular, and spessartine, were

identified. Spessartine and rutile are mostly present in

dunes. At the Vao beach, sillimanite and grossular

signatures were more dominant. On the Vao and

Samil beaches boundary, ilmenite and tourmaline

were classified on rock outcrops. Iron oxides were

found to be more abundant at Límens-Santa Marta.

Evaluation of EnMAP Hyperspectral Data for the Identiﬁcation of Placers in the Rias Baixas Region (Spain)

301

Figure 5: Classification maps derived from the first MTMF

classification using the USGS spectral library and

comparison between Límens-Santa Marta (A1), Vao-Samil

(B1), Patos (C1) and Alémans-Ratas-Canabal (D1)

beaches.

At Patos beach, the goethite signature appears in

higher abundance, especially when compared to Vao

beach. Sillimanite was the most abundant mineral

signature along the stretch that includes Alemáns-

ratas-Canabal beaches.

Using the spectra from HM concentrations for the

second classification (Fig. 6), the most abundant

endmember identified in the Límens-Santa Marta

beaches is UPO_1 (montmorillonite). Other

significant endmembers include UPO_2, UPO_3 and

UPO_4, which are interpreted to be amphibole

(possibly hornblende), with the differential

identification of montmorillonite), as well as UPO_5,

likely corresponding to chlorite/biotite. A smaller

abundance of UPO_6 (montmorillonite) was also

identified.

At the Vao beach, UPO_1 is present but in a much

lower abundance, with it increasing along the

transition northward to the Samil beach. UPO_2 and

UPO_3 are also present, and UPO_9

(tourmaline/garnet) can be observed in rock outcrops.

Patos beach exhibits a high abundance of UPO_1

(montmorillonite/illite), while in Alemáns-Ratas-

Canabal beaches, the same trend continues, with

UPO_1 and UPO_4 (amphibole) being the most

abundant endmembers.

Seasonal variations previously described can be

seen in the MTMF classifications since the highest

abundance of goethite was found in Límens-Santa

Marta beaches in the northern margin.

Figure 6: Classification maps derived from the second

MTMF classification using the HM concentrate spectral

library and comparison between Límens-Santa Marta (A2),

Vao-Samil (B2), Patos (C2) and Alémans-Ratas-Canabal

(D2) beaches.

5 CONCLUSIONS

EnMAP data, due to its high spectral resolution, was

essential to distinguish several HM signatures in the

Vigo beaches. Band ratios are mostly useful for the

detection of potential areas of primary mineralisation,

due to evidence of hydrothermal alteration signatures.

When combined with other datasets, these maps

could help understand the provenance of HM in

placers. Spectral unmixing, included in the MTMF

workflow, identified signatures from most HM

known to be present in the Vigo beaches, except

zircon. Límens-Santa Marta and Vao beaches have

the highest abundance of HM, being the signature of

sillimanite the most identified, although likely to be

overrepresented. Garnets (almandine, grossular and

spessartine), tourmaline, ilmenite signatures were

also identified. Goethite signature is more abundant

in Límens-Santa Marta beach due to seasonal

variations in the Ria’s hydrodynamics. In future

works, exploring fusion methods of EnMAP and

Sentinel-2 data may be of value to enhance EnMAP’s

spatial resolution. Additionally, ground truth data

may be collected to determine MTMF’s accuracy in

classifying and identifying HM in the Vigo beaches.

A2 B2

S34I 2025 - Special Session on S34I - From the Sky to the Soil

302

ACKNOWLEDGEMENTS

This study is funded by the European Union under

grant agreement no. 101091616 (https://doi.org/

10.3030/101091616), project S34I – SECURE AND

SUSTAINABLE SUPPLY OF RAW MATERIALS

FOR EU INDUSTRY. Portuguese National Funds

also support this work through the FCT – Fundação

para a Ciência e a Tecnologia, I.P. (Portugal), projects

UIDB/04683/2020 (https://doi.org/ 10.54499/UIDB/

04683/2020) and UIDP/04683/2020 (https://doi.org/

10.54499/UIDP/ 04683/2020).

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