open-source software for monitoring changes in the surface area of large glaciers, aligning with the primary goal of demonstrating the feasibility of accessible and cost-effective approaches to glacier monitoring.

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# Tracking the Progression of Burned Areas in Tropical Peat Swamp Forests by Integrating Sentinel Optical and SAR Imagery: A Case Study of Binsuluk Forest Reserve in Sabah, Malaysia

Nurul Aina Abdul Aziz<sup>a</sup>, Mckreddy Yaban<sup>b</sup>, Muhamad Zulfazli Zakaria<sup>c</sup>
and Siti Atikah Mohamed Hashim<sup>d</sup>

Malaysian Space Agency (MYSA), No. 13, Jalan Tun Ismail, 50480 Kuala Lumpur, Malaysia
aina@mysa.gov.my, mckreddy@mysa.gov.my, zulfazli@mysa.gov.my, atikah@mysa.gov.my

**Keywords:** Forest Fire, Burned Area, Sentinel-1, Sentinel-2, Peatland.

**Abstract:** Climate change and rising global temperatures are driving forest fires to become more intense and frequent worldwide, particularly in peat swamp forest. Since the predominant burning mechanism in peatland forest is smouldering combustion, it causes widespread air pollution and emits massive amounts of carbon due to prolonged episodes of fire events. Therefore, the development of a unique approach to monitor forest fire progressions through burned area mapping mainly in persistent cloud cover is vital for the estimation of fire extent, location, and land cover affected. Thus, this research aims to evaluate the capabilities of Sentinel-1 SAR and Sentinel-2 optical time series in boosting the frequency and accuracy of burn area progression mapping in peatland areas. Results from the forest fire series in Binsuluk Forest Reserve, which occurred from February to April 2024, indicated a reduction in the backscatter value of the cross-polarized (VH) signal in the burned area for Sentinel-1 SAR C band. Despite the cloud cover challenge, Sentinel-2 continues to deliver essential data on the positioning of active fires and smoke plumes, with burn area detection being more precise when utilizing the Normalized Difference Moisture Index (NDMI) compared to the Normalized Burn Ratio (NBR). The integration of Sentinel optical and SAR imagery has effectively facilitated an increased tracking frequency and precision for the evolution of burned areas.

## 1 INTRODUCTION

Tropical peat swamp ecosystems are widespread in Southeast Asia, particularly in Borneo. More than half of Malaysia's 2.6 million hectares of peat swamp forest are situated on Malaysian Borneo (Meiling L., 2016), while Sabah was believed to have 86,000 hectares of peat swamp forest, with roughly 60,000 ha of mixed peat swamp forest on the Klias Peninsula. Agricultural growth and fires caused by El Niño/Southern Oscillation have led to the fast disappearance of peat swamp forests. Kamlun, K. U., & Phua, M. H. (2024) indicate that agriculture is the most influential anthropogenic factor associated with the fire-affected areas while the distance to settlement played an increasingly important role in the fire

affected areas and contributes to the deforestation of the peat swamp forest in Klias Peninsula.

Satellite-based earth observation (EO) systems are able to provide consistent and frequent measurements over vast remote areas on the earth's surface. This allows the monitoring of forest fire progressions across the globe in a timely and cost-effective way (Engelbrecht et al., 2017; Chuvieco et al., 2020). Phua et al. (2007) examined the use of several vegetation indices in image differencing technique for detecting burned peat swamp forest. Phua et al. (2008) has further investigated into a fast approach for detecting disturbances in multiple change events.

In previous studies, Sentinel-2 MSI data has been effectively used to assess burn severity, or the degree to which an area has been affected by a fire. This is

<footnote>a https://orcid.org/0009-0003-0776-0045</footnote>

<footnote>b https://orcid.org/0009-0007-5083-327X</footnote>

<footnote>c https://orcid.org/0009-0002-8965-0722</footnote>

<footnote>d https://orcid.org/0009-0000-6179-2193</footnote>
because the MSI data is sensitive to changes in the chlorophyll content of vegetation, which is typically reduced in areas that have been burned. In general, the accuracy of burned area mapping increases with higher spatial resolution data. Using a lower spatial resolution image, such as the 20 m spatial resolution of Sentinel-2 data, may result in less accurate burned area maps.

In the context of identifying burn areas, Normalised Burn Ratio (NBR), and Normalised Difference Moisture Index (NDMI) are among the indices used in the context of determining burn regions; each has certain advantages and disadvantages. Phua et al. (2007) found that NBR is especially good at identifying burn intensity and defining burn scars. In dry conditions or when attempting to distinguish between different kinds of vegetation, NDMI may be less effective. However, it is useful for determining the moisture level of vegetation, which can indirectly suggest disease susceptibility or recovery.

In tropical rainforests, burned areas may fade within a few weeks as fresh foliage grows. Some satellites can detect actively burning places, but may not detect the entire charred area due to cloud cover or delays in satellite images. Thus, SAR could serve as an alternative data source of information since radar sensors can image day and night, and are capable of penetrating clouds, smoke, and smog. Further, SAR is sensitive to changes in vegetation structure and soil moisture following wildfire (Bourgeau-Chavez et al., 2007). These characteristics give SAR unique advantages in monitoring on-going forest fire event.

Tanase, Mihai A., et al (2010) has analyzed SAR data at X-, C-, and L-bands to investigate the relationship between backscatter and forest focusing on both HH and VV polarizations as well as on cross polarized (HV). Results obtained in Spain highlighted that for X- and C-bands, the copolarized (HH and VV) backscatter increased with burn severity, in detail: 1) for all frequencies, the cross polarized (HV) decreased with burn severity; 2) C- and L-bands cross-polarized backscatter showed better potential for burn severity; and 3) the small dynamic range observed for X-band data could prevent its use in vegetation affected by fires.

Gaveau, D., Descals, A., Salim, M., Sheil, D., & Sloan, S. (2021) present new and validated 2019 burned-area estimates for Indonesia using a time series of the atmospherically corrected surface reflectance multispectral images (level 2A product) taken by the Sentinel-2A and B satellites. The frequency-area distribution of the Sentinel-2 burn

scars follows the apparent fractal-like power law or Pareto pattern often reported in other fire studies, suggesting good detection over several magnitudes of scale with 97.9% accuracy.

This research aims to assess the effectiveness of both Sentinel-1 SAR and Sentinel-2 optical time series images in improving the frequency and precision of burn area progression mapping in peatland regions. Various approaches for optical and SAR will be suggested to monitor the size of the burned area in near real-time.

## 2 MATERIAL AND METHODS

The primary objective of the suggested methodology is to utilise image differencing techniques to detect burned areas in the Binsuluk Forest Reserve through optical and SAR imagery, hence assessing the evolution of fire in the affected region. The forest reserve boundaries provided by the Sabah Forestry Department is essential for identifying the source of fire and consistently calculating the area of land destroyed. Ultimately, we examined the impact of the fires on the current protective forest reserve.

<figure><img src="image_196.png" /><figcaption>Figure 1: Workflow of Burned Area Mapping.</figcaption></figure>

### 2.1 Area of Interest

The research was carried out in Binsuluk Forest Reserve which is a protected forest reserve on the Klias Peninsula, in Beaufort District of Interior
Division, Sabah, the Sabah Forestry Department in 1992. Its area is 12,106 hectares (121.06 km²). The reserve is mostly flat, consisting mostly of peat swamp forest, with a small area of mangroves. The forest type here is peat swamp forest over soils of the Klias Association. Most of the FR was badly burnt during the long drought of 1997-1998. Of the remaining trees, *Dryobalanops rappa* is the most dominant canopy tree species. Most of the burnt areas are dominated by small shrubs. The Binsuluk Forest Reserve Boundary was overlaid with the images as shown in Figure 2 to identify the actual AOI of the study area

<figure><img src="image_197.png" /><figcaption>Figure 2: Sentinel-2 true colour composite on April 24, 2024, of the study area. Sentinel-2 image available at https://scihub.copernicus.eu/.</figcaption></figure>

Previously in 2016 large fires in peat bogs occured, which were caused by fires spread to Binsuluk and other forest reserves from nearby open burning had contributed to the 2016 Malaysian haze. Over half of the reserve were burnt during this event. Open burning caused yet another forest fire in 2020, this time burning 274 hectares (2.74 km2). However, after some action and enforcement from the government, the trend of hotspots kept decreasing over the years as a result of mitigation action from Malaysia via the National Haze and Dry Weather Committee and ASEAN Agreement on Transboundary Haze Pollution.

## 2.2 Active Fire Data Collection

First, peat fires were identified by overlaying active fire data from NASA Fire Information for Resource Management System (FIRMS) data catalog. FIRMS distributes Near Real-Time (NRT) active fire data from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Aqua and Terra satellites, and the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (Suomi NPP) and NOAA 20 satellites. Combined (Terra and Aqua) MODIS NRT active fire products (MCD14DL) are processed using the standard MOD14/MYD14. Fire and thermal anomalies algorithms are automated pre-processed utilising Python scripts and ArcGIS software to input administrative boundary information such as division, district, and city, which is then merged into the ForFIS database for user access. Next, the NRT data from these four sensors for year 2024 are plotted to get the distribution as shown in Figure 3.

Aqua and Terra satellites, and the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (Suomi NPP) and NOAA 20 satellites. Terra/Aqua MODIS hotspots onto the existing peatland map. Fire and Thermal Anomalies algorithms are automated pre-processed utilising Python scripts and ArcGIS software to input administrative boundary information such as division, district, and city. Next, the NRT data from these four sensors from 2020 to 2024 are utilised to calculate the total number of hotspots, illustrate the hotspot distribution, and certify the high fire prone area.

<figure><img src="image_198.png" /><figcaption>Figure 3: The distribution of hotspot in FR Binsuluk within January-May 2024.</figcaption></figure>

In this study, the daily hotspot is downloaded from NASA Fire Information for Resource Management System (FIRMS) data catalog. FIRMS distributes Near Real-Time (NRT) active fire data from the Moderate Resolution Imaging spectroradiometer (MODIS) aboard the Aqua and Terra satellites, and the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (Suomi NPP) and NOAA 20 satellites. Combined (Terra and Aqua) MODIS NRT active fire products (MCD14DL) are processed using the standard MOD14/MYD14. Fire and thermal anomalies algorithms are automated pre-processed utilising Python scripts and ArcGIS software to input administrative boundary information such as division, district, and city, which is then merged into the ForFIS database for user access. Next, the NRT data from these four sensors for year 2024 are plotted to get the distribution as shown in Figure 3.
## 2.3 Sentinel Data Collection and Pre-Processing

Sentinel SAR and optical satellite images of Binsuluk Forest Reserve, acquired from January to April 2024, were retrieved from the ESA Copernicus Data Space Ecosystem portal. The dates picked are based on the existence of hotspot in continuous cycle of fire event. Basically, Sentinel-2 and Sentinel-1 are two key missions in a series of satellite missions initiated by the ESA to support global environmental monitoring and resource management. Sentinel-1 mission consists of a pair of polar-orbiting satellites that provide high-resolution, all-weather imaging of the Earth's surface. It includes C-band imaging operating in four modes with different resolutions (up to 5 m). ESA S1 mission provides global coverage of freely available dual polarization C-band SAR images with a repeat cycle at 6-days and revisit frequencies at 1-3 days taking into account of ascending and descending orbits and overlaying. Two bands of Sentinel-1 were used as SAR features, including VH and VV bands. The SAR images were exported from Sentinel-1 SAR GRD (Ground Range Detected) image collection named "COPERNICUS/S1_GRD" in the Copernicus Platform. Major pre-processing step are running using The Sentinel Application Platform (SNAP). The software is developed by Brockmann Consult, Skywatch, Sensar and C-S. SNAP software.

<table><caption>Table 1: Dates of satellite acquired in a descending pass, by Sentinel-1 (s1) and Sentinel-2 (s2).</caption><thead><tr><th>Acquisition Date</th><th>Imagery</th><th>Acquisition Date</th><th>Imagery</th></tr></thead><tbody><tr><td>5 Jan 2024</td><td>S2</td><td>5 March 2024</td><td>S2</td></tr><tr><td>10 Jan 2024</td><td>S1 and s2</td><td>10 March 2024</td><td>S1 and s2</td></tr><tr><td>15 Jan 2024</td><td>S2</td><td>15 March 2024</td><td>S2</td></tr><tr><td>20 Jan 2024</td><td>S2</td><td>20 March 2024</td><td>S2</td></tr><tr><td>22 Jan 2024</td><td>S1</td><td>25 March 2024</td><td>S2</td></tr><tr><td>25 Jan 2024</td><td>S2</td><td>30 March 2024</td><td>S2</td></tr><tr><td>30 Jan 2024</td><td>S2</td><td>3 April 2024</td><td>S1</td></tr><tr><td>3 Feb 2024</td><td>S1</td><td>4 April 2024</td><td>S2</td></tr><tr><td>4 Feb 2024</td><td>S2</td><td>9 April 2024</td><td>S2</td></tr><tr><td>9 Feb 2024</td><td>S2</td><td>14 April 2024</td><td>S2</td></tr><tr><td>14 Feb 2024</td><td>S2</td><td>19 April 2024</td><td>S2</td></tr><tr><td>19 Feb 2024</td><td>S2</td><td>24 April 2024</td><td>S2</td></tr><tr><td>24 Feb 2024</td><td>S2</td><td>27 April 2024</td><td>S1</td></tr><tr><td>27 Feb 2024</td><td>S1</td><td>29 April 2024</td><td>S2</td></tr><tr><td>29 Feb 2024</td><td>S2</td><td></td><td></td></tr></tbody></table>

Sentinel-2 is a multi-spectral imaging system that provides high-resolution imaging of the Earth's surface with 13 bands. It also comprises two satellites, each with a spatial resolution of up to 10 m. These two satellites can be combined to provide full coverage of the surface of the earth every five-day interval. Sentinel-2 images were atmospherically corrected.

Table 1 shows the total images used for tracking the forest fire. In the normal condition of monitoring peatland areas in Malaysia, only 12 out of a total of 24 S2 images have less than 30% cloud cover and can be used to generate the burnt area. The remaining S1 images totally can be utilized to complete the cycle.

## 2.4 Techniques of Burned Area Mapping

### 2.4.1 Multi-Spectral Burned Area Index

Ten bands of Sentinel-2 were selected as spectral features, including three visible bands, one Near-Infrared (NIR) band, four Red-edge bands, and two short-wave infrared (SWIR) bands. The raw image from band 2,3,4,8 and 12 were selected for displaying the burned area. The responses of these features in various spectral bands will show specific character during the visualization of burned area.

In the context of identifying burn areas in dense cloud cover area, we apply various indices like Normalized Difference Moisture Index (NDMI) and Normalized Burn Ratio (NBR) are utilized, each offering unique benefits and facing specific limitations. NBR generates values ranging from -1 to 1. Intense green vegetation will exhibit a high NBR value, whereas charred vegetation will have a low value. Regions characterized by dry, brown vegetation or exposed soil will have lower NBR values compared to green vegetation. Otherwise, NDMI is important in evaluating moisture levels in vegetation, which might indirectly reflect fire vulnerability or recovery; nevertheless, its efficacy may diminish in arid conditions or when distinguishing across vegetation kinds.

### 2.4.2 Sentinel-1 SAR GRD Backscatter

SAR-based burnt area mapping mainly relies on the resultant changes in radar backscattering, which depend on the modification degree caused by fire events in backscattering mechanisms. The total amount of energy scattered back to radar sensor can be influenced by sensor characteristic (signal wavelength and polarization), target properties (including vegetation structure, dielectric permittivity, canopy and water content, soil moisture and dielectric properties, and surface roughness) and observation geometry (Imperatore et al., 2017).

## 2.5 Field Verification

Using a mobile GPS device, we conducted an aerial survey in Binsuluk FR to validate the burned area.
The geographic coordinates and land cover categories are included in each sample point. In addition to the burned area, the image captures the nearly identical features of cleared land as a result of agricultural activities and flooded wetlands. In addition to aerial surveys, verification of the hotspot's status as an active fire was conducted using multispectral higher-resolution satellites, such as SPOT and Pleiades. We collaborate with the Sabah Forestry Department to conduct airborne surveys for field verification at 40 locations throughout the Binsuluk FR zone as shown in Figure 4.

<figure><img src="image_199.png" /><figcaption>Figure 4: Location of aerial survey and sample of photos of burnt areas.</figcaption></figure>

## 2.6 Multi-Temporal of Burned Area Progression

Considering the difference in acquisition time of SAR and optical observation, we assessed the initial until final stage of the progression maps, which mean assessing the full progressions of each fire event. This is deemed as unsuitable for validation since the fire could progress very fast during that period. In the normal condition of monitoring peatland areas in Malaysia, only 12 out of a total of 24 S2 images have less than 30% cloud cover and can be used to generate the burnt area. The remaining S1 images totally can be utilized to complete the cycle.

# 3 RESULTS AND DISCUSSION

## 3.1 Multi-Spectral Indices in Burned Area Mapping

The multi-spectral image was studied with three well-known band combinations which is true colour composite, near infrared (NIR) false colour composite image and shortwave infrared (SWIR) false colour composite image to decide which combination best to highlight the burned areas. Figure 5 shows SWIR colour composite that act as the best band combination to visualize the burned areas.

<figure><img src="image_200.png" /><figcaption>Figure 5: SWIR false colour composite (a) enhances the burned area features and active fire location in Sentinel-2 MSI dated 27 April 2024 compared to (b) true colour composite</figcaption></figure>

NBR excels at assessing burn intensity and outlining burn scars; however, its efficacy diminishes in areas with sparse vegetation or in identifying early post-fire regrowth. Using +0.1 as a criterion produces false positives on unburned cleared land and forest areas where foliage has dried prior to the fire. A far more cautious criterion (+0.3) produces a better outcome. Rahman et al. (2018) discovered that a dNBR threshold value of +0.1 is adequate for distinguishing burnt from unburnt areas using Sentinel-2.

This study indicates that specific conditions may result in false positives, since significant smoke in the post-burn image can distort the dNBR value. Regions that have had vegetation removal through alternative methods (logging, harvesting, and landslides) at the conclusion of the baseline period may erroneously appear as scorched.

The Normalized Difference Moisture Index (NDMI) is employed to assess vegetation water content and monitor drought conditions. Figure 6 indicates that the red and yellow regions are classified as burned areas. NDMI can distinguish between
clouds and shadows when identifying burned areas
compare to NBR. The NDMI value range is from -1
to 1. Negative NDMI values (approaching -1)
indicate infertile soil. Values near zero (-0.2 to 0.4)
typically indicate water stress. Elevated, positive
values indicate substantial canopy coverage without
water stress (about 0.4 to 1).

<figure><img src="image_201.png" /><figcaption>Figure 6: Comparison on Burned Area mapping on S2 imagery dated 24th April 2024 using difference indices NDMI and NBR.</figcaption></figure>

## 3.2 SAR-Based Burned Area Mapping

Figure 7 illustrate that VV and VH polarizations can be display into a false colour visualization. It uses the VV polarization in the red channel, the VH polarization in the green channel, and a ratio of VH/VV in the blue channel. It shows water areas in dark red (black), urban areas in yellow, vegetated areas in turquoise, and bare ground and burned area in dark purple. The grayscale visualization of the gamma0 of the VH polarization also can be displayed. The values for the cross polarization (VH) are generally lower (darker visualization) than for the co-polarization (HH, VV). The VH polarization has higher values for surfaces characterized by volume scattering, e.g., branches, dry coil bodies, or canopies (lighter color in the visualization) and lower for surfaces

<figure><img src="image_202.png" /><figcaption>Figure 7: Visualisation of Sentinel-1 SAR false colour composite VV:VH:VH/VV enhance the burned area features and grayscale VH.</figcaption></figure>

## 3.3 Tracking Burned Area Progression by Combination of Optical and SAR

Taking advantage of the equivalent spatial and
temporal resolutions of radar and optical information
is facilitated by the availability of near-concurrent
active (Sentinel-1) and passive (Sentinel-2) datasets.
Figure 8 shows the comparison of trends of SAR false
colour composite VV:VH:VH/VV and SWIR colour
composite in monitoring forest fire progression
within four month periods. Sentinel-2 images were
matched to the Sentinel-1 dates for each detection
period as follows when there was not any temporally
coincident image: for the pre-fire date, the closest
Sentinel-2 image acquired before was selected. Based
on observation, SWIR colour composite shows
higher percentage of cloud cover that distract burned
area delineation. While the delineation of burned
areas using SAR images can provide more
information on the area due to cloud penetration but
still limited for some landscapes, since the radar
signal reflected from the burned surface may be
similar in intensity to the signal from other
components of the landscape for example, areas of
open ground.
<figure><img src="image_203.png" /><figcaption>Figure 8: Comparing Burn Area Propagation visualized by SAR and SWIR false colour composite within January-April 2024.</figcaption></figure>

## 3.4 Incorporation of Burned Area Map into WebGIS Database

The validated burned area maps will be used in iForSABAH system which is an innovative application webGIS system that aims to address these challenges by using space technology and remote sensing to monitor the forest areas in Sabah, Malaysia. iForSABAH stands for Integrated Forest Resource Information System for Sabah, and it is a collaboration between the Malaysian Space Agency (MYSA) and the Sabah Forestry Department. The system uses Geographic Information System (GIS) technology and high-resolution satellite images to detect any changes in permanent forest reserves, and provide information on forest cover, forest type, forest degradation, forest fire, forest restoration, forest carbon stock, and forest biodiversity.

The system also supports the empowerment of indigenous peoples and local communities who depend on the forest resources for their livelihoods. iForSABAH is a cutting-edge solution that leverages the power of space technology to enhance the management of forest resources, support the implementation of the Sabah Forest Management Plan, and contribute to the national and international commitments on forest conservation and climate change mitigation. By using iForSABAH, users can access reliable, timely, and accurate information on the status and trends of the forest areas in Sabah, and make informed decisions for the benefit of the environment and the society.

## 4 CONCLUSION

In conclusion, the combining of Sentinel-1 and Sentinel-2 facilitates a higher frequency of data collecting and enhances the detection of burned areas, particularly in regions with significant cloud cover. The Sentinel-2 SWIR and NIR bands have demonstrated effectiveness in defining burned areas, in conjunction with the temporal backscatter patterns using Sentinel-1 SAR for forest fire propagation analysis. The multi-date of prefire and postfire data are crucial due to the characteristics of burned areas, which are often linked to agricultural clearance activities that precede the fire event, potentially resulting in misclassification between burned land and cleared land. Future research may demonstrate that NBR and NDMI differencing can yield a more effective methodology. Additionally, noise reduction techniques, such as cloud masking, are essential for improving detection outcomes. Information regarding the progression of burned areas, including segmented areas and hectares, can also be obtained from the final map.

## ACKNOWLEDGEMENT

The authors would like to thank the Malaysian Space Agency (MYSA) and Sabah Forestry Department for supporting and facilitating this research which part of the collaboration project IForSabah and Forest Fire
Information System (ForFIS). We acknowledge the use of data and/or imagery from NASA's Fire Information for Resource Management System (FIRMS) (https://earthdata.nasa.gov/firms), part of NASA's Earth Observing System Data and Information System (EOSDIS) and The Copernicus Data Space Ecosystem Browser.

forests." *IEEE Transactions on Geoscience and Remote Sensing* 48.10 (2010): 3663-3675.

## REFERENCES

Bourgeau-Chavez, L. L., Kasischke, E. S., Brunzell, S., Mudd, J. P., & Tukman, M. (2002). Mapping fire scars in global boreal forests using imaging radar data. *International Journal of Remote Sensing, 23*(20), 4211-4234.

Gaveau, D., Descals, A., Salim, M., Sheil, D., & Sloan, S. (2021). Refined burned-area mapping protocol using Sentinel-2 data increases estimate of 2019 Indonesian burning. *Earth System Science Data Discussions, 2021*, 1-23.

Imperatore, P. et al. Effect of the Vegetation Fire on Backscattering: An Investigation Based on Sentinel-1 Observations. *IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens.* **10**, 4478-4492, https://doi.org/10.1109/JSTARS.2017.2717039 (2017).

Kamlun, K. U., & Phua, M. H. (2024). Anthropogenic influences on deforestation of a peat swamp forest in Northern Borneo using remote sensing and GIS. *Forest Systems, 33*(1), eSC02-eSC02.

M. A. Tanase, F. Pèrez-Cabello, J. de la Riva, and M. Santoro, "TerraSAR-X data for burn severity evaluation in Mediterranean forests on sloped terrain," IEEE Trans. Geosci. Remote Sens., vol. 48, no. 2, pp. 917-929, Feb. 2010

Melling, L. (2016). Peatland in Malaysia. Tropical Peatland Ecosystems. Springer

Phua, M. H., Tsuyuki, S., Lee, J. S., & Sasakiwa, H. (2007). Detection of burned peat swamp forest in a heterogeneous tropical landscape: A case study of the Klias Peninsula, Sabah, Malaysia. *Landscape and urban planning, 82*(3), 103-116.

Rein, G., & Huang, X. (2021). Smouldering wildfires in peatlands, forests and the arctic: Challenges and perspectives. *Current Opinion in Environmental Science & Health, 24*, 100296.

Sirin, A., & Medvedeva, M. (2022, January 2). *Remote sensing mapping of peat-fire-burnt areas: Identification among other wildfires*. MDPI. https://www.mdpi.com/2072-4292/14/1/194

Sloan, S., Tacconi, L., and Cattau, M.: Fire prevention in managed landscapes: Recent success and challenges in Indonesia, Mitig. Adapt. Strat. Gl., 26, 32, https://doi.org/10.1007/s11027-021-09965-2, 2021

Suwanprasit, Chanida, and Shahnawaz. "Mapping burned areas in Thailand using Sentinel-2 imagery and OBIA techniques." *Scientific Reports* 14.1 (2024): 9609.

Tanase, Mihai A., et al. "Sensitivity of X-, C-, and L-band SAR backscatter to burn severity in Mediterranean pine
<figure><img src="image_204.png" /></figure>
SPECIAL SESSION ON S34I - FROM THE SKY TO THE SOIL
<figure><img src="image_205.png" /></figure>
FULL PAPERS
<figure><img src="image_206.png" /></figure>
# S34I Project: Secure and Sustainable Supply of Raw Materials for EU Industry

Ana Cláudia Teodoro¹ IDª, Joana Cardoso-Fernandes¹ IDᵇ, Mihaela Gheorghe² IDᶜ,

Francesco Falabella³ IDᵈ, Fabiana Calò³ IDᵉ, Antonio Pepe³ IDᵉ, Delira Hanelli⁴ ID⁵,

Andreas Knobloch⁴ ID⁵, Roberto De La Rosa⁴ ID⁶, Fahimeh Farahnakian⁵,⁶ ID⁷,

Georgios Periklis Georgalas⁷ ID⁸, Enoc Sanz-Ablanedo⁸ ID⁹, Vaughan Williams⁹, Krištof Oštir¹⁰ ID¹¹

¹Instituto de Ciências da Terra, Departamento de Geociências, Ambiente e Ordenamento do Território, Faculdade de Ciências, Universidade do Porto, Porto, Portugal

²GMV Innovating Solutions SRL, Bucharest, Romania

³CNR - National Research Council of Italy, Institute for the Electromagnetic Sensing of the Environment, Napoli, Italy

⁴Beak Consultants GmbH, Freiberg, Germany

⁵Geological Survey of Finland (GTK), Espoo, Finland

⁶Department of Computing, University of Turku, Turku, Finland

⁷Department of General Geology, Geological Mapping & Applications, Hellenic Survey of Geology & Mineral Exploration, Acharnae, Attica, Greece

⁸Grupo de Investigación en Geomática e Ingeniería Cartográfica (GEOINCA), Universidad de León, León, Spain

⁹Aurum Exploration Ltd, Ireland

¹⁰University of Ljubljana, Faculty of Civil and Geodetic Engineering, Ljubljana, Slovenia
{amteodor, joana.fernandes}@fc.up.pt, mgheorghe@gmv.com, {falabella,f, calo,f, pepe.a}@irea.cnr.it,
{delira.hanelli, andreas.knobloch, roberto.delarosa}@beak.de, fahimeh.farahnakian@gtk.fi, ggeorgalas@eagme.gr,
esana@unileon.es, vwilliams@aurumexploration.com, Kristof.Ostir@fgg.uni-lj.si

**Keywords:** Exploration, Extraction, Environmental Monitoring, Remote Sensing, Artificial Intelligence.

**Abstract:** The Secure and Sustainable Supply of Raw Materials for EU Industry – S34I project is researching and innovating new data-driven methods to analyze Earth Observation (EO) data, supporting systematic mineral exploration and continuous monitoring of extraction, closure, and post-closure activities to increase European autonomy regarding raw materials (RM) resources, and to use EO not only for the management of technical and environmental issues for a green transition but also to support public awareness, mining's social acceptance, and better legislation. S34I uses data from satellites, airborne, unmanned aerial vehicles, ground-based sensors, underwater hyperspectral imaging and conventional in-situ techniques/methods and fieldwork. The S34I project is supporting the technical experiments and pilot validations/demonstrations for the six pilot use cases and at different phases of the mining life-cycle to address the challenges of the topic: Onshore exploration (Aramo in Spain); Shallow water exploration (Ria de Vigo in Spain); Extraction (Gummern in Austria); and Closure/post-closure (Lausitz in Germany, Aijala and Outokumpu in Finland). The S34I project involves 19 partners from 12 European countries. The project started in January 2023 and ends in June 2025.

<footnote>a https://orcid.org/0000-0002-8043-6431</footnote>

<footnote>b https://orcid.org/0000-0001-8265-3897</footnote>

<footnote>c https://orcid.org/0000-0001-8171-6411</footnote>

<footnote>d https://orcid.org/0000-0002-3698-908X</footnote>

<footnote>e https://orcid.org/0000-0002-0174-5894</footnote>

<footnote>f https://orcid.org/0000-0002-7843-3565</footnote>

<footnote>g https://orcid.org/0009-0002-0012-1729</footnote>

<footnote>h https://orcid.org/0000-0001-7515-001X</footnote>

<footnote>i https://orcid.org/0000-0002-3004-7104</footnote>

<footnote>j https://orcid.org/0000-0002-7672-9346</footnote>

<footnote>k https://orcid.org/0009-0003-1180-2519</footnote>

<footnote>l https://orcid.org/0000-0001-9975-5726</footnote>

<footnote>m https://orcid.org/0000-0002-4887-7798</footnote>
# 1 INTRODUCTION

Sustainable mining practices, i.e., the minimization of environmental damage, social responsibility, reduction of ecological footprints, rehabilitating mined land, managing waste effectively, and conserving biodiversity, are a very hot topic and have been a concern for many years (Laurence, 2011; Chen et al., 2024).

The Critical Raw Materials Act (CRMA), proposed by the European Commission, seeks to address the challenges faced by the European Union (EU) in securing a stable and sustainable supply of critical raw materials (European Commission, 2023). Its primary goal is to reduce the EU's reliance on external sources for these materials while ensuring their availability to support strategic sectors, including those vital for decarbonization and advancing green technologies (Hool et al., 2024). The Act sets several benchmarks by 2030 along the strategic raw materials value chain and for the diversification of the EU supplies: (i) at least 10% of the EU's annual consumption for extraction; (ii) at least 40% of the EU's annual consumption for processing; and (iii) at least 25% of the EU's annual consumption for recycling no more than 65% of the EU's annual consumption from a single third country.

Satellite data is crucial in sustainable mining practices by offering tools and insights to minimize environmental impact, optimize operations, and ensure compliance with sustainability standards (Farahnakian et al., 2024; Li et al., 2023; Persello et al., 2022).

Regarding the applications of satellite data in sustainable mining, using these data and image-processing techniques reduces the environmental impact by identifying high-potential areas remotely (Rajan Girija & Mayappan, 2019). Remote sensing has the ability to help exploration companies explore much larger and often more remote and inaccessible areas whilst at the same time focusing time and costs on identifying specific target areas for further testing (Pour et al., 2019; Beiranvand Pour et al., 2018). This will lead to more efficient timelines for discovery. Mineral mapping can analyze mine waste (tailings) for recoverable resources, turning waste into reusable materials and reducing the need for fresh extraction (Gulicovski et al., 2024; Rodríguez-Hernández et al., 2019; Zoran et al., 2009).

The recent advances in Artificial Intelligence (AI) algorithms and Earth Observation (EO) free data are aspects that broadly support several Sustainable Development Goals and promote sustainable mining (Chen et al., 2024; Persello et al., 2022).

The S34I project has explored new data-driven methods to analyze EO data for systematic mineral exploration, continuous extraction monitoring, closure and post-closure activities, increasing European autonomy regarding raw materials (Farahnakian et al., 2024; Carvalho et al., 2025). The consortium is composed of 19 partners from 12 countries (11 from the EU, plus Norway) (see Figure 1). The partners are 50% from academia and national research centres and 50% from private companies/industries. The project started in January of 2023 and will end in June 2025.

<figure><img src="image_207.png" /><figcaption>Figure 1: S34I project partners.</figcaption></figure>

The main objective of this work is to present the project's main objectives and some preliminary results. The project tackles the entire mining life cycle (exploration, extraction, mine closure) by focusing on six distinct pilot sites. Each mining phase and pilot presents different challenges that were addressed using EO data and techniques. This work will summarize the S34I approach to address these challenges. First, all datasets used in S34I will be listed according to the mining phase. Then, an overview of the methods developed/adapted in the scope of S34I will be given. Preliminary results will be presented and discussed for selected methods. These methods and results will serve as a base to develop specific services to address the identified challenges.

# 2 DATA

S34I utilized data from Copernicus missions and Copernicus Contributing Missions (CCM) obtained from the European Space Agency and other satellite sensors, while additional platforms, which included airborne systems, unmanned aerial vehicles (UAVs), ground-based methods, in-situ techniques, and
fieldwork, were employed for calibration, validation, or to complement the satellite data. The data used varied depending on the mining phase and pilot site:

*   **Exploration Phase - Onshore Pilot (Aramo Mine, Spain):** Sentinel-1, Sentinel-2, Landsat-9, Hyperspectral Precursor of the Application Mission (PRISMA), Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR)-2, Constellation of Small Satellites for Mediterranean basin Observation (COSMO-SkyMed), airborne Light Detection and Ranging (LiDAR), hyperspectral data, and ground spectral libraries of rocks and soils.

*   **Exploration Phase - Shallow Waters Pilot (Rias Baixas, Spain):** Sentinel-1, Sentinel-2, Landsat-9, WorldView-2/-3, EnMap, Underwater Hyperspectral Imaging (UHI), complementary spectral libraries, and pre-existing or newly acquired geological data.

*   **Extraction Phase (Gummern, Austria):** Pléiades Neo tri-stereo, WorldView-2, Sentinel-1, Sentinel-2, COSMO-SkyMed, UAV data, and ground Global Navigation Satellite Systems (GNSS) stations.

*   **Closure and post-closure Phase (Lausitz and Outokumpu):** Sentinel-2, PRISMA, WorldView-3, UAV data, and geochemical water data; and Sentinel-1, Sentinel-2, and COSMO-SkyMed data (Aijala).

It should be noted that the S34I consortium worked together with the holders of the rights for exploration in Aramo and for exploitation in Gummern.

# 3 METHODS

The data and methodology employed in the S34I project depend on the pilot case and the mining phase addressed. However, the principal outcomes of the S34I project focus on processing Copernicus and CCM data.

Several approaches were applied and developed, from traditional methods to new ensemble machine learning (ML) algorithms. Figure 2 presents the methods developed in the primary pilot area. The techniques have been developed in a specific location but later implemented in other pilot areas.

The methods were developed according to the type of EO data exploited (satellite and other data), as shown in Figure 3.

<figure><img src="image_208.png" /><figcaption>Figure 2: Developed methods according to the primary pilot area.</figcaption></figure>

<figure><img src="image_209.png" /><figcaption>Figure 3: Developed methods according to the data.</figcaption></figure>

## 3.1 Exploration Phase

For the onshore pilot (Aramo, Spain), various analytical methods were employed, including RGB combinations, band ratios, Principal Component Analysis (PCA), K-means clustering, end-member extraction, minimum wavelength mapping, Spectral Angle Mapper (SAM), Self-Organizing Maps (SOM), and Artificial Neural Networks (ANNs). A novel ensemble AI method was developed by integrating Support Vector Machines (SVM), Random Forest (RF), and ANNs. Additionally, a specialized AI algorithm was designed for automated pre-processing of hyperspectral airborne data, requiring minimal ground truth input. This was aided by pre-existing geochemical datasets, which minimized the requirement for a large part of follow-up ground truthing.

In the shallow water exploration pilot (Rias Baixas, Spain), RGB combinations, band ratios, PCA, K-means clustering, spectral unmixing, and Object-Based Image Analysis (OBIA) enhanced feature detection and classification.
## 3.2 Extraction Phase

In the extraction pilot (Gummern, Austria), high-resolution Digital Elevation Models (DEMs) were generated, and UAV photogrammetry was effectively performed using Structure from Motion (SfM) for detailed surface modelling.

Interferometric Synthetic Aperture Radar (InSAR) and change detection techniques, including the Normalized Decorrelation Change Index (NDCI), were applied to Synthetic Aperture Radar (SAR) data for detailed surface analysis. Additionally, advanced AI models—Residual-in-Residual Dense Block (RRDB), Super-Resolution U-Net (SRUN), and Optical-Guided Super-Resolution Network (OGSRN)—were implemented to enhance the resolution and quality of SAR imagery.

A Low-cost GNSS Monitoring System (LGMS) using low-cost GNSS receivers to monitor displacements with high precision was implemented. The LGMS receives and stores GNSS observations continuously, which are later post-processed to estimate daily displacements of the monitoring locations.

## 3.3 Closure and Post-Closure Phase

For closed mines affected by Acid Mine Drainage (AMD) (Lausitz, Germany and Outokumpu, Finland), unsupervised learning methods such as SOM and K-means clustering were utilized for pattern recognition and data analysis. Supervised classification techniques were applied to improve predictive accuracy, including ANN, logistic regression, RF, and K-nearest Neighbors (KNN). Additionally, image enhancement and change detection techniques were implemented at the Aijala pilot (Finland).

# 4 RESULTS AND DISCUSSION

In this section, some preliminary results will be presented and discussed.

## 4.1 Exploration Phase

In the onshore pilot study, SOM was applied for data exploration to analyze geochemical sample points. A K-means clustering algorithm was also applied to the SOM output to categorize the data into distinct clusters. This combination allows for identifying meaningful groupings and enhances the interpretability of spatial and spectral patterns.

Ultimately, it improves understanding of Cobalt (Co) distribution captured by PRISMA and LiDAR data (Figure 4).

<figure><img src="image_210.png" /><figcaption>Figure 4: SOM results from geochemical sample points of PRISMA: (a-c) Example variable SOMs related to the band information (band 1, 40 and 106).</figcaption></figure>
A new ensemble AI method was developed by integrating SVM, RF, and ANN to exploit different satellite-based datasets. Emphasis was given to Copernicus data (Sentinel-2), Landsat-9 and PRISMA, combining multispectral and hyperspectral images. This classifier ensemble method employed a Soft Voting strategy. The ensemble classifier shows variable performance with different input data. PRISMA data allows for better class separability, but the model tends to overfit, indicating the need for better regularisation (Figure 5).

<figure><img src="image_211.png" /><figcaption>Figure 5: Examples of prediction map of hydrothermal alteration area (coloured pixels) generated from PRISMA dataset on the onshore exploration phase.</figcaption></figure>

A spectral library was created, including 311 samples of outcropping rocks, 371 samples of soils, and 208 samples from the old mine in Aramo. The mineralogical associations corresponding to the outcropping rocks and soils in the Aramo Plateau, their spectral signatures in the SWIR region and their relationship with the contents of Co and other elements (Nickel (Ni) and Copper (Cu)) were determined. Although no clear spectral signature for Co was identified, the study successfully defined nine distinct spectral signatures from the 11 mineralogical associations. This could be mainly attributed to the fact that the deposit is mainly enriched in Cu, but with associated Cu and Ni. Additionally, statistical correlations between mineralogical associations and geochemical data revealed that specific associations showed a higher probability of containing elevated Co, Ni, and Cu levels.

A method was also developed for pre-processing airborne hyperspectral data. This involves converting digital counts from hyperspectral sensors into radiance values through several key steps. Geometric

correction ensures that image pixels correspond to their correct geographic locations, while atmospheric correction mitigates the effects of the atmosphere, such as absorption and scattering, on the measured radiance. Additionally, spectral calibration ensures precise wavelength alignment to match known spectral features. A semi-automated workflow was implemented for the large-scale interpretation of hyperspectral data, integrating satellite, airborne platforms and UAVs, combined with ground spectroradiometer measurements to automate extracting meaningful features for geological interpretation.

A methodology for utilizing EO data from airborne and/or UAVs to develop predictive mineral maps for the Aramo plateau during the exploration mining phase was also developed.

Regarding the shallow waters pilot, a methodology was developed for identifying placer deposits using an OBIA and high-resolution satellite data. This analysis revealed distinct signatures for each material within the spectral resolution of the WorldView-3 satellite data, leading to the creation of a new band ratio for prospective deposit categorization (VNIR1-VNIR4 band ratio to identify Ilmenite) and one for determining the geological background from vegetation (VNIR6-VNIR8). Three different ML approaches are developed and compared with each other: a single-level model using the SVM algorithm, a multi-level model using the KNN algorithm, and a dynamic classification developed with a decision tree model. The outcome of implementing these methods is the production of maps illustrating the distribution of placer deposits within the coastal area of Vigo, which can be seen in Figure 6.

<figure><img src="image_212.png" /><figcaption>Figure 6: Examples of prediction map generated using Single-level OBIA on the WorldView-3 dataset on the shallow waters pilot.</figcaption></figure>
We also applied an innovative UHI for shallow water exploration to identify potential areas for the occurrence of CRMs. UHI data was acquired with field work and refers to three different sea zones in Ria de Vigo, along with seabed samples from the same areas. The methodology centres on the Mixture Tuned Matched Filtering (MTMF) algorithm to map seabed targets by matching known spectral signatures with UHI data. It also identified sediments with spectral signatures consistent with placer deposits found on Ria de Vigo beaches.

Finally, we integrate traditional geological methodologies with EO techniques for delineating CRM prospective areas for placer deposits.

## 4.2 Extraction Phase

The experimental results include images at a global scale, as collected in the Sentinel-1 and -2 datasets. All models' performance was evaluated using PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index Measure). The Sentinel-2 enhanced model (OGSRN) performs less than the Dense Residual-in-Residual Dense Block (RRDB) model, based on Sentinel-1 only. Overall, it demonstrated how deep learning models can improve the resolution of SAR data, showing an increase of 18% for the PSNR score and about 2% for the SSIM score compared to the corresponding baseline bicubic scores.

The Multigrid InSAR technique provides accurate measurements, with millimetre accuracy, of the ground deformations along the satellite radar line-of-sight (LOS) direction. Combining multi-band and multi-orbit SAR data, the obtained LOS displacement measurements can be profitably exploited to compute three-dimensional (3D) ground movement (Figure 7). Deformation is due to soil compaction, so the material added to the waste dump naturally keeps compacting.

The implementation of LGMSs at Gummern Mine includes two stable reference points (F1 and F3) placed on the stable ground and three observation points (ST1, ST2, and ST3), realized with metal poles, mounted in concrete pillars (Figure 8).

Based on the one-year testing period, it can be stated that LGMS performed well and detected slow movement with sub-centimetre accuracy. The results indicated that horizontal and vertical displacements of 10 and 20 mm occurred in ST1, while larger displacements were noticed in ST2, which moved 25 mm horizontally and 40 mm vertically.

<figure><img src="image_213.png" /><figcaption>Figure 7: Gummern ground deformation map (a) and time series obtained from COSMO-SkyMed (b).</figcaption></figure>

<figure><img src="image_214.png" /><figcaption>Figure 8: Locations of measuring stations.</figcaption></figure>

An innovative methodology was also developed based on satellite images to continuously monitor the life and evolution of mining waste deposits. Initially, land surveying is conducted to establish ground control points (GCPs). Following this, UAV flights are undertaken to produce high-resolution DEMs. The data acquisition process involves SfM processing. The next stage involves acquiring new satellite image datasets for specific epochs. For each epoch, a different DEM and orthophoto are obtained. Comparative analysis of successive DEMs allows calculating geometric or volumetric changes in the waste dumps over time. The Pléiades Neo tri-stereo dataset was the first satellite imagery used in the study, captured in October 2023, approximately one month after the UAV flight. These images already showed notable changes in the waste dump. To
compare the UAV-derived high-resolution DEM (HR DEM) with the Pléiades Neo-derived DEM, the UAV HR DEM was resampled to match the resolution of the Pléiades Neo DEM (30 cm). WorldView-2 images, taken in April 2024, six months after the Pléiades Neo tri-stereo images, provided an opportunity to study changes over a longer period. Figure 9 illustrates altitude differences between the DEM from WorldView-2 (12. 4. 2024) and the DEM from Pléiades Neo (1. 10. 2023). The volume changes in the areas where waste dumping occurred are summarized, with a total discharge volume of 101,872 m³.

<figure><img src="image_215.png" /><figcaption>Figure 9: Volumes of waste dumps between 12. 4. 2024 (DEM from Pléiades NEO) and 1. 10. 2023 (DEM from WorldView 2).</figcaption></figure>

## 4.3 Closure and Post-Closure Phase

Several ML algorithms were adapted to enhance pixel classification of AMD from Sentinel 2 imagery (Lausitz and Outokumpu) and also using space- and airborne- multispectral and hyperspectral imagery. SOM was also utilized to visualize and cluster high-dimensional data to interpret complex spatial data for AMD mapping. The study also evaluates the potential of spaceborne hyperspectral imagery for AMD mapping.

We also proposed ML algorithms, including RF, KNN, Logistic Regression (LR), and MLP. They are used to perform a pixel-based classification of the images into AMD or non-AMD classes, as well as to assess the severity of AMD by quantitative mapping of AMD constituents, such as iron concentration and pH values. The prediction map for three lakes in the Outokumpu area is shown in Figure 10. The visualization indicates that the RF model accurately classified the pixels, as the lakes were primarily contaminated by AMD rather than coastal areas.

Additionally, the SOM method was also used to visualize and cluster high-dimensional data, simplifying the understanding and interpretation of complex spatial data for AMD mapping. The output of the SOM method is grid data, such as heatmaps or U-matrix plots, which provide insights into the clustering and organization of the data within the grid. The main objective was the identification of AMD-affected areas. These experiments were conducted in three lakes located in Outokumpu, Finland.

<figure><img src="image_216.png" /><figcaption>Figure 10: Prediction map of the best model (RF) for three study lakes and their water samples in Outokumpu.</figcaption></figure>

Finally, we perform cross-sensor analysis over water bodies to harmonize Worldview-3 and UAV multispectral datasets to Sentinel-2. Given the free availability of Sentinel-2 data and the typically high costs of high-resolution commercial EO datasets, we propose a methodology where MLP is trained using Sentinel-2 data over a large area in conjunction with extensive geochemical monitoring data and the established dependencies are applied to commercial high-resolution datasets for targeted identification of AMD in specific areas. This approach enables a cost-efficient combination of free-of-charge and commercial EO datasets for AMD mapping.

Despite the cessation of mining activities, a sudden ground collapse in February 2017 near the Aijala refinery highlighted ongoing environmental risks, demonstrating the need to monitor post-closure mining sites continuously. SAR image coherence measures how similar two radar images of the same area are, taken at different times. For the temporal decorrelation analysis, we proposed calculating a new index, the Normalized Decorrelation Change Index (NDCI), presented in Figure 11. The method's effectiveness was validated by comparing results with
Sentinel 2A images and relevant background information about the Aijala site.

<figure><img src="image_217.png" /><figcaption>Figure 11: NDCI depicting ongoing high disturbances in the Aijala post-closure site between 2015-2017.</figcaption></figure>

## 5 CONCLUSIONS

In this work, we present the preliminary results of the S34I HORIZON project. At the moment, the methods are being validated and verified in the pilots and also with the end-users.

Based on the methods developed under the scope of S34I, we prototyped EO-based services that cater to the specific needs of mining stakeholders. These services aim to address three key areas:

1. **RM Deposits Mapping:** This involves using EO data to identify and map potential mineral deposits, both on land and in shallow waters.

2. **Early Warnings:** The focus here is on developing EO-based systems that can provide early warnings of potential hazards at mining sites, such as ground instability.

3. **Environmental Monitoring:** This encompasses the use of EO data to monitor the environmental impact of mining activities, such as the detection of AMD.

In the future, these services will be available for the stakeholders and end-users.

The result of this project will be an important step forward in monitoring all phases of the mining cycle using EO data, contributing to more sustainable mining practices.

## 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.

## REFERENCES

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# An Ensemble Modeling Approach for Mapping Critical Mineral Distribution with LiDAR and PRISMA Data

Fahimeh Farahnakian¹, a, Mahyar Yousefi², b and Ana Cláudia Teodoro³, c

¹Geological Survey of Finland (GTK), 02151, Finland

²Faculty of Engineering, Malayer University, Malayer, Iran

³Instituto de Ciências da Terra, Departamento de Geociências, Ambiente e Ordenamento do Território, Faculdade de Ciências, Universidade do Porto, Porto, Portugal
fahimeh.farahnakian@gtk.fi, m.yousefi.eng@gmail.com, amteodor@fc.up.pt

**Keywords:** Ensemble Modeling, Data Fusion, LiDAR, PRISMA, Machine Learning, Remote Sensing, Mineral Exploration.

**Abstract:** Traditional mining exploration techniques require significant effort, including drilling and sample collection, making the process highly challenging and costly. The application of machine learning (ML) in mineral exploration has revolutionized the field by improving efficiency and accuracy in identifying critical raw materials (CRM). This study presents a novel framework that integrates Light Detection and Ranging (LiDAR) and PRISMA hyperspectral data with ML techniques to enhance mineral exploration. By leveraging an ensemble model combining Random Forest (RF) and Multi-Layer Perceptron (MLP), this approach captures complex spatial and spectral patterns, improving the prediction of cobalt, copper, and nickel concentrations. To address the challenge of limited labeled data, synthetic samples were generated using the Gaussian Copula Synthesizer (GCS), enhancing model generalization. The proposed methodology was validated at the Áramo mine in Asturias, Spain, demonstrating that the fusion of multispectral and topographical features significantly improves predictive accuracy. The results show that the scalability and robustness of this framework for identifying CRM in geologically significant yet underexplored regions.

## 1 INTRODUCTION

Traditional mining exploration techniques rely heavily on extensive field surveys, drilling, and geochemical sampling, making the process time-consuming, labor-intensive, and costly. Additionally, these methods often struggle with accessibility in remote or geologically complex regions, limiting their efficiency in large-scale mineral prospecting.

Recent advancements in Remote Sensing (RS) technologies, particularly Light Detection and Ranging (LiDAR), have been significantly employed in geological mapping (Paniagua et al., 1988; Putkinen et al., 2017) and mineral exploration (Balaram, 2023). LiDAR provides high-resolution topographical data, which makes it a valuable resource for identifying the presence of minerals (Lo et al., 2021; Farahnakian et al., 2024a). Another useful data type for

monitoring and studying environmental phenomena is hyperspectral data from satellites like PRISMA. The PRISMA satellite offers high-resolution spectral imaging across a wide range of wavelengths, allowing for detailed analysis of surface compositions. This capability makes it particularly useful in detecting specific minerals and distinguishing between different rock types or vegetation (Bedini and Chen, 2020). Therefore, fusing these datasets provide a comprehensive foundation for mapping geological and mineralogical features (Farahnakian et al., 2024a).

Machine learning (ML) methods have been extensively applied in Mineral Prospectivity Mapping (MPM) to analyze complex spatial and geochemical patterns associated with mineralization. Early studies utilized traditional ML algorithms such as Support Vector Machines (SVM), which demonstrated effectiveness in binary classification tasks for predicting mineral deposits (Abedi et al., 2012). Random Forest (RF) has gained popularity due to its robustness in handling high-dimensional data and its ability

<footnote>a D https://orcid.org/0000-0002-7672-9346</footnote>

<footnote>b D https://orcid.org/0000-0002-8042-000X</footnote>

<footnote>c D https://orcid.org/0000-0002-8043-6431</footnote>
ity to provide feature importance metrics, enabling in-
sights into the relationships between explanatory vari-
ables and mineral occurrences (Parsa and Maghsoudi,
2021). Gradient Boosting methods, such as XGBoost,
have also been employed for MPM, achieving high
accuracy by sequentially minimizing errors in predic-
tion (Ibrahim et al., 2022). Additionally, Artificial
Neural Networks (ANNs) have shown promise due
to their universal approximation capabilities, partic-
ularly in capturing complex, nonlinear relationships
between geochemical, geophysical, and RS variables
(Brown et al., 2000). Besides traditional ML models,
deep learning models such as Convolutional Neural
Networks (CNNs) (Sun et al., 2024) and autoencoders
(Luo et al., 2020) have been recently introduced to
leverage spatial and spectral features from hyperspec-
tral data, further enhancing predictive performance.

Despite these advancements, a key limitation
across most ML methods is the dependency on large,
labeled datasets for training. In mineral explo-
ration, such datasets are often limited due to the
high cost and logistical constraints of field sam-
pling. To mitigate this issue, synthetic data genera-
tion methods, such as the Gaussian Copula Synthe-
sizer (GCS) and deep learning-based approaches like
Conditional Generative Adversarial Networks (CT-
GAN) and Tabular Variational Autoencoder (TVAE),
have been proposed to augment training datasets, en-
abling ML models to generalize better and enhance
prediction accuracy. CTGAN (Xu et al., 2019a) is
a deep learning-based model tailored for generating
synthetic tabular data, excelling at capturing complex
dependencies between features by conditioning on
discrete variables. Similarly, TVAE (Xu et al., 2019b)
leverages variational autoencoders to synthesize tabu-
lar data by effectively modeling intricate relationships
within the dataset. In this work (Farahnakian et al.,
2024a), the authors demonstrate that the GCS outper-
forms deep learning-based models such as TVAE and
CTGAN, particularly in scenarios where training data
is limited or lacks variability. Another method, SEDA
(Sheikh et al., 2024) integrates feature and distance
similarities to augment the minority samples. They
evaluated the impact of SEDA on the performance of
four ML models, including Multi-Layer Perceptron
(MLP), RF, Decision Tree (DT), and Logistic Regres-
sion (LR). Their results show that adding high-quality
synthetic samples can help ML models to generalize
better to unseen data, addressing the overfitting issue
commonly seen in imbalanced datasets.

This study proposes a novel approach to mineral
exploration that combines LiDAR and PRISMA hy-
perspectral data to predict concentrations of critical

minerals, including cobalt (Co), copper (Cu), and
nickel (Ni), at the Áramo mine in Asturias, Spain.
An ensemble model combining RF and Multi-Layer
Perceptron (MLP) was developed to leverage the
strengths of both algorithms. RF was utilized for its
robust feature selection capabilities and ability to han-
dle high-dimensional datasets, while MLP was em-
ployed for its ability to model complex nonlinear re-
lationships in the fused dataset. To address the limita-
tion of labeled data, synthetic samples were generated
using GCS (Xu et al., 2019a), augmenting the dataset
and improving model performance.

The ensemble approach, employing an averaging
strategy, was evaluated using both real and synthetic
geochemical data as ground truth, demonstrating su-
perior performance compared to individual models.
Results indicate that the integration of multispectral
and topographical features derived from LiDAR and
hyperspectral imagery significantly enhances the rep-
resentation of spatial and spectral characteristics nec-
essary for identifying mineralization zones. Addition-
ally, the results underscore the effectiveness of data
augmentation in improving ML ensemble methods
for predicting critical raw material concentrations.
This framework offers a reliable and scalable solu-
tion for mineral exploration, advancing data-driven
exploration strategies while supporting sustainable re-
source development.

2 STUDY AREA

The pilot site, Áramo, located in the Sierra del Áramo
in northern Spain (Figure 1), lies within the Saint
Patrick Exploration License, an area renowned for its
Co, Cu and Ni mineralization. This mineralization is
associated with the Late-Variscan Áramo Fault (Pani-
agua et al., 1988) and occurs within the allochthonous
Áramo Unit, which is part of the Cantabrian Zone
(Aller, 1983). The mineralization predominantly oc-
curs in karstified Upper Carboniferous limestones that
have undergone multiple phases of hydrothermal al-
teration, followed by a supergene stage (Álvarez et al.,
2018; Archibald, 2021). These distinct alteration fea-
tures, including the lithological and structural char-
acteristics of the mineralized rocks, make the Aramo
mine particularly suitable for testing and advanc-
ing RS techniques for mapping critical raw materi-
als (CRM). The diversity of alteration signatures and
the well-documented geological framework enable
researchers to calibrate and validate RS data, such as
LiDAR, hyperspectral imaging, and other methods,
for effective mineral exploration in geologically com-
plex and underexplored regions.
<figure><img src="image_218.png" /><figcaption>Figure 1: Location of the whole Aramo mine area. The St. Patrick mining area is the selected study area in this study.</figcaption></figure>

<figure><img src="image_219.png" /><figcaption>Figure 2: The PRISMA imagery used in this study, alongside the actual distribution of (a) Co, (b) Cu, and (c) Ni concentrations from geochemical data across the study area.</figcaption></figure>

# 3 DATA

## 3.1 PRISMA

The PRISMA (PRecursore IperSpettrale della Missione Applicativa)<fnref n="1" /> satellite provides hyperspectral imagery across 250 bands, offering continuous spectral coverage. It includes 66 bands in the Visible and Near-Infrared (VNIR) range (400–1010 nm) and 173 bands in the Short-Wave Infrared (SWIR) range (920–2505 nm), both with a spatial resolution of 30 meters. Additionally, PRISMA is equipped

with a panchromatic camera that captures a single band (400–700 nm) image at 5-meter spatial resolution. A PRISMA image of the study area, featuring 5.1% cloud coverage, was acquired on May 10, 2022 (Figure 3), at the L2D processing level. To ensure consistency, all bands were resampled using nearest-neighbor interpolation to a 5-meter resolution. To reduce data dimensionality and minimize noise, Principal Component Analysis (PCA) was employed, a technique proven effective in mineral exploration with satellite imagery (Carvalho et al., 2024) (Adiri et al., 2020). PCA compresses the information from the original bands into a smaller set of bands, known as principal components (PCs). Each PC rep

<footnote n="1">¹https://www.asi.it/en/earth-science/prisma/</footnote>
resents contributions from all the input bands and is
ranked according to the amount of variance it explains
(Carvalho et al., 2024).

## 3.2 Airborne Light Detection and Ranging (LiDAR)

In this study, high-resolution airborne LiDAR data was acquired by Eurosense<fnref n="2" /> to capture detailed topographic information of the study area. The LiDAR survey was conducted at an altitude of approximately 2,450 meters above mean sea level (AMSL) and 1,450 meters above ground level (AGL). To ensure thorough coverage, the LiDAR strips were flown with a 70-80% overlap, reducing gaps between flight paths and enhancing spatial continuity.

The average LiDAR point density exceeded 10 points per square meter, providing a fine spatial resolution, with each LiDAR spot having a ground diameter of 36 cm. This high-density data was crucial for producing accurate Digital Terrain Models (DTM) and Digital Surface Models (DSM), both generated at a 0.5-meter grid resolution. This ensured that terrain and surface details were captured with high precision. To interpolate ground elevation values between LiDAR points, Inverse Distance Weighting (IDW) interpolation was applied, assigning greater weight to points closer to the target location, thus ensuring smooth and accurate surface modeling.

## 3.3 Field Data

The study area, Áramo mine, comprises the Saint Patrick Exploration License, for Co, Cu, and Ni, currently being explored by AURUM Global Exploration<fnref n="3" />. Figure 3 illustrates the Co deposits and their concentration on the PRISMA image. The dataset consists of 729 samples, with Co concentrations ranging from 1 to 18,750 parts per million (ppm), with an average of 348.9 ppm. The Cu ranges from 2 to 500,000 ppm, with an average of 10,742.7 ppm. The Ni ranges from 1 to 16,800 ppm, with an average of 430.7 ppm. The distribution of metal concentrations, particularly Cu (Cu), has a large spread, with some extreme maximum values (e.g., 50% Cu and 500,000 ppm Cu). The standard deviation for Cu is quite high, indicating variability in the dataset. Co and Ni concentrations are much lower compared to Cu on average, and have more moderate spreads.

<figure><img src="image_220.png" /><figcaption>Figure 3: Correlation matrix heatmap for targets.</figcaption></figure>

To confirm that multi-target regression is justified, we plot the correlation heatmaps between targets as shown in Figure 3. From the visualization, we can see there is a strong positive correlation between Co and Ni (0.66). However, the correlation between Cu and Co (0.35) is weaker. For this reason and based on our extensive experiments, we found that using a multi-target regression for Co and Ni and a septate model for Cu can achieve better result based on our data.

The original distribution of data for the three minerals is skewed (see Figure 4a). To address this, we performed three preprocessing steps on the field data to mitigate issues such as skewed distributions, outliers, and variability in feature scales. The processed distribution of minerals is presented in Figure 4b. These preprocessing steps were crucial for enhancing the stability and performance of the machine learning models, as outlined below:

* **Logarithmic transformation** was applied to features with positively skewed distributions to normalize the data. Specifically, the natural logarithm with a small offset ($\log(1+x) = \log(1+x)$) was applied to numeric features to ensure the transformation was defined for zero values.

* **Winsorization** was used to handle outlier, where feature values were capped at the 99th percentile. This step was applied column-wise to numeric features to reduce the effect of extreme values while preserving the majority of the data distribution.

* **Standard scaling** was applied to all features to normalize their distributions. This transformation

<footnote n="2">²https://www.eurosense.com/</footnote>

<footnote n="3">³https://www.aurumexploration.com/
exploration-projects-in-asturias-spain/</footnote>
<figure><img src="image_221.png" /><figcaption>Figure 4: Comparison of (a) raw data and (b) processed distributions of mineral concentrations (Co, Cu, Ni).</figcaption></figure>

scaled the data to have a mean of zero and a standard deviation of one.

# 4 METHODOLOGY

Figure 5 illustrates the proposed methodology outlined in this study. The final dataset consists of five features, including DEM and DSM derived from LiDAR data, and three principal components (PCs) extracted from PRISMA hyperspectral data. The field dataset, comprising 729 samples, is divided into training and test sets in a 70:30 ratio. To address the limited labeled data, GCS was employed to generate synthetic data based on the training dataset. Both feature and target values were augmented, and the synthetic data was integrated with the original dataset to enhance the model training process.

Two models, RF and MLP, were used to solve the regression problem for exploring the target mineral's distribution patterns. Both models are evaluated with 10-fold Cross Validation (CV), and optimal hyperparameters were determined using a Grid Search tech-

nique (Pedregosa et al., 2011). The predictions from the two models were then combined through an averaging ensemble approach to produce the final output. The ensemble model's performance was evaluated on the test dataset using metrics such as RMSE, MAE, and R2. The best-performing model was subsequently utilized to generate high-resolution mineral prediction maps for Co, Cu, and Ni across the entire study area.

## 4.1 Synthetic Data Generation

GCS is reasonable if the dataset is small, as it helps introduce diversity while preserving relationships within the data. It was first introduced in the Synthetic Data Vault (SDV) as a method to generate synthetic data by modeling the statistical properties and dependencies within a single table (Patki et al., 2016). The process begins by identifying the appropriate probability distributions for each column, such as Gaussian, uniform, or other relevant distributions, using statistical tests like the Kolmogorov-Smirnov (Jr., 1951) test to determine the best fit. The Gaussian Copula Synthesizer captures the covariances between
<figure><img src="image_222.png" /><figcaption>Figure 5: Overall framework.</figcaption></figure>

columns by standardizing them to a normal distribution before computing their dependencies, which allows it to accurately replicate the relationships within the data. This approach ensures that the generated data maintains the original patterns and correlations observed in the real data, making it highly realistic and suitable for various data science tasks.

One of the key advantages of the GCS is its computational efficiency compared to more complex generative models, such as Generative Adversarial Networks (GANs). Since it relies on well-established statistical methods, this approach requires significantly less computational power and training time, making it faster and more scalable for large datasets. Additionally, GCS is inherently robust and easier to implement, as it does not involve the intricate training dynamics of adversarial models, which can be prone to instability and mode collapse. These strengths make GCS an attractive choice for generating high-quality synthetic data with minimal computational overhead, facilitating broader adoption in scenarios where data privacy and speed are critical concerns (Patki et al., 2016).

## 4.2 Ensemble Modeling

RF (Genuer et al., 2008) is an ensemble learning algorithm that constructs multiple decision trees during training and aggregates their outputs to enhance accuracy and robustness. For regression tasks, RF predicts by averaging the outputs of the individual trees, while for classification tasks, it determines the final output using the majority vote (mode) of the trees. RF excels at handling large, high-dimensional datasets and provides inherent estimates of feature importance, offering insights into the underlying data patterns (Farahnakian et al., 2024b). Its design mitigates overfitting by combining predictions from multiple trees, thereby improving generalization. Recent advancements have

further refined its computational efficiency and overfitting control, solidifying RF as a reliable and interpretable choice for both predictive and descriptive tasks.

MLP is a type of feedforward artificial neural network composed of an input layer, one or more hidden layers, and an output layer. Each layer consists of interconnected neurons that employ nonlinear activation functions to capture complex relationships in the data. During training, MLP adjusts its weights using optimization algorithms such as backpropagation to minimize the error between predicted and actual outputs. Known for its universal approximation properties, MLP is highly adaptable for tasks such as regression and classification. However, it is susceptible to overfitting, especially in models with excessive neurons or layers, which can be mitigated using techniques like dropout and regularization. Determining the optimal architecture typically requires experimentation and validation.

In this study, we employed "GridSearchCV", a systematic method from the scikit-learn library, to identify the optimal set of hyperparameters for our models. For RF, the optimal number of estimators was 50, and the maximum depth of the trees was set to 10. For MLP, the best configuration for the hidden layer size was 100, and the regularization term alpha was set to 0.001.

To further enhance predictive performance, we proposed an ensemble approach that combines the predictions from RF and MLP to leverage their complementary strengths. Ensemble learning integrates the outputs of multiple predictive models to enhance performance, robustness, and generalization. For regression tasks, we employed an averaging-based ensemble, which reduces variance and improves predictive accuracy by merging the outputs of RF and MLP. This approach capitalizes on RF's ability to handle
noisy, high-dimensional data and MLP's capacity to
learn from complex, multi-modal inputs, resulting in
a more robust and accurate predictive framework.

## 4.3 Performance Metrics

To evaluate the predictive performance of our models,
we used the following metrics:

* **Mean Absolute Error (MAE):** Measures the average absolute difference between predicted and actual values. It provides a straightforward interpretation of error magnitude, with smaller values indicating better performance.

* **Root Mean Squared Error (RMSE):** Represents the square root of the average squared differences between predicted and actual values. RMSE is sensitive to large errors and highlights the model's ability to handle outliers.

* **Coefficient of Determination (R²):** Indicates the proportion of variance in the target variable explained by the model. An R² value close to 1 suggests that the model explains most of the variability, while negative values indicate poor predictive performance.

# 5 RESULTS

We trained a single-target regression model for Cu to focus on its individual predictive patterns, while a multi-target regression model was developed for Co and Ni to leverage their strong correlation and enhance predictive performance. This dual modeling approach ensures that both independent and correlated variables are optimally modeled. The models were evaluated using RMSE, MAE, and R², providing a comprehensive assessment of predictive accuracy, residual error, and the proportion of variance explained by the models.

## 5.1 Models' Performance

Table 1 shows the performance of different models (RF, MLP, and Ensemble) applied to both single-target and multi-target regression tasks.

**Single-Target Regression (Cu):** The Ensemble model demonstrated the best performance when augmented data was used, achieving an RMSE of 1.66, an MAE of 0.83, and an R² score of 0.18. This improvement highlights the effectiveness of GCS in enhancing the diversity of training data, thereby reducing model bias and variance.

RF also benefited significantly from GCS augmentation, with an RMSE improvement from 2.45 (real data only) to 1.94 and a reduction in MAE from 0.92 to 0.87. However, the MLP exhibited diminished performance with GCS augmentation, as evidenced by an increase in RMSE (from 1.78 to 4.14) and a slight decline in R² (from 0.11 to 0.08). This suggests that the synthetic data may have introduced inconsistencies or overfitting tendencies for MLP. Overall, the Ensemble model with GCS augmentation emerged as the most robust approach for Cu prediction, outperforming both individual models in accuracy and generalization.

**Multi-Target Regression (Co, Ni):** For the multi-target regression of Co and Ni concentrations, GCS augmentation provided notable benefits to the Ensemble model. The Ensemble model achieved the best performance, with an RMSE improvement from 0.11 (real data only) to 0.10 and an increase in R² from 0.12 to 0.18. The RF model showed consistent performance, maintaining an RMSE of 0.11 and improving R² from 0.05 to 0.12, indicating that GCS successfully captured additional variability in the dataset. Similarly, the MLP model exhibited a slight improvement in R² (from 0.11 to 0.14) with GCS, while maintaining consistent RMSE and MAE values.

## 5.2 Impact of Data Augmentation on Model Performance

Table 1 also provides performance metrics (RMSE, MAE, and R²) for machine learning models trained with and without GCS-based data augmentation for both single-target and multi-target predictions. The column "Data Source" in Table 1 indicates whether the models were trained using only real data or a combination of real and synthetic data.

For the single-target predictions of Cu concentration, the RF model shows improvement with GCS augmentation, with RMSE decreasing from 2.45 to 1.94 and R² improving from -0.67 to -0.10. However, the MLP model's performance slightly deteriorates with GCS augmentation (RMSE increases from 1.78 to 4.14). The ensemble model benefits from GCS augmentation, achieving the best overall results for Cu with an RMSE of 1.66 and an R² of 0.18, showcasing the ensemble's ability to generalize better when leveraging augmented data.

For multi-target predictions (Co and Ni), the impact of GCS augmentation is less pronounced but still notable. While the RF model shows slight improvements in R² from 0.05 to 0.12, the MLP and en-
<table><caption>Table 1: Performance metrics for ML models with and without GCS Augmentation.</caption><thead><tr><th>Model Type</th><th>Target</th><th>Data Source</th><th>Model</th><th>RMSE</th><th>MAE</th><th>R<sup>2</sup></th></tr></thead><tbody><tr><td>Single-Target</td><td>Cu</td><td>Real Data</td><td>RF</td><td>2.45</td><td>0.92</td><td>-0.67</td></tr><tr><td>Single-Target</td><td>Cu</td><td>Real + GCS</td><td>RF</td><td>1.94</td><td>0.87</td><td>-0.10</td></tr><tr><td>Single-Target</td><td>Cu</td><td>Real Data</td><td>MLP</td><td>1.78</td><td>0.89</td><td>0.11</td></tr><tr><td>Single-Target</td><td>Cu</td><td>Real + GCS</td><td>MLP</td><td>4.14</td><td>1.15</td><td>0.08</td></tr><tr><td>Single-Target</td><td>Cu</td><td>Real Data</td><td>Ensemble</td><td>1.96</td><td>0.87</td><td>-0.07</td></tr><tr><td>Single-Target</td><td>Cu</td><td>Real + GCS</td><td>Ensemble</td><td>1.66</td><td>0.83</td><td>0.18</td></tr><tr><td>Multi-Target</td><td>Co,Ni</td><td>Real Data</td><td>RF</td><td>0.11</td><td>0.02</td><td>0.05</td></tr><tr><td>Multi-Target</td><td>Co,Ni</td><td>Real + GCS</td><td>RF</td><td>0.11</td><td>0.03</td><td>0.12</td></tr><tr><td>Multi-Target</td><td>Co,Ni</td><td>Real Data</td><td>MLP</td><td>0.11</td><td>0.04</td><td>0.11</td></tr><tr><td>Multi-Target</td><td>Co,Ni</td><td>Real + GCS</td><td>MLP</td><td>0.11</td><td>0.03</td><td>0.14</td></tr><tr><td>Multi-Target</td><td>Co,Ni</td><td>Real Data</td><td>Ensemble</td><td>0.11</td><td>0.03</td><td>0.12</td></tr><tr><td>Multi-Target</td><td>Co,Ni</td><td>Real + GCS</td><td>Ensemble</td><td>0.10</td><td>0.02</td><td>0.18</td></tr></tbody></table>

ensemble models benefit more significantly. The ensemble model achieves the best overall performance with GCS augmentation, as indicated by a decrease in RMSE to 0.10 and an improvement in R² to 0.18. These results suggest that GCS augmentation effectively enhances model performance for multi-target tasks, particularly for ensemble methods.

<figure><img src="image_223.png" /><figcaption>Figure 6: The performance metrics vs the number of synthetic data points for the ensemble model of (a) single target (Cu), and (b) multi-target (Co,Ni).</figcaption></figure>

## 5.3 Model Performance vs Number of Synthetic Samples

Different amounts of synthetic data are systematically tested and combined with real data to enhance model robustness. Figure 6 shows the performance metrics (MAE, RMSE and R²) versus the number of synthetic data points for the ensemble model of single target and multi-target. The performance of the ensemble model for single-target regression (Cu) improves consistently as the number of synthetic data points increases. As shown in Figure 6(a), the RMSE and MAE exhibit a gradual decline, while the R² score shows a steady increase. With the addition of up to 2,000 synthetic data samples, the RMSE reduces to 1.66, and the MAE decreases to 0.83, demonstrating enhanced accuracy. The R² improves to 0.18, indicating better variance explanation. These results confirm the effectiveness of GCS augmentation in enhancing model robustness and predictive performance for single-target regression tasks.

For the multi-target regression of Co and Ni, the ensemble model similarly benefits from the inclusion of synthetic data, as shown in Figure 6(b). The RMSE remains stable at 0.10, while the MAE maintains a low value of 0.02. The R² score increases slightly to 0.18, demonstrating a modest improvement in variance explanation with the addition of synthetic data. Unlike the single-target task, the improvements are more marginal, suggesting that the real dataset already captures much of the variability for these targets. Nevertheless, the synthetic data contributes to maintaining model stability and consistency across performance metrics.

In Figure 7, we also visualize the distribution of real data and the synthetic dataset generated using GCS only for the optimal number of synthetic data
<figure><img src="image_224.png" /><figcaption>Figure 7: Distribution of the real dataset versus synthetic dataset for the ensemble model predictions: (a) Cu and (b) Co and Ni.</figcaption></figure>

<figure><img src="image_225.png" /><figcaption>Figure 8: The prediction maps of (a) Co, (b) Cu, and (c) Ni concentrations across the study area.</figcaption></figure>
where ensemble model achieves the lowest RMSE. Both regression models achieve the lowest RMSE with 2,000 synthetic samples. Across all three targets (Cu, Co, Ni), the synthetic data closely approximates the real data distribution. This demonstrates the capability of GCS to model and replicate the statistical characteristics of the real dataset. The close alignment between real and synthetic distributions supports the validity of using the synthetic dataset for data augmentation in training machine learning models. This alignment ensures that the synthetic data does not introduce significant biases into the learning process.

## 5.4 Predicted Distribution Maps

The prediction maps of Co, Cu, and Ni concentrations (Figure 8) illustrate the spatial distribution of these critical raw materials across the study area. These maps were generated using the ensemble modeling approach that combines RF and MLP models using LiDAR and PRISMA data.

The Co prediction map (Figure 8(a)) reveals a concentrated distribution of Co in the central and northeastern portions of the study area, with estimated concentrations ranging from 40 ppm to 900 ppm. These areas coincide with regions of known geological features conducive to Co mineralization.

The Cu prediction map (Figure 8(b)) reveals a concentrated distribution of Cu in the central and southern portions of the study area, with estimated concentrations ranging from 30 ppm to 60,459 ppm.

The Ni prediction map (Figure 8(c)) highlights high Ni concentrations (up to 1,118 ppm) in the northern and southern parts of the study area, indicating potential overlap with Co mineralization zones. This spatial correlation underscores the strong geological relationship between Co and Ni, which was effectively captured by the multi-target regression model.

These prediction maps demonstrate the efficacy of the ensemble modeling approach in integrating multi-sensor data and leveraging synthetic data augmentation to produce high-resolution distribution maps. The results provide valuable insights for prioritizing exploration targets and guiding resource development strategies in geologically significant areas.

# 6 CONCLUSIONS

This study presents a comprehensive framework for mineral exploration, demonstrating the integration of LiDAR-derived elevation models (DEM, DSM)

with hyperspectral Principal Components (PCs) extracted from PRISMA imagery provided a richer feature space for ML models. By combining the predictive strengths of RF and MLP in an ensemble model, the approach effectively addresses the complexities of multi-sensor data analysis. The inclusion of synthetic data generated by the GCS significantly mitigates the challenges posed by limited labeled datasets, enhancing model performance and generalization. Experiments conducted at the Áramo mine in Asturias, Spain, validated the framework's ability to produce accurate distribution maps.

Future work will focus on extending this framework to incorporate additional data sources, such as geophysical measurements and soil geochemistry, to further enhance prediction accuracy and applicability in diverse geological settings. Additionally, exploring advanced machine learning techniques, such as deep learning models tailored for multi-sensor data fusion, and assessing their scalability across larger study areas will be prioritized.

# ACKNOWLEDGMENT

This study is funded by the European Union under grant agreement no. 101091616, project S34I - Secure and Sustainable Supply of Raw Materials for eu Industry.

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

Beatriz L. Araújo¹ IDª, Joana Cardoso-Fernandes¹,² IDᵇ, Antonio Azzalini ¹ IDᶜ, Morgana Carvalho¹,² IDᵈ,
Alexandre Lima ¹,² IDᵉ, Francisco J. González³ IDᵉf and Ana Cláudia Teodoro¹,² IDᵉg

¹Department of Geosciences, Environmental and Spatial Planning, Faculty of Sciences of the University of Porto,
Rua do Campo Alegre s/n, Porto, Portugal

²ICT (Institute of Earth Sciences) – Porto Pole, Portugal

³Marine Geology Resources and Extreme Environments, Geological Survey of Spain (IGME-CSIC), Madrid, Spain
up201811447@up.pt, joana.fernandes@fc.up.pt, antonio.azzalini@fc.up.pt, morgana.carvalho@fc.up.pt, allima@fc.up.pt,
fv.gonzales@igme.es, amteodor@fc.up.pt

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.

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

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

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

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

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

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

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

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

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).

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

Proceedings Copyright © 2025 by SCITEPRESS – Science and Technology Publications, Lda.
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><img src="image_226.png" /><figcaption>Figure 1: Geographical location of the Ria de Vigo in Galicia, Spain.</figcaption></figure>

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,
<figure><img src="image_227.png" /><figcaption>Figure 2: Geological map of the study area, based on the MAGNA 50 – Geological map of Spain, at scale 1:50000.</figcaption></figure>

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.
<table><caption>Table 1: Description of the band ratios performed.</caption><thead><tr><th>Band ratio</th><th>Type of mineral featured</th></tr></thead><tbody><tr><td><sup>153</sup>/<sub>189</sub></td><td>Alteration</td></tr><tr><td>(153 + 196)/189</td><td>Alunite/Kaolinite/Pyrophyllite</td></tr><tr><td><sup>189 * 204</sup>/<sub>196<sup>2</sup></sub></td><td>Clay</td></tr><tr><td><sup>204</sup>/<sub>189</sub></td><td>Kaolinite</td></tr><tr><td>(204 + 217)/208</td><td>Carbonate/Chlorite/Epidote</td></tr><tr><td>(196 + 217)/(204 + 208)</td><td>Epidote/Chlorite/Amphibole</td></tr><tr><td><sup>46</sup>/<sub>29</sub></td><td>Ferric iron</td></tr><tr><td><sup>189</sup>/<sub>68</sub> + <sup>29</sup>/<sub>45</sub></td><td>Ferrous Iron</td></tr><tr><td><sup>153</sup>/<sub>68</sub></td><td>Ferric Oxides</td></tr><tr><td><sup>189</sup>/<sub>153</sub></td><td>Ferrous Silicates</td></tr><tr><td><sup>187 + 197</sup>/<sub>194</sub></td><td>Al-sheetsilicates</td></tr><tr><td><sup>196</sup>/<sub>208</sub></td><td>Amphibole</td></tr><tr><td><sup>196 + 217</sup>/<sub>208</sub></td><td>Amphibole/ MgOH</td></tr></tbody></table>

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><caption>Table 2: Independent spectral libraries used for the MTMF classification and description of the endmembers.</caption><thead><tr><th rowspan="2">First Classification (USGS library)</th><th colspan="2">Second Classification (HM library)</th></tr><tr><th>Spectrum code</th><th>Manual interpretation</th></tr></thead><tbody><tr><td>Almandine</td><td>UPO_1</td><td>Montmorillonite, Illite</td></tr><tr><td>Goethite</td><td>UPO_2</td><td>Amphibole, Montmorillonite</td></tr><tr><td>Grossular</td><td>UPO_3</td><td>Amphibole, Montmorillonite</td></tr><tr><td>Ilmenite</td><td>UPO_4</td><td>Amphibole, Montmorillonite</td></tr><tr><td>Monazite</td><td>UPO_5</td><td>Chlorite, Biotite</td></tr><tr><td>Rutile</td><td>UPO_6</td><td>Montmorillonite, Illite</td></tr><tr><td>Sillimanite</td><td>UPO_7</td><td>Tourmaline, Garnet</td></tr><tr><td>Spessartine</td><td>UPO_8</td><td>Tourmaline, Garnet</td></tr><tr><td>Tourmaline</td><td>UPO_9</td><td>Tourmaline, Garnet</td></tr><tr><td>Zircon</td><td>UPO_10</td><td>Tourmaline, Garnet</td></tr></tbody></table>

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
<figure><img src="image_228.png" /><figcaption>Figure 3: A) Alteration band ratio; B) Ferric oxides band ratio; C) Amphibole band ratio.</figcaption></figure>

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><img src="image_229.png" /><figcaption>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.</figcaption></figure>

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.
<figure><img src="image_230.png" /></figure>

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><img src="image_231.png" /></figure>

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.
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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# AMD Mapping in the Lusatian Region: From Medium to Very High-Resolution R/S Data

Delira Hanelli<sup>a</sup>, Enis Sterjo<sup>b</sup>, Roberto De La Rosa<sup>c</sup> and Andreas Knobloch<sup>d</sup>

Beak Consultants GmbH, Am St.-Niclas-Schacht 13, 09599 Freiberg, Germany

{delira.hanelli, enis.sterjo, andreas.knobloch}@beak.de

**Keywords:** Acid Mine Drainage, Lusatia, Artificial Neural Networks, Remote Sensing, Multispectral Imagery.

**Abstract:** The Lusatian region is undergoing an extensive landscape rehabilitation program following the closure of lignite open-pit mines. Under this programme, former open-cast lignite mines are being converted into artificial water bodies. However, the region faces significant challenges related to the acidification of surface and groundwater primarily driven by the oxidation of pyrite. Recent geochemical analyses show that, surface waters exhibit a strong variation of pH and iron concentration. This study aims to elaborate the potential of free and commercial space- and airborne- multispectral Remote Sensing (R/S) datasets (Sentinel-2, Worldview-3 and Unmanned Aerial Vehicle (UAV)) for large-scale acid mine drainage (AMD) mapping and identify the most suitable data sources and approaches for practical case studies. Additionally, cross-sensor comparisons are performed to gain more insights into the agreement between the spectra from Sentinel-2 images with those from the Worldview-3 and UAV images over surface water. The cross-sensor agreement of the images is quantified by performing regression analyses between R/S data at different wavelengths. Finally, dependencies and relationships between AMD constituents and the spectral data are investigated using artificial neural networks (ANN) of type Multi-Layer Perceptron (MLP).

## 1 INTRODUCTION

The Lusatian region in Germany, well-known for lignite mining, is currently undergoing one of the largest landscape rehabilitation programs in Europe (LMBV, n.d.). Following the closure of open-pit lignite mines, the region comprises approximately 176 artificial water bodies, covering a total area of 185 km² (Hanelli, et al., 2023). Leading this large-scale transformation and rehabilitation, the Lausitzer und Mitteldeutsche Bergbau-Verwaltung (LMBV) is monitoring the water quality in these water bodies by periodic sampling campaigns and geochemical analyses.

The water quality across the region varies significantly due to differences in acid mine drainage (AMD) stages, water treatment methods, and geological conditions. For instance, newly flooded or untreated lakes often exhibit highly acidic conditions, with pH values between 2.5 and 4.5, whereas treated and naturally neutralized lakes typically range

between 6.5 and 8 (LMBV, n.d.). Given its complexity and large spatial extent, the Lusatian region presents an ideal case study for developing and validating cost-effective AMD mapping methods.

Remote sensing (R/S) technologies offer promising solutions for large-scale AMD monitoring (Hanelli et al., 2023; Farahnakian et al., 2024; Kopačková, 2019). This study evaluates the potential of free and commercial multispectral datasets from spaceborne and airborne platforms for AMD mapping in a selected area of the Lusatian region characterized by strong AMD variations. A key focus is on cross-sensor comparisons to assess spectral data consistency across platforms and the transferability of AMD-related spectral relationships.

The multispectral R/S datasets utilized in this study include Sentinel-2, WorldView-3, and Unmanned Aerial Vehicle (UAV) data. These datasets cover different areas of interest (AOI) depending on their availability, costs and accessibility (Figure 1). The free-of-charge Sentinel-2 data cover

<footnote><sup>a</sup> https://orcid.org/0009-0002-0012-1729</footnote>

<footnote><sup>b</sup> https://orcid.org/0009-0009-0993-0750</footnote>

<footnote><sup>c</sup> https://orcid.org/0000-0002-3004-7104</footnote>

<footnote><sup>d</sup> https://orcid.org/0000-0001-7515-001X</footnote>
an extensive area of post-mining water bodies and several AMD scenarios. In contrast, the commercial WorldView-3 data were acquired only for a 100 km² representative area with significant AMD activity.

In most of the post-mining lakes in the Lusatian region the access is restricted due to geotechnical instabilities (ground subsidence and landslides) and very acidic water environments (LMBV, n.d.). Given these limitations and the objectives of this study, two pilot sites were selected for UAV surveys: Scheibe See (685 hectares, no evidence of AMD) and Bergheider See (325 hectares, evidence of AMD).

<figure><img src="image_232.png" /><figcaption>Figure 1: AOIs for each of the used R/S datasets.</figcaption></figure>

The R/S datasets were utilised as key parameters for area-wide mapping of AMD in post-mining water bodies. At the same time, geochemical analyses of AMD components, such as iron concentration and pH values obtained from surface water samples, served as calibration data for the mapping process. The relationships between the AMD components and the spectral data are exploited by means of artificial neural networks (ANNs).

This application requires a synchronization of R/S datasets with geochemical analysis, in order to ensure that the measured water quality parameters correspond to the recorded surface reflectance information in the R/S datasets. This is particularly challenging considering the prerequisites of optical R/S imagery (e.g. cloud- and shadow-free, low-nadir-angle etc.). To increase the chances of getting suitable acquisitions, a temporal window of ± 10–20 days to the sampling date was applied, assuming no significant geochemical changes within this timeframe (e.g., through neutralization processes). This study integrates both R/S and geochemical datasets collected in late June 2024.

The study aims to improve AMD mapping techniques and identify the most effective and practical methodologies for environmental monitoring in post-mining landscapes.

# 2 DATA ACQUISITION

## 2.1 Geochemical Analysis

Geochemical analysis results for 31 post-mining water bodies were provided by LMBV for this study (Figure 2). The samples were collected from the epilimnion layer (0–15 m depth). Figure 1 shows the measured values of iron concentration as graduated symbols/colours, while the measured pH values are shown as column chart, whereas small columns indicate an acidic environment (pH varies between 2 and 3) and the big ones a neutral environment (pH varies between 6 and 8).

<figure><img src="image_233.png" /><figcaption>Figure 2: Water monitoring stations and recorded values of AMD constituents in the Lusatian post-mining water bodies.</figcaption></figure>

The water bodies in this region are characterized by strongly varying AMD levels (Table 1) and are therefore suitable for this research study.

<table><caption>Table 1: Statistics of the water geochemical parameters.</caption><thead><tr><td>Parameter</td><td>Count</td><td>Min</td><td>Max</td><td>Mean</td><td>Std</td></tr></thead><tbody><tr><td>pH</td><td>31</td><td>2,45</td><td>7,96</td><td>3,77</td><td>1,90</td></tr><tr><td>Fe (mg/L)</td><td>31</td><td>0,07</td><td>361</td><td>90,65</td><td>101,42</td></tr></tbody></table>

Figure 3 shows a plot of dependencies between the iron concentration and pH values. Water bodies with high iron concentrations are typically marked by low pH values (≤4), while water bodies with low iron concentrations typically have pH values between 6 and 8. Although pH is an optically non-active parameter and cannot be directly detected using optical R/S data, in this study we use the observed dependencies for large-scale mapping of pH values by leveraging patterns of optically active constituents, such as iron concentration.
<figure><img src="image_234.png" /><figcaption>Figure 3: Plot of dependencies between Log (Fe) and pH.</figcaption></figure>

## 2.2 R/S Datasets

An overview of the used R/S datasets for AMD mapping follows in Table 2. All data were projected to WGS1984/UTM 33N (WKID: 32633).

<table><caption>Table 2: Overview of the acquired imagery for AMD mapping (VNIR: visible and near-infrared, LWIR: Long-Wave Infrared, SWIR: Short-Wave Infrared).</caption><thead><tr><th>Type</th><th>Sensor</th><th>Acquisition Date</th><th>Spectral Range</th><th>Temporal Res.</th><th>Spatial Res.</th><th>Cloud Cover</th><th>AOI</th></tr></thead><tbody><tr><td>Space-Based<br>Multispectral Imagery</td><td>Sentinel-2</td><td>16 June 2024</td><td>VNIR -<br>SWIR<br>443-<br>2190 nm</td><td>2 - 3 days<br>Free of<br>Charge</td><td>10, 20, 60 m</td><td>0.0<sup>e</sup></td><td>Tile:<br>T33UVT</td></tr><tr><td>Space-Based<br>Super spectral<br>Imagery</td><td>Worldview-3</td><td>VNIR<br>26 June 2024<br>SWIR<br>29 June 2024</td><td>VNIR<br>400 -<br>1040 nm<br>SWIR<br>1195 -<br>2365 nm</td><td>&lt;1 day<br>Commercial<br>data</td><td>VNIR<br>1,24 m<br>SWIR<br>3,70 m</td><td>0.0<br>0.0</td><td>100 km<sup>2</sup><br>surrounding<br>Bergheider<br>See</td></tr><tr><td>Airborne-<br>Based<br>Multispectral Imagery</td><td>DJI Matrice<br>M300 RTK<br>Micasense<br>Altum-PT</td><td>10-25 June 2024</td><td>VNIR<br>475 to<br>842 nm<br>LWIR<br>10500 nm</td><td>Commercial<br>data</td><td>&lt; 0.05 m</td><td>variable</td><td>Bergheider<br>See and<br>Scheibe<br>See</td></tr></tbody></table>

### 2.2.1 Sentinel-2 Data

Sentinel-2 data were downloaded from https://code-<br>de.org/de/. CODE-DE is part of Germany's<br>geoinformation strategy and offers easy and efficient<br>access to remote sensing data as well as free cloud<br>resources for processing. More detailed information<br>on the Sentinel-2 acquisition resolutions can be found<br>on the Copernicus Sentinel-2 Mission website<br>(Copernicus, n.d.).

For the AOI there is a wide archive of historical and<br>actual data available. Top of Atmosphere (TOA) and<br>Bottom of Atmosphere (BOA) products were<br>downloaded respectively. The spatial resolution is set<br>to 10 m to benefit from the medium spatial resolution<br>of Sentinel-2 imagery.

### 2.2.2 Worldview-3 Data

The commercial high-resolution (HR) Worldview-3 data (VNIR+SWIR) was ordered from European Space Imaging (https://www.euspaceimaging.com/) for a 100 km² representative area. More detailed information on the WorldView-3 data can be found on the ESA Earth Online website (European Space Agency [ESA], n.d.). Important prerequisites aspects to consider for optical analysis are the cloud coverage and the low nadir angle.

The data has been made available as Ortho-Ready Standard Product (OR2A), with the spectral data as Digital Numbers (DN). The DN values are further processed to TOA Reflectance values using the radiometric calibration tool supported by NV5 Geospatial's software tools (NV5 Geospatial, n.d.). The conversion to BOA reflectance resulted in negative reflectance values in water areas, because of the low radiance. It is important to highlight that the atmospheric corrections are designed for land applications, and not for water bodies. In water applications they modify the reflectance drastically. For this reason, TOA reflectance data are used for further processing.

### 2.2.3 UAV Data

The very-high resolution (VHR) UAV data were acquired from Beak Consultants GmbH with the following equipment:

* UAV: DJI Matrice M300 RTK,

* Multispectral camera: Micasense Altum-PT (detailed information can be found in the Altum-PT Integration Guide – MicaSense Knowledge Base [MicaSense, n.d.]),

* GPS/GNSS System: Emlid RS2 GPS/GNSS (Global Positioning System / Global Navigation Satellite System) with NTRIP (Networked Transport of RTCM via Internet Protocol) connection to the national CORS system (Continuously Operating Reference Station).

The necessary approvals/permissions for the UAV flights were obtained in advance based on the regulations of the German Federal Aviation Authority (LBA, n.d.).

The processing of UAV acquisitions relies on the Structure from motion (SfM) photogrammetric range imaging technique. However, applying this technique over large water bodies presents several challenges:

<footnote n="1">¹ The cloud cover of Sentinel-2 acquisition over water bodies in the area of interest (AOI).</footnote>
* High reflectivity: The reflective properties of water create a mirror-like effect.

* Dynamic surface conditions: The continuous movement of water, influenced by factors such as wind, introduces discrepancies of overlapping areas in consecutive scenes.

* Stereo image similarity: The homogeneity of stereo-image pairs over water surfaces makes it difficult to identify tie and key points necessary for accurate image alignment.

To align UAV acquisitions in this case study, we employed the image block-adjustment by reference technique, as implemented in Agisoft Metashape (Agisoft LLC, n.d.). This workflow includes a yaw estimation process that analyses the drone's flight path between consecutive images to determine the camera's horizontal rotation. Assuming zero pitch and roll, yaw is the only rotational parameter considered. The drone's movement direction is calculated by a direction vector, obtained by subtracting the current camera's location from the next camera's location. This process effectively determines the camera's horizontal orientation based on its movement relative to the previous shot, aiding in initial camera alignment for photogrammetric processing.

Because of the large areas, UAV flight campaigns are conducted over multiple days, often under varying illumination conditions, leading to variations in ground surface brightness. Figure 4 shows the tiles representing the flight missions and the weather conditions, respectively. To mitigate these variations, the sun sensor correction (DLS) is applied, which partially compensates for differences in lighting conditions during data acquisition. However, this approach assumes a constant irradiance over time and lacks to develop irradiance series and compensate the DLS for movement (MicaSense, n.d.).

<figure><img src="image_235.png" /><figcaption>Figure 4: The flight missions and respective weather conditions in a) Bergheider See and b) Scheibe See.</figcaption></figure>

Finally, the recorded values have been divided by 32768 to get the reflectance values for each band instead of digital numbers (MicaSense, n.d.).

# 3 METHODS DESCRIPTION

Dependencies and relationships between spectral reflectance bands and AMD constituents are investigated using the supervised machine learning (ML) algorithm of ANNs of the multilayer perceptron type (MLP) (Haykin, 1998). Additionally, cross-sensor comparisons (Chastain, et al., 2019) are performed to gain more insights into the agreement between the spectra from Sentinel-2 images with those from the Worldview-3 and UAV images over surface water. Finally, transformation parameters are calculated to harmonize Worldview-3 and UAV spectral bands to Sentinel-2 over water bodies.

## 3.1 Prediction Modelling Using Artificial Neural Networks

MLP ANNs are implemented in the advangeo® Prediction Software from Beak Consultants GmbH (www.advangeo.com). The modeling and prediction software analyses complex relationships between a wide variety of spatial influencing parameters (in this case the multispectral data) and given AMD occurrences, by using methods of artificial intelligence (AI) within a Geographic Information System (GIS) environment. The base principle is the ability of ML algorithms to generalize and learn from non-linear relationships, and model natural complex processes and events, which are difficult or impossible to be described with analytical mathematics (Noack, et al., 2014).

The aim of the modelling is large-scale mapping of the iron concentration and pH based on R/S multispectral data and geochemical data and elaboration of the influence of spatial and spectral resolution in the modelling process.

The accuracy and robustness of the trained network is assessed by:

* Statistical evaluation: A comparison plot of the modelling results with the measured values of iron concentration and pH.

* The network mean squared error (MSE): A converging and stable model error indicates that the network is learning effectively.

* The model distribution weights: Balanced model distribution weights indicate that the network has appropriately distributed importance across all spectral bands without overemphasizing or neglecting any specific band.

* The distribution map: Predictions should closely align with the actual target values and present uniform and logical AMD clustering.
## 3.2 Cross-Sensor Comparison of Sentinel-2 and Worldview-3 TOA Products

The calibration of a reliable training network requires adequate and sufficient sampling data, covering a wide range of AMD scenarios. Given the free availability of Sentinel-2 data and the typically high costs of high-resolution commercial R/S datasets, we propose a methodology where ANNs are trained using Sentinel-2 data over a large area in conjunction with extensive geochemical monitoring data. The established dependencies and relationships are then applied to commercial high-resolution datasets for targeted identification of AMD in specific areas. This approach requires that the training and application models are provided with similar controlling parameters.

Though the R/S multispectral datasets used in this study can provide “similar” observations (VNIR-SWIR for Sentinel-2 and Worldview-3 data, and VNIR for UAV data), they differ in the field of view, spatial resolution, spectral bandwidth, and spectral response function. While the difference introduced by different field of view and spatial resolution can be reduced and solved by the orthorectification and data resampling, respectively, the difference caused by different spectral bandwidth and spectral response function (the so-called reflectance difference) is a more complex problem.

In this study, we apply the linear regression approach at different wavelengths to minimize reflectance difference between two similar satellite observations over water bodies. Figure 5 shows the cross-sensor agreement analyses between the Sentinel-2 and Worldview-3 TOA data. The Sentinel-2 bands B5, B7, B8A, B11 and B12 are paired to Worldview-3, based on the introduced concept of synthesised bands by (Gasparovic, et al., 2018).

The weakest correlations are observed in the low-resolution Sentinel-2 bands, specifically B1 (443 nm), B9 (940 nm), and the blue spectral band B2 (490 nm). The low correlation in the blue wavelength range can be addressed to the fact that the reflection in this part of the spectrum is more susceptible to atmospheric scattering, which can drastically affect measurements.

<figure><img src="image_236.png" /><figcaption>Figure 5: Cross-sensor agreement analyses between the Sentinel-2 and Worldview-3 TOA data.</figcaption></figure>

Figure 6 shows examples of cross-sensor comparison of the median spectra for three lakes: Kleinleipischer See (Fe = 195 mg/L, pH = 2,56), Bergheider See (Fe = 50,2 mg/L, pH = 2,74) and Poleysee (Fe = 0,15 mg/L, pH = 3,9). In all lakes, the Sentinel-2 and Worldview-3 spectra are in good agreement in terms of shape.

Additionally, we use the transformation parameters from the linear regression model to harmonize the reflectance of Worldview-3 to Sentinel-2. The Sentinel-2 and adjusted Worldview-3 spectra are in good agreement in terms of shape and magnitude (Figure 6). However, the adjusted spectra are slightly brighter than those of Sentinel-2 within the near- and short wavelength infrared (>700-nanometers).

<figure><img src="image_237.png" /><figcaption>(a)</figcaption></figure>
<figure><img src="image_238.png" /><figcaption>(b)</figcaption></figure>

<figure><img src="image_239.png" /><figcaption>(c)</figcaption></figure>

Figure 6: TOA reflectance spectra for a) Kleinleipischer See (Fe = 195 mg/L, pH = 2,56), b) Bergheider See (Fe = 50,2 mg/L, pH = 2,74) and c) Poleysee (Fe = 0,15 mg/L, pH = 3,9).

## 3.3 Cross-Sensor Comparison of Sentinel-2 BOA Products and UAV

Similar to section 3.2, cross-sensor agreement analyses were performed between Sentinel-2 BOA data and UAV data. The results of these analyses are presented in Figure 7. The Sentinel-2 bands B5 and B7 are paired to UAV, based on the introduced concept of synthesised bands by (Gasparovic, et al., 2018). The weakest correlations are observed in the visible part of the spectrum corresponding to Sentinel-2 B2 (490 nm), B3 (560 nm), B4 (665 nm).

<figure><img src="image_240.png" /></figure>

Figure 7: Cross-sensor agreement analyses between the Sentinel-2 BOA and UAV data.

A detailed view of the individual bands revealed that the visible bands are more susceptible to the ground surface brightness and reflectance differences coming from dynamically changing weather conditions.

These discrepancies are confirmed also when comparing the Sentinel-2 BOA and UAV median spectra for Scheibe See (Figure 8). Generally, both spectres in the two lakes (Bergheider and Scheibe See) are in good agreement both in terms of shape and magnitude. Due to their similarity, in this case no transformation is used to harmonize the reflectance of UAV data to Sentinel-2 BOA.

<figure><img src="image_241.png" /><figcaption>(a)</figcaption></figure>

<figure><img src="image_242.png" /><figcaption>(b)</figcaption></figure>

Figure 8: BOA reflectance spectra for a) Bergheider See (Fe = 50,2 mg/L, pH = 2,74), b) Scheibe See (Fe = 0,17 mg/L, pH = 7,38)

# 4 RESULTS

Two MLP training models were developed using Sentinel-2 and WorldView-3 data as controlling parameters. These models aim to evaluate the impact of WorldView-3's higher spatial and spectral resolution on AMD mapping. Furthermore, an application model leverages the established dependencies derived from Sentinel-2 training model and applies them to harmonized WorldView-3 data to assess the transferability of the knowledge gained in MLPs across similar remote sensing datasets.
On the other hand, the development of training models based on UAV-derived data for two lakes was deemed impractical due to the limited availability of training data. However, an application model was implemented to evaluate the transferability of the established dependencies for AMD mapping and was validated by the geochemical sampling in the two lakes.

## 4.1 Training Scenario Using Sentinel-2

Controlling parameters include Sentinel-2 multispectral bands of Level-1C and Level-2A products. Figure 9 shows a comparison plot of the modelling results with the measured values of iron concentration and pH values. The trained neural network has been able to reproduce the calibration data in case of the iron concentration, as an optically active parameter. In case of pH values, there are no clearly established dependencies between the controlling parameters and the calibration data, however there is a significant differentiation of the acidic from the neutral waters.

<figure><img src="image_243.png" /><figcaption>Figure 9: Plot of given and modelled a) iron concentration and b) pH values based on Sentinel-2 Level-2A BOA products.</figcaption></figure>

The MSE in both cases shows systematic convergence and remains stable, confirming the neural network's accuracy and robustness (Figure 10).

<figure><img src="image_244.png" /><figcaption>Figure 10: Plot of MSE for the MLP for a) iron concentration and b) pH values based on Sentinel-2 Level-2A BOA products.</figcaption></figure>

The model parameter weights revealed the Sentinel-2 Level-2A green (B03) and SWIR (B11 and B12) spectral bands to have the highest contribution for the modelling of iron concentration and B08 (NIR) and SWIR (B11 and B12) for pH values.

The result is a distribution map of iron concentration (Figure 11) and pH values (Figure 12) in the value ranges of input calibration data (0 – 361 mg/L and 2–8, respectively) over the water bodies in the AOI. The typical patterns of high iron concentrations and low pH values in the shores are mostly due to the mixed pixel information in shallow waters. Generally, the distribution map reflects the AMD severity as measured from the geochemical analysis.

<figure><img src="image_245.png" /><figcaption>Figure 11: Distribution map of iron concentration over the water bodies in the AOI; additionally, the measured Fe values are shown as a column chart.</figcaption></figure>

<figure><img src="image_246.png" /><figcaption>Figure 12: Distribution map of pH values over the water bodies in the AOI; additionally, the measured pH values are shown as a column chart.</figcaption></figure>
## 4.2 Training Scenario Using Worldview-3

Controlling parameters include Worldview-3 TOA multispectral bands. In this case, the geochemical analytic results are available for about 10 post-mining water bodies in the AOI. Figure 13 shows a comparison plot of the modelling results with the measured values of iron concentration and pH values. In this case, the trained neural network has been able to better reproduce the calibration data.

<figure><img src="image_247.png" /><figcaption>Figure 13: Plot of given and modelled a) iron concentration and b) pH values based on Worldview-3 TOA products.</figcaption></figure>

The MSE in both cases shows systematic convergence and remains stable, confirming the neural network's accuracy and robustness (Figure 14).

<figure><img src="image_248.png" /><figcaption>Figure 14: Plot of MSE for the MLP for a) iron concentration and b) pH values based on Worldview-3 TOA products.</figcaption></figure>

Similar to the training model in 4.1, the training model weights confirmed the Worldview-3 TOA green and SWIR (from SWIR2 to SWIR7) spectral bands to have the highest contribution for the modelling of iron concentration and NIR1/2 and SWIR (SWIR6 to SWIR8) for pH values.

The resulting distribution maps of iron concentration (Figure 15) and pH values (Figure 16) reflect the AMD severity as measured from the geochemical analysis, taking into consideration only those part of the WV3-image that are free of cirrus clouds.

<figure><img src="image_249.png" /><figcaption>Figure 15: Distribution map of iron concentration over the water bodies in the AOI; additionally, the measured Fe values are shown as a column chart.</figcaption></figure>

<figure><img src="image_250.png" /><figcaption>Figure 16: Distribution map of pH values over the water bodies in the AOI; additionally, the measured pH values are shown as a column chart.</figcaption></figure>

## 4.3 Application Scenario Using Harmonized Worldview-3

This scenario is useful when there are no sufficient calibration data inside the AOI of commercial Worldview-3 data. In this case, the harmonized Worldview-3 image bands to Sentinel-2 are used as controlling parameters and the established dependencies from the training scenario in 4.1 are used for AMD mapping. This approach does not
exploit the full potential of SWIR in the Worldview-3 data, since they are harmonized to SWIR bands of Sentinel-2. However, it enables AMD mapping in the shores and in small/narrow water bodies, which cannot be represented properly in medium resolution images.

Figure 17 shows a comparison plot of the application results with the measured values of iron concentration and pH values. The predicted iron concentration values resemble to the trend of measured values, but they are obviously overestimated, showing higher AMD-levels then the ones from geochemical analysis. This is reflected also in the modelled pH value, where all the water bodies in the AOI are predicted as very acidic.

<figure><img src="image_251.png" /><figcaption>Figure 17: Plot of given and modelled a) iron concentration and b) pH values based on harmonized Worldview-3 TOA products.</figcaption></figure>

The same observations are confirmed from the distribution maps of iron concentration (Figure 18) and pH values (Figure 19).

<figure><img src="image_252.png" /><figcaption>Figure 18: Distribution map of iron concentration over the water bodies in the AOI; additionally, the measured Fe values are shown as a column chart.</figcaption></figure>

<figure><img src="image_253.png" /><figcaption>Figure 19: Distribution map of pH values over the water bodies in the AOI; additionally, the measured pH values are shown as a column chart.</figcaption></figure>

## 4.4 Application Scenario Using UAV Data

In this scenario, a new neural network was trained by Sentinel-2 data using only bands in the VIS-VNIR. This network confirmed the Sentinel-2 Level-2A green band (B03) to have the highest contribution for modelling of iron concentration and NIR band (B08) for the pH value. The established dependencies in the trained network were used for large-scale mapping of AMD in Scheibe See and Bergheider See using UAV data.

Figure 20 and 21 show the distribution map of iron concentration and pH value over Bergheider See and Scheibe See. The median values of AMD parameters over both lakes and results of geochemical analysis are presented for comparison in Table 3.

<figure><img src="image_254.png" /><figcaption>Figure 20: Distribution map of a) iron concentration and b) pH value over the Bergheider See.</figcaption></figure>
<figure><img src="image_255.png" /><figcaption>Figure 21: Distribution map of a) iron concentration and b) pH value over the Scheibe See.</figcaption></figure>

<table><caption>Table 3: Comparison of geochemistry and modelling results for Bergheider See and Scheibe See.</caption><thead><tr><th rowspan="2"></th><th colspan="2">Bergheider See</th><th colspan="2">Scheibe See</th></tr><tr><th>Measured</th><th>Model</th><th>Measured</th><th>Model</th></tr></thead><tbody><tr><th>pH</th><td>2,74</td><td>3,84</td><td>7,38</td><td>6,62<sup>2</sup></td></tr><tr><th>Fe<br>(mg/L)</th><td>50,2</td><td>39,3</td><td>0,17</td><td>9,4</td></tr></tbody></table>

The varying weather conditions between the mission flights seem to have a very small effect in the modelling of iron concentration, but show a considerable influence in the modelling of pH values. This issue is further elaborated in chapter 6.

# 5 DISCUSSION

This study assesses the feasibility of using free and commercial multispectral R/S datasets in combination with supervised ML algorithms for the automatic mapping of AMD in water bodies.

Supervised ML algorithms require large and diverse training datasets that represent a wide range of AMD scenarios. However, in practical applications, such comprehensive datasets are often unavailable. To address this limitation, the study developed training models using a large variety of water bodies exhibiting different AMD levels. The transferability of the established models was further evaluated using commercial R/S datasets.

The application of optical R/S analyses requires cloud- and cirrus-free acquisitions. Sentinel-2 data are generally more available for this purpose due to their high temporal resolution. In contrast, acquiring commercial datasets such as WorldView-3 imagery often requires careful planning, as constraints such as low cloud coverage and low nadir angles significantly influence the availability of suitable acquisitions. UAV-flight campaigns also present logistical

challenges, especially for large water bodies, as they require several days of data collection under stable weather conditions to ensure consistent reflectance values across adjacent flight paths.

The high spatial and spectral resolution of WorldView-3 imagery, particularly in the SWIR bands, proved to be highly effective for AMD mapping. This may be related to the ability of SWIR bands to detect high concentrations of heavy metals typically associated with severely acidic conditions. Future research could further explore AMD patterns within the SWIR region using hyperspectral datasets, such as those provided by EnMAP or PRISMA. Additionally, the green spectral band was identified as an important feature, potentially due to the absence of vegetation or algae in water bodies with high acidity levels.

The MLP models developed in this study demonstrated robust performance but are limited by the concentration ranges defined by the calibration data. Consequently, quantitative predictions cannot be reliably extrapolated beyond the range of the calibration data. In this context, discrete sampling remains of critical importance for properly calibrating or validating the algorithms. However, the proposed approach enables large-scale AMD mapping of water bodies by significantly reducing the need on extensive sampling campaigns.

The flight campaign for a complete survey of large water bodies can take several days due to the European Union Aviation Safety Agency (EASA) restrictions for UAV (such as a maximum flight height of 120-meters). In practice, it is almost impossible to have constant weather conditions during such campaigns. The varying weather conditions (cloud, cirrus, haze, shadows) have a big influence on the surface reflectance of water bodies, leading to difficulties for balancing of the reflectance values and modelling inconsistencies over a water body.

To improve UAV-based monitoring, fixed-wing UAVs are recommended for their ability to cover larger areas efficiently, reducing weather-induced variability and resulting imbalances across flight missions. Additionally, multispectral cameras with wider spectral bands would enhance the detection of subtle water quality variations. On the other hand, increasing the UAV flight altitude would considerably reduce flight time and also improve the accuracy of photogrammetric reconstructions by enhancing feature variations between consecutive images.

<footnote n="2">² The median pH values for Scheibe See were derived only from tiles captured under cloudy weather conditions.</footnote>
# 6 CONCLUSIONS

The novelty of this study is the cross-sensor comparison of free and commercial space- and airborne- multispectral R/S datasets (Sentinel-2, Worldview-3 and UAV) with a focus on assessing the transferability of established dependencies between AMD parameters and spectral data across several datasets.

The cross-sensor analysis identified spectral discrepancies coming mainly from differences in spectral bandwidth and spectral response functions. To address these variations, transformation parameters were derived to align the spectral characteristics of commercial datasets with those of Sentinel-2, which was used as a reference due to its free availability and high temporal resolution. This makes Sentinel-2 a valuable dataset for training ML algorithms.

Results indicate that adjusted WorldView-3 data appear slightly brighter than Sentinel-2 data in the NIR and SWIR (>700 nm) regions. Consequently, the transferred neural network exhibited a tendency to overestimate AMD levels. Future research can focus on optimizing transformation parameters using larger and more diverse datasets, including time-series data and broader spatial coverage. Nevertheless, the correct relative distribution of iron concentrations suggests that the established dependencies from the training model remain transferable across these datasets. This approach fully elaborates the high spatial resolution of WV3-datasets and enables AMD mapping even in small-scale or narrow water bodies, offering a more efficient and cost-effective alternative, as running extensive training models on commercial datasets.

The training scenario with the best results was obtained when using Worldview-3 datasets as controlling parameters, due to their high spatial and spectral resolution, particularly in the SWIR bands. However, the trained network in this case is relied in a few number of water bodies and AMD scenarios.

The transferred neural network for UAV-based monitoring has shown also very promising results. While clear-sky and sunny conditions offer optimal reflectance, they can introduce sun-glint effects in UAV-based monitoring. The large-scale pH distribution map of Scheibe See (Figure 21) highlighted the significant impact of weather conditions on the modelling process. In Bergheider See, flight missions occurred under more consistent conditions, resulting in minimal weather-related influences. These findings suggest that bright, diffused sunlight represents the ideal weather conditions for UAV-based water quality monitoring.

Finally, despite not being included in any training scenarios, Scheibe See was correctly classified as a lake with no evidence of AMD, demonstrating the applicability of the trained neural network beyond the AOI. This demonstrates the robustness and application of the developed approach for large-scale mapping of the water quality in post-mining water bodies.

# ACKNOWLEDGEMENTS

This research has been done within the framework of the project S34I—Secure and sustainable supply of raw materials for EU industry—, coordinated by Ana C. Teodoro. This project has received funding from the European Union’s HORIZON Research and Innovation. Grant Agreement No. 101091616 (https://doi.org/10.3030/101091616). 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).

The authors are grateful to LMBV for providing the water monitoring data, which made this research study possible. The authors gratefully acknowledge also the comments and suggestions of three anonymous reviewers, which led to a substantial improvement in the manuscript.

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# Innovative Hyperspectral Data Fusion for Enhanced Mineral Prospectivity Mapping

Roberto De La Rosa<fnref n="1" />, Ina Storch<fnref n="1" />, Michael Steffen<fnref n="1" />, Ina Storch<fnref n="1" />, Andreas Knobloch<fnref n="1" />, Joana Cardoso-Fernandes<fnref n="2" />, Morgana Carvalho<fnref n="2" />, Mercedes Suárez Barrios<fnref n="3" />, Juan Morales Sánchez-Migallón<fnref n="3" />, Petri Nygren<fnref n="4" />, Vaughan Williams<fnref n="5" /> and Ana Cláudia Teodoro<fnref n="2" />, <fnref n="1" />,<fnref n="2" />, <fnref n="3" />,<fnref n="4" />

**Keywords:** Exploration, CRM Mapping, Hyperspectral Data, Mineral Prospectivity Mapping, Deep Learning, Bayesian Neural Networks, Self-Organizing Maps.

**Abstract:** To meet the European Union's growing demand for critical raw materials in the transition to green energy, this study presents a novel, cost-effective, and non-invasive methodology for mineral prospectivity mapping. By integrating hyperspectral data from satellite, airborne, and ground-based sources with deep learning techniques, we enhance mineral exploration efficiency. We employ Bayesian Neural Networks (BNNs) to predict mineral prospective areas while providing uncertainty estimates, improving decision-making. To address the challenge of obtaining reliable negative labels for supervised learning, Self-Organizing Maps (SOMs) are used for unsupervised clustering, identifying barren areas through co-registration with known mineral occurrences. We illustrate this approach in the Aramo Unit in Spain, a geologically complex region with Cu-Co-Ni mineralized veins. Our workflow integrates local geology, mineralogy, geochemistry, and structural data with hyperspectral data from PRISMA, airborne Specim AisaFenix, LiDAR and ground-based spectroradiometry. By leveraging learning techniques and high-resolution remote sensing, we accelerate exploration, reduce costs, and minimize environmental impact. This methodology supports the EU's S34I project by delivering high-value, unbiased datasets and promoting sustainable, cutting-edge mineral exploration technologies.

## 1 INTRODUCTION

The increasing global demand for critical raw materials (CRMs) necessary for renewable energy technologies, consumer electronics, electric vehicles and defence has intensified the urgency of developing efficient, sustainable, and innovative mineral exploration methods. The European Union (EU), in its transition toward green energy, faces significant challenges due to limited domestic production of

CRMs, necessitating reliance on imports. This dependence introduces risks related to supply chain disruptions and geopolitical instability. To address this challenge, the EU has launched several initiatives to promote the sustainable and responsible sourcing of CRMs, including the Secure and Sustainable Supply of Raw Materials for EU Industry (S34I) project. This project aims to develop new technologies and approaches for mineral exploration, extraction, and processing that minimize

<footnote n="1">¹Beak Consultants GmbH, 09599 Freiberg, Germany</footnote>

<footnote n="2">²Institute of Earth Sciences (ICT), Department of Geosciences, Environment and Land Planning, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal</footnote>

<footnote n="3">³Department of Geology, University of Salamanca, 37008 Salamanca, Spain</footnote>

<footnote n="4">⁴Spectral Mapping Services SMAPS Oy, 20320 Turku, Finland</footnote>

<footnote n="5">⁵Aurum Exploration Ltd, A82 HK12 Kells, Ireland</footnote>

<footnote>{roberto.delarosa, michael.steffen, ina.storch, andreas.knobloch}@beak.de, {joana.fernandes, morgana.carvalho, amteodor}@fc.up.pt, {msuarez, juan.morales}@usal.es, petri.nygren@smaps.fi, vwilliams@aurumexploration.com</footnote>

<footnote>a https://orcid.org/0000-0002-3004-7104</footnote>

<footnote>b https://orcid.org/0000-0001-7515-001X</footnote>

<footnote>c https://orcid.org/0009-0001-4332-2187</footnote>

<footnote>d https://orcid.org/0000-0002-8043-6431</footnote>
environmental impact and maximize resource efficiency.

Mineral prospectivity mapping (MPM) is a critical tool in addressing these challenges. MPM traditionally uses geographic information systems (GIS) to integrate diverse datasets as geological, geophysical, geochemical, and remote sensing to highlight areas with high mineralization potential. Traditional exploration techniques, while effective, often involve significant time, expense, and environmental disruption. Recent advancements in technology have revolutionized MPM, leveraging the power of GIS platforms, machine learning (ML), and artificial intelligence (AI) to improve the accuracy, efficiency, and sustainability of mineral exploration (Carranza & Hale, 2001, Carranza 2008, Nykänen et al., 2017, 2023, Yousefi et al., 2021, 2024 & Zhang et al., 2022).

MPM approaches are generally categorized into knowledge-driven, data-driven (Yousefi & Nykänen, 2016, Torppa et al., 2019, Lawley et al., 2022 & Nagasingha et al., 2024), and hybrid methods. Knowledge-driven techniques rely on expert interpretations of geological formations, making them particularly suitable for "greenfield" exploration regions with few known deposits. In contrast, data-driven methods empirically model relationships between explanatory variables and mineral occurrences, often applying ML techniques to established mining areas or "brownfield" regions. Hybrid approaches combine these methodologies, leveraging data-driven insights to enhance expert-driven interpretations. Deep learning models, particularly Bayesian Neural Networks (BNNs), have demonstrated exceptional capabilities in extracting complex patterns and relationships within large, multidimensional datasets, improving predictive accuracy and uncertainty quantification (Mao et al., 2023 and Jordão et al., 2023).

Despite these advancements, key challenges remain. One of the most significant is the scarcity and imbalance of labelled data, where known mineral deposits or mineralized samples (positive samples used as training points) are rare and rare compared to the amount of data available, which is effectively unknown in terms of a positive or negative binary classification for a CRM mapping. Traditionally, the unknown areas are considered barren regions from where the negative samples are randomly selected. Another traditional option is an expert driven negative sampling that requires extensive geological expertise, a good understanding of the study area and a vast knowledge of the parameters driving the mineralization event, which is not always available, possible or extremely expensive and time-consuming,

leading inevitably to an imbalance positive-negative training samples. This imbalance can lead to biased models and unreliable predictions (Mao *et al.*, 2023). Addressing this issue, our research introduces a novel data-driven approach to negative sampling selection by leveraging Self-Organizing Maps (SOMs). Instead of relying on arbitrary or expert-defined barren regions, we co-register SOM clusters with known mineral occurrences to identify geologically representative negative samples. This ensures that the training dataset accurately reflects the true background variability of the study area, leading to improved model robustness and generalization.

The integration of BNNs into our methodology provides another key innovation by incorporating uncertainty quantification into mineral prospectivity predictions. Unlike conventional neural networks that yield deterministic outputs, BNNs estimate probability distributions over model parameters, allowing them to quantify prediction uncertainty (Jordão et al., 2023). This uncertainty information is particularly valuable for mineral exploration, as it enables risk-aware decision-making and strategic resource allocation (Lauzon & Gloaguen 2024 & Zhang et al., 2024). Exploration efforts are prioritized in areas with high predictive confidence while regions with significant uncertainty can be flagged for further investigation. By embedding uncertainty quantification within the model, our approach enhances the interpretability and transparency of the mineral prospectivity mapping process, reducing the risk of false positives and missed discoveries.

Furthermore, our methodology offers several practical advantages over traditional exploration techniques. The non-invasive nature of hyperspectral remote sensing significantly reduces environmental impact by minimizing the need for extensive ground surveys. This is particularly beneficial for ecologically sensitive or remote regions where physical access is limited. The high spectral resolution of hyperspectral imaging allows for the precise identification of mineral signatures, capturing subtle spectral features that traditional methods may overlook. Additionally, by automating feature extraction, classification and post-processing of the data, we reduce the need for extensive manual interpretation, thereby increasing efficiency and cost-effectiveness.

By aligning with the objectives of the S34I project, our research contributes to the development of sustainable and technologically advanced mineral exploration methodologies. The integration of multi-scale hyperspectral remote sensing, SOM-driven negative sampling, and BNN-based prospectivity mapping and uncertainty quantification represents a
transformative step forward in mineral prospectivity mapping. As hyperspectral imaging technology continues to advance and larger, higher-quality datasets become available, the predictive accuracy and effectiveness of this approach will further improve. Additionally, continued innovations in machine learning architectures, Bayesian inference, and self-supervised learning will enhance the capabilities of this methodology, making it an increasingly powerful tool for mineral exploration.

Our proposed approach represents a significant advancement in mineral exploration by providing a scientifically rigorous, scalable, and environmentally responsible method for identifying potential mineral deposits. By addressing key challenges such as negative sampling bias and uncertainty estimation, we offer a robust framework that improves predictive reliability and supports informed decision-making in exploration projects. The integration of multi-scale hyperspectral data, SOM-based negative sampling, and deep learning via Bayesian Neural Networks not only enhances the accuracy and efficiency of mineral prospectivity mapping but also supports the broader transition toward sustainable resource management and a green energy future.

# 2 STUDY AREA & DATA

## 2.1 Geological Setting

The study area is the Aramo Unit, a thrust nappe within the Fold and Nappe Province of the Cantabrian Mountains in northern Spain (Figure 1). This region comprises a diverse sequence of Paleozoic sedimentary rocks, primarily from the Devonian and Carboniferous periods. The stratigraphic sequence includes Devonian shales, sandstones, and limestones, followed by Tournaisian-Visean grey and red nodular limestones. The Namurian succession is characterized by black, bituminous limestone, while the Bashkirian to Lower Moscovian sequence consists of shales interbedded with limestones and sandstones (Paniagua *et all.*, 1988, 1993). Structurally, the mineralization at the Aramo mine is controlled by the intersection of the E-W Aramo Fault and the Aramo Thrust front. The Aramo Fault is a major discontinuity traversing the Aramo Unit, while the Aramo Thrust front delineates the boundary between the Fold and Nappe Province and the Central Coal Basin. This structural interplay has induced extensive dolomitization and minor silicification in proximity to the orebody (Bruner & Smosna, 2000), Ordóñez *et al.*, 2005) and (Loredo & Ordóñez, 2008).

The Aramo mine hosts significant Cu-Co-Ni mineralization, occurring as mineralized veins with an average thickness of 25 cm. These veins are predominantly found within the Namurian limestone, situated along the thrust fault front. The deposit is epigenetic and carbonate-hosted, comprising Cu-Co-Ni sulfides and arsenides with minor precious metals (Paniagua *et al.*, 1988). The combination of structural controls and lithological characteristics has played a crucial role in the formation and localization of the mineralization.

<figure><img src="image_256.png" /><figcaption>Figure 1: Geological and structural map of the study area. Modified from: Aurum Exploration Ltd & Bergua *et al.*, 2019.</figcaption></figure>

## 2.2 Data Acquisition

A multi-scale approach to data acquisition was adopted for this study, integrating satellite, airborne, and ground-based measurements. Initially, multispectral and hyperspectral satellite data from Sentinel-2, Lansat-9 and PRISMA sensors were analyzed to delineate alteration zones and refine the definition of the main study area (Carvalho *et al.*, 2025). These delineated regions subsequently guided airborne data acquisition, ensuring targeted high-resolution imaging. Furthermore, the identified alteration areas informed and optimized ground-based sampling strategies for geochemical analysis and spectral validation, enhancing the overall effectiveness of the exploration process.
### 2.2.1 Hyperspectral & LiDAR Data

Regional hyperspectral data were acquired by the PRISMA satellite, providing broad coverage of the study area. This data was processed and analysed by the partners from the University of Porto and helped to the definition of alteration areas, the definition of the main study area, guided part of the ground-based sampling and furthermore an independent component analysis was performed (Carvalho et al., 2025) which produced informative input layers for the machine learning models.

<figure><img src="image_257.png" /><figcaption>Figure 2: Airborne hyperspectral data flight lines.</figcaption></figure>

High-resolution hyperspectral data were acquired using the Specim AisaFENIX sensor, flown on an airborne platform (Figure 2). This camera implements two sensors to cover the visible and near infrared (VNIR 380-1000 nm) and shortwave infrared (SWIR 1000-2500 nm) regions of the electromagnetic spectrum along 450 spectral bands. The acquisition and pre-processing of the data was performed by the partners from Smaps Oy. Due to persistent harsh weather conditions (typical of this region), it was no possible to fly all the planned lines, and this is the reason of the data gap at the east of the study area and the high percentage of cloud coverage. Unfortunately, this gap coincided with a prominent mineralization outcrop where most of the rock samples were located (Figure 1 and 2). The flight mission was on hold for more than one year waiting for the appropriated weather window. The pre-processing of the airborne

hyperspectral data was also performed by Smaps which consisted on the orthorectification and geometric correction, followed by the atmospheric correction performed with the ATCORE4 software resulting in reflectance data with ground sampling distance of approximately 1.2 meters per pixel.

The airborne LiDAR data was acquired in 2023 by the partners from Eurosense with the waveform processing Airborne Laser scanner Riegl VQ780 obtaining an average point density of 10pts/sqm and resulting in a digital elevation model (DEM) with 0.5m resolution per pixel (Figure 3).

<figure><img src="image_258.png" /><figcaption>Figure 3: Airborne LiDAR flight lines and impressions on the acquisition (Image credit from Eurosense partners).</figcaption></figure>

### 2.2.2 Ground-Based Techniques

The study area is currently being actively explored by Aurum Exploration Ltd., in collaboration with local partners from the Department of Geology at the University of Salamanca. Together, they have conducted multiple field campaigns for sample collection and analysis, as well as geological and structural mapping.

Field measurements were performed using an ASD portable spectroradiometer by the University of Salamanca to acquire high-quality point spectral signatures in the VNIR and SWIR regions of the electromagnetic spectrum. These measurements were taken from known mineral occurrences and background lithologies to build spectral libraries for spectral validation and the supervised processing of hyperspectral images.

Geological maps were used to extract the host Valdetejas Formation, and the distance to this unit was calculated and rasterized for use as an input
evidence layer in machine learning models. Additionally, the main fault structures were categorized into two groups based on their azimuth: E-W and N-S oriented faults. Finally, the distance to thrust front-related faults was calculated and incorporated as an input variable in the models.

Geochemical analyses were performed on the collected samples, and the results were used to select the samples that would serve as positive training points for the supervised machine learning methods. Specifically, samples with concentrations of Co > 0.05%, Cu > 0.4%, and Ni > 0.07% were selected, resulting in a dataset of 32 samples.

## 3 METHODOLOGY

The workflow of the proposed methodology is depicted in Figure 4. It begins with the acquisition of airborne hyperspectral data, which is already radiometrically calibrated and atmospherically corrected to reflectance. This data undergoes baseline correction and de-noising to produce corrected hyperspectral reflectance data. This corrected data is then processed using both unsupervised and supervised methods.

<figure><img src="image_259.png" /><figcaption>Figure 4: General workflow for hyperspectral data fusion with SOM and BNN for Mineral Prospectivity Mapping.</figcaption></figure>

In the supervised processing stage, ground spectroradiometer measurements are used for spectral validation and rasterized maps containing the relevant features extracted from the unsupervised process, along with geochemical data, are integrated to provide a more comprehensive understanding of the spectral data. Additionally, the geological and structural data are also incorporated to evidence layers stack as single bands raster files. These diverse

data sources undergo spatial co-registration to ensure that the spatial and spectral information aligns correctly. The data fusion is performed within the application of the Bayesian Neural Network (BNN), which models the relationships within the fused data. This approach leverages the capabilities of BNNs in handling complex, high-dimensional data to predict mineral distributions with higher accuracy and reliability, ultimately producing a mineral prediction map and the uncertainty associated to it.

<figure><img src="image_260.png" /><figcaption>Figure 5: Indices from band ratios along flight lines.</figcaption></figure>

<figure><img src="image_261.png" /><figcaption>Figure 6: Automated unsupervised hyperspectral data processing output. A: RGB. B: Cloud mask, C: PCA, D: Zoom-in to panel C, E: Zoom-in to panel D, F: End-member spectra, G: Minimum Wavelength Map from 1780 to 1990 nm. H: SAM for end-member of panel F.</figcaption></figure>
## 3.1 Automated Unsupervised Hyperspectral Data Processing

In the unsupervised processing stage, several techniques such as Minimum Wavelength maps, Spectral Angle Mapper, Band Ratios, Principal Component Analysis and N-member extraction are applied to extract meaningful features and patterns from the hyperspectral data (Figures 5 and 6). These processes facilitate the identification of potential mineralogical and geochemical signatures within the dataset. After data extraction, an automated process is applied to balance and normalize the products for each flight line, followed by the generation of the final mosaic raster layer (Figure 7). All the automated process is performed with an in-house and python-based develop methods thanks to the availability of publish methodologies for hyperspectral data processing (De La Rosa et al., 2021, 2022) and open source tools such as Spy Spectral Python library, Mephysto (Jakob et al., 2017) and Hylite (Thiele et al., 2020).

## 3.2 Self-Organizing Maps (SOM)

Self-Organizing Maps (SOM) are an unsupervised neural network technique used to cluster input evidence layer data while preserving its topological structure. The training process relies on competitive learning, where neurons compete to represent different regions of the data space. Each neuron in the SOM is associated with a weight vector, and when an input vector is presented, the neuron with the closest weight vector, known as the best matching unit (BMU), is selected. The BMU and its neighboring neurons are subsequently updated to better match the input vector. Repeating this process across all input data results in a self-organized map where similar input vectors form distinct clusters, providing an intuitive representation of underlying patterns in the data (Kohonen 1990, 1997, 2001) and (Wittek et al., 2017).

Visualization of SOM results facilitates cluster interpretation through various techniques such as color-coded maps and distance matrices, which highlight similarities among spectral signatures. To refine the clustering, k-means clustering is applied post-SOM computation. The iterative k-means algorithm randomly creates k centroids and assigns the data points to the nearest centroid. Then, it recalculates the centroids based on the mean of all points within a particular cluster and repeats this process until convergence. Multiple runs are performed across a user-defined range of cluster

numbers, with the best clustering results determined using the Davies-Bouldin index (David & Bouldin 1979). The three most optimal clustering outcomes are displayed in the user interface and stored for further analysis.

<figure><img src="image_262.png" /><figcaption>Figure 7: Automated mosaic raster layer for a Normalized Carbonate Index derived from band ratios.</figcaption></figure>

SOM results are visualized in geospatial and SOM spaces (Figure 8). Furthermore, the results are plotted and categorized into SOM space results, geo-space results, boxplots and scatterplots. SOM space plots include heatmaps representing the value of each codebook vector element, the U-matrix showing differences between neighboring SOM cells, k-means clustering results, and data point distributions per SOM cell. Geospace visualizations present k-means clustering results, BMU codebook vectors, and quantization errors in a geographical context. Additionally, boxplots illustrate the distribution of SOM data parameters across k-means clusters, while scatterplots provide cross-plots of different parameters, enabling deeper insight into data relationships. This combination of SOM and k-means clustering offers a powerful tool for pattern recognition and mineral prospectivity analysis.
## 3.3 Bayesian Neural Networks (BNN)

Bayesian Neural Networks (BNNs) can be a class of deep learning models when implementing multi-layered network architecture. This Neural networks integrate Bayesian inference to predict mineral prospectivity while quantifying associated uncertainties. Unlike traditional artificial neural networks (ANNs), which provide deterministic point estimates (Rosenblatt, 1958), BNNs estimate a distribution over model parameters, enabling a rigorous assessment of uncertainty (Jordão et al., 2023). This capability is particularly valuable in high-risk applications such as mineral exploration, where uncertainty estimation enhances decision-making. In this study, a BNN was trained using hyperspectral derived features layers, geological and structural derived input layers alongside positive samples derived from the geochemical analysis and negative labels derived from a random selection inside areas delimited by the self-organizing maps analysis.

The BNN represents the model weights and biases as probability distributions rather than fixed values and through Bayesian updating, these distributions are refined based on observed data, allowing the model to learn while maintaining an explicit quantification of uncertainty (Mao et al., 2023). Variational inference is employed to approximate the posterior distribution over model parameters, facilitating efficient learning. The implementation of the BNN model is developed in house as a python-based tool and utilizes the TensorFlow Probability python library to construct, train, and evaluate BNN architectures.

The BNN model is still under development and continuous improvement. This model was developed in part through the Critical Mineral Assessments with AI support (Critical MAAS) project. This project is a collaboration between our company Beak Consultants GmbH, the United States Geological Survey (USGS) and the Defense Advanced Research Projects Agency (DARPA). The Critical MAAS project aims to accelerate critical mineral resource assessments through re-design, automation, and human-centered AI engineering. The work developed in the frame of the project is classified as fundamental research, and the code is open-source and available in the following GitHub repository: https://github.com/DARPA-CRITICALMAAS/beak-ta3.

Key Bayesian contributions in the code include the incorporation of prior knowledge through prior distributions, the application of variational inference for posterior approximation, and the estimation of predictive uncertainty.

## 3.4 Data-Driven Negative Sampling

To address the challenge of obtaining reliable negative labels for training the BNN, we employed a data-driven approach using the SOM outputs. By co-registering the SOM clusters with known mineral occurrences (positive labels), we identified areas likely to be barren (negative labels) for targeted sampling. This approach ensured that the negative labels used for training the BNN were representative of the true background variability in the study area.

# 4 RESULTS

## 4.1 Som Results

<figure><img src="image_263.png" /><figcaption>Figure 8: Unsupervised clustering results from SOM.</figcaption></figure>

The SOM analysis successfully clustered the hyperspectral data, the geological and structural-based evidence layers into distinct groups highlighting areas with similar characteristics. By co-registering the SOM clusters with known mineral occurrences, we identified these clusters as 'very likely' to be areas showing characteristics that could be associated with the presence of mineralized samples and therefore, we exclude them and retain the rest of clusters that are identified (potentially) areas likely to be barren. These potentially barren clusters are the ones chosen for negative labels selection for training the BNN.