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A global soil spectral grid based on space sensing

Research output: Contribution to journalArticleResearchpeer review

Authors

  • José A.M. Demattê
  • Rodnei Rizzo
  • Nícolas Augusto Rosin
  • Raul Roberto Poppiel
  • Sabine Chabrillat

External Research Organisations

  • Universidade de Sao Paulo
  • University of Sydney
  • University of Florida (UF)
  • University of Nebraska-Lincoln (UNL)
  • Tel Aviv University
  • Czech University of Life Sciences Prague
  • Université Montpellier
  • Indian Institute of Science (IISc)
  • German Research Centre for Geosciences (GFZ)
  • Andalas University (UNAND)
  • National Authority for Remote Sensing And Space Sciences
  • Aristotle University of Thessaloniki (A.U.Th.)
  • Isfahan University of Technology
  • Indian Institute of Technology Kharagpur (IITKGP)
  • Universidade Federal de Vicosa
  • Chinese Academy of Sciences (CAS)
  • New South Wales Department of Climate Change, Energy, the Environment and Water (DCCEEW)
  • Institute of Soil Science and Plant Cultivation (IUNG)
  • Dokuchaev Soil Science Institute (SSI)
  • Commonwealth Scientific and Industrial Research Organisation (CSIRO)
  • Université Paris-Saclay
  • Peoples' Friendship University of Russia (RUDN)
  • Innovative Solutions for Decision Agriculture (iSDA)
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Details

Original languageEnglish
Article number178791
Number of pages14
JournalScience of the Total Environment
Volume968
Early online date20 Feb 2025
Publication statusPublished - 10 Mar 2025

Abstract

Soils provide a range of essential ecosystem services for sustaining life, including climate regulation. Advanced technologies support the protection and restoration of this natural resource. We developed the first fine-resolution spectral grid of bare soils by processing a spatiotemporal satellite data cube spanning the globe. Landsat imagery provided a 30 m composite soil image using the Geospatial Soil Sensing System (GEOS3), which calculates the median of pixels from the 40-year time series (1984–2022). The map of the Earth's bare soil covers nearly 90 % of the world's drylands. The modeling resulted in 10 spectral patterns of soils worldwide. Results indicate that plant residue and unknown soil patterns are the main factors that affect soil reflectance. Elevation and the shortwave infrared (SWIR2) band show the highest importance, with 78 and 80 %, respectively, suggesting that spectral and geospatial proxies provide inference on soils. We showcase that spectral groups are associated with environmental factors (climate, land use and land cover, geology, landforms, and soil). These outcomes represent an unprecedented information source capable of unveiling nuances on global soil conditions. Information derived from reflectance data supports the modeling of several soil properties with applications in soil-geological surveying, smart agriculture, soil tillage optimization, erosion monitoring, soil health, and climate change studies. Our comprehensive spectrally-based soil grid can address global needs by informing stakeholders and supporting policy, mitigation planning, soil management strategy, and soil, food, and climate security interventions.

Keywords

    Agri-environmental policy, Digital soil mapping, Earth observation, Soil reflectance spectra, Soil security

ASJC Scopus subject areas

Sustainable Development Goals

Cite this

A global soil spectral grid based on space sensing. / Demattê, José A.M.; Rizzo, Rodnei; Rosin, Nícolas Augusto et al.
In: Science of the Total Environment, Vol. 968, 178791, 10.03.2025.

Research output: Contribution to journalArticleResearchpeer review

Demattê, JAM, Rizzo, R, Rosin, NA, Poppiel, RR, Novais, JJM, Amorim, MTA, Rodriguez-Albarracín, HS, Rosas, JTF, Bartsch, BDA, Vogel, LG, Minasny, B, Grunwald, S, Ge, Y, Ben-Dor, E, Gholizadeh, A, Gomez, C, Chabrillat, S, Francos, N, Fiantis, D, Belal, A, Tsakiridis, N, Kalopesa, E, Naimi, S, Ayoubi, S, Tziolas, N, Das, BS, Zalidis, G, Francelino, MR, Mello, DCD, Hafshejani, NA, Peng, Y, Ma, Y, Coblinski, JA, Wadoux, AMJC, Savin, I, Malone, BP, Karyotis, K, Milewski, R, Vaudour, E, Wang, C, Salama, ESM & Shepherd, KD 2025, 'A global soil spectral grid based on space sensing', Science of the Total Environment, vol. 968, 178791. https://doi.org/10.1016/j.scitotenv.2025.178791
Demattê, J. A. M., Rizzo, R., Rosin, N. A., Poppiel, R. R., Novais, J. J. M., Amorim, M. T. A., Rodriguez-Albarracín, H. S., Rosas, J. T. F., Bartsch, B. D. A., Vogel, L. G., Minasny, B., Grunwald, S., Ge, Y., Ben-Dor, E., Gholizadeh, A., Gomez, C., Chabrillat, S., Francos, N., Fiantis, D., ... Shepherd, K. D. (2025). A global soil spectral grid based on space sensing. Science of the Total Environment, 968, Article 178791. https://doi.org/10.1016/j.scitotenv.2025.178791
Demattê JAM, Rizzo R, Rosin NA, Poppiel RR, Novais JJM, Amorim MTA et al. A global soil spectral grid based on space sensing. Science of the Total Environment. 2025 Mar 10;968:178791. Epub 2025 Feb 20. doi: 10.1016/j.scitotenv.2025.178791
Demattê, José A.M. ; Rizzo, Rodnei ; Rosin, Nícolas Augusto et al. / A global soil spectral grid based on space sensing. In: Science of the Total Environment. 2025 ; Vol. 968.
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abstract = "Soils provide a range of essential ecosystem services for sustaining life, including climate regulation. Advanced technologies support the protection and restoration of this natural resource. We developed the first fine-resolution spectral grid of bare soils by processing a spatiotemporal satellite data cube spanning the globe. Landsat imagery provided a 30 m composite soil image using the Geospatial Soil Sensing System (GEOS3), which calculates the median of pixels from the 40-year time series (1984–2022). The map of the Earth's bare soil covers nearly 90 % of the world's drylands. The modeling resulted in 10 spectral patterns of soils worldwide. Results indicate that plant residue and unknown soil patterns are the main factors that affect soil reflectance. Elevation and the shortwave infrared (SWIR2) band show the highest importance, with 78 and 80 %, respectively, suggesting that spectral and geospatial proxies provide inference on soils. We showcase that spectral groups are associated with environmental factors (climate, land use and land cover, geology, landforms, and soil). These outcomes represent an unprecedented information source capable of unveiling nuances on global soil conditions. Information derived from reflectance data supports the modeling of several soil properties with applications in soil-geological surveying, smart agriculture, soil tillage optimization, erosion monitoring, soil health, and climate change studies. Our comprehensive spectrally-based soil grid can address global needs by informing stakeholders and supporting policy, mitigation planning, soil management strategy, and soil, food, and climate security interventions.",
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TY - JOUR

T1 - A global soil spectral grid based on space sensing

AU - Demattê, José A.M.

AU - Rizzo, Rodnei

AU - Rosin, Nícolas Augusto

AU - Poppiel, Raul Roberto

AU - Novais, Jean Jesus Macedo

AU - Amorim, Merilyn Taynara Accorsi

AU - Rodriguez-Albarracín, Heidy Soledad

AU - Rosas, Jorge Tadeu Fim

AU - Bartsch, Bruno dos Anjos

AU - Vogel, Letícia Guadagnin

AU - Minasny, Budiman

AU - Grunwald, Sabine

AU - Ge, Yufeng

AU - Ben-Dor, Eyal

AU - Gholizadeh, Asa

AU - Gomez, Cecile

AU - Chabrillat, Sabine

AU - Francos, Nicolas

AU - Fiantis, Dian

AU - Belal, Abdelaziz

AU - Tsakiridis, Nikolaos

AU - Kalopesa, Eleni

AU - Naimi, Salman

AU - Ayoubi, Shamsollah

AU - Tziolas, Nikolaos

AU - Das, Bhabani Sankar

AU - Zalidis, George

AU - Francelino, Marcio Rocha

AU - Mello, Danilo Cesar de

AU - Hafshejani, Najmeh Asgari

AU - Peng, Yi

AU - Ma, Yuxin

AU - Coblinski, João Augusto

AU - Wadoux, Alexandre M.J.C.

AU - Savin, Igor

AU - Malone, Brendan P.

AU - Karyotis, Konstantinos

AU - Milewski, Robert

AU - Vaudour, Emmanuelle

AU - Wang, Changkun

AU - Salama, Elsayed Said Mohamed

AU - Shepherd, Keith D.

N1 - Publisher Copyright: © 2025 Elsevier B.V.

PY - 2025/3/10

Y1 - 2025/3/10

N2 - Soils provide a range of essential ecosystem services for sustaining life, including climate regulation. Advanced technologies support the protection and restoration of this natural resource. We developed the first fine-resolution spectral grid of bare soils by processing a spatiotemporal satellite data cube spanning the globe. Landsat imagery provided a 30 m composite soil image using the Geospatial Soil Sensing System (GEOS3), which calculates the median of pixels from the 40-year time series (1984–2022). The map of the Earth's bare soil covers nearly 90 % of the world's drylands. The modeling resulted in 10 spectral patterns of soils worldwide. Results indicate that plant residue and unknown soil patterns are the main factors that affect soil reflectance. Elevation and the shortwave infrared (SWIR2) band show the highest importance, with 78 and 80 %, respectively, suggesting that spectral and geospatial proxies provide inference on soils. We showcase that spectral groups are associated with environmental factors (climate, land use and land cover, geology, landforms, and soil). These outcomes represent an unprecedented information source capable of unveiling nuances on global soil conditions. Information derived from reflectance data supports the modeling of several soil properties with applications in soil-geological surveying, smart agriculture, soil tillage optimization, erosion monitoring, soil health, and climate change studies. Our comprehensive spectrally-based soil grid can address global needs by informing stakeholders and supporting policy, mitigation planning, soil management strategy, and soil, food, and climate security interventions.

AB - Soils provide a range of essential ecosystem services for sustaining life, including climate regulation. Advanced technologies support the protection and restoration of this natural resource. We developed the first fine-resolution spectral grid of bare soils by processing a spatiotemporal satellite data cube spanning the globe. Landsat imagery provided a 30 m composite soil image using the Geospatial Soil Sensing System (GEOS3), which calculates the median of pixels from the 40-year time series (1984–2022). The map of the Earth's bare soil covers nearly 90 % of the world's drylands. The modeling resulted in 10 spectral patterns of soils worldwide. Results indicate that plant residue and unknown soil patterns are the main factors that affect soil reflectance. Elevation and the shortwave infrared (SWIR2) band show the highest importance, with 78 and 80 %, respectively, suggesting that spectral and geospatial proxies provide inference on soils. We showcase that spectral groups are associated with environmental factors (climate, land use and land cover, geology, landforms, and soil). These outcomes represent an unprecedented information source capable of unveiling nuances on global soil conditions. Information derived from reflectance data supports the modeling of several soil properties with applications in soil-geological surveying, smart agriculture, soil tillage optimization, erosion monitoring, soil health, and climate change studies. Our comprehensive spectrally-based soil grid can address global needs by informing stakeholders and supporting policy, mitigation planning, soil management strategy, and soil, food, and climate security interventions.

KW - Agri-environmental policy

KW - Digital soil mapping

KW - Earth observation

KW - Soil reflectance spectra

KW - Soil security

UR - http://www.scopus.com/inward/record.url?scp=85217952120&partnerID=8YFLogxK

U2 - 10.1016/j.scitotenv.2025.178791

DO - 10.1016/j.scitotenv.2025.178791

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AN - SCOPUS:85217952120

VL - 968

JO - Science of the Total Environment

JF - Science of the Total Environment

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