Use of Remote Sensing for Soil Composition Determination
a sistematic review
DOI:
https://doi.org/10.70545/ran.v9i14.13571Keywords:
Engineering Reconnaissance, Remote Sensing, Military OperationsAbstract
Objective: To compare the accuracy of soil composition information obtained through remote sensing data, with what is prescribed by the regulatory standards of the Brazilian Association of Technical Standards and the current Military Doctrine. Methods: A systematic literature review was conducted following the PRISMA protocol. This review included scientific articles from 2016 to 2024 written in Portuguese, English, or Spanish, and published in journals indexed in databases. Those used were the Army Digital Library for military documentation, the Target company repository for technical standards research, and the Academia.edu repository for scientific articles. Results: Seven scientific articles, five technical standards, and five manuals from the Brazilian Army were included. The former established the status quaestionis of the topic, the latter presented the national technical and normative framework, and the manuals represented the current Military Doctrine. Discussion: Articles, army manuals, and technical standards were compared considering the fundamentals of engineering reconnaissance and the Engineering Branch. The study indicated the feasibility of using remote sensing for soil composition analysis in military operations.
Downloads
References
AAFAT EL JAZOULI; BARAKAT, A.; KHELLOUK, R.; RAIS, J.; EL BAGHDADI, M. Remote sensing and GIS techniques for prediction of land use land cover change effects on soil erosion in the high basin of the Oum Er Rbia River (Morocco). Remote Sensing Applications: Society and Environment, v. 13, p. 361–374, 2019. DOI: 10.1016/j.rsase.2018.12.004. DOI: https://doi.org/10.1016/j.rsase.2018.12.004
ALBRECHT, A. Large topsoil organic carbon variability is controlled by Andisol properties and effectively assessed by VNIR spectroscopy in a coffee agroforestry system of Costa Rica. Geoderma, 2016.DOI: https://doi.org/10.1016/j.geoderma.2015.08.026 DOI: https://doi.org/10.1016/j.geoderma.2015.08.026
ASSOCIAÇÃO BRASILEIRA DE NORMAS TÉCNICAS. NBR 13603:2022 – Solo – Avaliação da dispersibilidade de solos argilosos, por meio de ensaios químicos em amostra de água intersticial. Rio de Janeiro: ABNT, 2022. Disponível em: https://www.normas.com.br/visualizar/abnt-nbr-nm/10084/abnt-nbr13603-solo-avaliacao-da-dispersibilidade-de-solos-argilosos-por-meio-de-ensaios-quimicos-em-amostra-de-agua-intersticial.
ASSOCIAÇÃO BRASILEIRA DE NORMAS TÉCNICAS. NBR 6457:2024 – Solos — Preparação de amostras para ensaios de compactação, caracterização e determinação do teor de umidade. Rio de Janeiro: ABNT, 2024a. Disponível em: https://www.normas.com.br/visualizar/abnt-nbr-nm/1963/abnt-nbr6457-solos-preparacao-de-amostras-para-ensaios-de-compactacao-caracterizacao-e-determinacao-do-teor-de-umidade
ASSOCIAÇÃO BRASILEIRA DE NORMAS TÉCNICAS. NBR 7181:2025 – Solo — Análise granulométrica. Rio de Janeiro: ABNT, 2025. Disponível em: https://www.normas.com.br/visualizar/abnt-nbr-nm/1963/abnt-nbr6457-solos-preparacao-de-amostras-para-ensaios-de-compactacao-caracterizacao-e-determinacao-do-teor-de-umidade
ASSOCIAÇÃO BRASILEIRA DE NORMAS TÉCNICAS. NBR 9603:2023 – Sondagem a trado — Procedimento. Rio de Janeiro: ABNT, 2023. Disponível em: https://www.normas.com.br/visualizar/abnt-nbr-nm/5985/abnt-nbr9603-sondagem-a-trado-procedimento
ASSOCIAÇÃO BRASILEIRA DE NORMAS TÉCNICAS. NBR 9604:2024 – Solo — Abertura de poço ou trincheira de inspeção, com retirada de amostras deformadas e indeformadas — Procedimento. Rio de Janeiro: ABNT, 2024b. Disponível em: https://www.target.com.br/produtos/normas-tecnicas/27587/nbr9604-solo-abertura-de-poco-ou-trincheira-de-inspecao-com-retirada-de-amostras-deformadas-e-indeformadas-procedimento
BRADLEY, A. V. et al. A multi‐scale approach interpreting sediment processes and distribution from desert sand colour in Central Saudi Arabia. Earth Surface Processes and Landforms, v. 43, n. 14, p. 2847–2862, 2018. DOI: https://doi.org/10.1002/esp.4438. DOI: https://doi.org/10.1002/esp.4438
BRASIL. EXÉRCITO BRASILEIRO. COMANDO DE OPERAÇÕES TERRESTRES. Processo de Integração Terreno, Condições Meteorológicas, Inimigo e Considerações Civis (PITCIC). Brasília, DF: COTER, 2023a. (Manual de Campanha EB20-MC-10.711). Disponível em: http://bdex.eb.mil.br/.
BRASIL. EXÉRCITO BRASILEIRO. COMANDO DE OPERAÇÕES TERRESTRES. A engenharia nas operações. Brasília, DF: COTER, 2018. (Manual de Campanha EB70-MC-10.525). Disponível em: https://bdex.eb.mil.br/
BRASIL. EXÉRCITO BRASILEIRO. COMANDO DE OPERAÇÕES TERRESTRES. Manual Técnico Reconhecimento de Engenharia (EB70-MT-11.420), 1ª ed., 2021. Disponível em: http://bdex.eb.mil.br/.
BRASIL. EXÉRCITO BRASILEIRO. COMANDO DE OPERAÇÕES TERRESTRES. Manual de Campanha Grupamento de Engenharia: organização, capacidades e emprego. Brasília, DF: COTER, 2023b. Disponível em: https://bdex.eb.mil.br/.
BRASIL. EXÉRCITO BRASILEIRO. ESTADO-MAIOR DO EXÉRCITO. Geoinformação. Brasília, DF: EME, 2014. (Manual de Campanha EB20-MC-10.214). Disponível em: https://bdex.eb.mil.br/.
BRASIL. EXÉRCITO BRASILEIRO. ESTADO-MAIOR DO EXÉRCITO. Geointeligência. Brasília, DF: EME, 2016. (Manual de Campanha EB20-MC-10.215). Disponível em: http://bdex.eb.mil.br/.
BRASIL. EXÉRCITO BRASILEIRO. ESTADO MAIOR DO EXÉRCITO. Plano Estratégico do Exército 2024–2027. Brasília, DF, 2023. Disponível em: http://bdex.eb.mil.br/.
CALCINA, S. Automated Resistivity Profiling (ARP) to explore wide archaeological areas: the prehistoric site of Mont’e Prama, Sardinia, Italy. Remote Sensing, 2020. DOI: https://doi.org/10.3390/rs12030461. DOI: https://doi.org/10.3390/rs12030461
CAMPBELL, J. B. Introduction to Remote Sensing. 5th ed. New York: Guilford Press, 2011. DOI: https://doi.org/10.3390/rs5010282. DOI: https://doi.org/10.3390/rs5010282
GILLREATH-BROWN, A. A geospatial method for estimating soil moisture variability in prehistoric agricultural landscapes. PLoS One, 2019. DOI: https://doi.org/10.1371/journal.pone.0220457. DOI: https://doi.org/10.1371/journal.pone.0220457
GUPTA, R. K.; ONKAR, S. Remote sensing of soil moisture: recent advances and future prospects. Journal of Indian Society of Remote Sensing, v. 47, n. 3, p. 589–601, 2019. DOI: https://doi.org/10.1155/2013/424178. DOI: https://doi.org/10.1155/2013/424178
HIGGINS, J. P. T. et al. Chapter 8: Assessing risk of bias in a randomized trial. In: Cochrane Handbook for Systematic Reviews of Interventions. Disponível em: https://training.cochrane.org/handbook/current/chapter-08. Acesso em: 20 mar. 2025.
HOUSSEIN, et al. Formulating research questions for evidence-based studies. Journal of Medicine, Surgery, and Public Health, 2024. DOI: https://doi.org/10.1016/j.glmedi.2023.100046. DOI: https://doi.org/10.1016/j.glmedi.2023.100046
IBGE – INSTITUTO BRASILEIRO DE GEOGRAFIA E ESTATÍSTICA. Mapas de Solos do Brasil. Disponível em: https://www.ibge.gov.br/geociencias/informacoes-ambientais/solos/15819-solos.html. Acesso em: 7 abr. 2025.
ITUCHA, B.; TAKELE, C. Soil–landscape variability: mapping and building detail information for soil management. Soil Use and Management, v. 34, p. 111–123, 2018. DOI: https://doi.org/10.1111/sum.12404. DOI: https://doi.org/10.1111/sum.12404
IWUJI, M. C. et al. Assessment of land use changes and impacts of dam construction on the Mbaa River, Ikeduru, Nigeria. Journal of Geography, Environment and Earth Science International, v. 13, p. 1–10, 2017. DOI: 10.9734/JGEESI/2017/34984. DOI: https://doi.org/10.9734/JGEESI/2017/34984
KALLEM, S.; KUMAR, R.; BHARDWAJ, A. Soil mapping of Patiala-Ki-Rao watershed in Shivalik foothills using GIS. International Journal of Agricultural Science and Research (IJASR), v. 9, p. 1–8, 2019. DOI: https://doi.org/10.52151/jae2019561.1676. DOI: https://doi.org/10.24247/ijasrapr20191
LENZ, J. et al. Parameterization for EROSION-3D model under simulated rainfall conditions in Lower Shivaliks of India. Geosciences, v. 8, n. 11, p. 396, 2018. DOI: 10.3390/geosciences8110396. DOI: https://doi.org/10.3390/geosciences8110396
LISETSKII, F. et al. Postantique soils as a source of land use information: a case study of an ancient Greek agricultural area on the northern Black Sea coast. Applied and Environmental Soil Science, 2020. DOI: 10.1155/2020/8698179. DOI: https://doi.org/10.1155/2020/8698179
LILLESLAND, T. M. et al. Remote Sensing and Image Interpretation. 7th ed. Hoboken: Wiley, 2015.
MENDES, W. S. et al. Zoneamento pedoambiental da bacia do Rio São Bartolomeu, DF. Planaltina: Embrapa Cerrados, 2004. (Boletim de Pesquisa e Desenvolvimento, 118). Disponível em: https://ainfo.cnptia.embrapa.br/digital/bitstream/CNPAF-2009/18495/1/bolpd118.pdf. Acesso em: 7 abr. 2025.
MORELLI, S. Landslide mapping and characterization through infrared thermography (IRT): suggestions for a methodological approach from some case studies. Remote Sensing, v. 9, n. 12, p. 1281, 2017. DOI: https://doi.org/10.3390/rs9121281. DOI: https://doi.org/10.3390/rs9121281
NGETAR, N.; PHINZI, K. The assessment of water-borne erosion at catchment level using GIS-based RUSLE and remote sensing: a review. International Soil and Water Conservation Research, v. 7, n. 1, p. 24–46, 2019. DOI: https://doi.org/10.1016/j.iswcr.2018.12.002. DOI: https://doi.org/10.1016/j.iswcr.2018.12.002
PAGE, M. J. et al. Declaração PRISMA 2020: uma diretriz atualizada para relatar revisões sistemáticas. BMJ, Londres, v. 372, e71, 2021. DOI: http://dx.doi.org/10.1590/s1679-49742022000200033. DOI: https://doi.org/10.5123/S1679-49742022000200033
PANAGIOTIDIS, V. V. et al. Environmental aspects of ancient city planning: a pilot study on Ancient Thouria in the Peloponnese, Greece. STAR: Science & Technology of Archaeological Research, v. 5, n. 2, p. 257–268, 2019. DOI: https://doi.org/10.1080/20548923.2020.1761092. DOI: https://doi.org/10.1080/20548923.2020.1761092
PANDEY, P. GIS and remote sensing aided information for soil moisture estimation: a comparative study of interpolation techniques. Resources, v. 8, n. 2, 2019. DOI: https://doi.org/10.3390/resources8020070. DOI: https://doi.org/10.3390/resources8020070
PUBLICATION, IJRASET. Study of spectral reflectance pattern of red soils under varying moisture conditions. International Journal for Research in Applied Science and Engineering Technology, 2020. DOI: https://doi.org/10.22214/IJRASET.2019.11149. DOI: https://doi.org/10.22214/ijraset.2019.11149
RAZVANCHY, H. Characterization and modeling surface soil physicochemical properties using Landsat images: a case study in the Iraqi Kurdistan Region. Photogrammetric Image Analysis & Munich Remote Sensing Symposium, 2019. DOI: https://doi.org/10.5194/isprs-archives-XLII-2-W16-21-2019. DOI: https://doi.org/10.5194/isprs-archives-XLII-2-W16-21-2019
SHARMA, R. P. et al. Technique of large scale soil mapping using remote sensing satellite data in basaltic terrain of peninsular region in the North-West Gujarat, India. Journal of the Indian Society of Soil Science, v. 67, n. 2, p. 151, 2019. DOI: https://doi.org/10.5958/0974-0228.2019.00016.1. DOI: https://doi.org/10.5958/0974-0228.2019.00016.1
SOUZA, E. de et al. Pedotransfer functions to estimate bulk density from soil properties and environmental covariates: Rio Doce basin. Scientia Agricola, v. 73, n. 6, p. 525–534, 2016. DOI: 10.1590/0103-9016-2015-0485. DOI: https://doi.org/10.1590/0103-9016-2015-0485
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Mateus Fiamoncini Lopes, Elmar de Azevedo Burity (Autor)
This work is licensed under a Creative Commons Attribution 4.0 International License.
