CoChat
Sign Up
Cited by 100Remote Sensing in AgricultureRemote-Sensing Image ClassificationRemote Sensing and Land Use

The DOI matches its Crossref record; the references it cites are not checked yet.

Earth Observation Data Cubes for Brazil: Requirements, Methodology and Products

Open access
Ferreira, Karine R., Queiroz, Gilberto R., Vinhas, Lubia, Marujo, Rennan F. B., Simoes, Rolf E. O., Picoli, Michelle C. A., Camara, Gilberto, Cartaxo, Ricardo, Gomes, Vitor C. F., Santos, Lorena A., Sanchez, Alber H., Arcanjo, Jeferson S., Fronza, José Guilherme, Noronha, Carlos Alberto, Costa, Raphael W., Zaglia, Matheus C., Zioti, Fabiana, Korting, Thales S., Soares, Anderson R., Chaves, Michel E. D., Fonseca, Leila M. G.·2020·Remote Sensing
DOI 10.3390/rs12244033
Open this paper in CoChat — free accountOpen PDF

Abstract

Recently, remote sensing image time series analysis has being widely used to investigate the dynamics of environments over time. Many studies have combined image time series analysis with machine learning methods to improve land use and cover change mapping. In order to support image time series analysis, analysis-ready data (ARD) image collections have been modeled and organized as multidimensional data cubes. Data cubes can be defined as sets of time series associated with spatially aligned pixels. Based on lessons learned in the research project e-Sensing, related to national demands for land use and cover monitoring and related to state-of-the-art studies on relevant topics, we define the requirements to build Earth observation data cubes for Brazil. This paper presents the methodology to generate ARD and multidimensional data cubes from remote sensing images for Brazil. We describe the computational infrastructure that we are developing in the Brazil Data Cube project, composed of software applications and Web services to create, integrate, discover, access, and process the data sets. We also present how we are producing land use and cover maps from data cubes using image time series analysis and machine learning techniques.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3390/rs12244033

Cites

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

The citation lists haven’t been fetched yet

References 51 · Cited by 100

Research works printed but not linked: 48

  1. Foley, 2005, Global Consequences of Land Use, Science, 309, 570, 10.1126/science.1111772 · DOI · Paper page
  2. Gomez, 2016, Optical remotely sensed time series data for land cover classification: A review, J. Photogramm. Remote Sens., 116, 55, 10.1016/j.isprsjprs.2016.03.008 · DOI
  3. Soille, 2018, A versatile data-intensive computing platform for information retrieval from big geospatial data, Future Gener. Comput. Syst., 81, 30, 10.1016/j.future.2017.11.007 · DOI
  4. Gomes, V.C., Queiroz, G.R., and Ferreira, K.R. (2020). An Overview of Platforms for Big Earth Observation Data Management and Analysis. Remote Sens., 12. · DOI
  5. Woodcock, 2020, Transitioning from Change Detection to Monitoring with Remote Sensing: A Paradigm Shift, Remote Sens. Environ., 238, 111558, 10.1016/j.rse.2019.111558 · DOI
  6. Maus, 2016, A Time-Weighted Dynamic Time Warping Method for Land-Use and Land-Cover Mapping, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 9, 3729, 10.1109/JSTARS.2016.2517118 · DOI
  7. Belgiu, 2018, Sentinel-2 Cropland Mapping Using Pixel-Based and Object-Based Time-Weighted Dynamic Time Warping Analysis, Remote Sens. Environ., 204, 509, 10.1016/j.rse.2017.10.005 · DOI
  8. Picoli, 2018, Big earth observation time series analysis for monitoring Brazilian agriculture, ISPRS J. Photogramm. Remote Sens., 145, 328, 10.1016/j.isprsjprs.2018.08.007 · DOI
  9. Kong, Y.L., Huang, Q., Wang, C., Chen, J., Chen, J., and He, D. (2018). Long short-term memory neural networks for online disturbance detection in satellite image time series. Remote Sens., 10. · DOI
  10. Bendini, 2019, Detailed agricultural land classification in the Brazilian cerrado based on phenological information from dense satellite image time series, Int. J. Appl. Earth Obs. Geoinf., 82, 101872
  11. Simoes, 2020, Land use and cover maps for Mato Grosso State in Brazil from 2001 to 2017, Sci. Data, 7, 1, 10.1038/s41597-020-0371-4 · DOI
  12. Lewis, 2017, The Australian Geoscience Data Cube—Foundations and Lessons Learned, Remote Sens. Environ., 202, 276, 10.1016/j.rse.2017.03.015 · DOI
  13. Siqueira, A., Tadono, T., Rosenqvist, A., Lacey, J., Lewis, A., Thankappan, M., Szantoi, Z., Goryl, P., Labahn, S., and Ross, J. (August, January 28). CEOS Analysis Ready Data For Land–An Overview on the Current and Future Work. Proceedings of the IGARSS 2019—2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan. · DOI
  14. Giuliani, 2017, Building an Earth Observations Data Cube: Lessons Learned from the Swiss Data Cube (SDC) on Generating Analysis Ready Data (ARD), Big Earth Data, 1, 100, 10.1080/20964471.2017.1398903 · DOI
  15. Appel, M., and Pebesma, E. (2019). On-Demand Processing of Data Cubes from Satellite Image Collections with the Gdalcubes Library. Data, 4. · DOI
  16. Asmaryan, S., Muradyan, V., Tepanosyan, G., Hovsepyan, A., Saghatelyan, A., Astsatryan, H., Grigoryan, H., Abrahamyan, R., Guigoz, Y., and Giuliani, G. (2019). Paving the Way towards an Armenian Data Cube. Data, 4. · DOI
  17. Maso, J., Zabala, A., Serral, I., and Pons, X. (2019). A Portal Offering Standard Visualization and Analysis on top of an Open Data Cube for Sub-National Regions: The Catalan Data Cube Example. Data, 4. · DOI
  18. Killough, B. (August, January 28). The Impact of Analysis Ready Data in the Africa Regional Data Cube. Proceedings of the IGARSS 2019—2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan. · DOI
  19. Camara, G., Assis, L.F., Ribeiro, G., Ferreira, K.R., Llapa, E., and Vinhas, L. (2016, January 31). Big Earth Observation Data Analytics: Matching Requirements to System Architectures. Proceedings of the 5th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial ’16, San Francisco, CA, USA. · DOI
  20. Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. · DOI
  21. Giuliani, G., Camara, G., Killough, B., and Minchin, S. (2019). Earth Observation Open Science: Enhancing Reproducible Science Using Data Cubes. Data, 4. · DOI
  22. Ponzoni, 2008, In-flight absolute calibration of the CBERS-2 CCD sensor data, Acad. Bras. CiÊNcias, 80, 373, 10.1590/S0001-37652008000200015 · DOI
  23. Diniz, 2015, DETER-B: The New Amazon Near Real-Time Deforestation Detection System, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 8, 3619, 10.1109/JSTARS.2015.2437075 · DOI
  24. FG Assis, L.F., Ferreira, K.R., Vinhas, L., Maurano, L., Almeida, C., Carvalho, A., Rodrigues, J., Maciel, A., and Camargo, C. (2019). TerraBrasilis: A Spatial Data Analytics Infrastructure for Large-Scale Thematic Mapping. ISPRS Int. J. Geo-Inf., 8. · DOI
  25. Almeida, 2016, High spatial resolution land use and land cover mapping of the Brazilian Legal Amazon in 2008 using Landsat-5/TM and MODIS data, Acta Amaz., 46, 291, 10.1590/1809-4392201505504 · DOI
  26. Kintisch, 2007, Improved monitoring of rainforests helps pierce haze of deforestation, Science, 316, 536, 10.1126/science.316.5824.536 · DOI
  27. Maus, 2019, dtwSat: Time-Weighted Dynamic Time Warping for Satellite Image Time Series Analysis in R, J. Stat. Softw. Artic., 88, 1
  28. Xi, W., Du, S., Wang, Y.C., and Zhang, X. (2019). A Spatiotemporal Cube Model for Analyzing Satellite Image Time Series: Application to Land-Cover Mapping and Change Detection. Remote Sens. Environ. · DOI
  29. Vellido, A., Gibert, K., Angulo, C., and Martín Guerrero, J.D. (2020). Self-Organizing Maps in Earth Observation Data Cubes Analysis. Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization, Springer International Publishing. · DOI
  30. Lu, 2016, Spatio-temporal change detection from multidimensional arrays: Detecting deforestation from MODIS time series, ISPRS J. Photogramm. Remote Sens., 117, 227, 10.1016/j.isprsjprs.2016.03.007 · DOI
  31. Vinhas, 2017, Web services for big earth observation data, Rev. Bras. Cartogr., 69, 5, 10.14393/rbcv69n5-44004 · DOI
  32. Bernard, 2010, Moving code in spatial data infrastructures - Web service based deployment of geoprocessing algorithms, Trans. GIS, 14, 101, 10.1111/j.1467-9671.2010.01205.x · DOI
  33. Ferreira, K.R., Queiroz, G.R., Camara, G., Souza, R.C.M., Vinhas, L., Marujo, R.F.B., Simoes, R.E.O., Noronha, C.A.F., Costa, R.W., and Arcanjo, J.S. (2020, January 22–26). Using Remote Sensing Images and Cloud Services on AWS to Improve Land Use and Cover Monitoring. Proceedings of the 2020 IEEE Latin American GRSS ISPRS Remote Sensing Conference (LAGIRS), Santiago, Chile. · DOI
  34. Vermote, 2016, Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product, Remote Sens. Environ., 185, 46, 10.1016/j.rse.2016.04.008 · DOI
  35. 2013, Geração de imagens de reflectância de um ponto de vista geométrico, Rev. Bras. Geomat., 1, 23, 10.3895/rbgeo.v1n1.5434 · DOI
  36. Qiu, 2019, Fmask 4.0: Improved cloud and cloud shadow detection in Landsats 4–8 and Sentinel-2 imagery, Remote Sens. Environ., 231, 111205, 10.1016/j.rse.2019.05.024 · DOI
  37. Sanchez, A.H., Picoli, M.C.A., Camara, G., Andrade, P.R., Chaves, M.E.D., Lechler, S., Soares, A.R., Marujo, R.F.B., Simões, R.E.O., and Ferreira, K.R. (2020). Comparison of Cloud Cover Detection Algorithms on Sentinel–2 Images of the Amazon Tropical Forest. Remote Sens., 12. · DOI
  38. Sattler, K.U. (2016). Data Quality Dimensions. Encycl. Database Syst., 1–5. · DOI
  39. Gomes, 2017, Landscape-fragmentation change due to recent agricultural expansion in the Brazilian Savanna, Western Bahia, Brazil, Reg. Environ. Chang., 17, 411, 10.1007/s10113-016-0960-0 · DOI
  40. Hastie, T., and Tibshirani, R. (2009). The Elements of Statistical Learning. Data Mining, Inference, and Prediction, Springer. · DOI · Paper page
  41. Olofsson, 2014, Good Practices for Estimating Area and Assessing Accuracy of Land Change, Remote Sens. Environ., 148, 42, 10.1016/j.rse.2014.02.015 · DOI
  42. Chaves, M.E.D., Picoli, M.C.A., and Sanches, I.D. (2020). Recent Applications of Landsat 8/OLI and Sentinel-2/MSI for Land Use and Land Cover Mapping: A Systematic Review. Remote Sens., 12. · DOI
  43. Roy, 2016, A general method to normalize Landsat reflectance data to nadir BRDF adjusted reflectance, Remote Sens. Environ., 176, 255, 10.1016/j.rse.2016.01.023 · DOI
  44. Roy, 2017, Examination of Sentinel-2A multi-spectral instrument (MSI) reflectance anisotropy and the suitability of a general method to normalize MSI reflectance to nadir BRDF adjusted reflectance, Remote Sens. Environ., 199, 25, 10.1016/j.rse.2017.06.019 · DOI
  45. Claverie, 2018, The Harmonized Landsat and Sentinel-2 Surface Reflectance Data Set, Remote Sens. Environ., 219, 145, 10.1016/j.rse.2018.09.002 · DOI · Paper page
  46. Franch, B., Vermote, E., Skakun, S., Roger, J.c., Masek, J., Ju, J., Villaescusa-nadal, J.L., and Santamaria-artigas, A. (2019). A Method for Landsat and Sentinel 2 (HLS) BRDF Normalization. Remote Sens., 2. · DOI
  47. Steinhausen, 2018, Combining Sentinel-1 and Sentinel-2 data for improved land use and land cover mapping of monsoon regions, Int. J. Appl. Earth Obs. Geoinf., 73, 595
  48. Nicolau, 2021, Assessing SAR C-band data to effectively distinguish modified land uses in a heavily disturbed Amazon forest, Int. J. Appl. Earth Obs. Geoinf., 94, 102214

Metadata

Authors
Ferreira, K. R., Queiroz, G. R., Vinhas, L., Marujo, R. F. B., Simoes, R. E. O., Picoli, M. C. A., Camara, G., Cartaxo, R., Gomes, V. C. F., Santos, L. A., Sanchez, A. H., Arcanjo, J. S., Fronza, J. G., Noronha, C. A., Costa, R. W., Zaglia, M. C., Zioti, F., Korting, T. S., Soares, A. R., Chaves, M. E. D., & Fonseca, L. M. G.
Affiliations
  • Instituto Nacional de Pesquisas Espaciais (BR)
  • Earth Observation and Geoinformatics Division, National Institute for Space Research (INPE), Avenida dos Astronautas, 1758, Jardim da Granja, Sao Jose dos Campos, SP 12227-010, Brazil
Corresponding author
Ferreira, Karine R.
ORCID
Venue
Remote Sensing
Publisher
MDPI AG
Published
2020-12-09
Volume
12
Issue
24
Pages
4033
ISSN
2072-4292 (ISSN-L)
DOI
10.3390/rs12244033
OpenAlex
W3112926737
Cited by
100
References
51
Citations by year
2026: 20 · 2025: 20 · 2024: 20 · 2023: 12 · 2022: 13 · 2021: 15
FWCI
5.31
Type
journal article
Language
en
Open access
Licence
Locations
  • Remote Sensing (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Biblioteca Digital da Memória Científica do INPE (National Institute for Space Research) (submittedVersion)
Funders
  • Brazilian Development Bank (BNDES) (17.2.0536.1)
  • Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (001)
  • Conselho Nacional de Desenvolvimento Científico e Tecnológico (001)
  • Group on Earth Observations (GEO) and Amazon Web Services (AWS) (001)
  • Governo Brasil (001)
Topics
  • Physical Sciences › Environmental Science › Ecology › Remote Sensing in Agriculture
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Remote-Sensing Image Classification
  • Social Sciences › Geography, Planning and Development › Remote Sensing and Land Use
Keywords
Earth observation data cubes, analysis-ready data, data cubes, IT infrastructure

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Earth Observation Data Cubes for Brazil: Requirements, Methodology and Products. https://app.cochat.ai/papers/10.3390/rs12244033
The paper
Ferreira, K. R., Queiroz, G. R., Vinhas, L., Marujo, R. F. B., Simoes, R. E. O., Picoli, M. C. A., Camara, G., Cartaxo, R., Gomes, V. C. F., Santos, L. A., Sanchez, A. H., Arcanjo, J. S., Fronza, J. G., Noronha, C. A., Costa, R. W., Zaglia, M. C., Zioti, F., Korting, T. S., Soares, A. R., . . . Fonseca, L. M. G. (2020). Earth Observation Data Cubes for Brazil: Requirements, Methodology and Products. Remote Sensing. https://doi.org/10.3390/rs12244033