SPECTRAL-TEMPORAL PROFILE ANALYSIS OF MAIZE, SOYBEAN AND SUGARCANE BASED ON OLI/LANDSAT-8 DATA

Autores

  • Bruno Montibeller University of Tartu
  • Ieda Del'Arco Sanches National Institute for Space Research
  • Alfredo José Barreto Luiz Embrapa Environment
  • Fabio Gonçalves Canopy Remote Sensing Solutions
  • Daniel Alves de Aguiar Agrosatélite Geotecnologia Aplicada

DOI:

https://doi.org/10.37856/bja.v94i3.3612

Resumo

Remote sensing (RS) technology is a viable complementary alternative to current agriculture surveying methods. RS data spectral information is the main variable used for several purposes, such as crop type identification. However, different management practices (MP) adopted in crop cultivation may alter its spectral characteristics. The objective of this work is to analyze the spectral-temporal profile (STP) variation of soybean, maize and sugarcane cultivated under different MP. We used time series of the six spectral bands of the OLI/Landsat-8 sensor and of two vegetation indexes (VI) to investigate the intraspecific variation (same crop specie) and the interspecific variation (different crop species). We applied hierarchical cluster analyses to determine the crop´s STP variation. The bands results were more efficient than the VI. This shows that despite the widely use of VI, better results are retrieved when using the bands STP, which also allows differentiating and analyzing crops cultivated under different MP.

Biografia do Autor

Bruno Montibeller, University of Tartu

PhD student at University of Tartu

Ieda Del'Arco Sanches, National Institute for Space Research

Researcher at National Institute for Space Research

Alfredo José Barreto Luiz, Embrapa Environment

Researcher at Embrapa Environment

Fabio Gonçalves, Canopy Remote Sensing Solutions

Co-founder & CEO at Canopy Remote Sensing Solutions

Daniel Alves de Aguiar, Agrosatélite Geotecnologia Aplicada

Director at Agrosatélite Geotecnologia Aplicada

Referências

Adami M (2010) Estimativa da data de plantio da soja por meio de séries temporais de imagens MODIS. PhD Thesis, INPE - Pós-Graduação Em Sensoriamento Remoto, 163. https://doi.org/sid.inpe.br/mtc-m19/2010/09.15.21.47-TDI

Atzberger, C. (2013). Advances in remote sensing of agriculture: Context description, existing operational monitoring systems and major information needs. Remote Sensing, 5(2), 949–981. https://doi.org/10.3390/rs5020949

Battude, M., Al Bitar, A., Morin, D., Cros, J., Huc, M., Marais Sicre, C., … Demarez, V. (2016). Estimating maize biomass and yield over large areas using high spatial and temporal resolution Sentinel-2 like remote sensing data. Remote Sensing of Environment, 184, 668–681. https://doi.org/10.1016/j.rse.2016.07.030

Brown, J., Jepson, W., Kastens, J., Wardlow, B., Lomas, J., & Price, K. (2007). Multitemporal, Moderate-Spatial-Resolution Remote Sensing of Modern Agricultural Production and Land Modification in the Brazilian Amazon. GIScience & Remote Sensing, 44(2), 117–148. https://doi.org/10.2747/1548-1603.44.2.117

Companhia Nacional de Abastecimento -CONAB. Acompanhamento da safra brasileira de grãos. Boletim Grãos, v. 3, n. 11, 2016. Disponível em: <http://www.conab.gov.br/OlalaCMS/uploads/arquivos/16_06_09_09_00_00_boletim_graos_junho__2016_-_final.pdf>. Acesso em: 02 fev.2017.

Eberhardt, I. D. R., Schultz, B., Rizzi, R., Sanches, I. D. A., Formaggio, A. R., Atzberger, C., … Luiz, A. J. B. (2016). Cloud cover assessment for operational crop monitoring systems in tropical areas. Remote Sensing, 8(3), 1–14. https://doi.org/10.3390/rs8030219

Fehr, W. R., & Caviness, C. E. (1977). Stages of soybean development Recommended Citation. Retrieved from http://lib.dr.iastate.edu/specialreports/87

Ferguson, R., Rundquist, D., Shannon, D. K., Clay, D. ., & Kitchen, N. . (2003). Remote Sensing for Site-Specific Crop Management, 69(6), 647–664. https://doi.org/10.2134/precisionagbasics.2016.0092

Foerster, S., Kaden, K., Foerster, M., & Itzerott, S. (2012). Crop type mapping using spectral-temporal profiles and phenological information. Computers and Electronics in Agriculture, 89, 30–40. https://doi.org/10.1016/j.compag.2012.07.015

Huete, A. R., Liu, H. Q., Batchily, K., & Leeuwen, W. Van. (1995). A Comparison of Vegetation Indices over a Global Set of TM Images for EOS-MODIS, 4257.

IBGE. Mapa de solos do Brasil. 2004. Disponível em: <ftp://geoftp.ibge.gov.br/mapas/tematicos/sistematizacao/pedologia/>. Acesso em: 20 abr. 2017.

IBGE. (2019). Censo Agropecuário 2017. https://sidra.ibge.gov.br/tabela/6635

Johnson, D. M. (2014). An assessment of pre- and within-season remotely sensed variables for forecasting corn and soybean yields in the United States. Remote Sensing of Environment, 141, 116–128. https://doi.org/10.1016/j.rse.2013.10.027

Kastens, J. H., Brown, J. C., Coutinho, A. C., Bishop, C. R., & Esquerdo, J. C. D. M. (2017). Soy moratorium impacts on soybean and deforestation dynamics in Mato Grosso, Brazil. PLoS ONE, 12(4), 1–21. https://doi.org/10.1371/journal.pone.0176168

Kussul, N., Javier Gallego Pinilla, F., Skakun, S. V, Lavreniuk, M., Lemoine, G., Member, S., … Shelestov, A. Y. (2016). Parcel-Based Crop Classification in Ukraine Using Landsat-8 Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(6), 2500–2508. https://doi.org/10.1109/JSTARS.2016.2560141

Luiz, A. J. B., Sanches, I. D., Trabaquini, K., Eberhardt, I. D. R., & Formaggio, A. R. (2015). Dinâmica agrícola em área de sobreposição de órbitas adjacentes dos satélites Landsat. Anais XVII Simpósio Brasileiro de Sensoriamento Remoto - SBSR, João Pessoa-PB, Brasil, 25 a 29 de Abril de 2015, INPE, (1), 6381–6388. https://doi.org/10.1017/CBO9781107415324.004

Masialeti, I., Egbert, S., & Wardlow, B. D. (2010). A Comparative Analysis of Phenological Curves for Major Crops in Kansas. GIScience & Remote Sensing, 47(2), 241–259. https://doi.org/10.2747/1548-1603.47.2.241

Mello, M. P. (2013). Spectral-temporal and Bayesian methods for agricultural remote sensing data analysis. Mtc-M19.Sid.Inpe.Br, 120. Retrieved from http://urlib.net/8JMKD3MGP7W/3ERM89S

Mishra, A. K., Ines, A. V. M., Das, N. N., Prakash Khedun, C., Singh, V. P., Sivakumar, B., & Hansen, J. W. (2015). Anatomy of a local-scale drought: Application of assimilated remote sensing products, crop model, and statistical methods to an agricultural drought study. Journal of Hydrology, 526, 15–29. https://doi.org/10.1016/j.jhydrol.2014.10.038

Oliveira, J. C., Trabaquini, K., Epiphanio, J. C. N., Formaggio, A. R., Galvão, L. S., & Adami, M. (2014). Analysis of agricultural intensification in a basin with remote sensing data. GIScience & Remote Sensing, 51(3), 253–268. https://doi.org/10.1080/15481603.2014.909108

Parente, L., Ferreira, L., Faria, A., Nogueira, S., Araújo, F., Teixeira, L., & Hagen, S. (2017). Monitoring the brazilian pasturelands: A new mapping approach based on the landsat 8 spectral and temporal domains. International Journal of Applied Earth Observation and Geoinformation, 62(June), 135–143. https://doi.org/10.1016/j.jag.2017.06.003

Peña, M. A., & Brenning, A. (2015). Assessing fruit-tree crop classification from Landsat-8 time series for the Maipo Valley, Chile. Remote Sensing of Environment, 171, 234–244. https://doi.org/10.1016/j.rse.2015.10.029

Peña, M. A., Liao, R., & Brenning, A. (2017). Using spectrotemporal indices to improve the fruit-tree crop classification accuracy. ISPRS Journal of Photogrammetry and Remote Sensing, 128, 158–169. https://doi.org/10.1016/j.isprsjprs.2017.03.019

Risso, J., Rizzi, R., Rudorff, B. F. T., Adami, M., Shimabukuro, Y. E., Formaggio, A. R., & Epiphanio, R. D. V. (2012). Indices de vegetacao Modis aplicados na discriminao de areas de soja. Pesquisa Agropecuaria Brasileira, 47(9), 1317–1326. https://doi.org/10.1590/S0100-204X2012000900017

Rizzi, R., Risso, J., Friedrich, B., Rudorff, T., & Formaggio, A. R. (2009). Estimativa da área de soja no Mato Grosso por meio de imagens MODIS. 387–394.

Rouse, J. W., Hass, R. H., Schell, J. A., & Deering, D. W. (1972). Monitoring Vegetation Systems in the Great Plains with ERTS. Third Earth Resources Technology Satellite-1 Symposium.

Sakamoto, T., Wardlow, B. D., Gitelson, A. A., Verma, S. B., Suyker, A. E., & Arkebauer, T. J. (2010). A Two-Step Filtering approach for detecting maize and soybean phenology with time-series MODIS data. Remote Sensing of Environment, 114(10), 2146–2159. https://doi.org/10.1016/j.rse.2010.04.019

Vermote, E., Justice, C., Claverie, M., & Franch, B. (2016). Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sensing of Environment, 185, 46–56. https://doi.org/10.1016/j.rse.2016.04.008

Waldhoff, G., Curdt, C., Hoffmeister, D., & Bareth, G. (2012). Analysis of multitemporal and multisensor remote sensing data for crop rotation mapping. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 1(September), 177–182. https://doi.org/10.5194/isprsannals-I-7-177-2012

Wardlow, B. D., Egbert, S. L., & Kastens, J. H. (2007). Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains. Remote Sensing of Environment, 108(3), 290–310. https://doi.org/10.1016/j.rse.2006.11.021

Whitcraft, A. K., Becker-Reshef, I., Killough, B. D., & Justice, C. O. (2015). Meeting earth observation requirements for global agricultural monitoring: An evaluation of the revisit capabilities of current and planned moderate resolution optical earth observing missions. Remote Sensing, 7(2), 1482–1503. https://doi.org/10.3390/rs70201482

Downloads

Publicado

2019-12-23

Edição

v. 94 n. 3 (2019)

Seção

Artigos