Spatio-temporal variability of biophysical parameters of irrigated maize using orbital remote sensing
DOI:
https://doi.org/10.5433/1679-0359.2021v42n4p2181Keywords:
Agrometeorological models., Irrigation management, Phenological cycle.Abstract
This study proposes to estimate the actual crop evapotranspiration, using the SAFER model, as well as calculate the crop coefficient (Kc) as a function of the normalized difference vegetation index (NDVI) and determine the biomass of an irrigated maize crop using images from the Operational Land Imager (OLI) and Thermal Infrared (TIRS) sensors of the Landsat-8 satellite. Pivots 21 to 26 of a commercial farm located in the municipalities of Bom Jesus da Lapa and Serra do Ramalho, west of Bahia State, Brazil, were selected. Sowing dates for each pivot were arranged as North and South or East and West, with cultivation starting firstly in one of the orientations and subsequently in the other. The relationship between NDVI and the Kc values obtained in the FAO-56 report (KcFAO) revealed a high coefficient of determination (R2 = 0.7921), showing that the variance of KcFAO can be explained by NDVI in the maize crop. Considering the center pivots with different planting dates, the crop evapotranspiration (ETc) pixel values ranged from 0.0 to 6.0 mm d-1 during the phenological cycle. The highest values were found at 199 days of the year (DOY), corresponding to around 100 days after sowing (DAS). The lowest BIO values occur at 135 DOY, at around 20 DAS. There is a relationship between ETc and BIO, where the DOY with the highest BIO are equivalent to the days with the highest ETc values. In addition to this relationship, BIO is strongly influenced by soil water availability.Downloads
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References
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Bernardo, S., Mantovani, E. C., Silva, D. D., & Soares, A. A. (2019). Manual de irrigação (9a ed.). Viçosa, MG: Imprensa Universitária da UFV.
Bezerra, J. M., Moutra, G. B. A., Silva, B. B., Lopes, P. M. O., & Silva, E. F. F. (2014). Parâmetros biofísicos obtidos por sensoriamento remoto em região semiárida do estado do Rio Grande do Norte, Brasil. Revista Brasileira de Engenharia Agrícola e Ambiental, 18(1), 73-84.
Bhuiyan, C., Saha, A. K., Bandyopadhyay, N., & Kogan, F. N. (2017). Analyzing the impact of thermal stress on vegetation health and agricultural drought - a case study from Gujarat, India. Journal GIScience & Remote Sensing, 54(5), 1943-7226. doi: 10.1080/15481603.2017.1309737
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Formaggio, A. R., & Sanches, I. A. (2017). Sensoriamento remoto na agricultura. São Paulo: Oficina de Textos.
Frenck, G., Leitinger, G., Obojes, N., Hofmann, M., Newesely, C., Deutschmann, M.,... Tasser, E. (2018). Community-specific hydraulic conductance potential of soil water decomposed for two Alpine grasslands by small-scale lysimetry. Biogeosciences, 15(1), 1065-1078. doi: 10.5194/bg-15-1065-2018
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Ponzoni, F. J., & Shimabukuro, Y. E. (2007). Sensoriamento remoto no estudo da vegetação. São José dos Campos: Ed. Parêntese.
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Ribeiro, R. B., Filgueiras, R., Ramos, M. C. A., Almeida, L. T., Generoso, T. N., & Monteiro, L. I. B. (2017). Variação espacial-temporal da condição da vegetação na agricultura irrigada por meio de imagens sentinela. Revista Brasileira de Agricultura Irrigada, 11(1), 1884-1893. doi: 10.7127/rbai.v11 n600648
Rissini, A. L. L., Kawakami, J., Genú, A. M. (2015). Índice de vegetação por diferença normalizada e produtividade de cultivares de trigo submetidas a doses de nitrogênio. Revista Brasileira de Ciência do Solo, 39(6), 1703-1713. doi: 10.1590/01000683rbcs20140686
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Sá, P. C. C., Sobrinho, J. S., Silva, S. T. A., Ferreira, R. C., & Moura, M. S. B. (2016). Estimativa do saldo de radiação em cultivo irrigado de cana-de-açúcar utilizando dados de sensoriamento remoto orbital. Revista Brasileira de Geografia Física, 9(7), 2164-2178. doi: 10.5935/1984-2295.20160153
Santos, R. A., Venancio, L. P., Filgueiras, R., & Cunha, F. F. (2020). Remote sensing as a tool to determine biophysical parameters of irrigated seed corn crop. Semina: Ciências Agrárias, 41(2), 435-446. doi: 10. 5433/1679-0359.2020v41n2p435
Silva, A. P. N., Moura, G. B. A., Giongo, P. R., & Silva, B. B. (2011). Albedo de superfície estimado a partir de imagens Landsat 5 - TM no Semiárido Brasileiro. Revista de Geografia, 27(1), 154-168.
Silva, B. B., Braga, A. C., Braga, C. C., Oliveira, L. M. M., Montenegro, S. M. G., & Barbosa, B., Jr. (2016). Procedures for calculation of the albedo with OLI-Landsat 8 images: application to the Brazilian semi-arid. Revista Brasileira de Engenharia Agrícola e Ambiental, 20(1), 3-8. doi: 10.1590/1807-1929
Teixeira, A. H. C. (2010). Determining regional actual evapotranspiration of irrigated and natural vegetation in the São Francisco riverbasin (Brazil) using remote sensingan Penman-Monteith equation. Remote Sensing, 2(5), 1287-1319. doi: 10.3390/rs0251287
Teixeira, A. H. C., & Leivas, J. F. (2017). Determinação da produtividade da água com imagens Landsat 8 na região Semiárida do Brasil. Revista Conexões Ciência e Tecnologia, 11(1), 22-34. doi: 10.21439/ conexoes.v11i1.1064
Teixeira, A. H. C., Leivas, J. F., Andrade, R. G., & Hernandez, F. B. T. (2015). Water productivity assessments with Landsat 8 images in the Nilo Coelho irrigation scheme. Revista Irriga, 1(2), 1-10. doi: 10.15809/irriga.2015v1n2p01
Teixeira, A. H. C., Scherer-Warren, M., Hernandez, F. B., Andrade, R. G., & Leivas, J. F. (2013). Large-scale water productivity assessments with modis images in a changing semi-arid environment: a brazilian case study. Remote Sensing, Multidisciplinary Digital Publishing Institute, 5(11), 5783-5804. doi: 10.3390/rs5115783
Toureiro, C., Serralheiro, R., Shahidian, S., & Sousa, A. (2017). Irrigation management with remote sensing: Evaluating irrigation requirement for maiz eunder Mediterranean climate condition. Agricultural Water Management, 184(1), 211-220. doi: 10.1016/j.agwat.2016.02.010
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Vanhellemont, Q., & Ruddick, K. (2014). Turbid wakes associated with of shore wind turbines observed with landsat 8. Remote Sensing of Environment, 145(1), 105-115. doi: 10.1016/j.rse.2014.01.009
Veloso, G. A., Ferreira, M. E., Rosa, R., & Silva, B. B. (2015). Determinação do albedo de superfície em áreas irrigadas do projeto Jaíba (Minas Gerais) mediante imagens Landsat 5 - TM. Revista R. Ra’eGa, 35(1), 126-146. doi: 10.5380/raega.v35i0.39757
Wang, X., Ma, M., Huang, G., Veroustraete, F., Zhang, Z., Song, Y., & Tan, J. (2012). Vegetation primary production estimation at maize and alpine meadow over the Heihe River Basin, China. International Journal of Applied Earth Observation Geoinformation, 17(1), 94-101. doi: 10.1016/j.agrformet.2018. 01.005
Yuan, M., Zhang, L., Gou, F., Su, Z., Spiertz, J. H. J., & Van. W. W. (2013). Assessment of crop growth and water productivity for five C3 species in semi-arid Inner Mongolia. Agricultural Water Management, 122(5), 28-38. doi: 10.1016/j.agwat.2013.02.006
Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop evapotranspiration - guidelines for computing crop water requirements - FAO Irrigation and drainage paper 56 (9nd ed.). Rome: Food and Agriculture Organization of the United Nations.
Althoff, D., Santos, R. A., Bazame, H, C., Cunha, F. F., & Filgueiras, R. (2019). Improvement of hargreaves-samani reference evapotranspiration estimates with local calibration. Water, 11(11), 22-72. doi: 10. 3390/w11112272
Bastiaanssen, W. G. M., & Ali, S. (2003). A new crop yield forecasting model based on satellite meas urements applied across the Indus Basin, Pakistan. Agriculture, Ecosystems e Environment, 94(3), 321-340. doi: 10.1016/S0167-8809(02)00034-8
Bernardo, S., Mantovani, E. C., Silva, D. D., & Soares, A. A. (2019). Manual de irrigação (9a ed.). Viçosa, MG: Imprensa Universitária da UFV.
Bezerra, J. M., Moutra, G. B. A., Silva, B. B., Lopes, P. M. O., & Silva, E. F. F. (2014). Parâmetros biofísicos obtidos por sensoriamento remoto em região semiárida do estado do Rio Grande do Norte, Brasil. Revista Brasileira de Engenharia Agrícola e Ambiental, 18(1), 73-84.
Bhuiyan, C., Saha, A. K., Bandyopadhyay, N., & Kogan, F. N. (2017). Analyzing the impact of thermal stress on vegetation health and agricultural drought - a case study from Gujarat, India. Journal GIScience & Remote Sensing, 54(5), 1943-7226. doi: 10.1080/15481603.2017.1309737
Congedo, L. (2016). Semi-automatic classification plugin documentation: release 5.0.1.1. Roma: Sapienza University of Rome.
Formaggio, A. R., & Sanches, I. A. (2017). Sensoriamento remoto na agricultura. São Paulo: Oficina de Textos.
Frenck, G., Leitinger, G., Obojes, N., Hofmann, M., Newesely, C., Deutschmann, M.,... Tasser, E. (2018). Community-specific hydraulic conductance potential of soil water decomposed for two Alpine grasslands by small-scale lysimetry. Biogeosciences, 15(1), 1065-1078. doi: 10.5194/bg-15-1065-2018
Higuchi, N., Santos, J., Ribeiro, R. J., Minette, L., & Biot, Y. (1998) Biomassa da parte aérea da vegetação de floresta tropical úmida de terra-firme da Amazônia Brasileira. Acta Amazônica, 28(1), 153-165. doi: 10.1590/1809-43921998282166
Jensen, J. R., Bryan, M. L., Friedman, S. Z., Henderson, F. M., Holz, R. K., Lindgren. D.,… Wray, J. R. (1983). Urban/suburban land use analysis. In J. E. Estes (Ed.), Manual of remote sensing (vol. 2, pp. 1571-666, 2nd ed.). Falls Church, VA: American Society of Photogrammetry.
Martins, C. L., Busato, C., Silva, S. F., Rodrigues, W. N., & Reis, E. F. (2013). Avaliação do desempenho de sistemas de irrigação no sul do Estado do Espírito Santo. RevistaAgro@mbiente, 7(2), 236-241. doi: 10. 18227/1982-8470ragro.v7i2.1069
Monteith, J. L. (1972). Solar radiation and productivity in tropical ecosystems. Journal of Applied Ecology 9(3), 747-766. doi: 10.2307/2401901
Pandey, P. K., Dabral, P. P., & Pandey, V. (2016). Evaluation of reference evapotranspiration methods for the Northeastern region of India. International Soil and Water Conservation Research, 4(1), 56-67. doi: 10.1016/j.iswcr.2016.02.003
Ponzoni, F. J., & Shimabukuro, Y. E. (2007). Sensoriamento remoto no estudo da vegetação. São José dos Campos: Ed. Parêntese.
QGIS Development Team (2015). QGIS Geographic information system (3.16). Retcovered from http:// www.qgis.org/
R Core Team (2016). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from https://www.R-project.org/
Ribeiro, R. B., Filgueiras, R., Ramos, M. C. A., Almeida, L. T., Generoso, T. N., & Monteiro, L. I. B. (2017). Variação espacial-temporal da condição da vegetação na agricultura irrigada por meio de imagens sentinela. Revista Brasileira de Agricultura Irrigada, 11(1), 1884-1893. doi: 10.7127/rbai.v11 n600648
Rissini, A. L. L., Kawakami, J., Genú, A. M. (2015). Índice de vegetação por diferença normalizada e produtividade de cultivares de trigo submetidas a doses de nitrogênio. Revista Brasileira de Ciência do Solo, 39(6), 1703-1713. doi: 10.1590/01000683rbcs20140686
Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1974). Monitoring vegetation systems in the great plains with ERTS. Proceedings of the Earth Resources Technology Satellite-1, Symposium, Greenbelt, 3.
Sá, P. C. C., Sobrinho, J. S., Silva, S. T. A., Ferreira, R. C., & Moura, M. S. B. (2016). Estimativa do saldo de radiação em cultivo irrigado de cana-de-açúcar utilizando dados de sensoriamento remoto orbital. Revista Brasileira de Geografia Física, 9(7), 2164-2178. doi: 10.5935/1984-2295.20160153
Santos, R. A., Venancio, L. P., Filgueiras, R., & Cunha, F. F. (2020). Remote sensing as a tool to determine biophysical parameters of irrigated seed corn crop. Semina: Ciências Agrárias, 41(2), 435-446. doi: 10. 5433/1679-0359.2020v41n2p435
Silva, A. P. N., Moura, G. B. A., Giongo, P. R., & Silva, B. B. (2011). Albedo de superfície estimado a partir de imagens Landsat 5 - TM no Semiárido Brasileiro. Revista de Geografia, 27(1), 154-168.
Silva, B. B., Braga, A. C., Braga, C. C., Oliveira, L. M. M., Montenegro, S. M. G., & Barbosa, B., Jr. (2016). Procedures for calculation of the albedo with OLI-Landsat 8 images: application to the Brazilian semi-arid. Revista Brasileira de Engenharia Agrícola e Ambiental, 20(1), 3-8. doi: 10.1590/1807-1929
Teixeira, A. H. C. (2010). Determining regional actual evapotranspiration of irrigated and natural vegetation in the São Francisco riverbasin (Brazil) using remote sensingan Penman-Monteith equation. Remote Sensing, 2(5), 1287-1319. doi: 10.3390/rs0251287
Teixeira, A. H. C., & Leivas, J. F. (2017). Determinação da produtividade da água com imagens Landsat 8 na região Semiárida do Brasil. Revista Conexões Ciência e Tecnologia, 11(1), 22-34. doi: 10.21439/ conexoes.v11i1.1064
Teixeira, A. H. C., Leivas, J. F., Andrade, R. G., & Hernandez, F. B. T. (2015). Water productivity assessments with Landsat 8 images in the Nilo Coelho irrigation scheme. Revista Irriga, 1(2), 1-10. doi: 10.15809/irriga.2015v1n2p01
Teixeira, A. H. C., Scherer-Warren, M., Hernandez, F. B., Andrade, R. G., & Leivas, J. F. (2013). Large-scale water productivity assessments with modis images in a changing semi-arid environment: a brazilian case study. Remote Sensing, Multidisciplinary Digital Publishing Institute, 5(11), 5783-5804. doi: 10.3390/rs5115783
Toureiro, C., Serralheiro, R., Shahidian, S., & Sousa, A. (2017). Irrigation management with remote sensing: Evaluating irrigation requirement for maiz eunder Mediterranean climate condition. Agricultural Water Management, 184(1), 211-220. doi: 10.1016/j.agwat.2016.02.010
United States Geological Survey (2015). Landsat Project Description. Recuperado de http://landsat.usgs. gov/about_project_descriptions.php.
Vanhellemont, Q., & Ruddick, K. (2014). Turbid wakes associated with of shore wind turbines observed with landsat 8. Remote Sensing of Environment, 145(1), 105-115. doi: 10.1016/j.rse.2014.01.009
Veloso, G. A., Ferreira, M. E., Rosa, R., & Silva, B. B. (2015). Determinação do albedo de superfície em áreas irrigadas do projeto Jaíba (Minas Gerais) mediante imagens Landsat 5 - TM. Revista R. Ra’eGa, 35(1), 126-146. doi: 10.5380/raega.v35i0.39757
Wang, X., Ma, M., Huang, G., Veroustraete, F., Zhang, Z., Song, Y., & Tan, J. (2012). Vegetation primary production estimation at maize and alpine meadow over the Heihe River Basin, China. International Journal of Applied Earth Observation Geoinformation, 17(1), 94-101. doi: 10.1016/j.agrformet.2018. 01.005
Yuan, M., Zhang, L., Gou, F., Su, Z., Spiertz, J. H. J., & Van. W. W. (2013). Assessment of crop growth and water productivity for five C3 species in semi-arid Inner Mongolia. Agricultural Water Management, 122(5), 28-38. doi: 10.1016/j.agwat.2013.02.006
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Published
2021-05-20
How to Cite
Costa, T. S., Santos, R. A. dos, Santos, R. L., Filgueiras, R., Cunha, F. F. da, Pereira, A. de J., & Salles, R. A. de. (2021). Spatio-temporal variability of biophysical parameters of irrigated maize using orbital remote sensing. Semina: Ciências Agrárias, 42(4), 2181–2202. https://doi.org/10.5433/1679-0359.2021v42n4p2181
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