Remote sensing as a tool to determine biophysical parameters of irrigated seed corn crop
DOI:
https://doi.org/10.5433/1679-0359.2020v41n2p435Keywords:
Biomass, Evapotranspiration, Agricultural Management, Water Productivity, SAFER.Abstract
In recent years, many studies have been conducted combining orbital remote sensing data and crop growth models for vegetation monitoring, evapotranspiration estimation and quantification of biophysical parameters, e.g., NDVI, surface temperature, albedo, and biomass. The aim of the present study was to estimate evapotranspiration (ETr), biomass (BIO), and water productivity (WP) for irrigated seed corn crop using the SAFER algorithm and Landsat 8 satellite images. For this, eight cloud-free images were acquired at different phenological stages over the interest area on the United States Geological Survey website and meteorological data. ETr was estimated by the SAFER algorithm, BIO by the Monteith model, and WP by the BIO/ETr ratio. ETr values ranged from 0 to 6 mm d?1, with the highest values coinciding with the period of high vegetative crop vigor, while the lowest values were found at the sowing season. The highest biomass values were observed from images at 46 and 62 days after sowing (DAS), corresponding to 286 and 289 kg ha?1 d?1, respectively. The highest mean of water productivity was observed at 62 DAS, with 6.9 kg m?3 of water, corresponding to the period of maximum vegetative crop vigor. The application of the SAFER model together with Landsat 8 satellite images was an alternative to identifying the spatial and temporal variation of biophysical parameters of the corn crop. It could assist in the management of water in irrigated agriculture and decision making in large-sized farms.Downloads
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References
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Wang, N., Wang, E., Wang, J., Zhang, J., Zheng, B., Huang, Y., & Tan, M. (2018). Modelling maize phenology, biomass growth and yield under contrasting temperature conditions. Agricultural and Forest Meteorology, 250-251(2017), 319-329. doi: 10.1016/j.agrformet.2018.01.005
Wu, X., Wen, J., Xiao, Q., Yu, Y., You, D., & Hueni, A. (2017). Assessment of NPP VIIRS Albedo over heterogeneous crop land in Northern China. Journal of Geophysical Research: Atmospheres, 122(24), 13,138-154. doi: 10.1002/2017JD027262
Yan, W., & Hunt, L. A. (1999). An equation for modelling the temperature response of plants using only the cardinal temperatures. Annals of Botany, 84(5), 607-614. doi: 10.1006/anbo.1999.0955
Yuan, M., Zhang, L., Gou, F., Su, Z., Spiertz, J. H. J., & Van Der Werf, 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
Alvares, C. A., Stape, J. L., Sentelhas, P. C., Moraes Gonçalves, J. L. de, & Sparovek, G. (2013). Köppen’s climate classification map for Brazil. Meteorologische Zeitschrift, 22(6), 711-728. doi: 10.1127/0941-2948/2013/0507
Asrar, G., Myneni, R. B., & Choudhury, B. J. (1992). Spatial heterogeneity in vegetation canopies and remote sensing of absorbed photosynthetically active radiation: a modeling study. Remote Sensing of Environment, 41(2-3), 85-103. doi: 10.1016/0034-4257(92)90070-Z
Bastiaanssen, W. G. M., & Ali, S. (2003). A new crop yield forecasting model based on satellite measurements applied across the Indus Basin, Pakistan. Agriculture, Ecosystems and Environment, 94(3), 321-340. doi: 10.1016/S0167-8809(02)00034-8
Carroll, D. A., Hansen, N. C., Hopkins, B. G., & DeJonge, K. C. (2017). Leaf temperature of maize and Crop Water Stress Index with variable irrigation and nitrogen supply. Irrigation Science, 35(6), 549-560. doi: 10.1007/s00271-017-0558-4
Coaguila, D. N., Hernandez, F. B. T., Teixeira, A. H. de C., Franco, R. A. M., & Leivas, J. F. (2017). Water productivity using SAFER - Simple Algorithm for Evapotranspiration Retrieving in watershed. Revista Brasileira de Engenharia Agrícola e Ambiental, 21(8), 524-529. doi: 10.1590/1807-1929/agriambi. v21n8p524-529
Companhia Nacional de Abastecimento (2019). Milho. Recuperado de https://www.conab.gov.br/info-agro/safras/serie-historica-das-safras?start=20
Gomes, B. da, Silva, B. B, Cavalcanti, E. P., & Rocha, H. R. (2009). Balanço de radiação em diferentes biomas no estado de São Paulo mediante imagens landsat 5. Geociencias, 28(2), 153-164.
Hall, A. J., & Richards, R. A. (2013). Prognosis for genetic improvement of yield potential and water-limited yield of major grain crops. Field Crops Research, 143(3), 18-33. doi: 10.1016/j.fcr.2012.05.014
Hatfield, J. L., Asrar, G., & Kanemasu, E. T. (1984). Intercepted photosynthetically active radiation estimated by spectral reflectance. Remote Sensing of Environment, 14(1-3), 65-75. doi: 10.1016/0034-4257(84)90008-7
Instituto Nacional de Meteorologia (2018). Normais climatológicas (1961-2018). Recuperado de http://www.inmet.gov.br/portal/index.php?r=clima/normaisClimatologicas
Kamali, M. I., & Nazari, R. (2018). Determination of maize water requirement using remote sensing data and SEBAL algorithm. Agricultural Water Management, 209(10), 197-205. doi: 10.1016/j.agwat.2018.07.035
Lizaso, J. I., Ruiz-Ramos, M., Rodríguez, L., Gabaldon-Leal, C., Oliveira, J. A., Lorite, I. J.,… Otegui, M. E. (2017). Modeling the response of maize phenology, kernel set, and yield components to heat stress and heat shock with CSM-IXIM. Field Crops Research, 214(12), 239-254. doi: 10.1016/j.fcr.2017.09.019
Mdemu, M. V., Rodgers, C., Vlek, P. L. G., & Borgadi, J. J. (2009). Water productivity (WP) in reservoir irrigated schemes in the upper east region (UER) of Ghana. Physics and Chemistry of the Earth, 34(4-5), 324-328. doi: 10.1016/j.pce.2008.08.006
Monteith, J. L. (1972). Solar radiation and productivity in tropical ecosystems. Journal of Applied Ecology, 9(3), 747-766. doi: 10.2307/2401901
Moran, M. S., Maas, S. J., & Pinter, P. J. (1995). Combining remote sensing and modeling for estimating surface evaporation and biomass production. Remote Sensing Reviews, 12(3-4), 335-353. doi: 10.1080/02757259509532290
Parent, B., & Tardieu, F. (2012). Temperature responses of developmental processes have not been affected by breeding in different ecological areas for 17 crop species. New Phytologist, 194(3), 760-774. doi: 10.1111/j.1469-8137.2012.04086.x
Peng, J., Fan, W., Xu, X., Wang, L., Liu, Q., Li, J., & Zhao, P. (2015). Estimating crop Albedo in the application of a physical model based on the law of energy conservation and spectral invariants. Remote Sensing, 7(11), 15536-15560. doi: 10.3390/rs71115536
Silva, B. B. da, Braga, A. C., Braga, C. C., Oliveira, L. M. M. de, Montenegro, S. M. G. L., & 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/agriambi.v20n1p3-8
Taiz, L., Zeiger, E., Moller, I. M., & Murphy, A. (2017). Fisiologia e desenvolvimento vegetal (6a ed.). Porto Alegre, RS: Artmed.
Teixeira, A. H. de C. (2010). Determining regional actual evapotranspiration of irrigated crops and natural vegetation in the São Francisco River Basin (Brazil) Using remote sensing and penman-monteith equation. Remote Sensing, 2(5), 1287-1319. doi: 10.3390/rs0251287
Teixeira, A. H. de C., Bastiaanssen, W. G. M., Ahmad, M. D., & Bos, M. G. (2009). Reviewing SEBAL input parameters for assessing evapotranspiration and water productivity for the Low-Middle São Francisco River basin, Brazil. Part B: Application to the regional scale. Agricultural and Forest Meteorology, 149(3-4), 477-490. doi: 10.1016/j.agrformet.2008.09.014
Teixeira, A. H. de C., & Leivas, J. F. (2017). Determinação da produtividade da água com imagens Landsat 8 na região semiárida do Brasil. Conexões - Ciência e Tecnologia, 11(1), 22-34. doi: 10.21439/conexoes.v11i1.1064
Teixeira, A. H. de 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. IRRIGA, 1(2), 1-10. doi: 10.15809/irriga.2015v1n2p01
Toureiro, C., Serralheiro, R., Shahidian, S., & Sousa, A. (2017). Irrigation management with remote sensing: evaluating irrigation requirement for maize under Mediterranean climate condition. Agricultural Water Management, 184(4), 211–220. doi: 10.1016/j.agwat.2016.02.010
Wang, N., Wang, E., Wang, J., Zhang, J., Zheng, B., Huang, Y., & Tan, M. (2018). Modelling maize phenology, biomass growth and yield under contrasting temperature conditions. Agricultural and Forest Meteorology, 250-251(2017), 319-329. doi: 10.1016/j.agrformet.2018.01.005
Wu, X., Wen, J., Xiao, Q., Yu, Y., You, D., & Hueni, A. (2017). Assessment of NPP VIIRS Albedo over heterogeneous crop land in Northern China. Journal of Geophysical Research: Atmospheres, 122(24), 13,138-154. doi: 10.1002/2017JD027262
Yan, W., & Hunt, L. A. (1999). An equation for modelling the temperature response of plants using only the cardinal temperatures. Annals of Botany, 84(5), 607-614. doi: 10.1006/anbo.1999.0955
Yuan, M., Zhang, L., Gou, F., Su, Z., Spiertz, J. H. J., & Van Der Werf, 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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2020-03-06
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Santos, R. A. dos, Divincula, J. S. da, Oliveira, K. R. de, Venancio, L. P., Missio, M. F., Filgueiras, R., … Aleman, C. C. (2020). Remote sensing as a tool to determine biophysical parameters of irrigated seed corn crop. Semina: Ciências Agrárias, 41(2), 435–446. https://doi.org/10.5433/1679-0359.2020v41n2p435
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