Productivity and water demand of maize estimated by the modified satellite Priestley-Taylor algorithm
DOI:
https://doi.org/10.5433/1679-0359.2019v40n6Supl2p2991Keywords:
Landsat-8, Evapotranspiration, Biomass, Sustainable agriculture, Crop management.Abstract
The water demand of crops, as well as the relation of this variable to productivity and other important factors related to the sustainable management of agriculture, makes it relevant to estimate parameters that help in the most assertive and efficient decision-making in the agricultural environment. In this context, the work aims to estimate the actual evapotranspiration (ETa), biomass (Bio), water productivity (WP) and crop productivity (P), using the Landsat-8 satellite, through the Modified Satellite Priestley-Taylor Algorithm (MS-PT). For this, ETa was estimated for maize culture irrigated by central pivots, using the MS-PT with six images of Landsat-8, which were free of clouds. The ETa estimate was accurate in the first 60 days after emergence (DAE) of the crop. Subsequently, the variables Bio, P, and WP were estimated using the ETa and the assumptions of the Monteith (1972) model. Therefore, we sequentially calculated the dry biomass, crop productivity and water productivity. ETa presented a high correlation with Bio from the second image (06/10/2015), due to the canopy closure of the crop and, consequently, the predominance of transpiration in the evapotranspiration phenomenon. The water productivity was constant throughout the maximum vegetative stage until the reproductive phase R4 of the crop, verifying in this interval the best efficiency in the conversion of water in biomass. From the obtained results, it is verified that the set of algorithms used in the estimation of the parameters demonstrated the potential to increase the capacity to handle agriculture in a more efficient, assertive and sustainable way.Downloads
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References
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WESTERHOFF, R. S. Using uncertainty of Penman and Penman-Monteith methods in combined satellite and ground-based evapotranspiration estimates. Remote Sensing of Environment, Amsterdam, v. 169, p. 102-112, 2015. DOI: 10.1016/j.rse.2015.07.021
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ALLEN, R. G.; PEREIRA, L. S.; RAES, D.; SMITH, M. Crop evapotranspiration: guidelines for computing crop water requirements. Rome: Food and Agriculture Organization of the United Nations, 1998. 300 p.
ALLEN, R. G.; TASUMI, M.; TREZZA, R. Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC) Model. Journal of Irrigation and Drainage Engineering, Washington, v. 133, n. 4, p. 380-394, 2007. DOI: 10.1061/(ASCE)0733-9437(2007)133:4(380)
ARAÚJO, A. L.; SILVA, M. T.; SILVA, B. B.; SANTOS, C. A. C.; AMORIM, M. R. B. Modelagem simplificada para estimativa do balanço de energia à superfície em escala regional (R-SSEB). Revista Brasileira de Meteorologia, São José dos Campos, v. 32, n. 3, p. 433-446, 2017. DOI: 10.1590/0102-77863230010
BASTIAANSSEN, W. G. M.; ALI, S. A new crop yield forecasting model based on satellite measurements applied across the Indus Basin, Pakistan. Agriculture, Ecosystems & Environment, Amsterdam, v. 94, n. 3, p. 321-340, 2003. DOI: 10.1016/S0167-8809(02)00034-8
BASTIAANSSEN, W. G. M.; MENENTI, M.; FEDDES, R. A.; HOLTSLAG, A. A. M. A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation. Journal of Hydrology, Amsterdam, v. 212-213, p. 198-212, 1998. DOI: 10.1016/S0022-1694(98)00253-4
BERTOLIN, N. de O.; FILGUEIRAS, R.; VENANCIO, L. P.; MANTOVANI, E. C. Predição da produtividade de milho irrigado com auxílio de imagens de satélite. Revista Brasileira de Agricultura Irrigada, Fortaleza, v. 11, n. 4, p. 1627-1638, 2017. DOI: 10.7127/RBAI.V11N400567
CAMPOS, I.; NEALE, C. M. U.; ARKEBAUER, T. J.; SUYKER, A. E.; GONÇALVES, I. Z. Water productivity and crop yield: a simplified remote sensing driven operational approach. Agricultural and Forest Meteorology, Amsterdam, v. 249, p. 501-511, 2018. DOI: 10.1016/j.agrformet.2017.07.018
CARRILLO-ROJAS, G.; SILVA, B.; CÓRDOVA, M.; CÉLLERI, R.; BENDIX, J. Dynamic mapping of evapotranspiration using an energy balance-based model over an Andean páramo catchment of southern Ecuador. Remote Sensing, Basel, v. 8, n. 2, p. 160-184, 2016. DOI: 10.3390/rs8020160
CHAVEZ, P. S. An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sensing of Environment, Amsterdam, v. 24, n. 3, p. 459-479, 1988. DOI: 10.1016/0034-4257(88)90019-3
COAGUILA, D. N.; HERNANDEZ, F. B. T.; TEIXEIRA, A. H. C.; FRANCO, R. A. M.; LEIVAS, J. F. Water productivity using SAFER Simple Algorithm for Evapotranspiration Retrieving in watershed. Revista Brasileira de Engenharia Agrícola e Ambiental, Campina Grande, v. 21, n. 8, p. 524-529, 2017. DOI: 10.1590/1807-1929/agriambi.v21n8p524-529
FENG, Y.; CUI, N.; GONG, D.; ZHANG, Q.; ZHAO, L. Modeling reference evapotranspiration using extreme learning machine and generalized regression neural network only with temperature data. Computers and Electronics in Agriculture, Amsterdam, v. 136, p. 71-78, 2017a. DOI: 10.1016/j.compag.2017.01.027
FENG, Y.; PENG, Y.; CUI, N.; GONG, D.; ZHANG, K. Evaluation of random forests and generalized regression neural networks for daily reference evapotranspiration modelling. Agricultural Water Management, Amsterdam, v. 193, p. 163-173, 2017b. DOI: 10.1016/j.agwat.2017.08.003
KUSTAS, W. P.; HUMES, K. S.; NORMAN, J. M.; MORAN, M. S. Single and dual source modeling of surface energy fluxes with radiometric surface temperature. Journal of Applied Meteorology, Washington, v. 35, n. 1, p. 110-121, 1996. DOI: 10.1175/1520-0450(1996)035<0110:SADSMO>2.0.CO;2
LOBELL, D. B.; THAU, D.; SEIFERT, C.; ENGLE, E.; LITTLE, B. A scalable satellite-based crop yield mapper. Remote Sensing of Environment, Amsterdam, v. 164, p. 324-333, 2015. DOI: 10.1016/j.rse.2015.04.021
MELO, R. W.; FONTANA, D. C.; BERLATO, M. A.; DUCATI, J. R. An agrometeorological-spectral model to estimate soybean yield, applied to southern Brazil. International Journal of Remote Sensing, Oxfordshire, v. 29, n. 14, p. 4013-4028, 2008. DOI: 10.1080/01431160701881905
MINACAPILLI, M.; CONSOLI, S.; VANELLA, D.; CIRAOLO, G.; MOTISI, A. A time domain triangle method approach to estimate actual evapotranspiration: application in a Mediterranean region using MODIS and MSG-SEVIRI products. Remote Sensing of Environment, Amsterdam, v. 174, p. 10-23, 2016. DOI: 10.1016/j.rse.2015.12.018
MONTEITH, J. L. Solar radiation and productivity in tropical ecosystems. The Journal of Applied Ecology, Ann Arbor, v. 9, n. 3, p. 747-767, 1972. DOI: 10.2307/2401901
MORILLO, J. G.; RODRÍGUEZ, D. J. A.; CAMACHO, E.; MONTESINOS, P. Linking water footprint accounting with irrigation management in high value crops. Journal of Cleaner Production, Amsterdam, v. 87, p. 594-602, 2015. DOI: 10.1016/j.jclepro.2014.09.043
PONZONI, F.; SHIMABUKURO, Y.; KUPLICH, T. Sensoriamento remoto da vegetação. 2. ed. atualizada e ampliada. São Paulo: Oficina de Textos, 2012. 160 p.
PRIESTLEY, C. H. B.; TAYLOR, R. J. On the assessment of surface heat flux and evaporation using large-scale parameters. Monthly Weather Review, Washington, v. 100, n. 2, p. 81-92, 1972. DOI: 10.1175/1520-0493(1972)100<0081:OTAOSH>2.3.CO;2
RIZZI, R.; RUDORF, B. F. T. Imagens do sensor MODIS associadas a um modelo agronômico para estimar a produtividade de soja. Pesquisa Agropecuária Brasileira, Brasília, v. 42, n. 1, p. 73-80, 2007. DOI: 10.1590/S0100-204X2007000100010
ROUSE JUNIOR, J.; HAAS, R. H.; SCHELL, J. A.; DEERING, D. W. Monitoring vegetation systems in the Great Plains with ERTS. Washington: NASA, 1974. 309 p.
ROY, D. P.; WULDER, M. A.; LOVELAND, T. R. C. E. W.; ALLEN, R. G.; ANDERSON, M. C.; HELDER, D.; IRONS, J. R.; JOHNSON, D. M.; KENNEDY, R.; SCAMBOS, T. A.; SCHAAF, C. B.; SCHOTT, J. R.; SHENG, Y.; VERMOTE, E. F.; BELWARD, A. S.; BINDSCHADLER, R.; COHEN, W. B.; GAO, F.; HIPPLE, J. D.; HOSTERT, P.; HUNTINGTON, J.; JUSTICE, C. O.; KILIC, A.; KOVALSKYY, V.; LEE, Z. P.; LYMBURNER, L.; MASEK, J. G.; MCCORKEL, J.; SHUAI, Y.; TREZZA, R.; VOGELMANN, J.; WYNNE, R. H.; ZHU, Z. Landsat-8: science and product vision for terrestrial global change research. Remote Sensing of Environment, Amsterdam, v. 145, p. 154-172, 2014. DOI: 10.1016/j.rse.2014.02.001
SENAY, G. B.; FRIEDRICHS, M.; SINGH, R. K.; VELPURI, N. M. Evaluating Landsat 8 evapotranspiration for water use mapping in the Colorado River Basin. Remote Sensing of Environment, Amsterdam, v. 185, p. 171-185, 2016. DOI: 10.1016/j.rse.2015.12.043
SILVA, B. B.; MERCANTE, E.; BOAS, M. A. V.; WRUBLACK, S. C.; OLDONI, L. V. Satellite-based ET estimation using Landsat 8 images and SEBAL model. Revista Ciência Agronômica, Fortaleza, v. 49, n. 2, p. 221-227, 2018. DOI: 10.5935/1806-6690.20180025
SILVA, R. D.; SILVA, M. A. A.; CANTERI, M. G.; ROSISCA, J. R.; VIEIRA-JÚNIOR, N. A. Reference evapotranspiration for Londrina, Paraná, Brazil: performance of different estimation methods. Semina: Ciências Agrárias, Londrina, v. 38, n. 4, p. 2363-2374, 2017. DOI: 10.5433/1679-0359.2017v38n4Supl1p2363
TEIXEIRA, A. H. C. 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, Basel, v. 2, n. 5, p. 1287-1319, 2010. DOI: 10.3390/rs0251287
TEIXEIRA, A. H. C.; LEIVAS, J. F.; ANDRADE, R. G.; HERNANDEZ, F. B. T. Water productivity assessments with Landsat 8 images in the Nilo Coelho irrigation scheme. Irriga, Botucatu, v. 1, n. 2, p. 1-10, 2015. DOI:10.15809/irriga.2015v1n2p01
TOUREIRO, C.; SERRALHEIRO, R.; SHAHIDIAN, S.; SOUSA, A. Irrigation management with remote sensing: evaluating irrigation requirement for maize under Mediterranean climate condition. Agricultural Water Management, Amsterdam, v. 184, p. 211-220, 2017. DOI: 10.1016/j.agwat.2016.02.010
WESTERHOFF, R. S. Using uncertainty of Penman and Penman-Monteith methods in combined satellite and ground-based evapotranspiration estimates. Remote Sensing of Environment, Amsterdam, v. 169, p. 102-112, 2015. DOI: 10.1016/j.rse.2015.07.021
YAO, Y.; LIANG, S.; CHENG, J.; LIU, S.; FISHER, J. B.; ZHANG, X.; JIA, K.; ZHAO, X.; QIN, Q.; ZHAO, B.; HAN, S.; ZHOU, G.; ZHOU, G.; LI, Y.; ZHAO, S. MODIS-driven estimation of terrestrial latent heat flux in China based on a modified Priestley-Taylor algorithm. Agricultural and Forest Meteorology, Amsterdam, v. 171-172, p. 187-202, 2013. DOI: 10.1016/j.agrformet.2012.11.016
ZHANG, K.; KIMBALL, J. S.; RUNNING, S. W. A review of remote sensing based actual evapotranspiration estimation: A review of remote sensing evapotranspiration. Wiley Interdisciplinary Reviews: Water, New York, v. 3, n. 6, p. 834-853, 2016. DOI: 10.1002/wat2.1168
ZHANG, L.; YAO, Y.; WANG, Z.; JIA, K.; ZHANG, X.; ZHANG, Y.; WANG, X.; XU, J.; CHEN, X. Satellite-derived spatiotemporal variations in evapotranspiration over Northeast China during 1982-2010. Remote Sensing, Basel, v. 9, n. 11, p. 1140, 7 2017a. DOI: 10.3390/rs9111140
ZHANG, Y.; CHIEW, F. H. S.; PEÑA-ARANCIBIA, J.; SUN, F.; LI, H.; LEUNING, R. Global variation of transpiration and soil evaporation and the role of their major climate drivers: global variation in evapotranspiration components. Journal of Geophysical Research: Atmospheres, New York, v. 122, n. 13, p. 6868-6881, 2017b. DOI: 10.1002/2017JD027025
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2019-09-30
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Filgueiras, R., Mantovani, E. C., Althoff, D., Dias, S. H. B., Cunha, F. F. da, & Venancio, L. P. (2019). Productivity and water demand of maize estimated by the modified satellite Priestley-Taylor algorithm. Semina: Ciências Agrárias, 40(6Supl2), 2991–3006. https://doi.org/10.5433/1679-0359.2019v40n6Supl2p2991
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