SPECTRAL AGROMETEOROLOGICAL MODELING ADAPTED BY MEANS OF SIMPLIFIED TRIANGLE METHOD FOR SOYBEAN IN PARANÁ STATE – BRAZIL

Daniela Fernanda da Silva Fuzzo

Resumo


Agriculture is an economic activity with high dependence on weather and climate. Special geotechnology and agrometeorological modeling can be used to optimize productivity in regional and national systems, while minimizing costs. The aim was to test the agrometeorological model for estimating crop soybean yield proposed by Doorenbos and Kassam (1979), using only spectral data as input variable in the model obtained by a simplified triangle method applied in Paraná state, for crop years 2002/03 to 2011/12. A high accuracy of the data was found, the model values for the parameter d1 ("d1" modified Willmott) were between 0.8 and 0.95, whereas the root mean squared error showed that there was low variation between 30.81 to 116.88 (kg ha-1) and the p-value was used as the indicator significance of the model at the level of 5%, indicating that there was no statistically significant difference between the estimated and observed data, this means that the average of the data estimated by the model were statistically equal the average of the observed data. Thus, we can say that images of remote sensing can be used as tools in the absence of surface information, in agrometeorological modeling to estimate crop soybean yield.


Palavras-chave


Crop Yield; MODIS image; Remote Sensing; Vegetation Index

Referências


ARAUJO,G.K.D. Determinação e mapeamento do início do ciclo para culturas de verão no estado do Paraná por meio de imagens de satélites e dados de precipitação. 157p. Dissertação (Mestrado em Engenharia Agrícola), Universidade Estadual de Campinas – UNICAMP. Campinas. 2010.

ARVOR, D; SANT’ANNA NETO, J. L; DUBREUIL, V; ALMEIDA, I. R; MEIRELLES, M. S. P.; Análise dos perfis temporais de EVI/MODIS para o monitoramento da cultura da soja no Estado de Mato Grosso, Brasil. In: XIII Simpósio Brasileiro de Sensoriamento Remoto, Florianópolis. Anais...Florianópolis: (ANPEGE), 2007.

BERLATO, M.A.; FONTANA, D.C.; GONÇALVEZ, H.M. Relação entre o rendimento de grãos da soja e variáveis meteorológicas. Pesquisa Agropecuária Brasileira,. v. 27, n. 5, p. 695-702. 1992.

BRUNSELL, N.A.; ANDERSON, M.C. Characterizing the multi–scale spatial structure of remotely sensed evapotranspiration with information theory. Biogeosciences, v.8, n.8, p. 2269-2280, 2011.

CAMARGO, M.B.P.; MIRANDA, M.A.C.; PEDRO JUNIOR, M.J.; PEREIRA, J.C.V.N.; MASCARENHAS, H.A.A. Estimativa da produtividade potencial de cultivares de soja nas condições climáticas de Ribeirão Preto. Bragantia, Campinas, v. 22, n. 47, p. 277-288, 1988.

CAMARGO, A.P.; SENTELHAS, P.C. Avaliação do desempenho de diferentes métodos de estimativa da evapotranspiração potencial no Estado de São Paulo, Brasil. Revista Brasileira de Agrometeorologia, Santa Maria, v. 5, n. 1, p. 89-97, 1997.

CARLSON, T.N., PERRY, E.M. and SCHMUGGE, T.J. Remote estimation of soil moisture availability and fractional vegetation cover for agricultural fields. Agricultural and Forest Meteorology, v.52, n. 1-2, p.45–69, 1990.

CARLSON, T. An overview of the "Triangle Method" for estimating surface evapotranspiration and soil moisture from satellite imagery. Sensors v.7, p. 1612–1629, 2007

CARLSON, T.N. Triangle Models and Misconceptions. International Journal of Remote Sensing Applications. v. 3, n. 3,p. 155-158, 2013.

CARMELLO, V., SILVESTRE. M.R.;DUBREU, V.; SANT’ANA NETO, J.L. Chuva, Soja e Risco Agrícola na Vertente Sul da Bacia do Rio Paranapanema – Paraná. Anais... XV Simpósio Brasileiro de geografia Física Aplicada. Vitória –ES , 2013.

CARVALHO, L.G. de; SEDIYAMA, G.C.; CECON, P.R.; ALVES, H.M.R. Aplicação da análise harmônica por séries de Fourier para a previsão de produtividade da cultura do café no Estado de Minas Gerais. Engenharia Agrícola, Jaboticabal, v.25, n. 3, p.732-741, 2005.

CONGALTON, R.G. A review of assessing the accuracy classificatons of remotely sensed data. Remote Sensing Environment, v. 37, p.35-46, 1991.

CONGALTON, R.; GREEN, K.. Assessing the accuracy of remotely sensed data: principles and practices. CRC Press, Danvers, EUA, 1999.

DOORENBOS, J.; KASSAM, A. H. Yield response to water. Rome, FAO, 1979. 197p. (Irrigation and Drainage Paper, 33).

DUBREUIL. V. Clima e teledetecção: uma abordagem geográfica. Revista Brasileira de Climatologia, v. 1, n. 1. 2005.

DUBREUIL. V; LAMY, C; LECERF, R; PLANCHON, O. Monitoramento de secas na Bretanha: Reconstituição histórica e abordagem por teledetecção. Revista Mercator, Fortaleza, v. 9; Número especial (1), 2010.

ER-RAKI, S.; CHEHBOUNI, A.; GUEMOURIA, N.; DUCHEMIN, B.; EZZAHAR, J.; HADRIA, R. Combining FAO-56 model and ground-based remote sensing to estimate water conumptions of wheat crops in a semi-arid region. Agriculture Water Management, v.87, n.1, p.41-54, 2007.

FUZZO-SILVA. D.F.; PRELA-PANTANO, A. CAMARGO.M.B.P. Modelagem agrometeorológica para estimativa de produtividade de soja para o vale do Médio Paranapanema-sp. Irriga, Botucatu, v. 20, n. 3, p. 490-50, 2015.

GARCIA, M.; FERNÁNDEZ, N.; VILLAGARCÍA, L.; DOMINGO, F.; PUIGDEFÁBREGAS, J.; SANDHOLT, I. Accuracy of the Temperature–Vegetation Dryness Index using MODIS under water-limited vs. energy-limited evapotranspiration conditions. Remote Sensing of Environment, v. 149, p.100–117, 2014.

GILLIES, R.R. and CARLSON, T.N. Thermal remote sensing of surface soil water content with partial vegetation cover for incorporation into climate models. Journal of Applied Meteorology, v. 34, p. 745–56, 1995.

GILLIES, R.R., CARLSON, T.N., CUI, J. KUSTAS, W.P. and HUMES, K.S. Verification of the ‘triangle’ method for obtaining surface soil water content and energy fluxes from remote measurements of the Normalized Difference Vegetation Index NDVI and surface radiant temperature. International Journal of Remote Sensing, v.18, p.3145–66, 1997.

Goward, S.N., Xue, Y. and Czajkowski, K.P. Evaluating land surface moisture conditions from the remotely sensed temperature/vegetation index measurements: an exploration with the simplifi ed simple biosphere model. Remote Sensing of Environment , v. 79, p 225–42. 2002.

HUANG, C.; WYLIE, B.; YANG, L.; HOMER, C.; ZYLSTRA, G. Derivation of a tasselled cap transformation based on Landsat 7 at-satellite reflectance. International Journal of Remote Sensing, v. 23, n. 8, p.1741–1748, 2002.

JIANG, L.; ISLAM, S. Estimation of surface evaporation map over southern Great Plains using remote sensing data. Water Resources Research, v. 37, n. 2, p. 329-340, 2001.

JOHANN, J. A. Calibração de dados agrometeorológicos e agrícolas de verão no estado do Paraná. 201p Tese. (Doutorado em Engenharia Agrícola) Universidade Estadual de Campinas, Campinas, 2012.

LABUS, M.P.; NIELSEN, G.A.; LAWRENCE, R.L.; ENGEL, R.; LONG, D.S. Wheat yield estimates using multi-temporal NDVI satellite imagery. International Journal of Remote Sensing, v.23 n.20, p.4169-4180, 2002.

MANN, H. B.; WHITNEY, D. R. On a test of whether one of two random variables os stochastically larger than the other. The Annals of Mathematical Statistics, v. 18, n. 1, p. 50–60, 1947.

LANDIS, J.R.; KOCH, G.G. The measurement of observer agreement for categorical data. Biometrics, v. 33, n.1, p.159-174, 1977

MARTINS, A.N.; ORTOLANI, A.A. Estimativa de produção de laranja valência pela adaptação de um modelo agrometeorológico. Bragantia, Campinas, v. 65, n. 2, p. 355-361, 2006.

GATES, D. R.; MCCABE, G. J. Evaluating the use of ‘goodness- of-fit’ measures in hydrologic and hydroclimatic model validation. Water Resources Research, v.35, p.233-241, 1999.

MKHABELA, M.S.; MKHABELA, M.S.; MASHININI, N.N. Early maize yield forecasting in the four agro-ecological regions of Swaziland using NDVI data derived from NOAA’s- AVHRR. Agricultural Forest Meteorology,v.129, n.1–2, p.1–9, 2005.

MKHABELA, M.S.; BULLOCK, S.R.; WANG, Y. Y. Crop yield forecasting on the Canadian Prairies using MODIS NDVI data. Agricultural and Forest Meteorology, n. 151, v. 3, p . 385 – 393, 2011.

MORAES, A.V.C.; CAMARGO, M.B.P.; MASCARENHAS, H.A.A.; MIRANDA, M.A.C.; PEREIRA, J.C.V.N.A. Teste e análise de modelos agrometeorológicos de estimativa de produtividade para a cultura da soja na região de Ribeirão Preto. Bragantia, Campinas, v. 57, n. 2, p. 393-406. 1998.

NEPOMUCENO, A. L.; NEUMAIER, N.; FARIAS, J. R. B.; OYA, T. Tolerância à seca em plantas. Biotecnologia ciência e desenvolvimento, Brasília, v.23, 2001.

PEREIRA, A.R; ANGELOCCI, L.R; SENTELHAS, P.C. Agrometeorologia: fundamentos e aplicações práticas. Guaíba: Agropecuária, 478p, 2002.

PETROPOULOS, G.; CARLSON, T.N.;WOOSTER, M.J.; ISLAM, S. A review os Ts/VI remote sensing based methods for the retrieval of land surfasse energy fluxes and soil surface moisture. Progress in Physical Geography, v. 33, p. 224–250, 2009.

PRASAD, A.K.; CHAI, L.; SINGH, R.P.; KAFATOS, M. Crop yield estimation model for Iowa using remote sensing and surface parameters. International Journal of Applied Earth Observation and Geoinformation, v.8, n.1, p.26–33, 2006.

RAO, N.H.; SARMA, P.B.S.; CHANDER, S. A simple dated water-production function for use in irrigated agriculture. Agricultural Water Management, Amsterdam, v. 13, p. 25-32, 1988.

REN, J.; CHEN, Z.; ZHOU, Q.; TANG, H. Regional yield estimation for winter wheat with MODIS-NDVI data in Shandong, China. International Journal of Applied Earth Observation and Geoinformation, v.10, p.403-413, 2008.

Sun, Y.-J., Wang, J.-F., Zhang, R.-H., Gillies, R.R., Xue, Y. and Bo, Y.-C. Air temperature retrieval from remote sensing data based on thermodynamics. Theoretical and Applied Climatology, v. 80, p. 37–48, 2005:

SYMANZIK, J., GRIFFITHS, L. and GILLIES, R. Visual exploration of satellite images. In Proceedings of the Statistical Computing Section and Section on Statistical Graphics, Alexandria, VA: American Statistical Association, p. 10–19. 2000.

TIAN, J.; HONGBO, S.; XIAOMIN, S.; SHAOHUI, C.; HONGLIN, HE.; LINJUN, Z. Impacto of the Spatial Domain Size on the performance of the Ts-VI triangle method in terrestrial evapotranspiration estimation. Remote Sensing, v. 5, p. 1998-2013, 2013.

THORNTHWAITE, C.W.; MATHER, J.R. The water balance. In: Centerton, N. J. (ed), 104p. (Publ. in Climatology, v. 8, n. 1), 1955.

Willmott, C. J. On the validation of models. Physical geography, v.2, p.184-194, 1981.

Willmott, C. J.; Ackleson, S. G.; Davis, J. J.; Feddema, K. M.; Klink, D. R. Statistics for the evaluation and comparison of models. Journal of Geophysical Research, v.90, p.8995- 9005, 1985.




DOI: http://dx.doi.org/10.5380/raega.v44i0.48474

Direitos autorais 2018 Raega - O Espaço Geográfico em Análise

_____________________________________________

ISSN (IMPRESSO) 1516-4136 até 2008

ISSN (ELETRÔNICO) 2177-2738 a partir de 2009