Data assimilation in crop models: old experiences in new contexts
Resumo
Assimilação de dados é uma técnica que tem sido amplamente utilizada para melhorar as estimativas de modelos de crescimento de plantas, por exemplo, para incorporar os efeitos de eventos externos. Existem muitas abordagens bem estabelecidas para realizar assimilação com imagens de satélite, mas seu uso método em novos contextos, como cultivo protegido, requer a exploração de aspectos da metodologia que não estão tão bem estabelecidos para estes casos. Neste trabalho, avaliamos os impactos de diferentes níveis de incerteza associados às observações, realizando assimilação de dados em um modelo de crescimento de tomateiros, com observações artificiais de biomassa de frutos e frutos maduros.
Referências
Dorigo, W. A., Zurita-Milla, R., De Wit, A. J. W., et al. (may 2007). A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling. International Journal of Applied Earth Observation and Geoinformation, v. 9, n. 2, p. 165–193. doi: 10.1016/j.jag.2006.05.003
Fischer, A., Kergoat, L. and Dedieu, G. (19 feb 1997). Coupling Satellite Data with Vegetation Functional Models: Review of Different Approaches and Perspectives Suggested by the Assimilation Strategy. Remote Sensing Reviews, v. 15, n. 1–4, p. 283–303. doi: 10.1080/02757259709532343
Huang, J., Gómez-Dans, J. L., Huang, H., et al. (15 oct 2019). Assimilation of remote sensing into crop growth models: Current status and perspectives. Agricultural and Forest Meteorology, v. 276–277, p. 107609. doi: 10.1016/j.agrformet.2019.06.008
Jin, X., Kumar, L., Li, Z., et al. (jan 2018). A review of data assimilation of remote sensing and crop models. European Journal of Agronomy, v. 92, n. November 2017, p. 141–152. doi: 10.1016/j.eja.2017.11.002
Jones, J. W., Kenig, A. and Vallejos, C. E. (1999). Reduced state-variable tomato growth model. Transactions of the ASAE, v. 42, n. 1, p. 255–265. doi: 10.13031/2013.13203
Lei, F., Crow, W. T., Kustas, W. P., et al. (15 mar 2020). Data assimilation of high-resolution thermal and radar remote sensing retrievals for soil moisture monitoring in a drip-irrigated vineyard. Remote Sensing of Environment, v. 239, p. 111622. doi: 10.1016/j.rse.2019.111622
Liu, L., Yuan, J., Gong, L., Wang, X. and Liu, X. (20 nov 2022). Dynamic Fresh Weight Prediction of Substrate-Cultivated Lettuce Grown in a Solar Greenhouse Based on Phenotypic and Environmental Data. Agriculture, v. 12, n. 11, p. 1959. doi: 10.3390/agriculture12111959
Luo, L., Sun, S., Xue, J., et al. (aug 2023). Crop yield estimation based on assimilation of crop models and remote sensing data: A systematic evaluation. Agricultural Systems, v. 210, n. June, p. 103711. doi: 10.1016/j.agsy.2023.103711
Moon, T., Kim, D., Kwon, S., Ahn, T. I. and Son, J. E. (12 oct 2022). Non-Destructive Monitoring of Crop Fresh Weight and Leaf Area with a Simple Formula and a Convolutional Neural Network. Sensors, v. 22, n. 20, p. 7728. doi: 10.3390/s22207728
Nearing, G. S., Crow, W. T., Thorp, K. R., et al. (1 may 2012). Assimilating remote sensing observations of leaf area index and soil moisture for wheat yield estimates: An observing system simulation experiment. Water Resources Research, v. 48, n. 5. doi: 10.1029/2011WR011420
Oliveira, M. (2023). Leveraging high frequency data for improving crop growth estimates. doi: 10.5281/zenodo.7632419
Orlova, Y. and Linker, R. (2023). Data assimilation with sensitivity-based particle filter: A simulation study with AquaCrop. Computers and Electronics in Agriculture, v. 204, n. July 2022, p. 107538. doi: 10.1016/j.compag.2022.107538
Pellenq, J. and Boulet, G. (may 2004). A methodology to test the pertinence of remote-sensing data assimilation into vegetation models for water and energy exchange at the land surface. Agronomie, v. 24, n. 4, p. 197–204. doi: 10.1051/agro:2004017
Torres-Monsivais, J. C., López-Cruz, I. L., Ruíz-García, A., Ramírez-Arias, J. A. and Peña-Moreno, R. D. (2017). Data assimilation to improve states estimation of a dynamic greenhouse tomatoes crop growth model. Acta Horticulturae, n. 1170, p. 433–440. doi: 10.17660/ActaHortic.2017.1170.53
Valdes-Abellan, J., Pachepsky, Y., Martinez, G. and Pla, C. (2019). How Critical Is the Assimilation Frequency of Water Content Measurements for Obtaining Soil Hydraulic Parameters with Data Assimilation? Vadose Zone Journal, v. 18, n. 1, p. 1–30. doi: 10.2136/vzj2018.07.0142
Fischer, A., Kergoat, L. and Dedieu, G. (19 feb 1997). Coupling Satellite Data with Vegetation Functional Models: Review of Different Approaches and Perspectives Suggested by the Assimilation Strategy. Remote Sensing Reviews, v. 15, n. 1–4, p. 283–303. doi: 10.1080/02757259709532343
Huang, J., Gómez-Dans, J. L., Huang, H., et al. (15 oct 2019). Assimilation of remote sensing into crop growth models: Current status and perspectives. Agricultural and Forest Meteorology, v. 276–277, p. 107609. doi: 10.1016/j.agrformet.2019.06.008
Jin, X., Kumar, L., Li, Z., et al. (jan 2018). A review of data assimilation of remote sensing and crop models. European Journal of Agronomy, v. 92, n. November 2017, p. 141–152. doi: 10.1016/j.eja.2017.11.002
Jones, J. W., Kenig, A. and Vallejos, C. E. (1999). Reduced state-variable tomato growth model. Transactions of the ASAE, v. 42, n. 1, p. 255–265. doi: 10.13031/2013.13203
Lei, F., Crow, W. T., Kustas, W. P., et al. (15 mar 2020). Data assimilation of high-resolution thermal and radar remote sensing retrievals for soil moisture monitoring in a drip-irrigated vineyard. Remote Sensing of Environment, v. 239, p. 111622. doi: 10.1016/j.rse.2019.111622
Liu, L., Yuan, J., Gong, L., Wang, X. and Liu, X. (20 nov 2022). Dynamic Fresh Weight Prediction of Substrate-Cultivated Lettuce Grown in a Solar Greenhouse Based on Phenotypic and Environmental Data. Agriculture, v. 12, n. 11, p. 1959. doi: 10.3390/agriculture12111959
Luo, L., Sun, S., Xue, J., et al. (aug 2023). Crop yield estimation based on assimilation of crop models and remote sensing data: A systematic evaluation. Agricultural Systems, v. 210, n. June, p. 103711. doi: 10.1016/j.agsy.2023.103711
Moon, T., Kim, D., Kwon, S., Ahn, T. I. and Son, J. E. (12 oct 2022). Non-Destructive Monitoring of Crop Fresh Weight and Leaf Area with a Simple Formula and a Convolutional Neural Network. Sensors, v. 22, n. 20, p. 7728. doi: 10.3390/s22207728
Nearing, G. S., Crow, W. T., Thorp, K. R., et al. (1 may 2012). Assimilating remote sensing observations of leaf area index and soil moisture for wheat yield estimates: An observing system simulation experiment. Water Resources Research, v. 48, n. 5. doi: 10.1029/2011WR011420
Oliveira, M. (2023). Leveraging high frequency data for improving crop growth estimates. doi: 10.5281/zenodo.7632419
Orlova, Y. and Linker, R. (2023). Data assimilation with sensitivity-based particle filter: A simulation study with AquaCrop. Computers and Electronics in Agriculture, v. 204, n. July 2022, p. 107538. doi: 10.1016/j.compag.2022.107538
Pellenq, J. and Boulet, G. (may 2004). A methodology to test the pertinence of remote-sensing data assimilation into vegetation models for water and energy exchange at the land surface. Agronomie, v. 24, n. 4, p. 197–204. doi: 10.1051/agro:2004017
Torres-Monsivais, J. C., López-Cruz, I. L., Ruíz-García, A., Ramírez-Arias, J. A. and Peña-Moreno, R. D. (2017). Data assimilation to improve states estimation of a dynamic greenhouse tomatoes crop growth model. Acta Horticulturae, n. 1170, p. 433–440. doi: 10.17660/ActaHortic.2017.1170.53
Valdes-Abellan, J., Pachepsky, Y., Martinez, G. and Pla, C. (2019). How Critical Is the Assimilation Frequency of Water Content Measurements for Obtaining Soil Hydraulic Parameters with Data Assimilation? Vadose Zone Journal, v. 18, n. 1, p. 1–30. doi: 10.2136/vzj2018.07.0142
Publicado
08/11/2023
Como Citar
OLIVEIRA, Monique Pires Gravina de; RODRIGUES, Luiz Henrique Antunes.
Data assimilation in crop models: old experiences in new contexts. In: CONGRESSO BRASILEIRO DE AGROINFORMÁTICA (SBIAGRO), 14. , 2023, Natal/RN.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 40-47.
ISSN 2177-9724.
DOI: https://doi.org/10.5753/sbiagro.2023.26539.
