COMPARISON OF WEATHER MEASUREMENTS OBTAINED ON LAND SURFACE AND BY GOOGLE EARTH ENGINE (GEE) IN BOTUCATU - SÃO PAULO - BRAZIL

Authors

DOI:

10.31413/nat.v11i3.15887

Keywords:

Weather Measurements, Weather Station, Weather Data Correlation, Satellite Products, Geospatial Weather Data

Abstract

The aim of this study was to compare weather data obtained from geospatial products in Google Earth Engine with measurements collected by an automatic weather station at the School of Agricultural Sciences of UNESP in Botucatu (SP) - Brazil. Scatter plots were created and the statistical indicators MBE, rMBE, RMSE, rRMSE and r were generated from data on air temperature, precipitation, evapotranspiration, wind speed, shortwave solar irradiation, and atmospheric pressure, obtained in 2018 at hourly, daily, and monthly temporal resolutions. The investigation pointed to a strong positive correlation in most of the weather data, however, those related to the amount of water present in the atmosphere, such as precipitation and evapotranspiration, showed a lower correlation, mainly in the hourly temporal resolution. The study demonstrated that geospatial products were an efficient alternative to obtain weather data for the city of Botucatu (SP) - Brazil, mainly because they were obtained in a simplified way from the Google Earth Engine cloud computing platform, which demonstrated be a possible alternative to traditional weather measurements, collected on the earth's surface, in areas where the necessary technological resources are not available to meet this demand.

References

ATAÍDE, K. R. P.; LEDO, I. M. D.; OLIVEIRA, M. G. R.; BEZERRA, W. A. Avaliação da estimativa da temperatura de superfície obtida pelo sensor MODIS para o Estado de Goiás. In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, XIV, Anais... Natal, 2009. 3495-3502. Disponível em: http://marte.sid.inpe.br/rep/dpi.inpe.br/sbsr@80/2009/03.02.18.19?mirror=dpi.inpe.br/banon/2003/12.10.19.30.54&metadatarepository=dpi.inpe.br/sbsr@80/2009/03.02.18.19.38

BEZERRA, B. G.; SILVA, B. B; FERREIRA, N. J. Estimativa da Evapotranspiração Real Diária Utilizando-se Imagens Digitais TM-LANDSAT 5. Revista Brasileira de Meteorologia, v. 23, n. 3, p. 305-317, 2008. https://doi.org/10.1590/S0102-77862008000300005.

BHATTARAI, N.; WAGLE, P. Recent Advances in Remote Sensing of Evapotranspiration. Remote Sensing, v. 13, e4260, 2021. https://doi.org/10.3390/rs13214260.

CAMPAROTTO, L. D.; BLAIN, G. C.; GIAROLLA, A.; ADAMI, M.; CAMARGO, M. B. P. Validação de Dados Termopluviométricos Obtidos via Sensoriamento Remoto para o Estado de São Paulo. Revista Brasileira de Engenharia Agrícola e Ambiental, v. 17, n. 6, p. 665-671, 2023. https://doi.org/10.1590/S1415-43662013000600013.

DAL PAI, A.; ESCOBEDO, J.F; DAL PAI, E.; OLIVEIRA, A. P.; SOARES, J. R.; CODATO, G. MEO shadowring method for measuring diffuse solar irradiance: Corrections based on sky cover. Renewable Energy, v. 99, p. 754-763, 2016. http://dx.doi.org/10.1016/j.renene.2016.07.026.

ESTAÇÃO METEOROLÓGICA LAGEADO. Sobre a Estação Meteorológica. Disponível em: http://estacaolageado.fca.unesp.br/index.html. Acesso em: 06 nov. 2020.

GOOGLE EARTH ENGINE. Platform - Google Earth Engine. Disponível em: https://earthengine.google.com/platform. Acesso em: 04 nov. 2020.

GOODARZI, M. R.; POOLADI, R.; NIAZKAR, M. Evaluation of Satellite-Based and Reanalysis Precipitation Datasets with Gauge-Observed Data over Haraz-Gharehsoo Basin, Iran. Sustainability, v. 14, p. 13051, 2022. https://doi.org/10.3390/su142013051.

GORELICK, N.; HANCHER, M.; DIXON, M.; ILYUSHCHENKO, S.; THAU, D.; MOORE, R. Google earth engine: planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, v. 202, p. 18-27, 2017. https://doi.org/10.1016/j.rse.2017.06.031.

HUNTINGTON, J. L.; HEGEWISCH, K. C.; DAUDERT, B.; MORTON, C. G.; ABATZOGLOU, J. T.; MCEVOY, D. J.; ERICKSON, T. Climate Engine: cloud computing and visualization of climate and remote sensing data for advanced natural resource monitoring and process understanding. Bulletin of the American Meteorological Society, v. 98, n. 11, p. 2397-2410, 2017. https://doi.org/10.1175/BAMS-D-15-00324.1.

IBGE. Panorama do município de Botucatu - SP. Cidades: Instituto Brasileiro de Geografia e Estatística - IBGE: Rio de Janeiro, 2019. Disponível em: https://cidades.ibge.gov.br/brasil/sp/botucatu/panorama. Acesso em: 16 out. 2019.

KUMAR, L.; MUTANGA, O. Google Earth Engine applications since inception: Usage, trends, and potential. Remote Sensing, v. 10, n. 10, p. 1509, 2018. https://doi.org/10.3390/rs10101509.

LI, Y.; LIANG, K.; LIU, C.; LIU, W.; Bai, P. Evaluation of different evapotranspiration products in the middle Yellow River Basin, China. Hidrology Research, v. 48, p. 498-513, 2017. https://doi.org/10.3390/w10121884.

MENDELSOHN, R.; KURUKULASURIYA, P.; BASIST, A.; KOGAN, F.; WILLIAMS, C. Climate analysis with satellite versus weather station data. Climatic Change, v. 81, n. 1, p. 71-83, 2007. https://doi.org/10.1007/s10584-006-9139-x.

PEREIRA, G.; SILVA, M. E. S.; MORAES, E. C.; CARDOZO, F. S. Avaliação dos Dados de Precipitaão Estimados pelo Satélite TRMM para o Brasil. Revista Brasileira de Recursos Hídricos, v. 18, n. 3, 2023. https://doi.org/10.21168/rbrh.v18n3.p139-148

PESSI, D. D.; SANTOS, C. S. A.; NONATO, J. J.; DOURADO, L. G. A.; SILVA, O. P.; BASSINI, R. T.; JOSÉ, J. V. Validação das Estimativas de Precipitação do Satélite TRMM no Estado de Mato Grosso. Brasil. Revista de Ciências Agrárias, v. 42, n. 1, p. 79-88, 2019. Disponível em: https://doi.org/10.19084/RCA18217.

PORFIRIO, A. C. S.; CEBALLOS, J. C. Validação da Estimativa de Irradiação Solar Direta Normal por Satélite. In: Congresso Brasileiro de Energia Solar, V, Recife, Brasil, 2014. Anais... Disponível em: https://anaiscbens.emnuvens.com.br/cbens/article/view/2211.

RIBEIRO, H. J.; OLIVEIRA, W. N; SIQUEIRA, R. V.; OLIVEIRA, A. W. N.; OLIVEIRA, V. T. Estimativa da Evapotranspiração Diária para Diferentes Usos do Solo Utilizando imagens do Satélite LANDSAT 5. In: 7º Simpósio de Geotecnologias no Pantanal, Mato Grosso do Sul, Brasil, p.513-522, 2018. Disponível em: https://www.geopantanal.cnptia.embrapa.br/Anais-Geopantanal/pdfs/p73.pdf.

RINCÓN. A.; JORBA, O.; FRUTOS, M.; ALVAREZ, L.; BARRIOS, F. P.; GONZÁLEZ, J. A. Bias correction of global irradiance modelled with weather and research forecasting model over Paraguay. Solar Energy, v. 170, p. 201-211, 2018. https://doi.org/10.1016/j.solener.2018.05.061.

THOMAS, C.; WEY, E.; BLANC, P.; WALD, L. Validation of Three Satellite-Derived Databases of Surface Solar Radiation Using Measurements Performed at 42 Stations in Brazil. Advances in Science & Research, v. 13, n. 81-86, 2016. https://doi.org/10.5194/asr-13-81-2016.

TORRES, J. D.; MONTEIRO, I. O.; SANTOS, J. R.; ORTIZ, M. S. Aquisição de dados meteorológicos através da plataforma Arduino: construção de baixo custo e análise de dados. Scientia Plena, v. 11, n. 2, 2015.

XIA. Z.; GUO. X.; CHEN. R. Automatic extraction of aquaculture ponds base on Google Earth Engine. Ocean & Costal Management, v. 198, p. 105348, 2020. https://doi.org/10.1016/j.ocecoaman.2020.105348.

Published

2023-09-15

How to Cite

Rodrigues Raniero, M., Contes Calça, M. V., Franco, J. R., Stucchi , G., Ribeiro Roder , L., & Dal Pai, A. (2023). COMPARISON OF WEATHER MEASUREMENTS OBTAINED ON LAND SURFACE AND BY GOOGLE EARTH ENGINE (GEE) IN BOTUCATU - SÃO PAULO - BRAZIL. Nativa, 11(3), 331–337. https://doi.org/10.31413/nat.v11i3.15887

Issue

Section

Ciências Ambientais / Environmental Sciences