A tool for crop phenology metrics analysis from big Earth observation data
por Sansigolo, G.1; Reis Ferreira, K.1; De Queiroz, G. R.1; Körting, T.1; Pereira Garcia Leão, L.2; Adami, M1.
1Instituto Nacional de Pesquisas Espaciais (INPE)
2Tribunal de Contas da União (TCU)
Big Earth Data: https://www.tandfonline.com/journals/tbed20
Big Earth Data | Publicado em 22 de Março de 2026
Resumo
Phenological metrics are a set of measurements obtained from Earth observation (EO) satellite image time series that allow the estimation of phenological stages. These include indicators like the start of the greening season, the onset of senescence, and the growing season length. They are useful for crop monitoring. Today, large volumes of images are produced and made available by different EO satellites. These large EO data sets pose a challenge for storage and processing systems, exceeding the capacity of personal computers to handle them. This paper presents a free and open-source tool for phenological metrics analysis from large EO image collections that runs on server-side infrastructure and does not require local data downloads. The Web Crop Phenology Metrics Service (WCPMS) is the core of this tool, designed to estimate phenological metrics as a web service. The tool extracts phenological metrics associated with spatial locations, based on the Brazil Data Cube (BDC) platform. It calculates phenological metrics from data cubes of distinct remote sensing image collections. The potential of the tool is shown through an experiment estimating soybean sowing dates using phenological metrics compared with field data obtained in the Central-South region of Brazil.
Keywords: Big data, phenological metrics, Python package, remote sensing, spatio- temporal analysis, satellite image time series, web service.
Compartilhar e Citar
Sansigolo, G., Reis Ferreira, K., De Queiroz, G. R., Körting, T., Pereira Garcia Leão, L., & Adami, M. (2026). A tool for crop phenology metrics analysis from big Earth observation data. Big Earth Data, 1–24. https://doi.org/10.1080/20964471.2026.2641272.