A new study published in Big Earth Data presents phenological metrics derived from Earth observation (EO) satellite time series—such as greening onset, senescence, and growing season length—which are essential for crop monitoring but challenged by the massive scale of EO data exceeding local processing capacities, and introduces a free, open-source Web Crop Phenology Metrics Service (WCPMS) built on the Brazil Data Cube platform for server-side extraction from large datasets. It further demonstrates the tool's effectiveness by estimating soybean sowing dates in Brazil using phenological metrics and validating the results against field data. The data that support the findings of this study are openly available on Zenodo at https://doi.org/10.5281/zenodo.17260854 , and GitHub at https://github.com/GSansigolo/tool-for-crop-phenology-paper .
Citation
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
Abstract
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.
Big Earth Data is an interdisciplinary Open Access journal which aims to provide an efficient and high-quality platform for promoting the sharing, processing and analyses of Earth-related big data, thereby revolutionizing the cognition of the Earth's systems. The journal publishes a wide range of content, including Research Articles, Review Articles, Data Notes, Technical Notes, and Perspectives. It is now included in ESCI (IF=3.8, Q1), Scopus (CiteScore=9.0, Q1), Ei Compendex, GEOBASE, and Inspec. Starting from 2023, Big Earth Data has announced a new award series for authors: Best and Outstanding Paper Awards.
