A Data Cube of Big Satellite Image Time-Series for Agriculture Monitoring

TL;DR

This paper introduces the Agriculture monitoring Data Cube (ADC), an automated framework for managing and analyzing large-scale satellite image time-series to support agricultural monitoring and policy compliance.

Contribution

The paper presents a novel, modular data cube framework that efficiently handles big satellite data and integrates analysis tools for agricultural monitoring tasks.

Findings

Efficient storage and indexing of satellite data into a multidimensional cube.

Tools for generating analysis-ready features for machine learning.

Support for time-series analysis to detect trends and events.

Abstract

The modernization of the Common Agricultural Policy (CAP) requires the large scale and frequent monitoring of agricultural land. Towards this direction, the free and open satellite data (i.e., Sentinel missions) have been extensively used as the sources for the required high spatial and temporal resolution Earth observations. Nevertheless, monitoring the CAP at large scales constitutes a big data problem and puts a strain on CAP paying agencies that need to adapt fast in terms of infrastructure and know-how. Hence, there is a need for efficient and easy-to-use tools for the acquisition, storage, processing and exploitation of big satellite data. In this work, we present the Agriculture monitoring Data Cube (ADC), which is an automated, modular, end-to-end framework for discovering, pre-processing and indexing optical and Synthetic Aperture Radar (SAR) images into a multidimensional cube. We also offer a set of powerful tools on top of the ADC, including i) the generation of analysis-ready feature spaces of big satellite data to feed downstream machine learning tasks and ii) the support of Satellite Image Time-Series (SITS) analysis via services pertinent to the monitoring of the CAP (e.g., detecting trends and events, monitoring the growth status etc.). The knowledge extracted from the SITS analyses and the machine learning tasks returns to the data cube, building scalable country-specific knowledge bases that can efficiently answer complex and multi-faceted geospatial queries.