Satellite Image Time Series Analysis for Big Earth Observation Data

TL;DR

This paper introduces sits, an open-source R package designed for efficient satellite image time series analysis using machine learning, supporting the full land classification workflow across various computing environments.

Contribution

It presents a comprehensive software package that simplifies satellite data analysis with a focus on usability, flexibility, and accuracy in diverse computational settings.

Findings

High accuracy land cover maps achieved in case study

Supports multiple cloud environments for broad applicability

Includes methods for training data quality assessment

Abstract

The development of analytical software for big Earth observation data faces several challenges. Designers need to balance between conflicting factors. Solutions that are efficient for specific hardware architectures can not be used in other environments. Packages that work on generic hardware and open standards will not have the same performance as dedicated solutions. Software that assumes that its users are computer programmers are flexible but may be difficult to learn for a wide audience. This paper describes sits, an open-source R package for satellite image time series analysis using machine learning. To allow experts to use satellite imagery to the fullest extent, sits adopts a time-first, space-later approach. It supports the complete cycle of data analysis for land classification. Its API provides a simple but powerful set of functions. The software works in different cloud computing environments. Satellite image time series are input to machine learning classifiers, and the results are post-processed using spatial smoothing. Since machine learning methods need accurate training data, sits includes methods for quality assessment of training samples. The software also provides methods for validation and accuracy measurement. The package thus comprises a production environment for big EO data analysis. We show that this approach produces high accuracy for land use and land cover maps through a case study in the Cerrado biome, one of the world's fast moving agricultural frontiers for the year 2018.