TATSSI: A Free and Open-Source Platform for Analyzing Earth Observation Products with Quality Data Assessment

Inder Tecuapetla-Gómez,Gerardo López-Saldaña,María Isabel Cruz-López,Rainer Ressl

doi:10.3390/ijgi10040267

Abstract

Earth observation (EO) data play a crucial role in monitoring ecosystems and environmental processes. Time series of satellite data are essential for long-term studies in this context. Working with large volumes of satellite data, however, can still be a challenge, as the computational environment with respect to storage, processing and data handling can be demanding, which sometimes can be perceived as a barrier when using EO data for scientific purposes. In particular, open-source developments which comprise all components of EO data handling and analysis are still scarce. To overcome this difficulty, we present Tools for Analyzing Time Series of Satellite Imagery (TATSSI), an open-source platform written in Python that provides routines for downloading, generating, gap-filling, smoothing, analyzing and exporting EO time series. Since TATSSI integrates quality assessment and quality control flags when generating time series, data quality analysis is the backbone of any analysis made with the platform. We discuss TATSSI’s 3-layered architecture (data handling, engine and three application programming interfaces (API)); by allowing three APIs (a native graphical user interface, some Jupyter Notebooks and the Python command line) this development is exceptionally user-friendly. Furthermore, to demonstrate the application potential of TATSSI, we evaluated MODIS time series data for three case studies (irrigation area changes, evaluation of moisture dynamics in a wetland ecosystem and vegetation monitoring in a burned area) in different geographical regions of Mexico. Our analyses were based on methods such as the spatio-temporal distribution of maxima over time, statistical trend analysis and change-point decomposition, all of which were implemented in TATSSI. Our results are consistent with other scientific studies and results in these areas and with related in-situ data.

Highlights

When the National Aeronautics and Space Administration (NASA) launched the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the Terra satellite on December of 1999, its products were envisioned to contribute to the long-term monitoring and global scale research of Earth’s land and oceans [1,2], a mission that NASA complemented in 2002 by orbiting the Aqua sensor
The main goal of this paper is to present TATSSI and its capabilities to allow users to overcome common barriers when using Earth observation (EO) data for scientific purposes, focusing mainly on getting access to different datasets, using the quality information associated with each dataset and performing statistical analysis of time series
We studied the moisture dynamics of Marismas Nacionales through trend analysis of a time series of the normalized difference moisture index (NDMI), which according to Wilson and Sader [52] is an appropriate variable for our purpose

Summary

Introduction

When the National Aeronautics and Space Administration (NASA) launched the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the Terra satellite on December of 1999, its products were envisioned to contribute to the long-term monitoring and global scale research of Earth’s land and oceans [1,2], a mission that NASA complemented in 2002 by orbiting the Aqua sensor. The combination of MODIS products with those from other emerging higher resolution sensors made it possible to generate consistent long-term time series with information about burned area with an adequate quantification of error and uncertainty [7,8,9]; some of these products are instrumental for determining the unexpected global decline of burned area [10].

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