@article{a893ccff36bd4ef78926962cd28d5f63,
title = "{\textquoteleft}LandsatTS': an R package to facilitate retrieval, cleaning, cross-calibration, and phenological modeling of Landsat time series data",
abstract = "The Landsat satellites provide decades of near-global surface reflectance measurements that are increasingly used to assess interannual changes in terrestrial ecosystem function. These assessments often rely on spectral indices related to vegetation greenness and productivity (e.g. Normalized Difference Vegetation Index, NDVI). Nevertheless, multiple factors impede multi-decadal assessments of spectral indices using Landsat satellite data, including ease of data access and cleaning, as well as lingering issues with cross-sensor calibration and challenges with irregular timing of cloud-free acquisitions. To help address these problems, we developed the {\textquoteleft}LandsatTS' package for R. This software package facilitates sample-based time series analysis of surface reflectance and spectral indices derived from Landsat sensors. The package includes functions that enable the extraction of the full Landsat 5, 7, and 8 records from Collection 2 for point sample locations or small study regions using Google Earth Engine accessed directly from R. Moreover, the package includes functions for 1) rigorous data cleaning, 2) cross-sensor calibration, 3) phenological modeling, and 4) time series analysis. For an example application, we show how {\textquoteleft}LandsatTS' can be used to assess changes in annual maximum vegetation greenness from 2000 to 2022 across the Noatak National Preserve in northern Alaska, USA. Overall, this software provides a suite of functions to enable broader use of Landsat satellite data for assessing and monitoring terrestrial ecosystem function during recent decades across local to global geographic extents.",
keywords = "Google Earth Engine, Landsat, NDVI, cross-sensor calibration, greening and browning, spectral index",
author = "Berner, {Logan T.} and Assmann, {Jakob J.} and Signe Normand and Goetz, {Scott J.}",
note = "Funding Information: – Landsat 5 (doi.org/10.5066/P9IAXOVV), Landsat 7 (doi.org/10.5066/P9C7I13B), and Landsat 8 (doi.org/10.5066/P9OGBGM6) surface reflectance data courtesy of the U.S. Geological Survey. We thank two anonymous reviewers and the subject editor for their valuable feedback. – We acknowledge support from the National Aeronautics and Space Administration (NASA) Arctic Boreal Vulnerability Experiment (ABoVE) under grant no. 80NSSC19M0112 to S. J. G. and the NASA New Investigator Program (NIP) under grant no. 80NSSC21K1364 to L. T. B. This study was also supported by the National Science Foundation Navigating the New Arctic Big Idea under grant no. 2127273 to L. T. B. and S. J. G. The contributions from J. J. A. and S. N. to this study were funded by the Independent Research Fund of Denmark (grant no. 7027-00133B) and the EU Horizon 2020 CHARTER project (grant agreement no.: 869471). Logan T. Berner: Conceptualization (lead); Data curation (equal); Formal analysis (lead); Funding acquisition (equal); Investigation (lead); Methodology (lead); Software (lead); Visualization (lead); Writing – original draft (lead); Writing – review and editing (lead). Jakob J. Assmann: Conceptualization (equal); Data curation (equal); Formal analysis (equal); Investigation (equal); Methodology (lead); Software (lead); Visualization (equal); Writing – original draft (equal); Writing – review and editing (equal). Signe Normand: Funding acquisition (equal); Project administration (equal); Resources (supporting); Supervision (equal); Writing – original draft (supporting); Writing – review and editing (supporting). Scott J. Goetz: Conceptualization (supporting); Funding acquisition (lead); Methodology (supporting); Project administration (equal); Resources (equal); Supervision (lead); Writing – original draft (supporting); Writing – review and editing (supporting). – We acknowledge support from the National Aeronautics and Space Administration (NASA) Arctic Boreal Vulnerability Experiment (ABoVE) under grant no. 80NSSC19M0112 to S. J. G. and the NASA New Investigator Program (NIP) under grant no. 80NSSC21K1364 to L. T. B. This study was also supported by the National Science Foundation Navigating the New Arctic Big Idea under grant no. 2127273 to L. T. B. and S. J. G. The contributions from J. J. A. and S. N. to this study were funded by the Independent Research Fund of Denmark (grant no. 7027-00133B) and the EU Horizon 2020 CHARTER project (grant agreement no.: 869471). Funding Information: – We acknowledge support from the National Aeronautics and Space Administration (NASA) Arctic Boreal Vulnerability Experiment (ABoVE) under grant no. 80NSSC19M0112 to S. J. G. and the NASA New Investigator Program (NIP) under grant no. 80NSSC21K1364 to L. T. B. This study was also supported by the National Science Foundation Navigating the New Arctic Big Idea under grant no. 2127273 to L. T. B. and S. J. G. The contributions from J. J. A. and S. N. to this study were funded by the Independent Research Fund of Denmark (grant no. 7027‐00133B) and the EU Horizon 2020 CHARTER project (grant agreement no.: 869471). Publisher Copyright: {\textcopyright} 2023 The Authors. Ecography published by John Wiley & Sons Ltd on behalf of Nordic Society Oikos.",
year = "2023",
month = sep,
doi = "10.1111/ecog.06768",
language = "English (US)",
volume = "2023",
journal = "Ecography",
issn = "0906-7590",
publisher = "Wiley-Blackwell",
number = "9",
}