Next Article in Journal
Super-Resolution Restoration of MISR Images Using the UCL MAGiGAN System
Previous Article in Journal
Crop Classification Based on Temporal Information Using Sentinel-1 SAR Time-Series Data
Previous Article in Special Issue
Implications of Pixel Quality Flags on the Observation Density of a Continental Landsat Archive
Article Menu
Issue 1 (January-1) cover image

Export Article

Open AccessArticle
Remote Sens. 2019, 11(1), 51; https://doi.org/10.3390/rs11010051

Making Landsat Time Series Consistent: Evaluating and Improving Landsat Analysis Ready Data

1,2,†
,
1,3,4,†
,
1,* , 1
,
1
and
1,*
1
Department of Geosciences, Texas Tech University, Lubbock, TX 79409, USA
2
School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China
3
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
4
University of Chinese Academy of Sciences, Beijing 100049, China
Contributed equally to the work.
*
Authors to whom correspondence should be addressed.
Received: 18 November 2018 / Revised: 18 December 2018 / Accepted: 22 December 2018 / Published: 29 December 2018
(This article belongs to the Special Issue Science of Landsat Analysis Ready Data)
Full-Text   |   PDF [12666 KB, uploaded 4 January 2019]   |  

Abstract

Recently, the United States Geological Survey (USGS) has released a new dataset, called Landsat Analysis Ready Data (ARD), which is designed specifically for facilitating time series analysis. In this study, we evaluated the temporal consistency of this new dataset and recommended several processing streamlines for improving data consistency. Specifically, we examined the impacts of data resampling, cloud/cloud shadow detection, Bidirectional Reflectance Distribution Function (BRDF) correction, and topographic correction on the temporal consistency of the Landsat Time Series (LTS). We have four major observations. First, single-resampled data (ARD) are generally more consistent than double-resampled data (re-projected Collection 1 data), but the difference is very minor. Second, the improved cloud and cloud shadow detection approach (e.g., Fmask 4.0 vs. 3.3) moderately increased data consistency. Third, BRDF correction contributed the most in making LTS consistent. Finally, we corrected the topographic effects by using several widely used algorithms, including Sun-Canopy-Sensor (SCS), a semiempirical SCS (SCS+C), and Illumination Correction (IC) algorithms, however they were found to have very limited or even negative impacts on the consistency of LTS. Therefore, we recommend using Landsat ARD with the improved cloud and cloud shadow detection approach (Fmask 4.0), and with BRDF correction for routine time series analysis. View Full-Text
Keywords: Landsat time series; Analysis Ready Data; cloud and cloud shadow detection; BRDF correction; topographic correction; resampled data Landsat time series; Analysis Ready Data; cloud and cloud shadow detection; BRDF correction; topographic correction; resampled data
Figures

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Supplementary material

SciFeed

Share & Cite This Article

MDPI and ACS Style

Qiu, S.; Lin, Y.; Shang, R.; Zhang, J.; Ma, L.; Zhu, Z. Making Landsat Time Series Consistent: Evaluating and Improving Landsat Analysis Ready Data. Remote Sens. 2019, 11, 51.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top