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

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...
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Autor*in:	Shi Qiu [verfasserIn] Yukun Lin [verfasserIn] Rong Shang [verfasserIn] Junxue Zhang [verfasserIn] Lei Ma [verfasserIn] Zhe Zhu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Landsat time series Analysis Ready Data cloud and cloud shadow detection BRDF correction topographic correction resampled data

Übergeordnetes Werk:	In: Remote Sensing - MDPI AG, 2009, 11(2018), 1, p 51
Übergeordnetes Werk:	volume:11 ; year:2018 ; number:1, p 51

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/rs11010051

Katalog-ID:	DOAJ073873462

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authorswithroles_txt_mv	Shi Qiu @@aut@@ Yukun Lin @@aut@@ Rong Shang @@aut@@ Junxue Zhang @@aut@@ Lei Ma @@aut@@ Zhe Zhu @@aut@@
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author	Shi Qiu
spellingShingle	Shi Qiu misc Landsat time series misc Analysis Ready Data misc cloud and cloud shadow detection misc BRDF correction misc topographic correction misc resampled data misc Science misc Q Making Landsat Time Series Consistent: Evaluating and Improving Landsat Analysis Ready Data
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topic_title	Making Landsat Time Series Consistent: Evaluating and Improving Landsat Analysis Ready Data Landsat time series Analysis Ready Data cloud and cloud shadow detection BRDF correction topographic correction resampled data
topic	misc Landsat time series misc Analysis Ready Data misc cloud and cloud shadow detection misc BRDF correction misc topographic correction misc resampled data misc Science misc Q
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title	Making Landsat Time Series Consistent: Evaluating and Improving Landsat Analysis Ready Data
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title_sort	making landsat time series consistent: evaluating and improving landsat analysis ready data
title_auth	Making Landsat Time Series Consistent: Evaluating and Improving Landsat Analysis Ready Data
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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	Making Landsat Time Series Consistent: Evaluating and Improving Landsat Analysis Ready Data
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Making Landsat Time Series Consistent: Evaluating and Improving Landsat Analysis Ready Data

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