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.
Highlights
Landsat Time Series (LTS) has been widely used for a variety of time series analysis for monitoring environmental change [1], such as forest disturbance [2,3,4], surface water dynamics [5,6], urban expansion [7,8], and agricultural practice [9,10], especially since the open and free policy that was implemented by the United States Geological Survey (USGS) in 2008 [11]
Landsat Analysis Ready Data (ARD) is a new dataset for facilitating time series analysis and the data consistency is one of the most important factors
This new data collect images from different sensors (e.g., Thematic Mapper (TM), ETM+, and Operational Land Imager (OLI)/TIRS) and the consistency would be inherently effected [57,58]. Except for this effect, we explored four different scenarios (Table 2) for making LTS more consistent using Landsat ARD
Summary
Landsat Time Series (LTS) has been widely used for a variety of time series analysis for monitoring environmental change [1], such as forest disturbance [2,3,4], surface water dynamics [5,6], urban expansion [7,8], and agricultural practice [9,10], especially since the open and free policy that was implemented by the United States Geological Survey (USGS) in 2008 [11]. We will evaluate the impacts of data resampling (ARD vs Collection 1), better cloud/cloud shadow detection, Bidirectional Reflectance Distribution Function (BRDF) correction, and topographic correction on the data consistency of LTS. It would be interesting to evaluate whether the BRDF correction using a fixed set of BRDF spectral model parameters could improve the LTS consistency of Landsat ARD. Last, terrain shadows block the direct solar illumination and cause large fluctuations in LTS, as many topographic correction methods have been proposed, such as Sun-Canopy-Sensor (SCS) [40], their locations can change along with the solar and view angles To address this problem, many semiempirical (SCS+C).
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