Spectral Temporal Information for Missing Data Reconstruction (STIMDR) of Landsat Reflectance Time Series

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http://hdl.handle.net/10138/340674

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Tang , Z , Amatulli , G , Pellikka , P & Heiskanen , J 2022 , ' Spectral Temporal Information for Missing Data Reconstruction (STIMDR) of Landsat Reflectance Time Series ' , Remote Sensing , vol. 14 , no. 1 , 172 . https://doi.org/10.3390/rs14010172

Title: Spectral Temporal Information for Missing Data Reconstruction (STIMDR) of Landsat Reflectance Time Series
Author: Tang, Zhipeng; Amatulli, Giuseppe; Pellikka, Petri; Heiskanen, Janne
Contributor organization: Department of Geosciences and Geography
Doctoral Programme in Geosciences
Date: 2022-01
Language: eng
Number of pages: 27
Belongs to series: Remote Sensing
ISSN: 2072-4292
DOI: https://doi.org/10.3390/rs14010172
URI: http://hdl.handle.net/10138/340674
Abstract: The number of Landsat time-series applications has grown substantially because of its approximately 50-year history and relatively high spatial resolution for observing long term changes in the Earth's surface. However, missing observations (i.e., gaps) caused by clouds and cloud shadows, orbit and sensing geometry, and sensor issues have broadly limited the development of Landsat time-series applications. Due to the large area and temporal and spatial irregularity of time-series gaps, it is difficult to find an efficient and highly precise method to fill them. The Missing Observation Prediction based on Spectral-Temporal Metrics (MOPSTM) method has been proposed and delivered good performance in filling large-area gaps of single-date Landsat images. However, it can be less practical for a time series longer than one year due to the lack of mechanics that exclude dissimilar data in time series (e.g., different phenology or changes in land cover). To solve this problem, this study proposes a new gap-filling method, Spectral Temporal Information for Missing Data Reconstruction (STIMDR), and examines its performance in Landsat reflectance time series. Two groups of experiments, including 2000 x 2000 pixel Landsat single-date images and Landsat time series acquired from four sites (Kenya, Finland, Germany, and China), were performed to test the new method. We simulated artificial gaps to evaluate predicted pixel values with real observations. Quantitative and qualitative evaluations of gap-filled images through comparisons with other state-of-the-art methods confirmed the more robust and accurate performance of the proposed method. In addition, the proposed method was also able to fill gaps contaminated by extreme cloud cover for a period (e.g., winter in high-latitude areas). A down-stream task of random forest supervised classification through both gap-filled simulated datasets and the original valid datasets verified that STIMDR-generated products are relevant to the user community for land cover applications.
Subject: 1171 Geosciences
remote sensing
k-Nearest Neighbor regression
machine learning
random forest classification
time-series analysis
REMOTE-SENSING IMAGES
RANK-SUM TEST
CLOUD DETECTION
RANDOM FOREST
COVER
INTERPOLATION
REGRESSION
ALGORITHM
FUSION
CLASSIFICATION
Peer reviewed: Yes
Rights: cc_by
Usage restriction: openAccess
Self-archived version: publishedVersion


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