A method for predicting large-area missing observations in Landsat time series using spectral-temporal metrics

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http://hdl.handle.net/10138/333583

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Tang , Z , Adhikari , H , Pellikka , P & Heiskanen , J 2021 , ' A method for predicting large-area missing observations in Landsat time series using spectral-temporal metrics ' , International Journal of Applied Earth Observation and Geoinformation , vol. 99 , 102319 . https://doi.org/10.1016/j.jag.2021.102319

Title: A method for predicting large-area missing observations in Landsat time series using spectral-temporal metrics
Author: Tang, Zhipeng; Adhikari, Hari; Pellikka, Petri; Heiskanen, Janne
Contributor: University of Helsinki, Department of Geosciences and Geography
University of Helsinki, Earth Change Observation Laboratory (ECHOLAB)
University of Helsinki, Department of Geosciences and Geography
University of Helsinki, Institute for Atmospheric and Earth System Research (INAR)
Date: 2021-07
Language: eng
Number of pages: 13
Belongs to series: International Journal of Applied Earth Observation and Geoinformation
ISSN: 1569-8432
URI: http://hdl.handle.net/10138/333583
Abstract: Combined with increasing computing ability, the free and open access to Landsat archive has enabled the changes on the Earth’s surface to be monitored for almost 50 years. However, due to missing observations that result from clouds, cloud shadows, and scan line corrector failure, the Landsat data record is neither a continuous nor consistent time series. We present a new gap-filling method, Missing Observation Prediction based on Spectral-Temporal Metrics (MOPSTM), which uses spectral-temporal metrics computed from Landsat one-year time series and the k-Nearest Neighbor (k-NN) regression. Herein, we demonstrate the performance of MOPSTM by using five, nearly cloud-free, full scene Landsat images from Kenya, Finland, Germany, the USA, and China. Cloud masks from the images with extensive cloud cover were used to simulate large-area gaps, with the highest value we tested being 92% of missing data. The gap-filling accuracy was assessed quantitatively considering all five sites and different land use/land cover types, and the MOPSTM algorithm performed better than the spectral angle-mapper based spatiotemporal similarity (SAMSTS) gap-filling algorithm. The mean RMSE values of MOPSTM were 0.010, 0.012, 0.025, 0.012, and 0.018 for the five sites, while those of SAMSTS were 0.011, 0.017, 0.038, 0.014, and 0.023, respectively. Furthermore, MOPSTM had mean coefficient of determination (R2) values of 0.90, 0.86, 0.78, 0.92, and 0.89, which were higher than those for SAMSTS (0.84, 0.75, 0.55, 0.89, and 0.83). The performance of MOPSTM was not considerably affected by image gap sizes as images ranging from gap sizes of 51% of the image all the way to 92% of the image yielded similar gap-filling accuracy. Also, MOPSTM does not require local parametertuning except for the k values in the k-NN regression, and it can make a gap-free image from any acquisition date. MOPSTM provides a new spectral-temporal approach to generate the gap-free imagery for typical Landsat applications, such as land use, land cover, and forest monitoring.
Subject: 1171 Geosciences
Landsat
Remote sensing
Image reconstruction
Gap filling
k-Nearest Neighbor regression
FUSION
CLASSIFICATION
NORMALIZATION
REFLECTANCE
GAPS
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