A method for predicting large-area missing observations in Landsat time series using spectral-temporal metrics
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 r...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-07-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S030324342100026X |
| _version_ | 1811229377823768576 |
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| author | Zhipeng Tang Hari Adhikari Petri K.E. Pellikka Janne Heiskanen |
| author_facet | Zhipeng Tang Hari Adhikari Petri K.E. Pellikka Janne Heiskanen |
| author_sort | Zhipeng Tang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T10:14:00Z |
| format | Article |
| id | doaj.art-c9a233f0b47b46098a66ea160116b6ff |
| institution | Directory Open Access Journal |
| issn | 1569-8432 |
| language | English |
| last_indexed | 2024-04-12T10:14:00Z |
| publishDate | 2021-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj.art-c9a233f0b47b46098a66ea160116b6ff2022-12-22T03:37:15ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322021-07-0199102319A method for predicting large-area missing observations in Landsat time series using spectral-temporal metricsZhipeng Tang0Hari Adhikari1Petri K.E. Pellikka2Janne Heiskanen3Corresponding author.; Department of Geosciences and Geography, University of Helsinki, P.O. Box 68, FI-00014, Finland; Institute for Atmospheric and Earth System Research, Faculty of Science, University of Helsinki, FinlandDepartment of Geosciences and Geography, University of Helsinki, P.O. Box 68, FI-00014, Finland; Institute for Atmospheric and Earth System Research, Faculty of Science, University of Helsinki, FinlandDepartment of Geosciences and Geography, University of Helsinki, P.O. Box 68, FI-00014, Finland; Institute for Atmospheric and Earth System Research, Faculty of Science, University of Helsinki, FinlandDepartment of Geosciences and Geography, University of Helsinki, P.O. Box 68, FI-00014, Finland; Institute for Atmospheric and Earth System Research, Faculty of Science, University of Helsinki, FinlandCombined 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.http://www.sciencedirect.com/science/article/pii/S030324342100026XLandsatRemote sensingImage reconstructionGap fillingk-Nearest Neighbor regression |
| spellingShingle | Zhipeng Tang Hari Adhikari Petri K.E. Pellikka Janne Heiskanen A method for predicting large-area missing observations in Landsat time series using spectral-temporal metrics International Journal of Applied Earth Observations and Geoinformation Landsat Remote sensing Image reconstruction Gap filling k-Nearest Neighbor regression |
| title | A method for predicting large-area missing observations in Landsat time series using spectral-temporal metrics |
| title_full | A method for predicting large-area missing observations in Landsat time series using spectral-temporal metrics |
| title_fullStr | A method for predicting large-area missing observations in Landsat time series using spectral-temporal metrics |
| title_full_unstemmed | A method for predicting large-area missing observations in Landsat time series using spectral-temporal metrics |
| title_short | A method for predicting large-area missing observations in Landsat time series using spectral-temporal metrics |
| title_sort | method for predicting large area missing observations in landsat time series using spectral temporal metrics |
| topic | Landsat Remote sensing Image reconstruction Gap filling k-Nearest Neighbor regression |
| url | http://www.sciencedirect.com/science/article/pii/S030324342100026X |
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