{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T22:51:34Z","timestamp":1780354294542,"version":"3.54.1"},"reference-count":57,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2020,8,25]],"date-time":"2020-08-25T00:00:00Z","timestamp":1598313600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Monitoring vegetation changes over time is very important in dry areas such as Iran, given its pronounced drought-prone agricultural system. Vegetation indices derived from remotely sensed satellite imageries are successfully used to monitor vegetation changes at various scales. Atmospheric dust as well as airborne particles, particularly gases and clouds, significantly affect the reflection of energy from the surface, especially in visible, short and infrared wavelengths. This results in imageries with missing data (gaps) and outliers while vegetation change analysis requires integrated and complete time series data. This study investigated the performance of HANTS (Harmonic ANalysis of Time Series) algorithm and (M)-SSA ((Multi-channel) Singular Spectrum Analysis) algorithm in reconstruction of wide-gap of missing data. The time series of Normalized Difference Vegetation Index (NDVI) retrieved from Landsat TM in combination with 250m MODIS NDVI time image products are used to simulate and find periodic components of the NDVI time series from 1986 to 2000 and from 2000 to 2015, respectively. This paper presents the evaluation of the performance of gap filling capability of HANTS and M-SSA by filling artificially created gaps in data using Landsat and MODIS data. The results showed that the RMSEs (Root Mean Square Errors) between the original and reconstructed data in HANTS and M-SSA algorithms were 0.027 and 0.023 NDVI value, respectively. Further, RMSEs among 15 NDVI images extracted from the time series artificially and reconstructed by HANTS and M-SSA algorithms were 0.030 and 0.025 NDVI value, respectively. RMSEs of the original and reconstructed data in HANTS and M-SSA algorithms were 0.10 and 0.04 for time series 6, respectively. The findings of this study present a favorable option for solving the missing data challenge in NDVI time series.<\/jats:p>","DOI":"10.3390\/rs12172747","type":"journal-article","created":{"date-parts":[[2020,8,25]],"date-time":"2020-08-25T09:24:56Z","timestamp":1598347496000},"page":"2747","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Comparison of Harmonic Analysis of Time Series (HANTS) and Multi-Singular Spectrum Analysis (M-SSA) in Reconstruction of Long-Gap Missing Data in NDVI Time Series"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6083-1517","authenticated-orcid":false,"given":"Hamid Reza","family":"Ghafarian Malamiri","sequence":"first","affiliation":[{"name":"Department of Geography, Yazd University, Yazd 8915818411, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8799-8330","authenticated-orcid":false,"given":"Hadi","family":"Zare","sequence":"additional","affiliation":[{"name":"College of Natural Resources and Desert, Yazd University, Yazd 8915818411, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3694-6936","authenticated-orcid":false,"given":"Iman","family":"Rousta","sequence":"additional","affiliation":[{"name":"Department of Geography, Yazd University, Yazd 8915818411, Iran"},{"name":"Institute for Atmospheric Sciences-Weather and Climate, University of Iceland and Icelandic Meteorological Office (IMO), Bustadavegur 7, IS-108 Reykjavik, Iceland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haraldur","family":"Olafsson","sequence":"additional","affiliation":[{"name":"Institute for Atmospheric Sciences-Weather and Climate, and Department of Physics, University of Iceland, and Icelandic Meteorological Office (IMO), Bustadavegur 7, IS-108 Reykjavik, Iceland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2179-1262","authenticated-orcid":false,"given":"Emma","family":"Izquierdo Verdiguier","sequence":"additional","affiliation":[{"name":"Institute of Geomatics, University of Natural Resource and Live Science (BOKU), Peter-Jordan-strasse 82, 1190 Vienna, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Environmental Science and Engineering Jiangwan Campus, Fudan University, 2005 Songhu Road, Yangpu District, Shanghai 200438, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Terence Darlington","family":"Mushore","sequence":"additional","affiliation":[{"name":"Department of Physics, Faculty of Science, University of Zimbabwe, MP167 Mt Pleasant, Harare 00263, Zimbabwe"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ghafarian Malamiri, H., Rousta, I., Olafsson, H., Zare, H., and Zhang, H. 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