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However, in its application, we often encounter issues, such as identifying positively biased outliers for vegetation biochemical parameters and reducing the influence of long consecutive gaps. In this study, we improved the LACC algorithm to address the above two issues by (1) incorporating a procedure to remove outliers and (2) integrating the spatial information of neighboring pixels for large data gap filling. To evaluate the performance of the new version of LACC (namely LACC2.0), leaf chlorophyll content (LCC) was taken as an example. A reference LCC curve was generated for each pixel of the global map as the true value for global evaluation, and a time series of LCC with real gaps in the original data for each pixel was created by adding Gaussian noises into observations for testing the effectiveness of time series reconstruction algorithms. Results showed that the percentage of pixels with an RMSE smaller than 5 \u03bcg\/cm2 was improved from 81.2% in LACC to 96.4% in LACC2.0, demonstrating that LACC2.0 had the potential to provide a better reconstruction of global daily satellite-derived vegetation biochemical parameters. This finding highlights the significance of outlier removal and spatial-temporal fusion to enhance the accuracy and reliability of time series reconstruction.<\/jats:p>","DOI":"10.3390\/rs15133277","type":"journal-article","created":{"date-parts":[[2023,6,26]],"date-time":"2023-06-26T03:14:56Z","timestamp":1687749296000},"page":"3277","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["LACC2.0: Improving the LACC Algorithm for Reconstructing Satellite-Derived Time Series of Vegetation Biochemical Parameters"],"prefix":"10.3390","volume":"15","author":[{"given":"Mingzhu","family":"Xu","sequence":"first","affiliation":[{"name":"Key Laboratory for Humid Subtropical Ecogeographical Processes of the Ministry of Education, School of Geographical Sciences, Fujian Normal University, Fuzhou 350007, China"}]},{"given":"Rong","family":"Shang","sequence":"additional","affiliation":[{"name":"Key Laboratory for Humid Subtropical Ecogeographical Processes of the Ministry of Education, School of Geographical Sciences, Fujian Normal University, Fuzhou 350007, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8682-1293","authenticated-orcid":false,"given":"Jing M.","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory for Humid Subtropical Ecogeographical Processes of the Ministry of Education, School of Geographical Sciences, Fujian Normal University, Fuzhou 350007, China"},{"name":"Department of Geography and Planning, University of Toronto, Toronto, ON M5S 3G3, Canada"}]},{"given":"Lingfang","family":"Zeng","sequence":"additional","affiliation":[{"name":"Shanghai Surveying and Mapping Institute, Shanghai 200063, China"},{"name":"Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities, Shanghai 200333, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4259","DOI":"10.1038\/s41467-019-12257-8","article-title":"Vegetation Structural Change since 1981 Significantly Enhanced the Terrestrial Carbon Sink","volume":"10","author":"Chen","year":"2019","journal-title":"Nat. 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