@article{fba89f43c68f453eb68fb16eb223710f,
title = "A novel strategy to reconstruct ndvi time-series with high temporal resolution from modis multi-temporal composite products",
abstract = "Vegetation indices (VIs) data derived from satellite imageries play a vital role in land surface vegetation and dynamic monitoring. Due to the excessive noises (e.g., cloud cover, atmospheric contamination) in daily VI data, temporal compositing methods are commonly used to produce composite data to minimize the negative influence of noise over a given compositing time interval. However, VI time series with high temporal resolution were preferred by many applications such as vegetation phenology and land change detections. This study presents a novel strategy named DAVIR-MUTCOP (DAily Vegetation Index Reconstruction based on MUlti-Temporal COmposite Products) method for normalized difference vegetation index (NDVI) time-series reconstruction with high temporal resolution. The core of the DAVIR-MUTCOP method is a combination of the advantages of both original daily and temporally composite products, and selecting more daily observations with high quality through the temporal variation of temporally corrected composite data. The DAVIR-MUTCOP method was applied to reconstruct high-quality NDVI time-series using MODIS multi-temporal products in two study areas in the continental United States (CONUS), i.e., three field experimental sites near Mead, Nebraska from 2001 to 2012 and forty-six AmeriFlux sites evenly distributed across CONUS from 2006 to 2010. In these two study areas, the DAVIR-MUTCOP method was also compared to several commonly used methods, i.e., the Harmonic Analysis of Time-Series (HANTS) method using original daily observations, Savitzky–Golay (SG) filtering using daily observations with cloud mask products as auxiliary data, and SG filtering using temporally corrected composite data. The results showed that the DAVIR-MUTCOP method significantly improved the temporal resolution of the reconstructed NDVI time series. It performed the best in reconstructing NDVI time-series across time and space (coefficient of determination (R2 = 0.93 ~ 0.94) between reconstructed NDVI and ground-observed LAI). DAVIR-MUTCOP method presented the highest robustness and accuracy with the change of the filtering parameter (R2 = 0.99 ~ 1.00, bias = 0.001, root mean square error (RMSE) = 0.020). Only MODIS data were used in this study; nevertheless, the DAVIR-MUTCOP method proposed a universal and potential way to reconstruct daily time series of other VIs or from other operational sensors, e.g., AVHRR and VIIRS.",
keywords = "DAVIR-MUTCOP method, Daily time-series reconstruction, MODIS, Multi-temporal composite products, NDVI",
author = "Linglin Zeng and Wardlow, {Brian D.} and Shun Hu and Xiang Zhang and Guoqing Zhou and Guozhang Peng and Daxiang Xiang and Rui Wang and Ran Meng and Weixiong Wu",
note = "Funding Information: Author Contributions: Conceptualization, L.Z. and S.H.; methodology, L.Z. and S.H.; software, L.Z. and S.H.; validation, L.Z., B.D.W. and S.H.; formal analysis, L.Z. and S.H.; investigation, S.H., X.Z., G.P., D.X., R.W., R.M. and W.W.; resources, B.D.W. and G.Z.; data curation, L.Z.; writing—original draft preparation, L.Z.; writing—review and editing, L.Z., B.D.W. and S.H., X.Z., G.Z., D.X., R.M.; visualization, S.H.; supervision, B.D.W. and S.H.; project administration, L.Z. and S.H.; funding acquisition, L.Z. All authors have read and agreed to the published version of the manuscript.” Funding: This work was supported by the National Nature Science Foundation of China program (Grant No. 41901353), the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) (No. 162301202679), Huazhong Agricultural University (No. 2662019QD054), the National Key Research and Development Program of China (Grant No. 2017YFC1502406-03), the Open Research Fund of Guangxi Key Laboratory of Water Engineering Materials and Structures, Guangxi Institute of water resources research (Grant No. GXHRI-WEMS-2019-03) and National Key Research and Development Program of Guangxi (Grant No. 2019AB20009). Funding Information: This work was supported by the National Nature Science Foundation of China program (Grant No. 41901353), the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) (No. 162301202679), Huazhong Agricultural University (No. 2662019QD054), the National Key Research and Development Program of China (Grant No. 2017YFC1502406-03), the Open Research Fund of Guangxi Key Laboratory of Water Engineering Materials and Structures, Guangxi Institute of water resources research (Grant No. GXHRI-WEMS-2019-03) and National Key Research and Development Program of Guangxi (Grant No. 2019AB20009).The authors would like to thank the anonymous reviewers for their valuable comments to improve the quality of the paper. We also acknowledge for the data support from Carbon Sequestration Program of University of Nebraska-Lincoln?s (UNL) (CSP, http://csp.unl.edu/public/, accessed on 20 March 2021) and AmeriFlux (AF) sites (AmeriFlux website: https://ameriflux.lbl.gov/, accessed on 20 March 2021). Publisher Copyright: {\textcopyright} 2021 by the authors. Licensee MDPI, Basel, Switzerland.",
year = "2021",
month = apr,
day = "1",
doi = "10.3390/rs13071397",
language = "English (US)",
volume = "13",
journal = "Remote Sensing",
issn = "2072-4292",
publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",
number = "7",
}