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.
Highlights
Satellite-derived Vegetation indices (VIs) have been being widely used in monitoring vegetation conditions and dynamics on regional or global scales [1,2]
To address the above challenges, this study presents a novel strategy named the DAVIR-MUTCOP (Daily Vegetation Index Reconstruction based on Multi-Temporal Composite Products) method to reconstruct high-quality normalized difference vegetation index (NDVI) time-series data by combining the Moderate Resolution Imaging Spectroradiometer (MODIS) daily product (MOD09GQ) with MODIS 8-day or 16-day composite product (MOD09Q1 or MOD13Q1) as an example without any other auxiliary data
SG35L2 method tends to underestimate NDVI values, especially during or approaching the NDVI peak period (Figure 4), and (2) in addition to cloud cover, other types of noise that exist in the dataset, including other atmospheric contamination and off-nadir viewing effects, can increase or decrease NDVI values [47,48]. 8- and 16-day composite data had much less noise than daily NDVI data (Figure 4b,c), because the CVMVC method generally selects the representative NDVI with higher accuracy in the given time interval to minimize the influence of the noises in daily time series data, though there were still remaining data noises in composite data, especially for the 8-day composite data product (Figure 4e,f)
Summary
Satellite-derived VIs have been being widely used in monitoring vegetation conditions and dynamics on regional or global scales [1,2]. The satellite-derived normalized difference vegetation index (NDVI) calculated from the spectral reflectance of near infrared (NIR). Accurate NDVI data with high temporal (e.g., daily) and spatial resolution was preferred and sometimes necessary in many applications like forest disturbances [5,6] and vegetation phenology detection [7,8]. A Moderate Resolution Imaging Spectroradiometer (MODIS), with a high temporal and moderate spatial resolution, has been a key instrument aboard the NASA Terra and. MODIS provide a near-daily global coverage multispectral imagery of the entire earth surface for 36 spectral bands including the visible red and near infrared (NIR) bands with moderate spatial resolution (up to 250 m) [13].
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