Development of an Approach for Generation of Temporally Complete Daily Nadir MODIS Reflectance Time Series

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Time Series, MODIS, NBAR, Phenology, Composting


Consistent, spatially and temporally complete reflectance time series are required for reliable terrestrial monitoring. The Moderate Resolution Imaging Spectroradiometer (MODIS), like other polar-orbiting wide field of view satellite sensors, can provide global observations on a nearly daily basis, but the sparseness of valid observations due to cloud, residual atmospheric effects, and sensor anomalies, may result in gaps in the derived reflectance time series. This paper presents an approach for the generation of temporally complete daily MODIS 500 m nadir view BRDF-adjusted reflectance (NBAR) time series. The research is illustrated and assessed quantitatively using two years of cloud and snow screened, daily MODIS Terra and Aqua reflectance data at four sites in Africa, and demonstrated for phenology monitoring using NBAR derived NDVI time series. The components of the approach include: 1) an outlier detection algorithm to remove residual anomalous daily observations undetected in the upstream processing, 2) the dynamic generation of NBAR time series on a daily basis when seven or more observations are available for a day under consideration over a 16-day period, and 3) the means to gap fill the NBAR time series where less than 7 observations are available. The MODIS Ross-Thick/Li-Sparse-Reciprocal BRDF model is used with a rolling approach whereby a 16-day BRDF inversion window is moved on a daily overlapping basis to provide more reliable outlier detection and daily NBAR. NBAR gap filling in periods of missing observations is investigated using static land cover specific archetype BRDF parameters and using BRDF parameters defined adaptively from the temporally closest 16-day periods with 7 or more observations. Scaling factor estimators using ordinary least squares (OLS) and median-based robust least squares regression are investigated, and the robust method is demonstrated to provide on average temporally more coherent gap filled NBAR values. For regions with persistent clouds, the utility of the adaptive NBAR gap filling method is demonstrated to be severely limited due to the decreased likelihood that the surface BRDF at each gap can be described reliably. The reliability of the NBAR gap filling methodology is evaluated statistically using a cross-validation approach. For the small number of study site considered, the adaptive method is shown to provide more accurate results than the archetype method when there are more than an average of ~4–5 observations per 16-day window, or when a gap day is on average less than about 30 days from a 16-day period with 7 or more observations. The resulting gap free daily NBAR time series and derived daily NBAR NDVI generated by the approach is shown to capture phenological variations in a coherent temporally consistent manner, suggesting that it is a fruitful avenue for future research and validation.

Publication Title

Remote Sensing of Environment



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