Multi-algorithm Method Research Articles

Uncertainty analysis of five satellite-based precipitation products and evaluation of three optimally merged multi-algorithm products over the Tibetan Plateau

This study is the first comprehensive examination of uncertainty with respect to region, season, rain rate, topography, and snow cover of five mainstream satellite-based precipitation products over the Tibetan Plateau (TP) for the period 2005–2007. It further investigates three merging approaches in order to provide the best possible products for climate and hydrology research studies. Spatial distribution of uncertainty varies from higher uncertainty in the eastern and southern TP and relatively smaller uncertainty in the western and northern TP. The uncertainty is highly seasonal, temporally varying with a decreasing trend from January to April and then remaining relatively low and increasing after October, with an obvious winter peak and summer valley. Overall, the uncertainty also shows an exponentially decreasing trend with higher rainfall rates. The effect of topography on the uncertainty tends to rapidly increase when elevation exceeds 4000 m, while the impact slowly decreases in areas lower than that topography. The influence of the elevation on the uncertainty is significant for all seasons except for the summer. Further cross-investigation found that the uncertainty trend is highly correlated with the MODIS-derived snow cover fraction (SCF) time series over the TP (e.g. correlation coefficient ≥0.75). Finally, to reduce the still relatively large and complex uncertainty over the TP, three data merging methods are examined to provide the best possible satellite precipitation data by optimally combining the five products. The three merging methods – arithmetic mean, inverse-error-square weight, and one-outlier-removed arithmetic mean – show insignificant yet subtle differences. The Bias and RMSE of the three merging methods is dependent on the seasons, but the one-outlier-removed method is more robust and its result outperforms the five individual products in all the seasons except for the winter. The correlation coefficient of the three merging methods is consistently higher than any of five individual satellite estimates, indicating the superiority of the method. This optimally merging multi-algorithm method is a cost-effective way to provide satellite precipitation data of better quality with less uncertainty over the TP in the present era prior to the Global Precipitaton Measurement Mission.

Satellite remote sensing of harmful algal blooms: A new multi-algorithm method for detecting the Florida Red Tide ( Karenia brevis)

In a continuing effort to develop suitable methods for the surveillance of harmful algal blooms (HABs) of Karenia brevis using satellite radiometers, a new multi-algorithm method was developed to explore whether improvements in the remote-sensing detection of the Florida Red Tide was possible. A Hybrid Scheme was introduced that sequentially applies the optimized versions of two pre-existing satellite-based algorithms: an Empirical Approach (using water-leaving radiance as a function of chlorophyll concentration) and a Bio-optical Technique (using particulate backscatter along with chlorophyll concentration). The long-term evaluation of the new multi-algorithm method was performed using a multi-year MODIS dataset (2002–2006; during the boreal Summer–Fall periods – July–December) along the Central West Florida Shelf between 25.75°N and 28.25°N. Algorithm validation was done with in situ measurements of the abundances of K. brevis; cell counts ≥1.5 × 10 4 cells l −1 defined a detectable HAB. Encouraging statistical results were derived when either or both algorithms correctly flagged known samples. The majority of the valid match-ups were correctly identified (∼80% of both HABs and non-blooming conditions) and few false negatives or false positives were produced (∼20% of each). Additionally, most of the HAB-positive identifications in the satellite data were indeed HAB samples (positive predictive value: ∼70%) and those classified as HAB-negative were almost all non-bloom cases (negative predictive value: ∼86%). These results demonstrate an excellent detection capability, on average ∼10% more accurate than the individual algorithms used separately. Thus, the new Hybrid Scheme could become a powerful tool for environmental monitoring of K. brevis blooms, with valuable consequences including leading to the more rapid and efficient use of ships to make in situ measurements of HABs.