MODIS ocean color product downscaling via spatio-temporal fusion and regression: The case of chlorophyll-a in coastal waters

Abstract

Detailed and accurate information on the spatial variation of chlorophyll-a concentration in coastal waters is a critical component of ocean ecology and environmental research. The daily MODIS chlorophyll-a products provided by NASA, with 1 km spatial resolution, are suitable for monitoring this variation globally, but these products are too coarse to apply in practice to obtain detailed information over coastal waters. Early studies have shown that spatiotemporal data fusion techniques can be used to predict higher spatial resolution land-cover data based on time-series information in MODIS and the detailed texture from Landsat. However, this technology hasn’t been tested to determine whether it can be used to predict higher spatial-resolution data in coastal waters with rapid water movement. This study aims to answer this question by providing a method to downscale the MODIS chlorophyll-a products from 1 km spatial resolution to 30 m. The spatiotemporal data fusion model U-STFM and the regression model NASA OC2M-HI were used to combine the texture and chlorophyll-a information from Landsat and MODIS. An area with rapid water movement in Bohai Bay of the Bohai Sea, northeast China, was selected for this study. Twelve matched images from MODIS in Aqua platform and Landsat 8, taken over a period of five years (2013–2017), were used to better predict detailed remote-sensing reflectance (Rrs) on the targeted days. Landsat 8 Rrs was used as ground-truth data to assess the output. The results on Mar 10th, 2016, show: 1) The downscaled results (30 m) from the U-STFM model indicate a more stable prediction of Rrs with RMSE of 0.00177 and 0.00202 and R-squared of 0.868 and 0.881 for the blue and green bands, respectively. Results from STARFM and ESTARFM fusion models are also compared in this study. 2) High correlation between log10(U-STFM Blue/ U-STFM Green) and log10(MODIS Chl) captured by OC2M-HI regression model at 1 km scale with R-squared up to 0.85 and RMSE up to 0.742 mg/m^3. This correlation was further used to predict the final chlorophyll-a concentration prediction at 30 m scale on Mar 10th, 2016; 3) The Landsat 8 chlorophyll-a product was used as reference data to evaluate the final chlorophyll-a concentration prediction (30 m) and the original MODIS chlorophyll-a product. The result shows the final prediction (30 m) maintains the accuracy of MODIS chlorophyll-a product and highly improved the local texture details near coastal waters. Predictions on nine other targeted dates with similar conclusions were also evaluated in this paper. The results in this study suggest that low spatial-resolution (1 km) daily MODIS chlorophyll-a products can be downscaled to higher resolution (30 m) products based on the U-STFM image fusion model and NASA’s OC2M-HI regression model to better understand the dynamic patterns of chlorophyll-a concentration in coastal waters.