Fan, XW; Liu, YB; Wu, GP; Zhao, XS (2020). Compositing the Minimum NDVI for Daily Water Surface Mapping. REMOTE SENSING, 12(4), 700.

Capturing high frequency water surface dynamics via optical remote sensing is important for understanding hydro-ecological processes over seasonally flooded wetlands. However, it is a difficult task due to the presence of clouds on satellite images. This study proposed the MODerate-resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) Minimum Value Composite (MinVC) algorithm to generate daily water surface data at a 250-m resolution. The algorithm selected pixelwise minimum values from the combined daily Terra and Aqua MODIS NDVI data within a 15-day moving window. Consisting mainly of cloud and water surface information, the MinVC NDVI data were segmented for water surfaces over the Poyang Lake, China (2000-2017) by using an edge detection model. The water surface mapping result was strongly correlated with the Landsat based result (R-2 = 0.914, root mean square error, RMSE = 223.7 km(2)), the cloud free MODIS image based result (R-2 = 0.824, RMSE = 356.7 km(2)), the recent Landsat-MODIS image fusion based result (R-2 = 0.765, RMSE = 403 km(2)), and the hydrodynamic modeling result (R-2 = 0.799). Compared to the equivalent eight-day MOD13 NDVI based on the Constraint View-Angle Maximum Value Composite (CV-MVC) algorithm, the daily MinVC NDVI highlighted water bodies by generating spatially homogenous water surface information. Consequently, the algorithm provided spatially and temporally continuous data for calculating water submersion times and trends in water surface area, which contribute to a better understanding of hydro-ecological processes over seasonally flooded wetlands. Within the framework of sensor intercalibration, the algorithm can be extended to incorporate multiple sensor data for improved water surface mapping.