Abstract
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 (R2 = 0.914, root mean square error, RMSE = 223.7 km2), the cloud free MODIS image based result (R2 = 0.824, RMSE = 356.7 km2), the recent Landsat-MODIS image fusion based result (R2 = 0.765, RMSE = 403 km2), and the hydrodynamic modeling result (R2 = 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.
Highlights
A water body is identifiable from satellite images due to its unique spectral features in a wide range of electromagnetic spectrums [1,2,3,4,5]
To deal with cloud effects on water surface mapping and derive temporally continuous water surface area data, this study proposed a MODerate-resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) based Minimum Value Composite (MinVC) algorithm by exploiting the combined daily 250-m The product is released as MOD13Q1 (Terra) and Aqua MODIS visible and near-infrared (VNIR) bands reflectance data
We focused on the baseline MinVC NDVI data, because the MOD13 NDVI product was generated by using SUR reflectance data with a similar compositing period
Summary
A water body is identifiable from satellite images due to its unique spectral features in a wide range of electromagnetic spectrums [1,2,3,4,5]. With daily global observing abilities, the Terra/Aqua MODerate-resolution Imaging Spectroradiometer (MODIS), the ENVISAT MEdium Resolution Imaging Spectrometer (MERIS), the Sentinel-3 Ocean and Land Colour Instrument (OCLI), and the National Ocean and Atmosphere Administrator (NOAA)/MetOp Advanced Very High Resolution Radiometer (AVHRR) sensors can collect more cloud free images [28,29,30,31,32] These coarser-resolution data have been combined with finer-resolution data for detailed water surface mapping [33,34,35,36,37]. To deal with cloud effects on water surface mapping and derive temporally continuous water surface area data, this study proposed a MODIS NDVI based Minimum Value Composite (MinVC) algorithm by exploiting the combined daily 250-m Terra and Aqua MODIS VNIR bands reflectance data. OLI NorsmuraflaiczeedshDriinffkeirnegncpeeVrieogde. taTthioenLIannddesxat(-N8 DOVLI)Nimoramgeal(icz)edshDowiffserceonncteraVsteignegtastpioenctrIanldbeexhaviors between(NthDeVwI)atiemrasguerf(acc)esahnodwsvecgoenttartaisotninagt saphecigtrhalwbaethearvsitoargseb. etween the water surface and vegetation at a high water stage
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