Development and Validation of a Dense 18-Year Time Series of Surface Water Fraction Estimates from MODIS for the Mediterranean Region

Abstract

Abstract. Detailed knowledge on surface water distribution and its changes is of high importance for water management and biodiversity conservation. Landsat-based assessments of surface water, such as the Global Surface Water (GSW) dataset developed by the European Commission Joint Research Centre (JRC), may not capture important changes in surface water during months with considerable cloud cover. This results in large temporal gaps in the Landsat record that prevent accurate assessment of surface water dynamics. Here we show that the frequent global acquisitions by the Moderate Resolution Imaging Spectrometer (MODIS) sensors can compensate for this shortcoming, and in addition allow for examining surface water changes at fine temporal resolution. To account for water bodies smaller than a MODIS cell, we developed a global rule-based regression model for estimating surface water fraction from a 500 m nadir reflectance product from MODIS (MCD43A4). The model was trained and evaluated with the GSW monthly water history datasets. A high estimation accuracy (R2 = 0.91, RMSE = 11.41 %, MAE = 6.39 %) was achieved. We then applied the algorithm to 18 years of MODIS data (2000–2017) to generate a time series of surface water fraction maps at 8-day interval for the Mediterranean. From these maps we derived metrics including the mean annual maximum, standard deviation and seasonality of surface water. The dynamic surface water extent estimates from MODIS were compared with the results from GSW and water level data measured in situ or by satellite altimetry, yielding similar temporal patterns. Our datasets complement surface water products at fine spatial resolution by adding more temporal detail, which permits effective monitoring and assessment of the seasonal, inter-annual and long-term variability of water resources, inclusive of small water bodies.

Review of the manuscript

“…4) The potential of the data set in water management could be better illustrated. High temporal resolution surface water areas can benefit some studies that use water area information as input for hydrological modeling in ungauged basin, e.g., Huang et al (2018) used river widths generated from Landsat and Sentinel-2 to calibrate the parameters of a distributed hydrology model in Upper Brahmaputra River.…”