DSWEmod — The Production of High‐Frequency Surface Water Map Composites from Daily MODIS Images

Abstract

Optical satellite imagery is commonly used for monitoring surface water dynamics, but clouds and cloud shadows present challenges in assembling complete water time series. To test whether the daily revisit rate of Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery can reduce cloud obstruction and improve high‐frequency surface water mapping, we compared map results derived from Landsat (30‐m) and MODIS (250‐m) data across the state of California for 2003–2019. We adapted the Dynamic Surface Water Extent (DSWE) model in Google Earth Engine to generate surface water map composites from MODIS imagery every 5, 10, 15, and 30 days, and compared products to monthly Landsat‐based DSWE maps. Results for DSWEmod (DSWE MODIS) in California suggest that more than 5% data loss (cloud obstruction, etc.) was present in only 2% of the 15‐day time series, as compared to 32% of the monthly Landsat DSWE time series. The five‐day DSWEmod composites averaged 8.4% obscuration in the winter months. Area estimates derived from cloud‐filtered MODIS and Landsat monthly products have the highest linear correlations compared to streamgage discharge records, suggesting that monthly scale analyses best explain the relationship between surface water area and general streamflow dynamics. Shorter‐interval DSWEmod products have lower correlations but utility for understanding the timing of surface water peaks and past flood events.

Analysis of Surface Water Trends for the Conterminous United States Using MODIS Satellite Data, 2003–2019

“…To test the accuracy of DSWEmod at a national scale, we implemented a stratified random sampling approach (Stehman & Foody, 2019) based on Level I Ecoregions; this approach is modeled after Soulard et al. (2022). We selected random samples for each ecoregion within maximum water footprints (1985‐2019) determined by an independent water product, JRC (Pekel et al., 2016), hereafter referred to as the JRC maximum water stratum.…”

“…However, data continuity is paramount in trend analysis, and techniques aiming to test the temporal limits of trend analysis using optical imagery data must measure the influence of cloud obstruction in creating data gaps. Soulard et al (2022) concluded that complete, cloud-free monthly records spanning multiple decades represent a reasonable temporal resolution for applications focused on intra-and inter-annual surface water dynamics. Recent literature suggests that dense time series are well-suited to capture processes of land surface change at monthly scales (Senay et al, 2017;Tulbure & Broich, 2013;Waylen et al, 2014).…”

“…Downscaling uses a ratio developed for the near-infrared (NIR) band to apply to the two MODIS short-wave infrared (SWIR) bands, and a ratio developed for the red band to apply to the other MODIS visible channels to generate synthetic 250-m MODIS multispectral data (Nigro et al, 2014). For full image processing details see Soulard et al (2022).…”

“…To make the gage sample more comparable to DSWEmod (e.g., moderate-scale water bodies), we only retained gages in each ecoregion where mean annual discharge was in the top three quartiles (i.e., 2-4). Using M-K trend tests, we measured annual discharge trends across all available months, adapting the approach used in and Soulard et al (2022). A majority rule was applied to identify the predominant M-K trend direction across all gages tested in each Level III Ecoregion (i.e., increasing or decreasing), and the result was compared to the overall DSWEmod surface water trend derived from the continuous monthly record.…”

“…Additionally, the number of images available within the Landsat archive varies significantly in different parts of the world (Wulder et al., 2016). Daily imagery from Moderate Resolution Imaging Spectroradiometer (MODIS) satellites Terra and Aqua have helped provide a useful alternative to Landsat‐based approaches for DSWE mapping, albeit at a coarser 250‐m resolution and for a shorter period of availability (2000 to present) (Soulard et al., 2022). The generation of MODIS‐based DSWE products (“DSWEmod”) for California leveraged daily data to create monthly and sub‐monthly surface water maps with substantially reduced cloud obstruction.…”