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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. [ABSTRACT FROM AUTHOR]

Abstract: Riparian vegetation provides important wildlife habitat in the southwestern United States, but limited distributions and spatial complexity often leads to inaccurate representation in maps used to guide conservation. We test the use of data conflation and aggregation on multiple vegetation/land-cover maps to improve the accuracy of habitat models for the threatened western yellow-billed cuckoo (Coccyzus americanus occidentalis). We used species observations (n = 479) from a state-wide survey to develop habitat models from 1) three vegetation/land-cover maps produced at different geographic scales ranging from state to national, and 2) new aggregate maps defined by the spatial agreement of cover types, which were defined as high (agreement = all data sets), moderate (agreement ≥ 2), and low (no agreement required). Model accuracies, predicted habitat locations, and total area of predicted habitat varied considerably, illustrating the effects of input data quality on habitat predictions and resulting potential impacts on conservation planning. Habitat models based on aggregated and conflated data were more accurate and had higher model sensitivity than original vegetation/land-cover, but this accuracy came at the cost of reduced geographic extent of predicted habitat. Using the highest performing models, we assessed cuckoo habitat preference and distribution in Arizona and found that major watersheds containing high-probably habitat are fragmented by a wide swath of low-probability habitat. Focus on riparian restoration in these areas could provide more breeding habitat for the threatened cuckoo, offset potential future habitat losses in adjacent watershed, and increase regional connectivity for other threatened vertebrates that also use riparian corridors. [Copyright &y& Elsevier]

• We aggregated imagery-derived monthly water extents over the California study area from 1984 to 2015. • Correlations of surface water extent and stream gage discharge data vary on a watershed basis. • Fitting linear regressions to data pairs with the strongest correlation can be used to fill imagery gaps. • We constructed continuous time series of monthly surface water extents for most watersheds. Accurate monitoring of surface water location and extent is critical for the management of diverse water resource phenomena. The multi-decadal archive of Landsat satellite imagery is punctuated by missing data due to cloud cover during acquisition times, hindering the assembly of a continuous time series of inundation dynamics. This study investigated whether streamflow volume measurements could be integrated with satellite data to fill gaps in monthly surface water chronologies for the Central Valley region of California, USA, from 1984 to 2015. We aggregated measurements of maximum monthly water extent within each of the study area's 50 8-digit hydrologic unit code (HUC) watersheds from two Landsat-derived datasets: the European Commission's Joint Research Centre (JRC) Monthly Water History and the U.S. Geological Survey Dynamic Surface Water Extent (DSWE). We calculated Spearman rank correlation coefficients between water extent values in each HUC and streamflow discharge data. Linear regression fits of the water extent/streamflow data pairs with the highest correlations served as the basis for interpolation of missing imagery surface water values on a HUC-wise basis. Results show strong (ρ > 0.7) maximum correlations in 11 (22.4%) and 25 (51.0%) HUCs for the DSWE and JRC time series, respectively, when comparisons were restricted to imagery and gages co-located in each HUC. Strong maximum correlations occurred in 39 (79.6%; DSWE) and 42 (85.7%; JRC) HUCs when imagery was paired with discharge data from any study area gage, providing a solid basis for reconstruction of water extent values. We generated continuous time series of 30+ years in 35 HUCs, demonstrating that this technique can provide quantitative estimates of historical surface water extents and elucidate flooding or drought events over the period of data collection. Results of a non-parametric trend analysis of the long-term time series on an annual, seasonal, and monthly basis varied among HUCs, though most trends indicate an increase in surface water over the past 30 years. [ABSTRACT FROM AUTHOR]