A new global raster water mask at 250 m resolution

By Mark L. Carroll, John R. Townshend, Charlene M. DiMiceli, Praveen Noojipady, R. Sohlberg. International Journal of Digital Earth, 2009

372 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/2eda37ac08c5c2ee78b855414aa7e5ae31a5f9d2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

1. Wolfgang Wagner
2. Eric F. Wood
3. Remko Uijlenhoet
4. Niko E. C. Verhoest
5. Eric F. Vermote
6. Tomislav Hengl
7. Arko Lucieer
8. Trenton Franz

In Global and Planetary Change

1. Tomislav Hengl
2. Matthew C. Hansen
3. Wolfgang Wagner
4. Gerard B. M. Heuvelink
5. Eric F. Wood
6. Remko Uijlenhoet
7. Niko E. C. Verhoest
8. Matthew F. McCabe

In Water Science and Technology

1. Gerard B. M. Heuvelink
2. Remko Uijlenhoet
3. Niko E. C. Verhoest
4. Tomislav Hengl
5. Bas Kempen

Citations in the corpus

• Water Bodies' Mapping from Sentinel-2 Imagery with Modified Normalized Difference Water Index at 10-m Spatial Resolution Produced by Sharpening the SWIR Band (2016, background): “produced a new global raster water mask at 250-m resolution from MODIS dataset [24].”
• The Future of Earth Observation in Hydrology. (2017, positive): “Since estimating water temperature requires delineating water bodies, a MODIS-based water mask has been developed for this purpose (Carroll et al., 2009).”
• A global, high-resolution (30-m) inland water body dataset for 2000: first results of a topographic–spectral classification algorithm (2016, methodology): “The MODIS water mask is a global, 250-m-resolution representation of global inland surface water bodies in 2000 (Carroll et al. 2009).”
• A Review of Wetland Remote Sensing (2017, background): “[78] produced a new water mask map globally at 250 m spatial resolution based on MODIS and radar based water body dataset.”
• Global characterization and monitoring of forest cover using Landsat data: opportunities and challenges (2012, methodology): “In the creation of a 250-m global land water mask, for all those areas north of the coverage of SRTM at 558N, Landsat data were used successfully to identify water bodies (Carroll et al. 2009) and that change detection is feasible.”
• Evaluating the Hydrological Cycle over Land Using the Newly-Corrected Precipitation Climatology from the Global Precipitation Climatology Centre (GPCC) (2017, methodology): “Comparison of the 2015 Release to the 2011 Release A significantly improved land mask was constructed by combining the land masks of GLDAS [16] and ISLSCP II [17]; in addition, island stations missing in both land masks have been added after a manual check of the station location with other geograph”
• SoilGrids1km — Global Soil Information Based on Automated Mapping (2014, methodology): “50% of pixel covered with urban areas), bare rock areas, water bodies [31], shifting sands, permanent snow and ice.”