Global Surface Water

Short description

The Global Surface Water dataset was developed by the European Commission's Joint Research Centre (JRC) within the framework of the Copernicus Programme. It is derived from Landsat 5, 7 and 8 imagery and shows various aspects of the spatio-temporal distribution of surface water between 1984 and 2020 (with annual revisions) at the global scale in six different layers. Surface water is considered as any uncovered stretch of water (fresh and salt water areas) greater than 30m² visible from space, including natural and artificial water bodies.

Please check the FAQ on the official Global Surface Water website for more details. Detailed information on the processing methodology for the included layers can be found in the Data Users Guide and Pekel et al. (2016) [1] as the associated scientific publication.

Band information

The Global Surface Water dataset contains six layers that are listed in the table below:

Table 1: Layer Overview

Name Data type Description
Occurrence unsigned int8 The occurrence value expressed as a percentage
Occurrence Change Intensity unsigned int8 The normalised difference in the mean occurrence value between the two epochs for homologous months epoch1-epoch2 /epoch1+epoch2
Seasonality unsigned int8 The number of months that water was present from January to December in the year of review (2019 or 2020)
Recurrence unsigned int8 The frequency with which water returns from years to year expressed as a percentage
Transitions unsigned int8 The type of transition between the first and last year
Extent unsigned int8 A simple flag indicating if water was detected or not

Occurrence

The water Occurrence layer shows the (intra- and inter-annual) variations of surface water presence in the time range between March 1984 and December 2020. Permanent water areas with 100% occurrence over the 37 years are shown in blue, while lighter shades of pink and purple indicate lower degrees of water presence, according to the official layer symbology (Table 2).

Occurrence Danube Delta in RomaniaOccurrence layer visualisation in EO Browser for the Danube Delta in Romania.

Table 2: Occurrence Symbology

Value Symbol Colour Label
0 0xffffff Not water
1 0xffcccc 1% occurrence
100 0x0000ff 100% occurrence
255 0xcccccc No data

A visualisation script for the Occurrence layer can be found in our custom scripts repository.

Occurrence Change Intensity

The Occurrence Change Intensity layer visualises changes in water occurrence between two different epochs, the first ranging from March 1984 to December 1999, and the other covering the period from January 2000 to December 2020. According to the official layer symbology, the increase of water occurrence is visualised in different shades of green, no change is shown in black and decrease is shown in shades of red (Table 3).

Occurrence Change Intensity Ganges in BangladeshOccurrence Change Intensity layer visualisation in EO Browser for a section of the Padma River, the main distributary of the Ganges, in Bangladesh.

Table 3: Occurrence Change Intensity Symbology

Value Symbol Colour Label
0 0xff0000 -100% loss of occurrence
100 0x000000 No change
200 0x00ff00 100% increase in occurrence
253 0xffffff Not water
254 0x888888 Unable to calculate a value due to no homologous months
255 0xcccccc No data

A visualisation script for the Occurence Change Intensity layer can be found in our custom scripts repository.

Seasonality

The Seasonality layer provides information on the intra-annual distribution of surface water in the particular review year. According to the number of months with water cover, the discrete colour map shows areas with permanent water bodies in dark blue and seasonal surface water in gradually lighter shades of blue (Table 4).

Seasonality Salar de Uyuni in BoliviaEo Browser visualisation of surface water Seasonality in the world's largest salt flat Salar de Uyuni in southwest Bolivia.

Table 4: Seasonality Symbology

Value Symbol Colour Label
0 0xffffff Not water
1 0x99d9ea 1 month of water
12 0x0000aa 12 months of water (permanent water)
255 0xcccccc No data

A visualisation script for the Seasonality layer can be found in our custom scripts repository.

Recurrence

The Recurrence layer captures the degree of interannual variability in surface water presence. It shows how frequently water returned to a particular location between years in a defined water period within the period from 1984 to 2020. The water period is defined individually for each pixel, ranging from the first month with an observation of surface water to the last month with surface water present throughout the 37-year time range. Areas with seasonal or permanent water presence are visualised in blue, while irregularly flooded areas are shown in orange (Table 5).

Recurrence paddies near Eluru in IndiaRecurrence layer visualisation in EO Browser showing Paddy fields near the city of Eluru in West Godavari, India.

Table 5: Recurrence Symbology

Value Symbol Colour Label
0 0xffffff Not water
1 0xff7f27 1% recurrence
100 0x99d9ea 100% recurrence
255 0xcccccc No data

A visualisation script for the Recurrence layer can be found in our custom scripts repository.

Transitions

The Transitions layer is derived from a seasonality comparison between the first and last year in the 37-year time period and visualises changes in the three surface water classes of not water, seasonal, and permanent water. A discrete colour map for the class transitions is provided in Table 6.

Transitions Paraná River near Santa Fe in ArgentinaEO Browser visualisation of the Transitions layer showing the Paraná River near santa Fe, Argentina.

Table 6: Transitions Symbology

Value Symbol Colour Label
0 0xffffff Not water
1 0x0000ff Permanent
2 0x22b14c New permanent
3 0xd1102d Lost permanent
4 0x99d9ea Seasonal
5 0xb5e61d New seasonal
6 0xe6a1aa Lost seasonal
7 0xff7f27 Seasonal to permanent
8 0xffc90e Permanent to seasonal
9 0x7f7f7f Ephemeral permanent
10 0xc3c3c3 Ephemeral seasonal
255 0xcccccc No data

A visualisation script for the Transitions layer can be found in our custom scripts repository.

Extent

The maximum water Extent layer combines all other layers by visualising all locations for which water presence has ever been detected over the 37-year period.

Extent of Lake Kyoga in UgandaExtent layer visualisation in EO Browser capturing the maximum water extent for Lake Kyoga, Uganda.

Table 7: Extent Symbology

Value Symbol Colour Label
0 0xffffff Not water
1 0x6666ff Water detected
255 0xcccccc No data

A visualisation script for the Extent layer can be found in our custom scripts repository.

More information

References

[1]: Pekel, J. F., Cottam, A., Gorelick, N., & Belward, A. S. (2016). High-resolution mapping of global surface water and its long-term changes. Nature, 540(7633), 418-422.