The extent of surface water has been changing
significantly due to climatic change and human activities. However, it is
challenging to capture the interannual changes of inland water bodies due to
their high seasonal variation and abrupt change. In this paper, a global
annual surface water cover frequency dataset (GLOBMAP SWF) was generated
from the MODIS land surface reflectance products during 2000–2020 to
describe the seasonal and interannual dynamics of surface water. Surface
water cover frequency (SWF) was proposed as the percentage of the time
period when a pixel is covered by water in a year. Instead of determination
of the water directly, the SWF was estimated indirectly by identifying land
observations among annual clear-sky observations to reduce the influence of
clouds and variability of water bodies and surface background
characteristics, which helps to improve the applicability of the algorithm
for different regions across the globe. The generated dataset shows better
performances for frozen water, saline lakes, bright surfaces and regions with frequent cloud cover compared with the two high-frequency surface water datasets derived
from MODIS data, and it captures more intermittent surface water but may
underestimate small water bodies when compared with two high-resolution
datasets derived from Landsat data. Compared with the high-resolution SWF
maps extracted from Sentinel-1 data in eight regions that cover lakes, rivers
and wetlands, the R2 reaches 0.46 to 0.97, RMSE ranges from 7.24 %
to 22.62 %, and MAE is between 2.07 % and 7.15 %. In 2020, the area of
global maximum surface water extent is 3.38×106 km2, of which the
permanent surface water accounts for approximately 54 % (1.83×106 km2), and the other 46 % is intermittent surface water (1.55×106 km2). The area of global maximum and permanent surface water has been
shrinking since 2001, with a change rate of -7577 and -4315 km2 yr-1 (p<0.05), respectively, while the intermittent surface
water with the SWF above 50 % has been expanding (1368 km2 yr-1, p<0.01). This dataset can be used to analyze the interannual
variation and change trend of highly dynamic inland waters extent with
consideration of its seasonal variation. The GLOBMAP SWF data are available
at 10.5281/zenodo.6462883 (Liu and Liu, 2022).
Introduction
Surface water, comprised of natural lakes, rivers, reservoirs and seasonally
flooded waters, supplies water resources for maintenance of the functions of
terrestrial ecosystem and livelihoods of human society. It plays a vital
role in the global hydrological cycle, carbon cycle and climate system (Karlsson
et al., 2021) and also provides habitats for aquatic animals and plants.
Inland waters usually show significant interannual and seasonal variations
due to seasonality and changes of precipitation and evaporation as well as
human activities (Konapala et al., 2020). The extent of surface water is
suggested to be a sensitive indicator of climatic change and also responds
to human activities (Zhang et al., 2019). And the changes of surface water extent impact global
hydrological and carbon cycles and the availability of water resources,
which would affect human society and ecosystems' sustainability (Padron et
al., 2020; Miara et al., 2017; Ran et al., 2021).
Surface water has been monitored using repeated satellite observations of
the Earth's surface. The extent of inland water bodies has been mapped with
active and passive microwave observations, which can penetrate clouds and
vegetation to a certain extent. Several global surface water datasets have
been generated from microwave observations and provide monthly or weekly
water cover maps at a spatial resolution of dozens of kilometers. For
example, the Global Inundation Extent from Multi-Satellites (GIEMS) datasets
were created by fusing multiple satellite observations of passive and active
microwaves along with visible and near-infrared imagery, which describe the
monthly distribution of global surface water extent at 0.25∘
resolution (Prigent et al., 2007; Papa et al., 2010; Prigent et al., 2020).
A weekly inland water fraction dataset (Global-SWAF) was produced at a
spatial resolution of 25 km based on L-band multi-angle and dual-polarization microwave satellite data from the Soil Moisture Ocean Salinity
(SMOS) mission over the period of 2010 to 2019 (Al Bitar et al., 2020). In
recent years, with the availability of the Sentinel-1 C-band Synthetic
Aperture Radar (SAR) data, a few regional 10 m resolution water body
datasets have been developed, such as the High Spatial-Temporal Water Body
dataset in China (HSWDC) during 2016–2018 (Li et al., 2020). But these
high-resolution datasets can only cover the period since the launch of
Sentinel-1.
Surface water was also mapped with optical satellite data, which can provide
long-term observations of the Earth's surface at tens to hundreds of meters'
resolution. Several global 30 m resolution surface water datasets have been
generated from optical high-resolution satellite data, such as Landsat
(e.g., Liao et al., 2014; Pekel et al., 2016; Feng et al., 2016; Pickens et
al., 2020). These datasets can describe the detailed spatial distribution of
inland water bodies, usually the maximum surface water extent during the
observing period. Surface water generally shows remarkable seasonal and
interannual variations and may fluctuate abruptly during a short period due
to rainfall or reservoir constructions (Berghuijs et al., 2014; Lutz et al.,
2014; Pickens et al., 2020). The approaches based on high-spatial-resolution
optical images only provide a limited number of the snapshots of water cover
and their average change rate of area over a specific period of several
years. The sparse temporal sampling of these satellites makes it difficult
for them to capture interannual and seasonal variations of inland waters,
even misrepresented by the abrupt fluctuation of water cover.
The Moderate Resolution Imaging Spectroradiometer (MODIS) carried on the
Terra and Aqua satellites, with its daily revisiting period, provides a
powerful tool to capture the dynamics of surface water. Several global and
regional high-frequency surface water products have been generated using
MODIS data. Daily global datasets of inland water bodies were generated at
250–500 m resolution (Klein et al., 2017; Ji et al., 2018), and 8 d
datasets were also created at 250 m resolution at global (Han and Niu, 2020)
and regional (Lu et al., 2019b) scales. Several datasets for reservoirs and
large lakes were also produced from MODIS observations at 8 d temporal and
250–500 m spatial resolution (Khandelwal et al., 2017; Tortini et al., 2020;
Li et al., 2021). These high-frequency datasets generally directly identify
water pixels for each daily or multi-day composite satellite scene using the
following steps. The satellite observation is usually preprocessed to
exclude the effects of cloud, ice/snow and shadow. Then, the water pixels
are identified for clear-sky observations using threshold or classification
methods based on reflectance on the visible, near-infrared (NIR), and shortwave
infrared (SWIR) bands and spectral indices. Finally, the missing data from
clouds and other contaminations are usually filled using temporal
interpolation to generate a gap-free time series of inland waters. The
high-frequency datasets can capture the seasonal variation and short-term
fluctuation of surface water extent with their daily or 8 d time-series
maps. However, since the timing of precipitation and human activity (such as
reservoir impoundment and drainage) may shift among years, it would be
incomparable for the snapshot of surface water even acquired on the same day
of the year (DOY), which would conceal the real change trend when directly
using these high-frequency datasets. Additionally, clouds and variable
characteristics of the water body and surface background may affect the
performance of the water mapping algorithm, making it challenging to accurately
extract surface water cover at a global scale. For example, special water
bodies, such as frozen water and saline lakes, may show different spectral
characteristics compared with those of pure water and reduce the
applicability of the algorithm, and it is difficult to accurately identify
all clouds and snow/ice pixels, which would introduce uncertainties to the
estimation results.
In this paper, a global annual surface water cover frequency dataset
(GLOBMAP SWF) was generated from MODIS land surface reflectance data with a
resolution of 500 m from 2000 to 2020. The seasonal variation of surface
water was simplified to the percentage of the time period when a pixel is
covered by water in a year (surface water cover frequency, SWF) to
characterize the seasonal and interannual dynamics of surface water. The SWF
transforms a discrete variable (water or land) into a continuous variable
that can describe the distribution and life cycle of intermittent surface
water. It can help to avoid the interannual mismatch issue mentioned above
by excluding the influence of different occurrence periods of water cover.
The SWF was estimated from MODIS observations annually. The estimation
results were compared with two high-frequency surface water products derived
from MODIS and two high-spatial-resolution products derived from Landsat
and validated with the SWF maps derived from Sentinel-1 SAR data. Several
examples were also provided to demonstrate its application for the
characterization of seasonal and interannual dynamics of inland water
bodies.
DatasetsMODIS land surface reflectance products for surface water extraction
The MOD09A1 land surface reflectance product (Version 6)
(https://search.earthdata.nasa.gov/, last access: 20 November 2021) was used
to generate the SWF dataset. This product contains the atmospherically
corrected surface spectral reflectance of MODIS 1–7 bands, including three
visible bands (red, blue and green), one NIR band and three SWIR bands (1.2,
1.6 and 2.1 µm) (Vermote, 2015). The 8 d composited surface
reflectance with low view angle and absence of clouds or cloud shadows and
aerosol loading if available are provided at 500 m resolution in the
sinusoidal projection. The red, NIR and SWIR bands are sensitive to the
boundaries and properties of water, land, cloud and aerosols. Here, the
reflectance of MODIS Band 1 (red, 0.620–0.670 µm), Band 2 (NIR,
0.841–0.876 µm) and Band 7 (SWIR, 2.105–2.155 µm) bands was
utilized to estimate the SWF. Among them, the red and SWIR bands were used
to determine land observations, and the NIR band was used to extract the
annual maximum surface water extent and distinguish water from cloud and
ice/snow.
Digital elevation model for mountain shadow exclusion
The digital elevation model (DEM) of the U.S. Geological Survey (USGS)
Global 30-Arc-Second Elevation (GTOPO30) (https://earthexplorer.usgs.gov/,
last access: 20 October 2021) was used to exclude mountain shadow in the
extraction of the annual maximum surface water extent. This product provides
a DEM of the entire Earth's surface with geographic coordinates and horizontal
datum of WGS84 in a resolution of 30 arcsec (approximately 900 m). The
elevation data were derived from eight sources of topographic information,
including Digital Terrain Elevation Data, Digital Chart of the World, USGS
1∘ DEMs, Army Map Service 1:1000000-scale maps, International Map
of the World 1:1000000-scale maps, Peru 1:1000000-scale map, New Zealand
DEM and Antarctic Digital Database. The elevation data were transferred to
the sinusoidal projection to be consistent with that of MODIS land surface
reflectance data and used to calculate the terrain slope for mountain
shadow exclusion.
Surface water datasets for comparison
Two high-frequency surface water datasets derived from MODIS data and two
high-resolution datasets derived from Landsat data were employed for
comparison purposes, including the global surface water change database from
Ji et al. (2018) (hereafter referred to as GSWCD) and Inland Surface Water
Dataset in China (ISWDC) (Lu et al., 2019b), Global Surface Water dataset
(GSW) from Pekel et al. (2016) and the global inland water dataset derived
by the Global Land Analysis and Discovery laboratory (hereafter referred to
as GLAD) (Pickens et al., 2020).
The GSWCD provides global daily water maps at 500 m resolution during
2001–2016 derived from the MODIS daily reflectance time series
(http://data.ess.tsinghua.edu.cn/modis_500_2001_2016_waterbody.html, last access: 12 January 2022). Water was identified on each single-date reflectance image
with the assumption that reflectance of water at the visible bands should be
higher than at the SWIR bands, as well as thresholds of reflectance in
visible and SWIR bands. For those pixels with low reflectance in visible
bands, the spectral property assumption may not be exhibited, thresholds of
visible and SWIR bands reflectance were used to identify water pixels and
normalized difference vegetation index (NDVI) was used to reduce the
confusion between water and dense vegetation. The shadow effects caused by
mountains and clouds were reduced with a terrain slope derived from ASTER DEM
data and cloud shadow flag of MODIS state quality assurance (QA) layer, respectively. The cloud,
ice/snow and no valid data were labeled with MODIS state QA layer and land
surface temperature data, and cloud and no valid data were filled with
temporal–spatial interpolation to produce a gap-free time series. The
producer's accuracy and user's accuracy of the GSWCD product were reported
better than 90 % when compared with classification results derived from
Landsat images and manually interpreted samples.
The ISWDC product maps water bodies larger than 0.0625 km2 within the
land mass of China for the period 2000–2016 with 8 d temporal and 250 m
spatial resolution (10.5281/zenodo.2616035; Lu et al., 2019a). The surface water boundary was extracted based on the
modified Otsu threshold method with reflectance of MODIS NIR band. The
threshold value was determined for four seasons with 423 selected samples of
lakes and rivers. The interferences were removed with a terrain slope derived
from SRTM DEM data. The producer's accuracy and user's accuracy of the ISWDC
product were reported to be 88.95 % and 91.13 % when compared with samples
from lakes and rivers derived from the China national 30 m land cover
dataset (Liu et al., 2014).
The GSW product provides global surface water maps for the period 1984–2020
with 30 m resolution (https://global-surface-water.appspot.com/download,
last access: 10 August 2022). The pixels in Landsat 5, 7 and 8 data were
classified as open water, land or non-valid observation using the
combination of expert systems, visual analytics and evidential reasoning.
The classifier produces less than 1 % of false water detections and misses
less than 5 % of water when measured using over 40 000 reference points. A
seasonality dataset is contained in the GSW products to describe the
intra-annual distribution of water and used to compare with our estimation
results. A permanent water surface is underwater throughout 12 months of the
year (with a seasonality value of 12), while a seasonal water surface has a value less than 12. For lakes that freeze for part of the year, the
dataset treats ice as a non-valid observation, and the observation period
corresponds only to the unfrozen months.
The GLAD product maps global inland water for the period 1999–2020 with 30 m
resolution (https://www.glad.umd.edu/dataset/global-surface-water-dynamics,
last access: 10 August 2022). The land and water were classified in all
Landsat 5, 7 and 8 scenes and performed a time-series analysis to produce
maps that characterize interannual and intra-annual open surface water
dynamics. Each Landsat scene was classified into land, water, cloud, shadow,
haze and snow/ice with ensembles of classification trees. The producer's
accuracy and user's accuracy of the GLAD monthly mapped water class were
reported to be 96.0 % and 93.7 % when compared with reference sample data.
The annual water percent dataset is contained in the GLAD products to
characterize the seasonality of the water cover and used to compare with our
results. The land and water observations of a given pixel were summed per
month and aggregated into water presence frequency, measured by the percent
of clear observations flagged as water.
Datasets for validation
The estimation results were validated with the SWF maps extracted from
Sentinel-1 data. To evaluate the performance of our dataset for different
surface water types, permanent and seasonal waters, and different latitudes, as
well as the presence of frequent cloud cover, eight regions were selected
for validation, including Lake Albert in the Democratic Republic of the
Congo and Uganda (30.98∘ E, 1.74∘ N), Lake Mai-Ndombe in
the Democratic Republic of the Congo in the southwestern part of Congo Basin
(18.32∘ E, 2.07∘ S), the Amazon River and Taparus River in
western Amazon in Brazil (54.87∘ W, 2.16∘ S), wetlands
in western Bangladesh (91.12∘ E, 24.65∘ N), Lake
Winnipegosis in Canada (99.91∘ W, 52.61∘ N), lakes in
western Russia (31.00∘ E, 64.10∘ N), Lake Maggiore in
Italy (8.65∘ E, 45.90∘ N) and Lake Wakatipu in New
Zealand (168.55∘ E, 45.10∘ S). These areas cover major
types of inland water bodies, including lakes, rivers and wetlands. Among them,
the six lake regions and the Taparus River are dominated by permanent surface
water, the Amazon River has seasonal water cover and wetlands in western
Bangladesh are dominated by seasonal surface water. Lake Albert, Lake
Mai-Ndombe, the Amazon River, the Taparus River and wetlands in western
Bangladesh are in the tropics and subtropics. Cloud and rain should
frequently occur in these four regions, especially for the Amazon River,
Taparus River, and Lake Mai-Ndombe in the Amazon and Congo Basin respectively,
which helps to evaluate the performance in frequently cloud-covered areas.
Lake Maggiore and Lake Wakatipu are in the middle latitudes of the Northern
and Southern Hemisphere, respectively. The former is surrounded by
mountains in the Alps in northern Italy, which shows an example of the
performance in mountainous regions. Lake Winnipegosis and lakes in western
Russia are in high latitudes of the Northern Hemisphere, where
a large number of small water bodies are concentrated.
The Sentinel-1 mission images the entire Earth every 6 d with a
constellation of two satellites orbiting 180∘ apart, and the
repeat frequency is just 3 d at the Equator and less than 1 d
over the Arctic. The C-band Synthetic Aperture Radar (SAR) it carries can
penetrate cloud and rain to provide an all-weather supply of imagery of the
Earth's surface, which helps to accurately characterize the inundation
frequency. All available vertical transmission and vertical reception (VV)
polarization data of Sentinel-1A and Sentinel-1B in 2020 were used to
extract the surface water extent of the eight regions at 10 m resolution
utilizing Google Earth Engine (GEE). A median filter method was used to
reduce speckle noise in SAR images (Bioresita et al., 2018). The water
pixels were identified for each available image based on the Otsu algorithm,
which maps the surface water extent with an unsupervised histogram-based
thresholding approach that automatically selects the optimal threshold of
water and non-water by maximizing the variance between classes (Otsu, 1979).
The Sentinel-1 SWF was mapped by calculating the percentage of the count of water observations to the total count of observations for each pixel. For
regions at high and middle latitudes, the observations covered with snow and
ice were excluded, and the SWF was calculated with Sentinel-1 observations
during the unfrozen period.
MethodologyExtraction of surface water cover frequency
Clouds and ice/snow may affect accurate detection of inland surface water
based on optical remote sensing, especially for water bodies with high
reflectivity. To reduce the interferences of clouds and ice/snow, this paper
does not directly detect water pixels but extracted surface water through
identifying land observations in annual MODIS observation series. We found
high reliability distinguishing features for the separation of land, water,
cloud and ice/snow. The former usually has a lower reflectivity in the
visible band than in the SWIR band, while the latter three are the opposite.
And the cloud and ice/snow can be excluded with higher reflectance in NIR
band compared to water and land. Based on these spectral characteristics,
the SWF was estimated using four steps from MOD09A1 land surface reflectance
data (Fig. 1), including counting the number of clear-sky land observations,
determining the maximum surface water extent, estimating the total number of
clear-sky observations over the maximum surface water extent and
calculating the SWF.
Workflow of the method for generating the GLOBMAP surface water cover
frequency dataset.
Firstly, the number of clear-sky land observations during a whole year
(NLand) was counted for each pixel from the MOD09A1 annual land surface
reflectance series. The land observations were separated from water and
cloud using the reflectance in the red band (RRed) and SWIR band with a wavelength of 2.1 µm (RSWIR2.1). Those pixels with RRed<RSWIR2.1 were labeled as land. Since RRed is generally
higher than RSWIR2.1 for water, cloud and snow/ice, the land
observations can be reliably identified without the help of cloud masks.
Then, the annual maximum surface water extent was determined from the six
observations with the lowest NIR reflectance during a specific year. Water
generally has low reflectance in the NIR band (RNIR), while the presence of
cloud and ice/snow would significantly increase the RNIR. Thus,
observations with the lowest RNIR should be inclined to the clear-sky
inundated observation, while the cloud and ice/snow pixels could be excluded
reliably. Here, six observations with the lowest RNIR in a year were
selected by weighing available valid observations and possible noise
observations, such as shadows, burned areas and occasional water cover.
These six observations were assumed to be clear-sky observations, and water
observations among them were determined using the criterion of RRed>RSWIR2.1. Those pixels with water count ≤1 were
identified as reliable land. To exclude possible residual shadows, burned
areas and occasional water cover, all pixels with water count ≥3 were
used to create the maximum surface water extent map for the specific year.
The mountain shadow was excluded using the criterion that the terrain slope
derived from DEM >30∘.
The number of clear-sky observations over the maximum surface water extent
(NClear) was estimated from the count of clear-sky observations of its
adjacent reliable land pixels. Here, clear-sky observation refers to the
valid MOD09A1 observation that is not covered with clouds and snow/ice. The
coverage of clouds is usually similar for land and water bodies in a small
area. This study assumes that the number of clear-sky observations over the
water bodies (NClear) is the same as that over adjacent land areas
(NClear_Land_adjacent). Here, for each
pixel in the maximum surface water extent, 100 spatial nearest
reliable land pixels were selected. The count of clear-sky observations for
those reliable land pixels is equal to NLand since all clear-sky
observations should be land for reliable land pixels. Then, the
NClear_Land_adjacent was estimated by
averaging the NLand values for the selected 100 nearest reliable
land pixels, and the NClear was set to equal to the estimated
NClear_Land_adjacent.
Finally, the number of water observations (NWater) was calculated for
the pixels within the range of the maximum surface water extent by
subtracting the land observation count (NLand) from the count of all clear-sky
observations (NClear). And the SWF was calculated by the water
count divided by the count of all clear-sky observations within a year (Eq. 1).
Those pixels with NLand of zero should be covered by water during the
whole year, and their SWF values were equal to 100 %, while those pixels with
NLand equal to NClear should be permanent land, and their SWF values were
equal to 0 %. For large inland water bodies, the adjacent reliable land
pixels that were used to estimate NClear over the maximum surface water
extent may be far away from the water pixels, which may result in
uncertainties in NClear estimation of water pixels and the SWF
consequently. Here, the SWF was set to 100 % for those pixels with
NLand less than a count of 15 for the global largest 100 inland water bodies
excluding rivers and water bodies with great seasonal variation in water
extent to reduce the influences of uncertainty in NClear on the SWF dataset.
SWF=NClear-NLandNClear×100%
Several post-processing procedures were then implemented to the generated
SWF maps. The water bodies with an area less than 2×2 pixels were
removed to reduce the influence of noise. The oceans were delineated using
the ocean label in the state QA flags of MOD09A1 products. The flag of
MOD09A1 was used as the initial ocean flag. Those pixels detected as land by
the proposed method were labeled as land, and those water pixels between the
land and the ocean flagged by MOD09A1 were labeled as ocean. For water
bodies that were not marked as oceans in the state flag of MOD09A1, we
extended the land boundary toward the water. If the extended land boundaries
meet with each other, the water bodies were labeled as inland waters; if the
extended land boundaries meet the ocean pixels, the adjacent water pixels
were labeled as ocean.
Validation and inter-comparison with other products
The estimation results were validated with the SWF maps extracted from
Sentinel-1 SAR observations in the eight regions (Sect. 2.4). The spatial
distribution of our results was compared with the Sentinel-1 results. And
the Sentinel-1 SWF maps were resampled to 500 m resolution by averaging the
valid SWF estimations from Sentinel-1 data within the MODIS 500 m grid and
then compared with GLOBMAP SWF maps pixel by pixel. The root mean standard
error (RMSE), absolute mean difference (MAE) and coefficient of
determination (R2) were estimated to evaluate the accuracy of GLOBMAP
SWF maps.
The estimation results were also compared with the GSWCD and ISWDC products
that derived from MODIS observations as well as GSW and GLAD products that
derived from Landsat data for characterizing the seasonal variation of
surface water. The surface water maps of the five datasets were demonstrated
in three lake regions as examples to evaluate their performance in unfrozen
water, frozen water and saline lakes, as well as the presence of clouds
and bright surfaces. These include Taihu Lake in eastern China
(30.62–31.78∘ N, 119.66–120.82∘ E), lakes in the northeastern Tibetan Plateau
(34.48–36.10∘ N, 89.71–91.53∘ E) and Qarhan Salt Lake in the southern Qaidam Basin in northwestern China
(36.56–37.24∘ N, 94.48–96.08∘ E).
ResultsDistribution of global surface water cover frequency
The estimated global SWF map in 2020 is illustrated in Fig. 2a to describe
the temporal coverage of inland surface water. The maximum extent, minimum
extent (permanent surface water) and intermittent surface water extent are
also shown in Fig. 2b–d to characterize the different status of inland
water bodies. The intermittent surface water refers to the areas covered by
water for part of a year. Some lakes freeze for part of the year. Since snow
and ice observations are excluded in estimation of the SWF in the proposed
method, the observations in unfrozen periods are used to estimate the surface
water cover frequency for the year. If area is underwater for part of the
observation period (i.e., the unfrozen period), it is considered to be the
intermittent surface water, while if water is present throughout the
unfrozen period, the water body is considered to be a permanent surface
water. Considering possible uncertainty of the algorithm and quality of
satellite observations, here we use SWF ≥10 % for identification of
the maximum water extent, SWF ≥90 % for the minimum water extent
identification and 10 % ≤ SWF < 90 % for intermittent
water identification. For visualization, the SWF was aggregated to 10×10 km grids by averaging all valid SWF values in each grid.
In 2020, the area of the maximum extent of global surface water is 3.38×106 km2, of which the permanent surface water (the minimum extent)
is 1.83×106 km2, and the intermittent surface water is 1.55×106 km2. About 46 % of the global total surface water cover (the maximum
extent) is intermittent water, which demonstrates the remarkable seasonal
dynamics of inland water cover. Compared with the global high-resolution
surface water datasets of GSW (Pekel et al., 2016), GLAD (Pickens et al.,
2020) and the Global 3 arc-second Water Body Map (G3WBM) (Yamazaki et al., 2015)
derived from multi-temporal Landsat images, our estimation results extract
less permanent surface water (2.78, 2.93 and 3.25×106 km2 for GSW,
GLAD and G3WBM respectively) and the maximum surface water (4.46 and 4.82×106 km2 for GSW and GLAD respectively). This may be related to the
limited spatial resolution of MODIS and post-processing of the dataset,
which makes GLOBMAP SWF dataset able to detect inland water body larger than 1 km × 1 km open to the sky, including fresh and salt water. More
intermittent surface water is captured compared with the three
high-resolution datasets (0.81, 0.74 and 0.49×106 km2 for GSW,
GLAD and G3WBM respectively) with the aid of frequent MODIS observations to
separate the seasonal and permanent water bodies. The inland water bodies
are widely distributed across the globe except for the deserts and permanent
snow-/ice-covered areas. They are mainly concentrated in midlatitudes to high latitudes
of the Northern Hemisphere, such as the northeast of North America, northwest of
Europe, north of Russia and the Tibetan Plateau. About 67 % of the maximum
surface water is distributed above 35∘ N, and this percentage
reaches 79 % and 54 % for the permanent surface water and intermittent
surface water, respectively. The permanent surface water cover is
concentrated in the lake areas, such as the Great Lakes in North America,
Arctic lakes and lakes in the Tibetan Plateau. The intermittent surface water
is widely distributed across the globe, especially in the high latitudes of
the Northern Hemisphere, which may be related to the seasonal melting of
permafrost. It is also scattered in Africa, Australia, the Pacific
Islands and south parts of Eurasia and North America, which may be related
to the notable seasonal variations in precipitation.
Global distribution of surface water cover frequency in 2020. (a) Global SWF map, (b) the maximum surface water extent, (c) the permanent
surface water extent and (d) the intermittent surface water extent. The SWF
was aggregated to 10×10 km grids by averaging the valid SWF in
each grid for visualization.
Comparison with existing surface water datasets
The performance of our estimates was evaluated for unfrozen water, frozen
water and saline lakes and compared with the surface water datasets of GLAD,
GSW, GSWCD and ISWDC. The effects of clouds and bright surfaces were also
evaluated. The comparison was performed in three regions as examples,
including Taihu Lake in eastern China, lakes in the northeastern Tibetan Plateau
and Qarhan Salt Lake in the southern Qaidam Basin.
The performance of unfrozen water and the effects of clouds were evaluated in
the Taihu Lake region, the third largest freshwater lake in China. It is
located in the subtropical East Asian monsoon region, where clouds
frequently occur in summer. Since the average water temperature of Taihu
Lake in January is 4 ∘C, water rarely freezes in winter, with only
a little thin ice with a thickness of 1–2 cm in the bay or lee shore. Figure 3
shows the distribution of GLOBMAP SWF, annual water percent dataset of GLAD,
seasonality dataset of GSW and the surface water extent map of GSWCD and
ISDWC products in January (DOY001) and July (DOY200) in 2015. A Google Earth
high-resolution image is presented for reference (Fig. 3a). The results show
that the spatial pattern of our estimates is in good agreement with that of
the GLAD and GSW. The GLOBMAP SWF reaches 100 % in Taihu Lake and
surrounding lakes, indicating that our algorithm successfully extracts the
distribution of unfrozen water and reduces the influence of clouds in this
region (Fig. 3b). The two Landsat-based products capture more small lakes
and narrow rivers with their fine spatial resolution, but the water
occurrence of some areas in the northwest part of the Taihu Lake is
underestimated, which is probably due to frequent clouds. The surface water
maps of GSWCD are generally consistent with our estimation results, GLAD and
GSW, suggesting that the interpolation algorithm of GSWCD successfully
reconstructs the water cover series in this area. Many lake areas are not
identified in the ISDWC maps especially for July (Fig. 3h), which indicates
that surface water cover may be underestimated in this dataset due to
clouds. Seasonal water cover is observed in our estimates with SWF lower
than 30 % in the south and east of Taihu Lake. These intermittent water
cover may be related to the seasonal irrigation of paddy rice that is
widely planted in this area.
Comparison of surface water map of GLOBMAP SWF, GLAD, GSW, GSWCD
and ISDWC in frequently cloud-covered areas around Taihu Lake in eastern
China (30.62–31.78∘ N, 119.66–120.82∘ E) in 2015. (a) Google Earth high-resolution image (from
Google Earth), (b) GLOBMAP surface water cover frequency, (c) GLAD annual
water percent and (d) GSW seasonality (source: EC JRC/Google). Surface water
extent of GSWCD in (e) DOY001 and (f) DOY200 in 2015 and surface water extent
of ISDWC in (g) DOY001–008 and (h) DOY193–200 in 2015.
The performance of frozen water and impact of bright surfaces were compared
in lakes in the northeast part of the Tibetan Plateau (Fig. 4). Several
lakes are located in this barren area. The altitude reaches around 5000 m,
and these lakes are frozen in winter due to extreme cold weather. The
GLOBMAP SWF map captures the distribution of lakes in Google Earth images,
with the SWF reaching 100 % in the lake areas (Fig. 4b). GLAD and GSW show
similar spatial extent of lakes with GLOBMAP, but GLAD seems to
underestimate the water occurrence in this region. The surface water cover
maps of GSWCD and ISDWC products in July are consistent with our estimation
results and Google Earth imagery (Fig. 4f and h). But when it comes to
winter in January, some frozen water cover is undetected for the GSWCD
product (red circles in Fig. 4e), and many barren land pixels are confused
with frozen water. This may be related to the similar high reflectivity in
the visible band and low land surface temperature for frozen water and
barren land in winter. The ISDWC product fails to detect the lakes in this
area in DOY001–008 in 2015 due to cloud contamination (Fig. 4g).
Comparison of surface water map of GLOBMAP SWF, GLAD, GSW, GSWCD
and ISDWC for frozen lakes over bright surface in the northeastern Tibetan
Plateau (34.48–36.10∘ N, 89.71–91.53∘ E) in 2015. (a) Google Earth high-resolution image (from
Google Earth), (b) GLOBMAP surface water cover frequency map; (c) GLAD
annual water percent and (d) GSW seasonality (source: EC JRC/Google). Surface
water extent of GSWCD in (e) DOY001 and (f) DOY200 in 2015 and surface water
extent of ISDWC in (g) DOY001–008 and (h) DOY193–200 in 2015.
Figure 5 shows the comparison results of Qarhan Salt Lake, which is located
in the Qaidam Basin on the northwestern part of the Tibetan Plateau. As the
largest saline lake in China, the lake is rich in inorganic salts such as
sodium chloride, potassium chloride and magnesium chloride. Corresponding to
the high-resolution image of Google Earth (Fig. 5a), our estimation results,
GLAD and GSW successfully extract the distribution of the saline lake. The
estimated SWF is approximately 100 % in the lake areas (Fig. 5b), and the
derived saline lake map agrees well with the high-resolution images for the
four subregions shown by the red rectangles in Fig. 5a (the third row in
Fig. 5). GLAD and GSW show more spatial details of the salt lakes. The GSWCD
product identifies the majority of the lake, but some lake areas in
southern and western parts are missed (red circles in Fig. 5f). Although
clear-sky observations were obtained in this area during DOY001–008 and
DOY193–200 in 2015 according to MOD09A1 data, many salt water areas are
missed in the ISDWC product (red circles in Fig. 5g and h), indicating that
the extent of saline lakes may be underestimated in this dataset.
Additionally, our estimates also capture the signals of the endorheic Golmud
River that flows into the southeast of the saline lake (subregion 2 in
Google Earth image).
Comparison of surface water map of GLOBMAP SWF, GLAD, GSW, GSWCD
and ISDWC for Qarhan Salt Lake in the southern Qaidam Basin
in northwestern China (36.56–37.24∘ N,
94.48–96.08∘ E) in 2015. (a) Google Earth
high-resolution image (from Google Earth), (b) GLOBMAP surface water cover
frequency, (c) GLAD annual water percent and (d) GSW seasonality (source: EC
JRC/Google). Surface water extent of GSWCD in (e) DOY001 and (f) DOY200 in
2015 and surface water extent of ISDWC in (g) DOY001–008 and (h) DOY193–200 in
2015. The last row shows the Google Earth high-resolution images for the
four subregions shown with the red circles in Fig. 6a (from Google Earth).
Validation
The accuracy of GLOBMAP SWF dataset was assessed with the 10 m resolution
SWF maps extracted from Sentinel-1 SAR data in eight regions that cover
lakes, rivers and wetlands. Figure 6 presents the SWF maps of our results and
Sentinel-1 as well as the linear regression results of the two datasets.
The surface water extent of GLOMAP SWF is generally consistent with that of
Sentinel-1 in these regions, while Sentinel-1 SWF describes more small water
bodies and narrow rivers with its high-spatial-resolution observations. Good
positive correlation is observed for SWF maps between our estimates and
Sentinel-1 results, with R2 up to above 0.75 for most regions except
for lakes in western Russia (0.46). For the lakes that are mainly covered by
permanent surface water in the middle and low latitudes without frequent
cloud covers, such as the Lake Albert in the Democratic Republic of the
Congo and Uganda, Lake Maggiore in Italy and Lake Wakatipu in New Zealand,
the SWF maps of GLOBMAP and Sentinel-1 agree well, with the RMSE ranging
from 7.24 % to 13.20 % and MAE from 2.07 % to 2.45 %. For Lake
Maggiore that is surrounded by mountains, most of the water extent was
extracted compared with the Sentinel-1 results. The performance of the
dataset may be affected by frequent cloud cover in tropical regions. For
Lake Mai-Ndombe in the southwestern part of the Congo Basin, our dataset
can characterize the spatial extent of the lake, but the SWF may be
underestimated compared with the Sentinel-1 results, and the RMSE and MAE
are increased to 11.28 % and 3.80 % respectively, which may be due to
the lack of clear-sky observations in this tropic region. In the western
Amazon, both the two SWF maps show widespread seasonal water cover in the
Amazon River and permanent water cover in the Taparus River, with an RMSE
and MAE of 7.93 % and 2.24 %, respectively. Our estimation results
present scattered detection with SWF <10 % in the middle and
southern parts of the image, which may also be related to the frequent
occurrence of clouds and rain. For the wetlands in western Bangladesh,
widespread intermittent water cover and complex surface conditions make it
challenging to extract the SWF. Our results generally agree well with the
Sentinel-1 SWF map in this region, both showing higher inundation frequency
in the northern and middle parts of the wetlands than in the southern part
and margins, and the RMSE and MAE are still within 10.8 % and 7.2 %. For
lakes in high latitudes, including Lake Winnipegosis and lakes in western
Russia, the dataset captures the distribution of large water bodies but may
underestimate scattered small lakes in these regions due to the coarse
resolution of MODIS data, which makes the RMSE and MAE increase to
16.22 %–22.62 % and 6.17 %–7.04 %, respectively. The comparison
indicates that our dataset can also provide reasonable estimates for
intermittent inland water bodies, and it is more reliable for large water
bodies with less seasonal water cover and clouds.
Comparison of GLOBMAP SWF against SWF maps derived from Sentinel-1
in eight regions. These include Lake Albert in the Democratic Republic of
the Congo and Uganda (30.98∘ E, 1.74∘ N), Lake
Mai-Ndombe in western Democratic Republic of the Congo (18.32∘ E,
2.07∘ S), the Amazon River and Taparus River in western Amazon in
Brazil (54.87∘ W, 2.16∘ S), wetlands in western
Bangladesh (91.12∘ E, 24.65∘ N), Lake Winnipegosis in
Canada (99.91∘ W, 52.61∘ N), lakes in western Russia
(31.00∘ E, 64.10∘ N), Lake Maggiore in Italy
(8.65∘ E, 45.90∘ N) and Lake Wakatipu in New Zealand
(168.55∘ E, 45.10∘ S). The linear regression results are
presented at the top of the figure.
Interannual variation and change trend of global surface water
The interannual variation and change trend of global maximum, minimum and
intermittent surface water were analyzed using the GLOBMAP SWF dataset from
2001 to 2020. Since the MODIS data are incomplete in 2000, the results of
2000 were not used in this analysis. Figure 7 shows interannual variation of
the area of global inland water bodies with different inundation
frequencies. During the past 2 decades, the average area of global maximum
surface water (SWF ≥10 %) is 3.57±0.10×106 km2, with
the largest area of 3.72×106 km2 in 2008 and the smallest area of
3.38×106 km2 in 2016. The average area of the minimum surface water
(permanent surface water, SWF ≥90 %) is 1.89±0.03×106 km2, which is 53 % of the area of maximum water extent. The permanent
water reached the largest extent of 1.95×106 km2 in 2001 and the
smallest extent of 1.83 km2 in 2016. The average area of global
intermittent water (10 % ≤ SWF < 90 %) is 1.68±0.08×106 km2, accounting for 47 % of the maximum water area. Among
them, about 79 % of intermittent water occurred in less than half a
year (10 % ≤ SWF < 50 %).
Interannual variation of the area of global surface water with
different inundation frequency from 2001 to 2020. (a) Areas of the maximum
surface water extent with SWF ≥ 10 %. (b) Areas of the minimum surface
water extent with SWF ≥ 90 %. Areas of the intermittent surface water
extent with (c) 10 % ≤ SWF < 90 %, (d) 10 % ≤ SWF < 50 % and (e) 50 % ≤ SWF < 90 %.
A decreasing trend is observed for the area of global maximum and minimum
surface water since 2001. The maximum water extent shrank at a
rate of -7577 km2 yr-1 (p=0.04) during 2001–2020, with the downward
trend mainly occurring after 2012 (Fig. 7a). The area of permanent surface
water has been decreasing continuously since 2001 at a rate of -4315 km2 yr-1 (p<0.01) (Fig. 7b). The intermittent surface water also
shows an insignificant weak decreasing trend (-3262 km2 yr-1, p=0.29).
The intermittent surface water was divided up into two parts based on the
value of SWF – intermittent water cover with 10 % ≤ SWF < 50 % and that with 50 % ≤ SWF < 90 % – and the areas were
then calculated for these two types separately (Fig. 7d and e). The results
show that the area of intermittent surface water with SWF less than 50 %
also showed a decreasing trend (-4629 km2 yr-1, p=0.13) like the
maximum water extent, indicating that the extent of global surface water in
the wet season was shrinking. In contrast, an increasing trend is observed
for the area of intermittent water with SWF above 50 % (1368 km2 yr-1,
p<0.01), indicating that the temporal coverage period of some
permanent water bodies was reduced but still longer than half a year.
A linear trend of SWF was mapped to demonstrate the monotonic changes of
surface water inundation frequency during 2001–2020. For visualization, the
trend maps were aggregated to 10 km resolution and selected to display the
fraction of positive slopes or negative slopes (p<0.05), whichever
is larger in each 10 km grid, to represent the main monotonic change type of
surface water (Fig. 8a). Grids with a dominantly positive (negative) slope
were labeled as inundation frequency increasing (decreasing) areas (positive
(negative) fraction). Similarly, we compared the average rate of positive
slopes and negative slopes within each 10 km grid, and chose the faster
change rate to represent the intensity of surface water changes (Fig. 8b).
Grids with positive (negative) slope rate mean that the water occurrence is
increasing (decreasing) rapidly. The results show notable changes of water
cover extent in the high latitudes of the Northern Hemisphere. In the
Arctic, there are more expanded lakes in the south, while the shrinking
lakes are concentrated in the north, especially in the northern Arctic
regions of Russia and Canada. This is consistent with the findings of
Carroll et al. (2011) in Canada. The SWF has increased rapidly in the
northern Tibetan Plateau at a rate of above 1.5 % yr-1 (Fig. 8b), which is
consistent with the observed extensive lake expansion and new lakes on the
plateau due to increased glacial meltwater and precipitation (Zhang et al.,
2017). A similar increase of SWF is also observed in southeastern Siberia,
northern India, and central and northeastern parts of North America. In
contrast, the inundation frequency has been mainly decreased for water
bodies of Central Asia, Southeast Asia and southern China, as well as southern
parts of South America.
Linear trend of surface water cover frequency during 2001 and
2020. The trend maps were aggregated to 10 km resolution for visualization.
(a) Dominant SWF slope fraction (%). The positive (negative) fraction
means that the fraction of pixels has increasing (decreasing) SWF (p<0.05) in each 10 km grid, indicating whether the inundation
frequency is dominantly increasing (decreasing). (b) Dominant SWF change
rate (% yr-1). The positive (negative) slope rate means that the mean
linear slope rate of pixels has increasing (decreasing) SWF (p<0.05) in each 10 km grid, whichever is faster, indicating whether the
inundation frequency is increasing (decreasing) rapidly. The light grey
refers to non-water-covered areas.
Application examples for surface water dynamic analysis
Two examples are provided in this section to demonstrate the application of
GLOBMAP SWF dataset in surface water dynamic analysis. These include the
seasonal variation and interannual change of Poyang Lake in southeastern
China and global top 10 lakes with the largest seasonal dynamics.
Seasonal and interannual dynamics of Poyang Lake
Analysis of the seasonal and interannual dynamics of inland water body is
illustrated for Poyang Lake (28.28–29.89∘ N,
115.62–117.05∘ E), which is a large shallow lake
located on the south bank of the middle and lower reaches of the Yangtze
River. It receives water from five rivers and the surrounding areas and
flows into the Yangtze River from the northern lake outlet. The lake shows
significant seasonal variations of the water cover area due to the great
seasonal fluctuations of regional precipitation and the runoff of the
Yangtze River and the five rivers entering the lake, making it challenging
to evaluate its interannual change. Figure 9a presents the spatial
distribution of GLOBMAP SWF in 2020. The SWF value of most of the lake is
ranging from 20 % to 70 %, indicating that the lake is mainly covered by
intermittent water. The minimum lake area (SWF ≥ 90 %) during the dry
season of 2020 is 545 km2, while the maximum area (SWF ≥ 10 %)
during the flood season reaches more than 7.4 times the former (4062 km2). Figure 9b shows the interannual series of water cover areas with
different inundation frequencies. The maximum lake area shows remarkable
fluctuation among years. The area of the maximum lake extent exceeded 4900 km2 in 2002, 2010 and 2012, while it reduced to below 4000 km2 in
2004, 2007, 2008, 2013–2015, 2017 and 2018, and the smallest area was only
3055 km2 in 2011. The maximum lake area is closely related to the
amount of water entering the lake during the flood season. The Poyang Lake
basin and the Yangtze River basin are located in the East Asia monsoon
region. The precipitation is mainly concentrated in summer and has
significant interannual fluctuations, resulting in notable interannual
variations of the lake area in the flood season. The interannual variation
of lake area decreases gradually with the increase of SWF and reaches the
lowest for the minimum lake extent that occurred during the dry season.
Precipitation in the dry season (winter) is much less frequent and less affected by
abnormal climate, which may reduce the year-by-year fluctuation of the lake
area in the dry season consequently. In 2003, the permanent lake area
decreased abruptly from 947 to 512 km2 and then remained at a
low value, with the area ranging from 500 to 660 km2 for most
years after 2003, which coincides with the time of the impoundment of the
Three Gorges Dam in 2003. These results are consistent with the decline of
the annual minimum inundation area (Feng et al., 2012) and the rapid
increase of wetland vegetation coverage in this region after 2002 (Han et
al., 2015). The available count of clear-sky observations was averaged over the
maximum surface water extent in the Poyang Lake region for each year during
2001–2020 (purple line in Fig. 9b). The average NClear was between 33
and 39 during this 20-year period. Correlation was observed between the area
of maximum surface water extent and the NClear. More clear-sky
observations mean less precipitation, which may lead to a smaller lake area, while fewer clear-sky observations mean more precipitation, and the lake area
should be larger. However, the two variables do not correspond exactly,
which indicates that the maximum surface water area does not depend on the
available number of clear-sky observations. The minimum surface water area
shows no obvious correlation with NClear, and its interannual
fluctuation should be related to precipitation and the amount of water
entering the lake in the dry season.
The seasonal variation and interannual change of surface water
cover of Poyang Lake (28.28–29.89∘ N,
115.62–117.05∘ E). (a) The GLOBMAP SWF map in 2020.
(b) The interannual variation of the area of Poyang Lake with different
inundation frequency and average value of the count of available clear-sky observations (Nclear) over the maximum surface water extent from 2001 to 2020.
Top 10 lakes with significant seasonal variation
The 10 lakes with the largest seasonal variation in 2020 were identified to
illustrate the seasonal fluctuation of inland open surface water. Seasonal
variation was evaluated with the proportion of the intermittent water area
to the maximum water area in this year. All lakes whose maximum water cover
extent >3000 km2 were ranked with their seasonal variation,
and the top 10 lakes are listed in Table 1.
The results show that the intermittent water area of these 10 lakes
accounts for more than 30 % of the maximum water area. Poyang Lake in
eastern China presents the largest seasonal fluctuation, with the seasonal
variation reaching 84.29 %. These 10 lakes can be divided into three
types: two natural freshwater lakes, four natural saltwater lakes and four
reservoirs. Among them, natural freshwater lakes include Poyang Lake and
Lake Peipus. Both lakes are shallow in depth, and the relief of the bottom
and surrounding area is flat, which means the water area may rise
dramatically in the flood season and fall during the dry season. For example,
the shores of the Lake Peipus are usually flooded in the spring, with the
flooding area reaching up to 1000 km2. Saltwater lakes include the Aral
Sea, Lake Gairdner, Lake Eyre and Great Salt Lake. These lakes are all
endorheic lakes that are located in the arid regions of Central Asia, Australia
and North America. Similar to the two freshwater lakes, the water depth is
also shallow for these four saltwater lakes. In the wet season, the river
runoff and local precipitation make the lake extent expand, while in the dry
season, the lakes shrink significantly due to the strong evaporation. Four
reservoirs, including Lake Kariba, Rybinsk Reservoir, La Grande River
reservoir and Lake Nasser, are also listed in the top 10 lakes. The notable
seasonal fluctuation of reservoir area should be related to artificial
impoundment and drainage of the reservoir dam.
Global top 10 lakes with the largest seasonal variation in 2020.
Lake nameCountryLake typeMaximumMinimumIntermittent waterSeasonalarea (km2)area (km2)area (km2)variation (%)Poyang LakeChinaFreshwater lake3465.66544.582921.0884.29Lake KaribaZambia, ZimbabweReservoir4558.921427.273131.6668.69Aral SeaUzbekistan, KazakhstanSaltwater lake24 188.819677.0314 511.7959.99Rybinsk ReservoirRussiaReservoir4344.691761.922582.7759.45Lake EyreAustraliaSaltwater lake3870.941734.232136.7155.20Lake GairdnerAustraliaSaltwater lake3958.951852.502106.4553.21La Grande River reservoirCanadaReservoir4626.112363.392262.7248.91Lake EyreAustraliaSaltwater lake3870.942230.091640.8542.39Lake PeipusEstonia, RussiaFreshwater lake3518.902069.951448.9541.18Great Salt LakeAmericaSaltwater lake7385.984446.012939.9739.80Lake NasserEgypt, SudanReservoir5033.323397.401635.9132.50Discussions
It is challenging to capture the interannual variation and change trend of
inland water bodies due to their significant seasonal variations. The extent
of surface water usually varies during a year due to the seasonal cycle of
precipitation and evaporation, and it may also change abruptly due to large
amount of rainfall and human activities, such as reservoir construction,
mining and irrigation (Tao et al., 2015). The timing of seasonal variation
in surface water extent often varies among years due to interannual shifts
of the timing of precipitation and human activities. Thus, it may be
incomparable for the snapshot of surface water acquired at the same period
or during a specific period such as the high-water period that is usually
analyzed (e.g., in summer or wet season), which would misinterpret its
interannual change and long-term trend. Here, we generated a global surface
water cover frequency dataset from high-frequency MODIS data to characterize
the seasonal variation and interannual change of inland water bodies. This
dataset simplifies the multi-period water cover maps to the percentage of
period that a pixel is covered by water in a year. It can characterize the
temporal coverage frequency of surface water, which is suitable to represent
the spatiotemporal characteristics of intermittent waters. The extent of
maximum, minimum and different inundation frequency of surface water can be
estimated from the dataset without the influence of the occurrence period,
which helps to avoid misidentifying seasonal changes in water cover as
interannual changes.
This paper developed a method for surface water extraction from a new
perspective, which estimates the SWF indirectly by identifying land
observations in annual observation series to reduce the influence of clouds,
snow/ice and variable characteristics of water body and surface background.
Water generally absorbs more solar radiation in spectral bands with longer
wavelengths, resulting in the greater reflectivity of visible bands than
that of NIR and SWIR bands. This spectral contrast has been widely used to
extract surface water extent directly (e.g., GSWCD), and several spectral
indices have been proposed for surface water extraction with the reflectance
in the visible band (usually green) and NIR or SWIR band, such as the Normalized
Difference Water Index (NDWI) (McFeeters, 1996) and the Improved Normalized
Difference Water Index (MNDWI) (Xu, 2006). To reduce the effects of clouds,
the threshold of index is usually set to greater than zero in surface water
mapping. When it comes to special water bodies with high reflectivities,
such as frozen water, saline lakes and turbid water bodies, the value of
these indices may be below the threshold, resulting in misdetection.
Additionally, variation of surface background may also result in confusion
in water extraction, which has been demonstrated in the misdetection of
lakes on the bright surface of the northeastern Tibetan Plateau (Sect. 4.2).
These may introduce substantial uncertainties in global water cover mapping
with the direct water extraction algorithm. In the generation of global water
datasets, it is not only needed to propose good water cover extraction
algorithm, but also to consider data quality, noise and applicability
of the algorithm in different regions. We found a reliable and robust method
to separate land from water, cloud and snow/ice. The RRed of the former
is generally lower than that of RSWIR, while it is opposite for the
latter three. If the SWF values were estimated indirectly by identifying land, the
interference of cloud and snow/ice in water identification would be avoided.
In this paper, instead of identifying water cover directly, the frequency of
surface water cover was estimated by subtracting the count of land observations
from the count of total clear-sky observations, which avoids directly
distinguishing water from cloud and snow/ice. The annual maximum water
surface extent was extracted based on the minimum near-infrared reflectance
composition method, which automatically excludes the influence of clouds,
ice and snow. Moreover, the land identification method (RRed<RSWIR) was applicable for major types of water bodies and surface
background and can exclude cloud and snow/ice observations. Through these
procedures, the proposed algorithm is ubiquitous for various water bodies
and surface background and reduces the interference of cloud and snow/ice,
which helps to improve the applicability of our algorithm across the globe.
Several factors may affect the performance of the proposed approach,
including clouds, shadows, thawing of snow and ice, and spatial resolution.
Clouds can obscure surface water signals in optical remote sensing. They
usually occur more frequently during the rainy season, while the clear-sky
observations are inclined to occur in the dry season. Since our algorithm
uses the percentage of water observations in all clear-sky observations to
estimate the water cover frequency within a whole year, the cloud
observations that are concentrated in the rainy season are not taken into
account, which may lead to underestimation of the SWF (Lake Mai-Ndombe in
Fig. 6). The number of available clear-sky snow/ice-free observations in a
year (NClear) was counted during the period 2001–2020 over global
terrestrial surface. There are on average 4285 pixels with NClear≤6, accounting for 0.0008 % of the total terrestrial surface pixels
(550 215 315) in a year. This percentage is 0.02 % (460 pixels out of
total 1 901 338 water pixels) for the inland water bodies. Since the
proportion of pixels with extreme sparse clear-sky observation is very
small, its influences should be limited at global scale. Figure 10 shows the
global map of NClear in 2020. Fortunately, NClear is generally above
40 in arid and semi-arid areas, where water bodies may show significant
season variation in their extent. The low NClear values are concentrated
in the tropics and subtropics, such as the Gulf of Guinea, the Amazon, the
Southeast Asia, and the Sichuan Basin in southwestern China, where
NClear mostly ranges from 25 to 35. Since surface water generally
shows relatively small seasonal changes in the tropics and subtropics, the
available clear-sky observations should be able to capture the distribution
of surface water. In high latitudes in the Northern Hemisphere,
NClear is generally reduced to 10–25 due to a long period of snow/ice
cover and the polar night in winter. The proposed algorithm excludes
snow/ice observations and uses the observations in unfrozen period to
estimate the surface water cover frequency. In the glacial areas, such as
Greenland and glacial areas of the Tibetan Plateau, NClear is less than
10 as snow and ice observations are excluded in the counting of clear-sky
observations, but it should have little impact on the dataset due to
limited water bodies in these regions. In some areas in the central part of
several huge lakes (e.g., Caspian Sea), since they are far away from the
land pixels on the shore and their clear-sky observations may be different
from that of the adjacent reliable land pixels, NClear values are set to fill
value to reduce the uncertainties in NClear estimation. The SWF of these
regions is usually estimated to be 100 %, as its Nland is usually less
than 15. The limited number of valid observations is a common problem for
optical remote sensing. The MODIS onboard Terra and Aqua satellites observe
the Earth's surface every 1 to 2 d. Their dense time series can be
acquired to generate more clear-sky observations. Additionally, in the
proposed method, all pixels with water count ≥3 among six observations
with the lowest NIR reflectance were used to create the maximum surface
water extent map. This means that the algorithm can be implemented with
three valid observations during a year, which helps to improve the global
applicability of the algorithm.
Global map of the number of clear-sky snow/ice-free MOD09A1
observations in 2020.
The mountain shadows were masked using the criterion that the terrain slope
derived from DEM data is greater than 30∘. In areas with
complex terrain, this simplification may result in uncertainties of the
estimation results. The variation of solar angle along latitudes and seasons
was not considered in the slope criteria for shadow estimation, which may
cause water that is outside of shadow to be removed in mountainous areas.
Here, the DEM data were mainly used to exclude large areas mountain shadows,
such as shadows in the margin of the Tibetan Plateau. For mountain shadows
with a small range, since the local time when MODIS passes changes among
days, the distribution of shadows will change due to different solar and
viewing geometry. MOD09A1 selects the best possible observation during an
8 d composition period, and its spatial resolution is coarse (500 m),
which helps to reduce the effects of mountain shadows with a small range. It
would help to improve the identification of terrain shadows by considering
solar angle variation and using fine-resolution DEM data, such as GMTED2010.
In snow-/ice-covered areas, the meltwater on the ice would reduce the
reflectivity in the NIR band. This may lead to overestimation of the maximum
water area since the six observations with the lowest NIR reflectivity are
used to extract the annual maximum water extent. Here, we create the maximum
surface water extent map using those pixels with water count no less than 3
to remove possible false detections.
Additionally, the spatial resolution of MODIS may limit the identification
for narrow rivers and small water bodies, resulting in underestimation of
surface water extent. It is difficult for the dataset to capture small water
bodies and the subtle changes of surface water, especially in high latitudes
in the Northern Hemisphere, where a large number of small water bodies are
located. Satellite data often have certain advantages in terms of temporal or
spatial resolution and time coverage, etc., but it is difficult to take into
account all of these aspects. MODIS provides daily spectral measurements of
the Earth surface since 2000. Its long-term high-frequency observations have
unique advantages in monitoring of the seasonal and interannual changes in
surface water. High-resolution images such as from Sentinel-1 and Sentinel-2 would
help to improve the surface water extraction in these areas.
Data availability
The GLOBMAP SWF dataset is available on the Zenodo repository at
10.5281/zenodo.6462883 (Liu and Liu, 2022). The number of
MOD09A1 clear-sky snow-/ice-free observations (NClear) data is also
provided as a quality dataset. The dataset is provided by 296 1200 km × 1200 km tiles at annual temporal and 500 m spatial resolutions in
the sinusoidal projection in Geotiff format for each year during
2000–2020. The SWF file is named “GLOBMAPSWF.AYYYY001.hHHvVV.V01.tif”,
while the NClear file is named “GLOBMAPClearCount.AYYYY001.hHHvVV.V01.tif”, where “YYYY” refers to the year of the file,
and “HH” and “VV” explain the number of tiles that are the same as the
MODIS standard tile. For the SWF dataset, the valid range is 0–100, the scale
factor is 1.0 and the unit is percent. For the NClear dataset, the valid range
is 0–46, and the scale factor is 1.0.
Conclusions
In this paper, a global annual surface water cover frequency dataset
(GLOBMAP SWF) was generated at 500 m resolution from MODIS land surface
reflectance data from 2000 to 2020. The SWF was proposed to quantitatively
describe the seasonal dynamics of inland water bodies by estimating the
percentage of water cover occurrence in a year. The count of a pixel covered
by water was estimated indirectly by subtracting the land observation count
from total clear-sky observation count. The SWF was calculated by dividing
the water count by the total number of clear-sky observations without the
help of cloud masks.
In 2020, the area of global maximum surface water extent is 3.38×106 km2, of which the permanent surface water is 1.83×106 km2
(54 %), and the intermittent surface water is 1.55×106 km2
(46 %). The inland water bodies are mainly concentrated in midlatitudes–high
latitudes of the Northern Hemisphere above 35∘ N. Compared with
the high-frequency GSWCD and ISWDC datasets derived from MODIS data, the
regional analysis demonstrates that our estimation results show better
performances for frozen water and saline lakes; the influence of clouds is
successfully reduced, with the estimated SWF reaching 100 % for permanent
water bodies in cloud frequently covered regions. And the false detection
was also reduced over the bright surface in winter. When compared with the
high-resolution GLAD and GSW datasets derived from Landsat data, the
generated dataset captures more intermittent surface water, but small water
bodies may be underestimated due to the coarse spatial resolution of MODIS.
Our estimates are validated with the 10 m resolution SWF maps extracted from
Sentinel-1 SAR observations in eight regions that cover lakes, rivers and
wetlands. Consistent spatial patterns and good positive correlations are
observed between the two results, with the R2 up to 0.46–0.97, RMSE
ranging from 7.24 % to 22.62 %, and MAE between 2.07 % and 7.15 %.
During 2001–2020, a decreasing trend is observed for the area of global
maximum (-7577 km2 yr-1, p=0.04) and minimum (-4315 km2 yr-1, p<0.01) surface water. The intermittent water also showed an insignificant
weak decreasing trend (-3262 km2 yr-1, p=0.29), while that with SWF
above 50 % has been expanding since 2001 (1368 km2 yr-1, p<0.01).
The GLOBMAP SWF dataset condenses the seasonal variation of inland water
bodies to inundation frequency during a year. It can characterize the
spatial distribution of permanent water extent in the dry season and maximum
water extent in the rainy season, as well as the distribution of
intermittent water and the length of inundation period. The dataset can be
used to analyze the interannual variation and change trend of surface water
with consideration of its seasonal variation and may guide the scientific
management of water resources and the investment in water infrastructures.
Author contributions
RL designed the method, processed the MODIS
data and generated the surface water cover frequency dataset. YL analyzed
and validated the dataset and wrote the manuscript. RS also analyzed and
wrote the manuscript. All authors have read and approved the final paper.
Competing interests
The contact author has declared that none of the authors has any competing interests.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
The authors
would like to thank the NASA Land Processes Distributed Active Archive
Center for providing the MODIS products.
Financial support
This research has been supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (grant no. XDA19080303), the Youth Innovation Promotion Association of Chinese Academy of Sciences (grant no. 2019056) and the Major Special Project: the China High-Resolution Earth Observation System (grant no. 30-Y30F06-9003-20/22).
Review statement
This paper was edited by Sander Veraverbeke and reviewed by two anonymous referees.
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