Remote Sensing of Surface Water Dynamics in the Context of Global Change—A Review
Abstract
:1. Introduction
1.1. Surface Water in a Societal and Environmental Context
1.2. Remote Sensing Perspective
1.3. Objective of this Review
- How did the research field develop over time?
- Where are the hotspots of surface water dynamics research?
- What spatial and temporal scales are employed?
- What sensors are being used?
- What methods are utilized for delineating surface water?
- What are the strengths and limitations of available dynamic global surface water products?
- What are the predominant research foci?
2. Materials and Methods
3. Results
3.1. Development of Research Interest over Time
3.2. Discrepancy betweeen Areas of Interest and Authorships
3.3. Spatial and Temporal Scales of the Studies
3.4. Sensors and Sensor Types
3.5. Methods for Surface Water Delineation
3.6. Review of Thematic Foci in Research Hotspots
3.6.1. Africa
3.6.2. Asia
3.6.3. Australia
3.6.4. Europe
3.6.5. North America
3.6.6. South America
4. Discussion
4.1. Revisiting the Duality of Spatial vs. Temporal Resolution
4.2. Analysis-Ready Datasets
4.3. Drivers of Surface Water Change
- Human impact, specifically the construction of reservoirs, intensification of agriculture, rapid urbanization, and ineffective water management, has a great impact on global surface water. As is visible in Figure 10, humans are most often identified as drivers in densely populated areas with mostly high population increases. This interpretation is backed by other studies that show human impact on free flowing rivers [271] and surface water bodies in general [180,272].
- Climate change is often attributed with the highest or second highest impact on surface water dynamics. In contrast to human interventions, climate change impacts are reported for highly populated as well as nearly untouched regions of the world. Especially in cold and arctic regions, as well as those with acute water scarcity, these impacts are significant. In water scarce regions, the impacts of climate change (mainly temperature increase and more variable precipitation) often worsen the already tense situation. In cold and arctic regions, rising temperatures and precipitation changes have multiple effects: higher precipitation generally leads to increasing water body sizes. Water bodies fed by meltwater additionally increase in size due to higher temperature. Water bodies that are not fed by meltwater shrink if precipitation rates remain similar or decrease. This can be explained by simultaneously increasing temperatures and ET rates. Areas underlain by permafrost, due to rising temperatures, lead to a thawing process that can destabilize lake edges and rapidly drain lakes.
- Especially in tropical and subtropical regions, large scale oscillations affect the inter-annual dynamics of surface water. The impact of such climate modes spans multiple continents and can lead to adverse water situations in multiple and distant regions at the same time; even more so when they occur in combination with temperature increases and higher precipitation variations and human-induced changes in water dynamics.
4.4. Future Developments
- As discussed above, all sensor types utilized in RS all have their respective shortcomings. Optical sensors, for example, are highly sensitive towards cloud coverage. SAR sensors have limitations depending on the wavelength they operate in. Shorter wavelength sensors like those operating in the X- and C-band are influenced by soil moisture and atmospheric influences, while sensors operating in longer wavelengths like L-band SAR are limited by their coarse spatial resolution.
- Depending on the classification scheme, the quality of results can vary substantially. Accurate measurements of RS-based analyses are especially complicated for large-scale applications such as global mapping initiatives. On this level, ground truth comparisons become unfeasible and accuracy has to be measured via proxies like visual image interpretation or comparison with high resolution sensors. All of these approaches carry over the uncertainties of the used proxies.
- Lastly, the duality of high spatial vs. high temporal resolution sensors can be seen as the largest hurdle that limits the applicability of RS-based approaches.
5. Conclusions
- We identified an overall increase in research activity over time. From 2006 onwards, multiple peer-reviewed contributions are identified each year. From 2014 onwards, a steep increase in research activity was identified.
- Research hotspots are foremost located in Asia. China alone was covered by ~33% of the publications on surface water dynamics. Further, areas in Central, South, and Southeast Asia are investigated in 19%, 20%, and 19%, respectively. Further, a high concentration of studies was found for the Amazon River Basin (20%), the Congo River Basin (18%), Australia (19%), and North America (20%). Most first authorships come from China (36%), the USA (20%), France, and Germany (each 12%). This shows a discrepancy in the spatial distributions of research areas and first author countries. A significant number of studies investigates surface water dynamics globally (15%).
- On the temporal scale, we differentiated between studies considering inter-annual dynamics versus those including intra-annual dynamics. We found that earlier studies including intra-annual dynamics use shorter timeframes than more recent works. Generally, studies focusing on inter-annual dynamics observe longer timeframes than those analyzing intra-annual dynamics. There is a duality between high spatial resolution and high temporal resolution approaches. While many studies working on a local or regional scale employ high spatial resolution and low temporal resolution data, studies on a large geographical scale mostly work with low spatial resolution but high temporal resolution data.
- Most studies include optical data (91%). Often, studies rely solely on optical data (72%). Landsat data is utilized in 62% of studies and MODIS data is used in 20% of studies. Further, microwave data is used in a significant number of studies. In terms of active microwave data, especially synthetic aperture radar (SAR), sensors are used in many studies (18%). Ten percent of studies include passive microwave data.
- Most studies use a custom approach to identify the surface water area (79%). In many cases, this approach is based on a threshold-based classification of water (29%). Supervised classifications are used in 17% of studies. Twenty-one percent of studies rely on analysis-ready datasets to describe surface water area. In 5% of studies, surface water is monitored using the Global Surface Water (GSW) used by Ref. [11]. Additionally, the Global Inundation Extent from Multi-Satellites (GIEMS) by Ref. [181] or newer iterations of the same product are used in 3% of studies.
- Global surface water products have the potential to provide comparable surface water observations. Landsat-based products offer the longest timeframes (e.g., Global Surface Water (GSW): 1984-present). However, due to the low temporal resolution of Landsat, its use for the analysis of highly dynamic surface water bodies is limited. Particularly for GSW, low accuracies for specific regions (e.g., the Sahel [166,167]) are reported. High temporal resolution products are exclusively based on MODIS data. To our knowledge, the Global WaterPack (GWP) by Ref. [84] provides the highest temporal resolution of any available global product at 250 m spatial resolution. Additionally, all products based on optical sensors are limited by weather-related data gaps and cannot accurately depict water under canopy. Multi-sensor products like Global Inundation Extent from Multi-Satellites (GIEMS) have an advantage here, but are limited in spatial and temporal resolution (~25 km, monthly). The mentioned duality of spatial vs. temporal resolution is therefore also visible in global surface water products. We postulate this duality will end due to the increasingly long time series of high spatial and high temporal resolution satellite constellations.
- We categorized studies based on their thematic focus. Three spheres were identified: ~49% of studies have a thematic focus on the hydrosphere, ~24% on the biosphere, and ~26% on the anthroposphere. We divided studies into further subgroups. Within the respective identified spheres, the largest groups would be publications with a focus on surface water area dynamics (40%), natural hazards (17%), or landscape change (22%). The respective foci are distinctly spatially distributed. There are more global studies with a hydrosphere focus than studies with a biosphere or anthroposphere focus. Hydrosphere research hotspots are the Arctic and cold regions, the Amazon River Basin, the Congo River Basin, China, and Australia. Biosphere research hotspots are concentrated in North America, Iran, and China. Anthroposphere research hotspots are situated in the USA, the Sahel, the East African Rift, Central Asia, South Asia, China, and Australia.
Supplementary Materials
Author Contributions
Funding
Conflicts of Interest
References
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Name | Spatial Resolution | Temporal Resolution | Timeframe | Sensor Type | Availability | Sources |
---|---|---|---|---|---|---|
GIEMS (Global Inundation Extent from Multi-Satellites) | Native: ~25,000 m GIEMS-D15: ~500 m GIEMS-D3: ~90 m | Monthly | 1993–2007 | Active Microwave Passive Microwave Optical | Upon request | [82,180,181,182,183] |
GIEMS-2 (Global Inundation Extent from Multi-Satellites 2) | ~25,000 m | Monthly | 1992–2015 | Active Microwave Passive Microwave Optical | Upon request | [139] |
GLAD Surface Water (Global Land Analysis & Discovery Surface Water) | 30 m | Monthly | 1999–2020 | Optical | Project Website 1 | [173] |
GRSAD (Global Reservoir Surface Area Dataset) | 30 m | Monthly | 1984–2015 | Optical | Project Website 2 | [120] |
GSW (Global Surface Water) | 30 m | Monthly | 1984–2020 | Optical | Project Website 3 GEE | [11] |
GSWED (Global Surface Water Extent Dataset) | 250 m | 8 Days | 2000–2020 | Optical | Project Website 4 | [73] |
GWP (Global WaterPack) | 250 m | Daily | 2003–2020 | Optical | EOCGeoService 5 Upon request | [75,84] |
SWAMPS (Surface Water Microwave Product Series) | ~25,000 m | Daily | 1992–2020 | Active MicrowavePassive MicrowaveOptical | Project Website 6 | [140,184] |
Daily Global Surface Water Change Database | 500 m | Daily | 2001–2016 | Optical | Project Website 7 | [178] |
Continent | Hotspots | Main Findings | Drivers | Challenges | Sources |
---|---|---|---|---|---|
Africa | Sahel Congo River Basin East African Rift |
|
|
| [9,14,37,62,87,92,102,104,105,114,127,137,151,152,153,154,161,166,167,194,197,209,210,211,212,213,214,215,216,217,218] |
Asia | Siberian Tundra Central Asia China High Mountain AsiaSouth & Southeast Asia |
|
| [4,5,6,10,12,13,16,17,24,25,27,31,33,36,39,43,46,60,67,68,69,70,71,72,74,77,78,79,80,91,94,95,97,98,100,103,104,106,107,108,109,111,112,113,115,116,117,118,121,123,127,133,141,142,145,147,150,155,156,158,162,169,172,188,189,192,193,196,198,200,201,216,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251] | |
Australia | Southeast Australia (Murray-Darling-Basin) |
|
| [42,90,124,165,174,175,177,252,253] | |
Europe | Mediterranean Alpine regions Western Europe |
|
| [99,121,127,141,149,163,168,236,254,255,256] | |
North America | Arctic tundra Boreal regions Continental USA |
|
| [5,23,43,44,47,93,96,101,110,127,144,148,157,169,170,171,199,236,252,257,258,259,260,261,262] | |
South America | Amazon River Basin Pampas |
|
| [22,37,40,88,104,127,135,138,141,185,186,263,264,265,266,267] |
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Sogno, P.; Klein, I.; Kuenzer, C. Remote Sensing of Surface Water Dynamics in the Context of Global Change—A Review. Remote Sens. 2022, 14, 2475. https://doi.org/10.3390/rs14102475
Sogno P, Klein I, Kuenzer C. Remote Sensing of Surface Water Dynamics in the Context of Global Change—A Review. Remote Sensing. 2022; 14(10):2475. https://doi.org/10.3390/rs14102475
Chicago/Turabian StyleSogno, Patrick, Igor Klein, and Claudia Kuenzer. 2022. "Remote Sensing of Surface Water Dynamics in the Context of Global Change—A Review" Remote Sensing 14, no. 10: 2475. https://doi.org/10.3390/rs14102475