The Global Monitoring for Environment and Security (GMES) Sentinel-3 mission

Donlon, Craig; Berruti, B.; Buongiorno, A.; Ferreira, Maria-Isabel; Féménias, Pierre; Frerick, J.; Goryl, Philippe; Klein, Ulf; Laur, Henri; Mavrocordatos, C.; Nieke, Jens; Rebhan, H.; Seitz, B.; Stroede, J.; Sciarra, R.

doi:10.1016/j.rse.2011.07.024

Cited by 631 publications

(380 citation statements)

References 45 publications

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“…Large TWSAs caused by consecutive weekly or daily precipitation with low runoff would be excluded from the flood confirmation based on FPI. Furthermore, as FPI was first proposed in 2009 [14,35] and is therefore still fairly new to most researchers. As a consequence, FPI is associated with several challenges.…”

Section: Flood Analysis Using Fpi Over Yrbmentioning

confidence: 99%

“…If such data were not available, more accurate TWSA data and/or other remote sensing data would be required. For example, the Sentinel-3 mission [35] provides surface reflectance data with high spatial and temporal resolution and, therefore, has the potential to allow more accurate monitoring of floods at small scales and in urban environments. At present, GRACE data are mainly applied to the study of hydrological extremes and terrestrial water storage in large regions, and for some parts of the world where hydrological records are in decline for economic and/or political reasons [36].…”

Section: Hydrological Evaluation Based On Grace Datamentioning

confidence: 99%

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Assessing Terrestrial Water Storage and Flood Potential Using GRACE Data in the Yangtze River Basin, China

et al. 2017

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Floods have caused tremendous economic, societal and ecological losses in the Yangtze River Basin (YRB) of China. To reduce the impact of these disasters, it is important to understand the variables affecting the hydrological state of the basin. In this study, we used Gravity Recovery and Climate Experiment (GRACE) satellite data, flood potential index (FPI), precipitation data (Tropical Rainfall Measuring Mission, TRMM 3B43), and other meteorological data to generate monthly terrestrial water storage anomalies (TWSA) and to evaluate flood potential in the YRB. The results indicate that the basin contained increasing amounts of water from 2003 to 2014, with a slight increase of 3.04 mm/year in the TWSA. The TWSA and TRMM data exhibit marked seasonal characteristics with summer peaks and winter dips. Estimates of terrestrial water storage based on GRACE, measured as FPI, are critical for understanding and predicting flooding. The 2010 flood (FPI~0.36) was identified as the most serious disaster during the study period, with discharge and precipitation values 37.95% and 19.44% higher, respectively, than multi-year average values for the same period. FPI can assess reliably hydrological extremes with high spatial and temporal resolution, but currently, it is not suitable for smaller and/or short-term flood events. Thus, we conclude that GRACE data can be effectively used for monitoring and examining large floods in the YRB and elsewhere, thus improving the current knowledge and presenting potentially important political and economic implications.

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Section: Flood Analysis Using Fpi Over Yrbmentioning

confidence: 99%

Section: Hydrological Evaluation Based On Grace Datamentioning

confidence: 99%

Assessing Terrestrial Water Storage and Flood Potential Using GRACE Data in the Yangtze River Basin, China

et al. 2017

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“…Currently, the sea and land surface temperature radiometer (SLSTR) aboard the Sentinel-3A satellite, which was launched on 16 February 2016, can be used to obtain quasi-real-time observations with two viewing angles of approximately nadir and oblique (55 • ) [45]. This sensor represents a continuation of the ATSR sensor series and makes it possible to invert large-scale component temperatures from operational satellite data.…”

Section: Resultsmentioning

confidence: 99%

A Robust Inversion Algorithm for Surface Leaf and Soil Temperatures Using the Vegetation Clumping Index

Bian

Cao

et al. 2017

Remote Sensing

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Abstract:The inversion of land surface component temperatures is an essential source of information for mapping heat fluxes and the angular normalization of thermal infrared (TIR) observations. Leaf and soil temperatures can be retrieved using multiple-view-angle TIR observations. In a satellite-scale pixel, the clumping effect of vegetation is usually present, but it is not completely considered during the inversion process. Therefore, we introduced a simple inversion procedure that uses gap frequency with a clumping index (GCI) for leaf and soil temperatures over both crop and forest canopies. Simulated datasets corresponding to turbid vegetation, regularly planted crops and randomly distributed forest were generated using a radiosity model and were used to test the proposed inversion algorithm. The results indicated that the GCI algorithm performed well for both crop and forest canopies, with root mean squared errors of less than 1.0 • C against simulated values. The proposed inversion algorithm was also validated using measured datasets over orchard, maize and wheat canopies. Similar results were achieved, demonstrating that using the clumping index can improve inversion results. In all evaluations, we recommend using the GCI algorithm as a foundation for future satellite-based applications due to its straightforward form and robust performance for both crop and forest canopies using the vegetation clumping index.

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“…In 20% of the answers, the current global standard spatial resolution (300-1000 m) would even be sufficient. Finally, a spatial resolution of 150 m was found to be suitable for communities studying large-scale global dynamics and monitoring of the Earth's surface at 250 m or coarser with satellite data observations like MODIS, Envisat MERIS, PROBA-V and their continuity ensured by Sentinel-3 [47].…”

Section: Discussionmentioning

confidence: 99%

Compilation and Validation of SAR and Optical Data Products for a Complete and Global Map of Inland/Ocean Water Tailored to the Climate Modeling Community

et al. 2017

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Accurate maps of surface water extent are of paramount importance for water management, satellite data processing and climate modeling. Several maps of water bodies based on remote sensing data have been released during the last decade. Nonetheless, none has a truly (90 • N/90 • S) global coverage while being thoroughly validated. This paper describes a global, spatially-complete (void-free) and accurate mask of inland/ocean water for the 2000-2012 period, built in the framework of the European Space Agency (ESA) Climate Change Initiative (CCI). This map results from the synergistic combination of multiple individual SAR and optical water body and auxiliary datasets. A key aspect of this work is the original and rigorous stratified random sampling designed for the quality assessment of binary classifications where one class is marginally distributed. Input and consolidated products were assessed qualitatively and quantitatively against a reference validation database of 2110 samples spread throughout the globe. Using all samples, overall accuracy was always very high among all products, between 98% and 100%. The CCI global map of open water bodies provided the best water class representation (F-score of 89%) compared to its constitutive inputs. When focusing on the challenging areas for water bodies' mapping, such as shorelines, lakes and river banks, all products yielded substantially lower accuracy figures with overall accuracies ranging between 74% and 89%. The inland water area of the CCI global map of open water bodies was estimated to be 3.17 million km 2 ± 0.24 million km 2 . The dataset is freely available through the ESA CCI Land Cover viewer.

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The Global Monitoring for Environment and Security (GMES) Sentinel-3 mission

Cited by 631 publications

References 45 publications

Assessing Terrestrial Water Storage and Flood Potential Using GRACE Data in the Yangtze River Basin, China

Assessing Terrestrial Water Storage and Flood Potential Using GRACE Data in the Yangtze River Basin, China

A Robust Inversion Algorithm for Surface Leaf and Soil Temperatures Using the Vegetation Clumping Index

Compilation and Validation of SAR and Optical Data Products for a Complete and Global Map of Inland/Ocean Water Tailored to the Climate Modeling Community

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