Validation

Validation as defined by the Cal/Val working group of the Committee on Earth Observation Satellites (CEOS) is the process of assessing, by independent means, the quality of the data products derived from those system outputs.  Validation and uncertainty assessment are crucial requirements from the end user perspective of a satellite data product  and only through confidence in quantifiable uncertainties will there be increased uptake of these data products. Currently, there is a lack of documentation of satellite validation strategies and methods across the different terrestrial, aquatic and climate communities that utilize Earth Observations (EO) (Loew et al., 2017).

Validation has been identified as a key issue within the water quality-remote sensing community.  Palmer et al. (2015) pointed to the need for the inland community to be actively engaged in cal/val activities for Sentinel and future EO missions. Mouw et al. (2015) suggested an increased need of in situ observations for algorithm development and product validation efforts.  AquaWatch, the Group on Earth Observations (GEO) water quality community of practice met in August of 2018 to discuss the work plans and future activities and there was overwhelming consensus that issues and shortcomings surrounding validation of satellite-derived products was a priority facing the community (https://www.geoaquawatch.org/about/2018-joint-globolakes-geo-aquawatch-meeting-stirling-uk/ ).

Approaches to validation of EO-derived products vary across research and operational efforts. Validation activities generally involves the match up of satellite-derived values with in situ measurements.  However, most studies have a different validation approaches and the varying degrees of representativeness of the matchup values utilized.  Spatial and temporal differences including pixel size (smoothing) vs. point sample, differences in collection time and surface and integrated samples vs. light penetration depth need to be considered. This was highlighted by Nechad et al. (2015), who utilized in situ water quality (Chl-a, total suspended matter, non-algal particulate, particulate inorganic and organic matter, and colored dissolved organic matter), in situ reflectances and simulated water-leaving reflectances in the evaluation of algorithm robustness, remote sensing uncertainty, range of application and performance in varying conditions. Li et al. (2019) further highlighted the importance of relative uncertainty contributions to total uncertainty by developing an uncertainty budget, to improve overall accuracy.

References

Li, J.; Jamet, C.; Zhu, J.; Han, B.; Li, T.; Yang, A.; Guo, K.; Jia, D. Error Budget in the Validation of Radiometric Products Derived from OLCI around the China Sea from Open Ocean to Coastal Waters Compared with MODIS and VIIRS. Remote Sens. 2019, 11, 2400.

Loew A, Bell W, Brocca L, Bulgin CE, Burdanowitz J, Calbet X, Donner RV, Ghent D, Gruber A, Kaminski T, Kinzel J, Klepp C, Lambert J-C, Schaepman-Strub G, Schröder M, Verhoelst T (2017) Validation practices for satellite-based Earth observation data across communities. Rev Geophys 55:779–817.

Mouw, C.B., Greb, S., Aurin, D., DiGiacomo, P.M., Lee, Z., Twardowski, M., Binding, C., Hu, C., Ma, R., Moore, T., Moses, W., Craig, S.E., 2015. Aquatic color radiometry remote sensing of coastal and inland waters:  Challenges and recommendations for future satellite missions. Remote Sensing of Environment 160, 15–138630. https://doi.org/10.1016/j.rse.2015.02.001

Nechad, Bouchra; Ruddick, Kevin; Schroeder, Thomas; Oubelkheir, Kadija; Blondeau-Patissier, David; Cherukuru, Nagur; Brando, Vittorio E; Dekker, Arnold G; Clementson, Lesley; Banks, Andrew; Maritorena, Stéphane; Werdell, P Jeremy; Sá, Carolina; Brotas, Vanda; Caballero de Frutos, Isabel; Ahn, Yu-Hwan; Salama, Suhyb; Tilstone, Gavin; Martinez-Vicente, Victor; Foley, David; McKibben, Morgaine; Nahorniak, Jasmine; Peterson, Tawnya D; Siliò-Calzada, Ana; Röttgers, Rüdiger; Lee, Zhongping; Peters, Marco (2015): CoastColour Round Robin data sets: a database to evaluate the performance of algorithms for the retrieval of water quality parameters in coastal waters. Earth System Science Data, 7(2), 319-348.