Validation practices for satellite‐based Earth observation data across communities

Loew, Alexander; Bell, William; Brocca, Luca; Bulgin, Claire E.; Burdanowitz, Jörg; Calbet, Xavier; Donner, Reik V.; Ghent, Darren; Gruber, A.; Kaminski, Thomas W.; Kinzel, Julian; Klepp, Christian; Lambert, Jean-Christopher; Schaepman‐Strub, Gabriela; Schröder, Marc; Verhoelst, Tijl

doi:10.1002/2017rg000562

Cited by 172 publications

(141 citation statements)

References 194 publications

(228 reference statements)

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“…Such understanding should assist in informing how a synergistic monitoring system between satellite and strategically deployed ground based observations (in locations where ambiguity and/or temporal decorrelation would limit the confidence in the measurements made) fit for validating the InSAR ground motion measurements could operate. Lessons learned from other EO validation campaigns will be invaluable here [63].…”

Section: Discussionmentioning

confidence: 99%

Long-Term Peatland Condition Assessment via Surface Motion Monitoring Using the ISBAS DInSAR Technique over the Flow Country, Scotland

et al. 2018

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Satellite Earth Observation (EO) is often used as a cost-effective method to report on the condition of remote and inaccessible peatland areas. Current EO techniques are primarily limited to reporting on the vegetation classes and properties of the immediate peat surface using optical data, which can be used to infer peatland condition. Another useful indicator of peatland condition is that of surface motion, which has the potential to report on mass accumulation and loss of peat. Interferometic SAR (InSAR) techniques can provide this using data from space. However, the most common InSAR techniques for information extraction, such as Persistent Scatterers' Interferometry (PSI), have seen limited application over peat as they are primarily tuned to work in areas of high coherence (i.e., on hard, non-vegetated surfaces only). A new InSAR technique, called the Intermittent Small BAseline Subset (ISBAS) method, has been recently developed to provide measurements over vegetated areas from SAR data acquired by satellite sensors. This paper examines the feasibility of the ISBAS technique for monitoring long-term surface motion over peatland areas of the Flow Country, in the northeast of Scotland. In particular, the surface motions estimated are compared with ground data over a small forested area (namely the Bad a Cheo forest Reserve). Two sets of satellite SAR data are used: ERS C-band images, covering the period 1992-2000, and Sentinel-1 C-band images, covering the period 2015-2016. We show that the ISBAS measurements are able to identify surface motion over peatland areas, where subsidence is a consequence of known land cover/land use. In particular, the ISBAS products agree with the trend of surface motion, but there are uncertainties with their magnitude and direction (vertical). It is concluded that there is a potential for the ISBAS method to be able to report on trends in subsidence and uplift over peatland areas, and this paper suggests avenues for further investigation, but this requires a well-resourced validation campaign.

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Section: Discussionmentioning

confidence: 99%

Long-Term Peatland Condition Assessment via Surface Motion Monitoring Using the ISBAS DInSAR Technique over the Flow Country, Scotland

et al. 2018

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“…Second, an investigation of the vegetation vitality and atmospheric conditions preceding extreme temperature events could clarify the actual impact of a specific event itself on vegetation. Third, further validation of the obtained results based upon different remote sensing data sets (Scheftic et al, 2014) as well as ground-based observations (Loew et al, 2017) appears necessary. In this context, we particularly highlight the necessity to consider additional observables related to vegetation stress in order to study the robustness of our approach and reveal further details on the associated plant physiological processes.…”

Section: Discussionmentioning

confidence: 99%

Impacts of temperature extremes on European vegetation during the growing season

et al. 2017

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Abstract. Temperature is a key factor controlling plant growth and vitality in the temperate climates of the midlatitudes like in vast parts of the European continent. Beyond the effect of average conditions, the timings and magnitudes of temperature extremes play a particularly crucial role, which needs to be better understood in the context of projected future rises in the frequency and/or intensity of such events. In this work, we employ event coincidence analysis (ECA) to quantify the likelihood of simultaneous occurrences of extremes in daytime land surface temperature anomalies (LSTAD) and the normalized difference vegetation index (NDVI). We perform this analysis for entire Europe based upon remote sensing data, differentiating between three periods corresponding to different stages of plant development during the growing season. In addition, we analyze the typical elevation and land cover type of the regions showing significantly large event coincidences rates to identify the most severely affected vegetation types. Our results reveal distinct spatio-temporal impact patterns in terms of extraordinarily large co-occurrence rates between several combinations of temperature and NDVI extremes. Croplands are among the most frequently affected land cover types, while elevation is found to have only a minor effect on the spatial distribution of corresponding extreme weather impacts. These findings provide important insights into the vulnerability of European terrestrial ecosystems to extreme temperature events and demonstrate how event-based statistics like ECA can provide a valuable perspective on environmental nexuses.

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“…In this case, validation is not against independent measurements, but it is performed by using repeated observations by the same instrument. Such analyses would be more robust if the geophysical standard could be traced to a more controlled reference, which would require more support for repeated accurate measurements of the Earth target from the ground (Loew et al, 2017). For categorical ECVs, such as land cover type, a degree of uncertainty validation can be obtained by verifying that the estimated misclassification rates in the product are stable with respect to reasonable ranges of classification parameters.…”

Section: Validation Of Uncertaintymentioning

confidence: 99%

Uncertainty information in climate data records from Earth observation

Merchant

Paul

Popp

et al. 2017

Earth Syst. Sci. Data

125

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Abstract. The question of how to derive and present uncertainty information in climate data records (CDRs) has received sustained attention within the European Space Agency Climate Change Initiative (CCI), a programme to generate CDRs addressing a range of essential climate variables (ECVs) from satellite data. Here, we review the nature, mathematics, practicalities, and communication of uncertainty information in CDRs from Earth observations. This review paper argues that CDRs derived from satellite-based Earth observation (EO) should include rigorous uncertainty information to support the application of the data in contexts such as policy, climate modelling, and numerical weather prediction reanalysis. Uncertainty, error, and quality are distinct concepts, and the case is made that CDR products should follow international metrological norms for presenting quantified uncertainty. As a baseline for good practice, total standard uncertainty should be quantified per datum in a CDR, meaning that uncertainty estimates should clearly discriminate more and less certain data. In this case, flags for data quality should not duplicate uncertainty information, but instead describe complementary information (such as the confidence in the uncertainty estimate provided or indicators of conditions violating the retrieval assumptions). The paper discusses the many sources of error in CDRs, noting that different errors may be correlated across a wide range of timescales and space scales. Error effects that contribute negligibly to the total uncertainty in a single-satellite measurement can be the dominant sources of uncertainty in a CDR on the large space scales and long timescales that are highly relevant for some climate applications. For this reason, identifying andPublished by Copernicus Publications. 512 C. J. Merchant et al.: Uncertainty information in climate data records characterizing the relevant sources of uncertainty for CDRs is particularly challenging. The characterization of uncertainty caused by a given error effect involves assessing the magnitude of the effect, the shape of the error distribution, and the propagation of the uncertainty to the geophysical variable in the CDR accounting for its error correlation properties. Uncertainty estimates can and should be validated as part of CDR validation when possible. These principles are quite general, but the approach to providing uncertainty information appropriate to different ECVs is varied, as confirmed by a brief review across different ECVs in the CCI. User requirements for uncertainty information can conflict with each other, and a variety of solutions and compromises are possible. The concept of an ensemble CDR as a simple means of communicating rigorous uncertainty information to users is discussed. Our review concludes by providing eight concrete recommendations for good practice in providing and communicating uncertainty in EO-based climate data records.

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Validation practices for satellite‐based Earth observation data across communities

Cited by 172 publications

References 194 publications

Long-Term Peatland Condition Assessment via Surface Motion Monitoring Using the ISBAS DInSAR Technique over the Flow Country, Scotland

Long-Term Peatland Condition Assessment via Surface Motion Monitoring Using the ISBAS DInSAR Technique over the Flow Country, Scotland

Impacts of temperature extremes on European vegetation during the growing season

Uncertainty information in climate data records from Earth observation

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