Surface ALbedo VALidation (SALVAL) Platform: Towards CEOS LPV Validation Stage 4—Application to Three Global Albedo Climate Data Records

Sánchez-Zapero, Jorge; Martínez-Sánchez, Enrique; Camacho, Fernando; Wang, Zhuosen; Carrer, Dominique; Schaaf, Crystal B.; Garcı́a-Haro, Francisco Javier; Nickeson, Jaime; Cosh, Michael H.

doi:10.3390/rs15041081

Cited by 4 publications

(3 citation statements)

References 59 publications

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“…A similar concept of a web-based validation platform was recently developed in the frame of CEOS-LPV, focusing on land surface albedo products [178]. Another successful example of an operational validation tool is the ESA Atmospheric Validation Data Centre (EVDC) [179], which follows the same philosophy of becoming a one-stop-shop, providing centralised open access to satellite sub-settings and spatiotemporal collocated groundbased reference data of atmospheric variables.…”

Section: Validation Tools and Databasementioning

confidence: 99%

“…Database and tools-The final stage of an operational Cal/Val system consists of providing the users with a consistent and fully automated tool, including satellite sub-settings, and embedded analytics for satellite products comparison with reference data using standardised procedures. This is the framework originally developed by CEOS-LPV with the OLIVE tool [175], which is now being replicated to other terrestrial ECVs [178]. A centralised database with associated tools is key to enhance uptake of Cal/Val data within the community and for fostering the adoption of community-agreed standardised procedures.…”

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confidence: 99%

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European Space Agency (ESA) Calibration/Validation Strategy for Optical Land-Imaging Satellites and Pathway towards Interoperability

et al. 2021

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Land remote sensing capabilities in the optical domain have dramatically increased in the past decade, owing to the unprecedented growth of space-borne systems providing a wealth of measurements at enhanced spatial, temporal and spectral resolutions. Yet, critical questions remain as how to unlock the potential of such massive amounts of data, which are complementary in principle but inherently diverse in terms of products specifications, algorithm definition and validation approaches. Likewise, there is a recent increase in spatiotemporal coverage of in situ reference data, although inconsistencies in the used measurement practices and in the associated quality information still hinder their integrated use for satellite products validation. In order to address the above-mentioned challenges, the European Space Agency (ESA), in collaboration with other Space Agencies and international partners, is elaborating a strategy for establishing guidelines and common protocols for the calibration and validation (Cal/Val) of optical land imaging sensors. Within this paper, this strategy will be illustrated and put into the context of current validation systems for land remote sensing. A reinforced focus on metrology is the basic principle underlying such a strategy, since metrology provides the terminology, the framework and the best practices, allowing to tie measurements acquired from a variety of sensors to internationally agreed upon standards. From this general concept, a set of requirements are derived on how the measurements should be acquired, analysed and quality reported to users using unified procedures. This includes the need for traceability, a fully characterised uncertainty budget and adherence to community-agreed measurement protocols. These requirements have led to the development of the Fiducial Reference Measurements (FRM) concept, which is promoted by the ESA as the recommended standard within the satellite validation community. The overarching goal is to enhance user confidence in satellite-based data and characterise inter-sensor inconsistencies, starting from at-sensor radiances and paving the way to achieving the interoperability of current and future land-imaging systems.

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Section: Validation Tools and Databasementioning

confidence: 99%

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confidence: 99%

European Space Agency (ESA) Calibration/Validation Strategy for Optical Land-Imaging Satellites and Pathway towards Interoperability

et al. 2021

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“…Yue et al used high-resolution remote sensing images to upscale ground point-scale albedo measurements to the 500 m image scale [26], and Sanchez-Zapero et al combined the spatial heterogeneity of the subsurface. They employed geostatistical methods that combined the subsurface's spatial heterogeneity and scaled the observation tower's albedo measurements to the image element scale by the geostatistical method and the Simple Average method [27].…”

Section: Introductionmentioning

confidence: 99%

A Scale Conversion Model Based on Deep Learning of UAV Images

et al. 2023

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As a critical component of many remote sensing satellites and model validation, pixel-scale surface quantitative parameters are often affected by scale effects in the acquisition process, resulting in deviations in the accuracy of image scale parameters. Consequently, various successive scale conversion methods have been proposed to correct the errors caused by scale effects. In this study, we propose ResTransformer, a deep learning model for scale conversion of surface reflectance using UAV images, which fully extracts and fuses the features of UAV images in the sample area and sample points and establishes a high-dimensional nonlinear spatial correlation between sample points and sample area in the target sample area, so that the scale conversion of surface reflectance at the pixel-scale can be completed quickly and accurately. We collected and created a dataset of 500k samples to verify the accuracy and robustness of the model with other traditional scale conversion methods. The results show that the ResTransformer deep learning model works best, providing average MRE, average MRSE, and correlation coefficient R values of 0.6440%, 0.7460, and 0.99911, respectively, and the baseline improvements compared with the Simple Average method are 92.48%, 92.45%, and 16.59%, respectively. The ResTransformer model also shows the highest robustness and universality and can adapt to surface pixel-scale conversion scenarios with different sizes, heterogeneous sample areas, and arbitrary sampling methods. This method provides a promising, highly accurate, and robust method for converting pixel-scale surface reflectance scale.

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Four decades of global surface albedo estimates in the third edition of the CM SAF cLoud, Albedo and surface Radiation (CLARA) climate data record

Riihelä,

Jääskeläinen,

Kallio-Myers

2024

Earth Syst. Sci. Data

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Abstract. We present the surface albedo data in the third edition of the CM SAF cLoud, Albedo and surface Radiation (CLARA) data record family. The temporal coverage of this edition is extended from 1979 until the near-present day. The core algorithms and data format remain unchanged from previous editions, but now white- and blue-sky albedo estimates are also available for the first time in CLARA data. We present an overview of the retrieval, followed by an assessment of the accuracy and stability of the data record, based on collocated comparisons with reference surface albedo measurements and intercomparisons with preceding satellite-based albedo data records. Specific attention is paid to addressing the spatial representativeness problem inherent in the “point-to-pixel” validation of satellite-based coarse surface albedo estimates against in situ measurements. We find the CLARA-A3 albedo data to match or improve upon the accuracy and robustness of the predecessor record (CLARA-A2), with good agreement found when compared to in situ measurements. In cases of a large bias, the spatial representativeness of the measurement site typically explains most of the increase. We conclude with a summarizing discussion on the observed strengths and weaknesses of the new data record, including guidance for potential users. The data are available at https://doi.org/10.5676/EUM_SAF_CM/CLARA_AVHRR/V003 (Karlsson et al., 2023b).

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Surface ALbedo VALidation (SALVAL) Platform: Towards CEOS LPV Validation Stage 4—Application to Three Global Albedo Climate Data Records

Cited by 4 publications

References 59 publications

European Space Agency (ESA) Calibration/Validation Strategy for Optical Land-Imaging Satellites and Pathway towards Interoperability

European Space Agency (ESA) Calibration/Validation Strategy for Optical Land-Imaging Satellites and Pathway towards Interoperability

A Scale Conversion Model Based on Deep Learning of UAV Images

Four decades of global surface albedo estimates in the third edition of the CM SAF cLoud, Albedo and surface Radiation (CLARA) climate data record

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