Having already shown its potential of deriving the vector fields representing the ocean-surface advection from sequential 1.1-km-resolution local area coverage (LAC) Advanced Very High Resolution Radiometer (AVHRR) images, the maximum cross-correlation (MCC) technique here is applied to four 4.4-km-resolution global area coverage (GAC) AVHRR images. The resulting three vector fields are compared to the vector fields obtained from the LAC imagery corresponding to the same satellite passages. To quantify the reduction in accuracy inevitable when applying the method to the lower resolution imagery, the LAC vector fields were assumed to be error free. The deviation of the GAC vectors from the LAC vectors is expressed as percentage errors of the signal variance of meridional u and zonal v velocity components, and they are 16%/30%, respectively, for the best case and 62%/117% and 92%/111% for the other two cases. These results indicate that, in its present state, the GAC data do not allow the MCC technique to extract reliable current-vector information from it.
The Ocean and Land Colour Instrument (OLCI) on-board Sentinel-3 (2016–present) was designed with similar mechanical and optical characteristics to the Envisat Medium Resolution Imaging Spectrometer (MERIS) (2002–2012) to ensure continuity with a number of land and marine biophysical products. The Sentinel-3 OLCI Terrestrial Chlorophyll Index (OTCI) is an indicator of canopy chlorophyll content and is intended to continue the legacy of the Envisat MERIS Terrestrial Chlorophyll Index (MTCI). Despite spectral similarities, validation and verification of consistency is essential to inform the user community about the product’s accuracy, uncertainty, and fitness for purpose. This paper aims to: (i) describe the theoretical basis of the Sentinel-3 OTCI and (ii) evaluate the spatiotemporal consistency between the Sentinel-3 OTCI and the Envisat MTCI. Two approaches were used to conduct the evaluation. Firstly, agreement between the Sentinel-3 OTCI and the Envisat MTCI archive was assessed over the Committee for Earth Observation Satellites (CEOS) Land Product Validation (LPV) core validation sites, enabling the temporal consistency of the two products to be investigated. Secondly, intercomparison of monthly Level-3 Sentinel-3 OTCI and Envisat MTCI composites was carried out to evaluate the spatial distribution of differences across the globe. In both cases, the agreement was quantified with statistical metrics (R2, NRMSD, bias) using an Envisat MTCI climatology based on the MERIS archive as the reference. Our results demonstrate strong agreement between the products. Specifically, high 1:1 correspondence (R2 >0.88), low relative difference (6.4–8.7%), low absolute bias (<0.1), and minimal error (NRMSD ~0.1) are observed. The temporal profiles reveal consistency in the expected range of values, amplitudes, and seasonal trajectories. Biases and discrepancies may be attributed to changes in land management practices, land cover change, and extreme climatic events occurred during the time gap between the missions; however, this requires further investigation. This research confirms that Sentinel-3 OTCI dataset can be used along with the Envisat MTCI to provide a data coverage over the last 20 years.
Continuity to the Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) will be provided by the Ocean and Land Colour Instrument (OLCI) on-board the Sentinel-3 missions. To ensure its utility in a wide range of operational applications, validation efforts are required. In the past, direct validation has been constrained by the need for costly airborne hyperspectral data acquisition, due to the lack of freely available high spatial resolution imagery incorporating appropriate spectral bands. The Multispectral Instrument (MSI) on-board the Sentinel-2 missions now offers a promising alternative. We explored the synergetic use of MSI data for validation of the OLCI Terrestrial Chlorophyll Index (OTCI) over the Valencia Anchor Station, a large agricultural site in the Valencian Community, Spain. Using empirical and machine learning techniques applied to MSI data, in-situ measurements were upscaled to the moderate spatial resolution of the OTCI. An RMSECV of 0.09 g m-2 (NRMSECV = 20.93%) was achieved, highlighting the valuable information MSI data can provide when used in synergy with OLCI data for land product validation. Good agreement between the OTCI and upscaled in-situ measurements was observed (r = 0.77, p < 0.01), providing increased confidence to users of the product over vineyard dominated Mediterranean environments.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.