Harry Morris

Satellite remote sensing is an important method for forest phenological studies at continental or global scales. Sentinel-1 (S1), a polar orbit satellite with a spatial resolution of 10 m, provides an opportunity to observe high-resolution forest phenology. The sensitivities of S1 C-band backscatter measurements to vegetation phenology, such as crops, meadows, and mixed forests, have been discussed, whereas their performance for different forest types has not yet been quantitatively assessed. It is necessary to evaluate accuracy before adapting S1 datasets in forest phenological studies. This study discusses the seasonal variations in S1 backscatter measurements and assesses the accuracy of S1-based forest phenological metrics in two types of typical forests: deciduous and coniferous. S1 C-band SAR dual-polarization backscatter measurements for the period 2017–2019 were used to extract forest phenology metrics using the Fourier transform (FT) and double logistic (DL) functions. Phenological metrics from the ground-based PhenoCam dataset were used for evaluation. The S1 backscatter VV-VH signal peaks for deciduous and coniferous forests occur in the winter and summer, respectively. The S1 backscatter could reasonably characterize the start of season (SOS) of deciduous forests, with R² values up to 0.8, whereas the R² values for coniferous forest SOS were less than 0.30. Moreover, the retrieved end of season (EOS) was less accurate than the SOS. The differences in accuracy of S1 backscatter phenological metrics between deciduous and coniferous forests can be explained by the differences in seasonal changes in their corresponding canopy structures. To conclude, S1 C-band backscatter has a reasonable performance when monitoring the SOS of deciduous broadleaf forests (R² = 0.8) and relatively poor performance when extracting EOS of deciduous broadleaf forests (R² = 0.25) or phenology of evergreen needleleaf forests (R² = 0.2).

With a wide range of satellite-derived vegetation bio-geophysical products now available to users, validation efforts are required to assess their accuracy and fitness for purpose. Substantial progress in the validation of such products has been made over the last two decades, but quantification of the uncertainties associated with in situ reference measurements is rarely performed, and the incorporation of uncertainties within upscaling procedures is cursory at best. Since current validation practices assume that reference data represent the truth, our ability to reliably demonstrate compliance with product uncertainty requirements through conformity testing is limited. The Fiducial Reference Measurements for Vegetation (FRM4VEG) project, initiated by the European Space Agency, is aiming to address this challenge by applying metrological principles to vegetation and surface reflectance product validation. Following FRM principles, and in accordance with the International Standards Organisation’s (ISO) Guide to the Expression of Uncertainty in Measurement (GUM), for the first time, we describe an end-to-end uncertainty evaluation framework for reference data of two key vegetation bio-geophysical variables: the fraction of absorbed photosynthetically active radiation (FAPAR) and canopy chlorophyll content (CCC). The process involves quantifying the uncertainties associated with individual in situ reference measurements and incorporating these uncertainties within the upscaling procedure (as well as those associated with the high-spatial-resolution imagery used for upscaling). The framework was demonstrated in two field campaigns covering agricultural crops (Las Tiesas–Barrax, Spain) and deciduous broadleaf forest (Wytham Woods, UK). Providing high-spatial-resolution reference maps with per-pixel uncertainty estimates, the framework is applicable to a range of other bio-geophysical variables including leaf area index (LAI), the fraction of vegetation cover (FCOVER), and canopy water content (CWC). The proposed procedures will facilitate conformity testing of moderate spatial resolution vegetation bio-geophysical products in future validation exercises.

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 global mean percentage difference (−1.86 to 0.61), 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.