The Sentinel-3 OLCI Terrestrial Chlorophyll Index (OTCI): Algorithm Improvements, Spatiotemporal Consistency and Continuity with the MERIS Archive

: 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 di ﬀ erences across the globe. In both cases, the agreement was quantiﬁed with statistical metrics (R 2 , 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 (R 2 > 0.88), low global mean percentage di ﬀ erence ( − 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.


Introduction
Chlorophyll plays a key role in the chain of reactions that convert solar radiation, carbon dioxide, and water molecules into chemical energy and plant biomass [1]. Leaf chlorophyll concentration (LCC) is used as an indicator of photosynthetic capacity and productivity, plant phenology, and nutrition status [2][3][4]. The amount of chlorophyll in leaves is regulated by nutrient availability, disease, and environmental stress [5][6][7]. Natural vegetation exposed to pronounced seasonal variation in climatic conditions (e.g., temperate deciduous forests) experiences increases in chlorophyll as leaves unfold and

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To introduce the Sentinel-3 OTCI and describe updates and algorithm improvements with respect to the Envisat MTCI algorithm • To assess the spatiotemporal consistency between the Sentinel-3 OTCI and Envisat MTCI using a comparison with the MERIS climatology

Description of the OTCI Product
The Level-2 (L2) OLCI land product Terrestrial Chlorophyll Index (OTCI) that provides continuity to the Envisat MTCI is freely available to the public through the Copernicus Open Access Hub (https://scihub.copernicus.eu/). The Sentinel-3 OTCI is produced at full resolution (300 m) and reduced resolution (1200 m), and is computed from OLCI red, red-edge and NIR bands (Bands 10, 11, and 12) using Equation (1 (1) where Band 10, Band 11, Band 12 are the OLCI sensor reflectance bands centred at 681. 25 (2) where Band 8, Band 9, Band 10 are the MERIS sensor reflectance bands centred at 681. 25, 708.75, 753.75 nm. The signal-to-noise ratio (SNR) in OLCI bands is improved by a factor of 2 compared to MERIS bands [52,53] (Table 1). The outilne of the Sentinel-3 OTCI algorithm is shown in Figure 1, while the Sentinel-3 OLCI spectral bands and the comparison to Envisat MERIS spectral bands are shown in Figure 2. At present, surface reflectance products are not available directly from OLCI, although it is worth noting third-party atmospheric correction algorithms suitable for OLCI data are now available (e.g., De Keukelaere et al. [54]). Consistent with MERIS, in the operational processing chain, partial atmospheric correction is carried out prior to calculation of the OTCI. First, top of atmosphere (TOA) reflectance is computed from L1B TOA radiance using the solar spectral irradiance and the sun zenith angle [55]. In the next step, gaseous correction is performed to remove the effect of O 3 , O 2 , and H 2 O absorption [56]. Using a separate subroutine, Rayleigh correction is applied to TOA reflectance to account for the effect of molecular scattering [57]. Then, the across track field of view gradient caused by the variation in central wavelength ('smile effect') is corrected. An initial spectral test is carried out on land pixels using the NIR band to identify non-vegetated pixels. OTCI is then computed only on the pixels that passed the spectral test. Finally, the quality flags described in the next section are developed.
Uncertainties in land products affect their reliability and fitness for purpose. A key improvement over the Envisat MTCI is the per-pixel uncertainties estimate that is provided with the Sentinel-3 OTCI which are in turn computed from the TOA radiance uncertainties. This is, within the Sentinel-3 OTCI product, uncertainties of the input data are propagated through the algorithm according to the law of propagation of uncertainties as described by Miura [58]. This way, the product has a measure of uncertainty associated to each pixel.

Quality Flags
Documentation of traceable pixel-level quality information is a fundamental principle of the Quality Assurance Framework for Earth Observation [59]. The Sentinel-3 OTCI is accompanied by a data layer with 8-bit encoded information related to individual aspects of data quality: (i) bad data, (ii) view angle, (iii) aerosols, and (iv) soil. The sensitivity analyses and error assessments described below were carried out in the algorithm development and are addressed in more detail in the Sentinel-3 OTCI Algorithm Theoretical Basis Document (ATBD) [60].
The Sentinel-3 OTCI bad data flag consists of spectral tests conducted on pixels classified as land, followed by a range check. The spectral tests, performed on normalised surface reflectance OLCI bands are: (1) band 10 (red) < 0.2, (2) band 12 (NIR) > 0.1, and (3) the difference between band 12 and 10 must be greater than 0.1. Once the three conditions are met, pixel values are verified to fall within the Sentinel-3OTCI expected range (≥0.0 and ≤6.5). Passing the spectral tests and the verification indicates no presence of highly reflective pixels (possibly due to cloud and snow and good data quality). On the contrary, failing the tests indicates presence of clouds or poor data quality. A difference between Envisat MTCI and Sentinel-3 OTCI is the range of acceptable pixel values; empirical evidence suggested that Envisat MTCI maximum value could be increased from 5.5 to 6.5 without risk of saturation.

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A viewing and illumination quality flag was developed as follows. First, geometry effects on the L2 product were modelled over a variety of Sun and viewing geometries. Per pixel total uncertainty was modelled for the full OLCI view angle for combinations of solar zenith angle (SZA), solar azimuth angle (SAA), and Sentinel-3 OTCI values. The experiment revealed that the combination of SZA and SAA that resulted in maximum and minimum variation were SZA = 38; SAA = 158 and SZA = 23; SAA = 100, respectively. For a given configuration of SZA and SAA, total variation increases with increase in Sentinel-3 OTCI value. Then, to derive angular ranges for the acquisition and illumination geometry flag, pre-computed uncertainty values for each pixel were stored in a look-up-table (LUT). The LUT contained maximum uncertainty estimations for discrete classes of SZA and view zenith angle (VZA) as a function of predefined Sentinel-3 OTCI ranges. For a given pixel, the following auxiliary data is needed to query the pixel's uncertainty from the LUT: OTCI, SZA, SAA, and VZA. Example values of the view angle flag are presented in Table 2. This was the workflow to establish the angular ranges for the quality flag but this is not implemented in the processor itself. The scattering in the red region of the spectrum caused by aerosols translates into an increase in the OLCI red band reflectance. Due to its proximity, aerosols have similar effect on the red edge. On the contrary, a decrease in the NIR band is observed due to aerosols absorption in the NIR region. These responses cause a variation of Sentinel-3 OTCI of~3% at AOT 440 < 0.3, and up to 21.6% at AOT 440 > 1.4 [60]. To date, there are no operational AOT estimates available from OLCI (though there is an OLCI and Sea and Land Surface Temperature Radiometer (SLSTR) Synergy AOT product). When AOT information becomes available from OLCI, the flags presented in Table 2 will be implemented. At the time of this paper, these are set as very good condition.
Pixels with bare soil can have Sentinel-3 OTCI values between 1.5 and 1.9 that realistically do not correspond to any CCC. Soil contribution is evaluated using the soil discrimination index (SDI) algorithm (Equation (3)). The SDI, which uses the spectral features of the green, red, and NIR bands, is a combination of the simple ratio index (SR) [61] and the green difference index (GD). The SR is positively correlated to vegetation density whereas the GD is negatively correlated to chlorophyll content with values >3 corresponding to bright bare soils. Establishing an SDI threshold that distinguishes between Sentinel-3 OTCI values <2 due to soil and Sentinel-3 OTCI <2 due to sparse vegetation is necessary. Tests revealed that SDI threshold of 0.9 effectively identifies soil pixels while maintaining vegetated pixels [60].

Methods
The consistency of the Sentinel-3 OTCI and Envisat MTCI was evaluated at two distinct scales. Firstly, agreement between the Sentinel-3 OTCI and the Envisat MTCI archive was assessed over specific validation sites, enabling the temporal consistency of the two products to be investigated. Secondly, monthly Sentinel-3 OTCI composites were compared with a monthly Envisat MTCI climatology for the entire globe, enabling the consistency of spatial patterns to be assessed, and facilitating examination by latitude and land cover. In all cases, the comparisons were conducted against the full-mission Envisat MTCI climatology (2002-2012).

Evaluating Temporal Consistency at Specific Validation Sites
To assess the temporal consistency of Sentinel-3 OTCI and Envisat MTCI values over specific validation sites, pixel extractions were obtained from Level-2 Sentinel-3 OTCI and third reprocessing (3RP) Envisat MTCI data over 37 validation sites, representing a variety of land cover types, regions, and species (Table S1). The selection of sites included the CEOS WGCV LPV sub-group supersites, which were adopted on the basis of a series of requirements for the validation of land products, and was supplemented by several 'core' sites identified by the Sentinel-3 Validation Team (S3VT). The sites are well characterised in terms of canopy structure and biophysical variables, and have long-term monitoring operations with established protocols. The extractions covered a footprint of 3 × 3 full resolution (300 m) pixels to compensate for geometric inaccuracy and the point spread function of the sensors.

Evaluating Spatial Consistency at the Global Scale
To assess the spatial consistency of Sentinel-3 OTCI and Envisat MTCI values over the entire globe, monthly Level-3 Sentinel-3 OTCI composites were generated using the Sentinel Application Platform (SNAP) binning tool at a 4.6 km spatial resolution. All available Level-2 reduced resolution (RR) products were used as an input. Pixels were filtered according to a Land Quality Science Flag (LQSF) expression that removes clouds, snow, ice, and pixels with out of range Sentinel-3 OTCI inputs/outputs, allowing only the highest quality pixels. The specific LQSF values considered invalid were: LQSF.CLOUD, LQSF.CLOUD_AMBIGUOUS, LQSF.CLOUD_MARGIN, LQSF.SNOW_ICE, and LQSF.OTCI_FAIL. Valid pixels were aggregated using mean value downsampling and mapped to a fixed grid.
For comparison with the monthly Level-3 Sentinel-3 OTCI composites, monthly Level-3 4.6 km Envisat MTCI composites were obtained from the UK Centre for Environmental Data Analysis (CEDA) [62,63]. In this case, the flags used to screen poor quality Envisat MTCI values were: L2_FLAGS.CLOUD, L2_FLAGS.WATER, and LS_FLAGS.PCD_17. Note L2_FLAGS.PCD_17 is a product confidence flag raised if the algorithm input or output falls beyond a valid range (Table 3). Once obtained, the mean Envisat MTCI value for each month over all years (2002 to 2012) was computed. Version 2.0 of the Copernicus Global Land Service Land Cover (CGLS_LC_100) product [64] was used to evaluate global consistency per land cover type. The CGLS_LC_100 is based on PROBA-V time-series (2015 as reference year), a database of land cover training sites, and other ancillary data. The land cover product is mapped to the Sentinel-2 tiling grid and consists of 23 discrete classes at 100 m spatial resolution. The Level-1 map, which is formed by broad cover classes (i.e., bare/sparse vegetation, snow/ice, and permanent water) has an overall accuracy is 80.2 +/−0.7%. The map where closed and open forests are separated (Level-2), has an overall accuracy of 75.1 +/−0.7% [60]. The CGLS_LC_100 product was resampled to match the 4.6 km spatial resolution of the monthly Level-3 OTCI/MTCI composites, using majority (modal) aggregation.

Statistical Analysis
To quantify agreement between the both products, descriptive statistics, the bias, the coefficient of determination, and the normalised root mean square difference (NRMSD) were used. Comparisons were conducted taking the Envisat MTCI archive as the reference. The bias represents the average difference between the Sentinel-3 OTCI and Envisat MTCI. The sign of the bias is interpreted as over (positive sign) or under (negative sign) estimation. Ideally, the bias should be close to zero. The coefficient of determination (R 2 ) is a measure of the linear relationship between the compared products. The root mean square error (RMSE) is a common statistical indicator of accuracy. However, RMSE is scale dependent and thus specific to a particular variable. The normalisation of the RMSE by the mean (i.e., NRMSD) allows the accuracy of Sentinel-3 OTCI to be compared between other datasets and land products. Similarly, the cultivated site IT-Lison has maximum Envisat MTCI and Sentinel-3 OTCI values of 2.5 to 3.5 between DOY 200 to 230 (mid-July to mid-August). When individual observations were inspected, Sentinel-3 OTCI acquisitions depict a small amount of dispersion, but realistically capture the annual trend of the Envisat MTCI climatology in both hemispheres (middle panel in Figure 3). The comparison of monthly mean values shows moderate to strong agreement (R 2 = 0.55 to 0.99), low bias (−0.36 to 0.40), and low NRMSD (0.02 to 0.12) (Figure 3 and Table 4).    Figure 4 presents the monthly mean of all 37 sites for the entire archive of both products (12 years for Envisat MTCI and 4 years of Sentinel-3 OTCI). There is very strong agreement between both products (R 2 = 0.9; NRMSD < 0.10). There is more dispersion towards higher values; the Sentinel-3 OTCI shows minimal underestimation for DBF in values >3, whereas EBF depicts slight overestimation. In general, however, the observed bias is marginal (0.006).

Spatial Consistency at the Global Scale
This section compares Envisat MTCI and Sentinel-3 OTCI global composites. Northern hemisphere winter (summer in the southern hemisphere) and summer (winter in the southern hemisphere), periods were investigated as they correspond to times of contrasting vegetation photosynthetic activity. Overall, both products realistically capture the spatial patterns of CCC. Key findings are described below.

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AU-Zigzag-Creek EBF −37.47 148. 34 (Table 4). Red and grey lines represent the modelled and 1:1 lines, respectively. OLCI data acquisition is up to 15 January 2020 (S3A orbital cycle 53). In both products, the frequency distribution of pixel values is skewed to the left (i.e., indicating a higher concentration of lower values) and bimodal, dominated by a high peak around 1.5 and a small peak around 3. The mean of the Sentinel-3 OTCI (1.82) is slightly larger than the Envisat MTCI (1.80), whilst the dispersion is greater in MTCI (standard deviation = 0.61; range = 2.46) compared to Sentinel-3 OTCI (standard deviation = 0.56; range = 2.26).
The scatterplot indicates good agreement between the products in winter, showing a linear fit close to the 1:1 line (R 2 = 0.89). The statistical metrics depicts low error (NRMSD = 0.11) but slight overestimation when compared to the Envisat MTCI (bias = 0.02). The percentage difference histogram is unimodal it has a flattened nearly symmetrical shape. The mean percentage difference is −1.86 (10.05% standard deviation) and the range between 5th and 95th percentile is 55.78%. In addition, 67% of land mass has a difference less than 10%. Remote Sens. 2020, 12, x FOR PEER REVIEW 13 of 23 The spatial pattern of the Envisat MTCI and Sentinel-3 OTCI for summer (Jun, Jul, Aug) is shown in Figure 6a,b. The products reflect higher vegetation activity in the northern hemisphere and depict strong spatial agreement. Maximum values are observed in east of the United States, the Amazon and Congo rainforests, Europe and European Russia, the north of India, the east of China, and South East Asia. Areas of lower vegetation activity are the world's tundra (the north of Canada and Russia) and the herbaceous landscapes of Kazakhstan, South America, the south and horn of Africa, and central and west of Australia. Summer maps have substantially less missing data when compared to winter.
With respect to the frequency distribution of values, like the winter, Figure 6d shows the histograms are skewed to the left and bimodal. A dominant peak of values is located between 1.5 and 2.0, and a second smaller peak is located between 2.0 and 3.0. Overall, the mean and spread is marginally greater for Envisat MTCI (mean = 1.94; standard deviation = 0.63; range = 2.58) than for Sentinel-3 OTCI (mean = 1.93; standard deviation = 0.77; range = 2.56).  Feb (a,b)-and spatial distribution of differences (c). Index value frequency distribution (d), density scatterplot of the agreement between the two products (e), and frequency distribution of differences (f). For (c) and (f), difference is expressed in percentage computed as the ratio of per pixel absolute difference to the mean of the two pixels. Blue indicates areas where the Sentinel-3 OTCI is greater than the Envisat MTCI, conversely, red indicates areas where the Envisat MTCI is greater than the Sentinel-3 OTCI. Grey pixels are areas where the difference between the Envisat MTCI and Sentinel-3 OTCI is within ±5%. Figure 5c shows the gridded spatial variability of MTCI-OTCI difference for winter. Major negative differences (in blue), where the Sentinel-3 OTCI is greater than the Envisat MTCI, occur in East and South Central China, the Indo-Gangetic Plain, the southern portion of European Russia, Europe, and the east of South America (predominately agricultural region). Most of the positive differences (in red, where the Envisat MTCI is greater than the Sentinel-3 OTCI, occur in the Amazon and Congo basins, the north of Eastern Europe, the north of Australia, and South East Asia.

Northern Hemisphere Summer (Jun, Jul, Aug)
The spatial pattern of the Envisat MTCI and Sentinel-3 OTCI for summer (Jun, Jul, Aug) is shown in Figure 6a  Index value frequency distribution (d), density scatterplot of agreement between the two products (e), and frequency distribution of differences (f). For (c) and (f), difference is expressed in percentage computed as the ratio of per pixel absolute difference to the mean. Blue indicates areas where the Sentinel-3 OTCI is greater than the Envisat MTCI, conversely, red indicates areas where the Envisat MTCI is greater than the Sentinel-3 OTCI. Grey pixels are areas where the difference between the Envisat MTCI and Sentinel-3 OTCI is within ±5%.
The scatterplot further reflects the strong agreement. Quantitatively, the 1:1 comparison showed R 2 = 0.92, NRMSD = 0.09, and absolute bias = 0.01. Geographically, discrepancies where the Sentinel-3 OTCI is higher than the Envisat MTCI (blue) are located in the Midwest United States, Ukraine, and Northeast China (dominated agricultural land). On the contrary, areas where the Envisat MTCI is higher than the Sentinel-3 OTCI (red a), are found in the west United States and Mexico, the Amazon rainforest, west Africa, and southern Asia. The frequency distribution of percentage differences is symmetrical and unimodal with a mean and median of 0.61% (7.51 standard deviation), the range between the 5th and 95th percentile is 46.36%. Finally, the percentage of land mass with less than 10% is 81%.  Aug (a,b)-and spatial distribution of differences (c). Index value frequency distribution (d), density scatterplot of agreement between the two products (e), and frequency distribution of differences (f). For (c) and (f), difference is expressed in percentage computed as the ratio of per pixel absolute difference to the mean. Blue indicates areas where the Sentinel-3 OTCI is greater than the Envisat MTCI, conversely, red indicates areas where the Envisat MTCI is greater than the Sentinel-3 OTCI. Grey pixels are areas where the difference between the Envisat MTCI and Sentinel-3 OTCI is within ±5%.
With respect to the frequency distribution of values, like the winter, Figure 6d shows the histograms are skewed to the left and bimodal. A dominant peak of values is located between 1.5 and 2.0, and a second smaller peak is located between 2.0 and 3.0. Overall, the mean and spread is marginally greater for Envisat MTCI (mean = 1.94; standard deviation = 0.63; range = 2.58) than for Sentinel-3 OTCI (mean = 1.93; standard deviation = 0.77; range = 2.56).
The scatterplot further reflects the strong agreement. Quantitatively, the 1:1 comparison showed R 2 = 0.92, NRMSD = 0.09, and absolute bias = 0.01. Geographically, discrepancies where the Sentinel-3 OTCI is higher than the Envisat MTCI (blue) are located in the Midwest United States, Ukraine, and Northeast China (dominated agricultural land). On the contrary, areas where the Envisat MTCI is higher than the Sentinel-3 OTCI (red a), are found in the west United States and Mexico, the Amazon rainforest, west Africa, and southern Asia. The frequency distribution of percentage differences is symmetrical and unimodal with a mean and median of 0.61% (7.51 standard deviation), the range between the 5th and 95th percentile is 46.36%. Finally, the percentage of land mass with less than 10% is 81%.

Latitudinal Variation
Latitudinal variation data comparison has been presented in Figure 7.

Latitudinal Variation
Latitudinal variation data comparison has been presented in Figure 7.  Figure 7 compares the mean (±1 standard deviation) latitudinal gradient of the products over four seasons. Maximum values (>2.5) are found at the tropics (±10⁰), whereas minimum values at higher latitudes (>50° north and >30° south). In the region of maximum vegetation activity (±10°), the Envisat MTCI remains marginally higher than the Sentinel-3 OTCI. However, overall the products exhibit high correlation (R 2 > 0.96), with no apparent bias. The products exhibit latitudinal seasonal variations. For instance, in summer a peak emerges at 20° latitude that persists until autumn, and dispersion increases between 30° and 45°.

Consistency by Land Cover
Statistical results of product comparisons for six land cover classes (ENF, EBF, DNF, DBF, nonforest, and cultivated) across four seasons is presented in Table 5. In general, in the northern hemisphere, the strongest agreement occurred in spring and autumn (R 2 > 0.87), particularly for ENF and DBF. In the southern hemisphere, the strongest agreement was for winter and autumn in particular for DBF and non-forest (R 2 > 0.87). In the north, the poorest agreement was for DNF whereas in the south it was for cultivated land. In both hemispheres, the largest NRMSD was observed in the cultivated land. On average, NRMSD remained ≤0.11 across seasons and land cover types. Similarly, bias remained ≤0.10 across seasons except for EBF and DBF in winter in the south. The monthly evolution of the products further depicts their seasonal consistency in Figure 8. Notably, for EBF, DNF, non-forest, and cultivated, the winter and autumn months show a greater discrepancy, where the Envisat MTCI interquartile range is greater and the Sentinel-3 OTCI median is higher, reflecting an overestimation.

Consistency by Land Cover
Statistical results of product comparisons for six land cover classes (ENF, EBF, DNF, DBF, non-forest, and cultivated) across four seasons is presented in Table 5. In general, in the northern hemisphere, the strongest agreement occurred in spring and autumn (R 2 > 0.87), particularly for ENF and DBF. In the southern hemisphere, the strongest agreement was for winter and autumn in particular for DBF and non-forest (R 2 > 0.87). In the north, the poorest agreement was for DNF whereas in the south it was for cultivated land. In both hemispheres, the largest NRMSD was observed in the cultivated land. On average, NRMSD remained ≤0.11 across seasons and land cover types. Similarly, bias remained ≤0.10 across seasons except for EBF and DBF in winter in the south. The monthly evolution of the products further depicts their seasonal consistency in Figure 8. Notably, for EBF, DNF, non-forest, and cultivated, the winter and autumn months show a greater discrepancy, where the Envisat MTCI interquartile range is greater and the Sentinel-3 OTCI median is higher, reflecting an overestimation.

Product Performance
The results of the temporal and spatial comparison between the Envisat MTCI and Sentinel-3 OTCI revealed strong agreement despite the time gap between the two missions. Globally, both , non-forest, and cultivated. The boxes embody the median, the first, and the third quartile of the long-term monthly Envisat MTCI and Sentinel-3 OTCI values. ENF and DNF are missing for the southern hemisphere because these land cover types did not have a significant number of pixels.

Product Performance
The results of the temporal and spatial comparison between the Envisat MTCI and Sentinel-3 OTCI revealed strong agreement despite the time gap between the two missions. Globally, both products are strongly correlated (R 2 ≥ 0.89) with low absolute difference (NRMSD ≤ 0.11) and absolute bias (≤0.02). This agreement is not surprising as OLCI has a similar mechanical and optical design to MERIS (Figure 2), albeit with improvements including higher signal-to-noise ratio (Table 1), increased radiometric stability, shorter revisit times (~2 days). This agreement provides a solid case to use Sentinel-3 OTCI as a continuity of the Envisat MTCI time-series. Thus, potentially building a unique data set of canopy chlorophyll content going back to 2002 (with data gap 2012-2014). Further research on spectral similarity between the MERIS and OLCI sensor and their continuity would strengthen the case.
Notably, the global maps show differences up to 16-20% in some regions such as the Amazon basin, sub-Saharan Africa, and north of Australia. The spatial distribution and the magnitude of discrepancies varied between seasons. For instance, higher agreement is observed in summer (Jun, Jul, Aug) where 81% of the land mass had a difference of less than 10% (Figures 5f and 6f). Discrepancies between the products occurred in agricultural and densely forested areas. Whilst some discrepancies might theoretically be explained by differences in sensor viewing geometry and geolocation, experimental data demo3nstrated the impact of viewing geometry on the computed index values is marginal [60]. Furthermore, in this study, geolocation inaccuracies were minimized by using reduced resolution products. Overpass time could be discarded as a contributing factor as both, Envisat and Sentinel-3 were configured to fly in a sun-synchronous orbit at a local equatorial crossing time of 10:00 a.m. [53]. Differences in land management practices, land conversion and extreme climatic events resulting in productivity fluctuations during the gap between missions could be contributing factors. However, further investigation is necessary to improve our understanding. In the first instance, a detailed analysis of these factors over the validation sites used in this research could be a starting point.
Limitations with this study should be noted. Firstly, the compared datasets are not contemporaneous but there is a gap of four years between them. MERIS ceased operations in 2012 and OLCI was launched in 2016. Generally, intercomparisons are conducted with time-overlapping products. This introduces uncertainties due to interannual variations and extreme events. Furthermore, data available for Envisat MTCI span 10 years (2002-2012) whereas the OTCI archive is 3 years (2016-2019), which makes the Sentinel-3 OTCI more sensitive to outliers. This can be seen in Figure 1 (middle column) where Sentinel-3 OTCI presents abrupt transitions. Nevertheless, in the absence of other operational CCC products, the approach offers useful insights into product performance.

OTCI Applications and Future Work
The results of this study give confidence to users that the Sentinel-3 OTCI can provide continuity to the established MERIS archive. By taking advantage of guaranteed data availability through the Copernicus programme and leveraging several algorithm improvements, the Sentinel-3 OTCI has the potential for applications in risk management such as livestock and crop insurance, gross primary productivity (GPP) modelling, the investigation of vegetation responses to climate change, and food security.
To ensure continued utility of the Sentinel-3 OTCI, future work should focus on direct validation using traceable in situ measurements of CCC (as is being considered in the Fiducial Reference Measurements for Vegetation (FRM4VEG) project). Another opportunity is the indirect verification of OLCI A/B. Although at the moment Sentinel-3 B mission duration is too short (May 2018-present) to allow robust intercomparisons, eventually, comparisons between S3 units will help establish relative uncertainty characterisations. Initial results suggest there is no difference between A and B units [65], this should continue into the future to investigate any potential impact of sensor degradation on the Sentinel-3 OTCI.
The CEOS LPV and ESA Core validation sites selected in this research include a wide range of latitudes (52N to 43S) and vegetation types. However, these sites are concentrated in the United States, Europe, and Australia. This highlights the need to expand the geographical representativeness.
Future work may consider including tropical and higher latitude sites (e.g., tundra) in addition to underrepresented regions such as Central and South America, Africa, and Asia.
Quantitative analysis of between-sensor difference is crucial for long-term climate change studies. Although Sentinel-3 OLCI has similar design as Envisat MERIS with nearly identical spectral bands (Figure 2), contribution of differences in solar-viewing geometry, spectral bandwidth, and spectral response function cannot be entirely ruled out. BRDF corrections and spectral adjustment are ways to remove these effects and harmonise the two products, which could be considered in future work (e.g., Claverie, et al. [66]). Performing these corrections was beyond the scope of this research, because the aim is to assess the consistency between Envisat MTCI and Sentinel-3 OTCI that are currently available to the end user.
An additional area of opportunity to maximise the utility of the Sentinel-3 OTCI is the production and dissemination of Level-3 composites, since spatiotemporal syntheses rather than Level-2 products are required by many downstream applications. Within the Sentinel-3 Mission Performance Centre (S3MPC), production of Level-3 products mapped on a common grid, projection, and spatial extent are planned in the near future.

Conclusions
Sentinel-3 has been in operation since April 2016 delivering the OTCI. This product provides continuity to the related land products derived from Envisat MTCI. The Sentinel-3 OTCI has been released to the public, therefore, there is a need to evaluate the product's spatial and temporal consistency with the Envisat MTCI archive. This paper presents a description of the Sentinel-3 OTCI algorithm and the intercomparison of 3 years (2016-2019) of Sentinel-3 OTCI data against 10 years (2002-2012) of Envisat MTCI data, making use of a MERIS climatology. The strong spatial and temporal consistency between the Sentinel-3 OTCI and Envisat MTCI demonstrated in this study provides confidence to users of the product. Future work will involve direct validation using in situ reference measurements following best practices and protocols, Sentinel-3 A and B unit intercomparisons, and L3 product development.