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

2020 ◽  
Vol 12 (16) ◽  
pp. 2652
Author(s):  
J. Pastor-Guzman ◽  
L. Brown ◽  
H. Morris ◽  
L. Bourg ◽  
P. Goryl ◽  
...  
Keyword(s):  
Management Practices ◽  
Temporal Consistency ◽  
Imaging Spectrometer ◽  
Percentage Difference ◽  
Medium Resolution ◽  
Absolute Bias ◽  
Chlorophyll Index ◽  
Earth Observation Satellites ◽  
Temporal Profiles ◽  
Spatiotemporal Consistency

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.

scholarly journals Estimación de la cosecha de trigo en Andalucía usando series temporales de MERIS Terrestrial Chlorophyll Index (MTCI)

2018 ◽  
pp. 99 ◽  
Author(s):  
V. Egea-Cobrero ◽  
V. Rodriguez-Galiano ◽  
E. Sánchez-Rodríguez ◽  
M.A. García-Pérez
Keyword(s):  
Vegetation Index ◽  
Net Primary Production ◽  
Vegetation Indices ◽  
Yield Data ◽  
Imaging Spectrometer ◽  
Wheat Production ◽  
Production Studies ◽  
Normalised Difference Vegetation Index ◽  
Chlorophyll Index ◽  
The One

<p>There is a relationship between net primary production of wheat and vegetation indices obtained from satellite imaging. Most wheat production studies use the Normalised Difference Vegetation Index (NDVI) to estimate the production and yield of wheat and other crops. On the one hand, few studies use the MERIS Terrestrial Chlorophyll Index (MTCI) to determine crop yield and production on a regional level. This is possibly due to a lack of continuity of MERIS. On the other hand, the emergence of Sentinel 2 open new possibilities for the research and application of MTCI. This study has built two empirical models to estimate wheat production and yield in Andalusia. To this end, the study used the complete times series (weekly images from 2006–2011) of the MTCI vegetation index from the Medium Resolution Imaging Spectrometer (MERIS) sensor associated with the Andalusian yearbook for agricultural and fishing statistics (AEAP—Anuario de estadísticas agrarias y pesqueras de Andalucía). In order to build these models, the optimal development period for the plant needed to be identified, as did the time-based aggregation of MTCI values using said optimal period as a reference, and relation with the index, with direct observations of production and yield through spatial aggregation using coverage from the Geographic Information System for Agricultural Parcels (SIGPAC—Sistema de información geográfica de parcelas agrícolas) and requests for common agricultural policy (CAP) assistance. The obtained results indicate a significant association between the MTCI index and the production and yield data collected by AEAP at the 95% confidence level (R<sup>2</sup> =0.81 and R<sup>2</sup> =0.57, respectively).</p>

scholarly journals Evaluation of the MERIS aerosol product over land with AERONET

2008 ◽  
Vol 8 (1) ◽  
pp. 3721-3759 ◽  
Author(s):  
J. Vidot ◽  
R. Santer ◽  
O. Aznay
Keyword(s):  
Optical Properties ◽  
Surface Reflectance ◽  
Aerosol Optical Properties ◽  
Simple Criterion ◽  
Imaging Spectrometer ◽  
Inherent Optical Properties ◽  
Look Up Table ◽  
Medium Resolution ◽  
Reflectance Model ◽  
Resolution Imaging

Abstract. The Medium Resolution Imaging Spectrometer (MERIS) launched in February 2002 on-board the ENVISAT spacecraft is making global observations of top-of-atmosphere (TOA) radiances. Aerosol optical properties are retrieved over land using Look-Up Table (LUT) based algorithm and surface reflectances in the blue and the red spectral regions. We compared instantaneous aerosol optical thicknesses retrieved by MERIS in the blue and the red at locations containing sites within the Aerosol Robotic Network (AERONET). Between 2002 and 2005, a set of 500 MERIS images were used in this study. The result shows that, over land, MERIS aerosol optical thicknesses are well retrieved in the blue and poorly retrieved in the red, leading to an underestimation of the Angstrom coefficient. Correlations are improved by applying a simple criterion to avoid scenes probably contaminated by thin clouds. To investigate the weakness of the MERIS algorithm, ground-based radiometer measurements have been used in order to retrieve new aerosol models, based on their Inherent Optical Properties (IOP). These new aerosol models slightly improve the correlation, but the main problem of the MERIS aerosol product over land can be attributed to the surface reflectance model in the red.

scholarly journals Estimation of Gross Primary Productivity (GPP) Phenology of a Short-Rotation Plantation Using Remotely Sensed Indices Derived from Sentinel-2 Images

2020 ◽  
Vol 12 (13) ◽  
pp. 2104
Author(s):  
Maral Maleki ◽  
Nicola Arriga ◽  
José Miguel Barrios ◽  
Sebastian Wieneke ◽  
Qiang Liu ◽  
...  
Keyword(s):  
Primary Productivity ◽  
Seasonal Pattern ◽  
Vegetation Indices ◽  
Canopy Structure ◽  
Growing Season ◽  
Gross Primary Productivity ◽  
Imaging Spectrometer ◽  
Short Rotation ◽  
Medium Resolution ◽  
Sentinel 2

This study aimed to understand which vegetation indices (VIs) are an ideal proxy for describing phenology and interannual variability of Gross Primary Productivity (GPP) in short-rotation coppice (SRC) plantations. Canopy structure- and chlorophyll-sensitive VIs derived from Sentinel-2 images were used to estimate the start and end of the growing season (SOS and EOS, respectively) during the period 2016–2018, for an SRC poplar (Populus spp.) plantation in Lochristi (Belgium). Three different filtering methods (Savitzky–Golay (SavGol), polynomial (Polyfit) and Harmonic Analysis of Time Series (HANTS)) and five SOS- and EOS threshold methods (first derivative function, 10% and 20% percentages and 10% and 20% percentiles) were applied to identify the optimal methods for the determination of phenophases. Our results showed that the MEdium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) had the best fit with GPP phenology, as derived from eddy covariance measurements, in identifying SOS- and EOS-dates. For SOS, the performance was only slightly better than for several other indices, whereas for EOS, MTCI performed markedly better. The relationship between SOS/EOS derived from GPP and VIs varied interannually. MTCI described best the seasonal pattern of the SRC plantation’s GPP (R2 = 0.52 when combining all three years). However, during the extreme dry year 2018, the Chlorophyll Red Edge Index performed slightly better in reproducing growing season GPP variability than MTCI (R2 = 0.59; R2 = 0.49, respectively). Regarding smoothing functions, Polyfit and HANTS methods showed the best (and very similar) performances. We further found that defining SOS as the date at which the 10% or 20% percentile occurred, yielded the best agreement between the VIs and the GPP; while for EOS the dates of the 10% percentile threshold came out as the best.

scholarly journals Estimating Meltwater Drainage Onset Timing and Duration of Landfast Ice in the Canadian Arctic Archipelago Using AMSR-E Passive Microwave Data

2020 ◽  
Vol 12 (6) ◽  
pp. 1033
Author(s):  
Yasuhiro Tanaka
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Polynomial Regression ◽  
The Arctic ◽  
Imaging Spectrometer ◽  
Earth Observing System ◽  
Medium Resolution ◽  
Onset Timing ◽  
Landfast Ice ◽  
Heat Budgets

Meltwater drainage onset (DO) timing and drainage duration (DD) related to snowmelt-water redistribution are both important for understanding not only the Arctic energy and heat budgets but also the salt/heat balance of the mixed layer in the ocean and sea-ice ecosystem. We present DO and DD as determined from the time series of Advanced Microwave Scanning Radiometer-Earth observing system (AMSR-E) melt pond fraction (MPF) estimates in an area with Canadian landfast ice. To address the lack of evaluation on a day-by-day basis for the AMSR-E MPF estimate, we first compared AMSR-E MPF with the daily Medium Resolution Imaging Spectrometer (MERIS) MPF. The AMSR-E MPF estimate correlates significantly with the MERIS MPF (r = 0.73–0.83). The estimate has a product quality similar to the MERIS MPF only when the albedo is around 0.5–0.7 and a positive bias of up to 10% in areas with an albedo of 0.7–0.9, including melting snow. The DO/DD estimates are determined by using a polynomial regression curve fitted on the time series of the AMSR-E MPF. The DOs/DDs from time series of the AMSR-E and MERIS MPFs are compared, revealing consistency in both DD and DO. The DO timing from 2006 to 2011 is correlated with melt onset timing. To the best of our knowledge, our study provides the first large-scale information on both DO timing and DD.

scholarly journals The Retrieval of Land Surface Pressure from MERIS Measurements in the Oxygen A Band

2009 ◽  
Vol 26 (7) ◽  
pp. 1367-1377 ◽  
Author(s):  
Rasmus Lindstrot ◽  
Rene Preusker ◽  
Jürgen Fischer
Keyword(s):  
Surface Pressure ◽  
Land Surface ◽  
Sea Level Pressure ◽  
Imaging Spectrometer ◽  
Medium Resolution ◽  
Digital Elevation ◽  
Sensitivity Studies ◽  
Ice Surfaces ◽  
Elevation Model ◽  
Digital Elevation Model Data

Abstract Measurements of the Medium-Resolution Imaging Spectrometer (MERIS) on the Environmental Satellite (Envisat) are used for the retrieval of surface pressure above land and ice surfaces. The algorithm is based on the exploitation of gaseous absorption in the oxygen A band at 762 nm. The strength of absorption is directly related to the average photon pathlength, which in clear-sky cases above bright surfaces is mainly determined by the surface pressure, with minor influences from scattering at aerosols. Sensitivity studies regarding the influences of aerosol optical thickness and scale height and the temperature profile on the measured radiances are presented. Additionally, the sensitivity of the retrieval to the accuracy of the spectral characterization of MERIS is quantified. The algorithm for the retrieval of surface pressure (SPFUB) is presented and validated against surface pressure maps constructed from ECMWF sea level pressure forecasts in combination with digital elevation model data. The accuracy of SPFUB was found to be within 10 hPa above ice surfaces at Greenland and 15 hPa above desert and mountain scenes in northern Africa and southwest Asia. In a case study above Greenland the accuracy of SPFUB could be enhanced to be better than 3 hPa by spatial averaging over areas of 40 km × 40 km.

Sign in / Sign up

Export Citation Format

Share Document