Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation
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In this article, the authors evaluated the potential of the future ESA Sentinel-2 (S-2) satellite for estimation of canopy chlorophyll content, leaf area index (LAI) and leaf chlorophyLL concentration (LCC) using data from multiple field campaigns.Abstract:
The red edge position (REP) in the vegetation spectral reflectance is a surrogate measure of vegetation chlorophyll content, and hence can be used to monitor the health and function of vegetation. The Multi-Spectral Instrument (MSI) aboard the future ESA Sentinel-2 (S-2) satellite will provide the opportunity for estimation of the REP at much higher spatial resolution (20 m) than has been previously possible with spaceborne sensors such as Medium Resolution Imaging Spectrometer (MERIS) aboard ENVISAT. This study aims to evaluate the potential of S-2 MSI sensor for estimation of canopy chlorophyll content, leaf area index (LAI) and leaf chlorophyll concentration (LCC) using data from multiple field campaigns. Included in the assessed field campaigns are results from SEN3Exp in Barrax, Spain composed of 35 elementary sampling units (ESUs) of LCC and LAI which have been assessed for correlation with simulated MSI data using a CASI airborne imaging spectrometer. Analysis also presents results from SicilyS2EVAL, a campaign consisting of 25 ESUs in Sicily, Italy supported by a simultaneous Specim Aisa-Eagle data acquisition. In addition, these results were compared to outputs from the PROSAIL model for similar values of biophysical variables in the ESUs. The paper in turn assessed the scope of S-2 for retrieval of biophysical variables using these combined datasets through investigating the performance of the relevant Vegetation Indices (VIs) as well as presenting the novel Inverted Red-Edge Chlorophyll Index (IRECI) and Sentinel-2 Red-Edge Position (S2REP). Results indicated significant relationships between both canopy chlorophyll content and LAI for simulated MSI data using IRECI or the Normalised Difference Vegetation Index (NDVI) while S2REP and the MERIS Terrestrial Chlorophyll Index (MTCI) were found to have the strongest correlation for retrieval of LCC.read more
Citations
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First Experience with Sentinel-2 Data for Crop and Tree Species Classifications in Central Europe
TL;DR: The study presents the preliminary results of two classification exercises assessing the capabilities of pre-operational Sentinel-2 (S2) data for mapping crop types and tree species and confirmed its expected capabilities to produce reliable land cover maps.
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Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis
TL;DR: Object-based time-weighted dynamic time warping (TWDTW) method achieved comparable classification results to RF in Romania and Italy, but RF achieved better results in the USA, where the classified crops present high intra-class spectral variability.
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Comparison of Sentinel-2 and Landsat 8 in the estimation of boreal forest canopy cover and leaf area index
TL;DR: In this article, a comparison of Sentinel-2A (S2) MSI and Landsat 8 (L8) OLI (Operational Land Imager) data in the retrieval of forest canopy cover, effective canopy cover (ECC), and leaf area index (LAI) is presented.
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Sentinel-2 Data for Land Cover/Use Mapping: A Review
Darius Phiri,Matamyo Simwanda,Serajis Salekin,Vincent R. Nyirenda,Yuji Murayama,Manjula Ranagalage +5 more
TL;DR: Sentinel-2 has a positive impact on land cover/use monitoring, specifically in monitoring of crop, forests, urban areas, and water resources and the literature shows that the use of Sentinel-2 data produces high accuracies with machine-learning classifiers such as support vector machine (SVM) and Random forest (RF).
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Remote sensing for drought monitoring & impact assessment: Progress, past challenges and future opportunities
TL;DR: In this paper, the authors chart the rise of remote sensing for drought monitoring, examining key milestones and technologies for assessing meteorological, agricultural and hydrological drought events, and reflect on challenges the research community has faced to date, such as limitations associated with data record length and spatial, temporal and spectral resolution.
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