研究目的
To explore the synergetic use of Sentinel-2 MSI data for validation of the Sentinel-3 OLCI Terrestrial Chlorophyll Index (OTCI) over the Valencia Anchor Station, a large agricultural site in the Valencian Community, Spain.
研究成果
The study demonstrated the high retrieval accuracy of CCC using Sentinel-2 MSI data and its utility in validating Sentinel-3 OLCI land products. The Sentinel-2 missions represent a significant advance towards the routine validation of satellite-derived CCC products, offering a cost-effective alternative to airborne hyperspectral data acquisition.
研究不足
The study was limited by the range of CCC values experienced over the study site, which affected the strength of the relationship between the OTCI and CCC. Additionally, the study did not directly investigate the potential for deriving spectral vegetation indices similar to the OTCI at a higher spatial resolution using Sentinel-2 MSI data.
1：Experimental Design and Method Selection:
The study utilized a machine learning approach to generate a high spatial resolution CCC reference map from Sentinel-2 MSI data. An artificial neural network (ANN) was trained using radiative transfer models (RTMs) to simulate spectra and retrieve CCC.
2：Sample Selection and Data Sources:
Field data collection was carried out over 26 grapevine elementary sampling units (ESUs) within the Valencia Anchor Station. Measurements included leaf area index (LAI) and leaf chlorophyll concentration (LCC).
3：List of Experimental Equipment and Materials:
Equipment used included a handheld GPS device (Garmin eTrex 10), digital hemispherical photography (Nikon Coolpix 4500 with FC-E8 fisheye lens), and an optical chlorophyll meter (Konica Minolta SPAD-502).
4：2). Experimental Procedures and Operational Workflow:
4. Experimental Procedures and Operational Workflow: Field measurements were processed to yield estimates of LAI and LCC, which were then used to derive CCC. The ANN was applied to Sentinel-2 MSI data to generate a CCC reference map.
5：Data Analysis Methods:
The accuracy of the CCC retrievals was assessed using root mean square error (RMSE). The OTCI-derived CCC was validated against the MSI-derived CCC reference map.
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