A detailed list of the components available on Orbify for water quality monitoring.
Chlorophyll-a concentration
Component Type: Chart
Data Type: Optical, Radar
Accuracy: High
Spatial Resolution: 10m
Temporal Resolution: quarterly
Description: This algorithm returns the near-surface concentration of chlorophyll-a (chlor_a) in mg m-3, calculated using empirical relationships derived from in situ measurements of chlor_a and remote sensing reflectances (Rrs). The implementation is contingent on the availability of three or more sensor bands spanning the 440 - 670 nm spectral regime. The algorithm is applicable to all current ocean color sensors.
The chlor_a product is included as part of the standard Level-2 OC product suite and the Level-3 CHL product suite. The current implementation for the standard chlorophyll product (chlor_a), as applied in version R2022 of NASA's multi-mission ocean color processing, is a blend between the updated OC3/OC4 (OCx) band ratio algorithm (O'Reilly and Werdell 2019) and the color index (CI) of Hu et al. (2019). The combined algorithm and theoretical basis is described in Hu et al. (2019).
Chlorophyll-a concentration
Component Type: Layer
Data Type: Optical, Radar
Accuracy: High
Spatial Resolution: 10m
Temporal Resolution: quarterly
Description: This algorithm returns the near-surface concentration of chlorophyll-a (chlor_a) in mg m-3, calculated using empirical relationships derived from in situ measurements of chlor_a and remote sensing reflectances (Rrs). The implementation is contingent on the availability of three or more sensor bands spanning the 440 - 670 nm spectral regime. The algorithm is applicable to all current ocean color sensors.
The chlor_a product is included as part of the standard Level-2 OC product suite and the Level-3 CHL product suite. The current implementation for the standard chlorophyll product (chlor_a), as applied in version R2022 of NASA's multi-mission ocean color processing, is a blend between the updated OC3/OC4 (OCx) band ratio algorithm (O'Reilly and Werdell 2019) and the color index (CI) of Hu et al. (2019). The combined algorithm and theoretical basis is described in Hu et al. (2019).
Salinity Index
Component Type: Layer
Data Type: Optical
Accuracy: Very High
Spatial Resolution: 10m
Description: The salinity value is obtained from Sentinel-2A image processing, using the salinity estimator algorithm's calculation called Cilamaya Algorithm
Sea Surface Temperature
Component Type: Layer
Data Type: Other
Spatial Resolution: 4638.3 m
Description: The product measures sea surface temperature with an update latency of 3-4 days. It's part of the GCOM-C project, which aims for continuous global observation to understand radiation budget fluctuations and the carbon cycle. This data aids in making accurate future temperature projections. By collaborating with research institutions that use climate numerical models, GCOM-C enhances the accuracy of temperature rise predictions. The SGLI sensor, mounted on GCOM-C, succeeds the Global Imager from ADEOS-II and observes radiation across 19 channels, ranging from near-ultraviolet to thermal infrared. With its wide observation span, it allows for global observation roughly every two days around mid-latitude near Japan. Compared to similar sensors, SGLI boasts higher resolution and features both polarized and multi-angle observation capabilities.
Suspended Matter Concentration
Component Type: Chart
Data Type: Optical, Radar
Accuracy: High
Spatial Resolution: 10m
Description: Suspended Matter Concentration (gm−3) using the algorithm of Nechad et al. (2010) re- calibrated by Bouchra Nechad in September 2016, specifically for Landsat 8 and Sentinel-2.
Suspended Matter Concentration
Component Type: Layer
Data Type: Optical, Radar
Accuracy: High
Spatial Resolution: 10m
Description: Suspended Matter Concentration (gm−3) using the algorithm of Nechad et al. (2010) re- calibrated by Bouchra Nechad in September 2016, specifically for Landsat 8 and Sentinel-2.
Water mask
Component Type: Layer
Data Type: Optical
Accuracy: High
Spatial Resolution: 10m
Description: The dataset provides a binary mask that identifies water bodies within a specified region based on the NDWI (Normalized Difference Water Index) derived from the Sentinel-2 Surface Reflectance collection.
Water quality
Component Type: Layer
Data Type: Optical
Accuracy: High
Spatial Resolution: 10m
Description: The water quality was assessed with Normalised Difference Chlorophyll Index (NDCI), that acts as a measure for the amount of chlorophyll on the surface of a waterbody.
Water salinity
Component Type: Layer
Data Type: Optical
Accuracy: High
Spatial Resolution: 10m
Description: The salinity value is obtained from Sentinel-2A image processing, using the salinity estimator algorithm's calculation called Cilamaya Algorithm.
Conclusion
For more information, check the complete list of components available on Orbify.