Highest Quality Remote Sensing Reflectance Database Compiled from 20+ years of MODIS-Aqua measurements

1. Data Overview This database provides rigorously screened, highest-quality remote sensing reflectance (Rrs) products from the MODIS Aqua sensor at 4 km spatial resolution. It includes both 8-day and monthly composites. The HQ_flag files indicate whether each pixel in the corresponding MODIS Rrs product meets the criteria of CHQR (Criteria of the Highest-Quality Rrs). A pixel with: HQ_flag == 1 can be considered highest-quality Rrs, meaning the associated Rrs spectrum satisfies all of the following conditions: ⚫ Optimal observation conditions (excluding clouds, sun glints, etc.) ⚫ Spectral consistency with true water optical properties ⚫ Minimal residual uncertainties This dataset is suitable for research in ocean color remote sensing inversion, water quality monitoring, algorithm development, and long-term trends. 2. Data Screening Method Fig. 1. Criteria for the highest-quality Rrs (CHQR). Criteria of the Highest-Quality Rrs are shown in Fig. 1, which specifically includes the following three aspects: 1) Filtering based on observation condition or failure in data processing. Any Rrs product having the following level-2 quality-control flags (l2-flags) are excluded: ATMFAIL (1), LAND (2), HIGLINT (4), HILT (5), HISATZEN (6), STRAYLIGHT (9), CLDICE (10), COCCOLITH (11), HISOLZEN (13), LOWLW (15), CHLFAIL (16), NAVWARN (17), MAXAERITER (20), CHLWARN (22), ATMWARN (23), and NAVFAIL (26); 2) Spectral shape consistency. The QA system will be applied to a target Rrs spectrum and only QA Score = 1 will be kept. This helps ensure the data product represents realistic water conditions (Wei et al. 2016). 3) Minimum to no residual errors. We can implement a constraint based on the absorption coefficient (CBAC) to screen “error-free” Rrs. We estimated the absorption coefficient at 443 nm (a(443)) using two algorithms: the Rrs band ratio method (Lee et al., 1998), which is sensitive to atmospheric correction errors, and the band-difference method (Lee et al., 2023), which is less sensitive to such errors. By comparing the results from both methods, Rrs data were considered to have minimal residual errors when the two estimates of a(443) were approximately consistent. Algorithm details: The band-ratio algorithm for a(443) (a(443)BR) 𝑎(443)𝐵𝑅 =10𝛼0+𝛼1×𝜌25+𝛼2×𝜌25 2+𝛼3×𝜌35+𝛼4×𝜌35 2 (1a) 𝜌25 = 𝑙𝑜𝑔10[𝑅𝑟𝑠(443) 𝑅𝑟𝑠(547)],𝜌35 = 𝑙𝑜𝑔10[𝑅𝑟𝑠(488) 𝑅𝑟𝑠(547)] (1b) where 𝛼0−4 are the fitting coefficients (Lee et al. 1998), 𝛼0= −0.883 , 𝛼1= −0.955 , 𝛼2= −0.010, 𝛼3= −0.450, and 𝛼4= 0.345. On the other hand, the band-difference algorithm for a(443) (a(443)BD) is: 𝑀𝐵𝐷𝑅𝑟𝑠(443)= 𝑅𝑟𝑠(547)−[𝑅𝑟𝑠(443)+547−443 667−443(𝑅𝑟𝑠(667)−𝑅𝑟𝑠(443))] (2a) 𝑎(443)𝐵𝐷 =10𝛽0+𝛽1 exp(𝛽2 × 𝑀𝐵𝐷𝑅𝑟𝑠(443)) (2b) with 𝛽0−2 the fitting coefficients (Lee et al. 2023), 𝛽0= −2.12, 𝛽1= 0.91, and 𝛽2=247.38. Constraint Based on Absorption Coefficient (CBAC): Following the constraint scheme based on chlorophyll concentration (CBCC) (Hu et al. 2013), the relative difference between a(443)BD and a(443)BR is calculated as: δ𝑎(443) = |𝑎(443)𝐵𝐷−𝑎(443)𝐵𝑅| 𝑎(443)𝐵𝐷 (3) The δa(443) threshold is 0.15, and the highest quality Rrs spectra having δa(443) ≤ 0.15. 3. Data Structure 3.1 Directory Organization TEXT ├── 8_day_Rrs/ # 8-day composite products │ └── HQ_flag/ # Quality flags (1=highest-quality) ├── monthly_Rrs/ # Monthly composites (same structure) └── readme.pdf 3.2 Data Access Here is an example of how to read the data in MATLAB: MATLAB % Read quality flag HQ_flag=ncread('AQUA_MODIS.20020704_20020711.L3m.8D.RRS.HQ_fla g.4km.nc','HQ_flag'); % Read masked Rrs data Rrs_443=ncread('AQUA_MODIS.20020704_20020711.L3m.8D.RRS.443nm.4 km.nc','Rrs_443'); 3.3 Variable Description Variable Type Description Valid Range HQ_flag int8 1=highest-quality, 0=others 0/1 Reference: Hu, C., Feng, L., & Lee, Z. (2013). Uncertainties of SeaWiFS and MODIS remote sensing reflectance: Implications from clear water measurements. Remote Sensing of Environment, 133, 168-182 Lee, Z., Zhao, L., Hu, C., Wang, D., Lin, J., & Shang, S. (2023). Absorption Coefficient and Chlorophyll Concentration of Oceanic Waters Estimated from Band Difference of Satellite-Measured Remote Sensing Reflectance. Journal of Remote Sensing, 3, 0063 Lee, Z.P., Carder, K.L., Steward, R.G., Peacock, T.G., Davis, C.O., & Patch, J.S. (1998). An empirical algorithm for light absorption by ocean water based on color. Journal of Geophysical Research-Oceans, 103, 27967-27978 Wei, J.W., Lee, Z.P., & Shang, S.L. (2016). A system to measure the data quality of spectral remote-sensing reflectance of aquatic environments. Journal of Geophysical Research-Oceans, 121, 8189-8207