The Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR) in Lagrange-1 (L1) orbit provides observations of the Earth’s surface lit by the Sun at a cadence of 13 to 22 images/day and optical resolution of 16 km in 10 spectral bands from 317 to 780 nm. The EPIC data collected in the bands centered on 443, 551, and 680 nm are used to estimate daily mean photosynthetically available radiation (PAR) reaching the surface of the global, ice-free oceans. The solar irradiance reaching the surface is obtained by subtracting from the extraterrestrial irradiance (known), the irradiance reflected to space (estimated from the EPIC measurements), while taking into account atmospheric transmission (modeled). Clear and cloudy regions within a pixel do not need to be distinguished, i.e., the methodology is adapted to the relatively large EPIC pixels. A first daily mean EPIC PAR imagery is generated. Comparison with estimates from sensors in polar and geostationary orbits, namely MODIS and AHI, shows good agreement, with coefficients of determination of 0.79 and 0.92 and RMS differences of 8.2 and 5.7 E/m2/d, respectively, but overestimation by 1.08 E/m2/d (MODIS) and 3.44 E/m2/d (AHI). The advantages of using observations from L1 orbit are: 1) the daily cycle of cloudiness is well described (unlike from polar orbit) and 2) spatial resolution is not significantly degraded at high latitudes (unlike from geostationary orbit). The methodology can be easily extended to estimate ultraviolet (UV) surface irradiance using the spectral bands centered on 317, 325, 340, and 388 nm, all the more as ozone content, a key variable controlling atmospheric transmittance, is retrieved from the measurements.
Algorithms to retrieve ocean color from space, deterministic or statistical, often use a simplified water reflectance model, specified by a few parameters (e.g., chlorophyll concentration, backscattering and absorption coefficients at a given wavelength). The model, however, may not be representative of the worldwide ocean conditions, since many variables affecting reflectance are fixed at some average values. In this context, the semi-analytical model of Park and Ruddick (2005), PR05, used in the spectral matching POLYMER algorithm (Steinmetz et al., 2011), is examined in terms of its ability to represent properly water reflectance. The PR05 model depends on chlorophyll-a concentration, a parameter specifying the contribution of algal and non-algal particles to the backscattering coefficient, and a parameter allowing different absorption coefficients for dissolved organic matter. Model estimates at MODIS wavelengths, obtained for a representative set of Case 1 and Case 2 waters, are compared with Hydrolight calculations that include fluorescence and Raman scattering and AERONET-OC measurements. The accuracy of retrieving inherent optical properties (IOPs) using the reconstructed reflectance is also evaluated. The model parameters that give the best fit with the simulated data are determined. Agreement is generally good between the two- or three-parameter model results and Hydrolight/AERONETOC values, even in optically complex waters, with discrepancies much smaller than typical atmospheric correction errors. Significant differences exist in some cases, but having a more intricate model (i.e., using more parameters) might not guarantee convergence of the inversion scheme. The trade-off is between efficiency/robustness and accuracy. Significant errors are observed when using the model estimates to retrieve IOPs. Importantly, the model parameters that best fit the input data, in particular chlorophyll-a concentration, may not represent adequately actual values. The reconstructed water reflectance, not the retrieved model parameters, should be used in bio-optical algorithms.
The goal of the Photosynthetically available radiation (PAR) for Primary Production (3P) project is to provide robust, complete, and user-friendly satellite radiation products for ecosystem modeling, carbon cycle investigations, and climate change monitoring. A specific objective is to design and distribute a daily PAR product from MERIS and potentially the recent OLCI. In view of this, a PAR algorithm, based on the NASA Ocean Biology Processing Group (OBPG) operational algorithm, has been developed. The algorithm takes into account statistical diurnal variability of clouds using 3-hourly International Satellite Cloud Climatology (ISCCP) data. The PAR modeling, simplified to accommodate the information available, is evaluated using a Monte Carlo tool that simulates the satellite radiance and corresponding daily PAR. The daily PAR estimates obtained from reduced resolution (i.e., 1 km) MERIS data are evaluated against in situ measurements routinely collected from fixed buoys and platforms, namely BOUSSOLE in the Mediterranean Sea, CCE- 1 and -2 off the West coast of the United States, and COVE in the coastal Atlantic Ocean. The agreement between estimated and measured values is good on a daily time scale and substantially improved on a monthly time scale, with a bias of 2.7 (7.7%) E/m2/day and RMS errors of 8.5 (24.9%) and 4.5 (12.9%) E/m2/day. The bias is reduced significantly (by 1.8%) when using diurnal cloud climatology. Overestimation in cloudy conditions is partly explained by decoupling the clear atmosphere from the cloud/surface layer. Large gaps in regions affected by sun glint (not processed because incorrectly interpreted as cloudy) are adequately filled in the monthly PAR imagery. The statistical performance is satisfactory for long-term studies of aquatic primary production, especially in view of the much larger uncertainties on the fraction of PAR absorbed by live algae and the quantum yield of carbon fixation.