Inherent optical properties (IOPs) are crucial parameters for assessing water quality, with widely applied estimation methods established for open oceans. The estimation of IOPs for inland and coastal waters, however, remains a longstanding challenge due to their complex optical properties. In order to address this, we developed a deep learning-assisted quasi-analytical algorithm (QAA-DL) for estimating IOPs in inland and coastal waters. This method enhances traditional QAA procedures by using a neural network to reparameterize the algorithm for extremely turbid waters. Additionally, we introduced a soft-wired classification scheme to ensure smooth retrieval of IOPs in slightly turbid waters. Validation analyses showed that the IOPs retrievals using QAA-DL agreed well with the worldwide in situ measurements. Compared to other standard IOPs algorithms, QAA-DL provided more than double the valid data coverage in turbid waters. Additionally, when applied to moderate resolution imaging spectroradiometer (MODIS) imagery, the QAA-DL algorithm demonstrates consistent spatial patterns in IOPs retrievals. The QAA-DL algorithm can be adopted in different ocean color missions to produce high-quality IOPs retrievals for global inland and coastal waters.

Correcting atmospheric effects over inland and coastal waters poses a significant challenge in ocean color applications due to the prevalence of strongly absorbing aerosols, often resulting in overcorrection during atmospheric correction (AC) processes. To address this challenge, our previous study introduced novel global gridded aerosol models tailored for inland and coastal regions. However, further comprehensive validation is needed to assess the performance of these new aerosol models in obtaining remote sensing reflectance (Rrs) under different water conditions and across various satellite missions. In this study, we compiled global in situ Rrs data to compare the performance of the new gridded aerosol models with NASA’s standard aerosol models in AC across three satellite missions: MODIS Aqua (MODISA), Visible Infrared Imaging Radiometer Suite (VIIRS), and Landsat 8 Operational Land Imager (OLI). Our findings indicate that the new aerosol model consistently outperforms the standard aerosol model across nearly all spectral bands of the three satellite missions. Importantly, it notably mitigates the overcorrections typically observed with the standard models. These results underscore the effectiveness and applicability of globally gridded aerosol models for AC over inland and coastal waters across different satellite missions.

Although the 5% mission goal for NASA’s standard atmospheric correction (AC) algorithm (i.e., the near-infrared (NIR) algorithm) for oligotrophic oceans has been met, this algorithm applies only to blue bands and is highly sensitive to contamination from cloud straylight and sunglint. Here, we developed an AC algorithm for clear waters based on deep learning (namely, DLAC). The algorithm was trained using 3.6 million pairs of MODIS-Aqua high-quality Rrs from the NIR algorithm and Rayleigh-corrected reflectances selected across the global oceans and from all seasons. Validations usingin situdata and a chlorophyll (Chl) constraint-based approach showed that the uncertainties in the Rrsretrievals for DLAC are lower than those for the NIR algorithm, especially for the green and red bands. More importantly, the DLAC algorithm is more tolerant to cloud adjacency effects and moderate sunglint. As a result, the number of valid observations increased by ~50%, and the coverage of monthly global Level-3 Rrscomposites increased by up to 20%. More spatially and temporally consistent patterns were also found for the Level-3 Rrsand Chl products, and large changes in their magnitudes (up to 20% for Rrsand 30% for Chl) were detected in some oceanic regions. With these improvements in the quality and quantity of data, our DLAC algorithm may be valuable as another option for processing global data.

A practical Atmospheric Correction algorithm for inLand and Nearshore Coastal waters (ACLANC) is proposed in this study. The ACLANC algorithm uses interpolated aerosol optical depth (AOD) products (AOD$_{\mathrm {interp}}$) from nearby land surfaces and simulates the corresponding aerosol reflectance spectrum using a combination of the continental model in the satellite signal in the solar spectrum-vector (6SV) radiative transfer code and an approximate aerosol model (r85f20) in the Sea-viewing Wide Field-of view Sensor (SeaWiFS) Data Analysis System (SeaDAS). Validations with worldwidein situmeasurements show that the ACLANC-derived remote-sensing reflectance ($R_{\mathrm {rs}}$) for nine Moderate Resolution Imaging Spectroradiometer (MODIS) bands agreed well with thein situdatasets, where the mean$R^{2}$was 0.77 ± 0.09 and the mean unbiased percent difference was 28.7% ± 9.8%. ACLANC outperformed the existing atmospheric correction algorithms in not only the accuracy of the$R_{\mathrm {rs}}$retrievals but also data coverage. Vicarious calibration over the ACLANC algorithm showed minor improvement in the derived$R_{\mathrm {rs}}$products. Error budget analysis revealed that the uncertainties in AODinterprepresent >50% of the errors for ACLANC and that this proportion increases with decreasing AOD. Further efforts can also be applied to improve the aerosol models, especially for turbid aerosol environments, where the fixed aerosol model in SeaDAS contributes up to 30% of the error budget. The ACLANC algorithm can potentially be implemented in ocean color missions other than MODIS to obtain$R_{\mathrm {rs}}$with high accuracy and wide coverage for global inland and nearshore coastal waters.

Various approaches have been proposed to estimate surface ocean chlorophyll a concentrations (Chl, mg m-3) from spectral reflectance measured either in the field or from space, each with its own strengths and limitations. Here, we develop a machine learning approach to reduce the impact of spectral noise and improve algorithm performance at the global scale for multiple satellite sensors. Among several candidates, the support vector regression (SVR) approach was found to yield the best algorithm performance as gauged by several statistical measures against field-measured Chl. While statistically the performance of the SVR is slightly worse than the empirical color index (CI) algorithm proposed in Hu et al. (2012) for Chl-3, its applicability to global waters is much extended, from the CIs 0.01-0.25 mg m-3(about 75% of the global oceans) to its 0.01-1 mg-3[about 96% of global oceans according to Sea-viewing Wide Field-of-view Sensor (SeaWiFS) statistics]. Within this range, not only does the SVR show much improved performance over the traditional band-ratio OC x approaches, but the SVR leads to much reduced image noise and much improved cross-sensor consistency between SeaWiFS and Moderate Resolution Spectroradiometer (MODIS)/Aqua and between MODIS/Aqua and Visible Infrared Imaging Radiometer Suite (VIIRS). Furthermore, compared with the hybrid Ocean CI (OCI) algorithm currently used by the U.S. NASA as the default algorithm for all mainstream ocean color sensors, the SVR avoids the need to merge two different algorithms for intermediate Chl (band subtraction for CI and band ratio for OC x), thus may serve as an alternative approach for global data processing.

Nearly all calibration/validation activities for the satellite ocean color missions have focused on data quality to produce data products of the highest quality (i.e., science quality) for climate-related research. Little attention, however, has been paid to data quantity, particularly on how data quality control during data processing impacts downstream data quality and data quantity. In this letter, we attempt to fill this knowledge gap using measurements from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP). For this sensor, the same level-1B data are processed independently using different quality control methods by NASA and NOAA, respectively, allowing for an in-depth evaluation of the interplay between data quantity and quality. The results indicate that the methods to identify stray light and sun glint are the two primary quality control procedures affecting data quantity, where the criteria for flagging pixels “contaminated” by stray light and sun glint may be relaxed in the NASA ocean color data processing to increase data quantity without compromising data quality.

Ocean color satellite missions to measure the biophysical and geochemical properties of the surface ocean need to consider not only the spectral and spatial requirements of the sensors but also the satellite overpass time to maximize valid observations. The valid observations are impacted not only by cloud cover but also by other perturbations such as sun glint and stray light. Using Level-3 global composites of three ocean products (chlorophyll a or Chl-a, normalized florescence line height or nFLH, and sea surface temperature or SST), the daily percentage valid observations (DPVOs) over the global oceans were calculated, from which the differences between MODIS Aqua (afternoon pass) and MODIS Terra (morning pass) have been analyzed. For all three products, Aqua shows more valid observations than Terra over the Southern Ocean, the ocean near Peru and Chile, and the ocean around Angola and Namibia, with relatively >30% more valid observations in boreal winter months due to lower cloud coverage in the afternoon. In contrast, more than 20% of valid Chl-a and nFLH observations are obtained by Terra in the North Indian Ocean, and 10%-30% more valid observations by Terra are also found for the Equatorial Pacific and Atlantic oceans. These can be possibly linked to the lower presence of sun glint for Terra. Compared with Chl-a and nFLH, SST retrievals are more tolerant to sun glint and other perturbation factors, leading to much higher DPVOs. The implications of these findings to future satellite mission design and field campaigns are also discussed.

The advantages of geostationary observations of sediment plumes and phytoplankton blooms have been reported for coastal waters in the southern North Sea and west Pacific. So far, similar observations have not been possible for the Gulf of Mexico where blooms of Trichodesmium erythraeum often occur. Here, using data collected by the Geostationary Operational Environmental Satellite (GOES) Imager, we document diurnal changes of a Trichodesmium bloom first identified by the Moderate Resolution Imaging Spectroradiometer (MODIS). Despite the low-signal-to-noise ratio ( ~ 46 : 1 for typical ocean radiance), the 550-750-nm band revealed clear patterns of Trichodesmium mats floating on the ocean surface and their temporal changes between 14:15 and 22:30 GMT on May 22, 2004. Normalization of the delineated bloom against the ocean background provided an effective atmospheric correction that enabled quantification of the changes in bloom size (i.e., area) and bloom intensity over the course of a day. The area coverage increased by about eightfold from midmorning (14-15 GMT) to reach its maximum around 18:30 GMT, whereas the mean intensity of the bloom area increased by ~ 22% from midmorning to 17:30 GMT. In the afternoon, while the bloom area remained relatively stable on the water surface, bloom intensity sharply decreased. These temporal patterns may be caused by physical aggregation and/or vertical migration of the Trichodesmium cells, and they agree well with the diurnal changes of a harmful algal bloom of the dinoflagellate Prorocentrum donghaiense in the East China Sea observed by the Geostationary Ocean Color Imager.

Precipitation is important in many fields. It is meaningful and valuable to estimate the spatial distribution of precipitation. However, existing methods introduced for precipitation interpolation are not satisfactory. In this paper, geographic and topographic factors are taken into consideration and put into Cokriging method to interpolate the precipitation maps of annual precipitation in Poyang Lake Watershed of China. At the same time, IDW (Inverse distance weight) method, Ordinary Kriging method and Cokriging method considering elevation only has been used to interpolate the precipitation. Evaluating by MAE (mean absolute error), MRE (mean relative error), as well as RMSIE(Root mean squared interpolation error). The results indicate that Cokriging method considering geographic and topographic facotors is suoprior than IDW method and Cokriging method considering elevation only, and it has no obvious advantage compared with ordinary Kriging method.