Reliable atmospheric correction (AC) is a prerequisite for quantitative water color remote sensing. However, the complex and variable optical properties of coastal and inland waters—particularly in regions with spatial intermixing of clear and turbid waters—present significant challenges to achieving stable and consistent results with existing AC algorithms. To solve this problem, this study introduces an atmospheric correction combination strategy for Sentinel-2 MSI, termed Atmospheric Correction Approach combining POLYMER and ACOLITE (ACAPO-AC) , which integrates the strengths of POLYMER in clear waters and ACOLITE-DSF in turbid waters. By constructing a logistic function based on the ratio of near-infrared to green bands, the strategy effectively captures the continuous transition between clear and turbid waters.The performance of ACAPO-ACwas evaluated using a global in situ dataset. The results indicate that the ACAPO-ACachieves a coefficient of determination (R2) exceeding 0.64 for all bands, with (R2) exceeding 0.77 from 492 nm to 783 nm, and the mean relative percentage deviation (MAPD) for visible bands remaining below 50%. Compared to POLYMER and ACOLITE-DSF, the ACAPO-ACachieved slopes closest to 1 and the lowest bias and root mean square error (RMSE) across all bands (|bias|-1,RMSE-1).Furthermore, the spatial pattern analyses of Rrsand total suspended solids (TSS) confirm that this strategy enables smooth and consistent transitions between clear and turbid waters.

The geostationary ocean color imager (GOCI) has provided eight observations per day since 2010 and has been widely used in coastal dynamics of bio-optical parameters. However, accurate atmospheric correction (AC) of GOCI data over coastal waters is still a challenge, hindering the quantitative retrieval of biogeochemical parameters. Here, we proposed a new AC method for coastal waters based on a neural network (denoted NN_3S). The NN_3S algorithm was designed to derive remote sensing reflectance ($R_{\mathrm {rs}}$) from Rayleigh-corrected reflectance ($R_{\mathrm {rc}}$) and the training of NN_3S used 0.85 million pairs of high-quality$R_{\mathrm {rs}}$–$R_{\mathrm {rc}}$for 2019 generated by the near-infrared (NIR) iterative algorithm (NIR_AC) in SeaDAS. The performance of NN_3S was evaluated with ground measurements from three aerosol robotic network-ocean color (AERONET-OC) stations. The results showed a notable reduction in the band-averaged mean absolute percentage difference (MAPD) for the 412-, 443-, 490-, 555-, and 667-nm$R_{\mathrm {rs}}$retrievals when utilizing NN_3S, with decreases of 17.4%, 32.2%, and 16.59% observed in comparison to retrievals by NIR_AC, the Korea Ocean Satellite Center AC algorithm (KOSC) in GOCI data processing system version 2.0 (GDPS 2.0), and the ocean color-simultaneous marine and aerosol retrieval tool (OC-SMART), respectively. More importantly, the practical application of the NN_3S algorithm indicated successful retrievals over turbid waters. Furthermore, the daily percentage of valid observations (DPVOs) for NN_3S compared to NIR_AC and KOSC increased by 1.97% and 12.82% in June and by 4% and 10.83% in December, respectively. Smoother spatial patterns than NIR_AC were also found. These results indicate that NN_3S can be a reliable AC option for GOCI over coastal areas.

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.

High-resolution precipitation products are essential for accurate hydrological and meteorological applications. To improve the spatial resolution and accuracy of monthly satellite precipitation products, we developed a new downscaling-calibration framework with three key steps: 1) coarse-resolution satellite precipitation data are downscaled to 1-km resolution precipitation data with multivariate adaptive regression spline (MARS) model; 2) residual correction is applied to bridge the difference between the satellite precipitation data and downscaled precipitation data; and 3) the geographical differential analysis (GDA) calibration method is implemented to improve accuracy by merging the residual-corrected data with rain gauge data. In this study, geolocation variables (longitude and latitude), a digital elevation model (DEM) data, daytime and nighttime land surface temperatures, and four remote sensing indices were used to downscale monthly Tropical Rainfall Measuring Mission (TRMM) 3B43 precipitation datasets over the Yangtze River Economic Belt. The downscaled results showed that the MARS model can avoid “boxy artifact” and pixel-level anomalies, which are often found in geographically weighted regression (GWR) and random forest (RF) results. According to the validation, the step of residual correction is not necessary. With GDA calibration, the MARS-based estimated results were more accurate than the results of the other methods (i.e., GWR and RF) and original TRMM products. Therefore, the developed MARS-based downscaling-calibration procedure can improve not only the spatial resolution but also the quality of the TRMM 3B43 products.

Satellite sensors with large view angles can provide wide-swath imaging for earth observations, which pose new challenges for cross-calibration due to bidirectional reflectance distribution function (BRDF). To address these challenges, we adopted global searching (GS) algorithms to “search” calibration coefficients with BRDF considered. The GS-based methods were implemented to calibrate two Chinese large-view-angle sensors: the Gaofen-1 first wide-field-of-view (WFV1) camera and Gaofen-4 panchromatic multispectral sensor (PMS) with Landsat-8/Operational Land Imager (OLI) as references. Validations were conducted by evaluating the top of atmosphere (TOA) radiance and surface reflectance using synchronous OLI data. The mean relative biases (MRBs) of the GS-derived TOA radiance for the WFV1 were smaller than 5.5% compared with the OLI-simulated TOA radiance, while those of using official coefficients were up to 13%. The performances of the GS method and traditional method using the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF products for BRDF correction are comparable. The GS-based scheme has the potential to correct BRDF during cross-calibration and thus free cross-calibration of large-view-angle sensors from BRDF models and products.

Atmospheric aerosol has aroused increasing interests because of its mounting evidence of the importance of aerosol radiative forcing of climate, and its effect on cloud microphysics and albedo. Correct estimation of aerosol information is also essential to satellite remote sensing interpretation, especially to atmospheric correction of ocean color remote sensing. It is now being realized that aerosols are very variable in time and space. Therefore, more measurements of the prime aerosol quantities are needed. In this paper, aerosol data derived from handheld MICROTOPS II sun photometer, observed in March, April and September, 2003, September and October, 2006, were used to study the spatio-temporal variations of aerosol optical thickness (AOT) tauaand Angstrom exponent alpha over China Sea. The measured data showed a high spatial and temporal variability of the aerosol optical thickness and Angstrom exponent in the study period. Some results were given as follows: The averaged AOT tauawas highest in the South China Sea and lowest in the East China Sea. The highest spatio-temporal variation was in the East China Sea.