A long-term reliable satellite chlorophyll-a (chl-a) data record is essential in understanding the state of ocean biology and quantifying its changes. Creating a long-term data record requires a combination/merger of multiple satellite products into one data record, since the lifetime of any single ocean color sensor is finite. However, because of differences in sensor design, calibration, and retrieval models, apparent cross-mission biases are usually observed between different sensor products. To attain a coherent multisensor chl-a data record, the observed cross-mission biases should be accurately addressed in the data combination/merging schemes. In this study, a multivariable data interpolating empirical orthogonal functions (M-DINEOF) approach was used to create long-term chl-a records by applying the sea-viewing wide field-of-view sensor and moderate resolution imaging spectroradiometer products. Under the assumption that the single-sensor chl-a product is free from spurious temporal artifacts and can be reference time series representing the actual variability of chl-a, the discrepancies of trends derived from different chl-a series were quantitatively evaluated based on statistical t-test and Taylor diagram analyses. Compared with direct concatenation and linear regression methods, the M-DINEOF method more effectively reproduced the main trend patterns observed in reference data series during their overlapped periods. The results highlight the importance of a cross-mission bias correction when combining multisensor satellite data records and suggest that the M-DINEOF reconstruction provides a simple and effective path forward for creating reliable multisensor ocean color records suitable for long-term trend analysis.

Retrieval of satellite remotely sensed chlorophyll-a (chl-a) concentrations in coastal regions such as the Bohai and Yellow Seas (BYS) is challenging due to their complex oceanic and atmospheric optical properties. The standard OC3M (ocean chl-a three-band algorithm for MODIS (moderate-resolution imaging spectroradiometer)) algorithm has been widely used in the BYS, despite well-known uncertainties about its accuracy in terms of absolute magnitude. This was based on the belief that OC3M chl-a is capable of representing reliable relative spatial and temporal patterns of sea surface chl-a concentrations. In this study, the ability of the standard OC3M chl-a algorithm to reproduce accurate seasonality patterns was evaluated, based on comparisons with in situ chl-a measurements in the BYS. The results quantified the overestimation by the standard OC3M algorithm with a median absolute percentage difference of 98.48% and a median relative difference of 1.13 mg m⁻³.More importantly, the seasonality from OC3M chl-a was significantly biased relative to the seasonal patterns of in situ chl-a. In addition, a regional GAM (generalized additive model)-based satellite chl-a algorithm was evaluated and compared with OC3M chl-a. The results showed the GAM chl-a improved accuracy in both magnitude and seasonality when compared with in situ chl-a, relative to that from OC3M chl-a.

Chlorophyll fronts are important to monitor and map the oceanic front, especially in the season when sea surface temperature (SST) fronts weaken. In this study, surface chlorophyll-a (chl-a) fronts in the Yellow and Bohai seas were characterized for the first time using satellite data. Five distinct chl-a fronts (i.e., the Bohai Strait, Shandong Peninsula, Jiangsu, Liaodong Peninsula, and Korean Peninsula fronts) were observed in summer along the 40 m isobaths and faded in other seasons. Notably, these fronts coincided with SST fronts. Strong chl-a fronts emerged during summer due to chl-a blooms in eutrophic coastal waters paired with surface chl-a fading in strongly stratified offshore waters and coastal physical fronts. Although SST fronts were strong during winter, light limitation and strong vertical mixing in offshore waters led to low chl-a in both coastal and offshore waters, suppressing chl-a front formation. Both chl-a and SST fronts coincided with steep seabed slopes (slope ratio > 1), suggesting that seabed slope may be an indicator of oceanic front location.

The retrieval of aerosol properties over land from satellite sensors has always been a challenge. At present, several different algorithms for retrieving aerosol optical depth (AOD) have been developed from different satellite sensors. While each algorithm has its own advantages, the accuracy of AOD retrieval still needs to be further improved. To improve the retrieval accuracy of aerosol algorithms, it is necessary to provide a better method to describe the surface properties. In the current study, a new aerosol retrieval algorithm for Moderate Resolution Imaging Spectroradiometer (MODIS) images at a high spatial resolution of 500 m is proposed based on $a$ priori bidirectional reflectance distribution function (BRDF) shape parameters database, which is reconstructed via the 3-D discrete cosine transform (DCT-PLS) method. Then, the surface reflectances are calculated from BRDF model (i.e., RossThick-LiSparse), and a non-Lambertian forward model used to describe the surface anisotropy. The new algorithm is used for processing the MODIS over the Beijing–Tianjin–Hebei of China, and Southeastern United States of America regions, and results are validated against AERONET AOD measurements as well as compared with the MODIS AOD products. The comparison showed that the estimation scheme of surface reflectance in this new algorithm significantly improved the AOD retrievals accuracy, with average correlation coefficient ~0.965 and root-mean-square error ~0.125; the number of AOD retrievals falling within expected error has increased to ~80.1%, and the overestimation uncertainty has been reduced compared with MODIS products. Due to the high spatial resolution and continuous spatial distributions of the AOD retrievals by the new algorithm, therefore, it can well-captured aerosol details over mixed surfaces and better useful for air pollution studies than the MODIS products at local and urban scales.

East Asia is a region that is highly vulnerable to drought disasters during the spring season, as this period is critical for planting, germinating, and growing staple crops such as wheat, maize, and rice. The climate in East Asia is significantly influenced by three large-scale climate variations: the Pacific Decadal Oscillation (PDO), the El Niño–Southern Oscillation (ENSO), and the Indian Ocean Dipole (IOD) in the Pacific and Indian Oceans. In this study, the spring meteorological drought was quantified using the standardized precipitation evapotranspiration index (SPEI) for March, April, and May. Initially, coupled climate networks were established for two climate variables: sea surface temperature (SST) and SPEI. The directed links from SST to SPEI were determined based on the Granger causality test. These coupled climate networks revealed the associations between climate variations and meteorological droughts, indicating that semi-arid areas are more sensitive to these climate variations. In the spring, PDO and ENSO do not cause extreme wetness or dryness in East Asia, whereas IOD does. The remote impacts of these climate variations on SPEI can be partially explained by atmospheric circulations, where the combined effects of air temperatures, winds, and air pressure fields determine the wet/dry conditions in East Asia.