HiSea-II, designed for monitoring coastal-inland biogeochemical properties and launched into orbit on June 11, 2021, is the first small satellite (SmallSat) simultaneously satisfying high spatial resolution (20 m), high signal-to-noise ratio (SNR) (~300) and wide swath (~200 km). In this article, we provide detailed descriptions of this SmallSat, introduce its data products, highlight its unique features, also share its shortcomings so future such SmallSat could be improved. SmallSat for ocean color is at its early stage, we advocate more investment and efforts to advance this useful platform, so a constellation of ocean-color SmallSats could be established in the future for effective monitoring of coastal-inland aquatic ecosystems.

Primary production (PP) of the South China Sea (SCS) basin area (waters depth deeper than 200 m) is estimated using satellite products, with an overarching goal to reliably characterize the spatial distribution and temporal variation of PP of this important marginal sea. Among the PP models used, the absorption-based model (AbPM) showed better performance ($R^{2}=0.47$and$N =39$). In comparison, the$R^{2}$value is 0.26 for a chlorophyll-based model [vertically generalized production model (VGPM)] and 0.15 for the carbon-based model (CbPM). Furthermore, we observed that the PP spatial patterns obtained from these models were similar but disagree on the annual PP magnitude, where VGPM and CbPM, respectively, obtained$\sim $50% lower and$\sim $40% higher annual PP compared to that obtained by AbPM. In particular, after analysis using empirical orthogonal functions (EOFs), the upwelling-induced high PP off Luzon (winter) and Vietnam coast (summer) was clearly reflected in the first EOF mode of the AbPM results, and its principal component 1 has shown a decreasing trend for the period of 2003–2019 (−15.0% yr$^{-1}$for winter,$p < 0.05$; −14.7% yr$^{-1}$for summer,$p < 0.05$), which reflects the impact of weakening wind and higher sea surface temperature in the SCS. For the results of VGPM and CbPM, however, no strong relationships were found with the main regional oceanographic features. These results suggested that the spatiotemporal variations of SCS PP obtained from AbPM are more reasonable and further highlight the importance of a robust model in reliably capturing large-scale spatiotemporal dynamics of PP in marine environments.

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For the first time the vertical variation in light quality in the global ocean is quantified with a single parameter—the hue angle ($\alpha _{E}$, in degree) in chromaticity of downwelling irradiance. For oceanic waters,$\alpha _{E}$is ~140° at surface, but it becomes ~230° at the bottom of the euphotic zone;$\alpha _{E}$changes rapidly near the surface, and we term this layer of rapid change in light quality as chromocline, analogous to the thermocline or pycnocline in oceanography. The 3-D variations in light quality are further highlighted with data from satellite ocean color measurements, where global distributions of$\alpha _{E}$for depths of 99%, 37%, and 1% of surface photosynthetically available radiation (PAR) are presented. As an example to demonstrate the importance to consider the change in light quality, the relationship between the light quality and the ratio of phytoplankton absorbed light to PAR is presented where this ratio may vary by a factor of 3 or more under different chlorophyll-a concentrations; otherwise, the ratio would be constant vertically. We advocate quantitative measurement and report the light quality in the upper ocean with such a single and objective parameter to accompany the routine measurement and report the light intensity, which will greatly improve our understanding of light-related processes and further bridge ocean optics and oceanography.

Phaeocystis globosa (P. globosa) is a unique causative species of harmful algal blooms, which can form gelatinous colonies. We, for the first time, used unmanned aerial vehicle (UAV) measurements to identify P. globosa blooms and to quantify the biomass. Based on in situ measured remote sensing reflectance (${R_{\mathrm{ rs}}}$), it is found that, for P. globosa blooms, the maximum of the second-derivative (${d\lambda ^{2}{R}_{\mathrm{ rs}}}$) of${R_{\mathrm{ rs}}(\lambda)}$in the 460–480-nm domain is beyond 466 nm. An analysis of the absorption properties from algal cultures suggested that this feature comes from the absorption of chlorophyll${c_{3}}$(Chl$-/{c_{3}}$) around 466 nm, a prominent feature of P. globosa. This position of$ {d\lambda ^{2}{R}_{\mathrm{ rs}}}$maximum was, thus, selected as the criterion for P. globosa identification. The spatial extent of P. globosa blooms in two bays off southern China was then mapped by applying the criterion to UAV-measured${R_{\mathrm{ rs}}}$. Twelve out of 16 UAV and in situ match-up stations were consistently identified as dominated by P. globosa, indicating the accuracy of 75%. Furthermore, using localized empirical models, chlorophyll a (Chl$-/{a}$) concentration and colony numbers of P. globosa were estimated from UAV-derived${R_{\mathrm{ rs}}}$, where P. globosa colonies were found in a range of ~3–37 gel matrix/L, indicating the occurrence of weak to moderate P. globosa blooms during the surveys. The promising results suggest a high potential for detection and quantification of P. globosa blooms in near-shore bays or harbors using UAV-based hyperspectral remote sensing, where conventional ocean color satellite remote sensing runs into difficulties.

Mangroves are valuable contributors to coastal ecosystems; knowledge of the dynamics of mangrove ecosystems is important in the context of global change. To obtain this knowledge, remote sensing is an indispensable means, yet it poses challenges since the accuracy is sometimes unsatisfactory in distinguishing mangroves from other land cover types with traditional classification methods. In this letter, we proposed a modified artificial immune algorithm (AIA), in which the antibodies represent the candidate solutions and the antigens are expressed by the fitness function. Multiclass coevolution was combined with the concept of clonal selection to ensure computation of an optimal clustering center in parallel for each land cover type. A cluster-center-oriented decimal encoding method for antibodies was adopted, and the inner class variance and the between-class difference together were used to formulate the fitness function. Furthermore, a design of the antibody solubility-based selection operator and nonuniform mutation operator was undertaken. Applying this modified AIA to a Landsat Thematic Mapper multispectral remote sensing imagery in the Zhangjiang estuary in southeastern China, we found that the AIA substantially improved classification accuracy over traditional methods, showing an overall accuracy of 90% (kappa coefficient = 0.88) and was capable to discern mangrove well (commission of 10% and omission of 22%).