RESUMEN
Some species of algae such as cyanobacteria can vertically migrate through water during a day, which is a notable floating feature of harmful algae blooms. To date, this process has been observed and quantified using visible and near-infrared (VNIR) bands from spaceborne sensors with high temporal resolution (i.e., Geostationary Ocean Color Imager; GOCI). In this study, we conducted an in-situ measurement at Taihu Lake in China to investigate the ultraviolet (UV) reflection spectra of floating cyanobacteria blooms, and identified that they have significant UV reflection features (higher than that of background water) associated with their floating status. This has been demonstrated using spaceborne UV images at both 355 and 385 nm from the Ultraviolet Imager (UVI) onboard Haiyang-1C (HY-1C) of China. Compared with synchronous optical images from the Chinese Ocean Color and Temperature Scanner (COCTS), we found that UVI has a special ability to distinguish cyanobacteria floating on water surface. Additionally, the intensity of the UV signals obtained is positively correlated with the cyanobacterial equivalent density. Ultraviolet remote sensing, therefore, can work as a new approach for the detection of harmful algae blooms and help determine the floating status of them, which deserves further research.
Asunto(s)
Cianobacterias , Agua , Monitoreo del Ambiente/métodos , Floraciones de Algas Nocivas , Lagos/microbiología , Rayos UltravioletaRESUMEN
In oceanographic study, satellite-based sea surface temperature (SST) retrieval has always been the focus of researchers. This paper investigates several multi-channel SST retrieval algorithms for the thermal infrared band, and evaluates the accuracy of the COCTS/HY-1C SST products. NEAR-GOOS in situ SST data are utilized for validation and improvement, and a three-step matching procedure including geographic location screening, cloud masking, and homogeneity check is conducted to match in situ SST data with satellite SST data. Two improvement schemes, including nonlinear regression and regularization iteration, are proposed to improve the accuracy of the COCTS/HY-1C SST products and the typical application scenarios and the algorithm characteristics of these two schemes are discussed. The standard deviation of residual between retrieved SST and measured SST for these two data improvement algorithms, which are considered as the main indexes for assessment, result in an improvement of 13.245% and 14.096%, respectively. In addition, the generalization ability of the SST models under two data improvement methods is quantitatively compared, and the factors affecting the model accuracy are also carefully evaluated, including the in situ data acquisition method and measurement time (day/night). Finally, future works about SST retrieval with COCTS/HY-1C satellite data are summarized.