Atmospheric correction (AC) for coastal waters is an important issue in ocean color remote sensing. AC performance is fundamental in retrieving reliable water-leaving radiances and then bio-optical parameters. Unlike polar-orbiting satellites, geostationary ocean color sensors allow high-frequency (15–60 min) monitoring of ocean color over the same area. The first geostationary ocean color sensor, i.e., the Geostationary Ocean Color Imager (GOCI), was launched in 2010. Using GOCI data acquired over the Yellow Sea in summer 2017 at three principal overpass times (02:16, 03:16, 04:16 UTC) with ±1 and ±3 h match-up times, this study compared four GOCI AC algorithms: (1) the standard near infrared (NIR) algorithm of NASA (NASA-STD), (2) the Korea Ocean Satellite Center (KOSC) standard algorithm for GOCI (KOSC-STD), (3) the diffuse attenuation coefficient at 490 nm Kd (490)-based NIR correction algorithm (Kd-based), and (4) the Management Unit of the North Sea Mathematical Models (MUMM). The GOCI-estimated remote sensing reflectance (Rrs), aerosol parameters [aerosol optical thickness (AOT), Angström Exponent (AE)], and chlorophyll-a (Chla) were validated using in situ data. For Rrs, AOT, AE, and Chla, GOCI-retrieved results performed well within the ±1 h temporal window, but the number of match-ups was extended within the ±3 h match-up window. For ±3 h GOCI-derived Rrs, all algorithms had an absolute percentage difference (APD) at 490 and 555 nm of <40%, while other bands showed larger differences (APD > 60%). Compared with in situ values, the APD of the Rrs(490)/Rrs(555) band ratio was <20% for all ACs. For AOT and AE, the APD was >40% and >200%, respectively. Of the four algorithms, the KOSC-STD algorithm demonstrated satisfactory performance in deriving Rrs for the region of interest (Rrs APD: 22.23%–73.95%) in the visible bands. The Kd-based algorithm worked well obtaining Ocean Color 3 GOCI Chla because Rrs(443) is more accurate than the KOSC-STD. The poorest Rrs retrievals were achieved using the NASA-STD and the MUMM algorithms. Statistical analysis indicated that all methods had optimal performance at 04:16 UTC.
Abstract:Remote sensing reflectance (R rs ) classification of coastal waters is a useful tool to monitor environmental processes and manage marine environmental resources. This study presents classification work for data sets that were collected in the Yellow Sea during six cruises (spring and autumn, 2003; summer and winter, 2006/2007; and spring and autumn, 2007). Specifically, we analyzed classification features of R rs spectra and obtained spatio-temporal characteristics of reflectance and bio-optical properties in the coastal waters. Yellow Sea waters were classified into the following four typical regions based on their spatial distribution characteristics: middle of the Yellow Sea (MYS), north Yellow Sea (NYS), coastal Shandong (CS), and Jiangsu shoal (JS), and five water type categories consisting of Classes A-E were used to represent water colors from clear to very turbid. Application of this classification scheme to Medium Resolution Imaging Spectrometer (MERIS) imagery revealed seasonal variations in the data, which suggests that the water types have both significant temporal and spatial distributions. In particular, the area of Class E waters in the Jiangsu shoal tended to gradually shrink in summer and expand in winter. The spatio-temporal variability was due to the influence of various environmental factors such as currents, tidal activity, fresh water discharges, monsoon winds, and typhoons.
Novice-expert interaction plays an important role in teacher professional development for Chinese vocational education and training (VET). Both Chinese and international research shows that expert-teachers' support is associated with the improvement of novice-teachers' teaching. However, insights into how exactly novice teachers learn with the help of expert teachers are lacking. The learning processes of four novice VET teachers were explored in the context of a professional development project. Data were collected by semi-structured interviews with novice teachers and recordings of novice-expert interactions. A learning model was constructed based on the interconnected model of professional growth. The results showed that novice teachers internalised comments from expert teachers by active reflection and practice. Moreover, this study suggests that teachers' professional development is a complicated long-term process, and that during their development the support from expert teachers is an important external source for novice-teachers. Expert-teachers' support not only provides feedback and suggestions for alternative teaching methods, but also encourages and maintains novice-teachers' learning. The results are discussed in relation to the cultural (Chinese) and educational context (VET)
The accuracy of remote-sensing reflectance (Rrs) estimated from ocean color imagery through the atmospheric correction step is essential in conducting quantitative estimates of the inherent optical properties and biogeochemical parameters of seawater. Therefore, finding the main source of error is the first step toward improving the accuracy of Rrs. However, the classic validation exercises provide only the total error of the retrieved Rrs. They do not reveal the error sources. Moreover, how to effectively improve this satellite algorithm remains unknown. To better understand and improve various aspects of the satellite atmospheric correction algorithm, the error budget in the validation is required. Here, to find the primary error source from the OLCI Rrs, we evaluated the OLCI Rrs product with in-situ data around the China Sea from open ocean to coastal waters and compared them with the MODIS-AQUA and VIIRS products. The results show that the performances of OLCI are comparable to those MODIS-AQUA. The average percentage difference (APD) in Rrs is lowest at 490 nm (18%), and highest at 754 nm (79%). A more detailed analysis reveals that open ocean and coastal waters show opposite results: compared to coastal waters the satellite Rrs in open seas are higher than the in-situ measured values. An error budget for the three satellite-derived Rrs products is presented, showing that the primary error source in the China Sea was the aerosol estimation and the error on the Rayleigh-corrected radiance for OLCI, as well as for MODIS and VIIRS. This work suggests that to improve the accuracy of Sentinel-3A in the coastal waters of China, the accuracy of aerosol estimation in atmospheric correction must be improved.
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