A green-red quasi-analytical algorithm, QAA-GRI, was calibrated to derive inherent optical properties (IOPs) using an in situ dataset from Lake Qiandaohu (QDH). First, 510 nm was chosen as the reference band based on the general structure of the quasi-analytical algorithm (QAA). Second, a green-red index (GRI), which was calculated from the remote sensing reflectance at the three wavelengths (510, 560 and 620 nm), was used to retrieve the total absorption coefficients at the reference band, a(510) . A semi-analytical model based on a(510) and the GRI was proposed to replace the empirical model in original QAA. Subsequently, QAA-GRI, is calibrated to analytically retrieve total absorption coefficient for Lake Qiandaohu (QDH). The algorithm was further validated using the in situ data set collected in East China Sea (ECS) on January 1-12, 2016 and May 25-June 2, 2017. The QAA-GRI’s performance in ECS was compared with that of QAA-v5. Our results show that the QAA-GRI performs better in ECS with mean R2 of 0.87, compared with that the QAA-v5 of 0.53, and a mean absolutely percentage error of 19.2%, compared with that the QAA-v5 of 24.8%, respectively. These results indicate the potential of QAA-GRI to accurately estimate the IOPs for coastal and inland waters.
Thin semitransparent cloud removal is not an easy task, because the data optical remote sensing satellite acquired contains not only the information from the terrain object but also the information from the thin semitransparent cloud. For this reason, an atmospheric correction method was used in this study to remove the effect of cloud. A case study is carried out in the world famous cloudy area in the coastal area of the South China Sea. The qualitative and quantitative results show this method works well in the coastal area of cloudy South China Sea. It will make full use of the potential of GF-1 and Landsat data in this area by using this method.
Satellite remote sensing technology provides the only viable means for global monitoring of atmosphere systems, such as ozone. The ozone mapping and profiler suite (OMPS) onboard Suomi-NPP satellite, which was launched in the year 2011, has a primary purpose of measuring ozone. Suomi-NPP has been on operation for more than 3 years, and it is crucial to keep the satellite data precise and trusted. By using 17 months of satellite and ground-based total ozone column (TOC) data, this study performs an evaluation of OMPS products. Ozone monitoring instrument (OMI) data generated from a similar satellite instrument were also used to compare with the OMPS TOC data, and both the TOC products were generated using TOMS version 8.5 (TOMS-V8.5) algorithm. The evaluation consists of intercomparisons with ground-based Brewer measurements, similar satellite instruments, and accuracy analysis as a function of time and solar zenith angle. Results show that after 3 years of operation, OMPS-derived TOC data still have good correlation (R2 > 0.99, RMSE = 1.51 % ) with ground-based measurements. The results also give some evidence that the OMPS TOC data have better accuracy than those from OMI using the same algorithm.
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