Inland Water Atmospheric Correction Based on Turbidity Classification Using OLCI and SLSTR Synergistic Observations

Shun Bi, Yunmei Li, Qiao Wang, Heng Lyu, Ge Liu, Zhubin Zheng, Chenggong Du, Meng Mu, Jie Xu, Shaohua Lei, Song Miao
<span title="2018-06-24">2018</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/kay2tsbijbawliu45dnhvyvgsq" style="color: black;">Remote Sensing</a> </i> &nbsp;
Atmospheric correction is an essential prerequisite for obtaining accurate inland water color information. An inland water atmospheric correction algorithm, ACbTC (Atmospheric Correction based on Turbidity Classification), was proposed in this study by using OLCI (Ocean and Land Color Instrument) and SLSTR (Sea and Land Surface Temperature Radiometer) synergistic observations for the first time. This method includes two main steps: (1) water turbidity classification by the GRA index (GRAdient
more &raquo; ... the spectrum index); and (2) atmospheric correction by synergistic use of OLCI and SLSTR images. The algorithm was validated with 72 in situ sampling sites in Lake Erhai, Lake Hongze, and Lake Taihu, and compared with other atmospheric correction methods, i.e., C2RCC (Case 2 Regional Coast Colour processor), MUMM (Management Unit of the North Seas Mathematical Models), FLAASH (Fast Line-of-sight Atmospheric Analysis of Hypercubes), POLYMER (POLYnomial based algorithm applied to MERIS), and BPAC (Bright Pixel Atmospheric Correction). The results show that (1) the GRA index performed better than the proposed turbidity classification indices, i.e., the Diff (spectral difference index) and the Tind (turbid index), in inland lakes by using the reflectance peak at 1020 nm in clean water; (2) the synergistic use of OLCI and SLSTR performed feasibly for atmospheric correction, and the ACbTC algorithm achieved full-band average values of the mean absolute percentage error (MAPE) = 29.55%, mean relative percentage error (MRPE) = 13.98%, and the root mean square of error (RMSE) = 0.0039 sr −1 , which were more reliable than C2RCC, MUMM, FLAASH, POLYMER, and BPAC; and (3) the synergistic use of the 17th band (865 nm) on OLCI and the 5th band (1613 nm) on SLSTR are suitable for clean inland lakes, while both the 5th band (1613 nm) and 6th band (2250 nm) on SLSTR are advisable for the turbidity. satellite measurements can provide synoptic observations of the water signal for the entire lake, with the advantages of high rates of temporal and spatial coverage [3, 4] . However, the signals received by the satellite sensors include not only the water, which is the desired part, ultimately, but also atmospheric molecules and aerosols accounting for a significant portion [5] . Thus, the atmospheric correction, i.e., the removal of atmospheric effects, is an essential prerequisite for obtaining accurate water color information. Many algorithms have been proposed for atmospheric correction [6] [7] [8] [9] [10] [11] . The standard algorithm is the dark pixel method, which is based on the assumption that the signal of water in the near-infrared region (NIR) is zero [11, 12] . This algorithm has been successfully used in the open ocean to determine the aerosol model. However, this hypothesis is invalid for turbid water, such as offshore water or inland lakes in which suspended particles and algae still have a strong signal in the NIR band [8, 13] . To solve this problem, some researchers have tried to satisfy the assumption of dark pixels by subtracting the calculated water signal in the NIR [10, [14] [15] [16] [17] . However, the uncertainty of calculating the water signal in the NIR arises by this method because of the variety of the water components [18] . On the other hand, some researchers have tried to extend the band to the shortwave infrared region (SWIR) to satisfy the assumption because of the strong water absorption by SWIR [8, 19] . Unfortunately, the substantially lower signal-noise ratio (SNR) values in SWIR [20-22] may reduce the accuracy of aerosol estimation in clean water. To overcome these shortcomings, Shi and Wang [23] promoted a method to detect water turbidity before the atmospheric correction, which has been widely used in coastal waters [18, 24, 25] . First, the turbid water is identified by a turbid water index; and, second, the atmospheric correction is conducted by SWIR algorithm for the turbid water, whereas the standard algorithm [11] is applied for non-turbid water. This algorithm uses different band combinations to calculate the aerosols' reflectance for the different waters to decrease the error from the incorrect dark band assumption. This method hypothesizes that the reflectance at NIR is close to zero in the open ocean. However, this hypothesis is invalid in inland waters even for relatively clean water [18, 26] . Thus, the algorithm might not be suitable for detecting the turbidity of inland waters. In fact, the turbidity of inland water varies strongly. For example, the concentration of total suspended matter (TSM), which is considered as an essential indicator of lake turbidity [27] , is approximately 1.24 mg/L in Lake Fuxian [28] . That is about 1/20 of Lake Taihu and Lake Dianchi, and 1/60 of Lake Hongze and Lake Poyang [29] . Among the inland lakes, the reflectance of the relatively clean waters such as Lake Fuxian is close to zero after 800 nm, while in some turbid waters, the reflectance is non-zero even after 1000 nm [30, 31] . Therefore, it is necessary to use different correction bands to calculate the aerosol scattering based on different water types and facilitate the atmospheric corrections for inland waters. In order to increase the available band for atmospheric correction, the combination of multiple sensors has been attempted and obtained better results than a single sensor [32] . However, the synergistic ability of different sensors is strongly related to the consistency of the radiative resolution, acquisition time, geo-location, etc., between the sensors. Additionally, the conditions will be more complicated when the sensors are on different satellites [33] . The new sensors of OLCI (21 bands in the range of 400-1020 nm) and SLSTR (six bands in the range of 555-2250 nm), which are the legacy of ENVISAT MERIS (Medium Resolution Imaging Spectrometer Instrument) and AATSR (Advanced Along Track Scanning Radiometer), respectively, are payloads on the Sentinel-3 satellite and can obtain synchronized data. Despite the fact that the sensors have an overlap of the spectrum range, the OLCI provides higher quality and finer water pixels in the visible region (VISR) to NIR, while the SLSTR expands to the SWIR, thus, the aerosol condition over the mostly turbid inland lakes can be observed [34] . Up to the present time the application of one independent sensor on Sentinel-3 has been conducted in several areas: the OLCI data has been widely used in ocean color research [22, [35] [36] [37] , while the applications of the SLSTR have been focused on the land and atmosphere [38, 39] . Therefore, there is an urgent need to combine the Sentinel-3 synergistic data to improve the present algorithms by fully utilizing the advantages of the sensors.
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