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

Shun Bi,Zhubin Zheng,Jie Xu,Heng Lyu,Qiao Wang,Yunmei Li,Chenggong Du,Song Miao,Shaohua Lei,Meng Mu,Ge Liu

doi:10.3390/rs10071002

Abstract

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 of 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.

Highlights

The monitoring of inland lakes is becoming gradually more critical due to the increase of anthropogenic pressures and the effects of climate change [1,2]
The turbid water is identified by a turbid water index; and, second, the atmospheric correction is conducted by shortwave infrared region (SWIR) algorithm for the turbid water, whereas the standard algorithm [11] is applied for non-turbid water
Processing procedure of the ACbTC algorithm in this study is mainly divided into three steps (Figure 3): First, the image was pre-processed according to Section 2.2.1; second, the Rayleigh correction was conducted by Seadas LUTs and 6SV for OLCI and SLSTR, respectively; and, lastly, the aerosol reflectance was calculated based on turbidity detection by the GRA index using the dark pixel method [11] according to Section 3.1

Summary

Introduction

The monitoring of inland lakes is becoming gradually more critical due to the increase of anthropogenic pressures and the effects of climate change [1,2]. 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. 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]. 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

Methods

Findings

Discussion

Conclusion