Water Constituents and Water Depth Retrieval from Sentinel-2A - A First Evaluation in an Oligotrophic Lake.
REMOTE SENSING(2016)
摘要
Satellite remote sensing may assist in meeting the needs of lake monitoring. In this study, we aim to evaluate the potential of Sentinel-2 to assess and monitor water constituents and bottom characteristics of lakes at spatio-temporal synoptic scales. In a field campaign at Lake Starnberg, Germany, we collected validation data concurrently to a Sentinel-2A (S2-A) overpass. We compared the results of three different atmospheric corrections, i.e., Sen2Cor, ACOLITE and MIP, with in situ reflectance measurements, whereof MIP performed best (r = 0.987, RMSE = 0.002 sr(-1)). Using the bio-optical modelling tool WASI-2D, we retrieved absorption by coloured dissolved organic matter (a(CDOM)(440)), backscattering and concentration of suspended particulate matter (SPM) in optically deep water; water depths, bottom substrates and a(CDOM)(440) were modelled in optically shallow water. In deep water, SPM and a(CDOM)(440) showed reasonable spatial patterns. Comparisons with in situ data (mean: 0.43 m(-1)) showed an underestimation of S2-A derived a(CDOM)(440) (mean: 0.14 m(-1)); S2-A backscattering of SPM was slightly higher than backscattering from in situ data (mean: 0.027 m(-1) vs. 0.019 m(-1)). Chlorophyll-a concentrations (similar to 1 mg.m(-3)) of the lake were too low for a retrieval. In shallow water, retrieved water depths exhibited a high correlation with echo sounding data (r = 0.95, residual standard deviation = 0.12 m) up to 2.5 m (Secchi disk depth: 4.2 m), though water depths were slightly underestimated (RMSE = 0.56 m). In deeper water, Sentinel-2A bands were incapable of allowing a WASI-2D based separation of macrophytes and sediment which led to erroneous water depths. Overall, the results encourage further research on lakes with varying optical properties and trophic states with Sentinel-2A.
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关键词
WASI,atmospheric correction,bathymetry,submerged vegetation,sun glint,water quality,validation,inland waters,inverse modelling
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