A semi-analytical scheme to estimate Secchi-disk depth from Landsat-8 measurements

Lee, Zhongping; Shang, Shaoling; Qi, Lin; Yan, Jing; Gong, Lin

doi:10.1016/j.rse.2016.02.033

Cited by 156 publications

(95 citation statements)

References 35 publications

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“…SDD is correlated with K d (PAR) as they are both influenced by the absorption and scatter characteristics of the OACs; in effect, both measurements represent the penetration of light in the water [56]. The difference between them is the varied contributions of the extent of spectral bands as SDD is related to the visible domain (410-665 nm) [57,58] and K d (PAR) is 400-700 nm [5,33]. K d (PAR) and SDD are inversely correlated since higher K d (PAR) values indicate lower water clarity.…”

Section: Relationship Between K D (Par) and Sddmentioning

confidence: 99%

Spatial Distribution of Diffuse Attenuation of Photosynthetic Active Radiation and Its Main Regulating Factors in Inland Waters of Northeast China

Song

Wen

et al. 2016

Remote Sensing

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Light availability in lakes or reservoirs is affected by optically active components (OACs) in the water. Light plays a key role in the distribution of phytoplankton and hydrophytes, thus, is a good indicator of the trophic state of an aquatic system. Diffuse attenuation of photosynthetic active radiation (PAR) (K d (PAR)) is commonly used to quantitatively assess the light availability. The PAR and the concentration of OACs were measured at 206 sites, which covered 26 lakes and reservoirs in Northeast China. The spatial distribution of K d (PAR) was depicted and its association with the OACs was assessed by grey incidences(GIs) and linear regression analysis. Most importantly, we have demonstrated that the TSM concentration is a reliable measurement for the estimation of the K d (PAR) as 74% of the data produced a relative error (RE) of less than 0.4 in a leave-one-out cross validation (LOO-CV) analysis. Spatial transferability assessment of the model also revealed that TSM performed well as a determining factor of the K d (PAR) for the majority of the lakes. However, a few exceptions were identified where the optically regulating dominant factors were chlorophyll-a (Chl-a) and/or the chromophroic dissolved organic matter (CDOM). These extreme cases represent lakes with exceptionally clear waters.

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Section: Relationship Between K D (Par) and Sddmentioning

confidence: 99%

Spatial Distribution of Diffuse Attenuation of Photosynthetic Active Radiation and Its Main Regulating Factors in Inland Waters of Northeast China

Song

Wen

et al. 2016

Remote Sensing

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“…The improved capabilities of Landsat 8 Operational Land Imager (L8/ OLI) data, including an additional shorter blue band, a narrower near-infrared band, a 12 bit radiometric resolution, and higher signal-to-noise ratios, may improve capacities to monitor high turbid waters [Olmanson et al, 2016]. L8 data have recently been used to estimate Secchi-disk depths, sediment plumes, colored dissolved organic matter, water clarity levels, total suspended matter, and macroalgal blooms with considerable success [Barnes et al, 2015;Concha and Schott, 2016;Lee et al, 2016;Lymburner et al, 2016;Olmanson et al, 2016;Slonecker et al, 2016;Xing and Hu, 2016]. However, to our knowledge, SPM estimations from L8/ OLI have been seldom applied for the Yellow River estuary despite their evident potential.…”

Section: Introductionmentioning

confidence: 99%

Using Landsat 8 data to estimate suspended particulate matter in the Yellow River estuary

et al. 2017

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The distribution of suspended particulate matter (SPM) and its variations in estuary regions are key to promoting carbon, oxygen, and nutrient cycling in coastal regions and nearby seas. This study presents SPM estimations for the Yellow River estuary from Landsat 8 Operational Land Imager (L8/OLI) data from 2013 to 2016. L8/OLI‐measured remote sensing reflectance (Rrs) was cross‐validated with Moderate Resolution Imaging Spectroradiometer (MODIS) measurements, and SPM concentrations calculated from the tuned retrieval model were validated with in situ observations. The validation shows that L8/OLI can provide reasonably Rrs, which can be used to quantify SPM distributions and variations in the Yellow River estuary. Three year averaged SPM maps show that highly turbid waters are mostly found in an ovate area surrounding the mouth of the Yellow River. The corresponding area proportion is less than 30%, with SPM concentrations greater than 100 g m−3. High variations of SPM distributions are consistent with high SPM concentrations, and vice versa. Significant difference is observed between dry and wet seasons. Higher SPM in the dry season is observed both in range and intensity compared to those of the wet season. Furthermore, multiyear averaged SPM distributions with high concentrations are mainly attributable to currents. Significant seasonal variations are mainly controlled by sediment resuspension processes driven by wind‐wave forces. Due to human interventions, seasonal variability in river runoff and sediment discharge from the Yellow River has decreased in recent years. Accordingly, seasonal variability in SPM distributions in the Yellow River estuary due to sediment discharge has decreased.

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“…Synergetic multi-sensor use may increase temporal coverage and allow for cloud-free data [10]. Landsat 8 has the capabilities for retrieving water constituents [30][31][32][33], water depths [31,34], Secchi disk depth [35] and bottom substrates [34]. Model based sensitivity and field data analyses have revealed a high potential of Sentinel-2 for water constituent retrieval [36,37] and bottom substrate mapping such as coral reefs [38].…”

Section: Introductionmentioning

confidence: 99%

Water Constituents and Water Depth Retrieval from Sentinel-2A—A First Evaluation in an Oligotrophic Lake

et al. 2016

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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 (~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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A semi-analytical scheme to estimate Secchi-disk depth from Landsat-8 measurements

Cited by 156 publications

References 35 publications

Spatial Distribution of Diffuse Attenuation of Photosynthetic Active Radiation and Its Main Regulating Factors in Inland Waters of Northeast China

Spatial Distribution of Diffuse Attenuation of Photosynthetic Active Radiation and Its Main Regulating Factors in Inland Waters of Northeast China

Using Landsat 8 data to estimate suspended particulate matter in the Yellow River estuary

Water Constituents and Water Depth Retrieval from Sentinel-2A—A First Evaluation in an Oligotrophic Lake

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