Atmospheric correction assessment of SPOT-6 image and its influence on models to estimate water column transparency in tropical reservoir

Rotta, Luiz; Alcântara, Enner; Watanabe, Fernanda; Rodrigues, Thanan; Imai, Nilton Nobuhiro

doi:10.1016/j.rsase.2016.09.001

Cited by 15 publications

(15 citation statements)

References 28 publications

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“…Several empirical algorithms available from the literature were tested for the Alqueva reservoir, with a poorer performance with respect to the algorithm proposed here as shown in Table 5. Rotta et al [48] proposed an algorithm for KD retrieval applied to Nova Avanhandava Reservoir, using the 660 nm band. In this work the equivalent MSI band (band 4) is used, adjusting the linear regression coefficients to Alqueva reservoir, yielding Equation (8).…”

Section: Empirical Algorithmsmentioning

confidence: 99%

“…In this work the equivalent MSI band (band 4) is used, adjusting the linear regression coefficients to Alqueva reservoir, yielding Equation (8). The results of R 2 and NRMSE obtained for the Alqueva reservoir with the algorithm proposed by Rotta et al [48] are presented in Table 6. The scatter plots, with the best fit for each algorithm, are shown in Figure 4.…”

Section: Empirical Algorithmsmentioning

confidence: 99%

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Temporal and Spatial Variations of Secchi Depth and Diffuse Attenuation Coefficient from Sentinel-2 MSI over a Large Reservoir

et al. 2020

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The Alqueva reservoir (South of Portugal) in the Guadiana river basin constitutes the most important water resource in southern Portugal for domestic and agricultural consumption. We present a methodology developed to characterize spatial and temporal variations of Secchi depth and diffuse attenuation coefficient (both related to dissolved/suspended particles and to water transparency), using high spatial resolution satellite images from Sentinel-2 Multi-Spectral Instrument (MSI). Empirical relations between satellite retrievals of surface reflectances and in situ measurements of water parameters were defined and applied to the entire reservoir for spatial and temporal analysis in the period July 2017-June 2019, useful in the identification of microalgae blooms and rapid variations in water characteristics, which allowed us to differentiate five zones. Water estimates with lower transparency and higher attenuation of radiation were found in the northern area of Alqueva reservoir during the months characterized by higher water temperatures, with Secchi depth monthly averages near 1.0 m and diffuse attenuation coefficient near or above 1.5 m −1 . Satellite retrievals of water with greater transparency in the reservoir were obtained in the southern area in months with low water temperature and atmospheric stability, presenting some monthly Secchi depth averages above 3 m, and diffuse attenuation coefficient below 0.8 m −1 . January 2018 presented great transparency of water with a Secchi depth of 7.5 m for pixels representing the 95th percentile and diffuse attenuation coefficient of 0.36 m for pixels representing the 5th percentile in the Southern region.

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Section: Empirical Algorithmsmentioning

confidence: 99%

Section: Empirical Algorithmsmentioning

confidence: 99%

Temporal and Spatial Variations of Secchi Depth and Diffuse Attenuation Coefficient from Sentinel-2 MSI over a Large Reservoir

et al. 2020

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“…Remotely sensed images present many advantages reported in literature, such as the spatial coverage and the temporal resolution to provide a long-term dataset of water quality monitoring (Baylei and Werdell, 2006;Neukermans et al, 2009;Bonansea et al, 2015;Zheng et al, 2015;Rotta et al, 2016). However, the use of remotely sensed information, represented by remote sensing reflectance (R rs , units in sr À1 ), depends on minimizing the influence of atmosphere compounds (gases and aerosols) (Chavez, 1988;Song et al, 2001;Tkacik et al, 2012;He and Chen, 2014;Lobo et al, 2014) that attenuate about 80% of remote-sensed signal (Roy et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Atmospheric correction issues for retrieving total suspended matter concentrations in inland waters using OLI/Landsat-8 image

Bernardo

Watanabe

Rodrigues

et al. 2017

Advances in Space Research

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The atmospheric effects that influence on the signal registered by remote sensors might be minimized in order to provide reliable spectral information. In aquatic systems, the application of atmospheric correction aims to minimize such effects and avoid the under or overestimation of remote sensing reflectance (R rs). Accurately R rs provides better information about the state of aquatic system, it means, establishing the concentration of aquatic compounds more precisely. The aim of this study is to evaluate the outputs from several atmospheric correction methods (Dark Object Subtraction-DOS; Quick Atmospheric Correction-QUAC; Fast Line-of-sight Atmospheric Analysis of Hypercubes-FLAASH; Atmospheric Correction for OLI 'lite'-ACOLITE, and Provisional Landsat-8 Surface Reflectance Algorithm-L8SR) in order to investigate the suitability of R rs for estimating total suspended matter concentrations (TSM) in the Barra Bonita Hydroelectrical Reservoir. To establish TSM concentrations via atmospherically corrected Operational Land Imager (OLI) scene, the TSM retrieval model was calibrated and validated with in situ data. Thereby, the achieved results from TSM retrieval model application demonstrated that L8SR is able to provide the most suitable R rs values for green and red spectral bands, and consequently, the lowest TSM retrieval errors (Mean Absolute Percentage Error about 10% and 12%, respectively). Retrieved R rs from near infrared band is still a challenge for all the tested algorithms.

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“…Linear regressions were tested between K d(PAR) and R SPOT rs (λ) (visible bands). The best model was selected based on the coefficient of determination (R 2 ) and the Root-Mean-Square-Error (RMSE) [26]. Due to the low number of samples for model calibration, the Leave-One-Out Cross Validation (LOOCV) method was used to evaluate the models, so that it was not necessary to split samples for validation.…”

Section: Kd(par) Mappingmentioning

confidence: 99%

“…In situ Rrs spectra acquired by RAMSES/TriOS sensors were predicted to match the SPOT-6 sensor bands and were compared with SPOT-6 atmospherically corrected sampling points for performance validation. More details of atmospheric correction of SPOT-6 in Rotta et al [16] and Rotta et al [26]. The spatial distribution of Kd(PAR) was divided into 12 classes based on the minimum and maximum values.…”

Section: Kd(par) Mappingmentioning

confidence: 99%

Kd(PAR) and a Depth Based Model to Estimate the Height of Submerged Aquatic Vegetation in an Oligotrophic Reservoir: A case study at Nova Avanhandava

et al. 2019

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Submerged aquatic vegetation (SAV) carry out important biological functions in freshwater systems, however, uncontrolled growth can lead to many negative ecologic and economic impacts. Radiation availability is the primary limiting factor for SAV and it is a function of water transparency measured by Kd(PAR) (downwelling attenuation coefficient of Photosynthetically Active Radiation) and depth. The aim of this study was to develop a Kd(PAR) and depth based model to estimate the height of submerged aquatic vegetation in a tropical oligotrophic reservoir. This work proposed a new graphical model to represent the SAV height in relation to Kd(PAR) and depth. Based on the visual analysis of the model, it was possible to establish a set of Boolean rules to classify the SAV height and identify regions where SAV can grow with greater or lesser vigor. Kd(PAR) was estimated using a model based on satellite data. Overall, the occurrence and height of SAV were directly influenced by the Kd(PAR), depending on the depth. This study highlights the importance of optical parameters in examining the occurrence and status of SAV in Brazilian Reservoirs. It was concluded that the digital model and its graphical representation overcomes the limitations found by other researchers to understand the SAV behavior in relation to those independent variables: depth and Kd(PAR).

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Atmospheric correction assessment of SPOT-6 image and its influence on models to estimate water column transparency in tropical reservoir

Cited by 15 publications

References 28 publications

Temporal and Spatial Variations of Secchi Depth and Diffuse Attenuation Coefficient from Sentinel-2 MSI over a Large Reservoir

Temporal and Spatial Variations of Secchi Depth and Diffuse Attenuation Coefficient from Sentinel-2 MSI over a Large Reservoir

Atmospheric correction issues for retrieving total suspended matter concentrations in inland waters using OLI/Landsat-8 image

Kd(PAR) and a Depth Based Model to Estimate the Height of Submerged Aquatic Vegetation in an Oligotrophic Reservoir: A case study at Nova Avanhandava

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