Remote sensing monitoring of the suspended particle size in Hongze Lake based on GF-1 data

Lei, Shaohua; Wu, Dexing; Li, Yunmei; Wang, Qiao; Huang, Changchun; Liu, Ge; Zheng, Zhuo; Du, Chunyu; Mu, Meng; Xu, Jie; Lv, Hongbo

doi:10.1080/01431161.2018.1541109

Cited by 34 publications

(21 citation statements)

References 57 publications

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“…The spatial distribution of bbp(676) in Lake Hongze was mainly affected by the discharge of input rivers, of which the Huaihe River accounts for 70% of the total inflow [47]. Suspended sediment from the upper reaches of Huaihe River is carried into Lake Hongze [6,48], which leads to higher bbp(676) in the nearby area. Seasonal variation in bbp(676) in Lake Taihu and Lake Hongze is illustrated in Fig.…”

Section: Spatiotemporal Variation Features Of Particulate Backscattering Properties In Lake Hongze and Lake Taihumentioning

confidence: 99%

“…Therefore, the Chla/TSM ratio was chosen to indicate particle composition in this study. At the same time, the median particle diameter of the volume distribution (Dv50) is generally used to characterize the particle size [6,69]. Before analyzing the influence of particle composition and particle size on backscattering characteristics, TSM was first adopted to normalize bbp to eliminate the effect of total suspended matter concentration.…”

Section: The Relationship Between Particulate Features and Backscattering Characteristicsmentioning

confidence: 99%

“…Recently, bbp(λ) has been widely used to indicate some important parameters representing water quality and biochemical characteristics, such as total suspended matter (TSM), particulate organic carbon (POC) and particle size [3][4][5]. As a result, understanding the particulate backscattering characteristics can further grasp the horizontal transport of suspended particulate matter, as well as the characteristics of vertical flocculation, sedimentation, resuspension and siltation [6].…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Particulate Backscattering Coefficient in Turbid Inland Water Using Sentinel 3A-OLCI Image

Bian

Lyu

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Section: Spatiotemporal Variation Features Of Particulate Backscattering Properties In Lake Hongze and Lake Taihumentioning

confidence: 99%

Section: The Relationship Between Particulate Features and Backscattering Characteristicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Estimation of Particulate Backscattering Coefficient in Turbid Inland Water Using Sentinel 3A-OLCI Image

Bian

Lyu

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…The R rs (λ) data were field-measured while using an Analytical Spectral Devices (ASD) Inc. FieldSpec Pro. Full details of the measurement process of R rs (λ) are provided by previous studies [22,23]. At each sampling site, the total radiance (L t ), the sky-viewing radiance (L sky ), and the radiance reflected by a standard gray panel (L p ) were collected.…”

Section: In-situ Water Quality Data and Spectra Data Collectionmentioning

confidence: 99%

Retrieval of Secchi Disk Depth in Turbid Lakes from GOCI Based on a New Semi-Analytical Algorithm

Zeng

Lei

et al. 2020

Remote Sensing

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The accurate remote estimation of the Secchi disk depth (ZSD) in turbid waters is essential in the monitoring the ecological environment of lakes. Using the field measured ZSD and the remote sensing reflectance (Rrs(λ)) data, a new semi-analytical algorithm (denoted as ZSDZ) for retrieving ZSD was developed from Rrs(λ), and it was applied to Geostationary Ocean Color Imager (GOCI) images in extremely turbid waters. Our results are as follows: (1) the ZSDZ performs well in estimating ZSD in turbid water bodies (0.15 m < ZSD < 2.5 m). By validating with the field measured data that were collected in four turbid inland lakes, the determination coefficient (R2) is determined to be 0.89, with a mean absolute square percentage error (MAPE) of 22.39%, and root mean square error (RMSE) of 0.24 m. (2) The ZSDZ improved the retrieval accuracy of ZSD in turbid waters and outperformed the existing semi-analytical schemes. (3) The developed algorithm and GOCI data are in order to map the hourly variation of ZSD in turbid inland waters, the GOCI-derived results reveal a significant spatiotemporal variation in our study region, which are significantly driven by wind forcing. This study can provide a new approach for estimating water transparency in turbid waters, offering important support for the management of inland waters.

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“…Actually, two main categories of remote sensing sensors, optical sensors and Synthetic Aperture Radar (SAR), both play important roles in flood monitoring [13]. Out of significant advantages in long historical data and easy access with low cost, numerous typical optical sensors have been applied to flood monitoring, such as Terra Moderate Resolution Imaging Spectroradiometer (MODIS) [14][15][16], Landsat-8 OLI [17,18], GF-1 WFV [19,20], HJ-1 A/B CCD [21,22], etc. However, flood events are usually accompanied by rainy weather and heavy clouds.…”

Section: Introductionmentioning

confidence: 99%

Full Lifecycle Monitoring on Drought-Converted Catastrophic Flood Using Sentinel-1 SAR: A Case Study of Poyang Lake Region during Summer 2020

Yang

Wang

et al. 2021

Remote Sensing

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During summer 2020, the most catastrophic flood in the 21st century attacked the Poyang Lake region, one of the flood-prone areas in China. To explore the occurrence mechanism and evolution patterns of this drought-converted flood better, a full lifecycle model is developed in this article. Employing Sentinel-1 Synthetic Aperture Radar (SAR) images, with the advantages of high spatial–temporal resolution and all-day and all-weather working capacity, a bimodal threshold was applied to efficiently extract flood inundation mapping. Thus, 61 Sentinel-1 SAR images in 2020 were used to establish inundation sequences for full lifecycle monitoring. This flood presented an abrupt transformation from drought, a long duration, and the slow receding of water, and its area exceeded 3000 km2 from July to early October. In addition, inundation models that reflect the lake area and water level relationship were introduced to assist near-real-time monitoring. Through hydrological and meteorological analysis, compared with results of previous years (from 2010 to 2019), this study found that the water level from July to October in 2020 was at least 17% higher than the mean level at the same period in history and water volume had increased about 44.13 billion m3 during the flooding period. Similarly, the average precipitation from June to September was significantly higher than the same period of previous years. It was the abnormal sustained heavy precipitation and sharp rising of the water level that caused this catastrophic flood. In particular, the Standardized Precipitation Index (SPI) increased from −1.02 in April to 1.31 in July, indicating that the flood was abruptly converted from drought. The inundated areas of several land types during different periods of the full lifecycle were calculated for damage assessment. It was found that cropland was the most heavily impaired with a maximum inundated area of 1375.67 km2, while other land types including forest, grassland, wetland, and impervious surface were relatively less damaged. The study results demonstrate that flood full lifecycle monitoring based on SAR data is helpful to explore the patterns of flood evolution, analyze causes, and assess damage. Simultaneously, focusing on drought-converted floods contributes to the understanding of flood patterns, which provides relevant management departments with decision support for disaster prevention and mitigation.

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Remote sensing monitoring of the suspended particle size in Hongze Lake based on GF-1 data

Cited by 34 publications

References 57 publications

Estimation of Particulate Backscattering Coefficient in Turbid Inland Water Using Sentinel 3A-OLCI Image

Estimation of Particulate Backscattering Coefficient in Turbid Inland Water Using Sentinel 3A-OLCI Image

Retrieval of Secchi Disk Depth in Turbid Lakes from GOCI Based on a New Semi-Analytical Algorithm

Full Lifecycle Monitoring on Drought-Converted Catastrophic Flood Using Sentinel-1 SAR: A Case Study of Poyang Lake Region during Summer 2020

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