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The bottom depth of coastal benthic habitats plays a vital role in the coastal ecological environment and navigation. In optically shallow waters (OSWs), seafloor reflectance has an impact on the remotely sensed data, and thus, water depth can be retrieved from the remote sensing reflectance (Rrsλ) values provided by satellite imagery. Empirical me...
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... Multispectral Instrument (MSI) onboard the twin satellites has 13 spectral bands, a 290 km swath width, high spatial resolution (10 m), and frequent revisit capability (5-day revisit period), providing a new perspective of the coastal and ocean ecological environment [36]. The band information is listed in Table 1. The spatial resolution of Sentinel-2 can capture the detailed and small-scaled features caused by bottom depth variations, tidal differences, and steep slopes, which is beneficial for bottom depth estimation. ...Similar publications
The coffee leaf miner (Leucoptera coffeella) is a key coffee pest in Brazil that can cause severe defoliation and a negative impact on the productivity. Thus, it is essential to identify initial pest infestation for the sake of appropriate time control to avoid further economic damage to the coffee crops. A fast non-destructive method is an importa...
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... In our research, we used Ahmad RT LUT because 6SV and MODTRAN LUT lack the capability of handling the water surface reflected sky radiance component. Differences in numerical solution methods such as nonlinear optimization [19,27,29] or the look-up-table method [21,[30][31][32] have negligible effects on the accuracy of the result and are simply a matter of choice. ...
The estimation of depth in optically shallow waters using satellite imagery can be efficient and cost-effective. Active sensors measure the distance traveled by an emitted laser pulse propagating through the water with high precision and accuracy if the bottom peak intensity of the waveform is greater than the noise level. However, passive optical imaging of optically shallow water involves measuring the radiance after the sunlight undergoes downward attenuation on the way to the sea floor, and the reflected light is then attenuated while moving back upward to the water surface. The difficulty of satellite-derived bathymetry (SDB) arises from the fact that the measured radiance is a result of a complex association of physical elements, mainly the optical properties of the water, bottom reflectance, and depth. In this research, we attempt to apply physics-based algorithms to solve this complex problem as accurately as possible to overcome the limitation of having only a few known values from a multispectral sensor. Major analysis components are atmospheric correction, the estimation of water optical properties from optically deep water, and the optimization of bottom reflectance as well as the water depth. Specular reflection of the sky radiance from the water surface is modeled in addition to the typical atmospheric correction. The physical modeling of optically dominant components such as dissolved organic matter, phytoplankton, and suspended particulates allows the inversion of water attenuation coefficients from optically deep pixels. The atmospheric correction and water attenuation results are used in the ocean optical reflectance equation to solve for the bottom reflectance and water depth. At each stage of the solution, physics-based models and a physically valid, constrained Levenberg–Marquardt numerical optimization technique are used. The physics-based algorithm is applied to Landsat Operational Land Imager (OLI) imagery over the shallow coastal zone of Guam, Key West, and Puerto Rico. The SDB depths are compared to airborne lidar depths, and the root mean squared error (RMSE) is mostly less than 2 m over water as deep as 30 m. As the initial choice of bottom reflectance is critical, along with the bottom reflectance library, we describe a pure bottom unmixing method based on eigenvector analysis to estimate unknown site-specific bottom reflectance.
... Unfortunately, few studies have considered the heterogeneous density and weak connectivity of ICESat-2 photon point cloud data in feature space. Currently, employing density-based clustering algorithms for ICESat-2 photon denoising is the main focus in most related works [4,[21][22][23][24][25][26], but it is still insufficient to addressing the abovementioned challenges effectively using only the proximity of density. K-nearest neighbors (KNNs) are density-independent metrics that consider the directional uniformity of the data distribution [27,28]. ...
The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) can obtain underwater elevation due to its strong penetration ability. However, the photons recorded by ICESat-2 include a large amount of noise that needs to be removed. Although density-based clustering methods can finish signal photon extraction, heterogeneous density and weak connectivity in photon data distribution impede their denoising performance, especially for sparse signals in deep water and drastic topographic change areas. In this paper, a novel fused denoising method based on the local outlier factor and inverse distance metric is proposed to overcome the above problems. The local outlier factor and inverse distance metric are calculated based on K-nearest neighbors (KNNs), taking into account not only the difference in density but also the directional uniformity of the data distribution. Using six trajectories under various seabed topographies, the proposed method is compared with state-of-the-art ICESat-2 photon denoising algorithms and official ATL03 results. The results indicate that the overall accuracy of the proposed method can surpass 96%, and the proposed method maintains higher recall but also has a lower false positive rate. Compared with the results of other methods, the proposed method can better adopt areas with abrupt topographic changes and deep water. The extracted signal strips are more unbroken and continuous. This study can contribute to pioneering a new perspective for ICESat-2 photon-counting data denoising research that is limited to using only density-based algorithms.
... A previous study has shown that optical bathymetry can estimate water depth up to 20 m in clear water and 1-3 m in turbid water . Conventional approaches for optical bathymetry include analytical methods and empirical models, such as linear functions, band ratios of logarithmic-transformed models, nonlinear inversion models and physics-based models (Ma et al. 2020c;Wang et al. 2022c). Recently, machine learning methods have been increasingly utilized in satellite-derived bathymetry, leveraging their capability to address nonlinear problems (Niroumand-Jadidi et al. 2022;Surisetty et al. 2021;Yang et al. 2022a). ...
Clean Water and Sanitation, the sixth goal of Sustainable Development Goals (SDGs 6) is a call for action by the United Nations aiming at balancing the water cycle for sustainable life on the earth. For water security and regional sustainable development, the quantity and quality of inland waters are key variables. Over the past decades, satellite remote sensing offers global information about inland water dynamics in a real-time and low-cost way. Amongst, the Sentinel satellites designed by the European Space Agency can provide global monitoring with a spatial resolution of up to 10 m and several days of revisit time. Although Sentinel satellites have been explored in inland water monitoring for a long time period, a systematical review on the research progress and challenges of their applications has not been documented well. This review aims to present a comprehensive review of the Sentinel satellites (especially for Sentinel-1, Sentinel-2, and Sentinel-3) in monitoring inland water, both on the quantity and quality dimensions, including the water extent, level, depth, volume and water quality (e.g., chlorophyll-a, phycocyanin, suspended particulate matter, colored dissolved organic matter, and Secchi disk depth). A total of 690 publications are involved and the bibliometric quantitative approach is used to analyze the areas in which Sentinel instrument excelled and their performance with different processing methods. The implications for virtual constellation construction using Sentinel satellites from different missions and the contribution of a virtual constellation in support of the SDG 6 are also discussed. According to the initial investigation and characteristics of various satellites, we have proposed several schemes for Sentinel virtual constellation toward different missions covering water quantity measurement and water quality monitoring, which can maximize the observation capability of the satellite. The optimal Sentinel virtual constellation constructing scheme theoretically enables a coverage of 10 m spatial resolution and less than 2 days temporal resolution for all-weather inland water monitoring. These solutions will significantly enhance the observational capacity to obtain high-quality, long-term water security parameters in supporting SDG 6. Nevertheless, there remains a scarcity of freely available Sentinel-derived products, widely applicable data processing algorithms, and unified platform, capable of supporting water security monitoring on a broad scale.
River bank erosion is the major issue along large rivers such as Brahmaputra in Bangladesh. This study is aimed to develop the method for estimating river morphology along river bank to monitor movement of sand bars and erosion near river structures. The method combines satellite imagery, which is used to estimate water extent, and observed water level. Elevation of each pixel is estimated with several water level gauges and merged with an inverse distance weight method to consider slope of water surface. Developed method was applied to Brahmaputra river. This study used Sentinel-1 satellite images with Synthetic Apeture Radar (SAR) and optical satellite Sentinel-2 with MultiSpectral Instrument (MSI), both operated by European Space Agency. By comparison between observed and estimated cross section, the developed method effectively captured shape of river cross section. The results demonstrate that this method is used to detect sand bars movement and scouring around river structures. However when the method is applied to relatively small scale phenonmenum of less than 100 meter horizontally, applicability is affected by several factors including availability of satellite images influenced by cloud cover, observation frequency, and range of water fluctuation. These factors should be considered when practical application is considerd.
