Satellite estimates of wide-range suspended sediment concentrations in Changjiang (Yangtze) estuary using MERIS data

The Changjiang (Yangtze) estuarine and coastal waters are characterized by suspended sediments over a wide range of concentrations from 20 to 2,500 mg l-1. Suspended sediment plays important roles in the estuarine and coastal system and environment. Previous algorithms for satellite estimates of suspended sediment concentration (SSC) showed a great limitation in that only low to moderate concentrations (up to 50 mg l-1) could be reliably estimated. In this study, we developed a semi-empirical radiative transfer (SERT) model with physically based empirical coefficients to estimate SSC from MERIS data over turbid waters with a much wider range of SSC. The model was based on the Kubelka–Munk two-stream approximation of radiative transfer theory and calibrated using datasets from in situ measurements and outdoor controlled tank experiments. The results show that the sensitivity and saturation level of remote-sensing reflectance to SSC are dependent on wavelengths and SSC levels. Therefore, the SERT model, coupled with a multi-conditional algorithm scheme adapted to satellite retrieval of wide-range SSC, was proposed. Results suggest that this method is more effective and accurate in the estimation of SSC over turbid water

Hyperspectral remote sensing of cyanobacterial pigments as indicators for cell populations and toxins in eutrophic lakes

The growth of mass populations of toxin-producing cyanobacteria is a serious concern for the ecological status of inland waterbodies and for human and animal health. In this study we examined the performance of four semi-analytical algorithms for the retrieval of chlorophyll a (Chl a) and phycocyanin (C-PC) from data acquired by the Compact Airborne Spectrographic Imager-2 (CASI-2) and the Airborne Imaging Spectrometer for Applications (AISA) Eagle sensor. The retrieval accuracies of the semi-analytical models were compared to those returned by optimally calibrated empirical band-ratio algorithms. The best-performing algorithm for the retrieval of Chl a was an empirical band-ratio model based on a quadratic function of the ratio of re!ectance at 710 and 670 nm (R2=0.832; RMSE=29.8%). However, this model only provided a marginally better retrieval than the best semi-analytical algorithm. The best-performing model for the retrieval of C-PC was a semi-analytical nested band-ratio model (R2=0.984; RMSE=3.98 mg m−3). The concentrations of C-PC retrieved using the semi-analytical model were correlated with cyanobacterial cell numbers (R2=0.380) and the particulate and total (particulate plus dissolved) pools of microcystins (R2=0.858 and 0.896 respectively). Importantly, both the empirical and semi-analytical algorithms were able to retrieve the concentration of C-PC at cyanobacterial cell concentrations below current warning thresholds for cyanobacteria in waterbodies. This demonstrates the potential of remote sensing to contribute to early-warning detection and monitoring of cyanobacterial blooms for human health protection at regional and global scales

A Semi-analytical Model for Remote Sensing Retrieval of Suspended Sediment Concentration in the Gulf of Bohai, China

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Remote sensing for Mapping TSM concentration in Mahakam Delta: an analytical approach

The Indonesian coastal zones have always been under heavy pressures, including those from fisheries, oil industries and sea transportation. The presence of these activities carry a large portion of risk in damaging the environment as well as in destroying the marine resources, leading to the need for an integrated management approach based on an environmental information system that is comprehensive and multi-disciplinary in nature. The Mahakam Delta has the same general problems as other coastal regions in Indonesia. The method is based on bio optical modeling. The forward water analysis comprised the laboratory measurements of water quality (TSM and Chl) and Inherent Optical Properties (IOPs) to derive Spesific Inherent Optical properties (SIOPs). SIOPs (of water, TSM, Chl and CDOM), coefficient f and B were used to developed R(0-) model. The inverse atmosphere analysis comprised the image preprocessing (i.e. geometric correction, atmospheric correction, air-water interface correction). The last step is inverse water analysis, which comprised the development of algorithm and image processing to develop TSM concentration maps. The spectrometer measurements collected in the field were used for obtaining the subsurface irradiance reflectance. The subsurface irradiance reflectance R(0-) is the ratio of upwelling (Ewu) and downwelling irradiance (Ewd) just beneath the water surface. There are some discrepancies from matching R(0-) model and R(0-) measured in the field, especially in the blue region and NIR region. The reason of the discrepancies could be due to the fact that the Q factor (the angular distribution factor of spectral radiance) is still not understood completely. This model is very susceptible to the decrease of the proportional factor f, and to the increase of the backscattering probability B. The results indicates that red band of satellite sensor is sensitive to detect higher TSM concentration. For Mahakam Delta, red band algorithm was used to derive TSM map, since higher TSM concentration occurred in the delta

유류오염 탐지를 위한 해양표면 마이크로파 후방산란 분석기반의 준경험적 임계모델

학위논문(석사) -- 서울대학교대학원 : 자연과학대학 지구환경과학부, 2023. 8. 김덕진.In an automatic oil spill monitoring system that utilizes SAR satellites, the dark spot detection step, which is responsible for the segmentation of potential oil spills, is undeniably significant. As the initial stage of automatic oil spill detection, this process is typically the most time-consuming and substantially influences the system performance. Considering the vast expanses of ocean that require thorough surveillance, it is crucial to have an efficient method that accurately identifies oil spill candidates at this critical early stage. In this study, a semi-empirical model was carefully proposed, grounded in a comprehensive analysis of the physical characteristics governing the interaction between electromagnetic waves and the sea surface, as well as oil spill observation data from SAR. This model utilizes wind speed, relative wind direction, and incidence angle as independent variables to calculate the threshold radar backscatter coefficient, to differentiate oil spill candidates from the ocean. To determine the parameters of the proposed model, large oil spill observational data was collected from the Sentinel-1 satellite, and the corresponding wind field data was derived from the ECMWF ERA5 reanalysis data. When compared to widely used dark spot detection methods such as the Otsu, Bradley, and active contour methods, the proposed model demonstrated outstanding performance. The model achieved an average F1 score of 0.7948 on the evaluation dataset, while the aforementioned methods showed 0.3315, 0.6400, and 0.5191, respectively. The proposed model exhibited distinguished accuracy with a straightforward implementation process, balancing effectiveness with simplicity, which makes it particularly suitable for real-time oil spill detection where efficiency is paramount. A notable feature of the proposed model is its ability to compute threshold at the pixel-level, unlike conventional patch-level methods that require iterative processes to detect oil spill candidates of varying sizes. This allows the model to identify oil spills in a single operation regardless of their sizes. While the proposed model is flexible in using diverse wind input sources such as buoys, scatterometers, or geophysical model functions, it is crucial to note that its performance depends on the accuracy of the wind field information, specifically, how well it reflects the wind conditions at the exact SAR acquisition time. In conclusion, this study has thoroughly investigated the behavior of the radar backscatter coefficient under both slick-free and slick-covered sea surfaces, leading to the development of a semi-empirical model that can enhance the efficiency of oil spill monitoring systems. The practical implications of the model extend beyond improving system performance; it can be used to create balanced deep-learning datasets by selectively choosing patches with dark spots. Moreover, the physically-grounded nature of the model enables advanced future research, such as distinguishing types of oil or estimating slick thickness.SAR 위성을 활용한 자동 유류오염 모니터링 시스템에서 유류오염 후보를 탐지하는 과정인 dark spot detection 단계는, 시스템의 첫 번째 단계로서 일반적으로 가장 많은 시간이 소요되고, 최종 탐지 성능에 결정적인 영향을 미친다. 넓은 해상 영역을 효과적으로 감시하기 위해서는, 이와 같이 초기 단계에서 유류오염 후보를 정확하고 효율적으로 식별할 필요성이 강조된다. 본 논문에서는 전자기파와 유류막으로 덮인 해양 표면 간의 상호작용에 대한 광범위한 분석과 유류오염 위성 관측값을 기반으로 준경험적 모델을 제시하였다. 제안된 모델은 풍속, 상대 풍향, 입사각을 독립변수로 가지며 바다와 유류오염 후보를 효과적으로 구분하는 레이더 후방산란 계수의 임계값을 산출한다. 모델의 매개변수를 결정하기 위해, Sentinel-1 위성에서 대량의 유류오염 관측 데이터를 수집하였고, 이에 해당하는 바람장 데이터를 ECMWF ERA5 재분석 데이터로부터 추출하여 사용하였다. 본 연구에서 제안한 모델의 segmentation 성능 평가 결과, 평균 F1 점수는 0.7948로 나타났으며, 기존의 대표적인 접근방법인 Otsu, Bradley, active contour model의 성능이 각각 0.3315, 0.6400, 0.5191인 것과 비교하여 우수한 탐지 성능을 보였다. 해당 모델은 물리적 특성을 고려한 직관적인 알고리즘과 높은 segmentation 정확도로, 특히 효율성이 강조되는 실시간 유류오염 모니터링에 매우 적합하다. 또한 제안된 모델은 픽셀 단계의 임계값 계산이 가능하여, 다른 patch 단위로 동작하는 탐지 모델들과 달리, 다양한 크기의 유류오염을 탐지하기 위해 여러 크기의 윈도우를 반복적으로 사용하여 탐지할 필요가 없다. 해당 모델은 해상 부이, 산란계와 같은 다양한 바람장 정보를 입력값으로 사용할 수 있으나, 모델의 성능은 해당 데이터의 정확도에 크게 의존하며, 특히 SAR 이미지 취득 시점의 바람 상태를 얼마나 정확히 반영하는지에 따라 달라진다는 특징을 가진다. 결론적으로, 본 논문에서는 유류오염이 없는 바다 표면과 있는 표면에서의 레이더 후방산란 계수의 변화를 세밀하게 분석하여, 유류오염 모니터링 시스템의 효율성을 강화할 수 있는 준경험적 유류오염 후보 추출 임계값 모델을 개발하였다. 제안된 모델은 모니터링 시스템의 성능을 향상시킬 수 있을 뿐만 아니라, dark spot이 있는 patch를 선별하여 균형잡힌 딥러닝 데이터셋을 생성하는 데에도 활용될 수 있다. 또한, 본 모델은 유류오염과 해양의 물리적 특성에 근거하므로, 유종 식별 또는 유류 두께추정과 같은 후속 연구의 가능성을 제시한다.Chapter 1. Introduction 1 1.1 Research Background 1 1.2 Literature Review 4 1.3 Research Objective 9 Chapter 2. Microwave Backscattering Properties from the Sea Surface 11 2.1 Microwave backscattering from the Slick-Free Ocean Surface 11 2.1.1 Radar Scattering Model 11 2.1.2 Geophysical Model Function 13 2.2 Microwave backscattering from the Slick-Covered Ocean Surface 18 2.2.1 The Action Balance Equation 18 2.2.2 The Damping Ratio 27 Chapter 3. Development and Validation of the Semi-Empirical Model 29 3.1 Formulation of the Theoretical Framework for the Semi-Empirical Model 29 3.2 Data Acquisition 34 3.2.1 Acquisition of SAR Image Data 34 3.2.2 Acquisition of Wind Field Data 40 3.3 Parameter Determination of the Semi-Empirical Model 44 Chapter 4. Performance Evaluation of the Semi-Empirical Model 47 4.1 Experimental Design 47 4.2 Performance Evaluation with Other Methods 51 4.3 Performance Evaluation for Different Wind Conditions and Regions 60 Chapter 5. Application of the Semi-Empirical Model 69 Chapter 6. Conclusion 72 Bibliography 75 Abstract in Korean 82석

Theoretical modeling of dual-frequency scatterometer response: improving ocean wind and rainfall effects

Ocean surface wind is a key parameter of the Earth’s climate system. Occurring at the interface between the ocean and the atmosphere, ocean winds modulate fluxes of heat, moisture and gas exchanges. They reflect the lower branch of the atmospheric circulation and represent a major driver of the ocean circulation. Studying the long-term trends and variability of the ocean surface winds is of key importance in our effort to understand the Earth’s climate system and the causes of its changes. More than three decades of surface wind data are available from spaceborne scatterometer/radiometer missions and there is an ongoing effort to inter-calibrate all these measurements with the aim of building a complete and continuous picture of the ocean wind variability. Currently, spaceborne scatterometer wind retrievals are obtained by inversion algorithms of empirical Geophysical Model Functions (GMFs), which represent the relationship between ocean surface backscattering coefficient and the wind parameters. However, by being measurement-dependent, the GMFs are sensor-specific and, in addition, they may be not properly defined in all weather conditions. This may reduce the accuracy of the wind retrievals in presence of rain and it may also lead to inconsistencies amongst winds retrieved by different sensors. Theoretical models of ocean backscatter have the big potential of providing a more general and understandable relation between the measured microwave backscatter and the surface wind field than empirical models. Therefore, the goal of our research is to understand and address the limitations of the theoretical modeling, in order to propose a new strategy towards the definition of a unified theoretical model able to account for the effects of both wind and rain. In this work, it is described our approach to improve the theoretical modeling of the ocean response, starting from the Ku-band (13.4 GHz) frequency and then broadening the analysis at C-band (5.3 GHz) frequency. This research has revealed the need for new understanding of the frequency-dependent modeling of the surface backscatter in response to the wind-forced surface wave spectrum. Moreover, our ocean wave spectrum modification introduced to include the influences of the surface rain, allows the interpretation/investigation of the scatterometer observations in terms not only of the surface winds but also of the surface rain, defining an additional step needed to improve the wind retrievals algorithms as well as the possibility to jointly estimate wind and rain from scatterometer observations

A semi-analytical model for estimating total suspended matter in highly turbid waters

Total suspended matter (TSM) is related to water quality. High TSM concentrations limit underwater light availability, thus affecting the primary productivity of aquatic ecosystems. Accurate estimation of TSM concentrations in various waters with remote sensing technology is particularly challenging, as the concentrations and optical properties vary greatly among different waters. In this research, a semi-analytical model was established for Hangzhou Bay and Lake Taihu for estimating TSM concentration. The model construction proceeded in two steps. 1) Two indices of the model were calculated by deriving absorption and backscattering coefficients of suspended matter (ap(λ) and bbp(λ)) from the reflectance signal using a semi-analytical method. 2) The two indices were then weighted to derive TSM. The performance of the proposed model was tested using in situ reflectance and Geostationary Ocean Color Imager (GOCI) data. The derived TSM based on in situ reflectance and GOCI images both corresponded well with the in situ TSM with low mean relative error (32%, 41%), root mean square error (20.1 mg/L, 43.1 mg/L), and normalized root mean square error (33%, 55%). The model was further used for the slightly turbid Xin’anjiang Reservoir to demonstrate its applicability to derive ap(λ) and bbp(λ) in other water types. The results indicated that the form Rrs −1(λ1) − Rrs −1(λ2) could minimize the effect of CDOM absorption in deriving ap(λ) from the total absorption. The model exploited the different relationships between TSM concentration and multiband reflectance, thus improving the performance and application range in deriving TSM

A review of protocols for fiducial reference measurements of water-leaving radiance for validation of satellite remote-sensing data over water

© 2019 by the authors. This paper reviews the state of the art of protocols for measurement of water-leaving radiance in the context of fiducial reference measurements (FRM) of water reflectance for satellite validation. Measurement of water reflectance requires the measurement of water-leaving radiance and downwelling irradiance just above water. For the former there are four generic families of method, based on: (1) underwater radiometry at fixed depths; or (2) underwater radiometry with vertical profiling; or (3) above-water radiometry with skyglint correction; or (4) on-water radiometry with skylight blocked. Each method is described generically in the FRM context with reference to the measurement equation, documented implementations and the intra-method diversity of deployment platform and practice. Ideal measurement conditions are stated, practical recommendations are provided on best practice and guidelines for estimating the measurement uncertainty are provided for each protocol-related component of the measurement uncertainty budget. The state of the art for measurement of water-leaving radiance is summarized, future perspectives are outlined, and the question of which method is best adapted to various circumstances (water type, wavelength) is discussed. This review is based on practice and papers of the aquatic optics community for the validation of water reflectance estimated from satellite data but can be relevant also for other applications such as the development or validation of algorithms for remote-sensing estimation of water constituents including chlorophyll a concentration, inherent optical properties and related products

Chlorophyll-a Algorithms for Oligotrophic Oceans: A Novel Approach Based on Three-Band Reflectance Difference

A new empirical algorithm is proposed to estimate surface chlorophyll-a concentrations (Chl) in the global ocean for Chl less than or equal to 0.25 milligrams per cubic meters (approximately 77% of the global ocean area). The algorithm is based on a color index (CI), defined as the difference between remote sensing reflectance (R(sub rs), sr(sup -1) in the green and a reference formed linearly between R(sub rs) in the blue and red. For low Chl waters, in situ data showed a tighter (and therefore better) relationship between CI and Chl than between traditional band-ratios and Chl, which was further validated using global data collected concurrently by ship-borne and SeaWiFS satellite instruments. Model simulations showed that for low Chl waters, compared with the band-ratio algorithm, the CI-based algorithm (CIA) was more tolerant to changes in chlorophyll-specific backscattering coefficient, and performed similarly for different relative contributions of non-phytoplankton absorption. Simulations using existing atmospheric correction approaches further demonstrated that the CIA was much less sensitive than band-ratio algorithms to various errors induced by instrument noise and imperfect atmospheric correction (including sun glint and whitecap corrections). Image and time-series analyses of SeaWiFS and MODIS/Aqua data also showed improved performance in terms of reduced image noise, more coherent spatial and temporal patterns, and consistency between the two sensors. The reduction in noise and other errors is particularly useful to improve the detection of various ocean features such as eddies. Preliminary tests over MERIS and CZCS data indicate that the new approach should be generally applicable to all existing and future ocean color instruments