最新刊期
卷 26 ， 期 1 ， 2022
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摘要：Lake remote sensing is an important branch of limnology and remote sensing science as a new interdisciplinary discipline. In general, lake remote sensing includes water color remote sensing, lake water environment remote sensing, and lake hydrology remote sensing. The key to lake remote sensing is to initially study the lake’s specific problems to address individual or a type of sensitive factors. To accomplish the monitoring of these lake factors through remote sensing, the scientific questions about the preprocessing of remote sensing data, atmospheric correction, algorithm development, and validation, as well as the reconstruction of a long time series data record, were introduced one by one.With the reviews in published studies, this research discussed the research object, content, and method of lake remote sensing science and summarized five development progresses of lake remote sensing by reviewing the research progress, as follows:(1) Concerns: from interest-oriented to problem-oriented. Lake remote sensing has gradually expanded from the water color remote sensing to water environment, water ecology, and hydrology remote sensing, with diverse research fields.(2) Observation equipment: from ground-based remote sensing and medium resolution satellite to high-resolution/hyperspectral/drone. Satellite instruments for remote sensing of lakes have developed from scratch and achieved a development stage from low to high spatial, radiometric, and spectral resolution.(3) Algorithm and computing force: from stand-alone experience/mechanism model to machine learning algorithms based on cloud computing. The machine learning models have been used to retrieve water constituents in the lakes with complicated optical properties, which were difficult for traditional empirical and semi-analytical algorithms.(4) Research dimension: from surface to vertical profile. The remote sensing reflectance was related to the vertical distribution of lake water quality parameters (e.g., algae biomass) in the water column; aquatic remote sensing has been gradually developed in a 3D scale.(5) Spatial coverage: from individual/regional lake to national/continental/global scale. A number of aquatic parameters in national and global lakes, such as lake boundary, algae blooms, and water clarity, have been monitored by remote sensing data through cloud computing platforms.Finally, the future development directions of lake remote sensing are identified, as follows: (1) Launch geostationary satellites or small satellite constellation to satisfy the requirements for lake observation; (2) Develop standard algorithms of lake remote sensing and establish the global lake satellite remote sensing monitoring network; (3) strengthen remote sensing of salinity, temperature, and carbon cycle in lakes under the background of global change; (4) conduct research with respect to satellites, aircraft, and ground remote sensing monitoring and simulation over lakes and the entire watershed.关键词：lake remote sensing;machine learning;network;satellite constellation;SDG;big data;water color remote sensing- 2589
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发布时间：2022-04-30 -
摘要：Case II waters, including inland and inshore waters, are affected by many factors, such as phytoplankton, suspended particles, and colored dissolved organic matter, leading to complex and changeable optical characteristics of the water body. Hence, establishing a unified remote sensing quantitative estimation model for retrieving water environmental parameters is difficult. According to the optical characteristics of water, the method of water optical classification and water environmental parameter inversion can not only improve the accuracy of parameter estimation but also facilitate the model to be popularized in similar waters. This study aims to review the state-of-the-art concepts and methods of water optical classification on remote sensing technology for case II water monitoring. The classification-based applications on retrieving environmental parameters as well as the limitations and prospects are discussed.The criteria for considering studies for this review are based on the general development of water optical classification technology and ongoing studies from authors and their collaborators. The selection of studies is classified by different methods and applications for parameter retrieval. The main concept and advantages of water optical classification are illustrated with several examples presented in this study.According to the optical characteristics of water, the method of water optical classification and water environmental parameter inversion can not only improve the accuracy of parameter estimation but also facilitate the model to be popularized in similar waters. Water optical classification methods mainly include optical classification based on inherent optical characteristics, remote sensing reflectance waveform characteristics, and parameter inversion. The classification inversion strategy includes the fusion of classification and model algorithm, the optimization algorithm based on water optical type, and the hybrid calculation based on optimization of multi-model.Water optical classification is an effective tool for remotely recording the water quality and improving the estimation of the parameters especially in optically complex case II waters. The water retrieval of one predominated optical type should be based on its optimal model. However, accurate estimation of water composed of various types with spatiotemporal dynamics requires the determination of optimal models for each type and the blending strategy. The fuzzy-logic-based blending supports the production of seamless contiguities by considering weight factors. However, different classification methods and parameter estimation strategies should be reconsidered according to the complexity of water optical characteristics and research purposes.关键词：Water optical classification;case II water;water environmental parameters;remote sensing monitoring;remote sensing quantitative estimation model- 1766
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发布时间：2022-04-30 -
摘要：Phycocyanin (PC), as the signature pigment of cyanobacteria, is usually used for remote sensing monitoring of cyanobacteria bloom. In recent years, the water quality of inland water has deteriorated, the eutrophication has intensified, and the algal blooms frequently occur. The research of remote sensing inversion of PC has attracted more and more attention. Therefore, it is urgent to sort out and write a comprehensive overview paper. In this paper, 178 relevant literatures were reviewed, and the development history and trend of PC remote sensing inversion research of PC in the past 30 years (1990—2020) were comprehensively summarized from the perspectives of PC optical characteristics, development of inversion algorithms, application of satellite sensors, difficulties and interference factors of quantitative remote sensing inversion of PC. This paper helps us to understand the new ideas and new methods emerging at home and abroad, master the new development trends of PC remote sensing inversion in the future, and provide data basis for the monitoring and management of water environment, water quality and water resources. Over the past 30 years, PC remote sensing inversion study number rising, and a breakthrough in the algorithm, has a lot of classic algorithms, such as band ratio method, the baseline method, nested band ratio method, biological optical model, derivative algorithm and machine learning algorithms, algorithm successfully isolated PC spectral characteristic of absorption coefficients at wavelength of 620 nm. Decreased the influence of other optically active substances (Chla、TSM) and obtained the high precision of inversion and validation . In addition, the development of PC inversion algorithms mostly based on in situ hyperspectral data or aerial images (CASI-2, AISA Eagle). In order to meet the needs of PC concentration distribution in a certain space and frequency, satellite image data sources are mostly used. PC. There are many types of multi-spectral satellite data sources to choose, such as Landsat series, MODIS, MERIS, Sentinel-2 MSI, Sentinel-3 OLCI, etc. However, due to the more appropriate band setting, MERIS and Sentinel-3 OLCI are still the most used data sources for PC remote sensing inversion research. Because PC spectrum signal is weak, and vulnerable to the interference of chlorophyll a, TSM, there is still a major difficult to get an accurate estimation result. Based on the above analysis, the future development direction of PC remote sensing inversion can be summarized as the following aspects: first, an international standard measurement method is urgently needed in PC extraction testing; secondly, the development of algorithm, adhere to the mechanism research related to the inherent optical properties, and the integrating machine learning algorithm to bring higher inversion accuracy; third, the scale of study area water body in space and time will toward a larger geographical space scale, longer time series history tracing and future prediction; fourth, at the aspect of the expansion of application, PC remote sensing inversion is not limited to the quantitative estimation of cyanobacteria biomass, but will predict distribution of algal toxins and related diseases based on the relationships between these parameters and PC concentration, furtherly establish a water risk factor rating system based on remote sensing in the future.关键词：lake remote sensing;inland waters;Phycocyanin;Optical properties;Inversion algorithm;Satellite sensor- 1113
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发布时间：2022-04-30 -
摘要：lake carbon cycle is an important segment in the global carbon cycle. Growing attention has been received to lake carbon cycle for its virtual effect on the global carbon cycle and climate change. However, comprehensive monitoring and assessment of the global lake carbon cycle is still challenging due to the fragmentary distribution and diversity in ecology, type and climatic zone of lake. Remote sensing technology with advantages of large area continuously synchronous observation could conquer the limitations of conventional observation method, supporting the research of global lake carbon cycle with huge of observation data. Meanwhile, the estimation of organic carbon source and composition via the remote sensing technology could be combined with biogeochemical technology for the advantage of spectral detection by remote sensing. In this paper, recent studies about the remote sensing application and research on lake basin and water were reviewed based on the active demand of remote sensing in the lake carbon cycle. The application of remote sensing in a geography of lake carbon cycling was proposed due to the highly variable among lakes within basin characteristics. Much more precision and higher spatial resolution results of land use, vegetation canopy, primary productivity, soil properties, population density and other watershed attribute data from remote sensing should be considered in geography of lake carbon cycling to improve the estimation of carbon input in lake. The remote sensing retrieval of particulate and dissolved organic carbon concentration in the lake water have been widely used, yet the carbon pool estimation is flimsy for the difficulty in the acquirement of carbon vertical distribution. Meanwhile, the sources of organic carbon significantly affect the turnover time of organic carbon, presenting the short turnover time of endogenous organic carbon and relative long turnover time of terrestrial organic carbon. The remote sensing should be cooperatively estimated endogenous and terrestrial organic carbon with isotopic geochemistry technology, which can distinguish the source of organic carbon effectively. The retrieval algorithms of inorganic carbon, such as CO2 and CH4, are being developed by the active and passive remote sensing. The black carbon from incomplete combustion of fossil fuel and biomass is a higher aromatic content and different from other types of organic carbon (such as: terrestrial, endogenous organic carbon) should be taken as a new inversion parameter from remote sensing. The estimation of physicochemical characteristics of lake water, which significantly affected the lake carbon cycle, should be concerned and combined in the research of lake carbon cycle. The virtual sensors with high temporal, spectral and spatial resolution should be established due to the limitation of current remote sensing satellite data. Multi-source remote sensing data fusion is a recommendable method to overcome the limitation application of remote sensing in lake carbon cycle due to the exclusive highly temporal, spectral or spatial resolution. The opportunities and challenges of remote sensing application in the lake carbon cycle were discussed according to biogeochemical processes of carbon in the lake and the recent advances of big data and artificial intelligence in remote sensing technology, as well as the development of lake carbon cycle studies.关键词：lake carbon cycle;remote sensing;Biogeochemistry;Big data and artificial intelligence;water environment;greenhouse gase- 1318
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发布时间：2022-04-30 -
摘要：In shallow lakes or reservior, aquatic vegetation plays an important role in purifying water, maintaining the balance of lake ecosystems, supporting socioeconomic functions and protecting lake ecological environment. However, an excessive amount of macrophytes, especially floating-leaved vegetation, can have some negative effects on lake ecology. For example, the addition of large amounts of plant material to the lake bottom can cause lake silting and accelerate lake swamping; the release of pollutants into the lake water when the plants die and decay can result in water pollution. Therefore, it is very important to map spatiotemporal distribution and their changes of aquatic vegetation and then to retrieve biochemical parameters such as coverage and biomass for ecological restoration and management of lakes. Remote sensing techniques have become powerful and effective tools for mapping aquatic vegetation types and their changes over a large area and a long period. In this paper, with the theme of aquatic vegetation remote sensing, we reviewed and summarized the major progresses and methods of remote sensing application in aquatic vegetation in shallow lakes by literature review. We found the research topics in aquatic vegetation remote sensing mainly included hyperspectral analyses, classification and mapping, parameter inversion, change detection, and so on. We also offered a literature statistical diagram of classification methods for mapping aquatic vegetation, and found decision tree was the most popular and machine learning was becoming more and more popular in all mapping methods. Finally, we discussed existing major challenges, potential solutions and future prospects in aquatic vegetation remote sensing, including developing a multi-parameter method for mapping different species of submerged vegetation, expanding the spatial-temporal scale of inversion models in parameters in application and making full use of the advantages of UAV (unmanned aerial vehicle) coupled with hyperspectral and multispectral sensors for mapping and parameter inversion in aquatic vegetation.关键词：Aquatic vegetation;submerged aquatic vegetation;Biomass;Remote sensing;Classification;Change detection- 1239
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- 428
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- 1
发布时间：2022-04-30 -
摘要：Lakes are the main components of water resources on the earth surface and are closely related to natural environment, human life, and social economics. The variation of lake ecosystem triggered by natural changes and human activities has attracted attention of scientists and governments worldwide. As a major lake ecological problem, lake eutrophication can lead to algal blooms, causing ecosystem disaster and drinking water risk. Therefore, effective monitoring of lake eutrophication process is an important cornerstone to accurately grasp the lake ecological dynamics and strictly control the lake environment pollution. This study mainly discusses the research progress on remote sensing assessment of lake nutrient status and retrieval algorithms of characteristic parameters.Through in-depth analysis of a large number of relevant literatures in recent years, this study systematically summarizes the existing methods for remote sensing assessment of lake nutrient status and introduces the research progress on retrieval algorithms of characteristic parameters. In addition, suggestions and prospects for the studies of lake eutrophication are put forward from the perspective of remote sensing big data. Thus, the objectives of this study are to provide an overview of remote sensing algorithms as useful reference and demonstrate the feasibility of remote sensing big data for the assessment of lake nutrient status.Accurate, real-time, and large-scale monitoring of lake nutrient status is an important basis for understanding the characteristics of lake environment change, through analysis, evaluation, remediation, and management of lake eutrophication. Compared with the traditional survey approaches, remote sensing has the advantages of fast, wide and periodicity. It has been broadly used in monitoring various lake environmental parameters, such as chlorophyll, transparency, and nutrient status. This study focuses on remote sensing assessments based on Trophic State Index (TSI) and Trophic Level Index (TLI). Moreover, the latest studies on retrieval algorithms (including empirical model, semi mechanism model, and machine learning model) of characteristic parameters are summarized. Therefore, the reliability of remote sensing assessment of lake nutrient status has been fully demonstrated.Through combing the research progress on the conventional assessments (i.e., TSI and TLI) and the retrieval algorithms of key characteristic parameters (i.e., ZSecchi Disk, Forel—Ule index, chlorophyll a, total nitrogen, and total phosphorus), the potential correlation between the two methods is clarified. The results can provide reference for the studies on lake ecological environment and the possibility for improving the remote sensing technology of lake optics and water color in the future.In recent years, with the continuous improvement of quantitative retrieval algorithm and satellite sensor technology, research progress on remote sensing assessment of lake nutrient status has entered a rapid development stage. The review of related studies has advanced our understanding of lake eutrophication by remote sensing data and technology. In summary, remote sensing plays a significant role in the research of lake eutrophication and provides practical contribution to the monitoring and protection of lake ecological environment in China and even the world.关键词：lake eutrophication;remote sensing assessment of nutritional status;water parameter retrieval algorithm;remote sensing big data- 1325
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发布时间：2022-04-30 -
摘要：Lakes play an important role in supplying water resources and sustaining human living. The broad geographical extent and complex environment of China result in the different changing patterns and complicated driving factors of lakes. The two-epoch national lake surveys provide the groundbreaking knowledge of lake distribution and change characteristics in China. Thus, an increasing number of researchers have conducted the national-scale remote sensing monitoring of lake changes with the rapid development of satellite techniques in recent years. However, evident discrepancies exist in the results and conclusions among these prior studies due to differences in level of manual quality control, lake mapping method, and the acquisition time (year and month) of used remote sensing images. Thus, comprehensively investigating their differences and causes and producing an updated lake inventory are required.We reviewed and compared the data sources, methods, and results of the existing works on national-scale remotely sensed lake changes and analyzed the causes of such differences. We also produced a new national lake inventory by interpreting the maximum water inundation area (1980—2010), which can minimize the differences of manual interpretation and lake mapping in different time periods. With the spatial constrain of maximum water extent for each lake from the new inventory, this study developed a novel approach, namely, the Probability Equivalent Area method, to detect the long-term change trajectory of China’s lakes during 1980—2010.A total of 3741 lakes in China had a maximum area exceeding 1 km2 in the past 30 years. The Qinghai-Tibet Plateau lake zone accounted for approximately one-third in count and half in area of the inventoried lakes in China. The total area of China’s lakes showed an overall upward trend, but it had strong spatial heterogeneity. The Qinghai-Tibet Plateau and Xinjiang lake zones significantly increased lake areas, while the Eastern Plain, Inner Mongolia Plateau, and Yunnan-Guizhou Plateau lake zones significantly decreased lake areas. The area changes in the Northeast Plain and Mountain lake zone were statistically insignificant.This study suggested that the main causes of different results in previous remote sensing research of China’s lakes include inconsistent timing of satellite imagery, the manual interpretation standards for lakes and reservoirs, and quality assurance procedures. To produce a more accurate estimation of lake area, a new inventory dataset, which minimized interpretation differences and provided a unified spatiotemporal constraint, was proposed on the basis of our findings. A statistics-based method named the Probability Equivalent Area, which eliminated differences in satellite imagery selection, was also proposed. The lake area changing patterns and distribution of lake water resources in different lake zones are obviously imbalanced in China. China’s sparsely populated areas are generally experiencing a dramatic increase in lake area. However, the densely populated areas are experiencing a significant decline in lake area. This situation may further increase the imbalance of water resources per capita in China. Effective measures should be taken to slow down this trajectory.关键词：lake remote sensing;Water Area;remote sensing monitoring;China;National Scale;global change- 1261
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发布时间：2022-04-30
Review
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摘要：Water level is an important variable that indicates the variations in the vertical dimension of inland water bodies. However, in-situ monitoring of water level of lakes and reservoirs is expensive. Thus, the coverage of gauging network is relatively low. The satellite altimetry technology, originally used for ocean research, has been widely applied for inland water research on a local to regional and global scale. As the success of several altimetry missions, the situation of data scarcity has been mitigated, especially in the recent decade.The literature review shows that the mainstream of altimetry research for lakes and reservoirs focuses on one specific or few lakes/reservoirs aiming at a detailed investigation. Regarding the altimetry data sets, most studies use high-level products (i.e. water level data instead of raw signals) from one certain database, such as Hydroweb, DAHITI, etc., but very few exploit the low-level products. The major research foci include temporal variations of water level, and the attribution of water level changes in the context of climate changes. Besides, some publications research the water storage changes and thus assess the water resources and management issues, while some others focus on catchment hydrologic modeling with water levels of lakes/reservoirs as constraints.In this short review article, we first briefly introduced the theory of inland altimetry, followed by the descriptions of major freely open-access products of different levels. Then, we summarized the common data processing procedures including data screening, waveform retracking, outlier removal, time series construction, etc. We intend to guide the newcomers to prepare data for their own studies of interest when dealing with low-level products if necessary. Moreover, we reviewed the latest progresses of inland water altimetry, especially for lakes and reservoirs research. The progresses are reported in three main directions, i.e., water level monitoring and analysis, dynamics of water storage, and catchment hydrologic modeling. The latter two directions involve more data sets other than altimetry-derived water levels and still need more research for further advancement.We concluded this study with recommendations on future research topics, such as new data processing techniques (e.g. Fully-Focused SAR and Wide swath InSAR processing, Machine Learning, etc.) to extract water levels that are more accurate. We also provide introductions of several proposed or planed future altimetry missions (e.g. SWOT, Sentinel-3 Next Generation Topography, Sentinel-6-Next Generation, etc.) that will provide many opportunities for lake and reservoir research beyond just lake/reservoir monitoring. Moreover, we highlight the value of multi-mission (or constellations) data sets for high spatio-temporal resolution mapping of inland water bodies. Meanwhile, it is also very important to develop freely open-access high-level databases for end users, such as hydrological modelers.关键词：satellite altimetry;water level;lake and reservoir;monitoring;review- 1139
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发布时间：2022-04-30 -
摘要：Lakes are very sensitive to the impacts of climate change and human activities. The lakes over the Tibetan Plateau (TP) are numerous and extensively distributed; they are an important part of the Asian water towers. Understanding the interactions of the Earth system’s circles and the mechanism of environmental changes on the TP requires less disturbance from human activities. What is the response of TP’s lakes to climate change as sensitive indicators in the context of rapid global warming? Based on the lake area mapping with multispectral images, lake water level changes from satellite altimetry data, and lake water volume changes with digital elevation model. This study synthesizes the research progress of area, level, and water volume changes of lakes (larger than 1 km2) on the TP in the past nearly 50 years. The main conclusions are as follows: (1) the total number of lakes on the TP increased from 1080 in the 1970s to 1424 in 2018 (+32%), the total lake area expanded from 40,000 km2 to 50,000 km2 (+25%), the average water level of lakes increased by approximately 4 m, and the lake water storage increased by nearly 170 billion tons. (2) The changes in lake area, water level, and water volume decreased slightly from the 1970s to 1995, and then showed a rapid but nonlinear increase. The lake area, water level, and volume increased in the north-central plateau but decreased in the south. (3) A quantitative lake water balance based on multisource remote sensing data reveals that increased precipitation is the main driver of lake expansion, followed by glacier ablation contribution. Several scientific frontiers facing the challenge are also summarized as follows: (1) quantitative evaluation of the causes of individual lake change. At present, a quantitative study on the causes of lake change indicates the contribution of glacial mass loss to the increase in lake water volume, and precipitation, evaporation, and permafrost underground ice ablation that contribute to the increase in lake water. New driving data sets should be developed and hydrological models from the watershed scale should be further combined to estimate lake water balance. (2) Driving mechanisms of lake changes. The driving mechanisms of lake changes on the TP are currently analyzed mainly to enhance precipitation on the plateau. In the future, climate dynamics theory and hydrological models should be combined to further improve understanding of the driving mechanisms of spatial and temporal differences between the climate system and the cryosphere affecting lake changes on the TP. (3) New satellite remote sensing technology should be combined to understand the past, present, and future lake evolution on the TP. Remote sensing, as an indispensable modern technical means of air-sky-earth, plays a greater role with the implementation of the Second TP Scientific Expedition and Research plan on the TP, and more new satellites are launched one after another to improve understanding of the evolution pattern and change mechanism of lakes on the TP.关键词：lake;remote sensing;area;level;volume;Tibetan Plateau- 1751
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发布时间：2022-04-30 -
摘要：Tibetan Plateau (TP) lakes are located in the high-altitude and rough-terrain region. These lakes are effective indicators and sentinels of climate changes because of the absence of direct anthropogenic influence and their dominant distribution in endorheic basins. Altimetry satellites can be used to monitor the water level changes of inland water bodies. However, satellites cannot easily obtain accurate and continuous observations of Tibetan lakes with steep terrain. This paper presents a robust scheme for constructing accurate and long-term lake level time series using multi-altimeters. We demonstrate the robust scheme over La-ang Co.A robust strategy is presented to obtain lake levels on the TP using multi-altimeter data. The consistency of atmospheric path delay corrections should be carefully checked to integrate various altimeter products issued in different periods. Apparent biases are found in troposphere corrections from different altimeter products and updated by ERA-5 model. ICE retracker is used to correct the altimeter range. A two-step method is proposed for outlier removal, which has accurate performance without any a prior information. Bias adjustment is an essential step in the fusion of multi-altimeters. Tandem mission data of altimeters are used to estimate inter-satellite bias. Finally, a 28-year-long lake level time series are constructed using TOPEX/Poseidon and Jason-1/2/3 altimeter data from 1992 to 2020. The relationship among lake level, area, precipitation, temperature, and evaporation in the basin from 1992 to 2020 is analyzed.The mean lake level for each cycle is estimated after outlier removal. As an example, About 38% of the observations are rejected as outliers in Jason-2 period. The T/P-family satellites share the same ground track and have an overlap between two successive satellites for intersatellite calibration. As a result, Jason-1 has a mean lake level bias of 0.15 m with respect to T/P. The bias of Jason-2 with respect to Jason-1 is 0.02 m. The bias of Jason-3 with respect to Jason-2 is -0.23 m after removing an outlier. Biases between different missions are adjusted, and a 28-year monthly lake level time series is generated. Compared to the in situ data and available lake level databases, our result is the most robust time series for La-ang Co, with high accuracy and considerably continuous samples from 1992 to 2020. The mean STD is about 13.10 cm for T/P-family satellites. From 1992 to 2020, the level of La-ang Co decreased by 6.00 m, with an average change trend of -0.21±0.01 m/a.This result showed that the lake level extraction in this study is more accurate than that of available lake level databases, and the change of lake levels in La-ang Co is similar with the previous studies. Annual and semi-annual variations as well as inter-annual oscillations can be clearly observed in the time series. Evaporation is greater than precipitation, which is the main factor leading to the decrease of lake level. The water level of La-ang Co will continue to decline in the near term due to global warming.关键词：satellite altimetry;TOPEX/Poseidon;Jason-1/2/3;La-ang Co;lake level;Tibetan Plateau- 1193
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发布时间：2022-04-30 -
摘要：Lake level elevation and variation are important indicators for the global climate change, and satellite altimetry especially the laser altimetry data is a valuable data source. GF-7 laser altimeter as the first business application load with full-waveform can be used to measure the lake level, except for elevation control points. To evaluate the GF-7 satellite laser altimetry data on the lake, the basic parameters are introduced, and the relative and elevation accuracy of ICESat and ICESat-2 laser points located on large lakes is compared. The absolute elevation accuracy is analyzed by the field surveying result.A method to extract the GF-7 satellite laser altimetry points located on a large lake with high precision and reliability is presented, and the side-sway of the satellite, the atmospheric scatter, and echo waveform saturation that influence the elevation accuracy are discussed. The laser point with 1° side-angle can induce 0.106 m elevation error and the points on the lake less than 0.3° side-angle can be used to measure the lake level. The unique footprint image of GF-7 satellite is introduced to extract the laser points located on the lake. It can be used to judge the location of the laser point, whether it is in the lake or on the land and whether it is influenced by the cloud or dense fog and haze. Moreover, the saturation of echo waveform and the error elimination by median absolute deviation are introduced to ensure the reliability of the laser points.The absolute elevation accuracies of beam 1 and beam 2 are -0.030±0.109 m and -0.195±0.049 m, respectively, according to the field RTK-GPS surveying on the frozen Hulun Lake. The internal consistency of GF-7 laser points is better than that of ICESat laser points on Qinghai Lake, and beam 1 is slightly better than beam 2, with standard deviation of 0.056 and 0.080 m, respectively, which are equal to ICESat’s 0.079 m. Compared with ICESat-2, the lake surface points of GF-7 are sparser, but the accuracy is the same. The mean difference between the two beams of GF-7 and ICESat-2 in the same area of Hala Lake in adjacent time is -5.2 and -8.0 cm, respectively.The laser altimeter of GF-7 satellite can effectively obtain the water level of large lakes, and the relative and absolute elevation accuracy of GF-7 satellite laser altimetry points after extraction is equal to the ICESat and ICESat-2 laser points in the large lake. Under certain conditions, GF-7 laser altimeter can measure lake water level in terms of accuracy, but some problems, such as low repetition frequency of laser and weak observation ability, still exist. The conclusion can be viewed as reference for the next generation laser altimetry satellite, such as the land and sea laser satellite of China. Also, the combined of different satellite laser altimetry data on the lake maybe an effective way for the future application.关键词：GF-7;satellite laser altimetry;ICESat-2;lake water level;accuracy evaluation- 1201
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发布时间：2022-04-30 -
摘要：The real-time dynamic monitoring of water volume has great value for risk assessment, prediction, and early warning and disposal decision-making of dammed lakes. In view of the difficulty in obtaining underwater topographic data of dammed lake in areas without gauged data on the plateau, directly, quantitatively, and timely estimating the water volume of dammed lake by using remote sensing technology is difficult. This study aims to solve the problem of rapid quantitative estimation of water volume of plateau dammed lake under unknown underwater terrain scenario by remote sensing and perform the risk monitoring and disaster assessment of dammed lake.According to the existing remote sensing data and digital elevation information, this study puts forward a remote sensing quantitative estimation method of water volume of dammed lake on the plateau without underwater terrain by fully using remote sensing data. The details are as follows. First, the submerged area of dammed lake is extracted from remote sensing images. Second, the center line of the complex polygon of the dammed lake is calculated. Specifically, according to the water area of the dammed lake, the Tyson polygon algorithm is used to calculate the position information of the dammed lake centerline. Third, through the location of the polygonal center line of the dammed lake, the fixed-point elevation measurement is carried out to complete the fitting calculation. Then, according to the fitting estimation of the middle line elevation and combined with the slope elevation information, the unknown underwater terrain of the dammed lake is adaptively simulated. Finally, based on the simulated underwater terrain and the submerged area of the dammed lake, the capacity of the dammed lake is calculated by 3D curved surface space discrete integration.The dammed lake, namely, Sarez Lake in the Pamirs was selected as the research area. Remote sensing survey and empirical research on water volume were carried out using the proposed method. The research results show that the water area of Sarez dammed lake is approximately 89.09 km2, and the water volume of Sarez Lake is approximately 16.25 billion m3. This result is consistent with the expert’s estimated water volume of 15.5 billion m3 to 16.5 billion m3. The accuracy verification of the local simulation experiment shows that the overall dynamic error between the simulation data and the measured data is controlled within 10%, and the correlation coefficient is 0.95 (P<0.01, double tailed). This finding further proves the robustness of the algorithm and the credibility of the estimation results.This method can rapidly estimate the water volume of plateau dammed lake, with high accuracy and strong technical universality. It provides an efficient method for remote sensing estimation of plateau dammed lake water volume under none or lack of underwater terrain data scenario. It also solves the problem of quantitative calculation of plateau dammed lake water volume with unknown underwater terrain.关键词：dammed lake;water volume estimation;underwater terrain;hydrologic remote sensing;sarez lake- 913
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发布时间：2022-04-30
Lake Hydrology
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摘要：Organic Suspended Matter (OSM) is an important component of lake organic carbon pool, which is important to the study of lake ecological environment and primary productivity. At present, the research of OSM in water mainly focuses on its source composition, migration and transformation, and flux into the sea. Therefore, constructing a new method for estimating OSM in inland water is urgently needed. This method can obtain the spatial and temporal distribution characteristics of OSM concentration in the whole lake and even the whole region.In this study, the combined Taihu, Chaohu, Geui, Xiaoxingkai, Dianchi, Hongze, Hulun, and Nanyi lakes are selected as the research area, and a remote sensing method for estimating the concentration of organic suspended solids in inland water is developed. According to the slope of B6 and B7 bands and the slope of B10 and B11 bands, the water body is divided into two types, namely, the Type A water dominated by inorganic suspended matter and the Type B water dominated by OSM. On this basis, remote sensing estimation models of OSM concentration for different types of water bodies are established. The remote sensing estimation model of OSM concentration for Type A water is OSM=32.75·B17/B3-1.3537, and the remote sensing estimation model of OSM concentration for Type B water is: OSM=43.098+15.751·B10/Bl1. The band ratio B17/B3 can be used to estimate the OSM concentration of Type A water, and the band ratio B11/B10 can be used to estimate the OSM concentration of Type B water. The proposed method obtained RMSE of 5.38 mg/L and MAPE of 28.93%, which were decreased by 11.10 mg/L and 41.66%, respectively, compared with those of the traditional nonclassified OSM estimation empirical method.An empirical algorithm for retrieving OSM concentration in inland lakes is proposed on the basis of the band characteristics of Sentinel 3A-OLCI image. According to the spectral characteristics of inland water bodies, which are the slope of OLCI band B6 and B7 and the slope of band B10 and B11, the algorithm, which uses the strategy of first classification and then retrieval, can divide the water into two types: the water dominated by inorganic suspended matter and the water dominated by OSM. The OSM concentration of the water dominated by inorganic suspended matter can be estimated by using OLCI bands B3 and B17, while the OSM concentration of the water dominated by organic suspended solids can be estimated by using OLCI bands B10 and B11. Independent validation datasets show that the RMSE and MAPE estimated by this method are 5.38 mg/L and 28.93%, respectively. The estimation accuracy of this method is significantly improved compared with those of the existing retrieval algorithms for the concentration of OSM in inland water. This method has been successfully applied to obtain the temporal and spatial distribution characteristics of the OSM concentration in Taihu and Chaohu lakes in China. The method constructed in this study provides a new algorithm with higher accuracy for obtaining the OSM concentration of inland water at regional scale.The classification method of this study and the construction of the retrieval algorithm are based on several typical lakes on the basis of field sampling. However, the robustness of the algorithm still needs further examination, especially when it is applied to water with different biological optical characteristics from sampling lake. The performance of the algorithm also should be tested using a great number of field measured datasets. In future studies, the performance of the algorithm will be further tested by collecting more lake field data.关键词：inland water;organic suspended matter;OLCI;optical classification;quantitative estimation;empirical algorithm- 1086
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发布时间：2022-04-30 -
摘要：China’s ZY-1 02D satellite was successfully launched on September 12, 2019. It carries the new-generation Advanced Hyperspectral Imager (AHSI), which has 166 bands in the visible to short-wave infrared bands. AHSI can acquire images at 30 m spatial resolution with a 60 km swath. ZY-1 02D satellite shows great potential for inland water quality monitoring application, owing to its abundant narrow bands and relatively high spatial resolution. However, this satellite has been launched for a short period, and the applicability of this data needs to be further analyzed and tested.Taihu Lake (eutrophic), Yuqiao reservoir (eutrophic), and Xiaolangdi Reservoir (mesotrophic) in China were used as study areas for the Chlorophyll-a (Chla) retrieval based on the ZY-1 02D hyperspectral images. Within one day of the ZY-1 02D satellite overpass, in situ spectra, and Chla concentrations were collected at sampling sites in these study areas. We selected five typical Chla semi-empirical models based on spectral indices, namely, Band Ratio (BR), Normalized Difference Chlorophyll Index (NDCI), Three-Band Index (TBI), Enhanced Three-Band Index (ETBI), and the Baseline Height (BH). We used in situ measured Chla concentration at 46 sampling sites in the three study areas and simultaneously acquired ZY-1 02D images to optimize the parameters in these models. We evaluated the accuracies of image-derived Rrs at sampling sites, and then conducted accuracy analysis for estimated Chla concentrations using optimized empirical models.ZY-1 02D image-derived Rrs were consistent with in situ measured Rrs in the 671 and 705 nm, whereas the 731 and 748 nm band Rrs had greater uncertainties because they were more likely to be affected by the image noise. In addition, the accuracy analysis for the estimated Chla concentrations shows that the model based on the 705 to 671 nm band ratio achieves the highest accuracy, with an R2 of 0.78. In addition, the mean unbiased relative error (AURE) and Root Mean Square Error (RMSE) are 13.5% and 4.5 mg/m3, respectively. On the contrary, models based on the ETBI and BH yield Chla concentration estimates with low accuracies.In conclusion, ZY-1 02D hyperspectral data show good potential in terms of accurate retrieval of Chla concentration for inland waters. We plan to conduct more in situ experiment when the ZY-1 02D satellite overpasses to improve the Chla concentration retrieval model applied on the ZY-1 02D data. In the future, the monitoring capacity should be improved through establishing a hyperspectral satellite constellation, and noise reduction and atmospheric correction methods should be developed for ZY-1 02D’s inland water application.关键词：ZY-1 02D satellite;hyperspectral remote sensing;inland water;Chlorophyll-a retrieval;lake remote sensing- 1626
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发布时间：2022-04-30 -
摘要：The width of high-resolution satellite is generally very small. Affected by cloud and rain and orbital return visit cycle, the coverage capacity of a single satellite is limited in a short period of time. Therefore, A single high-resolution satellite is often unable to meet the needs of black and odorous water monitoring in a certain period of time, and multi-source satellites are needed to monitor black and odorous water. In order to analyze the applicability of multi-source high-resolution image to the remote sensing monitoring of black and odorous water, based on the water remote sensing reflectance data measured by the surface object spectrometer, the equivalent calculation was carried out with GeoEye-1, WorldView-2,DMC3, SuperView-1 (SV1) and GF-PMS series (GF-1/1B/1C/1D, GF-2, GF-6) sensor bands.Take the multi-source sensor remote sensing images as the research object. First compared the GeoEye-1, WorldView-2, DMC3, SuperView (SV1) and GF-PMS series (GF-1/1B/1C/1D, GF-2, GF-6) image spatial resolution, spectral response function, and band Settings; then, based on the BOI (Black and Odorous Water Index) recognition model, the applicability of multi-source sensor image monitoring is analyzed with the same threshold value, and a new model is proposed for DMC3 which is not suitable for BOI model, and the high-quality multi-source images are selected and applied; Finally, some suggestions are put forward for cooperative monitoring of black and smelly water with multi-source sensor remote sensing image.The research results show that: (1) It is found that GeoEye-1, WorldView-2, SuperView-1 and GF-1/1B/1C/1D/2/6 images can use the same threshold for black and odorous water Monitoring, with good identification accuracy; The normalized differential water body index (NDWI) for DMC3 can effectively identify the general water body and the black and smelly water body. (2) High quality multi-source images were selected with the threshold of BOI=0.05 and NDWI=0.55 for the application of black and smelly water monitoring. It was found that the collaboration of multi-source remote sensing images could provide continuous supervision for river water quality monitoring. (3) In the process of black smelly water monitoring, comprehensive consider price and spatial resolution image, when the river width in 2—10 meters, select GF-2, SV1 or DMC3 image as a conventional remote sensing image, GeoEye-1, WorldView-2 images as a supplement; When most of the river width is more than 10 meters, GF-1 or GF-6 images are selected as conventional remote sensing images, and the supplementary data sources are GF-2, SV1, DMC3, GF-1B/1C/1D, Geoeye-1, and WorldView-2 images, respectively.关键词：multi-source image;black-odor water monitoring;applicability;BOI;threshold- 1195
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发布时间：2022-04-30
Lake Water Environment
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摘要：Lake ice phenology refers to the dates of lake freeze-up and break-up and period of ice cover; it is considered a valuable indicator of regional climate change. The shifts of lake ice phenology in association with a warming climate is widely interesting because it not only serves as evidence of the changes in climate but could show substantial impacts on regional hydrological processes and the aquatic ecosystem. Ground-based records of lake ice phenology over the Tibetan Plateau are limited because of the harsh geographical conditions and the high observation costs. Satellite-based observation and modeling are expected to be effective in investigating the long-term changes in lake ice phenology for regions with poor ground observations. We aim to reconstruct the lake ice phenology time series and to identify the long-term changes of lake ice phenology in responding to the climate of Nam Co Lake at the Tibetan Plateau and for the past 60 years based on a process-based model, where remotely sensed lake surface water temperature is used to calibrated the process-based model.The research framework includes retrieving lake surface water temperature and lake ice phenology information from remotely sensed data, calibrating the process-based model against the remotely sensed lake surface water temperature, determining lake ice phenology according to the simulated water temperature, validating the simulated lake ice phenology by comparing against that derived from the remotely sensed data, detecting the long-term trends in the reconstructed lake ice phenology, and modeling the response of lake ice phenology to changes in air temperature. Four different remotely sensed datasets and the corresponding approaches are used to retrieve lake ice phenology of the Nam Co for the period 2000—2015. The process-based model (LAKE 2.3) is a 1D lake surface energy balance model. It is used to reconstruct lake ice phenology of Nam Co for the period 1963 to 2018 and investigate the sensitivity of lake ice phenology to climate change. The Mann–Kendall nonparametric statistical test approach is used in detecting the trend of lake ice phenology.Lake ice phenology derived using different remotely sensed data and approaches with consistency in the trend but with considerable uncertainties due to the temporal and spatial resolution of the sensors. The reconstructed lake ice breaking-up date based on the model is more comparable to that remotely sensed data than the other lake ice phenology indicators. The reconstructed time series of lake ice phenology shows that, during the previous 57 years, the freezing-up date was significantly delayed whereas the breaking-up date was earlier, thereby resulting in a shortened ice cover duration. The ice cover duration is shortened at a rate of 6.4 days/10a during the period 1963 to 2018. Sensitivity analysis shows that the breaking-up date would be significantly earlier in a warm climate. Under the 2 °C warmer scenario, the breaking-up date would be 12.4 days earlier on the average, and the ice cover duration would be shortened by 19.7 days, on the average.This study combines the strengths of remote sensing and numerical modeling in forming a novel research framework to reconstruct lake ice phenology of regions with poor ground-observation, such as the Tibetan Plateau. The results show that the framework is reliable and valuable to explore the long-term changes in lake ice phenology and its response to climate change. However, uncertainties exist in the remotely sensed lake ice phenology and the numerical modeling, which needs to be improved and further validated where or when ground-based observations are available.关键词：Lake ice phenology;multi-source remote sensing;lake model;Nam Co;Tibetan Plateau- 1144
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发布时间：2022-04-30 -
摘要：Seasonal freeze—thaw of lake ice is an important indicator of climate change. As a boundary lake between China and Russia, Khanka Lake has annual ice cover due to its low air temperature. Changes in ice phenology greatly affect the physical, chemical, and biological lake processes. Therefore, this study aims to obtain the ice phenology variations of Khanka Lake and analyze its influencing factors from 1979 to 2019.An algorithm based on moving t test method is applied to determine the daily status of passive microwave calibrated enhanced resolution passive microwave (CETB) pixels, and then the ice phenology dates can be obtained by the thresholds of 5% and 95% of all the pixels. Subsequently, ice phenology results extracted from moderate-resolution imaging spectroradiometer (MODIS) daily snow product are used to compare with the results from passive microwave data. In addition, the meteorological data from Jixi station are used to analyze the reason for ice phenology variations of Khanka Lake.The results show that the passive microwave and MODIS remote sensing data have good consistency in the extraction of lake ice phenology. On the average, lake ice begins to freeze on November 13 and completely freezes on November 23 every year, and the freezing duration of lake ice is 9.80 days. On April 23 of the following year, the lake ice begins to melt, and on April 30, the lake ice completely melted, lasting for 8.03 days. The complete freezing duration of lake ice is 150.50 days, and the ice cover duration is 168.03 days. In more than 41 years, the freeze-up start date has no evident change, but the freeze-up end date has been pushed back at 0.19 day/year. In addition, the break-up start date and break-up end date have advanced at 0.16 day/year and 0.13 day/year, respectively. The complete freezing duration and ice cover duration have shortened by 12.71 days and 2.87 days, respectively. The delay of freeze-up dates is closely correlated with wind speed up, whereas the advancement of break-up dates and shortening of the complete freezing duration and ice cover duration are significantly correlated with the increasing air temperature.The consistency between ice phenology results from CETB dataset and MODIS daily snow product indicates that the extracting lake ice phenology from passive microwave brightness temperature with low frequency is feasible, and the results are reliable. Khanka Lake has experienced subsequent freeze-up end date and earlier break-up dates from 1979 to 2019, thereby shortening the complete freezing duration and ice cover duration. The increasing wind speed may be the main reason for the subsequent freeze-up dates, while the variations in break-up dates and ice cover durations can be explained by the increasing air temperature.关键词：lake remote sensing;Lake ice phenology;passive remote sensing;MODIS;climate change;Khanka Lake- 1065
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发布时间：2022-04-30
Lake Ice Remote Sensing
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摘要：Water-leaving radiance (Lw) or remote sensing reflectance (Rrs) is a fundamental parameter of water color remote sensing. It has been a long-standing and challenging goal to precisely measure Lw. Skylight-Blocked Approach (SBA), a novel approach for in-situ water spectrum measurement, can observe Lw directly screening the impact of the skylight in above-water method. It is not necessary to fulfill complicated post-processing to derive Lw by using SBA, which makes it has great potential to be used in different types of water body. However, there is not an automatic portable instrument to obtain water spectrum through SBA until now. In this study, a portable floating optical buoy (FOBY-P) is developed and tested. FOBY-P has its advantages with a smaller self-shading and an easier deployment compared with previous versions of FOBY with a circular floating body. The in-situ measurements in the coast of China through FOBY-P were carried out from October to November 2018 to test the buoy system. The results showed that 1) The self-shading effect of the floating body on the Lw observation under a large solar zenith angle can be effectively avoided by the tripod design of FOBY-P. The errors caused by the self-shading were less than 5% for the Rrs of 400—700 nm when it was used in turbid water. And the self-shading effect was only 1%—3% used in clear water; 2) FOBY-P can keep the sensor stable in the different sea state levels. Its effective observation ratio (the tilt angle less than 5°) is over 98% in the 1st sea state. The sensor tilt angle would be greater and greater when the sea state becomes worse. However, the ratio can still reach approximately 50% for the 3rd and 4th sea state; 3) The derived results of FOBY-P are in good agreement with those of TriOS RAMSES sensors. The correlation coefficient r between the Rrs of FOBY-P and that of TriOS RAMSES is larger than 0.9, and the Rrs difference of them is less than 5% for 490—565 nm. The difference may be caused by the different processing procedures of the two systems with different approaches. The water-air interface correction processing may be one of the factors to cause the uncertainty of TriOS RAMSES observations with above-water method. The result shows that FOBY-P has some advantages in platform stability, ease of use, and measurement accuracy as an automatic water spectrum acquisition instrument based on SBA. In general, FOBY-P can satisfy the requirements of in-situ measurement of Rrs in optically complex coastal waters, even during moderate sea state to provide high-quality data. Furthermore, it is only the first version of FOBY-P and further optimization in the design and data processing would improve the performance of the instrument.关键词：water color remote sensing;water spectrum;insitu measurement;the skylight-blocked approach;the Portable Floating Optical Buoy (FOBY-P)- 850
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发布时间：2022-04-30
Scientific Instruments
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摘要：Lake water transparency can comprehensively reflect the lake water environment, has significant relationships to some water quality parameters, and greatly important for water environment monitoring. This study aims to introduce the generation processes, characteristics, and application values of a new monthly mean water transparency dataset for large lakes in China with a water area of >20 km2. The remote sensing algorithm for retrieving water transparency proposed by Liu et al. (2020) was applied to MODIS surface reflectance data and stored on the Google Earth Engine cloud platform to realize rapid calculation and mapping of monthly mean water transparency in different lakes in China from 2000 to 2020. The MODIS surface reflectance data contain one state band, which was used to remove nonwater pixels such as cloud, cloud shadow, and land. The output data were stored in GeoTIFF grid format, which saved the pixel-based water transparency value and the geographic coordinate information. The GeoTIFF format file was also convenient for different software platforms. The dataset covers 412 large lakes in different lake zones of China. Specifically, Inner Mongolia-Xinjiang Lake Zone (IMXL), the Tibetan Plateau Lake Zone (TPL), the Yunnan-Guizhou Plateau Lake Zone (YGPL), the Northeast Plain and Mountain Lake Zone (NPML), and the Eastern Plain Lake Zone (EPL) have 40, 262, 11, 20, and 79 lakes, respectively.This study also provided some application examples of the dataset. First, the dataset indicates that the lakes in China had high water transparency values in the west but low values in the east. In 2019, the area-weighted water transparency values in the IMXL, TPL, YGPL, NPML, and EPL zones were 174.54 cm, 276.67 cm, 254.93 cm, 43.41 cm, and 53.93 cm, respectively. Second, the comparison results of lakes Fuxian and Poyang show the two typical types of seasonal variations in water transparency. For the deep Lake Fuxian, water transparency was determined by phytoplankton content; it had low values in summer. On the contrary, for the shallow Lake Poyang, water transparency was controlled by sediment resuspension; it had low values in winter with strong wind. Third, according to water transparency, we divided the Chinese lakes into four types. Lakes in Type I with high water clarity were majorly located in the west. Lakes in Type IV with low water clarity were mainly distributed in the east. Fourth, water transparency was applied to assess the lake water environment under the sustainable development goals. In the previous two decades, water transparency values in the IMXL, TPL, and NPML zones showed a significantly increasing trend, but water transparency values in the EPL and YGPL zones showed a decreasing trend.To our knowledge, this dataset is the first monthly mean water transparency dataset, which covers nearly all large lakes in China. The monthly scale time resolution allows the dataset to obtain outstanding advantages for dynamically monitoring the lake water environment in China. In the future, the open sharing dataset is greatly important to promote the development of lake water environment research in China.关键词：China;lakes;water transparency;remote sensing;MODIS- 1771
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发布时间：2022-04-30 -
摘要：Lakes play an important role in the global carbon cycle. The dissolved carbon dioxide concentration (- 1104
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发布时间：2022-04-30
Data Articles
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