Retrieval study of lake water depth by using multi-spectral remote sensing in Bangong Co Lake

This paper reviewed studies on remote sensing of water depth retrieval. Four water depth retrieval models （single-band, dou- ble-ratio-band, multi-band, and BP network models） were evaluated using TM image and water data from Bangong Co Lake, which is located in China＇s Tibet Autonomous Region and Indian Kashmir. Tested by independent data, comparison of these four models demonstrates that BP network model performed better than the multi-band model, with the single-band model performing the worst. To sum up, this study demonstrates that first, BP network model performed better than the traditional model; second, precise atmospheric correction and radiation study, affected by different water level sand sediment, could improve the precision of water depth retrieval.

Remote Sensing Applied to the Evaluation of Spatial and Temporal Variation of Water Quality in a Coastal Environment, Southeast Brazil

The monitoring of water quality in large coastal regions demands great analytical efforts through the collection of many samples, over long periods. Remote sensing is a reliable tool that can provide valuable information on the spatial and temporal variations of environmental parameters, particularly turbidity and chlorophyll a. The aim of the present research was to evaluate the spatial and temporal distribution of water quality from 2005 to 2017 along the north coast of São Paulo and its responses to the implementation of industrial developments and to variations in rainfall. Fifty-two MODIS images were used, showing concentrations of chlorophyll a and turbidity, in the dry season and wet season, from 2005 to 2017. The results showed that dilution processes (due to rainfall) control chlorophyll a concentrations. However, a notable increase in concentrations could be identified after the installation of some of the developments in the region, particularly roads and ports. Turbidity was also shown to be affected by dilution processes, and during the wet season this parameter presented lower values. No effect in the results of turbidity could be identified from the installation of roads or ports, showing that vegetation cover exerts an important control on the erosional processes.

River Water Quality Model Based on Remote Sensing Information Methods——A Case Study of Lijing River in Guilin City

River water quality models based on remote sensing information models are superior to pure water quality models because they combine the inevitability and risk of geographical phenomena and can take complex geographical characteristics into account. A water quality model for forecasting COD has been established with remote sensing in- formation modeling methods by monitoring and analyzing water quantity and water quality of the Lijing River reach which flows through a complicated Karst mountain area. This model provides a good tool to predict water quality of complex rivers. It is validated by simulating contaminant concentrations of the study area. The results show that remote sensing information models are suitable for complex geography. It is not only a combined model of inevitability and risk of the geographical phenomena, but also a semi-theoretical and semi-empirical formula, providing a good tool to study organic contaminants in complicated rivers. The coefficients and indices obtained have limited value and the model is not suitable for all situations. Some improvements are required.

Integrated Approach Using Remote Sensing and GIS for Assessment of Groundwater Quality and Hydrogeomorphology in Certain Parts of Tummalapalle Area, Cuddapah District, Andhra Pradesh, South India

The present work deals with the assessment of groundwater potential zones and their suitability for drinking in the severely drought affected villages of Vemula mandal of Cuddapah District, Andhra Pradesh. This study is based on remote sensing and GIS approach. In this approach the IRS P6 LISS III Data (23.5 m Spatial Resolution) with Path: 100;Row: 063 of Indian Remote Sensing Satellite, Resourcesat IRS-P6 LISS-III has been utilized to analyse the onscreen interpretation and delineated different geomorphological units, lithological formations and geological structures. By integrating the above said parameters the hydrogeomorphological map is prepared on 1:50,000 scale. The study area is characteristically occupied by the Papaghni and Chitravati group of rocks. In the present study, the lithological formations on the basis of their genesis have been classified as fluvial, denudational and structural. Majority of lineaments are trending in NE-SW and NW-SE directions. The fluvial landforms namely valley fill moderate and valley have good groundwater prospects while shallow weathered buried pediplain has moderate to poor groundwater prospects. The chemical quality parameters of groundwater samples have indicated that the water samples are found to be suitable for drinking, agricultural, and industrial purposes.

A new algorithm of retrieving a petroleum substances absorption coefficient in sea water based on a remote sensing image

Establishing the remote sensing algorithm of retrieving the absorption coefficient of seawater petroleum substances is an efficient way to improve the accuracy of retrieving a seawater petroleum concentration using a remote sensing technology. A remote sensing reflectance is a basic physical parameter in water color remote sensing. Apply it to directly retrieve the absorption coefficient of seawater petroleum substances is of potential advantage. The absorption coefficient of waters containing petroleum [ACWCP, a_o（λ）], consists of the absorption coefficient of pure water [ACPW, a_w（λ）], plankton [ACP, a_（ph）（λ）], colored scraps [ACCS, a_（d,g）（λ）], and petroleum substance [ACPS, a_（oil）（λ）]. Among those, ACCS consists of the absorption coefficient of nonalgal particle [ACNP, a_d（λ）] and colored dissolved organic matter [ACCDOM, a_g（λ）]. For waters containing petroleum, the retrieved ACCS using the existing method is a combination absorption coefficient of ACNP,ACCDOM and ACPA [CAC, a_（d,g,oil）（λ）]. Therefore, the principle question is how to extract ACPS from CAC.Through the analysis of the three proportion tests conducted between the year of 2013 and 2015 and the corresponding remote sensing data, an algorithm of retrieving the absorption coefficient of petroleum substances is proposed based on remote sensing reflectance. First of all, ACPS and CAC are retrieved from the reflectance using the quasi-analytical algorithm（QAA）, with some parameter modified. Secondly, given the fact that the backscatter coefficient [BC, b_（bp）（555）] of total particles at 555 nm can be obtained completely from the reflectance, the relation between BC and ACNP in petroleum contaminated water can be established. As a result, ACNP can be calculated. Then, combining the remote sensing retrieving algorithm of a_g（440）, the method of achieving the spectral slope of the absorption coefficient can be established, from which ACCDOM,can be calculated. Finally, ACPS can be computed as the residual. The accuracy of ACPS based on this algorithm is 86% compared with the in situ measurements.

Spectral Analysis of Water Reflectance for Hyperspectral Remote Sensing of Water Quailty in Estuarine Water

Hyperspectral remote sensing offers an effective approach for frequent, synoptic water quality measurements over a large spatial extent. However, the optical complexity of case 2 water makes the water quality monitoring by remote sensing in estuarine water a challenge. The prime objective of this study was to develop algorithms for hyperspectral remote sensing of water quality based on in situ spectral measurement of water reflectance. In this study, water reflectance spectra R(λ) were acquired by a pair of Ocean Optic 2000 spectroradiometers during the summers from 2008 to 2011 at Patuxent River, a tributary of Chesapeake Bay, USA. Simultaneously, concentrations of chlorophyll a and total suspended solids (TSS), as well as absorption of colored dissolved organic matter (CDOM) were measured. Empirical models that based on spectral features of water reflectance generally showed good correlations with water quality parameters. The retrieval model that using spectral bands at red/NIR showed a high correlation with chlorophyll a concentration (R2 = 0.81). The ratio of green to blue spectral bands is the best predictor for TSS (R2 = 0.75), and CDOM absorption is best correlated with spectral features at blue and NIR regions (R2 = 0.85). These empirical models were further applied to the ASIA Eagle hyperspectral aerial imagery to demonstrate the feasibility of hyperspectral remote sensing of water quality in the optical complex estuarine waters.

Fuzzy Integral Based Information Fusion for Water Quality Monitoring Using Remote Sensing Data

To improve the monitoring precision of lake chlorophyll a (Chl-a), this paper presents a fusion method based on Choquet Fuzzy Integral (CFI) to estimate the Chl-a concentration. A group of BPNN models are designed. The output of multiple BPNN model is fused by the CFI. Meanwhile, to resolve the over-fitting problem caused by a small number of training sets, we design an algorithm that fully considers neighbor sampling information. A classification experiment of the Chl-a concentration of the Taihu Lake is conducted. The result shows that, the proposed approach is superior to the classification using a single neural network classifier, and the CFI fusion method has higher identification accuracy.

Evaluation of Ordinary Least Square(OLS) and Geographically Weighted Regression(GWR) for Water Quality Monitoring:A Case Study for the Estimation of Salinity

Landsat-5 Thematic Mapper(TM) dataset have been used to estimate salinity in the coastal area of Hong Kong. Four adjacent Landsat TM images were used in this study, which was atmospherically corrected using the Second Simulation of the Satellite Signal in the Solar Spectrum(6S) radiative transfer code. The atmospherically corrected images were further used to develop models for salinity using Ordinary Least Square(OLS) regression and Geographically Weighted Regression(GWR) based on in situ data of October 2009. Results show that the coefficient of determination(R^2) of 0.42 between the OLS estimated and in situ measured salinity is much lower than that of the GWR model, which is two times higher(R^2 = 0.86). It indicates that the GWR model has more ability than the OLS regression model to predict salinity and show its spatial heterogeneity better. It was observed that the salinity was high in Deep Bay(north-western part of Hong Kong) which might be due to the industrial waste disposal, whereas the salinity was estimated to be constant(32 practical salinity units) towards the open sea.

An Approach to Improving the Retrieval Accuracy of Oceanic Constituents in Case Ⅱ Waters

In the present paper, a method is proposed to improve the performance of Artificial Neural Network (ANN) based algorithms for the retrieval of oceanic constituents in Case Ⅱ waters. The ANN-based algorithms have been developed based on a constraint condition, which represents, to a certain degree, the correlation between suspended particulate matter (SPM) and pigment (CHL), coloured dissolved organic matter (CDOM) and CHL, as well as CDOM and SPM, found in Case Ⅱ waters. Compared with the ANN-based algorithm developed without a constraint condition, the performance of ANN-based algorithms developed with a constraint conditions is much better for the retrieval of CHL and CDOM, especially in the case of high noise levels; however, there is not significant improvement for the retrieval of SPM.

Application of Multivariate Model Based on Three Simulated Sensors for Water Quality Variables Estimation in Shitoukoumen Reservoir,Jilin Province,China

This study applied a multivariate model based on three simulated sensors to estimating water quality variables in Shitoukoumen Reservoir,Changchun City,Jilin Province,China,including concentration of total suspended matter,concentration of chlorophyll-a and non-pigment matter absorption.Two field campaigns for spectra measurements with a total of 40 samples were carried out on June 13 and September 23,2008.The in-situ spectra were recalculated to the spectral bands and sensitivities of the instruments applied in this paper,i.e.Landsat TM,Alos and P6,by using the average method.And the recalculated spectra were used for estimating water quality variables by the single model and multivariate model.The results show that the multivariate model is superior to the single model as the multivariate model takes the combined effects of water components into consideration and can estimate water quality variables simultaneously.According to R2 and RMSE,Alos is superior to other sensors for water quality variables estimation although the precision of non-pigment matter absorption inversion performed the second.

Long-Term Application of Remote Sensing Chlorophyll Detection Models: Jordanelle Reservoir Case Study

Algae blooms pose a threat to water quality by depleting oxygen during decomposition and also cause other issues with water quality and water use. Algae biomass is traditional monitored through field samples analyzed for chlorophyll-a, a pigment present in all algae. Field sampling can be time- and cost-intensive, especially in areas that are difficult to access and provides only limited spatial coverage. Estimations of algal biomass based on remote sensing data have been explored over the past two decades as a supplement to information obtained from limited field samples. We use Landsat data to develop and demonstrate seasonal remote sensing models, a relatively recent method, to evaluate spatial and temporal algae distributions for the Jordanelle Reservoir, located in north-central Utah. Remote sensing of chlorophyll as a monitoring and analysis method can provide a more spatially complete representation of algae distribution and biomass;information that is difficult to obtain using point samples.

Effects of Ulva prolifera dissipation on the offshore environment based on remote sensing images and field monitoring data

Outbreaks of Ulva prolifera have continued in the South Yellow Sea of China(SYS)since 2007,becoming a serious marine ecological disaster.Large amounts of U.prolifera drift to the coast of the Shandong Peninsula to dissipate under the action of southeast monsoons and ocean surface currents.This causes serious harm to the ecological environment and economic activities of coastal cities.To investigate the impact of U.prolifera dissipation,this study extracted the spatiotemporal distribution of U.prolifera in the SYS from 2012 to 2020 based on the Google Earth Engine.The outbreak cycle of U.prolifera was determined by fitting analysis of outbreak time and coverage area through MATLAB.This study also looked at the effect of U.prolifera dissipation on water quality through field monitoring data.The results showed that the growth curve of the U.prolifera has a significant Gaussian distribution.The U.prolifera dissipates in Haiyang,China,in July and August every year and affects the offshore environment.Water quality parameters of seawater at different depths had significant differences after the U.prolifera dissipation.Changes in pH,chemical oxygen demand,nitrite nitrogen,nitrate nitrogen,ammonia nitrogen,chlorophyll a,total phosphorus,and suspended solids were more significant in surface seawater than in deeper water.Changes in the concentrations of dissolved oxygen and total nitrogen were more significant in the deep seawater(1.63 and 1.1 times higher than those in the surface seawater,respectively).The dissipation of U.prolifera releases a large amount of carbon and nitrogen into the seawater,which provides rich nutrients for phytoplankton and may cause secondary disasters such as red tide.These findings are useful for further understanding the rules of U.prolifera dissipation,as well as preventing and controlling green tide disasters.

Introduction to the airborne marine surveillance platform and its application to water quality monitoring in China

China Marine Surveillance Force was equipped with modern aerial equipments for marine lawexecute with the advantage of functioning agilely at a large scale of surveillance coverage, providing powerful all-round safeguard, which is of benefit to the harmonious and sustainable development of coastal economy. Onboard the planes, three kinds of remote sensing sensors have been installed, including a marine airborne multi-spectrum scanner （MAMS）, an optical-electronic platform, and an airborne hyper-spectral system AISA＋. The specifications of remote sensing platforms were introduced briefly first, then examples of water quality monitoring by airborne remote sensing were presented, including the monitoring in coastal suspended material, oil-spill and abnormal warm water, etc.

Hyper-spectrum models for monitoring water quality in Dianshan Lake,China

The correlation between water quality parameters and hyper-spectral reflectance is studied with models established for each parameter and applied in Dianshan Lake, in the upstream of the Huangpu River running through Shanghai, China. Models are for dissolved oxygen (DO in mg/L): R720/R680 = 20.362×(R720/R680)2?31.438×(R720/R680)+19.156; for turbidity (NTU): R*714.5 = 206.07× (R*714.5)2?582.5×R*714.5 + 423.24; and for total phosphorus (TP in mg/L): R*509.5 = 16.661× (R*509.5)2?32.646×R*509.5+16.116. The R2 values are 0.770 8, 0.660 4 and 0.738 7, respectively, showing strong positive relationships. The models were then applied to assess water quality of different times. Results are quite satisfactory for some samples.

An integrated measurement and modeling methodology for estuarine water quality management

This paper describes research undertaken by the authors to develop an integrated measurement and modeling methodology for water quality management of estuaries. The approach developed utilizes modeling and measurement results in a synergistic manner. Modeling results were initially used to inform the field campaign of appropriate sampling locations and times, and field data were used to develop accurate models. Remote sensing techniques were used to capture data for both model development and model validation. Field surveys were undertaken to provide model initial conditions through data assimilation and determine nutrient fluxes into the model domain. From field data, salinity re- lationships were developed with various water quality parameters, and relationships between chlorophyll a concentrations, transparency, and light attenuation were also developed. These relationships proved to be invaluable in model development, particularly in modeling the growth and decay of chlorophyll a. Cork Harbour, an estuary that regularly experiences summer algal blooms due to anthropogenic sources of nutrients, was used as a case study to develop the methodology. The integration of remote sensing, conventional fieldwork, and modeling is one of the novel aspects of this research and the approach developed has widespread applicability.

Geospatial Evaluation for Ecological Watershed Management: A Case Study of Some Chesapeake Bay Sub-Watersheds in Maryland USA

Geospatial technology is increasingly being used for various applications in environmental management as the need for sustainable development becomes more evident in today’s rapidly-developing world. As a decision tool, Geographic Information system (GIS) and Global positioning System (GPS) can support major decisions dealing with natural phenomena distributed in space and time. Such is the case for land use/cover known to impact ecosystems health in very direct ways. Our study examined one such application in managing land use of some sub-watersheds in the eastern Shore of Maryland, USA. We conducted a 20-year historical land use/cover evaluation using Landsat-TM remotely sensed images and GIS analysis and water monitoring data acquired during the period by Maryland Department of Natural Resources, including sewage discharge of some municipalities in the area. The results not only showed general trends in land use patterns, but also detailed dynamics of land use-land cover classes, impact on water quality, as well as other useful information for guiding both terrestrial and aquatic ecosystems management decisions of the sub-watersheds. The use of this technology for evaluating trends in land use/cover on a decade-by-decade basis is recommended as standard practice for managing ecosystem health on a sustainable basis.

Water quality monitoring in a slightly-polluted inland water body through remote sensing-Case study of the Guanting Reservoir in Beijing,China

This study focused on the water quality of the Guanting Reservoir,a possible auxiliary drinking water source for Beijing.Through a remote sensing(RS)approach and using Landsat 5 Thematic Mapper(TM)data,water quality retrieval models were established and analyzed for eight common water quality variables,including algae content,turbidity,and concentrations of chemical oxygen demand,total nitrogen,ammonia nitrogen,nitrate nitrogen,total phosphorus,and dissolved phosphorus.The results show that there exists a statistically significant correlation between each water quality variable and remote sensing data in a slightly-polluted inland water body with fairly weak spectral radiation.With an appropriate method of sampling pixel digital numbers and multiple regression algorithms,retrieval of the algae content,turbidity,and nitrate nitrogen concentration was achieved within 10% mean relative error,concentrations of total nitrogen and dissolved phosphorus within 20%,and concentrations of ammonia nitrogen and total phosphorus within 30%.On the other hand,no effective retrieval method for chemical oxygen demand was found.These accuracies were acceptable for the practical application of routine monitoring and early warning on water quality safety with the support of precise traditional monitoring.The results show that performing the most traditional routine monitoring of water quality by RS in relatively clean inland water bodies is possible and effective.

Spectral Geometric Triangle Properties of Chlorophyll-A Inversion in Taihu Lake Based on TM Data

The main objective of this study was to develop and validate the applicability of the Area Chlorophyll-a Concentration Retrieved Model (ACCRM), Height Chlorophyll-a Concentration Retrieved Model (HCCRM), Angle Chlorophyll-a Concentration Retrieved Model (AgCCRM), and Ratio Model of TM2/TM3 (RM) in estimating the chlorophyll-a concentration in Case II water bodies, such as Taihu Lake in Jiangsu Province, China. Water samples were collected from 23 stations on the 27th and 28th of October, 2003. The four empirical models were calibrated against the calibration dataset (samples from 19 stations) and validated using the validation dataset (samples from 4 stations). The regression analysis showed higher correlation coefficients for the ACCRM and the HCCRM than for the AgCCRM and the Ratio Model;and the HCCRM was slightly superior to the ACCRM. The performance of the ACCRM and the HCCRM was validated, and the ACCRM underestimated concentration values more than the HCCRM. The distribution of chlorophyll-a concentrations in Taihu Lake on October 27, 2003 was estimated based on the Landsat/TM data using the ACCRM and the HCCRM. Both models indicated higher chlorophyll-a concentrations in the east, north and center of the lake, but lower concentrations in the south. The accuracy of results obtained from the HCCRM and the ACCRM were also supported by the validation dataset. The study revealed that the HCCRM and the ACCRM had the best potential for accurately assessing the chlorophyll-a concentration in the highly turbid water bodies.

Using a semi-analytical model to retrieve Secchi depth in coastal and estuarine waters

Secchi depth(SD,m)is a direct and intuitive measure of water's transparency,which is also an indicator of water quality.In 2015,a semi-analytical model was developed to derive SD from remote sensing reflectance,thus able to provide maps of water's transparency in satellite images.Here an in-situ dataset(338 stations)is used to evaluate its potential ability to monitor water quality in the coastal and estuarine waters,with measurements covering the Zhujiang(Pearl)River Estuary,the Yellow Sea and the East China Sea where measured SD values span a range of 0.2–21.0 m.As a preliminary validation result,according to the whole dataset,the unbiased percent difference(UPD)between estimated and measured SD is 23.3%(N=338,R^2=0.89),with about 60%of stations in the dataset having relative difference(RD)≤20%,over 80%of stations having RD≤40%.Furthermore,by excluding the field data which with relatively larger uncertainties,the semi-analytical model yielded the UPD of 17.7%(N=132,R^2=0.92)with SD range of 0.2–11.0 m.In addition,the semi-analytical model was applied to Landsat-8 images in the Zhujiang River Estuary,and retrieved high-quality mapping and reliable spatial-temporal patterns of water clarity.Taking into account the uncertainties associated with both field measurements and satellite data processing,and that there were no tuning of the semi-analytical model for these regions,these findings indicate highly robust retrieval of SD from spectral techniques for such turbid coastal and estuarine waters.The results suggest it is now possible to routinely monitor coastal water transparency or visibility at high-spatial resolutions from measurements,like Landsat-8 and Sentinel-2 and newly launched Gaofen-5.

THE STUDY OF RETRIEVAL THEORY AND METHODS FROM SATELLITE REMOTE SENSING FOR METEOROLOGICAL PARAMETERS OVER EASTERN ASIA—PART Ⅱ:ISPRM AND VSPRM2

Based on the practice of improved simultaneous physical retrieval model(ISPRM),in the light of the functional analysis approach,the variational simultaneous physical retrieval model (VSPRM)has been developed.Its approximation of 1st degree is VSPRM1,which is identical with the ISPRM.Its approximation of 2nd degree is VSPRM2,more advanced than the VSPRM1. This paper has analyzed the function of VSPRM2,pointing out the potentiality of synergy retrieval of this model.Also,it has dealt with the problem of parameterization of water vapor's kernel functions and retrieval of water vapor remote sensing. Because of the characteristics of this strong ill posed inverse problem,prior information must be used wisely in order to get the accurate calculation of radiance R.In the previous paper,we discussed how to build the best first guess field,the way to determine the P_s and to correct the calculation of radiance.In this paper,we continue discussing in depth about the calculation of transmittance,the determination of surface parameters and the selection for an optimum combination of channels for the low-level sounding. The long-term experiment and comparison work under operational environment have shown that the ISPRM is useful for retrieval of temperature and water vapor parameters over China including the Tibetan Plateau,and it further proves the scientific nature of well-posed inverse theory.