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Variability in efficiency of particulate organic carbon export: A model study

GLOBAL BIOGEOCHEMICAL CYCLES 2015年第1期29卷 33-45页

作者： Henson, Stephanie A. Yool, Andrew Sanders, Richard Natl Oceanog Ctr Southampton Hants England

The flux of organic carbon from the surface ocean to mesopelagic depths is a key component of the global carbon cycle and is ultimately derived from primary production (PP) by phytoplankton. Only a small fraction of organic carbon produced by PP is exported from the upper ocean, referred to as the export efficiency (herein e-ratio). Limited observations of the e-ratio are available, and there is thus considerable interest in using remotely sensed parameters such as sea surface temperature to extrapolate local estimates to global annual export flux. Currently, there are large discrepancies between export estimates derived in this way;one possible explanation is spatial or temporal sampling bias in the observations. Here we examine global patterns in the spatial and seasonal variability in e-ratio and the subsequent effect on export estimates using a high-resolution global biogeochemical model. The model used here represents export as separate slow- and fast-sinking detrital material whose remineralization is respectively temperature dependent and a function of ballasting minerals. We find that both temperature and the fraction of export carried by slow-sinking particles are factors in determining e-ratio, suggesting that current empirical algorithms for e-ratio that only consider temperature are overly simple. We quantify the temporal lag between PP and export, which is greatest in regions of strong variability in PP where seasonal decoupling can result in large e-ratio variability. Extrapolating global export estimates from instantaneous measurements of e-ratio is strongly affected by seasonal variability and can result in errors in estimated export of up to 60%.

关键词： POC flux carbon export seasonal variability biogeochemical model empirical algorithm

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Ocean Wave Integral Parameter Measurements Using Envisat ASAR Wave Mode Data

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2011年第1期49卷 155-174页

作者： Li, Xiao-Ming Lehner, Susanne Bruns, Thomas German Aerosp Ctr DLR Remote Sensing Technol Inst D-82234 Wessling Germany German Weather Serv DWD D-20359 Hamburg Germany

An empirical algorithm to retrieve integral ocean wave parameters such as significant wave height (SWH), mean wave period, and wave height of waves with period larger than 12 s (H-12) from synthetic aperture radar (SAR) images over sea surface is presented. The algorithm is an extension to the Envisat Advanced SAR (ASAR) wave mode data based on the CWAVE approach developed for ERS-2 SAR wave mode data and is thus called CWAVE_ENV (CWAVE for Envisat). Calibrated ASAR images are used as the only source of input without needing prior information from an ocean wave model (WAM) as the standard algorithms used in weather centers. This algorithm makes SAR an independent instrument measuring integrated wave parameters like SWH and mean wave period to altimeter quality. A global data set of 25 000 pairs of ASAR wave mode images and collocated reanalysis WAM results from the European Centre for Medium-Range Weather Forecasts (ECMWF) is used to tune CWAVE_ENV model coefficients. Validation conducted by comparing the retrieved SWH to in situ buoy measurements shows a scatter index of 0.24 and 0.16 when compared to the ECMWF reanalysis WAM. Two case studies are presented to evaluate the performance of the CWAVE_ENV algorithm for high sea state. A North Atlantic storm during which SWH is above 18 m as observed by SAR and Radar Altimeter simultaneously is analyzed. For an extreme swell case that occurred in the Indian Ocean, the potential of using SWH measurements from ASAR wave mode data derived by the CWAVE_ENV algorithm is demonstrated.

关键词： empirical algorithm integral wave parameter synthetic aperture radar (SAR) wave mode data

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Remote sensing regression models to estimate water quality indicators in continental waters in North-East Argentina 7

Remote sensing regression models to estimate water quality i...

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2024 Congress of Argentina

作者： Hugo Gauto, Victor Marta Utges, Enid Ivonne Hervot, Elsa Daniela Tenev, Maria Farias, Alejandro Bonansea, Matias Ferral, Anabella UTN FRRe GISTAQ Resistencia Argentina Consejo Nacl Invest Cient & Tecn ICBIA Rio Cuarto Argentina UNC CONAE Inst Mario Gulich Cordoba Argentina

ISBN: (纸本)9798350365948;9798350365931

To ensure water preservation is relevant to constantly monitor water quality. To facilitate this task, remote sensing techniques are applied combining water property measurements and spectral information. Two empirical regression models were developed to estimate chlorophyll-a concentration and turbidity from a water body located in North-East Argentina, in the Metropolitan Area of Gran Resistencia, Chaco Province. The spectral platform used was Sentinel-2 MultiSpectral Instrument, and the physicochemical characterization was achieved by multiple field campaigns where water samples were collected. In the training step several candidate models were compared. Performance metrics (coefficient of determination, R-2, and root mean squared error, RMSE) were calculated and used for the final models selection. The obtained algorithms were linear combinations of spectral bands. Chlorophyll-a algorithm validated performance metrics were R-2 = 0.819 and RMSE = 13.71 mg/m(3). For the turbidity model, R-2 = 0.968 and RMSE = 1.62 NTU. Correlation between physicochemical parameters were asserted and maps were created applying the validated models. A correlation between chlorophyll-a and turbidity was observed. Due this, the spatial distribution in the maps of both parameters followed the same trend.

关键词： remote sensing water quality empirical algorithm chlorophyll-a turbidity Sentinel-2

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Retrieval of total suspended particulate matter in highly turbid Hangzhou Bay waters based on Geostationary Ocean Color Imager

Retrieval of total suspended particulate matter in highly tu...

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Conference on Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions

作者： Liu, Jia Liu, Jiahang He, Xianqiang Chen, Tieqiao Zhu, Feng Wang, Yihao Hao, Zengzhou Chen, Peng Chinese Acad Sci Xian Inst Opt & Precis Mech Lab Remote Sensing & Intelligent Informat Syst Xian 710119 Shaanxi Peoples R China State Ocean Adm Inst Oceanog 2 State Key Lab Satellite Ocean Environm Dynam Hangzhou 310012 Zhejiang Peoples R China

ISBN: (纸本)9781510613096;9781510613089

Hangzhou Bay waters are often characterized by extremely high total suspended particulate matter (TSM) concentration due to terrestrial inputs, bottom sediment resuspension and human activities. The spatial-temporal variability of TSM directly contributes to the transport of carbon, nutrients, pollutants, and other materials. Therefore, it is essential to maintain and monitor sedimentary environment in coastal waters. Traditional field sampling methods limit observation capability for insufficient spatial-temporal resolution. Thus, it is difficult to synoptically monitor high diurnal dynamics of TSM. However, the in-orbit operation of the world's first geostationary satellite ocean color sensor, GOCI, thoroughly changes this situation with hourly observations of covered area. Taking advantage of GOCI high spatial-temporal resolution, we generated TSM maps from GOCI Level-1B data after atmospheric correction based on six TSM empirical algorithms. Validation of GOCI-retrieved normalized water-leaving radiances and TSM concentration was presented in comparison with matched-up in-situ measurements. The mean absolute percentage differences of those six TSM regional algorithms were 24.52%, 163.93%, 195.50%, 70.50%, 121.02%, 82.72%, respectively. In addition, the discrepancy reasons were presented, taking more factors such as diversified satellite data, various study area, and different research season into consideration. It is effective and indispensable to monitor and catch the diurnal dynamics of TSM in Hangzhou Bay coastal waters, with hourly GOCI observations data and appropriate inversion algorithm.

关键词： GOCI Total suspended particulate matter Hangzhou Bay Atmospheric correction empirical algorithm validation spatial and temporal variability

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Assessment of satellite retrieval algorithms for chlorophyll-a concentration under high solar zenith angle

Assessment of satellite retrieval algorithms for chlorophyll...

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Conference on Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions

作者： Li, Hao He, Xianqiang Bai, Yan Chen, Xiaoyan Gong, Fang Zhu, Qiankun Hu, Zifeng State Ocean Adm Inst Oceanog 2 State Key Lab Satellite Ocean Environm Dynam Hangzhou 310012 Zhejiang Peoples R China

ISBN: (数字)9781510604032

ISBN: (纸本)9781510604025;9781510604032

Numerous empirical algorithms have been operationally used to retrieve the global ocean chlorophyll-a concentration (Chla) from ocean color satellite data, e.g., the OC4V4 algorithm for SeaWiFS and OC3M for MODIS. However, the algorithms have been established and validated based on the in situ data mainly measured under low to moderate solar zenith angle (< 70 degrees). Currently, with the development of the geostationary satellite ocean color remote sensing which observes from early morning to later afternoon, it is necessary to know whether the empirical Chla algorithms could be applied to high solar zenith angle. In this study, the performances of seven widely-used Chla algorithms under high solar zenith angles, i.e., OC2, OC3M, OC3V, OC4V4, CLARK, OCI, and YOC algorithms, were evaluated using the NOMAD global in situ ocean color dataset. The results showed that the performances of all the seven algorithms decreased significantly under high solar zenith angles as compared to those under low-moderate solar zenith angles. For instance, for the OC4V4 algorithm, the relative percent difference (RPD) and root-mean-square error (RMSE) were 13.78% and 1.66 mu g/l for the whole dataset, and 3.95% and 1.49 mu g/l for the solar zenith angles ranged from 30 degrees to 40 degrees, respectively. However, the RPD and RMSE increased to 30.45% and 6.10 mu g/l for solar zenith angle larger than 70 degrees.

关键词： Ocean color remote sensing chlorophyll-a concentration empirical algorithm high solar zenith angle

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Remote Sensing Estimates of Particulate Organic Carbon Sources in the Zhanjiang Bay Using Sentinel-2 Data and Carbon Isotopes

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REMOTE SENSING 2023年第15期15卷 3768-3768页

作者： Yu, Guo Zhong, Yafeng Liu, Sihai Lao, Qibin Chen, Chunqing Fu, Dongyang Chen, Fajin Guangdong Ocean Univ Coll Ocean & Meteorol Zhanjiang 524088 Peoples R China Guangdong Ocean Univ Coll Elect & Informat Engn Zhanjiang 524088 Peoples R China Guangdong Ocean Univ Coll Chem & Environm Sci Zhanjiang 524088 Peoples R China Guangdong Ocean Univ Key Lab Climate Resources & Environm Continental S Zhanjiang 524088 Peoples R China

The source information of coastal particulate organic carbon (POC) with high spatial and temporal resolution is of great significance for the study of marine carbon cycles and marine biogeochemical processes. Over the past decade, satellite ocean color remote sensing has greatly improved our understanding of the spatiotemporal dynamics of ocean particulate organic carbon concentrations. However, due to the complexity of coastal POC sources, remote sensing methods for coastal POC sources have not yet been established. With an attempt to fill the gap, this study developed an algorithm for retrieving coastal POC sources using remote sensing and geochemical isotope technology. The isotope end-member mixing model was used to calculate the proportion of POC sources, and the response relationship between POC source information and in situ remote sensing reflectance (R-rs) was established to develop a retrieval algorithm for POC sources with the following four bands: (R-rs(443)/R-rs(492)) x (R-rs(704)/R-rs(665)). The results showed that the four-band algorithm performed well with R-2, mean absolute percentage error (MAPE) and root mean square error (RMSE) values of 0.78, 33.57% and 13.74%, respectively. Validation against in situ data showed that the four-band algorithm derived calculated the proportion of marine POC accurately, with an MAPE and RMSE of 27.49% and 13.58%, respectively. The accuracy of the algorithm was verified based on the Sentinel-2 data, with an MAPE and RMSE of 28.02% and 15.72%, respectively. Additionally, we found that the proportion of marine POC sources was higher outside the Zhanjiang Bay than inside it using in situ survey data, which was consistent with the retrieved results. Influencing factors of POC sources may be due to the occurrence of phytoplankton blooms outside the bay and the impact of terrestrial inputs inside the bay. Remote sensing in combination with carbon isotopes provides important technical assistance in comprehending the biogeo

关键词： remote sensing geochemical isotope POC sources empirical algorithm Zhanjiang Bay

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Diffuse Attenuation of Clear Water Tropical Reservoir: A Remote Sensing Semi-Analytical Approach

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REMOTE SENSING 2020年第17期12卷 2828-2828页

作者： Curtarelli, Victor Pedroso Faria Barbosa, Claudio Clemente Maciel, Daniel Andrade Flores Junior, Rogerio Carlos, Felipe Menino Leao de Moraes Novo, Evlyn Marcia Curtarelli, Marcelo Pedroso Freire da Silva, Edson Filisbino Brazilian Inst Space Res Image Proc Div BR-12227010 Sao Jose Dos Campos SP Brazil Brazilian Inst Space Res Appl Comp BR-12227010 Sao Jose Dos Campos SP Brazil Brazilian Inst Space Res Remote Sensing Div BR-12227010 Sao Jose Dos Campos SP Brazil Fundacao CERTI Green Econ Ctr BR-88040970 Florianopolis SC Brazil

The diffuse attenuation coefficient of downwelling irradiance (Kd) is an essential parameter for inland waters research by remotely sensing the water transparency. Lately,Kdsemi-analytical algorithms substituted the empirical algorithms widely employed. The purpose of this research was to reparametrize a semi-analytical algorithm to estimateKdand then apply it to a Sentinel-2 MSI time-series (2017-2019) for the Tres Marias reservoir, Brazil. The results for theKdsemi-analytical reparametrization achieved good accuracies, reaching mean absolute percentage errors (MAPE) for bands B2, B3 and B4 (492, 560 and 665 nm), lower than 21% when derived from in-situ remote sensing reflectance (Rrs), while for MSI Data, a derived MAPE of 12% and 38% for B2 and B3, respectively. After the application of the algorithm to Sentinel-2 images time-series, seasonal patterns were observed in the results, showing highKdvalues at 492 nm during the rainy periods, mainly in the tributary mouths, possibly due to an increase in the surface runoff and inflows and outflow rates in the reservoir watershed.

关键词： inland waters remote sensing of water transparency downwelling irradiance diffuse attenuation coefficient semi-analytical algorithm empirical algorithm

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Estimation of Soil Freeze Depth in Typical Snowy Regions Using Reanalysis Dataset: A Case Study in Heilongjiang Province, China

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REMOTE SENSING 2022年第23期14卷 5989页

作者： Wang, Xiqiang Chen, Rensheng Han, Chuntan Yang, Yong Liu, Junfeng Liu, Zhangwen Guo, Shuhai Chinese Acad Sci Northwest Inst Ecoenvironm & Resources Qilian Alpine Ecol & Hydrol Res Stn Lanzhou 730000 Peoples R China Northwest Univ Coll Urban & Environm Sci Xian 710127 Peoples R China

Soil freeze depth variations greatly affect energy exchange, carbon exchange, ecosystem diversity, and the water cycle. Given the importance of these processes, obtaining freeze depth data over large scales is an important focus of research. This paper presents a simple empirical algorithm to estimate the maximum seasonally frozen depth (MSFD) of seasonally frozen ground (SFG) in snowy regions. First, the potential influences of driving factors on the MSFD variations were quantified in the baseline period (1981-2010) based on the 26 meteorological stations within and around the SFG region of Heilongjiang province. The three variables that contributed more than 10% to MSFD variations (i.e., air freezing index, annual mean snow depth, and snow cover days) were considered in the analysis. A simple multiple linear regression to estimate soil freeze depth was fitted (1981-2010) and verified (1975-1980 and 2011-2014) using ground station observations. Compared with the commonly used simplified Stefan solution, this multiple linear regression produced superior freeze depth estimations, with the mean absolute error and root mean square error of the station average reduced by over 20%. By utilizing this empirical algorithm and the ERA5-Land reanalysis dataset, the multi-year average MSFD (1981-2010) was 132 cm, ranging from 52 cm to 186 cm, and MSFD anomaly exhibited a significant decreasing trend, at a rate of -0.38 cm/decade or a net change of -28.14 cm from 1950-2021. This study provided a practical approach to model the soil freeze depth of SFG over a large scale in snowy regions and emphasized the importance of considering snow cover variables in analyzing and estimating soil freeze depth.

关键词： seasonally frozen ground snow cover air temperature empirical algorithm

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Remote sensing estimation of the concentration and sources of coloured dissolved organic matter based on MODIS: A case study of Erhai lake

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ECOLOGICAL INDICATORS 2021年 131卷 108180-108180页

作者： Zhang, Hao Yao, Bo Wang, Shengrui Wang, Guoqiang Beijing Normal Univ Ctr Water Res Guangdong Hong Kong Joint Lab Water Secur Zhuhai 519087 Peoples R China Beijing Normal Univ Coll Water Sci Engn Res Ctr Minist Educ Groundwater Pollut Control & Remediat Beijing 100875 Peoples R China Chinese Acad Sci Inst Mech Key Lab Mech Fluid Solid Coupling Syst Beijing 100190 Peoples R China

The use of satellite remote sensing to estimate coloured dissolved organic matter (CDOM) and identify its potential sources is important for monitoring lake water quality and implementing management strategies. In this study, taking Erhai Lake as an example and based on MODIS/Aqua satellite images and in-situ measured data, we used empirical and semi-empirical methods to develop algorithms for CDOM and the fluorescence index (FI370) from remote sensing reflectance (Rrs(lambda)). The temporal and spatial distributions of the CDOM concentration and FI370 in Erhai Lake during 2013-2019 were retrieved. The results show the following. (1) The band ratio (Rrs (469)+Rrs(645))/Rrs(555) model could estimate the CDOM absorption coefficient at 412 nm (aCDOM(412)) (R2=0.507), and it was relatively stable. Using the band ratio Rrs(645)/Rrs(469) combined with the chlorophylla (Chl-a) APProach by ELimination (APPEL) model, a semi-empirical inversion model of FI370 performed with satisfactory accuracy (R2=0.550) and was more accurate than the empirical algorithm (R2=0.505). (2) During the period of 2013-2019, the CDOM concentration in Erhai Lake generally decreased from the northern to the central to the southern parts of the lake, and the CDOM concentration was higher in summer and autumn than in spring and winter. FI370 was higher in the northern and western coastal waters and lower in the central, southern and eastern parts of the lake. FI370 in autumn and winter was higher than that in spring and summer. CDOM was affected by both terrestrial and internal sources, and their relative contributions were not the same in different seasons. (3) For different Chl-a concentrations, different CDOM concentration models had better retrieval effects, i.e., Rrs(645))/Rrs(555) and (Rrs(469)+Rrs(645))/Rrs(555) had the best performance when Chl-a 10 mu g/L and Chl-a 10 mu g/L, respectively. The inversion models established in this study offer improved quantifications of the CDOM concentration

关键词： CDOM Fluorescence index empirical algorithm Semi-empirical algorithm Erhai Lake

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Total suspended solids characterization and management implications for lakes in East China

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SCIENCE OF THE TOTAL ENVIRONMENT 2022年第Part4期806卷 151374-151374页

作者： Du, Yunxia Song, Kaishan Wang, Qiang Li, Sijia Wen, Zhidan Liu, Ge Tao, Hui Shang, Yingxin Hou, Junbin Lyu, Lili Zhang, Bai Chinese Acad Sci Northeast Inst Geog & Agroecol Changchun 130102 Peoples R China Hainan Normal Univ Haikou 571158 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Liaocheng Univ Sch Environm & Planning Liaocheng 252000 Shandong Peoples R China

In this study, we empirically developed a robust model (the Root Mean Square Error (RMSE), bias, NSE and RE were 26.63 mg/L, -4.86 mg/L, 0.47 and 16.47%, respectively) for estimating the total suspended solids (TSS) concentrations in lakes and reservoirs (Hereinafter referred to as lakes) across the Eastern Plain Lake (EPL) Zone. The model was based on 700 in-situ TSS samples collected during 2007-2020 and logarithmic transformed red band reflectance of Landsat data. Based on the Google Earth Engine (GEE), the TSS concentrations in 16,804 lakes were mapped from 1984 to 2019. The results demonstrated a decreasing tendency of TSS in 82.2% of the examined lakes (72.5% of the basins) indicating that the pollutants carried by TSS flowing into the lakes were decreasing. Statistically significant variation (p < 0.05) was found in half of these lakes (28.6% of the basins). High TSS level (>100 mg/L) was observed in 0.31% of lakes (1.1% of the basins). The changing rates of TSS in 47.8% of the lakes (52.7% of the basins) ranged between -50 mg/L/yr and 0. We found high and significantly increased relative spatial heterogeneity of TSS in 4.6% and 6.5% of lakes, respectively. Likewise, the environmental factors, i.e., fertilizer usage, domestic wastewater, industrial wastewater, precipitation, wind speed and Normalized Difference Vegetation Index (NDVI) exhibited a significant correlation with interannual TSS in 38, 21, 20,11,17 and 15 of the 91 basins, respectively. This analysis indicated that only precipitation and fertilizer usage were signifi-cantly (p < 0.05) related to the spatial distribution of TSS. The relative contributions of the six factors to the interannual TSS changes were varied in different basins. Overall, the NDVI (the representation of vegetation cover) had a high mean contribution to the interannual TSS changes with an average contribution of 7.2%, and contributions of fertilizer were varied greatly among the basins (0.01%-68%). Human activities (fertilize

关键词： Total suspended solids Remote sensing The Eastern Plain Lake Zone Water quality parameter empirical algorithm

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