Developing a semi-analytical algorithm to estimate particulate organic carbon (POC) levels in inland eutrophic turbid water based on MERIS images: A case study of Lake Taihu

Particulate organic carbon (POC) plays an important role in the carbon cycle in water due to its biological pump process. In the open ocean, algorithms can accurately estimate the surface POC concentration. However, no suitable POC-estimation algorithm based on MERIS bands is available for inland turbid eutrophic water. A total of 228 field samples were collected from Lake Taihu in different seasons between 2013 and 2015. At each site, the optical parameters and water quality were analyzed. Using in situ data, it was found that POC-estimation algorithms developed for the open ocean and coastal waters using remote sensing reflectance were not suitable for inland turbid eutrophic water. The organic suspended matter (OSM) concentration was found to be the best indicator of the POC concentration, and POC has an exponential relationship with the OSM concentration. Through an analysis of the POC concentration and optical parameters, it was found that the absorption peak of total suspended matter (TSM) at 665 nm was the optimum parameter to estimate POC. As a result, MERIS band 7, MERIS band 10 and MERIS band 12 were used to derive the absorption coefficient of TSM at 665 nm, and then, a semi-analytical algorithm was used to estimate the POC concentration for inland turbid eutrophic water. An accuracy assessment showed that the developed semi-analytical algorithm could be successfully applied with a MAPE of 31.82% and RMSE of 2.68 mg/L. The developed algorithm was successfully applied to a MERIS image, and two full-resolution MERIS images, acquired on August 13, 2010, and December 7, 2010, were used to map the POC spatial distribution in Lake Taihu in summer and winter.     

Bathymetrical influences on spatial and temporal characteristics of chlorophyll-a concentrations in the Southern Ocean from 2002 to 2012 (October to March) using MODIS

Phytoplankton blooms, particularly in the Southern Ocean, can have significant impact on global biogeochemistry cycling. To investigate the accuracy of chlorophyll-a distribution, and to better understand the spatial and temporal dynamics of phytoplankton biomass, we examine chlorophyll-a estimates (October–March from 2002 to 2012) derived from Moderate Resolution Imaging Spectrometer (MODIS) data following the ocean chlorophyll-a 3 model (OC3M) algorithm. Noticeable seasonality occurs in the temporal distribution of chlorophyll-a concentrations, which shows the highest value in December and January and an increasing tendency during the 2002–2012 period. The spatial distribution of chlorophyll-a varies greatly with latitude, as higher latitudes experience more phytoplankton blooms (chlorophyll-a concentration larger than 1 mg/m³) and marginal seas (Ross Sea and Amundsen Sea) show different bloom anomalies caused by two dominant algae species. Areas at higher latitudes and shallow water (<500 m) experience the shorter ice-free periods with greater seasonality. A noticeable bathymetry gradient exists at 2500-m isobaths, while water at the 500–2500-m depth experiences quite long ice-free periods with a stable water environment. Blooms generally occur near topographic features where currents have strong interactions when the water depth is more than 2500 m. Based on these findings, we can classify the Southern Ocean into two bloom subregions, 0–500 m as an enhanced bloom zone (EBZ), and 500–2500 m as a moderate bloom zone (MBZ). The EBZ has a quite high-bloom probability of about 30%, while the MBZ has only 10%.     

利用氧气和水汽吸收波段暗像元假设的MERIS影像二类水体大气校正方法

摘 要:MERIS数据以其更为合理的水色波段设置和300m较高的空间分辨率,在内陆湖泊水环境遥感监测中有较大的应用潜力, 对其进行有效的大气校正则是水环境参数定量化反演的前提。以太湖为研究区, 研究基于氧气和水汽吸收波段的暗象元假设, 改进传统的近红外波段暗像元假设的大气校正方法。采用MERIS L2p数据辅助获取湖区气溶胶参数, 并利用2007年11月11日、2008年11月20日以及2009年4月25日三景MERIS影像进行方法验证。结果表明, 该方法能够快速、有效地完成MERIS影像的大气校正, 与地面准同步实测数据相比, 三次校正的RMSP都在25%以下; 与BEAM自带的二类水体大气校正算法、气溶胶厚度辅助的6S大气校正以及改进的暗象元算法进行精度比较, 表明该算法校正精度较高。由于该算法不需要同步实测气溶胶数据, 因此具有一定的适用性。     

Potential fish habitat mapping using MODIS-derived sea surface salinity,temperature and chlorophyll-a data: South China Sea Coastal areas,Malaysia

This paper demonstrates the use of moderate resolution imaging spectro-radiometer (MODIS) data for fish forecasting mapping of seasonal spatial distribution of sea surface salinity (SSS), temperature (SST) and chlorophyll-a in the ocean waters off the coast of Semporna, Malaysia. Multi-linear regression analysis was performed to estimate SSS and the Brown and Minnet algorithm was used for the SST. The extracted parameters were validated using in situ measurement taken with Hydro-Lab equipment. The extracted parameters from MODIS images reveal the signature values which establish the relationships between these parameters, and thus delineating the potential fish zonation (PFZ) map. These developed models will help for accurate monitoring of large coverage areas at low cost and within short period of time. Furthermore, such models will allow the prediction of the total fish catch in different seasons, thus contributing to fish industry management and marketing. This research recommends the use of PFZ map for mass scale fish harvesting in short time for larger areas. Finally, the research has developed a potential fish zone model amalgamating all the above parameters. The PFZ mapping was carried out off the coast of Semporna, Sabah as there were sufficient fish catch data for accuracy assessment. The R was computed as 0.93 and the higher fish catch areas have coincided very well with the higher PFZ values, meaning the tool is ready for use for operational near real-time fish forecasting.     

Application of Aqua MODIS sensor data for estimating chlorophyll a in the turbid Case 2 waters of Lake Erie using bio-optical models

There is considerable interest in accurately estimating water quality parameters in turbid (Case 2) and eutrophic waters such as the Western Basin of Lake Erie (WBLE). Lake Erie is a large, open freshwater body that supports diverse ecosystem, and over 12 million people in the mid-western part of the United States depend on it for drinking water, fisheries, navigational, and recreational purposes. The increasing utilization of the freshwater has deteriorated the water severely and currently the lake is experiencing recurring harmful algal blooms (HABs). Improving the water quality of Lake Erie requires the use of robust monitoring tools that help water quality managers understand sources and pathways of influxes that trigger HABs. Satellite-based remote sensing sensor such as the moderate resolution imaging spectroradiometer (MODIS) may provide frequent and synoptic view of the water quality indices. In this study, data set from field measurements was used to evaluate the performance of 14 existing ocean color algorithms. Results indicated that MODIS data consistently underestimated the chlorophyll a concentrations in the WBLE, with the largest source of errors from dissolved organic matter and xanthophyll accessory pigments in this data set. Most of the global algorithms, including OC4v4 and the Baltic model, generated near-identical statistical parameters with an average R² of ~0.57 and RMSE ~2.9 μg/l. MODIS performed poorly (R² ~0.18) when its NIR/red bands were used. A slightly improved model was developed using similar band ratio approach generating R² of ~0.62 and RMSE ~1.8 μg/l.     

Distributed cache replacement method for geospatial data using spatiotemporal locality-based sequence

Specific features of tile access patterns can be applied in a cache replacement strategy to a limited distributed high-speed cache for the cloud-based networked geographic information services (NGISs), aiming to adapt to changes in the access distribution of hotspots. By taking advantage of the spatiotemporal locality, the sequential features in tile access patterns, and the cache reading performance in the burst mode, this article proposes a tile sequence replacement method, which involves structuring a Least Recently Used (LRU) stack into three portions for the different functions in cache replacement and deriving an expression for the temporal locality and popularity of the relevant tile to facilitate the replacement process. Based on the spatial characteristics of both the tiles and the cache burst mode with regard to reading data, the proposed method generates multiple tile sequences to reflect spatiotemporal locality in tile access patterns. Then, we measure the caching value by a technique based on a weighted-based method. This technique draws on the recent access popularity and low caching costs of tile sequences, with the aim of balancing the temporal and spatial localities in tile access. It ranks tile sequences in a replacement queue to adapt to the changes in accessed hotspots while reducing the replacement frequency. Experimental results show that the proposed method effectively improves the hit rate and utilization rate for a limited distributed cache while achieving satisfactory response performance and high throughput for users in an NGIS. Therefore, it can be adapted to handle numerous data access requests in NGISs in a cloud-based environment.     

模拟退火法及其在抗差估计中的应用

抗差估计结果的好坏与初值的选取密切相关。当迭代初值偏离较大时,ρ函数不是严凸时,采取的选权迭代法往往不能获得全局最优值,而只能得到局部的最优解。本文提出一种新的抗差估计求解过程,即把它看作是关于未知参数的全局最优化问题,应用模拟退火法进行求解。计算结果表明:相对于传统的选权迭代法,模拟退火法对初值的选取不敏感,能给出全局最优值。     

Verification of net primary production estimation method in the Mongolian Plateau using landsat ETM+data

We plan to estimate global net primary production (NPP) of vegetation using the Advanced Earth Observing Satellite-II (ADEOS-II) Global Imager (GLI) multi-spectral data. We derive an NPP estimation algorithm from ground measurement data on temperate plants in Japan. By the algorithm, we estimate NPP using a vegetation index based on pattern decomposition (VIPD) for the Mongolian Plateau. The VIPD is derived from Landsat ETM+multi-spectral data, and the resulting NPP estimation is compared with ground data measured in a semi-arid area of Mongolia. The NPP estimation derived from satellite remote sensing data agrees with the ground measurement data within the error range of 15% when all above-ground vegetation NPP is calculated for different vegetation classifications.     

Verification of Net Primary Production Estimation Method in the Mongolian Plateau Using Landsat ETM＋ Data

We plan to estimate global net primary production (NPP) of vegetation using the Advanced Earth Observing Satellite-Ⅱ (ADEOS-Ⅱ) Global Imager (GLI) multi-spectral data. We derive an NPP estimation algorithm from ground measurement data on temperate plants in Japan. By the algorithm, we estimate NPP using a vegetation index based on pattern decomposition (VIPD) for the Mongolian Plateau. The VIPD is derived from Landsat ETM multi-spectral data, and the resulting NPP estimation is compared with ground data measured in a semi-arid area of Mongolia. The NPP estimation derived from satellite remote sensing data agrees with the ground measurement data within the error range of 15% when all above-ground vegetation NPP is calculated for different vegetation classifications.     

抗差估计的选权迭代法分析与比较

在抗差稳健估计中,选权迭代法应用最广,较易理解,M估计法计算简单。基于M估计的基本理论,深入分析Huber、Hampel、IGG3种常用的选权迭代法,并利用水准网数据,分析这3种常用选权迭代法的适用范围和相应的抗差效果,表明IGG法比其他两种的抗粗差效果要好,且易实现。     

基于MODIS与GPS的D-InSAR大气延迟改正量提取

受GPS站点密度的限制,利用GPS数据改正D-InSAR中大气延迟误差往往达不到很好的效果。为此,研究了GPS与MODIS联合实现大气延迟改正量提取方法,利用两期GPS观测数据及相应时间的MODIS数据分析GPS-PWV与MODIS-PWV的关系,进一步得到MODIS水汽的校正模型。经过GPS+MODIS算法改正后,大气延迟改正精度为3.618mm,满足形变测量的要求。实验结果表明:在大气状态变化缓慢时,利用GPS结合MODIS数据对D-InSAR大气延迟改正有一定的效果。     

Tracing high time-resolution fluctuations in dissolved organic carbon using satellite and buoy observations: Case study in Lake Taihu,China

Field measurements of dissolved organic carbon (DOC) concentration and remote-sensing reflectance were conducted to develop a regional, empirical red-blue algorithm to retrieve surface DOC from Geostationary Ocean Color Imager (GOCI) data for Lake Taihu, China. The auxiliary data (in-situ observations of the optical properties and water quality, buoy measurements of hydrodynamic data and water chemical parameters) were used to investigate the spatial and temporal variations in DOC. GOCI was shown to be capable of successfully obtaining hourly variations in DOC, with a root mean square error percentage (RMSP) of 17.29% (RMSE = 0.69 mg/L) for the match-up data. The GOCI-derived DOC in Lake Taihu confirms that the highest DOC concentration is in northwest Lake Taihu, followed by Meiliang Bay, Gonghu Bay and northeast Lake Taihu. Hourly DOC variation is significant and presents a different trend for each lake segment due to the variety of influencing factors. Discharge of DOC from surrounding rivers is an important factor to the variation of DOC in northeast Lake Taihu. However, organic products of algae will be the primary contributor to DOC when algal bloom occurred. During the period of algal bloom, high DOC levels in Lake Taihu can lead to hypoxia when coupled with high temperatures and low disturbance.     

An improved method of joint inversion using GPS and gravity observation data

On the basis of the dislocation theory and gravity observation, a joint inversion model is presented with a fitting factor A scaling amplitudes between the gravity and GPS observation data. The test results show that the new joint model is better than that taking the scale factor ）, as a constant from the inversion result of MSE （mean square error）. In addition, the random cost method used in the inversion algorithm is revised and improved, which shows that the improved random cost method can easily get the local minimum value and greatly decrease the iteration steps.     

A visible band index for remote sensing leaf chlorophyll content at the canopy scale

Leaf chlorophyll content is an important variable for agricultural remote sensing because of its close relationship to leaf nitrogen content. The triangular greenness index (TGI) was developed based on the area of a triangle surrounding the spectral features of chlorophyll with points at (670 nm, R₆₇₀), (550 nm, R₅₅₀), and (480 nm, R₄₈₀), where R_λ is the spectral reflectance at wavelengths of 670, 550 and 480, respectively. The equation is TGI = −0.5[(670 − 480)(R₆₇₀ − R₅₅₀) − (670 − 550)(R₆₇₀ − R₄₈₀)]. In 1999, investigators funded by NASA's Earth Observations Commercialization and Applications Program collaborated on a nitrogen fertilization experiment with irrigated maize in Nebraska. Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data and Landsat 5 Thematic Mapper (TM) data were acquired along with leaf chlorophyll meter and other data on three dates in July during late vegetative growth and early reproductive growth. TGI was consistently correlated with plot-averaged chlorophyll-meter values at the spectral resolutions of AVIRIS, Landsat TM, and digital cameras. Simulations using the Scattering by Arbitrarily Inclined Leaves (SAIL) canopy model indicate an interaction among TGI, leaf area index (LAI) and soil type at low crop LAI, whereas at high LAI and canopy closure, TGI was only affected by leaf chlorophyll content. Therefore, TGI may be the best spectral index to detect crop nitrogen requirements with low-cost digital cameras mounted on low-altitude airborne platforms.     

Evaluating the influences of harvesting activity and eutrophication on loss of aquatic vegetations in Taihu Lake,China

A rapid degradation of aquatic vegetations in Taihu Lake has roused a wide attention in recent years. Giving large-scale harvesting activity on aquatic vegetation since 2012, whether water eutrophication or the human harvest activity induced the degradation remains controversial and unclear. In this study, based on Landsat and HJ-CCD data acquired from 1984 to 2016 and a 12-year field observation (2005–2016) of water quality, a method was proposed to quantitatively assess impacts of harvesting activity and water quality change on degradations of both floating-leaved aquatic vegetation (FAV) and submerged aquatic vegetation (SAV) in Taihu Lake. First, areas of FAV and SAV covers from 1984 to 2016 in Taihu Lake were mapped using the satellite data, and then the mapped areas were modified to those on a reference date by using phenological curves of FAV and SAV covers. Next, correlations between water quality data and FAV and SAV covers were analyzed by using Pearson correlation analysis based on the data before implementing the human harvesting activity (i.e., before 2012), and multiple general linear models were established based on the selected water quality variables with p-value <0.01 for estimating covers of FAV and SAV from 2012 to 2016. Finally, based on the predicted areas of FAV and SAV covers by the models and the modified areas mapped from satellite data, the influences of water eutrophication and the human harvesting activity on the degradation of FAV and SAV covers were quantitatively assessed. The results indicated that (1) FAV cover exhibited a significant increase from 1984 to 2011 and then a rapid decrease, while SAV cover increased significantly before 2003 and then obviously declined; (2) water level (WL) and total nitrogen (TN) showed significantly negative correlations with FAV and SAV covers, while secchi disk depth (SDD) and SDD/WL had significantly positive correlations with FAV and SAV covers; (3) the human harvesting activity made a major contribution to the loss of FAV cover, and the degradation of SAV cover was mainly due to an increased lake eutrophication and deteriorated underwater light environment. The findings derived from this study could offer a guidance for Taihu Lake ecological restoration and effective management.     

Estimating fractional vegetation cover and the vegetation index of bare soil and highly dense vegetation with a physically based method

Normalized difference vegetation index (NDVI) of highly dense vegetation (NDVI_v) and bare soil (NDVI_s), identified as the key parameters for Fractional Vegetation Cover (FVC) estimation, are usually obtained with empirical statistical methods However, it is often difficult to obtain reasonable values of NDVI_v and NDVI_s at a coarse resolution (e.g., 1 km), or in arid, semiarid, and evergreen areas. The uncertainty of estimated NDVI_s and NDVI_v can cause substantial errors in FVC estimations when a simple linear mixture model is used. To address this problem, this paper proposes a physically based method. The leaf area index (LAI) and directional NDVI are introduced in a gap fraction model and a linear mixture model for FVC estimation to calculate NDVI_v and NDVI_s. The model incorporates the Moderate Resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectance Distribution Function (BRDF) model parameters product (MCD43B1) and LAI product, which are convenient to acquire. Two types of evaluation experiments are designed 1) with data simulated by a canopy radiative transfer model and 2) with satellite observations. The root-mean-square deviation (RMSD) for simulated data is less than 0.117, depending on the type of noise added on the data. In the real data experiment, the RMSD for cropland is 0.127, for grassland is 0.075, and for forest is 0.107. The experimental areas respectively lack fully vegetated and non-vegetated pixels at 1 km resolution. Consequently, a relatively large uncertainty is found while using the statistical methods and the RMSD ranges from 0.110 to 0.363 based on the real data. The proposed method is convenient to produce NDVI_v and NDVI_s maps for FVC estimation on regional and global scales.     

Land surface temperature and emissivity estimation for Urban Heat Island assessment using medium- and low-resolution space-borne sensors: A review

The Urban Heat Island (UHI) phenomenon, a typical characteristic on urban landscapes, has been recognised as a key driver to the transformation of local climate. Reliable retrieval of urban and intra-urban thermal characteristics using satellite thermal data depends on accurate removal of the effects of atmospheric attenuations, angular and land surface emissivity. Several techniques have been proposed to retrieve land surface temperature (LST) from coarse resolution sensors. Medium spatial resolution sensors like the Advanced Space-borne Thermal Emission and Reflection Radiometer and the Landsat series offer a viable option for assessing LST within urban landscapes. This paper reviews the theoretical background of LST estimates from the thermal infrared part of the electromagnetic spectrum, LST retrieval algorithms applicable to each of the commonly used medium-resolution sensors and required variables for each algorithm. The paper also highlights LST validation techniques and concludes by stipulating the requirements for LST temporal and spatial configuration.     

一种利用哨兵1号数据提取南极接地线方法

针对因IW工作模式数据在南极地区覆盖不全面导致对南极接地线的全面提取造成影响的问题,提出利用双差干涉测量提取接地线的基本原理,分别利用工作模式为IW和EW的Sentinel-1雷达卫星数据,对东南极毛德皇后地沿岸冰川接地线进行提取,将提取结果分别与MEa-SUREs接地线产品进行对比,同时再将这两种模式下的提取结果进行对比分析。结果表明,双差干涉测量可以去除冰流相位的影响,使用EW工作模式的Sentinel-1数据进行南极接地线提取可以忽略其地面分辨率较低这一缺陷,同时,这一模式的Sentinel-1数据在幅宽、南极覆盖范围等方面更具优势。因此,综合利用IW和EW工作模式的Sentinel-1数据,对南极接地线的全面、高效、长期动态监测具有重要意义。     

An accurate selectivity estimation method for window queries and an implementation thereof

Spatial selectivity estimation is crucial to choose the cheapest execution plan for a given query in a query optimizer. This article proposes an accurate spatial selectivity estimation method based on the cumulative density (CD) histograms, which can deal with any arbitrary spatial query window. In this method, the selectivity can be estimated in original logic of the CD histogram, after the four corner values of a query window have been accurately interpolated on the continuous surface of the elevation histogram. For the interpolation of any corner points, we first identify the cells that can affect the value of point (x, y) in the CD histogram. These cells can be categorized into two classes: ones within the range from (0, 0) to (x, y) and the other overlapping the range from (0, 0) to (x, y). The values of the former class can be used directly, whereas we revise the values of any cells falling in the latter class by the number of vertices in the corresponding cell and the area ratio covered by the range from (0, 0) to (x, y). This revision makes the estimation method more accurate. The CD histograms and estimation method have been implemented in INGRES. Experiment results show that the method can accurately estimate the selectivity of arbitrary query windows and can help the optimizer choose a cheaper query plan.