Retrieving chlorophyll content and equivalent water thickness of Moso bamboo(Phyllostachys pubescens) forests under Pantana phyllostachysae Chao-induced stress from Sentinel-2A/B images in a multiple LUTs-based PROSAIL framework

Biochemical components of Moso bamboo(Phyllostachys pubescens)are critical to physiological and ecological processes and play an important role in the material and energy cycles of the ecosystem.The coupled PROSPECT with SAIL(PROSAIL)radiative transfer model is widely used for vegetation biochemical component content inversion.However,the presence of leaf-eating pests,such as Pantana phyllostachysae Chao(PPC),weakens the performance of the model for estimating biochemical components of Moso bamboo and thus must be considered.Therefore,this study considered pest-induced stress signals associated with Sentinel-2A/B images and field data and established multiple sets of biochemical canopy reflectance look-up tables(LUTs)based on the PROSAIL framework by setting different parameter ranges according to infestation levels.Quantitative inversions of leaf area index(LAI),leaf chlorophyll content(LCC),and leaf equivalent water thickness(LEWT)were derived.The scale conversions from LCC to canopy chlorophyll content(CCC)and LEWT to canopy equivalent water thickness(CEWT)were calculated.The results showed that LAI,CCC,and CEWT were inversely related with PPC-induced stress.When applying multiple LUTs,the p-values were<0.01;the R2 values for LAI,CCC,and CEWT were 0.71,0.68,and 0.65 with root mean square error(RMSE)(normalized RMSE,NRMSE)values of 0.38(0.16),17.56μg cm-2(0.20),and 0.02 cm(0.51),respectively.Compared to the values obtained for the traditional PROSAIL model,for October,R2 values increased by 0.05 and 0.10 and NRMSE decreased by 0.09 and 0.02 for CCC and CEWT,respectively and RMSE decreased by 0.35μg cm-2 for CCC.The feasibility of the inverse strategy for integrating pest-induced stress factors into the PROSAIL model,while establishing multiple LUTs under different pest-induced damage levels,was successfully demonstrated and can potentially enhance future vegetation parameter inversion and monitoring of bamboo forest health and ecosystems.

基于Sentinel-2数据的山仔水库水华遥感监测

山仔水库位于福建省福州市连江县,利用Sentinel-2高分辨率多光谱遥感影像,结合多种水体提取算法和藻类水华识别方法,对山仔水库2019—2023年间的水华进行了时空分析。研究结果表明:1)在水体提取方面,NDWI在研究区域内表现最佳,能够有效识别水体边界。2)在水华反演方面,通过NDVI和FAI的阈值法以及随机森林(RF)、支持向量机(SVM)、梯度提升树(GBT)等监督分类方法的对比分析,结果显示RF方法在分类准确性上表现最佳,提取精度达到了95.75%。3)山仔水库的水华爆发具有显著的季节性和年际波动特征,春季(3—4月)为主要爆发时段,秋季(9—10月)次之,而夏季和冬季的水华强度较低且分布较为分散。随着气温下降与降水量增加,水华的面积在2021—2023年间持续回落。4)在空间分布方面,水华高发区集中在水库北部狭窄水道和东南、西南部弯曲支流区域,而中部开阔水域因水体流动性强,水华频率相对较低。本研究为山仔水库的藻类水华监测提供了有效的遥感技术支持,可为水生态环境管理和水华预警提供科学依据。

Mapping soil organic matter in cultivated land based on multi-year composite images on monthly time scales

Rapid and accurate acquisition of soil organic matter(SOM)information in cultivated land is important for sustainable agricultural development and carbon balance management.This study proposed a novel approach to predict SOM with high accuracy using multiyear synthetic remote sensing variables on a monthly scale.We obtained 12 monthly synthetic Sentinel-2 images covering the study area from 2016 to 2021 through the Google Earth Engine(GEE)platform,and reflectance bands and vegetation indices were extracted from these composite images.Then the random forest(RF),support vector machine(SVM)and gradient boosting regression tree(GBRT)models were tested to investigate the difference in SOM prediction accuracy under different combinations of monthly synthetic variables.Results showed that firstly,all monthly synthetic spectral bands of Sentinel-2 showed a significant correlation with SOM(P<0.05)for the months of January,March,April,October,and November.Secondly,in terms of single-monthly composite variables,the prediction accuracy was relatively poor,with the highest R^(2)value of 0.36 being observed in January.When monthly synthetic environmental variables were grouped in accordance with the four quarters of the year,the first quarter and the fourth quarter showed good performance,and any combination of three quarters was similar in estimation accuracy.The overall best performance was observed when all monthly synthetic variables were incorporated into the models.Thirdly,among the three models compared,the RF model was consistently more accurate than the SVM and GBRT models,achieving an R^(2)value of 0.56.Except for band 12 in December,the importance of the remaining bands did not exhibit significant differences.This research offers a new attempt to map SOM with high accuracy and fine spatial resolution based on monthly synthetic Sentinel-2 images.

Sentinel-2 MSI和Landsat 8 OLI数据在玉米秸秆覆盖度遥感估算应用中的比较研究

秸秆覆盖度(Crop residue cover, CRC)的遥感估算可以在短时间内获取大范围耕地秸秆覆盖度数据,对于政府部门监测保护性耕作的实施情况有重要的现实意义。本研究基于Sentinel-2 MSI和Landsat 8 OLI数据,分别计算了多种光谱指数,并与野外实测的秸秆覆盖度数据进行相关性分析,挑选出极显著性相关的光谱指数。在此基础上,构建其与秸秆覆盖度之间的相关模型,并通过决定系数(R2)和均方根误差(RMSE)所表征模型的精度比较Sentinel-2 MSI和Landsat 8OLI数据由于光谱和空间尺度的差异对秸秆覆盖度反演模型的影响。结果表明:6种光谱指数与CRC的相关性系数均大于0.4,相关性较高的是Sentinel-2 MSI 20 m分辨率数据获取的NDTI和STI,相关系数分别为0.878、0.894,相关性最低的为Sentinel-2 MSI 10 m分辨率数据获取的NDSVI,相关系数为0.476;利用一元线性回归法构建模型时,Sentinel-2 MSI 20 m分辨率数据构建的光谱指数STI和NDTI,模型精度最高,R^(2)分别为0.810和0.800,RMSE分别为6.84%和7.01%,而30 m重采样数据的R^(2)分别为0.770和0.771,RMSE分别为7.52%和7.50%,随着空间分辨率的降低呈现出下降趋势;Sentinel-2MSI 30 m重采样数据获取的光谱指数构建的所有模型精度均略大于Landsat 8 OLI数据构建的模型。因此,Sentinel-2 MSI数据获取NDTI和STI这两个光谱指数更加适合本研究区域秸秆覆盖度的估算。

Winter wheat yield estimation based on assimilated Sentinel-2 images with the CERES-Wheat model

Assimilating Sentinel-2 images with the CERES-Wheat model can improve the precision of winter wheat yield estimates at a regional scale. To verify this method, we applied the ensemble Kalman filter(EnKF) to assimilate the leaf area index(LAI) derived from Sentinel-2 data and simulated by the CERES-Wheat model. From this, we obtained the assimilated daily LAI during the growth stage of winter wheat across three counties located in the southeast of the Loess Plateau in China: Xiangfen, Xinjiang, and Wenxi. We assigned LAI weights at different growth stages by comparing the improved analytic hierarchy method, the entropy method, and the normalized combination weighting method, and constructed a yield estimation model with the measurements to accurately estimate the yield of winter wheat. We found that the changes of assimilated LAI during the growth stage of winter wheat strongly agreed with the simulated LAI. With the correction of the derived LAI from the Sentinel-2 images, the LAI from the green-up stage to the heading–filling stage was enhanced, while the LAI decrease from the milking stage was slowed down, which was more in line with the actual changes of LAI for winter wheat. We also compared the simulated and derived LAI and found the assimilated LAI had reduced the root mean square error(RMSE) by 0.43 and 0.29 m^(2) m^(–2), respectively, based on the measured LAI. The assimilation improved the estimation accuracy of the LAI time series. The highest determination coefficient(R2) was 0.8627 and the lowest RMSE was 472.92 kg ha^(–1) in the regression of the yields estimated by the normalized weighted assimilated LAI method and measurements. The relative error of the estimated yield of winter wheat in the study counties was less than 1%, suggesting that Sentinel-2 data with high spatial-temporal resolution can be assimilated with the CERES-Wheat model to obtain more accurate regional yield estimates.

基于Sentinel-2A MSI特征的毛竹林刚竹毒蛾危害检测

为快速、准确地检测毛竹林刚竹毒蛾(Pantana phyllostachysae Chao)危害,基于Sentinel-2A MSI数据分析不同刚竹毒蛾危害等级下毛竹林像元光谱的变化,从叶损量、绿度、含水率等多个维度选择对刚竹毒蛾危害具有响应能力的22个Sentinel-2A MSI光谱衍生指标;经单因素方差分析(ANOVA)以及递归特征消除法(Recursive feature elimination,RFE)优选后,得到可用于刚竹毒蛾危害识别的10个遥感特征,包括LAI、RVI、NDMVI、EVI、NDVI_(705)、NDVI_(783)、RegVI_(1)、RegVI_(2)、GVMI和NDWI;将上述指标作为自变量,虫害等级作为因变量,建立基于XGBoost模型的刚竹毒蛾危害检测模型。研究发现,Sentinel-2A MSI数据波段6、7、8、8a对刚竹毒蛾危害具有较强的响应能力;红边与近红外波段参与构建的指数有效反映了竹林的受害情况;XGBoost模型对刚竹毒蛾危害识别的总精度为83.70%,对不同刚竹毒蛾危害等级的识别精度依次为94.72%、72.06%、79.77%、92.41%。因此,利用ANOVA-RFE筛选Sentinel-2A MSI光谱特征建立的XGBoost虫害检测模型,具有较高的识别精度,可为毛竹林刚竹毒蛾危害遥感监测提供技术支持。

基于Sentinel-2影像的平寨水库水体透明度遥感反演

[目的]研究水体透明度的变化规律及其空间分异驱动因素,为管理湖库及恢复湖库生态系统提供科学依据。[方法]基于2020年5月18日,8月26日,11月14日的Sentinel-2 MSI卫星影像及准同步实测透明度数据构建平寨水库透明度遥感反演模型,利用地理探测器定量分析影响透明度空间分异的驱动因素。[结果]①平寨水库水体透明度与Sentinel-2 MSI的B_(3)波段最为敏感,利用波段组合B_(3)×B_(4)作为最佳敏感因子构建出的透明度反演模型具有较高的精度(R^(2)=0.81,RMSE=0.11 m,MRE=16.91%)。②平寨水库水体透明度呈现出中心库区高而各支流上游低,近水体两岸低的空间分布趋势,且水体透明度11月>8月>5月。[结论]总悬浮物、叶绿素a及总有机碳含量是平寨水库水体透明度空间分异的主要因素。总磷、总氮、水温及风速通过影响水体中的总悬浮物、叶绿素a及总有机碳含量进而影响水体透明度空间分布。

神经网络支持下的Sentinel-2卫星影像自动云检测

为解决利用Sentinel-2卫星影像进行地物信息提取时云层遮挡造成的信息误判问题,提出了一种基于深度学习的遥感影像云区高精度分割方法。该方法通过预处理的遥感样本数据构建出一种深度神经网络模型,自动提取高层次影像特征;再将影像特征输入分类器,实现遥感影像的像素级分类,从而分割出云覆盖矩阵;最后将云覆盖矩阵转化为云二值图,结合感兴趣区矢量准确获取指定区域云检测结果。选取典型区域进行测试,结果表明:该方法检测精度较高,速度较快,且无须辅助信息与人工干预,可用于Sentinel-2卫星影像不规则区域自动云检测。

基于Landsat-8陆地成像仪与Sentinel-2多光谱成像仪传感器的香港近海海域叶绿素a浓度遥感反演

为验证Landsat-8陆地成像仪(operational land imager,OLI)遥感数据与Sentinel-2多光谱成像仪(multispectral imager,MSI)遥感数据监测近海海域叶绿素a浓度可行性,以其为数据源,香港近海海域为研究区域,以半分析模型为方法,挑选与监测点实测叶绿素a浓度采集时间一致且遥感影像云覆盖率小于10%影像清晰的两类遥感影像。对两类遥感影像分别选取2/3的遥感影像数据经预处理后提取其对应实测日期监测点位置遥感反射率进行相关性分析,得到相关性最高的反演因子进行建模,并且利用剩下的1/3数据对其反演回复回归模型进行精度检验,其结果与OCx Ocean Chlorophyll X模型反演结果进行对比效果显著。基于Landsat-8遥感数据建立的最佳反演回归半分析模型决定系数R^(2)为0.906,略高于基于Sentinel-2遥感数据建立的最佳反演回归半分析模型,其R^(2)为0.801。与此同时证明了就香港近海海域叶绿素a浓度反演两类遥感数据的可行性,且两类数据的反演结果均呈现出香港近海海域内部海域叶绿素a浓度高于外部叶绿素a浓度的现象。

一种基于Sentinel-2的塑料大棚提取方法

采用Sentinel-2 MSI(multispectral instrument,MSI)作为数据源,以多尺度面向对象分析为基本方法,研究分析塑料大棚与其他地物在不同分割尺度下的典型特征,建立一组大棚指数,提出一种基于大棚指数集的塑料大棚提取方法。通过山东省潍坊市某地区的塑料大棚提取试验对方法进行验证,应用该方法提取大棚的生产者精度、用户精度和总体Kappa系数分别为96.6%、89.2%和0.9,测试精度表明该方法能较为有效地应用于塑料大棚提取研究。

Mapping soil organic matter content using Sentinel-2 syntheticimages at different time intervals in Northeast China

Mapping soil organic matter(SOM)content has become an important application of digital soil mapping.In this study,we processed all Sentinel-2 images covering the bare-soil period(March to June)in Northeast China from 2019 to 2022 and integrated the observation results into synthetic materials with four defined time intervals(10,15,20,and 30 d).Then,we used synthetic images corresponding to different time periods to conduct SOM mapping and determine the optimal time interval and time period beforefinally assessing the impacts of adding environmental covariates.The results showed the following:(1)in SOM mapping,the highest accuracy was obtained using day-of-year(DOY)120 to 140 synthetic images with 20 d time intervals,as well as with different time intervals,ranked as follows:20 d>30 d>15 d>10 d;(2)when using synthetic images at different time intervals to predict SOM,the best time period for predicting SOM was always within May;and(3)adding environmental covariates effectively improved the SOM mapping performance,and the multiyear average temperature was the most important factor.In general,our results demonstrated the valuable potential of SOM mapping using multiyear synthetic imagery,thereby allowing detailed mapping of large areas of cultivated soil.

A landslide extraction method of channel attention mechanismU-Net network based on Sentinel-2A remote sensing images

Accurate landslide extraction is significant for landslide disaster prevention and control.Remote sensing images have been widely used in landslide investigation,and landslide extraction methods based on deep learning combined with remote sensing images(such as U-Net)have received a lot of attention.However,because of the variable shape and texture features of landslides in remote sensing images,the rich spectral features,and the complexity of their surrounding features,landslide extraction using U-Net can lead to problems such as false detection and missed detection.Therefore,this study introduces the channel attention mechanism called the squeeze-and-excitation network(SENet)in the feature fusion part of U-Net;the study also constructs an attention U-Net landside extraction model combining SENet and U-Net,and uses Sentinel-2A remote sensing images for model training and validation.The extraction results are evaluated through different evaluation metrics and compared with those of two models:U-Net and U-Net Backbone(U-Net Without Skip Connection).The results show that proposed the model can effectively extract landslides based on Sentinel-2A remote sensing images with an F1 value of 87.94%,which is about 2%and 3%higher than U-Net and U-Net Backbone,respectively,with less false detection and more accurate extraction results.

The application of ResU-net and OBIA for landslide detection from multi-temporal Sentinel-2 images

Landslide detection is a hot topic in the remote sensing community,particularly with the current rapid growth in volume(and variety)of Earth observation data and the substantial progress of computer vision.Deep learning algorithms,especially fully convolutional networks(FCNs),and variations like the ResU-Net have been used recently as rapid and automatic landslide detection approaches.Although FCNs have shown cutting-edge results in automatic landslide detection,accuracy can be improved by adding prior knowledge through possible frameworks.This study evaluates a rulebased object-based image analysis(OBIA)approach built on probabilities resulting from the ResU-Net model for landslide detection.We train the ResU-Net model using a landslide dataset comprising landslide inventories from various geographic regions,including our study area and test the testing area not used for training.In the OBIA stage,we frst calculate land cover and image difference indices for pre-and post-landslide multi-temporal images.Next,we use the generated indices and the resulting ResU-Net probabilities for image segmentation;the extracted landslide object candidates are then optimized using rule-based classification.In the result validation section,the landslide detection of the proposed integration of the ResU-Net with a rule-based classification of OBIA is compared with that of the ResU-Net alone.Our proposed approach improves the mean intersection-over-union of the resulting map from the ResU-Net by more than 22%.

基于Sentinel-2 MSI影像的秦皇岛海域叶绿素a浓度遥感反演

基于2013-2018年秦皇岛海域实测遥感反射率和叶绿素a浓度数据,建立了该海域Sentinel-2 MSI影像的叶绿素a浓度遥感反演模型。结果表明:443 nm、490 nm和560 nm处的等效遥感反射率比值与叶绿素a浓度相关系数普遍高于其他波段或组合,通过经典的OC3Mv6算法拟合分析,得到秦皇岛海域叶绿素a浓度遥感反演的最佳算法,R^(2)=0.804,MAPE=40.2%,RMSE=4.73 mg/m^(3);利用2016年7月6日的实测叶绿素a浓度数据对Sentinel-2 MSI遥感反演结果进行了真实性检验,MAPE=35.9%,可以满足应用要求;采用2020年2月、5月、7月及10月Sentinel-2 MSI影像进行叶绿素a浓度反演,发现春、夏季秦皇岛海域叶绿素a浓度梯度变化显著,而秋、冬季叶绿素a浓度分布相对均匀,且春、夏季沿海海域叶绿素a浓度明显高于秋、冬季。

Unsupervised GRNN flood mapping approach combined with uncertainty analysis using bi-temporal Sentinel-2 MSI imageries

Floods occur frequently worldwide.The timely,accurate mapping of the flooded areas is an important task.Therefore,an unsupervised approach is proposed for automated flooded area mapping from bitemporal Sentinel-2 multispectral images in this paper.First,spatial–spectral features of the images before and after the flood are extracted to construct the change magnitude image(CMI).Then,the certain flood pixels and non-flood pixels are obtained by performing uncertainty analysis on the CMI,which are considered reliable classification samples.Next,Generalized Regression Neural Network(GRNN)is used as the core classifier to generate the initial flood map.Finally,an easy-toimplement two-stage post-processing is proposed to reduce the mapping error of the initial flood map,and generate the final flood map.Different from other methods based on machine learning,GRNN is used as the classifier,but the proposed approach is automated and unsupervised because it uses samples automatically generated in uncertainty analysis for model training.Results of comparative experiments in the three sub-regions of the Poyang Lake Basin demonstrate the effectiveness and superiority of the proposed approach.Moreover,its superiority in dealing with uncertain pixels is further proven by comparing the classification accuracy of different methods on uncertain pixels.

Integration of Multiple Spectral Data via a Logistic Regression Algorithm for Detection of Crop Residue Burned Areas:A Case Study of Songnen Plain,Northeast China

The burning of crop residues in fields is a significant global biomass burning activity which is a key element of the terrestrial carbon cycle,and an important source of atmospheric trace gasses and aerosols.Accurate estimation of cropland burned area is both crucial and challenging,especially for the small and fragmented burned scars in China.Here we developed an automated burned area mapping algorithm that was implemented using Sentinel-2 Multi Spectral Instrument(MSI)data and its effectiveness was tested taking Songnen Plain,Northeast China as a case using satellite image of 2020.We employed a logistic regression method for integrating multiple spectral data into a synthetic indicator,and compared the results with manually interpreted burned area reference maps and the Moderate-Resolution Imaging Spectroradiometer(MODIS)MCD64A1 burned area product.The overall accuracy of the single variable logistic regression was 77.38%to 86.90%and 73.47%to 97.14%for the 52TCQ and 51TYM cases,respectively.In comparison,the accuracy of the burned area map was improved to 87.14%and 98.33%for the 52TCQ and 51TYM cases,respectively by multiple variable logistic regression of Sentind-2 images.The balance of omission error and commission error was also improved.The integration of multiple spectral data combined with a logistic regression method proves to be effective for burned area detection,offering a highly automated process with an automatic threshold determination mechanism.This method exhibits excellent extensibility and flexibility taking the image tile as the operating unit.It is suitable for burned area detection at a regional scale and can also be implemented with other satellite data.

基于Sentinel数据与DNN算法的衡水市土壤墒情遥感反演研究

利用Sentinel-1 SAR和Sentinel-2 MSI数据与深度神经网络(DNN)算法,实现衡水市土壤墒情的遥感反演。结果表明,所提取的遥感指数能够准确捕捉地表环境特征;DNN算法通过构建样点尺度土壤墒情与遥感指数之间非线性关系,稳健预测空间尺度土壤墒情分布。独立验证结果显示,土壤墒情反演精度R2达0.854,MAE和RMSE分别为0.05、0.06。本试验证明基于Sentinel数据与DNN算法的土壤墒情遥感反演方法,在墒情监测与预测方面具有较高的精度和可靠性。

北方半干旱区典型湖泊--岱海透明度遥感反演(2013-2020年)

水体的透明度是评价水质的重要指标,在水生态系统中起着重要的作用.借助遥感技术可以获得大范围、实时数据,并且有节省人力物力的优点.本文利用岱海的野外实测透明度数据和光谱数据,针对Sentinel-2 MSI和Landsat-8 OLI卫星数据波段设置,建立了岱海水体透明度反演模型.结果表明:1)本文建立的透明度反演模型中,蓝红波段比二次模型反演精度最好,决定系数R^(2)=0.66,均方根误差(RMSE)为24.02 cm,平均绝对百分比误差(MAPE)为21.24%.2)将蓝红波段比二次模型应用于Landsat-8 OLI和Sentinel-2 MSI卫星数据,透明度反演精度较好,MAPE<28.82%,RMSE<23.26 cm,R^(2)>0.60.3)此算法应用于时间序列MSI和OLI影像,得到了岱海水体透明度时空分布特征.结果表明,岱海水体透明度年平均变化范围在90.71-120.77 cm,2015年的平均透明度最高,2013年的平均透明度最低;月平均变化范围在90.68-122.53 cm,7月的平均透明度最高,5月的平均透明度最低.岱海透明度在空间上的分布趋势大致表现为西北高,东南低,中部高,四周低.4)影响岱海水体透明度变化的主要因素为风速和降水,透明度与风速和降水分别具有显著的负相关和正相关关系.

Optimizing Sentinel-2 image selection in a Big Data context

Processing large amounts of image data such as the Sentinel-2 archive is a computationally demanding task.However,for most applications,many of the images in the archive are redundant and do not contribute to the quality of the final result.An optimization scheme is presented here that selects a subset of the Sentinel-2 archive in order to reduce the amount of processing,while retaining the quality of the resulting output.As a case study,we focused on the creation of a cloud-free composite,covering the global land mass and based on all the images acquired from January 2016 until September 2017.The total amount of available images was 2,128,556.The selection of the optimal subset was based on quicklooks,which correspond to a spatial and spectral subset of the original Sentinel-2 products and are lossy compressed.The selected subset contained 94,093 image tiles in total,reducing the amount of images to be processed to 4.42%of the full set.

基于Sentinel-2 MSI影像的河湖系统水体悬浮物空间分异遥感监测:以安徽省升金湖与连接长江段为例

开展河湖系统悬浮物监测对掌握水体泥沙运移规律、制定水环境治理措施具有重要意义.以安徽省升金湖与连接长江段水体为研究区,根据实测光谱模拟Sentinel-2 MSI影像波段反射率,结合同步水体悬浮物实测数据建立反演模型;而后根据2017~2019年28景MSI影像水体悬浮物反演结果,分析河湖系统水体悬浮物浓度的变化规律,并探究水位变化对其空间分异的影响.结果表明:①根据MSI影像第六波段与第三波段的比值建立的二次多项式模型具有较高的反演精度(R^2=0.863,RMSE=22.211 mg·L^-1),适用于高浊度水体悬浮物反演;②在空间上,升金湖入湖口附近、上中湖区西北部和下湖悬浮物浓度相对较高,除夏季外升金湖悬浮物浓度均高于其连接长江段;在时间上,升金湖悬浮物浓度在夏季相对较低,在其他季节较高,而与其连接的长江水体呈现相反的年内变化规律;③闸控影响下河湖连通性改变造成的水位变化,是影响升金湖-长江悬浮物空间分异的关键因素.在平水期与枯水期,升金湖对长江悬浮物浓度变化具有一定的贡献度,而在丰水期,升金湖与长江悬浮物浓度变化之间的相关性不明显.