Ocean remote sensing datasets often have the problem of missing values due to various reasons.However,many scientific applications require spatiotemporal seamless data.Data reconstruction methods are commonly used to ...Ocean remote sensing datasets often have the problem of missing values due to various reasons.However,many scientific applications require spatiotemporal seamless data.Data reconstruction methods are commonly used to obtain such gap-free datasets.In reconstructing satellite remote sensing data,randomly masking original data for progressive cross-validation is a common method to indicate the performance of reconstruction.In this study,the accuracy of this validation method is analysed.We artificially constructed two data missing patterns using the sea surface temperature(SST)data in the East China Sea,one simulating natural cloud coverage and the other randomly masking the same percentage of original data.The results of reconstruction for the two types of masking were compared.The root mean square error(RMSE)of dataset that simulate real cloud coverage is more than 50%higher than that of the dataset randomly masking data,regardless of the data missing rate.This result implies that the error of satellite data gap-filling is underestimated when random masking of original data is applied for progressive cross-validation,which should be treated with care in applications.展开更多
Chlorophyll-a(Chl-a)concentration is a primary indicator for marine environmental monitoring.The spatio-temporal variations of sea surface Chl-a concentration in the Yellow Sea(YS)and the East China Sea(ECS)in 2001-20...Chlorophyll-a(Chl-a)concentration is a primary indicator for marine environmental monitoring.The spatio-temporal variations of sea surface Chl-a concentration in the Yellow Sea(YS)and the East China Sea(ECS)in 2001-2020 were investigated by reconstructing the MODIS Level 3 products with the data interpolation empirical orthogonal function(DINEOF)method.The reconstructed results by interpolating the combined MODIS daily+8-day datasets were found better than those merely by interpolating daily or 8-day data.Chl-a concentration in the YS and the ECS reached its maximum in spring,with blooms occurring,decreased in summer and autumn,and increased in late autumn and early winter.By performing empirical orthogonal function(EOF)decomposition of the reconstructed data fields and correlation analysis with several potential environmental factors,we found that the sea surface temperature(SST)plays a significant role in the seasonal variation of Chl a,especially during spring and summer.The increase of SST in spring and the upper-layer nutrients mixed up during the last winter might favor the occurrence of spring blooms.The high sea surface temperature(SST)throughout the summer would strengthen the vertical stratification and prevent nutrients supply from deep water,resulting in low surface Chl-a concentrations.The sea surface Chl-a concentration in the YS was found decreased significantly from 2012 to 2020,which was possibly related to the Pacific Decadal Oscillation(PDO).展开更多
基金The National Key Research and Development Program of China under contract No.2022YFC3104900-2022YFC3104905the National Natural Science Foundation of China under contract Nos 42376172 and 52471303Guangdong Basic and Applied Basic Research Foundation under contract No.2024A1515012032.
文摘Ocean remote sensing datasets often have the problem of missing values due to various reasons.However,many scientific applications require spatiotemporal seamless data.Data reconstruction methods are commonly used to obtain such gap-free datasets.In reconstructing satellite remote sensing data,randomly masking original data for progressive cross-validation is a common method to indicate the performance of reconstruction.In this study,the accuracy of this validation method is analysed.We artificially constructed two data missing patterns using the sea surface temperature(SST)data in the East China Sea,one simulating natural cloud coverage and the other randomly masking the same percentage of original data.The results of reconstruction for the two types of masking were compared.The root mean square error(RMSE)of dataset that simulate real cloud coverage is more than 50%higher than that of the dataset randomly masking data,regardless of the data missing rate.This result implies that the error of satellite data gap-filling is underestimated when random masking of original data is applied for progressive cross-validation,which should be treated with care in applications.
基金Supported by the Fundamental Research Funds for the Central Universities(Nos.202341017,202313024)。
文摘Chlorophyll-a(Chl-a)concentration is a primary indicator for marine environmental monitoring.The spatio-temporal variations of sea surface Chl-a concentration in the Yellow Sea(YS)and the East China Sea(ECS)in 2001-2020 were investigated by reconstructing the MODIS Level 3 products with the data interpolation empirical orthogonal function(DINEOF)method.The reconstructed results by interpolating the combined MODIS daily+8-day datasets were found better than those merely by interpolating daily or 8-day data.Chl-a concentration in the YS and the ECS reached its maximum in spring,with blooms occurring,decreased in summer and autumn,and increased in late autumn and early winter.By performing empirical orthogonal function(EOF)decomposition of the reconstructed data fields and correlation analysis with several potential environmental factors,we found that the sea surface temperature(SST)plays a significant role in the seasonal variation of Chl a,especially during spring and summer.The increase of SST in spring and the upper-layer nutrients mixed up during the last winter might favor the occurrence of spring blooms.The high sea surface temperature(SST)throughout the summer would strengthen the vertical stratification and prevent nutrients supply from deep water,resulting in low surface Chl-a concentrations.The sea surface Chl-a concentration in the YS was found decreased significantly from 2012 to 2020,which was possibly related to the Pacific Decadal Oscillation(PDO).
