Satellite normalized difference vegetation index(NDVI)time series,essential for ecological and environmental applications,is still limited by a trade-off between the spatiotemporal resolution and time coverage despite...Satellite normalized difference vegetation index(NDVI)time series,essential for ecological and environmental applications,is still limited by a trade-off between the spatiotemporal resolution and time coverage despite various global products.The Advanced Very High-Resolution Radiometer(AVHRR)instrument can provide the longest continuous time series since 1982,but with the drawback of coarse spatial resolution and poor data quality.To address this issue,a spatiotemporal fusion-based long-term NDVI product(STFLNDVI)since 1982 was generated in this study at a 1-km spatial resolution with monthly intervals,by fusing with the Moderate Resolution Imaging Spectroradiometer(MODIS)data.A multi-step processing fusion framework,containing temporal filtering,normalization,spatiotemporal fusion,and residual error correction,was employed to combine the superior characteristics of the two products,respectively.Simulated comparison with MODIS data and real-data assessments with true 1 km AVHRR data both confirm the ideal accuracy of the fusion product in spatial distribution and temporal variation,providing stable long-term results similar to MODIS data.We believe that the STFLNDVI product will be of great significance in characterizing the spatial patterns and long-term variations of global vegetation and the historical radiometric calibrations in AVHRR data gaps around the Arctic and instrument differences between MODIS and AVHRR should be further considered in the future.展开更多
A good understanding of the quality of digital elevation model(DEM)is a perquisite for various applications.This study investigates the accuracy of three most recently released 1-arcsec global DEMs(GDEMs,Copernicus,NA...A good understanding of the quality of digital elevation model(DEM)is a perquisite for various applications.This study investigates the accuracy of three most recently released 1-arcsec global DEMs(GDEMs,Copernicus,NASA and AW3D30)in five selected terrains of China,using more than 240,000 high-quality ICESat-2(Ice,Cloud and land Elevation Satellite)ALT08 points.The results indicate the three GDEMs have similar overall vertical accuracy,with RMSE of 6.73(Copernicus),6.59(NASA)and 6.63 m(AW3D30).While the accuracy varies considerably over study areas and among GDEMs.The results show a clear correlation between the accuracy and terrain slopes,and some relationship between the accuracy and land covers.Our analysis reveals the land cover exerts a greater impact on the accuracy than that of the terrain slope for the study area.Visual inspections of terrain representation indicate Copernicus DEM exhibits the greatest detail of terrain,followed by AW3D30,and then by NASADEM.This study has demonstrated that ICESat-2 altimetry offers an important tool for DEM assessment.The findings provide a timely and comprehensive understanding of the quality of newly released GDEMs,which are informative for the selection of suitable DEMs,and for the improvement of GDEM in future studies.展开更多
Efficient and continuous monitoring of surface water is essential for water resource management.Much effort has been devoted to the task of water mapping based on remote sensing images.However,few studies have fully c...Efficient and continuous monitoring of surface water is essential for water resource management.Much effort has been devoted to the task of water mapping based on remote sensing images.However,few studies have fully considered the diverse spectral properties of water for the collection of reference samples in an automatic manner.Moreover,water area statistics are sensitive to the satellite image observation quality.This study aims to develop a fully automatic surface water mapping framework based on Google Earth Engine(GEE)with a supervised random forest classifier.A robust scheme was built to automatically construct training samples by merging the information from multi-source water occurrence products.The samples for permanent and seasonal water were mapped and collected separately,so that the supplement of seasonal samples can increase the spectral diversity of the sample space.To reduce the uncertainty of the derived water occurrences,temporal correction was applied to repair the classification maps with invalid observations.Extensive experiments showed that the proposed method can generate reliable samples and produce good-quality water mapping results.Comparative tests indicated that the proposed method produced water maps with a higher quality than the index-based detection methods,as well as the GSWD and GLAD datasets.展开更多
The tradeoffs between the spatial and temporal resolutions for the remote sensing instruments limit their capacity to monitor the eutrophic status of inland lakes.Spatiotemporal fusion(STF)provides a cost-effective wa...The tradeoffs between the spatial and temporal resolutions for the remote sensing instruments limit their capacity to monitor the eutrophic status of inland lakes.Spatiotemporal fusion(STF)provides a cost-effective way to obtain remote sensing data with both high spatial and temporal resolutions by blending multisensor observations.However,remote sensing reflectance(Rrs)over water surface with a relatively low signal-to-noise ratio is prone to be contaminated by large uncertainties in the fusion process.To present a comprehensive analysis on the influence of processing and modeling errors,we conducted an evaluation study to understand the potential,uncertainties,and limitations of using STF for monitoring chlorophyll a(Chla)concentration in an inland eutrophic water(Chaohu Lake,China).Specifically,comparative tests were conducted on the Sentinel-2 and Sentinel-3 image pairs.Three typical STF methods were selected for comparison,i.e.,Fit-FC,spatial and temporal nonlocal filter-based fusion model,and the flexible spatiotemporal data fusion.The results show as follows:(a)among the influencing factors,atmospheric correction uncertainties and geometric misregistration have larger impacts on the fusion results,compared with radiometric bias between the imaging sensors and STF modeling errors;and(b)the machine-learning-based Chla inversion accuracy of the fusion data[R^(2)=0.846 and root mean square error(RMSE)=17.835μg/l]is comparable with that of real Sentinel-2 data(R^(2)=0.856 and RMSE=16.601μg/l),and temporally dense Chla results can be produced with the integrated Sentinel-2 and fusion image datasets.These findings will help to provide guidelines to design STF framework for monitoring aquatic environment of inland waters with remote sensing data.展开更多
基金supported by the National Key Research and Development Program of China[2017YFA0604402]National Natural Science Foundation of China[42371364]+3 种基金National Natural Science Foundation of China[U23A2021]National Natural Science Foundation of China[42001371]China Postdoctoral Science Foundation Special Project[2022T150489]the China Postdoctoral Science Foundation Special Project(213926).
文摘Satellite normalized difference vegetation index(NDVI)time series,essential for ecological and environmental applications,is still limited by a trade-off between the spatiotemporal resolution and time coverage despite various global products.The Advanced Very High-Resolution Radiometer(AVHRR)instrument can provide the longest continuous time series since 1982,but with the drawback of coarse spatial resolution and poor data quality.To address this issue,a spatiotemporal fusion-based long-term NDVI product(STFLNDVI)since 1982 was generated in this study at a 1-km spatial resolution with monthly intervals,by fusing with the Moderate Resolution Imaging Spectroradiometer(MODIS)data.A multi-step processing fusion framework,containing temporal filtering,normalization,spatiotemporal fusion,and residual error correction,was employed to combine the superior characteristics of the two products,respectively.Simulated comparison with MODIS data and real-data assessments with true 1 km AVHRR data both confirm the ideal accuracy of the fusion product in spatial distribution and temporal variation,providing stable long-term results similar to MODIS data.We believe that the STFLNDVI product will be of great significance in characterizing the spatial patterns and long-term variations of global vegetation and the historical radiometric calibrations in AVHRR data gaps around the Arctic and instrument differences between MODIS and AVHRR should be further considered in the future.
基金supported by the National Natural Science Foundation of China[grant number 41201429,42171375].
文摘A good understanding of the quality of digital elevation model(DEM)is a perquisite for various applications.This study investigates the accuracy of three most recently released 1-arcsec global DEMs(GDEMs,Copernicus,NASA and AW3D30)in five selected terrains of China,using more than 240,000 high-quality ICESat-2(Ice,Cloud and land Elevation Satellite)ALT08 points.The results indicate the three GDEMs have similar overall vertical accuracy,with RMSE of 6.73(Copernicus),6.59(NASA)and 6.63 m(AW3D30).While the accuracy varies considerably over study areas and among GDEMs.The results show a clear correlation between the accuracy and terrain slopes,and some relationship between the accuracy and land covers.Our analysis reveals the land cover exerts a greater impact on the accuracy than that of the terrain slope for the study area.Visual inspections of terrain representation indicate Copernicus DEM exhibits the greatest detail of terrain,followed by AW3D30,and then by NASADEM.This study has demonstrated that ICESat-2 altimetry offers an important tool for DEM assessment.The findings provide a timely and comprehensive understanding of the quality of newly released GDEMs,which are informative for the selection of suitable DEMs,and for the improvement of GDEM in future studies.
基金supported by the National Natural Science Foundation of China[grants numbers 42171375 and 41801263].
文摘Efficient and continuous monitoring of surface water is essential for water resource management.Much effort has been devoted to the task of water mapping based on remote sensing images.However,few studies have fully considered the diverse spectral properties of water for the collection of reference samples in an automatic manner.Moreover,water area statistics are sensitive to the satellite image observation quality.This study aims to develop a fully automatic surface water mapping framework based on Google Earth Engine(GEE)with a supervised random forest classifier.A robust scheme was built to automatically construct training samples by merging the information from multi-source water occurrence products.The samples for permanent and seasonal water were mapped and collected separately,so that the supplement of seasonal samples can increase the spectral diversity of the sample space.To reduce the uncertainty of the derived water occurrences,temporal correction was applied to repair the classification maps with invalid observations.Extensive experiments showed that the proposed method can generate reliable samples and produce good-quality water mapping results.Comparative tests indicated that the proposed method produced water maps with a higher quality than the index-based detection methods,as well as the GSWD and GLAD datasets.
基金supported by the Science and Technology Major Project of Hubei Province,China(grant number 2023BCA003)in part by the Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation,Ministry of Natural Resources(grant number KF-2023-08-23)the National Natural Science Foundation of China(grant number 42171375).
文摘The tradeoffs between the spatial and temporal resolutions for the remote sensing instruments limit their capacity to monitor the eutrophic status of inland lakes.Spatiotemporal fusion(STF)provides a cost-effective way to obtain remote sensing data with both high spatial and temporal resolutions by blending multisensor observations.However,remote sensing reflectance(Rrs)over water surface with a relatively low signal-to-noise ratio is prone to be contaminated by large uncertainties in the fusion process.To present a comprehensive analysis on the influence of processing and modeling errors,we conducted an evaluation study to understand the potential,uncertainties,and limitations of using STF for monitoring chlorophyll a(Chla)concentration in an inland eutrophic water(Chaohu Lake,China).Specifically,comparative tests were conducted on the Sentinel-2 and Sentinel-3 image pairs.Three typical STF methods were selected for comparison,i.e.,Fit-FC,spatial and temporal nonlocal filter-based fusion model,and the flexible spatiotemporal data fusion.The results show as follows:(a)among the influencing factors,atmospheric correction uncertainties and geometric misregistration have larger impacts on the fusion results,compared with radiometric bias between the imaging sensors and STF modeling errors;and(b)the machine-learning-based Chla inversion accuracy of the fusion data[R^(2)=0.846 and root mean square error(RMSE)=17.835μg/l]is comparable with that of real Sentinel-2 data(R^(2)=0.856 and RMSE=16.601μg/l),and temporally dense Chla results can be produced with the integrated Sentinel-2 and fusion image datasets.These findings will help to provide guidelines to design STF framework for monitoring aquatic environment of inland waters with remote sensing data.
