Impervious surface mapping is essential for urban environmental studies.Spectral Mixture Analysis(SMA)and its extensions are widely employed in impervious surface estimation from medium-resolution images.For SMA,inapp...Impervious surface mapping is essential for urban environmental studies.Spectral Mixture Analysis(SMA)and its extensions are widely employed in impervious surface estimation from medium-resolution images.For SMA,inappropriate endmember combinations and inadequate endmember classes have been recognized as the primary reasons for estimation errors.Meanwhile,the spectral-only SMA,without considering urban spatial distribution,fails to consider spectral variability in an adequate manner.The lack of endmember class diversity and their spatial variations lead to over/underestimation.To mitigate these issues,this study integrates a hierarchical strategy and spatially varied endmember spectra to map impervious surface abundance,taking Wuhan and Wuzhou as two study areas.Specifically,the piecewise convex multiple-model endmember detection algorithm is applied to automatically hierarch-ize images into three regions,and distinct endmember combinations are independently developed in each region.Then,spatially varied endmember spectra are synthesized through neighboring spectra using the distance-based weight.Comparative analysis indicates that the proposed method achieves better performance than Hierarchical SMA and Fixed Four-endmembers SMA in terms of MAE,SE,and RMSE.Further analysis suggests that the hierarch-ical strategy can expand endmember class types and considerably improve the performance for the study areas in general,specifically in less developed areas.Moreover,we find that spatially varied endmember spectra facilitate the reduction of heterogeneous surface material variations and achieve the improved performance in developed areas.展开更多
Ensuring water resource security and enhancing resilience to extreme hydrological events demand a comprehensive understanding of water dynamics across various scales.However,monitoring water bodies with highly seasona...Ensuring water resource security and enhancing resilience to extreme hydrological events demand a comprehensive understanding of water dynamics across various scales.However,monitoring water bodies with highly seasonal hydrological variability,particularly using medium-resolution satellite imagery such as Landsat 4-9,presents substantial challenges.This study introduces the Normalized Difference Water Fraction Index(NDWFI)based on spectral mixture analysis(SMA)to improve the detection of subtle and dynamically changing water bodies.First,the effectiveness of NDWFI is rigorously assessed across four challenging sites.The findings reveal that NDWFI achieves an average overall accuracy(OA)of 98.2%in water extraction across a range of water-covered scenarios,surpassing conventional water indices.Subsequently,using approximately 11,000 Landsat satellite images and NDWFI within the Google Earth Engine(GEE)platform,this study generates a high-resolution surface water(SW)map for Jiangsu Province,China,exhibiting an impressive OA of 95.91%±0.23%.We also investigate the stability of the NDWFI threshold for water extraction and its superior performance in comparison to existing thematic water maps.This research offers a promising avenue to address crucial challenges in remote sensing hydrology monitoring,contributing to the enhancement of water security and the strengthening of resilience against hydrological extremes.展开更多
基金supported by the National Natural Science Foundation of China with grant numbers[41890820,42090012,41771452 and 41771454].
文摘Impervious surface mapping is essential for urban environmental studies.Spectral Mixture Analysis(SMA)and its extensions are widely employed in impervious surface estimation from medium-resolution images.For SMA,inappropriate endmember combinations and inadequate endmember classes have been recognized as the primary reasons for estimation errors.Meanwhile,the spectral-only SMA,without considering urban spatial distribution,fails to consider spectral variability in an adequate manner.The lack of endmember class diversity and their spatial variations lead to over/underestimation.To mitigate these issues,this study integrates a hierarchical strategy and spatially varied endmember spectra to map impervious surface abundance,taking Wuhan and Wuzhou as two study areas.Specifically,the piecewise convex multiple-model endmember detection algorithm is applied to automatically hierarch-ize images into three regions,and distinct endmember combinations are independently developed in each region.Then,spatially varied endmember spectra are synthesized through neighboring spectra using the distance-based weight.Comparative analysis indicates that the proposed method achieves better performance than Hierarchical SMA and Fixed Four-endmembers SMA in terms of MAE,SE,and RMSE.Further analysis suggests that the hierarch-ical strategy can expand endmember class types and considerably improve the performance for the study areas in general,specifically in less developed areas.Moreover,we find that spatially varied endmember spectra facilitate the reduction of heterogeneous surface material variations and achieve the improved performance in developed areas.
基金National Science Foundation for Distinguished Young Scholars of China under grant 42225107National Key Research and Development Program under grant 2022YFB3903402Natural Science Foundation of China under grants 61976234,42171409,and 42171410.
文摘Ensuring water resource security and enhancing resilience to extreme hydrological events demand a comprehensive understanding of water dynamics across various scales.However,monitoring water bodies with highly seasonal hydrological variability,particularly using medium-resolution satellite imagery such as Landsat 4-9,presents substantial challenges.This study introduces the Normalized Difference Water Fraction Index(NDWFI)based on spectral mixture analysis(SMA)to improve the detection of subtle and dynamically changing water bodies.First,the effectiveness of NDWFI is rigorously assessed across four challenging sites.The findings reveal that NDWFI achieves an average overall accuracy(OA)of 98.2%in water extraction across a range of water-covered scenarios,surpassing conventional water indices.Subsequently,using approximately 11,000 Landsat satellite images and NDWFI within the Google Earth Engine(GEE)platform,this study generates a high-resolution surface water(SW)map for Jiangsu Province,China,exhibiting an impressive OA of 95.91%±0.23%.We also investigate the stability of the NDWFI threshold for water extraction and its superior performance in comparison to existing thematic water maps.This research offers a promising avenue to address crucial challenges in remote sensing hydrology monitoring,contributing to the enhancement of water security and the strengthening of resilience against hydrological extremes.
