The mid-wave infrared band （3-5 #rn） has been widely used for atmospheric soundings. The sunglint impact on the atmospheric parameter retrieval using this band has been neglected because the reflected radiances in t...The mid-wave infrared band （3-5 #rn） has been widely used for atmospheric soundings. The sunglint impact on the atmospheric parameter retrieval using this band has been neglected because the reflected radiances in this band are significantly less than those in the visible band. In this study, an investigation of sunglint impact on the atmospheric soundings was conducted with Atmospheric InfraRed Sounder ob- servation data from 1 July to 7 July 2007 over the Atlantic Ocean. The impact of sunglint can lead to a brightness temperature increase of 1.0 K for the surface sensitive sounding channels near 4.58 #m. This contamination can indirectly cause a positive bias of 4 g kg-1 in the water vapor retrieval near the ocean surface, and it can be corrected by simply excluding those contaminated channels.展开更多
Vegetation is crucial for wetland ecosystems.Human activities and climate changes are increasingly threatening wetland ecosystems.Combining satellite images and deep learning for classifying marsh vegetation communiti...Vegetation is crucial for wetland ecosystems.Human activities and climate changes are increasingly threatening wetland ecosystems.Combining satellite images and deep learning for classifying marsh vegetation communities has faced great challenges because of its coarse spatial resolution and limited spectral bands.This study aimed to propose a method to classify marsh vegetation using multi-resolution multispectral and hyperspectral images,combining super-resolution techniques and a novel self-constructing graph attention neural network(SGA-Net)algorithm.The SGA-Net algorithm includes a decoding layer(SCE-Net)to preciselyfine marsh vegetation classification in Honghe National Nature Reserve,Northeast China.The results indicated that the hyperspectral reconstruction images based on the super-resolution convolutional neural network(SRCNN)obtained higher accuracy with a peak signal-to-noise ratio(PSNR)of 28.87 and structural similarity(SSIM)of 0.76 in spatial quality and root mean squared error(RMSE)of 0.11 and R^(2) of 0.63 in spectral quality.The improvement of classification accuracy(MIoU)by enhanced super-resolution generative adversarial network(ESRGAN)(6.19%)was greater than that of SRCNN(4.33%)and super-resolution generative adversarial network(SRGAN)(3.64%).In most classification schemes,the SGA-Net outperformed DeepLabV3+and SegFormer algorithms for marsh vegetation and achieved the highest F1-score(78.47%).This study demonstrated that collaborative use of super-resolution reconstruction and deep learning is an effective approach for marsh vegetation mapping.展开更多
基金partly supported by the National Oceanic and Atmospheric Administration (NOAA) GOES-R algorithm working group(AWG) and GOES-R Risk Reduction programs (Grant No.NA06NES4400002)
文摘The mid-wave infrared band （3-5 #rn） has been widely used for atmospheric soundings. The sunglint impact on the atmospheric parameter retrieval using this band has been neglected because the reflected radiances in this band are significantly less than those in the visible band. In this study, an investigation of sunglint impact on the atmospheric soundings was conducted with Atmospheric InfraRed Sounder ob- servation data from 1 July to 7 July 2007 over the Atlantic Ocean. The impact of sunglint can lead to a brightness temperature increase of 1.0 K for the surface sensitive sounding channels near 4.58 #m. This contamination can indirectly cause a positive bias of 4 g kg-1 in the water vapor retrieval near the ocean surface, and it can be corrected by simply excluding those contaminated channels.
基金supported by National Natural Science Foundation of China:[Grant Number 21976043,42122009]Guangxi Science&Technology Program:[Grant Number GuikeAD20159037]+1 种基金‘Ba Gui Scholars’program of the provincial government of Guangxi,and the Guilin University of Technology Foundation:[Grant Number GUTQDJJ2017096]Innovation Project of Guangxi Graduate Education:[Grant Number YCSW2022328].
文摘Vegetation is crucial for wetland ecosystems.Human activities and climate changes are increasingly threatening wetland ecosystems.Combining satellite images and deep learning for classifying marsh vegetation communities has faced great challenges because of its coarse spatial resolution and limited spectral bands.This study aimed to propose a method to classify marsh vegetation using multi-resolution multispectral and hyperspectral images,combining super-resolution techniques and a novel self-constructing graph attention neural network(SGA-Net)algorithm.The SGA-Net algorithm includes a decoding layer(SCE-Net)to preciselyfine marsh vegetation classification in Honghe National Nature Reserve,Northeast China.The results indicated that the hyperspectral reconstruction images based on the super-resolution convolutional neural network(SRCNN)obtained higher accuracy with a peak signal-to-noise ratio(PSNR)of 28.87 and structural similarity(SSIM)of 0.76 in spatial quality and root mean squared error(RMSE)of 0.11 and R^(2) of 0.63 in spectral quality.The improvement of classification accuracy(MIoU)by enhanced super-resolution generative adversarial network(ESRGAN)(6.19%)was greater than that of SRCNN(4.33%)and super-resolution generative adversarial network(SRGAN)(3.64%).In most classification schemes,the SGA-Net outperformed DeepLabV3+and SegFormer algorithms for marsh vegetation and achieved the highest F1-score(78.47%).This study demonstrated that collaborative use of super-resolution reconstruction and deep learning is an effective approach for marsh vegetation mapping.
基金Supported by Major State Basic Research Development Program (973 Program) under Grant 2009CB723905the 863 High Technology Program of China (No. 2007AA12Z148)the National Science Foundation of China (No. 40771139)
