[ Objective] The study aimed to improve methods of monitoring Karst Rocky Desertification （KRD） control projects and increase the working efficiency. [Method] Based on remote sensing images with medium and high spat...[ Objective] The study aimed to improve methods of monitoring Karst Rocky Desertification （KRD） control projects and increase the working efficiency. [Method] Based on remote sensing images with medium and high spatial resolution, KRD control projects in Disi River basin in Puan County were monitored, that is, information of the project construction in the study area was extracted using supervised classification and hu- man-computer interactive interpretation, and the monitoring results were testified with the aid of GPS. [Result] It was feasible to monitor KRD con- trol projects in Disi River basin based on remote sensing images with medium and high resolution, and the monitoring accuracy was satisfactory, reaching above 80% or 90%, so the method is worthy of popularizing. [ Conclusion] Remote sensing images with medium and high resolution can be used to monitor other KRD control Droiects.展开更多
In this paper,a thin cloud removal method was put forward based on the linear relationships between the thin cloud reflectance in the channels from 0.4 μm to 1.0 μm and 1.38 μm.Channels of 0.66 μm,0.86 μm and 1....In this paper,a thin cloud removal method was put forward based on the linear relationships between the thin cloud reflectance in the channels from 0.4 μm to 1.0 μm and 1.38 μm.Channels of 0.66 μm,0.86 μm and 1.38 μm were chosen to extract the water body information under the thin cloud.Two study cases were selected to validate the thin cloud removal method.One case was applied with the Earth Observation System Moderate Resolution Imaging Spectroradiometer(EOS/MODIS) data,and the other with the Medium Resolution Spectral Imager(MERSI) and Visible and Infrared Radiometer(VIRR) data from Fengyun-3A(FY-3A).The test results showed that thin cloud removal method did not change the reflectivity of the ground surface under the clear sky.To the area contaminated by the thin cloud,the reflectance decreased to be closer to the reference reflectance under the clear sky after the thin cloud removal.The spatial distribution of the water body area could not be extracted before the thin cloud removal,while water information could be easily identified by using proper near infrared channel threshold after removing the thin cloud.The thin cloud removal method could improve the image quality and water body extraction precision effectively.展开更多
The Medium Resolution Spectral ImagerⅡ(MERSI-Ⅱ)is a core payload onboard the Fengyun-3D(FY-3D)satellite.It is designed for monitoring atmospheric and environmental conditions and is crucial for retrieving key parame...The Medium Resolution Spectral ImagerⅡ(MERSI-Ⅱ)is a core payload onboard the Fengyun-3D(FY-3D)satellite.It is designed for monitoring atmospheric and environmental conditions and is crucial for retrieving key parameters such as cloud and aerosol properties.This study aims to evaluate the cloud detection and cloud top height(CTH)retrieval accuracy of FY-3D/MERSI-Ⅱin polar regions(Arctic and Antarctic)against the CALIOP(Cloud-Aerosol Lidar with Orthogonal Polarization)cloud products.The results demonstrate that MERSI-Ⅱ-derived cloud coverages in the Arctic are 7.2%lower than CALIOP observations,while they are overestimated(underestimated)in Antarctic in summer(other seasons).Seasonal variations in cloud detection accuracy are notable,with Arctic accuracy reaching90.4%in summer but declining to 51.3%in winter.The detection accuracy is consistently lower in Antarctic than in Arctic,though MERSI-Ⅱshows significantly better detection capability over water surfaces than snow/ice-covered areas.For CTH retrievals,correlation coefficients between MERSI-Ⅱand CALIOP reach 0.70(Arctic)and 0.71(Antarctic),with systematic underestimations of-0.46 km(Arctic)and-1.52 km(Antarctic)on average.Larger deviations occur in multi-layer clouds and ice cloud conditions.Notably,MERSI-Ⅱtends to overestimate(underestimate)CTH for low-level(high-level)clouds and demonstrates superior retrieval accuracy for water clouds than ice clouds.These findings provide robust data support for satellite algorithm optimization and polar environment monitoring.展开更多
基金Supported by the Key Science and Technology Projects of Guizhou Province,China[(2007)3017,(2008)3022]Major Special Project of Guizhou Province,China(2006-6006-2)
文摘[ Objective] The study aimed to improve methods of monitoring Karst Rocky Desertification （KRD） control projects and increase the working efficiency. [Method] Based on remote sensing images with medium and high spatial resolution, KRD control projects in Disi River basin in Puan County were monitored, that is, information of the project construction in the study area was extracted using supervised classification and hu- man-computer interactive interpretation, and the monitoring results were testified with the aid of GPS. [Result] It was feasible to monitor KRD con- trol projects in Disi River basin based on remote sensing images with medium and high resolution, and the monitoring accuracy was satisfactory, reaching above 80% or 90%, so the method is worthy of popularizing. [ Conclusion] Remote sensing images with medium and high resolution can be used to monitor other KRD control Droiects.
基金Under the auspices of National Nature Science Foundation of China(No.40901231,41101517)
文摘In this paper,a thin cloud removal method was put forward based on the linear relationships between the thin cloud reflectance in the channels from 0.4 μm to 1.0 μm and 1.38 μm.Channels of 0.66 μm,0.86 μm and 1.38 μm were chosen to extract the water body information under the thin cloud.Two study cases were selected to validate the thin cloud removal method.One case was applied with the Earth Observation System Moderate Resolution Imaging Spectroradiometer(EOS/MODIS) data,and the other with the Medium Resolution Spectral Imager(MERSI) and Visible and Infrared Radiometer(VIRR) data from Fengyun-3A(FY-3A).The test results showed that thin cloud removal method did not change the reflectivity of the ground surface under the clear sky.To the area contaminated by the thin cloud,the reflectance decreased to be closer to the reference reflectance under the clear sky after the thin cloud removal.The spatial distribution of the water body area could not be extracted before the thin cloud removal,while water information could be easily identified by using proper near infrared channel threshold after removing the thin cloud.The thin cloud removal method could improve the image quality and water body extraction precision effectively.
基金Supported by the National Natural Science Foundation of China(U2342206)Nature Science Foundation of Anhui Province(2208085UQ01)+3 种基金Anhui Meteorological Detection Equipment Engineering Technology Research Center Open Project Fund(2023QXTC04)Pioneer Program for Fengyun Satellite Applications(FY-APP-ZX-2023.01)Open Project Fund of China Meteorological Administration(CMA)Basin Heavy Rainfall Key Laboratory(2023BHR-Y06)CMA Special Project for Innovation and Development(CXFZ2026J137)。
文摘The Medium Resolution Spectral ImagerⅡ(MERSI-Ⅱ)is a core payload onboard the Fengyun-3D(FY-3D)satellite.It is designed for monitoring atmospheric and environmental conditions and is crucial for retrieving key parameters such as cloud and aerosol properties.This study aims to evaluate the cloud detection and cloud top height(CTH)retrieval accuracy of FY-3D/MERSI-Ⅱin polar regions(Arctic and Antarctic)against the CALIOP(Cloud-Aerosol Lidar with Orthogonal Polarization)cloud products.The results demonstrate that MERSI-Ⅱ-derived cloud coverages in the Arctic are 7.2%lower than CALIOP observations,while they are overestimated(underestimated)in Antarctic in summer(other seasons).Seasonal variations in cloud detection accuracy are notable,with Arctic accuracy reaching90.4%in summer but declining to 51.3%in winter.The detection accuracy is consistently lower in Antarctic than in Arctic,though MERSI-Ⅱshows significantly better detection capability over water surfaces than snow/ice-covered areas.For CTH retrievals,correlation coefficients between MERSI-Ⅱand CALIOP reach 0.70(Arctic)and 0.71(Antarctic),with systematic underestimations of-0.46 km(Arctic)and-1.52 km(Antarctic)on average.Larger deviations occur in multi-layer clouds and ice cloud conditions.Notably,MERSI-Ⅱtends to overestimate(underestimate)CTH for low-level(high-level)clouds and demonstrates superior retrieval accuracy for water clouds than ice clouds.These findings provide robust data support for satellite algorithm optimization and polar environment monitoring.
