Immediate remote sensing detection and diagnosis of surface anomalies is a critical requirement to ensure the healthy development of China's social economy and national security in the new era.However,this emergin...Immediate remote sensing detection and diagnosis of surface anomalies is a critical requirement to ensure the healthy development of China's social economy and national security in the new era.However,this emerging frontier field is still in its infancy.Current researches lack not only a clear definition and analysis of concepts related to surface anomalies,but also a systematic examination of research approaches and development prospects.This study systematically summarizes the concepts and manifestation characteristics of anomaly,surface anomaly,and potential surface anomaly,and analyzes the connections and differences between them.Based on the temporal incongruence,spatial incongruence,and spatiotemporal incongruence characteristics of potential surface anomalies,three research frameworks for immediate remote sensing detection of potential surface anomalies are proposed,and the key issues and development prospects under these three research frameworks are pointed out.This study can provide research approaches and theoretical basis for immediate remote sensing detection of surface anomalies.展开更多
The newly launched GF-2 satellite is now the most advanced civil satellite in China to collect high spatial resolution remote sensing data.This study investigated the capability and strategy of GF?2 multispectral data...The newly launched GF-2 satellite is now the most advanced civil satellite in China to collect high spatial resolution remote sensing data.This study investigated the capability and strategy of GF?2 multispectral data for land use and land cover (LULC) classification in a region of the North China Plain.The pixel-based and object-based classifications using maximum likelihood (MLC) and support vector machine (SVM) classifiers were evaluated to determine the classification strategy that was suitable for GF?2 multispectral data.The validation results indicated that GF-2 multispectral data achieved satisfactory LULC classification performance,and object-based classification using the SVM classifier achieved the best classification accuracy with an overall classification accuracy of 94.33% and kappa coefficient of 0.911.Therefore,considering the LULC classification performance and data characteristics,GF-2 satellite data could serve as a valuable and reliable high-resolution data source for land surface monitoring.Future works should focus on improving LULC classification accuracy by exploring more classification features and exploring the potential applications of GF-2 data in related applications.展开更多
Surface incident shortwave radiation(Rs)can promote the circulation of substance and energy,and the accuracy of its estimation is of great significance for climate studies.The Rs can be acquired from satellite retriev...Surface incident shortwave radiation(Rs)can promote the circulation of substance and energy,and the accuracy of its estimation is of great significance for climate studies.The Rs can be acquired from satellite retrievals,reanalysis predictions and general circulation model(GCM)simulations.Although Rs estimates have been evaluated and compared in previous studies,most of them focus on evaluating the Rs estimates over specific regions using ground measurements from limited stations.Therefore,it is essential to comprehensively validate Rs estimates from multiple data sources.In this study,ground measurements of 690 stations from BSRN,GEBA,CMA,GC-NET and buoys were employed to validate the Rs estimates from seven representative products(GLASS,GEWEX-SRB,CERES-EBAF,ERA5,MERRA2,CFSR and CMIP6).The validation results indicated that the selected products overestimated Rs globally,with biases ranged from 0.48 to 21.27 W/m^(2).The satellite retrievals showed relatively better accuracy among seven datasets compared to ground measurements at the selected stations.Moreover,the selected seven products were all in poor accuracy at high-latitude regions with RMSEs greater than 50 W/m^(2).The long-term variation trends were also analyzed in this study.展开更多
Land surface all-wave net radiation(R_(n))is crucial in determining Earth’s climate by contributing to the surface radiation budget.This study evaluated seven satellite and three reanalysis long-term land surface R_(...Land surface all-wave net radiation(R_(n))is crucial in determining Earth’s climate by contributing to the surface radiation budget.This study evaluated seven satellite and three reanalysis long-term land surface R_(n)products under different spatial scales,spatial and temporal variations,and different conditions.The results showed that during 2000-2018,Global Land Surface Satellite Product(GLASS)-Moderate Resolution Imaging Spectroradiometer(MODIS)performed the best(RMSE=25.54 Wm^(-2),bias=-1.26 Wm^(-2)),followed by ERA5(the fifth-generation of European Centre for Medium-Range Weather Forecast Reanalysis)(RMSE=32.17 Wm^(-2),bias=-4.88 Wm^(-2))and GLASS-AVHRR(Advanced Very-High-Resolution Radiometer)(RMSE=33.10 Wm^(-2),bias=4.03 Wm^(-2)).During 1983-2018,GLASS-AVHRR and ERA5 ranked top and performed similarly,with RMSE values of 31.70 and 33.08 Wm^(-2)and biases of-4.56 and 3.48 Wm^(-2),respectively.The averaged multi-annual mean R_(n)over the global land surface of satellite products was higher than that of reanalysis products by about 10~30 Wm^(-2).These products differed remarkably in long-term trends variations,particularly pre-2000,but no significant trends were observed.Discrepancies were more frequent in satellite data,while reanalysis products showed smoother variations.Large discrepancies were found in regions with high latitudes,reflectance,and elevation which could be attributed to input radiative components,meteorological variables(e.g.,cloud properties,aerosol optical thickness),and applicability of the algorithms used.While further research is needed for detailed insights.展开更多
A fractional vegetation cover(FVC)estimation method incorporating a vegetation growth model and a radiative transfer model was previously developed,which was suitable for FVC estimation in homogeneous areas because th...A fractional vegetation cover(FVC)estimation method incorporating a vegetation growth model and a radiative transfer model was previously developed,which was suitable for FVC estimation in homogeneous areas because the finer-resolution pixels corresponding to one coarseresolution FVC pixel were all assumed to have the same vegetation growth model.However,this assumption does not hold over heterogeneous areas,meaning that the method cannot be applied to large regions.Therefore,this study proposes a finer spatial resolution FVC estimation method applicable to heterogeneous areas using Landsat 8 Operational Land Imager reflectance data and Global LAnd Surface Satellite(GLASS)FVC product.The FVC product was first decomposed according to the normalized difference vegetation index from the Landsat 8 OLI data.Then,independent dynamic vegetation models were built for each finer-resolution pixel.Finally,the dynamic vegetation model and a radiative transfer model were combined to estimate FVC at the Landsat 8 scale.Validation results indicated that the proposed method(R^(2)=0.7757,RMSE=0.0881)performed better than either the previous method(R^(2)=0.7038,RMSE=0.1125)or a commonly used method involving look-up table inversions of the PROSAIL model(R^(2)=0.7457,RMSE=0.1249).展开更多
The all-wave net radiation(Rn)at the land surface represents surface radiation budget and plays an important role in the Earth's energy and water cycles.Many studies have been conducted to estimate from satellite ...The all-wave net radiation(Rn)at the land surface represents surface radiation budget and plays an important role in the Earth's energy and water cycles.Many studies have been conducted to estimate from satellite top-of-atmosphere(TOA)data using various methods,particularly the application of machine learning(ML)and deep learning(DL).However,few studies have been conducted to provide a comprehensive evaluation about various ML and DL methods in retrieving.Based on extensive in situ measurements distributed at mid-low latitudes,the corresponding Moderate Resolution Imaging Spectroradiometer(MODIS)TOA observations,and the daily from the fifth generation of European Centre for Medium-Range Weather Forecasts Reanalysis 5(ERA5)used as a priori knowledge,this study assessed nine models for daily estimation,including six classic ML methods(random forest-RF,adaptive boosting-Adaboost,extreme gradient boosting-XGBoost,multilayer perceptron-MLP,radial basis function neural network-RBF,and support vector machine-SVM)and three DL methods(multilayer perceptron neural network with stacked autoencoders-SAE,deep belief network-DBN and residual neural network-ResNet).The validation results showed that the three DL methods were generally better than the six ML methods except XGBoost,although they all performed poorly in certain conditions such as winter days,rugged terrain,and high elevation.ResNet had the most robust performance across different land cover types,elevations,seasons,and latitude zones,but it has disadvantages in practice because of its highly configurable implementation environment and low computational efficiency.The estimated daily values from all nine models were more accurate than the corresponding Global LAnd Surface Satellite(GLASS)product.展开更多
As a key component of digital earth,remotely sensed data provides the compelling evidence that the amount of water vapour transferred from the entire global surface to the atmosphere increased from 1984 to 2007.The va...As a key component of digital earth,remotely sensed data provides the compelling evidence that the amount of water vapour transferred from the entire global surface to the atmosphere increased from 1984 to 2007.The validation results from the earlier evapotranspiration(ET)estimation algorithm based on net radiation(Rn),Normalised Difference Vegetation Index(NDVI),air temperature and diurnal air temperature range(DTaR)showed good agreement between estimated monthly ET and ground-measured ET from 20 flux towers.Our analysis indicates that the estimated actual ET has increased on average over the entire global land surface except for Antarctica during 19842007.However,this increasing trend disappears after 2000 and the reason may be that the decline in net radiation and NDVI during this period depleted surface soil moisture.Moreover,the good correspondence between the precipitation trend and the change in ET in arid and semi-arid regions indicated that surface moisture linked to precipitation affects ET.The input parameters Rn,Tair,NDVI and DTaR show substantial spatio-temporal variability that is almost consistent with that of actual ET from 1984 to 2007 and contribute most significantly to the variation in actual ET.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.42192580,42192581)。
文摘Immediate remote sensing detection and diagnosis of surface anomalies is a critical requirement to ensure the healthy development of China's social economy and national security in the new era.However,this emerging frontier field is still in its infancy.Current researches lack not only a clear definition and analysis of concepts related to surface anomalies,but also a systematic examination of research approaches and development prospects.This study systematically summarizes the concepts and manifestation characteristics of anomaly,surface anomaly,and potential surface anomaly,and analyzes the connections and differences between them.Based on the temporal incongruence,spatial incongruence,and spatiotemporal incongruence characteristics of potential surface anomalies,three research frameworks for immediate remote sensing detection of potential surface anomalies are proposed,and the key issues and development prospects under these three research frameworks are pointed out.This study can provide research approaches and theoretical basis for immediate remote sensing detection of surface anomalies.
基金the National Natural Science Foundation of China (Grant No.41571422)the National Key Research and Development Program of China (No.2016YFA0600103).
文摘The newly launched GF-2 satellite is now the most advanced civil satellite in China to collect high spatial resolution remote sensing data.This study investigated the capability and strategy of GF?2 multispectral data for land use and land cover (LULC) classification in a region of the North China Plain.The pixel-based and object-based classifications using maximum likelihood (MLC) and support vector machine (SVM) classifiers were evaluated to determine the classification strategy that was suitable for GF?2 multispectral data.The validation results indicated that GF-2 multispectral data achieved satisfactory LULC classification performance,and object-based classification using the SVM classifier achieved the best classification accuracy with an overall classification accuracy of 94.33% and kappa coefficient of 0.911.Therefore,considering the LULC classification performance and data characteristics,GF-2 satellite data could serve as a valuable and reliable high-resolution data source for land surface monitoring.Future works should focus on improving LULC classification accuracy by exploring more classification features and exploring the potential applications of GF-2 data in related applications.
基金supported by the National Natural Science Foundation of China Major Program[Grant Number 42192584]the National Key Research and Development Program pf China[Grant Number 2020YFA0608702]the National Natural Science Foundation of China[Grant Number 42171320].
文摘Surface incident shortwave radiation(Rs)can promote the circulation of substance and energy,and the accuracy of its estimation is of great significance for climate studies.The Rs can be acquired from satellite retrievals,reanalysis predictions and general circulation model(GCM)simulations.Although Rs estimates have been evaluated and compared in previous studies,most of them focus on evaluating the Rs estimates over specific regions using ground measurements from limited stations.Therefore,it is essential to comprehensively validate Rs estimates from multiple data sources.In this study,ground measurements of 690 stations from BSRN,GEBA,CMA,GC-NET and buoys were employed to validate the Rs estimates from seven representative products(GLASS,GEWEX-SRB,CERES-EBAF,ERA5,MERRA2,CFSR and CMIP6).The validation results indicated that the selected products overestimated Rs globally,with biases ranged from 0.48 to 21.27 W/m^(2).The satellite retrievals showed relatively better accuracy among seven datasets compared to ground measurements at the selected stations.Moreover,the selected seven products were all in poor accuracy at high-latitude regions with RMSEs greater than 50 W/m^(2).The long-term variation trends were also analyzed in this study.
基金funded by the National Natural Science Foundation of China[grant numbers 42090012 and 41971291].
文摘Land surface all-wave net radiation(R_(n))is crucial in determining Earth’s climate by contributing to the surface radiation budget.This study evaluated seven satellite and three reanalysis long-term land surface R_(n)products under different spatial scales,spatial and temporal variations,and different conditions.The results showed that during 2000-2018,Global Land Surface Satellite Product(GLASS)-Moderate Resolution Imaging Spectroradiometer(MODIS)performed the best(RMSE=25.54 Wm^(-2),bias=-1.26 Wm^(-2)),followed by ERA5(the fifth-generation of European Centre for Medium-Range Weather Forecast Reanalysis)(RMSE=32.17 Wm^(-2),bias=-4.88 Wm^(-2))and GLASS-AVHRR(Advanced Very-High-Resolution Radiometer)(RMSE=33.10 Wm^(-2),bias=4.03 Wm^(-2)).During 1983-2018,GLASS-AVHRR and ERA5 ranked top and performed similarly,with RMSE values of 31.70 and 33.08 Wm^(-2)and biases of-4.56 and 3.48 Wm^(-2),respectively.The averaged multi-annual mean R_(n)over the global land surface of satellite products was higher than that of reanalysis products by about 10~30 Wm^(-2).These products differed remarkably in long-term trends variations,particularly pre-2000,but no significant trends were observed.Discrepancies were more frequent in satellite data,while reanalysis products showed smoother variations.Large discrepancies were found in regions with high latitudes,reflectance,and elevation which could be attributed to input radiative components,meteorological variables(e.g.,cloud properties,aerosol optical thickness),and applicability of the algorithms used.While further research is needed for detailed insights.
基金This work was supported by the National Natural Science Foundation of China under[Grant 41671332 and Grant 41571422]in part by the National Key Research and Development Program of China under[Grant 2016YFA0600103].
文摘A fractional vegetation cover(FVC)estimation method incorporating a vegetation growth model and a radiative transfer model was previously developed,which was suitable for FVC estimation in homogeneous areas because the finer-resolution pixels corresponding to one coarseresolution FVC pixel were all assumed to have the same vegetation growth model.However,this assumption does not hold over heterogeneous areas,meaning that the method cannot be applied to large regions.Therefore,this study proposes a finer spatial resolution FVC estimation method applicable to heterogeneous areas using Landsat 8 Operational Land Imager reflectance data and Global LAnd Surface Satellite(GLASS)FVC product.The FVC product was first decomposed according to the normalized difference vegetation index from the Landsat 8 OLI data.Then,independent dynamic vegetation models were built for each finer-resolution pixel.Finally,the dynamic vegetation model and a radiative transfer model were combined to estimate FVC at the Landsat 8 scale.Validation results indicated that the proposed method(R^(2)=0.7757,RMSE=0.0881)performed better than either the previous method(R^(2)=0.7038,RMSE=0.1125)or a commonly used method involving look-up table inversions of the PROSAIL model(R^(2)=0.7457,RMSE=0.1249).
基金supported by National Natural Science Foundation of China:[grant no 41971291,42090012]National Key Research and Development Program of China:[grant no 2020YFA0608704].
文摘The all-wave net radiation(Rn)at the land surface represents surface radiation budget and plays an important role in the Earth's energy and water cycles.Many studies have been conducted to estimate from satellite top-of-atmosphere(TOA)data using various methods,particularly the application of machine learning(ML)and deep learning(DL).However,few studies have been conducted to provide a comprehensive evaluation about various ML and DL methods in retrieving.Based on extensive in situ measurements distributed at mid-low latitudes,the corresponding Moderate Resolution Imaging Spectroradiometer(MODIS)TOA observations,and the daily from the fifth generation of European Centre for Medium-Range Weather Forecasts Reanalysis 5(ERA5)used as a priori knowledge,this study assessed nine models for daily estimation,including six classic ML methods(random forest-RF,adaptive boosting-Adaboost,extreme gradient boosting-XGBoost,multilayer perceptron-MLP,radial basis function neural network-RBF,and support vector machine-SVM)and three DL methods(multilayer perceptron neural network with stacked autoencoders-SAE,deep belief network-DBN and residual neural network-ResNet).The validation results showed that the three DL methods were generally better than the six ML methods except XGBoost,although they all performed poorly in certain conditions such as winter days,rugged terrain,and high elevation.ResNet had the most robust performance across different land cover types,elevations,seasons,and latitude zones,but it has disadvantages in practice because of its highly configurable implementation environment and low computational efficiency.The estimated daily values from all nine models were more accurate than the corresponding Global LAnd Surface Satellite(GLASS)product.
基金supported by the Key High-Tech Research and Development Program of China(No.2009AA122100)the Youth Natural Science Fund of Beijing Normal University,the Natural Science Fund of Zhejiang(No.Y5110343)the Natural Science Fund of China(No.40901167).
文摘As a key component of digital earth,remotely sensed data provides the compelling evidence that the amount of water vapour transferred from the entire global surface to the atmosphere increased from 1984 to 2007.The validation results from the earlier evapotranspiration(ET)estimation algorithm based on net radiation(Rn),Normalised Difference Vegetation Index(NDVI),air temperature and diurnal air temperature range(DTaR)showed good agreement between estimated monthly ET and ground-measured ET from 20 flux towers.Our analysis indicates that the estimated actual ET has increased on average over the entire global land surface except for Antarctica during 19842007.However,this increasing trend disappears after 2000 and the reason may be that the decline in net radiation and NDVI during this period depleted surface soil moisture.Moreover,the good correspondence between the precipitation trend and the change in ET in arid and semi-arid regions indicated that surface moisture linked to precipitation affects ET.The input parameters Rn,Tair,NDVI and DTaR show substantial spatio-temporal variability that is almost consistent with that of actual ET from 1984 to 2007 and contribute most significantly to the variation in actual ET.
