Powdery mildew is a disease that threatens wheat production and causes severe economic losses worldwide. Its timely diagnosis is imperative for preventing and controlling its spread. In this study, the multiangle cano...Powdery mildew is a disease that threatens wheat production and causes severe economic losses worldwide. Its timely diagnosis is imperative for preventing and controlling its spread. In this study, the multiangle canopy spectra and disease severity of wheat were investigated at several developmental stages and degrees of disease severity. Four wavelength variable-selected algorithms: successive projection(SPA), competitive adaptive reweighted sampling(CARS), feature selection learning(Relief-F), and genetic algorithm(GA), were used to identify bands sensitive to powdery mildew. The wavelength variables selected were used as input variables for partial least squares(PLS), extreme learning machine(ELM), random forest(RF), and support vector machine(SVM) algorithms, to construct a suitable prediction model for powdery mildew. Spectral reflectance and conventional vegetation indices(VIs) displayed angle effects under several disease severity indices(DIs). The CARS method selected relatively few wavelength variables and showed a relatively homogeneous distribution across the 13 viewing zenith angles.Overall accuracies of the four modeling algorithms were ranked as follows: ELM(0.70–0.82) > PLS(0.63–0.79) > SVM(0.49–0.69) > RF(0.43–0.69). Combinations of features and algorithms generated varied accuracies, with coefficients of determination(R^(2)) single-peaked at different observation angles. The constructed CARS-ELM model extracted a predictable bivariate relationship between the multi-angle canopy spectrum and disease severity, yielding an R^(2)> 0.8 at each measured angle. Especially for larger angles,monitoring accuracies were increased relative to the optimal VI model(40% at-60°, 33% at +60°), indicating that the CARS-ELM model is suitable for extreme angles of-60° and +60°. The results are proposed to provide a technical basis for rapid and large-scale monitoring of wheat powdery mildew.展开更多
Evolution of remote sensing sensors technologies is presented, with emphasis on its suitability in observing the polar regions. The extent of influence of polar regions on the global climate and vice versa is the spea...Evolution of remote sensing sensors technologies is presented, with emphasis on its suitability in observing the polar regions. The extent of influence of polar regions on the global climate and vice versa is the spearhead of climate change research. The extensive cover of sea ice has major impacts on the atmosphere, oceans, and terrestrial and marine ecosystems of the polar regions in particular and teleconnection on other processes elsewhere. Sea ice covers vast areas of the polar oceans, ranging from ~18 × 106 km2 to ~23 × 106 km2, combined for the Northern and Southern Hemispheres. However, both polar regions are witnessing contrasting rather contradicting effects of climate change. The Arctic sea ice extent is declining at a rate of 0.53 × 106 km2·decade–1, whereasAntarcticaexhibits a positive trend at the rate of 0.167 × 106 km2·decade–1. This work reviews literature published in the field of sea ice remote sensing, to evaluate and access success and failures of different sensors to observe physical features of sea ice. The chronological development series of different sensors on different satellite systems, sensor specifications and datasets are examined and how they have evolved to meet the growing needs of users is outlined. Different remote sensing technology and observational methods and their suitability to observe specific sea ice property are also discussed. A pattern has emerged, which shows that microwave sensors are inherently superior to visible and infrared in monitoring seasonal and annual changes in sea ice. Degree of successes achieved through remote sensing techniques by various investigators has been compared. Some technologies appear to work better under certain conditions than others, and it is now well accepted that there is no algorithm that is ideal globally. Contribution of Indian remote sensing satellites is also reviewed in the context of polar research. This review suggests different primary datasets for further research on sea ice features (sea ice extent, ice type, sea ice thickness, etc.). This work also examines past achievements and how far these capabilities have evolved and tap into current state of art/direction of sensor technologies. Effective monitoring and syntheses of past few decades of research pinpoint useful datasets for sea ice monitoring, thereby avoiding wastage of resources to find practical datasets to monitor these physically inaccessible regions.展开更多
A retrieval method of cloud top heights using polarizing remote sensing is proposed in this paper. Using the vector radiative transfer model in a coupled atmosphere-ocean system, the factors influencing the upwelling ...A retrieval method of cloud top heights using polarizing remote sensing is proposed in this paper. Using the vector radiative transfer model in a coupled atmosphere-ocean system, the factors influencing the upwelling linear polarizing radiance at top-of-atmosphere are analyzed, which show that the upwelling linear polarizing radiance varies remarkably with the cloud top height, but has negligible sensitivity with cloud albedo and aerosol scattering above the cloud layer. Based on this property, a cloud top height retrieval algorithm using polarizing remote sensing was developed. The algorithm has been applied to the polarizing remote sensing data of Polarization and Directionality of the Earth's Reflectances-2 (POLDER-2). The retrieved cloud top height from POLDER-2 compares well with the Moderate Resolution Imaging Spectroradiometer (MODIS) operational product with a bias of 0.83 km and standard deviation of 1.56 km.展开更多
Measurement of ice velocities of the Antarctic glaciers is very important for studies on Antarctic ice and snow mass balance. The polar area environmental change and its influences on the global environment. Conventio...Measurement of ice velocities of the Antarctic glaciers is very important for studies on Antarctic ice and snow mass balance. The polar area environmental change and its influences on the global environment. Conventional methods may be used for measuring the ice velocities, but they suffer from severe weather conditions in the Polar areas. Use of satellite multi-spectral and muki-temporal images makes it easier to measure the velocities of the glacier movements. This paper discusses a new method for monitoring the glacial change by means of multi-temporal satellite images. Temporal remotely sensed images in the Ingrid Christensen coast were processed with respect to geometric rectification, registration and overlay, The average ice velocities of the Polar Record Glacier and the Dark Glacier were then calculated, with the changing characteristics analyzed and evaluated. The advantages of the method reported here include promise of all-weather operation and potentials of dynamic monitoring through suitable selection of temporal satellite images.展开更多
Optical remote sensing is a crucial component of the ocean observation system.However,the complex interactions between the ocean and atmosphere limit its observation capability and hinder the advancement of quantitati...Optical remote sensing is a crucial component of the ocean observation system.However,the complex interactions between the ocean and atmosphere limit its observation capability and hinder the advancement of quantitative applications and support capacity.Polarimetric remote sensing,as an advanced detection technology,investigates the anisotropic characteristics of electromagnetic waves perpendicular to the direction of propagation.Serving as an extension of conventional optical remote sensing,it significantly improves the accuracy of feature identification and quantitative estimation.As the most classical polarization feature,the Degree of Polarization(DoP)feature has been widely applied in various scenarios.In this study,the spatial distribution of the DoP feature over the 2πobservation space under oceanic conditions is thoroughly investigated through theoretical simulations and sample measurements.Our findings suggest that the DoP feature lacks sufficient sensitivity and versatility to be used independently in ocean observation scenarios.To address this limitation,a novel feature,namely the Angular Polarization(AP)feature,is proposed for polarimetric remote sensing tailored to ocean applications.The effectiveness of this new feature is validated in three representative ocean observation scenarios,and its performance is compared against both conventional optical feature and DoP feature.The results demonstrate that the AP feature offers distinct advantages in differentiating ocean bodies with varying refractive indices and in emphasizing the differences between observed targets.Moreover,its application enhances the accuracy of unsupervised classification for ocean observations.The establishment of the AP feature greatly strengthens the information-sensing capacity of polarimetric ocean remote sensing,offering a promising pathway to enhance the overall performance of ocean observation systems.展开更多
The traditional remote sensing mainly detects the ground vertically to obtain the 2D information but it is hard to get adequate parameters for the quantitative remote sensing to invert land features. The multi-angle o...The traditional remote sensing mainly detects the ground vertically to obtain the 2D information but it is hard to get adequate parameters for the quantitative remote sensing to invert land features. The multi-angle observation can get more detailed and reliable 3D structural parameters of targets, so it makes the quantitative remote sensing applicable. During the process of reflecting, scattering and transmitting the electromagnetic wave, minerals and rocks could reveal the polarized features related to the nature of themselves. Therefore, it has become a new approach of quantitative remote sensing to detect multi-angle polarized information of minerals and rocks. In respect that the polarized reflectance always goes with the bidirectional one, we can obtain the 3D spatial distribution of targets by a polarized means together with detecting its bi-directional reflectance. From the perspective of multi-angle polarized remote sensing mechanism, the quantitative relationship between multi-angle polarized reflectance and the BRDF is studied in this paper. And it is testified that the bi-directional reflectance, polarized reflectance of 45° and the mean value of polarized reflectance are equal to that of the corresponding azimuth angle, zenith angle, detection angle and detection channels in 27t space by experiment.展开更多
Cloud top pressure(CTP)is one of the critical cloud properties that significantly affects the radiative effect of clouds.Multi-angle polarized sensors can employ polarized bands(490 nm)or O_(2)A-bands(763 and 765 nm)t...Cloud top pressure(CTP)is one of the critical cloud properties that significantly affects the radiative effect of clouds.Multi-angle polarized sensors can employ polarized bands(490 nm)or O_(2)A-bands(763 and 765 nm)to retrieve the CTP.However,the CTP retrieved by the two methods shows inconsistent results in certain cases,and large uncertainties in low and thin cloud retrievals,which may lead to challenges in subsequent applications.This study proposes a synergistic algorithm that considers both O_(2)A-bands and polarized bands using a random forest(RF)model.LiDAR CTP data are used as the true values and the polarized and non-polarized measurements are concatenated to train the RF model to determine CTP.Additionally,through analysis,we proposed that the polarized signal becomes saturated as the cloud optical thickness(COT)increases,necessitating a particular treatment for cases where COT<10 to improve the algorithm's stability.The synergistic method was then applied to the directional polarized camera(DPC)and Polarized and Directionality of the Earth’s Reflectance(POLDER)measurements for evaluation,and the resulting retrieval accuracy of the POLDER-based measurements(RMSEPOLDER=205.176 hPa,RMSEDPC=171.141 hPa,R^(2)POLDER=0.636,R^(2)DPC=0.663,respectively)were higher than that of the MODIS and POLDER Rayleigh pressure measurements.The synergistic algorithm also showed good performance with the application of DPC data.This algorithm is expected to provide data support for atmosphere-related fields as an atmospheric remote sensing algorithm within the Cloud Application for Remote Sensing,Atmospheric Radiation,and Updating Energy(CARE)platform.展开更多
Due to the small size,variety,and high degree of mixing of herbaceous vegetation,remote sensing-based identification of grassland types primarily focuses on extracting major grassland categories,lacking detailed depic...Due to the small size,variety,and high degree of mixing of herbaceous vegetation,remote sensing-based identification of grassland types primarily focuses on extracting major grassland categories,lacking detailed depiction.This limitation significantly hampers the development of effective evaluation and fine supervision for the rational utilization of grassland resources.To address this issue,this study concentrates on the representative grassland of Zhenglan Banner in Inner Mongolia as the study area.It integrates the strengths of Sentinel-1 and Sentinel-2 active-passive synergistic observations and introduces innovative object-oriented techniques for grassland type classification,thereby enhancing the accuracy and refinement of grassland classification.The results demonstrate the following:(1)To meet the supervision requirements of grassland resources,we propose a grassland type classification system based on remote sensing and the vegetation-habitat classification method,specifically applicable to natural grasslands in northern China.(2)By utilizing the high-spatial-resolution Normalized Difference Vegetation Index(NDVI)synthesized through the Spatial and Temporal Non-Local Filter-based Fusion Model(STNLFFM),we are able to capture the NDVI time profiles of grassland types,accurately extract vegetation phenological information within the year,and further enhance the temporal resolution.(3)The integration of multi-seasonal spectral,polarization,and phenological characteristics significantly improves the classification accuracy of grassland types.The overall accuracy reaches 82.61%,with a kappa coefficient of 0.79.Compared to using only multi-seasonal spectral features,the accuracy and kappa coefficient have improved by 15.94%and 0.19,respectively.Notably,the accuracy improvement of the gently sloping steppe is the highest,exceeding 38%.(4)Sandy grassland is the most widespread in the study area,and the growth season of grassland vegetation mainly occurs from May to September.The sandy meadow exhibits a longer growing season compared with typical grassland and meadow,and the distinct differences in phenological characteristics contribute to the accurate identification of various grassland types.展开更多
Accurately and timely monitoring the spatial distribution and composition of mangrove species is critical for assessing mangroves’health,dynamics,and biodiversity,as well as mangroves’degradation and restoration.Rec...Accurately and timely monitoring the spatial distribution and composition of mangrove species is critical for assessing mangroves’health,dynamics,and biodiversity,as well as mangroves’degradation and restoration.Recent advances in machine learning algorithms,coupled with spaceborne remote sensing technique,offer an unprecedented opportunity to map mangroves at species level with high resolution over large extents.However,a single data source or data type is insufficient to capture the complex features of mangrove species and cannot satisfy the need for fine species classification.Moreover,identifying and selecting effective features derived from integrated multisource data are essential for integrating high-dimensional features for mangrove species discrimination.In this study,we developed a novel framework for mangrove species classification using spectral,texture,and polarization information derived from 3-source spaceborne imagery:WorldView-2(WV-2),OrbitaHyperSpectral(OHS),and Advanced Land Observing Satellite-2(ALOS-2).A total of 151 remote sensing features were first extracted,and 18 schemes were designed.Then,a wrapper method by combining extreme gradient boosting with recursive feature elimination(XGBoost-RFE)was conducted to select the sensitive variables and determine the optical subset size of all features.Finally,an ensemble learning algorithm of XGBoost was applied to classify 6 mangrove species in the Zhanjiang Mangrove National Nature Reserve,China.Our results showed that combining multispectral,hyperspectral,and L-band synthetic aperture radar features yielded the best mangrove species classification results,with an overall accuracy of 94.02%,a quantity disagreement of 4.44%,and an allocation disagreement of 1.54%.In addition,this study demonstrated important application potential of the XGBoost classifier.The proposed framework could provide fine-scale data and conduce to mangroves’conservation and restoration.展开更多
Soil moisture is a key parameter in the exchange of energy and water between the land surface and the atmosphere.This parameter plays an important role in the dynamics of permafrost on the Qinghai-Xizang Plateau,China...Soil moisture is a key parameter in the exchange of energy and water between the land surface and the atmosphere.This parameter plays an important role in the dynamics of permafrost on the Qinghai-Xizang Plateau,China,as well as in the related ecological and hydrological processes.However,the region's complex terrain and extreme climatic conditions result in low-accuracy soil moisture estimations using traditional remote sensing techniques.Thus,this study considered parameters of the backscatter coefficient of Sentinel-1A ground range detected(GRD)data,the polarization decomposition parameters of Sentinel-1A single-look complex(SLC)data,the normalized difference vegetation index(NDVI)based on Sentinel-2B data,and the topographic factors based on digital elevation model(DEM)data.By combining these parameters with a machine learning model,we established a feature selection rule.A cumulative importance threshold was derived for feature variables,and those variables that failed to meet the threshold were eliminated based on variations in the coefficient of determination(R^(2))and the unbiased root mean square error(ubRMSE).The eight most influential variables were selected and combined with the CatBoost model for soil moisture inversion,and the SHapley Additive exPlanations(SHAP)method was used to analyze the importance of these variables.The results demonstrated that the optimized model significantly improved the accuracy of soil moisture inversion.Compared to the unfiltered model,the optimal feature combination led to a 0.09 increase in R^(2)and a 0.7%reduction in ubRMSE.Ultimately,the optimized model achieved a R²of 0.87 and an ubRMSE of 5.6%.Analysis revealed that soil particle size had significant impact on soil water retention capacity.The impact of vegetation on the estimated soil moisture on the Qinghai-Xizang Plateau was considerable,demonstrating a significant positive correlation.Moreover,the microtopographical features of hummocks interfered with soil moisture estimation,indicating that such terrain effects warrant increased attention in future studies within the permafrost regions.The developed method not only enhances the accuracy of soil moisture retrieval in the complex terrain of the Qinghai-Xizang Plateau,but also exhibits high computational efficiency(with a relative time reduction of 18.5%),striking an excellent balance between accuracy and efficiency.This approach provides a robust framework for efficient soil moisture monitoring in remote areas with limited ground data,offering critical insights for ecological conservation,water resource management,and climate change adaptation on the Qinghai-Xizang Plateau.展开更多
基金supported by the National Natural Science Foundation of China (31971791)the National Key Research and Development Program of China (2017YFD0300204)。
文摘Powdery mildew is a disease that threatens wheat production and causes severe economic losses worldwide. Its timely diagnosis is imperative for preventing and controlling its spread. In this study, the multiangle canopy spectra and disease severity of wheat were investigated at several developmental stages and degrees of disease severity. Four wavelength variable-selected algorithms: successive projection(SPA), competitive adaptive reweighted sampling(CARS), feature selection learning(Relief-F), and genetic algorithm(GA), were used to identify bands sensitive to powdery mildew. The wavelength variables selected were used as input variables for partial least squares(PLS), extreme learning machine(ELM), random forest(RF), and support vector machine(SVM) algorithms, to construct a suitable prediction model for powdery mildew. Spectral reflectance and conventional vegetation indices(VIs) displayed angle effects under several disease severity indices(DIs). The CARS method selected relatively few wavelength variables and showed a relatively homogeneous distribution across the 13 viewing zenith angles.Overall accuracies of the four modeling algorithms were ranked as follows: ELM(0.70–0.82) > PLS(0.63–0.79) > SVM(0.49–0.69) > RF(0.43–0.69). Combinations of features and algorithms generated varied accuracies, with coefficients of determination(R^(2)) single-peaked at different observation angles. The constructed CARS-ELM model extracted a predictable bivariate relationship between the multi-angle canopy spectrum and disease severity, yielding an R^(2)> 0.8 at each measured angle. Especially for larger angles,monitoring accuracies were increased relative to the optimal VI model(40% at-60°, 33% at +60°), indicating that the CARS-ELM model is suitable for extreme angles of-60° and +60°. The results are proposed to provide a technical basis for rapid and large-scale monitoring of wheat powdery mildew.
文摘Evolution of remote sensing sensors technologies is presented, with emphasis on its suitability in observing the polar regions. The extent of influence of polar regions on the global climate and vice versa is the spearhead of climate change research. The extensive cover of sea ice has major impacts on the atmosphere, oceans, and terrestrial and marine ecosystems of the polar regions in particular and teleconnection on other processes elsewhere. Sea ice covers vast areas of the polar oceans, ranging from ~18 × 106 km2 to ~23 × 106 km2, combined for the Northern and Southern Hemispheres. However, both polar regions are witnessing contrasting rather contradicting effects of climate change. The Arctic sea ice extent is declining at a rate of 0.53 × 106 km2·decade–1, whereasAntarcticaexhibits a positive trend at the rate of 0.167 × 106 km2·decade–1. This work reviews literature published in the field of sea ice remote sensing, to evaluate and access success and failures of different sensors to observe physical features of sea ice. The chronological development series of different sensors on different satellite systems, sensor specifications and datasets are examined and how they have evolved to meet the growing needs of users is outlined. Different remote sensing technology and observational methods and their suitability to observe specific sea ice property are also discussed. A pattern has emerged, which shows that microwave sensors are inherently superior to visible and infrared in monitoring seasonal and annual changes in sea ice. Degree of successes achieved through remote sensing techniques by various investigators has been compared. Some technologies appear to work better under certain conditions than others, and it is now well accepted that there is no algorithm that is ideal globally. Contribution of Indian remote sensing satellites is also reviewed in the context of polar research. This review suggests different primary datasets for further research on sea ice features (sea ice extent, ice type, sea ice thickness, etc.). This work also examines past achievements and how far these capabilities have evolved and tap into current state of art/direction of sensor technologies. Effective monitoring and syntheses of past few decades of research pinpoint useful datasets for sea ice monitoring, thereby avoiding wastage of resources to find practical datasets to monitor these physically inaccessible regions.
基金supported by the National Basic Research Program of China (973 Program, Grant No. 2009CB421202)the National Natural Science Foundation of China (Grant No. 40706061)the National High Technology Research and Development Program of China (863 Program, Grant Nos. 2007AA12Z137 and 2008AA09Z104)
文摘A retrieval method of cloud top heights using polarizing remote sensing is proposed in this paper. Using the vector radiative transfer model in a coupled atmosphere-ocean system, the factors influencing the upwelling linear polarizing radiance at top-of-atmosphere are analyzed, which show that the upwelling linear polarizing radiance varies remarkably with the cloud top height, but has negligible sensitivity with cloud albedo and aerosol scattering above the cloud layer. Based on this property, a cloud top height retrieval algorithm using polarizing remote sensing was developed. The algorithm has been applied to the polarizing remote sensing data of Polarization and Directionality of the Earth's Reflectances-2 (POLDER-2). The retrieved cloud top height from POLDER-2 compares well with the Moderate Resolution Imaging Spectroradiometer (MODIS) operational product with a bias of 0.83 km and standard deviation of 1.56 km.
文摘Measurement of ice velocities of the Antarctic glaciers is very important for studies on Antarctic ice and snow mass balance. The polar area environmental change and its influences on the global environment. Conventional methods may be used for measuring the ice velocities, but they suffer from severe weather conditions in the Polar areas. Use of satellite multi-spectral and muki-temporal images makes it easier to measure the velocities of the glacier movements. This paper discusses a new method for monitoring the glacial change by means of multi-temporal satellite images. Temporal remotely sensed images in the Ingrid Christensen coast were processed with respect to geometric rectification, registration and overlay, The average ice velocities of the Polar Record Glacier and the Dark Glacier were then calculated, with the changing characteristics analyzed and evaluated. The advantages of the method reported here include promise of all-weather operation and potentials of dynamic monitoring through suitable selection of temporal satellite images.
基金supported by the National Key R&D Program of China(Grant No.2023YFB3905703)the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDA19090144)+1 种基金the National Natural Science Foundation of China(Grant No.41842048)the Shenzhen Academician(Expert)Workstation of Orbbec Inc.(XHXS2023-115)。
文摘Optical remote sensing is a crucial component of the ocean observation system.However,the complex interactions between the ocean and atmosphere limit its observation capability and hinder the advancement of quantitative applications and support capacity.Polarimetric remote sensing,as an advanced detection technology,investigates the anisotropic characteristics of electromagnetic waves perpendicular to the direction of propagation.Serving as an extension of conventional optical remote sensing,it significantly improves the accuracy of feature identification and quantitative estimation.As the most classical polarization feature,the Degree of Polarization(DoP)feature has been widely applied in various scenarios.In this study,the spatial distribution of the DoP feature over the 2πobservation space under oceanic conditions is thoroughly investigated through theoretical simulations and sample measurements.Our findings suggest that the DoP feature lacks sufficient sensitivity and versatility to be used independently in ocean observation scenarios.To address this limitation,a novel feature,namely the Angular Polarization(AP)feature,is proposed for polarimetric remote sensing tailored to ocean applications.The effectiveness of this new feature is validated in three representative ocean observation scenarios,and its performance is compared against both conventional optical feature and DoP feature.The results demonstrate that the AP feature offers distinct advantages in differentiating ocean bodies with varying refractive indices and in emphasizing the differences between observed targets.Moreover,its application enhances the accuracy of unsupervised classification for ocean observations.The establishment of the AP feature greatly strengthens the information-sensing capacity of polarimetric ocean remote sensing,offering a promising pathway to enhance the overall performance of ocean observation systems.
基金Project KZCX3-S W-338-1 supported by Science and Technology Innovation Foundation of Chinese Academy of Science and 49771057 supported by theNational Natural Science Foundation of China
文摘The traditional remote sensing mainly detects the ground vertically to obtain the 2D information but it is hard to get adequate parameters for the quantitative remote sensing to invert land features. The multi-angle observation can get more detailed and reliable 3D structural parameters of targets, so it makes the quantitative remote sensing applicable. During the process of reflecting, scattering and transmitting the electromagnetic wave, minerals and rocks could reveal the polarized features related to the nature of themselves. Therefore, it has become a new approach of quantitative remote sensing to detect multi-angle polarized information of minerals and rocks. In respect that the polarized reflectance always goes with the bidirectional one, we can obtain the 3D spatial distribution of targets by a polarized means together with detecting its bi-directional reflectance. From the perspective of multi-angle polarized remote sensing mechanism, the quantitative relationship between multi-angle polarized reflectance and the BRDF is studied in this paper. And it is testified that the bi-directional reflectance, polarized reflectance of 45° and the mean value of polarized reflectance are equal to that of the corresponding azimuth angle, zenith angle, detection angle and detection channels in 27t space by experiment.
基金the National Natural Science Foundation of China(Grant Nos.42025504,No.41905023)National Natural Science Youth Science Foundation(Grant No.41701406)Youth Innovation Promotion Association of Chinese Academy of Sciences(Grant No.:2021122).
文摘Cloud top pressure(CTP)is one of the critical cloud properties that significantly affects the radiative effect of clouds.Multi-angle polarized sensors can employ polarized bands(490 nm)or O_(2)A-bands(763 and 765 nm)to retrieve the CTP.However,the CTP retrieved by the two methods shows inconsistent results in certain cases,and large uncertainties in low and thin cloud retrievals,which may lead to challenges in subsequent applications.This study proposes a synergistic algorithm that considers both O_(2)A-bands and polarized bands using a random forest(RF)model.LiDAR CTP data are used as the true values and the polarized and non-polarized measurements are concatenated to train the RF model to determine CTP.Additionally,through analysis,we proposed that the polarized signal becomes saturated as the cloud optical thickness(COT)increases,necessitating a particular treatment for cases where COT<10 to improve the algorithm's stability.The synergistic method was then applied to the directional polarized camera(DPC)and Polarized and Directionality of the Earth’s Reflectance(POLDER)measurements for evaluation,and the resulting retrieval accuracy of the POLDER-based measurements(RMSEPOLDER=205.176 hPa,RMSEDPC=171.141 hPa,R^(2)POLDER=0.636,R^(2)DPC=0.663,respectively)were higher than that of the MODIS and POLDER Rayleigh pressure measurements.The synergistic algorithm also showed good performance with the application of DPC data.This algorithm is expected to provide data support for atmosphere-related fields as an atmospheric remote sensing algorithm within the Cloud Application for Remote Sensing,Atmospheric Radiation,and Updating Energy(CARE)platform.
基金supported by the National Natural Science Foundation of China[grant number 42001386,42271407]within the ESA-MOST China Dragon 5 Cooperation(ID:59313).
文摘Due to the small size,variety,and high degree of mixing of herbaceous vegetation,remote sensing-based identification of grassland types primarily focuses on extracting major grassland categories,lacking detailed depiction.This limitation significantly hampers the development of effective evaluation and fine supervision for the rational utilization of grassland resources.To address this issue,this study concentrates on the representative grassland of Zhenglan Banner in Inner Mongolia as the study area.It integrates the strengths of Sentinel-1 and Sentinel-2 active-passive synergistic observations and introduces innovative object-oriented techniques for grassland type classification,thereby enhancing the accuracy and refinement of grassland classification.The results demonstrate the following:(1)To meet the supervision requirements of grassland resources,we propose a grassland type classification system based on remote sensing and the vegetation-habitat classification method,specifically applicable to natural grasslands in northern China.(2)By utilizing the high-spatial-resolution Normalized Difference Vegetation Index(NDVI)synthesized through the Spatial and Temporal Non-Local Filter-based Fusion Model(STNLFFM),we are able to capture the NDVI time profiles of grassland types,accurately extract vegetation phenological information within the year,and further enhance the temporal resolution.(3)The integration of multi-seasonal spectral,polarization,and phenological characteristics significantly improves the classification accuracy of grassland types.The overall accuracy reaches 82.61%,with a kappa coefficient of 0.79.Compared to using only multi-seasonal spectral features,the accuracy and kappa coefficient have improved by 15.94%and 0.19,respectively.Notably,the accuracy improvement of the gently sloping steppe is the highest,exceeding 38%.(4)Sandy grassland is the most widespread in the study area,and the growth season of grassland vegetation mainly occurs from May to September.The sandy meadow exhibits a longer growing season compared with typical grassland and meadow,and the distinct differences in phenological characteristics contribute to the accurate identification of various grassland types.
基金National Natural Science Foundation of China(42171379,42222103,42101379,and 42171372)Science and Technology Development Program of Jilin Province,China(20210101396JC)+2 种基金Youth Innovation Promotion Association of the Chinese Academy of Sciences(2017277 and 2021227)Young Scientist Group Project of Northeast Institute of Geography and Agroecology,Chinese Academy of Sciences(2022QNXZ03)Shenzhen Science and Technology Program(JCYJ20210324093210029).
文摘Accurately and timely monitoring the spatial distribution and composition of mangrove species is critical for assessing mangroves’health,dynamics,and biodiversity,as well as mangroves’degradation and restoration.Recent advances in machine learning algorithms,coupled with spaceborne remote sensing technique,offer an unprecedented opportunity to map mangroves at species level with high resolution over large extents.However,a single data source or data type is insufficient to capture the complex features of mangrove species and cannot satisfy the need for fine species classification.Moreover,identifying and selecting effective features derived from integrated multisource data are essential for integrating high-dimensional features for mangrove species discrimination.In this study,we developed a novel framework for mangrove species classification using spectral,texture,and polarization information derived from 3-source spaceborne imagery:WorldView-2(WV-2),OrbitaHyperSpectral(OHS),and Advanced Land Observing Satellite-2(ALOS-2).A total of 151 remote sensing features were first extracted,and 18 schemes were designed.Then,a wrapper method by combining extreme gradient boosting with recursive feature elimination(XGBoost-RFE)was conducted to select the sensitive variables and determine the optical subset size of all features.Finally,an ensemble learning algorithm of XGBoost was applied to classify 6 mangrove species in the Zhanjiang Mangrove National Nature Reserve,China.Our results showed that combining multispectral,hyperspectral,and L-band synthetic aperture radar features yielded the best mangrove species classification results,with an overall accuracy of 94.02%,a quantity disagreement of 4.44%,and an allocation disagreement of 1.54%.In addition,this study demonstrated important application potential of the XGBoost classifier.The proposed framework could provide fine-scale data and conduce to mangroves’conservation and restoration.
基金supported by the Scientific Research Foundation for High-level Talents of Anhui University of Science and Technology(13230550)the Coal Industry Engineering Research Center of Mining Area Environmental and Disaster Cooperative Monitoring,Anhui University of Science and Technology(KSXTJC202305)+1 种基金the State Key Laboratory of Geodesy and Earth's Dynamics,Innovation Academy for Precision Measurement Science and Technology(SKLGED2023-5-1)the China Postdoctoral Science Foundation(2023M733604).
文摘Soil moisture is a key parameter in the exchange of energy and water between the land surface and the atmosphere.This parameter plays an important role in the dynamics of permafrost on the Qinghai-Xizang Plateau,China,as well as in the related ecological and hydrological processes.However,the region's complex terrain and extreme climatic conditions result in low-accuracy soil moisture estimations using traditional remote sensing techniques.Thus,this study considered parameters of the backscatter coefficient of Sentinel-1A ground range detected(GRD)data,the polarization decomposition parameters of Sentinel-1A single-look complex(SLC)data,the normalized difference vegetation index(NDVI)based on Sentinel-2B data,and the topographic factors based on digital elevation model(DEM)data.By combining these parameters with a machine learning model,we established a feature selection rule.A cumulative importance threshold was derived for feature variables,and those variables that failed to meet the threshold were eliminated based on variations in the coefficient of determination(R^(2))and the unbiased root mean square error(ubRMSE).The eight most influential variables were selected and combined with the CatBoost model for soil moisture inversion,and the SHapley Additive exPlanations(SHAP)method was used to analyze the importance of these variables.The results demonstrated that the optimized model significantly improved the accuracy of soil moisture inversion.Compared to the unfiltered model,the optimal feature combination led to a 0.09 increase in R^(2)and a 0.7%reduction in ubRMSE.Ultimately,the optimized model achieved a R²of 0.87 and an ubRMSE of 5.6%.Analysis revealed that soil particle size had significant impact on soil water retention capacity.The impact of vegetation on the estimated soil moisture on the Qinghai-Xizang Plateau was considerable,demonstrating a significant positive correlation.Moreover,the microtopographical features of hummocks interfered with soil moisture estimation,indicating that such terrain effects warrant increased attention in future studies within the permafrost regions.The developed method not only enhances the accuracy of soil moisture retrieval in the complex terrain of the Qinghai-Xizang Plateau,but also exhibits high computational efficiency(with a relative time reduction of 18.5%),striking an excellent balance between accuracy and efficiency.This approach provides a robust framework for efficient soil moisture monitoring in remote areas with limited ground data,offering critical insights for ecological conservation,water resource management,and climate change adaptation on the Qinghai-Xizang Plateau.
