- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Munich, Germany (2019.6.23-2019.6.27)] 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Hypercube States for Sub-Planck Sensing
摘要： Land-cover datasets are crucial for research on eco-hydrological processes and earth system modeling. Many land-cover datasets have been derived from remote-sensing data. However, their spatial resolutions are usually low and their classification accuracy is not high enough, which are not well suited to the needs of land surface modeling. Consequently, a comprehensive method for monthly land-cover classification in the Heihe river basin (HRB) with high spatial resolution is developed. Moreover, the major crops in the HRB are also distinguished. The proposed method integrates multiple classifiers and multisource data. Three types of data including MODIS, HJ-1/CCD, and Landsat/TM and Google Earth images are used. Compared to single classifier, multiple classifiers including thresholding, support vector machine (SVM), object-based method, and time-series analysis are integrated to improve the accuracy of classification. All the data and classifiers are organized using a decision tree. Monthly land-cover maps of the HRB in 2013 with 30-m spatial resolution are made. A comprehensive validation shows great improvement in the accuracy. First, a visual comparison of the land-cover maps using the proposed method and standard SVM method shows the classification differences and the advantages of the proposed method. The confusion matrix is used to evaluate the classification accuracy, showing an overall classification accuracy of over 90% in the HRB, which is quite higher than previous approaches. Furthermore, a ground campaign was performed to evaluate the accuracy of crop classification and an overall accuracy of 84.09% for the crop classification was achieved.
关键词： Crop classification,river basin,multisource remotely sensed data,phenology,time-series analysis,multiple classifiers,multiple scales,HJ-1/CCD,land cover
更新于2025-09-19 17:13:59
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[IEEE 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL) - Sozopol, Bulgaria (2019.9.6-2019.9.8)] 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL) - Experimental Implementation of Non-uniformity Effects in Artificial Media : (Invited)
摘要： Land-cover datasets are crucial for research on eco-hydrological processes and earth system modeling. Many land-cover datasets have been derived from remote-sensing data. However, their spatial resolutions are usually low and their classification accuracy is not high enough, which are not well suited to the needs of land surface modeling. Consequently, a comprehensive method for monthly land-cover classification in the Heihe river basin (HRB) with high spatial resolution is developed. Moreover, the major crops in the HRB are also distinguished. The proposed method integrates multiple classifiers and multisource data. Three types of data including MODIS, HJ-1/CCD, and Landsat/TM and Google Earth images are used. Compared to single classifier, multiple classifiers including thresholding, support vector machine (SVM), object-based method, and time-series analysis are integrated to improve the accuracy of classification. All the data and classifiers are organized using a decision tree. Monthly land-cover maps of the HRB in 2013 with 30-m spatial resolution are made. A comprehensive validation shows great improvement in the accuracy. First, a visual comparison of the land-cover maps using the proposed method and standard SVM method shows the classification differences and the advantages of the proposed method. The confusion matrix is used to evaluate the classification accuracy, showing an overall classification accuracy of over 90% in the HRB, which is quite higher than previous approaches. Furthermore, a ground campaign was performed to evaluate the accuracy of crop classification and an overall accuracy of 84.09% for the crop classification was achieved.
关键词： land cover,river basin,time-series analysis,multisource remotely sensed data,phenology,Crop classification,HJ-1/CCD,multiple scales,multiple classifiers
更新于2025-09-19 17:13:59
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Hydrogenation of polycrystalline silicon films for passivating contacts solar cells
摘要： The strong temporal backscatter signature of rice growing above the water’s surface allows for the use of synthetic aperture radar (SAR) for paddy rice crop mapping in Southern Vietnam (Mekong Delta). In Northern Vietnam (Red River Delta), rice mapping using SAR is a challenge and is rarely performed because of the complex land-use/land-cover. Nevertheless, information about rice fields is needed for hydrological simulations in river basins such as the Cau River basin. The objective of this research is to investigate the potential of RADARSAT-2 band-C in identifying rice fields over a large and fragmented land-use area. Two methods are proposed, one for each data type, adapted to the land-use/land-cover of the study area. The thresholding technique, with a statistical analysis of the temporal variation of rice backscattering, was applied to the HH like-polarized ratio of dual-pol data. The support vector machine (SVM) algorithm was applied to the full quad-pol and a single HH-polarization calculated from polarimetric data. This study demonstrates that RADARSAT-2 dual- and quad-pol data can be successfully used to identify cultivated rice fields. However, the dual-pol data seems less efficient than the quad-pol data and the SVM classification is more flexible than the thresholding technique. Between the full quad-pol and a single polarization, the overall classification accuracy shows that the results derived from the single HH polarization are 3 to 10% less accurate than those derived from the classification of full quad-pol data. The results show the usefulness of polarimetric C-band data for the identification of rice fields in Northern Vietnam.
关键词： RADARSAT-2,support vector machine (SVM),thresholding,Cau river basin,rice identification
更新于2025-09-19 17:13:59
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[IEEE 2019 IEEE 4th International Future Energy Electronics Conference (IFEEC) - Singapore, Singapore (2019.11.25-2019.11.28)] 2019 IEEE 4th International Future Energy Electronics Conference (IFEEC) - A Comparative Study of Flexible Power Point Tracking Algorithms in Photovoltaic Systems
摘要： Land-cover datasets are crucial for research on eco-hydrological processes and earth system modeling. Many land-cover datasets have been derived from remote-sensing data. However, their spatial resolutions are usually low and their classification accuracy is not high enough, which are not well suited to the needs of land surface modeling. Consequently, a comprehensive method for monthly land-cover classification in the Heihe river basin (HRB) with high spatial resolution is developed. Moreover, the major crops in the HRB are also distinguished. The proposed method integrates multiple classifiers and multisource data. Three types of data including MODIS, HJ-1/CCD, and Landsat/TM and Google Earth images are used. Compared to single classifier, multiple classifiers including thresholding, support vector machine (SVM), object-based method, and time-series analysis are integrated to improve the accuracy of classification. All the data and classifiers are organized using a decision tree. Monthly land-cover maps of the HRB in 2013 with 30-m spatial resolution are made. A comprehensive validation shows great improvement in the accuracy. First, a visual comparison of the land-cover maps using the proposed method and standard SVM method shows the classification differences and the advantages of the proposed method. The confusion matrix is used to evaluate the classification accuracy, showing an overall classification accuracy of over 90% in the HRB, which is quite higher than previous approaches. Furthermore, a ground campaign was performed to evaluate the accuracy of crop classification and an overall accuracy of 84.09% for the crop classification was achieved.
关键词： HJ-1/CCD,multiple classifiers,phenology,river basin,multiple scales,time-series analysis,Crop classification,land cover,multisource remotely sensed data
更新于2025-09-16 10:30:52
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Improving Gpm Precipitation Data Over Yarlung Zangbo River Basin Using Smap Soil Moisture Retrievals
摘要： Precipitation plays an essential role in land surface processes, as a vital forcing variable of hydrology and ecosystem models. Satellite remote sensing is able to provide precipitation estimations at regional and global scales with high spatiotemporal resolutions. However, the accuracy of these products still need improvement especially at daily scale. In this study, we proposed a new method which can improve the rainfall product of the Global Precipitation Mission by using the soil moisture product of the Soil Moisture Active Passive mission over the Yarlung Zangbo River basin in the Tibetan Plateau. Compared to rain gauge observation, our method improve rainfall estimation at 97 of 108 GPM grids. Overall, the average correlation between improved GPM and in situ observation increased from 0.12 to 0.31, while the RMSE and RRMSE decreased by 1.16 and 0.42, respectively. It indicates that this approach can be used in a large scale to improve satellite-based rainfall products over the Tibetan Plateau.
关键词： Yarlung Zangbo River basin,GPM,rainfall estimation,SMAP,soil moisture
更新于2025-09-10 09:29:36
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Applications and Challenges of Geospatial Technology (Potential and Future Trends) || Flood Inundation and Hazard Mapping of 2017 Floods in the Rapti River Basin Using Sentinel-1A Synthetic Aperture Radar Images
摘要： Globally, the flood magnitude and flood-induced damage are increasing. Hence, the geospatial technology has been used to minimise the adverse effects of floods and to plan the floodplain for the betterment of floodplain dwellers. One of the major causes of floods in the Rapti River basin is heavy rainfall induced by the break-in-monsoon condition. These days, geoscientists and planners use Sentinel-1A IW GRD synthetic-aperture radar (SAR) image for flood extent mapping. Gauge level and flood duration data recorded at Bhinga, Balrampur, Bansi, Regauli, Birdghat, Kakarahi, Uska Bazar and Trimohinighat sites provide the basis for the selection of SAR images. Extensive floods occurred in the Rapti River basin during August 13–September 01, 2017. The flood duration in the Rapti River basin varied from 3 (Bhinga) to 18 days (Birdghat) in 2017. The flood duration, normally, increases from the upstream to downstream along the Rapti River due to decreasing slope and discharges contributed by the tributaries. In this study, Sentinel-1A GRD SAR images of August 21 and 25, 2017, have been selected for flood mapping in the Indian part of the Rapti River basin. The water level of rivers was above the danger level (DL) at Bansi, Regauli, Birdghat, Kakarahi, Uska Bazar and Trimohinighat gauge and discharge (G/D) sites on August 21 and 25, 2017. The propagation of flood peaks and affected areas has been analysed using water level data and SAR images for the mentioned periods. The actual flooded areas covered 2046.7 km2 area of the Indian part of the Rapti River basin during August 21–25, 2017. The validation of flooded areas has been done using GPS way points collected during field survey (November 2017) and Landsat 7 ETM+ images (August 24, 2017). Breach sites in flood-prone areas have been mapped using Sentinel-2A and B MSI images. The z-score method has been used for the standardisation of development block-wise flooded areas (km2) and number of flood-affected villages. After standardisation, these two parameters have been added to formulate development block-wise flood hazard index (FHI). High to very high FHI values have been observed in Siddharthnagar and Gorakhpur districts.
关键词： Unprecedented flood,Rapti River basin,Sentinel-1A IW GRD SAR,Backscatter values,Flood hazard index,Danger level
更新于2025-09-10 09:29:36
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ET Variations and influence factors in the Yangtze River Basin from multi-satellite remote sensing data
摘要： Evapotranspiration (ET) variations in the Yangtze River Basin (YRB) are influenced by environmental and climate changes related to planting of crops, forest vegetation, water use and other human activities. However, it is difficult to measure ET variations and analyse influencing factors in the YRB due to lack of in-situ measurements. In the present study, the ET variations were estimated and investigated in the whole, the upper, middle and lower reaches of the YRB using the Gravity Recovery and Climate Experiment, optical remote sensing data and hydrological models based on a water balance method, which was validated by MODerate Resolution Imaging Spectroradiometer (MODIS) observations and models. Furthermore, GRACE-ET verified the drought events in 2006 and 2011. The long-term variation rate of GRACE-ET is 0.79 mm/yr. The spatial distribution of seasonal ET variations indicates that ET is highest in summer and lowest in autumn-winter. It also shows that the completion of the Three Gorges Project has certainly increased ET. Precipitation and temperature have the largest impact on the ET variations; radiation and soil moisture have moderate effects. ET variations in the middle and lower reaches are greatly affected by precipitation, and temperature plays a more important role in the upper YRB reaches.
关键词： evapotranspiration,GRACE,climate change,Yangtze River Basin (YRB)
更新于2025-09-09 09:28:46