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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 - Effect of the Double-Bounce Contribution in Polinsar-Based Height Estimates of Rice Crops Using Tandem-X Bistatic Data
摘要： In bistatic acquisitions the presence of a double-bounce contribution at the ground affects the interferometric coherence with a decorrelation factor which is usually overlooked in studies employing polarimetric SAR interferometry. The standard acquisition mode of TanDEM-X is bistatic, so the influence of this contribution in the estimation of scene parameters (ground topography and vegetation height) is studied here. The analysis is carried out both with simulations and real data acquired over rice fields during the science phase of TanDEM-X. Results show that the error in height and topography is small when incidence angle is below 30 degrees, but may become noticeable for shallower incidences.
关键词： vegetation,rice,Polarimetric SAR interferometry,bistatic radar,TanDEM-X
更新于2025-09-23 15:21:21
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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 - A Integrated Inversion Method for Estimating Global Leaf Area Index from Chinese FY-3A Mersi Data
摘要： Global leaf area index (LAI) generally produced based on the satellite sensors with 1 km spatial resolution, such as the advanced very high resolution radiometer (AVHRR), moderate resolution imaging spectroradiometer (MODIS) and VEGETATION. At present, there isn’t a LAI product estimated from the Chinese Feng Yun No. 3 (FY-3) images. This study aims to generate a 10-day composite LAI product from FY-3A with a medium resolution spectral imaging (MERSI) at global scale in 2011. Making use of the land cover type as priori knowledge, the LAI for pure vegetation types was inversed from a lookup-table (LUT) based on an stochastic three-dimensional radiative transfer model (3D RTM). For the mixed water and vegetation types, LAI was inversed based on an improved linear decomposition method. The accuracy of LAI inversion from FY-3A MERSI was assessed by LAI field measurements from the Chinese ecosystem research network (CERN) in 2011.
关键词： FY-3A MERSI,LUT,LAI,mixed water and vegetation type,stochastic 3D RTM
更新于2025-09-23 15:21:21
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[Institution of Engineering and Technology 8th Renewable Power Generation Conference (RPG 2019) - Shanghai, China (24-25 Oct. 2019)] 8th Renewable Power Generation Conference (RPG 2019) - The Method of Grid Disturbance Test for Very Large Capacity Photovoltaic Inverter Based on Hardware-In-Loop Simulation Platform
摘要： A three-dimensional (3-D) finite-difference time-domain (FDTD) algorithm is used in order to simulate ground penetrating radar (GPR) for landmine detection. Two bowtie GPR transducers are chosen for the simulations and two widely employed antipersonnel (AP) landmines, namely PMA-1 and PMN are used. The validity of the modeled antennas and landmines is tested through a comparison between numerical and laboratory measurements. The modeled AP landmines are buried in a realistically simulated soil. The geometrical characteristics of soil’s inhomogeneity are modeled using fractal correlated noise, which gives rise to Gaussian semivariograms often encountered in the field. Fractals are also employed in order to simulate the roughness of the soil’s surface. A frequency-dependent complex electrical permittivity model is used for the dielectric properties of the soil, which relates both the velocity and the attenuation of the electromagnetic waves with the soil’s bulk density, sand particles density, clay fraction, sand fraction, and volumetric water fraction. Debye functions are employed to simulate this complex electrical permittivity. Background features like vegetation and water puddles are also included in the models and it is shown that they can affect the performance of GPR at frequencies used for landmine detection (0.5–3 GHz). It is envisaged that this modeling framework would be useful as a testbed for developing novel GPR signal processing and interpretations procedures and some preliminary results from using it in such a way are presented.
关键词： rough surface,GPR,water puddles,modeling,FDTD,antipersonnel (AP) landmines,roots,dispersive,fractals,Antennas,bowtie,GprMax,grass,vegetation
更新于2025-09-23 15:21:01
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[Sustainable Development Goals Series] Remote Sensing for Food Security || Application of Vegetation Health Data and Products for Monitoring Food Security
摘要： The year 2018. Almost one-fifth of the twenty-first century has already past and the Earth has still been continuing the previous tendencies for a rapid population growth, declining stock of natural resources, climate warming, land cover changes, increasing natural disasters, etc., which have intensified considerably world’s concerns about the future food supply/demand and global food security (USDA 2017; FAO 2017, 1999; Heibuch 2011). Most of the indicated problems are related to a deterioration of environmental conditions. As has never been before, decision makers of the world, countries, communities, international organizations, and businesses need reliable and timely information to understand, monitor, and predict impacts of weather/climate and environmentally based Earth’s changes on global food security (FS).
关键词： Environmental Monitoring,Food Security,Vegetation Health,Drought Monitoring,Satellite Data
更新于2025-09-23 15:21:01
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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 - Multi-Frequency Estimation of Canopy Penetration Depths from SMAP/AMSR2 Radiometer and Icesat Lidar Data
摘要： In this study, the ?? - ?? model framework is used to derive extinction coefficient and canopy penetration depths from multi-frequency SMAP and AMSR2 retrievals of vegetation optical depth together with ICESat LiDAR vegetation heights. The vegetation extinction coefficient serves as an indicator of how strong absorption and scattering processes within the canopy attenuate microwaves at L and C-band. Through inversion of the extinction coefficient, the penetration depth into the canopy can be obtained, which is analyzed on local (Sahel, Illinois) and continental scale (Africa, parts of North America) as well as for a one year time series (04/2015-04/2016). First analyses of the retrieved penetration depth estimates reveal strongest attenuation for densely forested areas, therefore vegetation attenuation should be accounted for when retrieving soil moisture in these areas. For the continents of North America and Africa penetration depths decrease in average with an increase in frequency from L- to C-band. Moreover penetration depth time series were found to match with expected seasonal variations (e.g. vegetation growth period & rainy season) for analyzed local regions.
关键词： ICESat,Vegetation attenuation,SMAP,AMSR2,canopy penetration,multi-sensor
更新于2025-09-23 15:21:01
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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 - Spatial and Temporal Properties of SMOS Retrieval Over Tropical Forests
摘要： In this paper, retrieval results obtained using the last version (V650) of SMOS level 2 algorithms are tested considering pixels of Africa and South America. Yearly average values of vegetation optical depth are compared against forest height estimates at continental scale. For selected areas of African woody savannah, multitemporal trends of SM and VOD are compared against environmental variables available from Climatic Research Unit data base.
关键词： Soil Moisture,Forests,Vegetation Optical Depth,SMOS
更新于2025-09-23 15:21:01
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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 - Frequency-Dependence of Vegetation Optical Depth-Derived Isohydriciy Estimates
摘要： Passive microwave radiometry-derived vegetation optical depth measurements can be used to map how different ecosystems are sensitive to drought. This is quantified using the plant physiological concept of isohydricity. VOD-derived effective ecosystem-scale isohydricity maps have recently become commonly used, but their sensitivity to the underlying VOD datasets is not yet well understood. In this work, the dependence of the isohydricity calculation on assumptions about canopy cover penetration - which depends on frequency - and observation time - which varies by sensor in a manner roughly consistent with the observation frequency - are reviewed, and isohydricity datasets from different VOD datasets are compared and validated.
关键词： SMAP,AMSR-E,Isohydricity,vegetation optical depth
更新于2025-09-23 15:21:01
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Method for Mapping Rice Fields in Complex Landscape Areas Based on Pre-Trained Convolutional Neural Network from HJ-1 A/B Data
摘要： Accurate and timely information about rice planting areas is essential for crop yield estimation, global climate change and agricultural resource management. In this study, we present a novel pixel-level classi?cation approach that uses convolutional neural network (CNN) model to extract the features of enhanced vegetation index (EVI) time series curve for classi?cation. The goal is to explore the practicability of deep learning techniques for rice recognition in complex landscape regions, where rice is easily confused with the surroundings, by using mid-resolution remote sensing images. A transfer learning strategy is utilized to ?ne tune a pre-trained CNN model and obtain the temporal features of the EVI curve. Support vector machine (SVM), a traditional machine learning approach, is also implemented in the experiment. Finally, we evaluate the accuracy of the two models. Results show that our model performs better than SVM, with the overall accuracies being 93.60% and 91.05%, respectively. Therefore, this technique is appropriate for estimating rice planting areas in southern China on the basis of a pre-trained CNN model by using time series data. And more opportunity and potential can be found for crop classi?cation by remote sensing and deep learning technique in the future study.
关键词： mapping rice ?elds,convolutional neural network,time series of vegetation index,complex landscape,transfer learning
更新于2025-09-23 15:21:01
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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 - Monitoring Key Agricultural CROPS in the Netherlands using Sentinel-1
摘要： In this study, we performed ground validation to support the interpretation of Sentinel-1 imagery during a full growing season of five key crop types in the Netherlands. Crop height and growth stage were monitored weekly in a total of 25 parcels of maize, potato, sugar beet, wheat and English ryegrass in the province of Flevoland. Hydrometeorological data were collected throughout the season. Here, these results are used to interpret time series of Sentinel-1 data processed for the province of Flevoland. Results demonstrate that Sentinel-1 data follow the phenological stages and can be used to identify key moments in crop development. Combined with the guaranteed availability of observations regardless of cloud cover, this makes Sentinel-1 data a valuable resource for agencies and commercial entities providing advice to farmers and agro-industrial co-operatives.
关键词： SAR,vegetation,crop monitoring,radar,agriculture
更新于2025-09-23 15:21:01
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[IEEE 2019 18th International Conference on Optical Communications and Networks (ICOCN) - Huangshan, China (2019.8.5-2019.8.8)] 2019 18th International Conference on Optical Communications and Networks (ICOCN) - Measurement of the position and orientation of mechanical arm based on laser tracker
摘要： Fractional vegetation cover (FVC) is one of the most important criteria for surface vegetation status. This criterion corresponds to the complement of gap fraction unity at the nadir direction and accounts for the amount of horizontal vegetation distribution. This study aims to directly validate the accuracy of FVC products over crops at coarse resolutions (1 km) by employing ?eld measurements and high-resolution data. The study area was within an oasis in the Heihe Basin, Northwest China, where the Heihe Watershed Allied Telemetry Experimental Research was conducted. Reference FVC was generated through upscaling, which ?tted ?eld-measured data with spaceborne and airborne data to retrieve high-resolution FVC, and then high-resolution FVC was aggregated with a coarse scale. The fraction of green vegetation cover product (i.e., GEOV1 FVC) of SPOT/VEGETATION data taken during the GEOLAND2 project was compared with reference data. GEOV1 FVC was generally overestimated for crops in the study area compared with our estimates. Reference FVC exhibits a systematic uncertainty, and GEOV1 can overestimate FVC by up to 0.20. This ?nding indicates the necessity of reanalyzing and improving GEOV1 FVC over croplands.
关键词： product validation,Coarse resolution,fractional vegetation cover,SPOT/VEGETATION
更新于2025-09-23 15:19:57