- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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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 - First Evaluation of Land Surface Emissivity Spectra Simulated with the Sail-Thermique Model
摘要： The SAIL-Thermique model was developed to simulate land surface emissivity. It is adapted from the original SAIL model. A specific experiment was set-up over a soybean canopy for evaluating spectral emissivity simulations. Multispectral data were obtained thanks to a CIMEL CE 312-2 radiometer and emissivities calculated using the TES method. Comparison of multispectral emissivity simulations and measurements at various leaf area index levels were comparable in terms of emissivity values and spectral behavior.
关键词： thermal infrared,vegetation canopy,Emissivity,SAIL-Thermique,radiative transfer model
更新于2025-09-10 09:29:36
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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 Modified Scattering Model of Row Wheat at X-Band
摘要： Cereal crops, contrary to natural vegetation, have the different characteristics for their regular planting. Further, the random assumption of the radiative transfer theory is not suitable for cereal canopy. The paper aimed to present a modified scattering model of row wheat at X-band (center frequency 3.2GHz). The modified scattering model considered both the surface scattering of soil and the volume scattering of wheat canopy. In different wheat growth stage, the weights of the two kinds of scattering phenomenon were set up based on an empirical growth model because of their visible area. A series of data including wheat growth parameters and backscatter coefficients, related to the interaction, were collected for the analyses of the model. The research results showed the model could better reflect the scattering phenomenon of regulate planting, which is helpful to agriculture remote sensing fields.
关键词： scatterometer,vegetation,soil,Modeling,backscatter
更新于2025-09-10 09:29:36
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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 - SMOS-IC Vegetation Optical Depth Index in Monitoring Aboveground Carbon Changes in the Tropical Continents During 2010–2016
摘要： Tropical aboveground carbon changes during 2010-2016 were estimated by a newly developed vegetation optical depth (VOD) product retrieved from the low-frequency L-band (1.4 GHz) passive microwave observations from the Soil Moisture and Ocean salinity (SMOS) satellite. The aboveground carbon changes estimated by VOD in the tropical region during 2010-2016 indicate the tropical region acts as a net carbon source of 111 Tg C yr-1 during 2010-2016. The declines in tropical aboveground carbon were found mainly in eastern America, African drylands and Indonesia.
关键词： carbon changes,SMOS-IC,aboveground biomass,vegetation optical depth,tropical region
更新于2025-09-10 09:29:36
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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 - Urban Vegetation Mapping Using Hyperspectral Imagery and Spectral Library
摘要： The development and expansion of urbanized areas around the cities, brings new challenging issues about the organization, the monitoring, and the distribution of green spaces within the cities (e.g. grass, trees, shrubs, etc.). Indeed, these spaces brings better life quality for population and preserve biodiversity. This study, aims to 1) investigate the feasibility of urban vegetation mapping by species using multiband imagery and spectral libraries and to 2) determine at what scale the mapping is reliable (e.g. trees scale, group of trees scale, high/short vegetation scale).
关键词： spectral library,regularization,Hyperspectral,band selection,vegetation mapping
更新于2025-09-10 09:29:36
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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 - Progress in Emulation For Radiative Transfer Modeling And Mapping
摘要： Physical radiative transfer models (RTMs) of leaf and canopies with sufficient realism enable the retrieval of biophysical variables from imaging spectroscopy through numerical inversion. However, advanced RTMs are computationally intensive, which hampers practical applicability of inversion schemes against remote sensing images. To bypass the computational load such RTMs, it has been proposed to approximate these models by means of statistical learning, i.e. emulation. Here we tested three machine learning regression algorithms, i.e. neural networks, kernel ridge regression and Gaussian processes regression, on their ability to emulate the advanced RTM SCOPE (Soil-Canopy-Observation of Photosynthesis and the Energy balance) for limited set of input variables. The best performing emulator was implemented into a numerical inversion scheme to process a subset of an hyperspectral image into a multitude of vegetation properties. Obtained maps are not only consistent, but also processing time was in the order of minutes - in comparison, by using SCOPE the processing would have taken days.
关键词： numerical inversion,vegetation properties mapping,Emulation
更新于2025-09-10 09:29:36
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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 - Automatic Derivation of Cropland Phenological Parameters by Adaptive Non-Parametric Regression of Sentinel-2 Ndvi Time Series
摘要： Satellite Image Time Series (SITS), such as the ones acquired by the new Sentinel-2 (S2), combine a large amount of information compared to previous satellite generations since a better trade-off in terms of spatial/spectral/temporal resolutions is guaranteed. The specific characteristic of acquiring images under overlapped orbits, offered by S2, results in: i) availability of irregularly sampled acquisitions and ii) increase of the probability to acquire cloud free images over time. This characteristic becomes relevant in the agricultural analysis, where availability of dense SITS is required to map and analyze fast working crop behaviors. In the literature, several methods exist that extract phenological parameters for agricultural analysis, but none of them is able to deal with irregularly sampled data. Thus, this paper presents an approach for derivation of cropland phenological parameters from irregularly sampled S2-SITS. Experimental results obtained on S2-SITS acquired over Barrax, Spain, confirm the effectiveness of the proposed approach.
关键词： Sentinel-2,Non-parametric regression,NDVI SITS,Vegetation phenology,Data smoothing
更新于2025-09-10 09:29:36
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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 - Improved Iterative Error Analysis Using Spectral Similarity Measures for Vegetation Classification in Hyperspectral Images
摘要： Iterative error analysis (IEA) is one of popular, sequential and linear constrained endmember extraction algorithm that uses spectral angle mapping (SAM) to calculate angles between spectral vectors. However, IEA has a limit that discriminating similar spectral vector is difficult because SAM does not consider positive and negative correlations. Since vegetation has similar spectral properties, it is difficult to classify different vegetation types. To improve IEA for various applications, such as crop classification and change detection, spectral similarity measures other than SAM have been applied to IEA. Many spectral similarity measures have been developed to calculate the similarities among spectral signatures and these are divided into the original methods and the newly developed hybrid algorithms. In this study, the original methods used were SAM, SCA, and SID, while the hybrid methods included SAMSID, SCASID, Jeffries-matusita measures-SAM (JMSAM), and normalized spectral similarity score (NS3). A Compact airborne spectrographic imager image including three crops and road was used and similarity values of four endmembers extracted by modified IEA were calculated. The CASI image was classified using endmembers and minimum distance classifier. The classification accuracy of the modified IEA with SMA, SCA, SID, SAMSID, SCASID, JMSAM, and NS3 were 84.45%, 85.56%, 61.47%, 65.83%, 62.11%, 93.47%, 90.29%. SID based algorithm has lower accuracy because SID tends to make two similar spectral signatures more similar. The results showed that JASAM was most effective to classify different vegetation types. The modified IEA with JMSAM could classify vegetation more effectively than the original IEA.
关键词： vegetation classification,spectral similarity measures,endmember,IEA
更新于2025-09-10 09:29:36
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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 - Estimating the NDVI from SAR by Convolutional Neural Networks
摘要： Since optical remote sensing images are useless in cloudy conditions, a possible alternative is to resort to synthetic aperture radar (SAR) images. However, many conventional techniques for Earth monitoring applications require specific spectral features which are defined only for multispectral data. For this reason, in this work we propose to estimate missing spectral features through data fusion and deep learning, exploiting both temporal and cross-sensor dependencies on Sentinel-1 and Sentinel-2 time-series. The proposed approach, validated focusing on the estimation of the normalized difference vegetation index (NDVI), shows very interesting results with a large performance gain over the linear regression approach according to several accuracy indicators.
关键词： synthetic aperture radar (SAR),Data fusion,multitemporal,deep learning,vegetation monitoring
更新于2025-09-10 09:29:36
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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 - Geophysical Relationship between Cygnss GNSS-R Bistatic Reflectivity and Smap Microwave Radiometry Brightness Temperature Over Land Surfaces
摘要： This work presents an assessment on the correlation between CyGNSS-derived Global Navigation Satellite Systems Reflectometry (GNSS-R) bistatic reflectivity rl? and SMAP-derived brightness temperature BT, over land surfaces. This parametric-study is performed as a function of Soil Moisture Content (SMC), and vegetation opacity τ. Several target areas are selected to evaluate potential differentiated geophysical effects on “active” (as many transmitters as navigation satellites are in view), and passive approaches. Although microwave radiometry has potentially a better sensitivity to SMC, the spatial resolution is poor ~ 40 km. On the other hand, GNSS-R bistatic coherent radar footprint is limited by half of the first Fresnel zone which provides about ~ 150 m of spatial resolution (depending on the geometry). The synergetic combination of both techniques could provide advantages with respect to active monostatic Synthetic Aperture Radar (SAR).
关键词： vegetation opacity,CyGNSS,GNSS-R,land,microwave radiometry,SMAP,multi-static radar,Soil Moisture Content (SMC)
更新于2025-09-10 09:29:36
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Utilization of single-image normalized difference vegetation index (SI-NDVI) for early plant stress detection
摘要： An imaging system was refined to monitor the health of vegetation grown in controlled conditions using spectral reflectance patterns. To measure plant health, the single- image normalized difference vegetation index (SI- NDVI) compares leaf reflectance in visible and near- infrared light spectrums. The SI- NDVI imaging system was characterized to assess plant responses to stress before visual detection during controlled stress assays. Images were analyzed using Fiji image processing software and Microsoft Excel to create qualitative false color images and quantitative graphs to detect plant stress. Stress was detected in Arabidopsis thaliana seedlings within 15 min of salinity application using SI- NDVI analysis, before stress was visible. Stress was also observed during ammonium nitrate treatment of Eruca sativa plants before visual detection. Early detection of plant stress is possible using SI- NDVI imaging, which is both simpler to use and more cost efficient than traditional dual- image NDVI or hyper- spectral imaging.
关键词： vegetation index,early stress detection,plant health monitoring,imaging
更新于2025-09-10 09:29:36