Assessing Non-Photosynthetic Cropland Biomass from Spaceborne Hyperspectral Imagery [0.03%]
基于空间超光谱遥感影像估算非光合农作物生物量
Katja Berger,Tobias Hank,Andrej Halabuk et al.
Katja Berger et al.
Non-photosynthetic vegetation (NPV) biomass has been identified as a priority variable for upcoming spaceborne imaging spectroscopy missions, calling for a quantitative estimation of lignocellulosic plant material as opposed to the sole ind...
Vegetation Types Mapping Using Multi-Temporal Landsat Images in the Google Earth Engine Platform [0.03%]
基于Google Earth Engine的多时相 landsat 影像植被类型制图研究
Masoumeh Aghababaei,Ataollah Ebrahimi,Ali Asghar Naghipour et al.
Masoumeh Aghababaei et al.
Vegetation Types (VTs) are important managerial units, and their identification serves as essential tools for the conservation of land covers. Despite a long history of Earth observation applications to assess and monitor land covers, the q...
Transboundary Basins Need More Attention: Anthropogenic Impacts on Land Cover Changes in Aras River Basin, Monitoring and Prediction [0.03%]
跨界流域需要更多关注：阿拉斯河流域土地利用变化的人为影响、监测与预测
Sajad Khoshnoodmotlagh,Jochem Verrelst,Alireza Daneshi et al.
Sajad Khoshnoodmotlagh et al.
Changes in land cover (LC) can alter the basin hydrology by affecting the evaporation, infiltration, and surface and subsurface flow processes, and ultimately affect river water quantity and quality. This study aimed to monitor and predict ...
Drift of the Earth's Principal Axes of Inertia from GRACE and Satellite Laser Ranging Data [0.03%]
基于GRACE和激光卫星轨道数据的地极运动估计
José M Ferrándiz,Sadegh Modiri,Santiago Belda et al.
José M Ferrándiz et al.
The location of the Earth's principal axes of inertia is a foundation for all the theories and solutions of its rotation, and thus has a broad effect on many fields, including astronomy, geodesy, and satellite-based positioning and navigati...
Global Sensitivity Analysis of Leaf-Canopy-Atmosphere RTMs: Implications for Biophysical Variables Retrieval from Top-of-Atmosphere Radiance Data [0.03%]
叶-冠层-大气辐射传输模型的全局敏感性分析：基于顶部光谱数据反演生物物理变量的影响
Jochem Verrelst,Jorge Vicent,Juan Pablo Rivera-Caicedo et al.
Jochem Verrelst et al.
Knowledge of key variables driving the top of the atmosphere (TOA) radiance over a vegetated surface is an important step to derive biophysical variables from TOA radiance data, e.g., as observed by an optical satellite. Coupled leaf-canopy...
Eco-Friendly Estimation of Heavy Metal Contents in Grapevine Foliage Using In-Field Hyperspectral Data and Multivariate Analysis [0.03%]
基于田间高光谱数据多变量分析的葡萄叶片重金属含量的环保预测模型研究
Mohsen Mirzaei,Jochem Verrelst,Safar Marofi et al.
Mohsen Mirzaei et al.
Heavy metal monitoring in food-producing ecosystems can play an important role in human health safety. Since they are able to interfere with plants' physiochemical characteristics, which influence the optical properties of leaves, they can ...
Monitoring Cropland Phenology on Google Earth Engine Using Gaussian Process Regression [0.03%]
基于高斯过程回归的谷歌地球引擎农作物物候监测研究
Matías Salinero-Delgado,José Estévez,Luca Pipia et al.
Matías Salinero-Delgado et al.
Monitoring cropland phenology from optical satellite data remains a challenging task due to the influence of clouds and atmospheric artifacts. Therefore, measures need to be taken to overcome these challenges and gain better knowledge of cr...
Optimal Spectral Wavelengths for Discriminating Orchard Species Using Multivariate Statistical Techniques [0.03%]
基于多元统计的果园树种分类的光谱最佳波段组合选取方法研究
Mozhgan Abbasi,Jochem Verrelst,Mohsen Mirzaei et al.
Mozhgan Abbasi et al.
Sustainable management of orchard fields requires detailed information about the tree types, which is a main component of precision agriculture programs. To this end, hyperspectral imagery can play a major role in orchard tree species mappi...
Assessment of Workflow Feature Selection on Forest LAI Prediction with Sentinel-2A MSI, Landsat 7 ETM+ and Landsat 8 OLI [0.03%]
Sentinel-2A MSI、Landsat 7 ETM+和Landsat 8 OLI支持的工作流特征选择在森林叶片面积指数预测中的评估研究
Benjamin Brede,Jochem Verrelst,Jean-Philippe Gastellu-Etchegorry et al.
Benjamin Brede et al.
The European Space Agency (ESA)'s Sentinel-2A (S2A) mission is providing time series that allow the characterisation of dynamic vegetation, especially when combined with the National Aeronautics and Space Administration (NASA)/United States...
A Survey of Active Learning for Quantifying Vegetation Traits from Terrestrial Earth Observation Data [0.03%]
基于地面观测数据量化植被性状的主动学习法综述
Katja Berger,Juan Pablo Rivera Caicedo,Luca Martino et al.
Katja Berger et al.
The current exponential increase of spatiotemporally explicit data streams from satellitebased Earth observation missions offers promising opportunities for global vegetation monitoring. Intelligent sampling through active learning (AL) heu...