How accurately does L band vegetation optical depth predict aboveground biomass? [0.03%]
L波段植被光学厚度对地上生物量的预测精度如何？
Yuan Zhang,Philippe Ciais,Jean-Pierre Wigneron et al.
Yuan Zhang et al.
L-band Vegetation Optical Depth (L-VOD) has emerged as a critical remote sensing proxy for monitoring global aboveground biomass (AGB) dynamics. Persistent methodological ambiguities, including the absence of standardized protocols for deri...
Geospatial impact evaluation of a low-cost agricultural intervention for enhancing environmental resilience [0.03%]
低成本的农业干预措施增强环境恢复力的空间影响评估
Pratap Khattri,Rachel Sayers,Kunwar K Singh et al.
Pratap Khattri et al.
Land degradation poses a significant threat to ecosystems and livelihoods, particularly in disaster-prone regions. In these settings, the promotion of certain agricultural practices with economic incentives, such as sugarcane (Saccharum off...
Optimizing the detection of emerging infections using mobility-based spatial sampling [0.03%]
基于流动性的空间抽样优化新兴感染的检测
Die Zhang,Yong Ge,Jianghao Wang et al.
Die Zhang et al.
Timely and precise detection of emerging infections is imperative for effective outbreak management and disease control. Human mobility significantly influences the spatial transmission dynamics of infectious diseases. Spatial sampling, int...
Unraveling near real-time spatial dynamics of population using geographical ensemble learning [0.03%]
基于地理集合学习的.population空间动态近实时解析
Yimeng Song,Shengbiao Wu,Bin Chen et al.
Yimeng Song et al.
Dynamic gridded population data are crucial in fields such as disaster reduction, public health, urban planning, and global change studies. Despite the use of multi-source geospatial data and advanced machine learning models, current framew...
The relationship between remotely-sensed spectral heterogeneity and bird diversity is modulated by landscape type [0.03%]
遥感光谱异质性与鸟类多样性关系受景观类型调节
Dominika Prajzlerová,Vojtěch Barták,Petr Keil et al.
Dominika Prajzlerová et al.
To identify areas of high biodiversity and prioritize conservation efforts, it is crucial to understand the drivers of species richness patterns and their scale dependence. While classified land cover products are commonly used to explain b...
Spatially explicit accuracy assessment of deep learning-based, fine-resolution built-up land data in the United States [0.03%]
基于深度学习的美国细分辨率建设用地数据的空间显式精度评价
Johannes H Uhl,Stefan Leyk
Johannes H Uhl
Geospatial datasets derived from remote sensing data by means of machine learning methods are often based on probabilistic outputs of abstract nature, which are difficult to translate into interpretable measures. For example, the Global Hum...
A deep learning approach for automatic identification of ancient agricultural water harvesting systems [0.03%]
一种用于自动识别古代农业集水系统的深度学习方法
Arti Tiwari,Micha Silver,Arnon Karnieli
Arti Tiwari
Despite the harsh climatic conditions in the Central Negev Desert, Israel, thousands of dry stonewalls were built across ephemeral streams between the fourth and seventh centuries CE to sustain productive agricultural activity. Since 640 CE...
Accuracy Assessment of NLCD 2011 percent impervious cover for selected USA Metropolitan Areas [0.03%]
美国典型大城市区域NLCD2011年不透水地表覆盖精度评价
J Wickham,S V Stehman,A C Neale et al.
J Wickham et al.
The emergence of high-resolution land cover data has created the opportunity to assess the accuracy of impervious cover (IC) provided by the National Land Cover Database (NLCD). We assessed the accuracy of the 900 m2 NLCD2011 %IC for 18 met...
Revealing geographic transmission pattern of COVID-19 using neighborhood-level simulation with human mobility data and SEIR model: A case study of South Carolina [0.03%]
基于人类流动性数据和SEIR模型的邻里级模拟揭示COVID-19地理传播模式：南卡罗来纳州案例研究
Huan Ning,Zhenlong Li,Shan Qiao et al.
Huan Ning et al.
Direct human physical contact accelerates COVID-19 transmission. Smartphone mobility data has emerged as a valuable data source for revealing fine-grained human mobility, which can be used to estimate the intensity of physical contact surro...
Simultaneous retrieval of sugarcane variables from Sentinel-2 data using Bayesian regularized neural network [0.03%]
利用贝叶斯正则化神经网络从Sentinel-2数据中同时反演甘蔗参数
Mohammad Hajeb,Saeid Hamzeh,Seyed Kazem Alavipanah et al.
Mohammad Hajeb et al.
Quantifying biophysical and biochemical vegetation variables is of great importance in precision agriculture. Here, the ability of artificial neural networks (ANNs) to generate multiple outputs is exploited to simultaneously retrieve Leaf a...