Yonglin Shen
YOU?
Author Swipe
View article: Disentangling vegetation physiological responses under extreme drought in the Amazon Rainforest: A multispectral remote sensing approach with insights from ET, SIF, and VOD
Disentangling vegetation physiological responses under extreme drought in the Amazon Rainforest: A multispectral remote sensing approach with insights from ET, SIF, and VOD Open
View article: Multi-layer feature fusion and attention-enhanced YOLOv9 for rapid field detection of greenhouse blueberry maturity at long and short distances
Multi-layer feature fusion and attention-enhanced YOLOv9 for rapid field detection of greenhouse blueberry maturity at long and short distances Open
Accurate maturity monitoring is critical for blueberry growth management and digitalization. Given the dense growth of blueberry plants and small fruit size, detecting blueberry maturity at long distances poses significant challenges. Ther…
View article: Low-cost video-based air quality estimation system using structured deep learning with selective state space modeling
Low-cost video-based air quality estimation system using structured deep learning with selective state space modeling Open
Air quality is crucial for both public health and environmental sustainability. An efficient and cost-effective model is essential for accurate air quality predictions and proactive pollution control. However, existing research primarily f…
View article: How coarse analysis can obscure key trends - a multi-scale study using very high-resolution intra-urban land data
How coarse analysis can obscure key trends - a multi-scale study using very high-resolution intra-urban land data Open
Land use change (LUC) mechanisms are vital for understanding change processes and policy formulation. However, most previous studies neglected intra-urban land use evolution and the influence of spatial resolution. This study designed a mu…
View article: A deep transfer learning based convolution neural network framework for air temperature classification using human clothing images
A deep transfer learning based convolution neural network framework for air temperature classification using human clothing images Open
Weather recognition is crucial due to its significant impact on various aspects of daily life, such as weather prediction, environmental monitoring, tourism, and energy production. Several studies have already conducted research on image-b…
View article: Exploring Causal Effects of Droughts on Air Quality for Environmental Sustainability in China Using VAR Model and Deep Learning Techniques
Exploring Causal Effects of Droughts on Air Quality for Environmental Sustainability in China Using VAR Model and Deep Learning Techniques Open
China has faced severe and persistent drought conditions over the past few decades. However, the impact of drought on air quality remains insufficiently understood. To address this issue, we employed a VAR model incorporating Granger causa…
View article: Evaluating the Ability of the Sentinel-1 Cross-Polarization Ratio to Detect Spring Maize Phenology Using Adaptive Dynamic Threshold
Evaluating the Ability of the Sentinel-1 Cross-Polarization Ratio to Detect Spring Maize Phenology Using Adaptive Dynamic Threshold Open
Accurate, timely, and fine-resolution crop phenology is essential for determining the optimal timing of agronomic management practices supporting precision agriculture and food security. Synthetic Aperture Radar (SAR) methods, unaffected b…
View article: Disentangling Vegetation Physiological Responses Under Extreme Drought with Multispectral Remote Sensing Approach: Insights from Et, Sif, and Vod
Disentangling Vegetation Physiological Responses Under Extreme Drought with Multispectral Remote Sensing Approach: Insights from Et, Sif, and Vod Open
View article: A novel approach to detect the spring corn phenology using layered strategy
A novel approach to detect the spring corn phenology using layered strategy Open
Accurate and continuous crop phenology information at a regional scale is important for agronomic management and yield estimation. However, detecting continuous crop phenology remains challenging due to the low sensitivity of remote sensin…
View article: AQE-Net: A Deep Learning Model for Estimating Air Quality of Karachi City from Mobile Images
AQE-Net: A Deep Learning Model for Estimating Air Quality of Karachi City from Mobile Images Open
Air quality has a significant influence on the environment and health. Instruments that efficiently and inexpensively detect air quality could be extremely valuable in detecting air quality indices. This study presents a robust deep learni…
View article: Multisource Remote Sensing Based Estimation of Soil NO<sub><i>x</i></sub> Emissions From Fertilized Cropland at High‐Resolution: Spatio‐Temporal Patterns and Impacts
Multisource Remote Sensing Based Estimation of Soil NO<sub><i>x</i></sub> Emissions From Fertilized Cropland at High‐Resolution: Spatio‐Temporal Patterns and Impacts Open
Soil nitrogen oxides (NO x ) emissions from agricultural areas currently dominate in some regions around the world. Since China is largely an agricultural country, an accurate estimation of soil NO x emissions from agricultural areas is es…
View article: Bromate reduction by Shewanella oneidensis MR-1 is mediated by dimethylsulfoxide reductase
Bromate reduction by Shewanella oneidensis MR-1 is mediated by dimethylsulfoxide reductase Open
Microbial bromate reduction plays an important role in remediating bromate-contaminated waters as well as biogeochemical cycling of bromine. However, little is known about the molecular mechanism of microbial bromate reduction so far. Sinc…
View article: Estimation of Ground PM2.5 Concentrations in Pakistan Using Convolutional Neural Network and Multi-Pollutant Satellite Images
Estimation of Ground PM2.5 Concentrations in Pakistan Using Convolutional Neural Network and Multi-Pollutant Satellite Images Open
During the last few decades, worsening air quality has been diagnosed in many cities around the world. The accurately prediction of air pollutants, particularly, particulate matter 2.5 (PM2.5) is extremely important for environmental manag…
View article: Modeling sensitivities of BVOCs to different versions of MEGAN emission schemes in WRF-Chem (v3.6) and its impacts over eastern China
Modeling sensitivities of BVOCs to different versions of MEGAN emission schemes in WRF-Chem (v3.6) and its impacts over eastern China Open
Biogenic volatile organic compounds (BVOCs) simulated by current air quality and climate models still have large uncertainties, which can influence atmospheric chemistry and secondary pollutant formation. These modeling sensitivities are p…
View article: Comment on gmd-2021-29
Comment on gmd-2021-29 Open
Abstract. Biogenic volatile organic compounds (BVOCs) simulated by current air quality and climate models still have large uncertainties, which can influence atmospheric chemistry and secondary pollutant formation. These m…
View article: Comment on gmd-2021-29
Comment on gmd-2021-29 Open
Abstract. Biogenic volatile organic compounds (BVOCs) simulated by current air quality and climate models still have large uncertainties, which can influence atmospheric chemistry and secondary pollutant formation. These m…
View article: Supplementary material to "Sensitivity of different BVOC emission schemes in WRF-Chem(v3.6) to vegetation distributions and its impacts over East China"
Supplementary material to "Sensitivity of different BVOC emission schemes in WRF-Chem(v3.6) to vegetation distributions and its impacts over East China" Open
View article: Sensitivity of different BVOC emission schemes in WRF-Chem(v3.6) to vegetation distributions and its impacts over East China
Sensitivity of different BVOC emission schemes in WRF-Chem(v3.6) to vegetation distributions and its impacts over East China Open
Biogenic volatile organic compounds (BVOCs) simulated by current air quality and climate models still have large uncertainties, which can influence atmosphere chemistry and secondary pollutant formation over East China. These uncertainties…
View article: Rotation Invariance Regularization for Remote Sensing Image Scene Classification with Convolutional Neural Networks
Rotation Invariance Regularization for Remote Sensing Image Scene Classification with Convolutional Neural Networks Open
Deep convolutional neural networks (DCNNs) have shown significant improvements in remote sensing image scene classification for powerful feature representations. However, because of the high variance and volume limitations of the available…
View article: Estimation of Field-Level NOx Emissions from Crop Residue Burning Using Remote Sensing Data: A Case Study in Hubei, China
Estimation of Field-Level NOx Emissions from Crop Residue Burning Using Remote Sensing Data: A Case Study in Hubei, China Open
Crop residue burning is the major biomass burning activity in China, strongly influencing the regional air quality and climate. As the cultivation pattern in China is rather scattered and intricate, it is a challenge to derive an accurate …
View article: A Gaussian Kernel-Based Spatiotemporal Fusion Model for Agricultural Remote Sensing Monitoring
A Gaussian Kernel-Based Spatiotemporal Fusion Model for Agricultural Remote Sensing Monitoring Open
Time series normalized difference vegetation index (NDVI) is the primary data for agricultural remote sensing monitoring. Due to the tradeoff between a single sensor's spatial and temporal resolutions and the impacts of cloud coverage, the…
View article: Deep Object-Centric Pooling in Convolutional Neural Network for Remote Sensing Scene Classification
Deep Object-Centric Pooling in Convolutional Neural Network for Remote Sensing Scene Classification Open
Remote sensing imagery typically comprises successive background contexts and complex objects. Global average pooling is a popular choice to connect the convolutional and fully connected (FC) layers for the deep convolution network. This a…
View article: An Adversarial Generative Network for Crop Classification from Remote Sensing Timeseries Images
An Adversarial Generative Network for Crop Classification from Remote Sensing Timeseries Images Open
Due to the increasing demand for the monitoring of crop conditions and food production, it is a challenging and meaningful task to identify crops from remote sensing images. The state-of the-art crop classification models are mostly built …
View article: Spatiotemporal Big Data for PM2.5 Exposure and Health Risk Assessment during COVID-19
Spatiotemporal Big Data for PM2.5 Exposure and Health Risk Assessment during COVID-19 Open
The coronavirus disease 2019 (COVID-19) first identified at the end of 2019, significantly impacts the regional environment and human health. This study assesses PM2.5 exposure and health risk during COVID-19, and its driving factors have …
View article: Mapping Paddy Rice Fields by Combining Multi-Temporal Vegetation Index and Synthetic Aperture Radar Remote Sensing Data Using Google Earth Engine Machine Learning Platform
Mapping Paddy Rice Fields by Combining Multi-Temporal Vegetation Index and Synthetic Aperture Radar Remote Sensing Data Using Google Earth Engine Machine Learning Platform Open
The knowledge of the area and spatial distribution of paddy rice fields is important for water resource management. However, accurate map of paddy rice is a long-term challenge because of its spatiotemporal discontinuity and short duration…
View article: Multilevel Deep Learning Network for County-Level Corn Yield Estimation in the U.S. Corn Belt
Multilevel Deep Learning Network for County-Level Corn Yield Estimation in the U.S. Corn Belt Open
Accurate and timely estimation of crop yield at a small scale is of great significance to food security and harvest management. Recent studies have proven remote sensing is an efficient method for yield estimation and machine learning, esp…
View article: Large-Scale Crop Mapping From Multisource Remote Sensing Images in Google Earth Engine
Large-Scale Crop Mapping From Multisource Remote Sensing Images in Google Earth Engine Open
Large-scale crop mapping is vitally important to agriculrural monitoring and management. However, traditional methods cannot well meet the needs of large-scale applications. Therefore, this study proposed a method for large-scale crop mapp…
View article: County-Level Soybean Yield Prediction Using Deep CNN-LSTM Model
County-Level Soybean Yield Prediction Using Deep CNN-LSTM Model Open
Yield prediction is of great significance for yield mapping, crop market planning, crop insurance, and harvest management. Remote sensing is becoming increasingly important in crop yield prediction. Based on remote sensing data, great prog…
View article: Fully Connected Conditional Random Fields for High-Resolution Remote Sensing Land Use/Land Cover Classification with Convolutional Neural Networks
Fully Connected Conditional Random Fields for High-Resolution Remote Sensing Land Use/Land Cover Classification with Convolutional Neural Networks Open
The interpretation of land use and land cover (LULC) is an important issue in the fields of high-resolution remote sensing (RS) image processing and land resource management. Fully training a new or existing convolutional neural network (C…
View article: Fully Connected Conditional Random Fields for High-Resolution Remote Sensing Land Use/Land Cover Classification with Convolutional Neural Networks
Fully Connected Conditional Random Fields for High-Resolution Remote Sensing Land Use/Land Cover Classification with Convolutional Neural Networks Open
The interpretation of land use and land cover (LULC) is an important issue in the fields of high-resolution remote sensing (RS) image processing and land resource management. Fully training a new or existing convolutional neural network (C…