Article
Engineering, Electrical & Electronic
Bo Ping, Fenzhen Su, Xingxing Han, Yunshan Meng
Summary: In this study, a deep learning-based SR model was proposed for SST reconstruction, achieving the lowest MAE and RMSE in specific regions. The ODRE model showed the highest or second-highest peak signal-to-noise ratio and structural similarity index. Additionally, the number of missing pixels and SST variety were identified as important factors in SR performance, while deeper networks did not necessarily enhance reconstruction accuracy.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2021)
Article
Environmental Sciences
Huoqing Li, Zonghui Liu, Ali Mamtimin, Junjian Liu, Yongqiang Liu, Chenxiang Ju, Hailiang Zhang, Zhibo Gao
Summary: The study proposes a new method for estimating broadband emissivity in arid regions using FTIR observations, MODIS emissivity, LAI, and reflectance products, which showed higher variations and finer distribution compared to global satellite and land model data. The proposed method accurately estimates broadband emissivity in arid regions and reveals a complex relationship between emissivity, land-use type, and soil moisture under an inhomogeneous surface. Future research will focus on testing the data in a land model.
Article
Geochemistry & Geophysics
Jose Luis Villaescusa-Nadal, Eric Vermote, Belen Franch, Andres E. Santamaria-Artigas, Jean-Claude Roger, Sergii Skakun
Summary: The goal of this study is to develop accurate and consistent surface reflectance and albedo products for analyzing global albedo trends. Distinguishing between cloud and snow is challenging due to the limitations of the sensor used. To address this issue, the researchers propose an algorithm based on spectral analysis to identify clear land and snow pixels using satellite and reanalysis data.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Xiaoqing Tian, Ling Gao, Jun Li, Lin Chen, Jingjing Ren, Chengcai Li
Summary: This study develops a novel algorithm for retrieving aerosol optical depth (AOD) from AVHRR using machine learning. The algorithm is compared and validated against MODIS, showing good performance in various conditions. This research is important for long-term monitoring of the role of aerosols in global climate change.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Meteorology & Atmospheric Sciences
Kameswara S. S. Vinjamuri, Marco Vountas, Luca Lelli, Martin Stengel, Matthew D. D. Shupe, Kerstin Ebell, John P. P. Burrows
Summary: The role of clouds in the Arctic radiation budget is not well understood. Ground-based and airborne measurements provide valuable data to test and improve our understanding. Passive remote sensing measurements from satellite sensors offer high spatial coverage and an evolving time series, having lengths potentially of decades. However, detecting clouds by passive satellite remote sensing sensors is challenging over the Arctic because of the brightness of snow and ice in the ultraviolet and visible spectral regions and because of the small brightness temperature contrast to the surface. The Cloud_CCI cloud data products investigated agree reasonably well with those retrieved from ground-based measurements made at the four high-latitude sites.
ATMOSPHERIC MEASUREMENT TECHNIQUES
(2023)
Article
Geochemistry & Geophysics
Jianglei Xu, Bo Jiang, Shunlin Liang, Xiuxia Li, Yezhe Wang, Jianghai Peng, Hongkai Chen, Hui Liang, Shaopeng Li
Summary: A downscaled scheme was developed to generate a high-resolution time-series of ocean surface net radiation using satellite observations, showing high accuracy in estimating the radiation. This downscaled data provided more detailed information, especially in hotspot regions, compared to other existing products.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2021)
Article
Geochemistry & Geophysics
Jun Li, Wenjun Tang, Kun Yang, Yu Xie, Christian A. Gueymard, Jun Qin, Manajit Sengupta
Summary: Cloud parameters have a critical impact on surface shortwave radiation computation, and introducing a parameterization of cloud transmittance and reflectance based on radiative transfer simulations improves the accuracy of existing models. The revised model shows higher accuracy and no underestimation under high cloud optical thickness, outperforming the MODIS official SSR product. The improved model can be used globally to map surface shortwave radiation with reliable performance.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Jianjun Liu, Fuzhong Weng, Zhanqing Li
Summary: This study develops an ensemble machine learning approach to estimate PM2.5 concentrations with a very high spatial resolution using satellite measurements. The model shows a high and stable performance, accurately capturing the distribution patterns and magnitudes of PM2.5 concentrations in a polluted area. This high-resolution model provides detailed information for air pollution-related studies and government monitoring and evaluation.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Environmental Sciences
Jacques Descloitres, Audrey Minghelli, Francois Steinmetz, Cristele Chevalier, Malik Chami, Leo Berline
Summary: This study successfully estimated the coverage of Sargassum by combining different remote sensing data with the AFAI algal index. The results showed that the AFAI deviation is proportional to the fractional coverage of Sargassum, providing important implications for further research on the variation and distribution of Sargassum.
Article
Geosciences, Multidisciplinary
Xinyan Liu, Tao He, Shunlin Liang, Ruibo Li, Xiongxin Xiao, Rui Ma, Yichuan Ma
Summary: This study proposed a spatiotemporal statistical data fusion framework based on cumulative distribution function (CDF) matching and the Bayesian maximum entropy (BME) method to produce a synthetic 1°x1° cloud fraction dataset in the Arctic during 2000-2020. The results showed that the proposed framework effectively reduced the inconsistencies between passive sensor products and reference data, and improved the accuracy of cloud fraction estimation. The fused product also addressed the temporal and spatial gaps in existing satellite datasets, providing valuable information for modeling and reanalysis studies. Rating: 8/10
EARTH SYSTEM SCIENCE DATA
(2023)
Article
Remote Sensing
Zhuting Tan, Zhengyu Tan, Juhua Luo, Hongtao Duan
Summary: This study proposes a new method for cotton sample selection and employs machine learning to effectively identify long time series cotton planting areas at a 30-meter resolution scale. The study uses Bortala and Shuanghe in Xinjiang, China as case studies to demonstrate the approach. The results show that the method can achieve high accuracy and reveal the spatiotemporal distribution characteristics of cotton planting areas.
GEO-SPATIAL INFORMATION SCIENCE
(2023)
Article
Geochemistry & Geophysics
Zhuocan Xu, Gerald G. Mace, Derek J. Posselt
Summary: This research demonstrates that precipitation affects the estimation of cloud droplet effective radius using the bispectral method. Precipitation leads to overestimation of the estimated radius and increased uncertainties. It also results in a loss of information on the total number concentration and liquid water content near the cloud top. Additional independent information is needed to supplement these estimates.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Remote Sensing
Yuanyuan Wang, Guicai Li
Summary: This paper reports the production of a global land cover classification map using MERSI-II data. By utilizing various image compositing techniques and supervised learning algorithms, the study achieved high accuracy in LC classification and conducted comparisons with other widely used LC products.
INTERNATIONAL JOURNAL OF REMOTE SENSING
(2022)
Article
Engineering, Electrical & Electronic
Bin Fang, Venkat Lakshmi, Michael Cosh, Christopher Hain
Summary: A downsampling algorithm based on the relationship between vegetation-modulated apparent thermal inertia and soil moisture was developed to improve the spatial resolution of soil moisture data, successfully downsampling high-resolution soil moisture data into contiguous United States and demonstrating good performance through validation with ground measurements.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2021)
Article
Geochemistry & Geophysics
Quan Zhang, Jie Cheng, Ninglian Wang
Summary: This study proposes an all-weather land surface temperature fusion method based on random forest, which effectively addresses the problem of oversmoothing and achieves more consistent results compared to the widely used Bayesian maximum entropy method.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Geochemistry & Geophysics
Kai Yan, Hanliang Li, Wanjuan Song, Yiyi Tong, Dalei Hao, Yelu Zeng, Xihan Mu, Guangjian Yan, Yuan Fang, Ranga B. Myneni, Crystal Schaaf
Summary: In this study, a hybrid algorithm called Topo-KD is proposed to address the issue of errors in surface bidirectional effects caused by rugged terrain. The algorithm adaptively selects the most suitable model (RLKB or LKBx005F;T) according to the terrain conditions and fitting residuals. The experiment using MODIS data sets demonstrates that the Topo-KD algorithm reduces fitting residuals in the red and NIR bands compared with the RTLSR model, indicating its better performance in mountainous areas.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geography, Physical
Weihan Liu, Jiancheng Shi, Shunlin Liang, Shugui Zhou, Jie Cheng
Summary: This paper extends a new temperature and emissivity separation (TES) algorithm for retrieving land surface temperature and emissivity (LST and LSE) to the Advanced Geosynchronous Radiation Imager (AGRI) onboard Fengyun-4A, China's newest geostationary meteorological satellite. The AGRI TES algorithm demonstrates good performance and potential in producing high-quality LST and LSE products.
INTERNATIONAL JOURNAL OF DIGITAL EARTH
(2022)
Article
Environmental Sciences
Dong Xu, Jie Cheng, Shen Xu, Jing Geng, Feng Yang, He Fang, Jinfeng Xu, Sheng Wang, Yubai Wang, Jincai Huang, Rui Zhang, Manqing Liu, Haixing Li
Summary: The rapid development of urbanization and population growth in China poses a major threat to the green sustainable development of the ecological environment. This study investigates the coupling mechanism between urbanization and the eco-environmental quality (EEQ) in China using a combined mathematical and graphics model. The results show that the development of urbanization has a greater impact on the coupling coordination degree (CCD) than the EEQ. This research provides a new research perspective for the sustainable development of China and the world in the future.
Article
Environmental Sciences
Shaopeng Li, Bo Jiang, Jianghai Peng, Hui Liang, Jiakun Han, Yunjun Yao, Xiaotong Zhang, Jie Cheng, Xiang Zhao, Qiang Liu, Kun Jia
Summary: The study proposes a RF-based model with ERA5 data to estimate daily all-wave net radiation at mid-low latitudes, achieving satisfactory performance and strong potential for long-term accurate global mapping. Validation against in situ measurements demonstrates the superiority of the model compared to existing products and its effectiveness in the presence of limited satellite observations or overcast skies.
Article
Meteorology & Atmospheric Sciences
Jiawen Xu, Xiaotong Zhang, Weiyu Zhang, Ning Hou, Chunjie Feng, Shuyue Yang, Kun Jia, Yunjun Yao, Xianhong Xie, Bo Jiang, Jie Cheng, Xiang Zhao, Shunlin Liang
Summary: Surface downward longwave radiation (SDLR) plays an important role in understanding the greenhouse effect and global warming. This study evaluated the simulated SDLR from 47 coupled models in the Coupled Model Intercomparison Project (CMIP6) general circulation models (GCMs) and compared them with ground measurements and CMIP5 results. The study found that the precision of the SDLR simulations varied at different sites and altitudes in the CMIP6 GCMs. Additionally, the Bayesian model averaging (BMA) method improved the correlation and accuracy of the SDLR predictions compared to individual CMIP6 GCMs.
ATMOSPHERIC RESEARCH
(2022)
Article
Remote Sensing
Jie Cheng, Qi Zeng, Jiancheng Shi
Summary: This paper proposes a direct algorithm for estimating the surface longwave net radiation (SLNR) using satellite radiances and water vapor data. The relationships were established using multivariate regression and extreme gradient boosting, showing that the direct algorithm performs better than the conventional method. Results indicate that there is a weak nonlinearity between clear-sky SLNR and predictors.
INTERNATIONAL JOURNAL OF REMOTE SENSING
(2022)
Article
Agronomy
Junming Yang, Yunjun Yao, Changliang Shao, Yufu Li, Joshua B. Fisher, Jie Cheng, Jiquan Chen, Kun Jia, Xiaotong Zhang, Ke Shang, Ruiyang Yu, Xiaozheng Guo, Zijing Xie, Lu Liu, Jing Ning, Lilin Zhang
Summary: A novel method for daily LE estimation using all-weather LST was proposed and validated in mainland China, demonstrating its accuracy at regional scale.
AGRICULTURAL AND FOREST METEOROLOGY
(2022)
Article
Green & Sustainable Science & Technology
Tingting Bai, Jie Cheng, Zihao Zheng, Qifei Zhang, Zihao Li, Dong Xu
Summary: In the past 18 years, the ecoenvironmental quality in more than 60% of China has improved, with natural factors and human activities jointly dominating 58% of the EEQ change. Climate water deficit is the most important natural factor. Human activities such as population, gross domestic product, and electricity consumption have significantly increased in the past 18 years, simultaneously dominating the EEQ change in about 18% of China's areas. Therefore, rational development of human activities is crucial for China's ecoenvironmental quality in the context of global climate change.
JOURNAL OF CLEANER PRODUCTION
(2023)
Article
Environmental Sciences
Qi Zeng, Jie Cheng, Mengfei Guo
Summary: This study comprehensively evaluates the accuracy of mainstream surface longwave (LW) radiation products (GLASS, CERES SYN and ERA5) in terms of surface longwave upward radiation (SLUR) and surface longwave downward radiation (SLDR). The GLASS product shows the best accuracy under clear-sky conditions, while ERA5 has the best overall accuracy. The global annual mean values of SLUR and SLDR are quantified, along with their temporal variations from 2003 to 2020. This evaluation and trend analysis contribute to understanding global energy balance and climate change.
Article
Environmental Sciences
Yaotao Luo, Donghui Xie, Jianbo Qi, Kun Zhou, Guangjian Yan, Xihan Mu
Summary: LiDAR is a widely used technology for acquiring three-dimensional information about physical objects and environments. A LiDAR simulation model was developed that can accurately simulate LiDAR waveforms and point clouds, and the performance of the simulator was compared with other models. The findings demonstrate that the proposed LiDAR simulator has great potential and offers faster simulation speeds.
Article
Engineering, Electrical & Electronic
Xiangchen Meng, Weihan Liu, Jie Cheng, Hao Guo, Beibei Yao
Summary: This article extends the current land surface temperature retrieval algorithms by incorporating a daily land surface emissivity database for high-temporal-resolution retrieval. The validation results show that using the daily land surface emissivity obtained from Feng Yun-4A/Advanced Geostationary Radiation Imager data can improve the accuracy of land surface temperature retrieval.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2023)
Article
Geochemistry & Geophysics
Shugui Zhou, Jie Cheng
Summary: Surface longwave upwelling radiation (SLUR) is an important parameter for studying water-energy balance. A new hybrid method was proposed to estimate SLUR using MODIS data. The method utilizes a physical four-channel algorithm to estimate atmospheric terms and a linear model to relate SLUR to MODIS radiance. Validation results showed that the new method outperformed the original method and was slightly better than the classical hybrid method. The new method can accurately estimate SLUR and has the potential to provide long-term high-resolution environmental data.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Geochemistry & Geophysics
Weihan Liu, Jie Cheng, Qiao Wang
Summary: This study proposes an effective method to estimate hourly all-weather land surface temperature (LST) using Advanced Geosynchronous Radiation Imager (AGRI) data. The method involves an improved temperature and emissivity separation algorithm to obtain high-quality clear-sky LST and a unique approach to solve the temperature difference between cloudy-sky LST and hypothetical clear-sky LST caused by cloud radiative effects. The estimated all-weather LST shows promising results in capturing diurnal variations and can be upscaled temporally.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Remote Sensing
Xiangchen Meng, Weihan Liu, Jie Cheng, Hao Guo
Summary: The adapted enterprise algorithm was used to retrieve land surface temperature from FY-4A thermal infrared data, and it was found that using the daily composite of emissivity data resulted in better accuracy. This study demonstrates the effectiveness of the algorithm.
REMOTE SENSING LETTERS
(2023)
Article
Remote Sensing
Linyuan Li, Xihan Mu, Francesco Chianucci, Jianbo Qi, Jingyi Jiang, Jiaxin Zhou, Ling Chen, Huaguo Huang, Guangjian Yan, Shouyang Liu
Summary: Accurate estimation of forest crown cover is crucial for ecological studies. This study proposes a method that combines deep learning with UAV imagery and photogrammetric point clouds for canopy mapping, demonstrating its effectiveness in separating understorey and overstorey vegetation components.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2022)